{"item_id": "item_0000", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total trash volume by source so I can see at a glance which categories account for the largest shares?", "table_markdown": "| Category | # Bags | # Pounds | # Sites |\n|---------------------------------|--------|-----------|---------|\n| Encampment Trash | 398 | 13,930 | 40 |\n| Inactive Encampment | 752 | 26,320 | 153 |\n| Stormwater Debris | 662 | 23,170 | 136 |\n| Dumping | 220 | 7,700 | 72 |\n| Litter | 27 | 945 | 12 |\n| Special Removal Needed | 189 | 6,615 | 43 |\n| Sum: | 2,248 | 78,680 | |"}
{"item_id": "item_0001", "chart_task_type": "growth_speed", "query": "Can you chart the growth for each income source so I can see at a glance which one expanded the fastest?", "table_markdown": "| Income | 2020 | 2019 |\n|---------------------------------------------|--------|--------|\n| EA under PCSO | 31,593 | 40,226 |\n| Agricultural Rates | 110,914| 108,295|\n| Special Levy ex ERYC | 57,358 | 55,741 |\n| Upland Water ex EA | 15,624 | 16,330 |\n| Bank Interest | 598 | 587 |\n| Other Income | 7,639 | 4,396 |\n| **Total Income** | | |"}
{"item_id": "item_0002", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total construction costs by responsible entity so I can see at a glance what share each government body contributes?", "table_markdown": "| Item | Federal | State | County | Town | Total |\n|-----------------------|---------|-------|--------|------|-------|\n| **Construction Costs**| | | | | |\n| Flood Control | 3,925,000 | 5,318,000 | 915,000(2) | - | 10,158,000 |\n| Recreation (1) | 370,000 | - | - | 370,000 | 740,000 |\n| **Total** | 4,295,000 | 5,318,000 | 915,000 | 370,000 | 10,898,000 |\n| **Annual Operation and Maintenance Costs** | | | | | |\n| Flood Control | 700 | 20,300 | - | - | 21,000 |\n| Recreation (1) | - | - | - | 5,000 | 5,000 |\n| **Total** | 700 | 20,300 | - | 5,000 | 26,000 |"}
{"item_id": "item_0003", "chart_task_type": "paired_gap", "query": "Can you chart the torque difference between strength grades 10.9 and 8.8 for each screw size, making it easy to spot which thread has the largest gap?", "table_markdown": "| | Strength grade | |\n|---|---|---|\n| | 8.8 | 10.9 |\n| Thread | Tightening torque (Nm) (*) | |\n| M5 | 5.5 (4) | 8.1 (6) |\n| M6 | 9.6 (7) | 14 (10) |\n| M8 | 23 (17) | 34 (25) |\n| M10 | 46 (34) | 67 (49) |\n| M12 | 79 (58) | 115 (85) |\n| M16 | 145 (107) | 215 (159) |"}
{"item_id": "item_0004", "chart_task_type": "growth_speed", "query": "Can you chart the growth for each electricity end-use category so I can see at a glance which one expanded the fastest?", "table_markdown": "| End-use | 2016 | 2017 |\n|-------------------------------|----------|----------|\n| Public Areas | 1,027,529| 1,023,183|\n| HVAC and Hot Water Systems & Guestrooms End-use | 2,032,003| 2,131,234|\n| Food & Beverage | 360,272 | 370,989 |"}
{"item_id": "item_0005", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the CBT-2 exam by section based on the number of questions, so I can easily see the relative weight of each part?", "table_markdown": "| Name of the Sections | No. of Questions | Marks | Duration |\n|---------------------------------------|------------------|-------|----------|\n| General Awareness | 15 | 15 | 120 min |\n| Physics & Chemistry | 15 | 15 | |\n| Basics of Computers & Applications | 10 | 10 | |\n| Basics of Environment & Pollution Control | 10 | 10 | |\n| Technical Abilities | 100 | 100 | |\n| **Total** | **150** | **150**| |"}
{"item_id": "item_0006", "chart_task_type": "paired_gap", "query": "Can you chart the gender gap in average new behaviors acquired by age group, making it easy to spot which age range has the largest advantage for females?", "table_markdown": "| Age | SPW | WW | BW | GM | average no. of new behaviors acquired |\n|-------|--------|--------|--------|--------|--------------------------------------|\n| | | | | | male | female |\n| 2~3 | 90.9% | 36.3% | 90.9% | 72.7% | 2.5 | 3.4 |\n| 4~5 | 87.5 | 75.0 | 100.0 | 62.5 | 3.2 | 3.2 |\n| 6~7 | 85.7 | 85.7 | 100.0 | 100.0 | 3.0 | 4.0 |\n| 8~11 | 91.6 | 25.0 | 33.3 | 100.0 | 2.8 | 3.2 |\n| 12~ | 18.1 | 0 | 18.1 | 63.6 | 1.1 | 1.0 |"}
{"item_id": "item_0007", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of total dispositions over the five quarters? I want it to be obvious at a glance where the sharpest drop in activity occurred.", "table_markdown": "| Period | Dispositions – Total | Pre-hearing |\n|---|---|---|\n| Q1-2012 | 930 | 287 |\n| Q2-2012 | 1005 | 341 |\n| Q3-2012 | 956 | 343 |\n| Q4-2012 | 1017 | 340 |\n| Q1-2013 | 888 | 280 |"}
{"item_id": "item_0008", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of map downloads across the six permanent orienteering course locations so I can see at a glance which parks account for the largest share of the total?", "table_markdown": "| Location | Downloads | total of numbers in groups |\n|---------------------------|-----------|----------------------------|\n| Eddison Park | 69 | 316 |\n| Fadden Pines | 88 | 468 |\n| Haig Park | 35 | 33 |\n| John Knight Memorial Park | 132 | 941 |\n| Tidbinbilla | 16 | 33 |\n| Weston Park | 166 | 1077 |\n| | 506 | 2868 |"}
{"item_id": "item_0009", "chart_task_type": "growth_speed", "query": "Can you chart the growth for the three membership categories so I can see at a glance which one grew the fastest?", "table_markdown": "| | 31 March 2019 | 31 March 2020 | Change |\n|---|---|---|---|\n| Active members (members who are currently in service with eir) | 1,663 | 1,539 | -124 |\n| Deferred members (members who are former employees of eir) | 4,020 | 3,344 | -676 |\n| Pensioners (those in receipt of benefits from the Scheme) | 10,630 | 11,213 | +583 |\n| Total | 16,313 | 16,096 | -217 |"}
{"item_id": "item_0010", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether our 2018 imports were heavily concentrated among a few key suppliers like China and the USA, so I can see at a glance how dominant the top sources are?", "table_markdown": "| | 2018 | 2019 Feb |\n|---|---|---|\n| Imports total, billion € (% change on year earlier) | 191.1 (+4.4%) | 14.7 (+9.0%) |\n| Biggest supplying countries (2018) | | |\n| 1. China | 51.5 (+3.9%) | 3.5 (+8.1%) |\n| 2. USA | 12.0 (+7.3%) | 0.9 (+10.7%) |\n| 3. Czech Republic | 10.7 (+6.3%) | 0.9 (+12.0%) |\n| 4. Poland | 8.6 (+9.7%) | 0.7 (+11.9%) |\n| 5. Hungary | 8.2 (+8.0%) | 0.7 (+7.6%) |\n| 6. Japan | 7.8 (+2.6%) | 0.6 (+2.2%) |\n| Export prices | 0.0% | +0.3% |\n| Import prices | -1.3% | -0.7% |"}
{"item_id": "item_0011", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of OATH headcount between full-time and full-time equivalent staff shifted from FY14 to FY24, so I can easily see the changing reliance on each type?", "table_markdown": "| | Actual | | | | | | | | | | |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| | FY14 | FY15 | FY16 | FY17 | FY18 | FY19 | FY20 | FY21 | FY22 | FY23 | FY24 |\n| Full-Time | 237 | 238 | 241 | 274 | 290 | 310 | 302 | 285 | 306 | 306 | 306 |\n| Full-Time Equivalent | 121 | 124 | 119 | 106 | 87 | 99 | 63 | 164 | 190 | 190 | 190 |\n| Total | 358 | 362 | 360 | 380 | 377 | 409 | 365 | 449 | 496 | 496 | 496 |"}
{"item_id": "item_0012", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each network statistic from 2004-2005 to 2011-2012? I want it to be obvious which one expanded the fastest.", "table_markdown": "| Network statistic | 2004-2005 | 2005-2006 | 2006-2007 | 2007-2008 | 2008-2009 | 2009-2010 | 2010-2011 | 2011-2012 |\n|------------------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|\n| Number of nodes | 374 | 293 | 298 | 313 | 320 | 326 | 316 | 325 |\n| Number of edges | 161 | 272 | 273 | 279 | 390 | 287 | 255 | 243 |\n| Clustering coefficient | 0.006 | 0.031 | 0.021 | 0.019 | 0.033 | 0.034 | 0.015 | 0.015 |\n| Network diameter | 3 | 10 | 9 | 8 | 8 | 5 | 6 | 5 |\n| Characteristic path length | 1.471 | 3.355 | 2.808 | 2.834 | 2.894 | 2.456 | 2.331 | 1.984 |\n| Network density | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |\n| Betweenness centrality > 0 | 0.051 | 0.112 | 0.124 | 0.115 | 0.128 | 0.101 | 0.073 | 0.077 |\n| Closeness centrality > 0 | 0.289 | 0.486 | 0.438 | 0.433 | 0.553 | 0.379 | 0.331 | 0.314 |"}
{"item_id": "item_0013", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of juvenile sentencing numbers from 2005 to 2011? I want it to be obvious at a glance when the growth trend started to weaken.", "table_markdown": "| Year | Sentenced | | Incarcerated* | |\n|---|---|---|---|---|\n| | Number | % of Total | Number | % of Total |\n| 2005 | 82,721 | 9.82% | 23,957 | 1.54% |\n| 2006 | 83,697 | 9.41% | 23,250 | 1.48% |\n| 2007 | 87,525 | 9.39% | 21,807 | 1.39% |\n| 2008 | 88,891 | 8.82% | 20,772 | 1.31% |\n| 2009 | 77,604 | 7.79% | 20,662 | 1.27% |\n| 2010 | 68,193 | 6.78% | 18,450 | 1.12% |\n| 2011 | 67,280 | 6.40% | 16,701 | 1.01% |"}
{"item_id": "item_0014", "chart_task_type": "composition_compare", "query": "Can you chart the ground cover composition for each vegetation group so I can easily compare their relative mixes of rock, crust, litter, grass, forbs, and bare ground?", "table_markdown": "| Vegetation Type/Forest/Yr | Rock | Crust | Litter | Grass | Forbs | Bare Ground | Total Ground Cover |\n|---------------------------|------|-------|--------|-------|-------|-------------|--------------------|\n| Mtn big sage (CNF 2001 n=3)a | 1.4 | 3.5 | 63.6 | 12.9 | 3.9 | 14.8 | 85.2 |\n| Mtn big sage (WCNF 2001 n=3) | 2.6 | 0.3 | 41.8 | 38.9 | 10.9 | 5.6 | 94.4 |\n| Mtn big sage (CNF 2001 n=41) | 2.0 | 0.1 | 34.6 | 5.2 | 5.0 | 53.3 | 46.7 |\n| Mtn big sage (WCNF 2001, 2004, 2005 n=10) | 0.9 | 0 | 53.7 | 3.6 | 3.6 | 38.2 | 61.8 |\n| Aspen (WCNF 2004, 2005 n=6) | 2.5 | 0.3 | 70.7 | 1.7 | 2.3 | 40.4 | 59.6 |\n| Conifer (2001 n=3)b | 1.1 | 0 | 42.5 | 7.7 | 9.8 | 38.9 | 61.1 |\n| Conifer (2004 n=3)c | 1.0 | 0.1 | 89.6 | 0.6 | 0.9 | 7.8 | 92.2 |"}
{"item_id": "item_0015", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of Pacific Gas & Electric's projected capacity and energy payments shifted from 1998 to 2026, so I can see at a glance how the relative reliance on each component changed over time?", "table_markdown": "| Year | Projected Capacity Payments | Projected Energy Payments | Total |\n|---|---|---|---|\n| 1998 | $541 | $897 | $1,438 |\n| 1999 | 529 | 779 | 1,308 |\n| 2000 | 531 | 665 | 1,196 |\n| 2001 | 528 | 651 | 1,179 |\n| 2002 | 525 | 646 | 1,171 |\n| 2003 | 520 | 664 | 1,184 |\n| 2004 | 508 | 686 | 1,194 |\n| 2005 | 505 | 706 | 1,211 |\n| 2006 | 504 | 729 | 1,233 |\n| 2007 | 484 | 752 | 1,236 |\n| 2008 | 472 | 782 | 1,254 |\n| 2009 | 430 | 805 | 1,235 |\n| 2010 | 406 | 831 | 1.237 |\n| 2011 | 391 | 857 | 1,248 |\n| 2012 | 366 | 887 | 1,253 |\n| 2013 | 354 | 913 | 1,267 |\n| 2014 | 336 | 942 | 1.278 |\n| 2015 | 304 | 972 | 1,276 |\n| 2016 | 283 | 933 | 1,216 |\n| 2017 | 262 | 892 | 1,154 |\n| 2018 | 214 | 753 | 967 |\n| 2019 | 166 | 603 | 169 |\n| 2020 | 100 | 375 | 475 |\n| 2021 | 60 | 232 | 292 |\n| 2022 | 52 | 209 | 261 |\n| 2023 | 52 | 215 | 267 |\n| 2024 | 52 | 222 | 274 |\n| 2025 | 52 | 229 | 281 |\n| 2026 | 14 | 65 | 79 |\n| Total | $9,541 | $18,892 | $28,433 |"}
{"item_id": "item_0016", "chart_task_type": "composition_compare", "query": "Can you chart the demographic breakdown for males versus females in Richard Locke's survey so I can easily spot how the composition of each gender group differs?", "table_markdown": "| Category | Number | Category | Number |\n|------------------------|--------|------------------------|--------|\n| Husbands | 2500 | Wives | 2525 |\n| Widowers | 600 | Widows | 1000 |\n| Bachelors | 700 | Spinsters | 300 |\n| Sons | 4500 | Daughters | 3500 |\n| Male servants | 950 | Female servants | 1025 |\n| Male apprentices | 400 | Female apprentices | 100 |\n| Male lodgers | 850 | Female lodgers | 800 |\n| **Total** | **10500** | **Total** | **9500** |"}
{"item_id": "item_0017", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of Accrued Expenses for 31-Mar-20 and 30-Jun-19 so I can see at a glance how the composition of costs differs between the two periods?", "table_markdown": "| Description | 31-Mar-20 | 30-Jun-19 |\n|------------------------------|-------------|-------------|\n| Salary & Wages Payable | 3,161,750 | 4,476,658 |\n| Listing Fees | 674,019 | 299,797 |\n| CDBL Fees | 144,179 | 144,179 |\n| Other expenses | 365,455 | 67,631 |\n| Audit Fee | 300,000 | 250,000 |\n| **Total** | **4,645,403** | **5,238,265** |"}
{"item_id": "item_0018", "chart_task_type": "composition_compare", "query": "Can you chart the cost breakdown for the proposed maximum security levels to show how the mix of expenses differs between International and Domestic passengers?", "table_markdown": "| | Current Price | Maintaining/enhancing security standards | ‘Smart Security’ Enhancements | BAU Cost Pressures | Rebuild/Maintain Reserves | Max Proposed (2021/22) |\n|----------------------|---------------|------------------------------------------|-------------------------------|--------------------|--------------------------|------------------------|\n| International | $8.70 | $3.44 | $0.43 | $0.43 | $0.12 | $13.12 |\n| (assuming transit passengers also pay) | | | | | | |\n| Domestic | $5.05 | $1.20 | $0.21 | $0.15 | $0.03 | $6.64 |"}
{"item_id": "item_0019", "chart_task_type": "paired_gap", "query": "Can you chart the shift in population distribution across BMI groups from the start to year 15, making it easy to spot which group experienced the largest change?", "table_markdown": "| Population Group | BMI Range | Initial Distribution (at Year 0) | Distribution at Year 15 (Agent-based Model) |\n|---|---|---|---|\n| 1 | 14-16 | 0.002 | 0 |\n| 2 | 16-18 | 0.0208 | 0.0003 |\n| 3 | 18-20 | 0.0677 | 0.0042 |\n| 4 | 20-22 | 0.1217 | 0.0329 |\n| 5 | 22-25 | 0.2053 | 0.1345 |\n| 6 | 25-28 | 0.1737 | 0.199 |\n| 7 | 28-30 | 0.0865 | 0.1205 |\n| 8 | 30-32 | 0.0768 | 0.1019 |\n| 9 | 32-34 | 0.0693 | 0.0821 |\n| 10 | 34-36 | 0.0452 | 0.072 |\n| 11 | 36-38 | 0.0403 | 0.0713 |\n| 12 | 38-40 | 0.029 | 0.0459 |\n| 13 | 40-42 | 0.0192 | 0.0397 |\n| 14 | 42-44 | 0.015 | 0.0296 |\n| 15 | 44-46 | 0.0114 | 0.0205 |\n| 16 | 46-50 | 0.012 | 0.0287 |\n| 17 | 50-58 | 0.0042 | 0.0169 |"}
{"item_id": "item_0020", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the expenditure is concentrated among a few top companies or spread out, so I can see at a glance how much the biggest spenders dominate the total?", "table_markdown": "| Company | Address | Amount |\n|-------------------------------|----------------------------------------------|----------|\n| Perth Parmelia Hilton | Mill Street, Perth WA 6000 | $11,549.00 |\n| Hyatt Regency Perth | 99 Adelaide Tce, Perth WA 6000 | $30,649.00 |\n| Jones Lang Wotten/ LaSalle | 3/225 St Georges Tce, Perth WA | $9,407.26 |\n| Jubilee Printing | 281 Guildford Road, Maylands WA 6051 | $7,759.40 |\n| Rave Design | 90 Edwards Street, East Perth WA 6004 | $6,993.00 |\n| JBW Enterprises | 26 Grove End Ridge, St John's Wood, Mt Claremont WA 6010 | $6,000.00 |\n| Sheraton Hotel | Adelaide Terrace, Perth WA 6000 | $13,155.00 |\n| EM Printers | 50 Francis Street, Perth WA 6000 | $3,249.00 |\n| Gillies Olver Design | 120 Churchill Avenue, Subiaco WA 6008 | $2,057.00 |\n| RF Court | 174 Hampden Road, Nedlands WA 6009 | $4,305.80 |\n| Baldivis Estate | 249A River Road, Baldivis WA 6171 | $1,553.55 |\n| Aust Post | Cloisters Square, Perth WA 6850 | $2,722.99 |"}
{"item_id": "item_0021", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total square footage for the General Education Programs, so I can see at a glance how much space each room type accounts for?", "table_markdown": "| PHILIP R. SMITH ED SPECS SPACE SUMMARY | | | | | |\n|---|---|---|---|---|---|\n| Room Description | | # of Students Per Room | Required # of Rooms | Square Feet Per Room | Total Area (Sq. Feet) |\n| | GENERAL EDUCATION PROGRAMS | | | | |\n| Early Childhood: Full-Day Kindergarten | | 24 | 3 | 1,100 | 3,300 |\n| Early Elementary: Grades 1 - 2 | | 24 | 6 | 890 | 5,340 |\n| Early Elementary: Classroom Toilets | | | 6 | 50 | 300 |\n| Intermediate: Grades 3-5 | | 24-26 | 9 | 890 | 8,010 |\n| Visual Arts – Classroom | | 24-26 | 1 | 1,100 | 1,100 |\n| Visual Arts - Kiln Room and Storage | | | 1 | 350 | 350 |\n| Music - Choral Room/General Music | | 24-26 | 1 | 1,000 | 1,000 |\n| Music - Instrumental Room | | | 1 | 1,000 | 1,000 |\n| Physical Education - Gymnasium | | | 1 | 4,700 | 4,700 |\n| Physical Education - Office and Storage | | | 1 | 600 | 600 |\n| Platform (Stage) | | | 1 | 900 | 900 |\n| STEM Lab Classroom | | 24-26 | 1 | 1,200 | 1,200 |\n| World Language Office | | | 1 | 150 | 150 |\n| Media Center | | | 1 | 2,240 | 2,240 |\n| Video Production Lab (in Media Center) | | | 1 | 100 | 100 |\n| General Ed. Intervention Services (Math, EL) | | | 2 | 400 | 800 |\n| General Ed. Intervention Services (Reading) | | | 1 | 700 | 700 |\n| Total | | | 38 | | 31,790 |\n| | SPECIAL EDUCATION PROGRAMS | | | | |\n| Special Education Resource Room | | | 2 | 350 | 700 |\n| K-5 OT/PT Resource Room | | | 1 | 500 | 500 |\n| Related Services Suite (Social Worker/ Psychologist/Speech) | | | 1 | 700 | 700 |\n| Total | | | 4 | | 1,900 |\n| | ADMINISTRATION AND SUPPORT FACILITIES | | | | |\n| Principal’s Office | | | 1 | 190 | 190 |\n| Main Office | | | 1 | 700 | 700 |\n| Conference Rooms | | | 2 | 200 | 400 |\n| Health Services | | | 1 | 650 | 650 |\n| Teacher Workroom | | | 1 | 440 | 440 |\n| Food Services - Cafeteria | | | 1 | 1,800 | 1,800 |\n| Food Services - Kitchen and Manager’s Office | | | 1 | 1,250 | 1,250 |\n| Faculty Lounge | | | 1 | 400 | 400 |\n| Custodial Office Area | | | 1 | 150 | 150 |\n| Instructional Storage | | | 1 | 400 | 400 |\n| General Storage | | | 1 | 700 | 700 |\n| Total | | | 12 | | 7,080 |"}
{"item_id": "item_0022", "chart_task_type": "paired_gap", "query": "Can you plot the discrepancy between Lot # 1 and Lot # 2 lactose readings for each cheese sample so I can see at a glance which one has the biggest gap?", "table_markdown": "| Samples ID | Samples Name | Lot # 1 | Lot # 2 | HPLC organic | Bias for Lot # 1 |\n|---|---|---|---|---|---|\n| 1 | Raw Blend Loaf | 6.55 | 6.43 | 6.45 | ‐0.10 |\n| 2 | Raw Blend Loaf | 7.53 | 6.84 | 7.13 | ‐0.40 |\n| 3 | Raw Blend Loaf | 7.20 | 6.88 | 6.81 | ‐0.39 |\n| 4 | Raw Blend Loaf | 6.77 | 6.75 | 7.15 | 0.38 |\n| 5 | Raw Blend Loaf | 7.78 | 7.01 | 7.68 | ‐0.10 |\n| 6 | Raw Blend Loaf | 6.77 | 7.25 | 7.60 | 0.83 |\n| 7 | Raw Blend Slice | 3.90 | 3.98 | 4.62 | 0.72 |\n| 8 | Raw Blend Slice | 4.45 | 4.21 | 3.86 | ‐0.58 |\n| 9 | Raw Blend Slice | 4.42 | 4.36 | 3.78 | ‐0.65 |\n| 10 | Finished Loaf | 6.12 | 5.95 | 6.41 | 0.29 |\n| 11 | Finished Loaf | 6.67 | 5.97 | 6.87 | 0.19 |\n| 12 | Finished Loaf | 6.12 | 5.88 | 7.00 | 0.88 |\n| 13 | Finished Loaf | 6.23 | 6.00 | 7.01 | 0.78 |\n| 14 | Finished Loaf | 6.30 | 5.69 | 7.14 | 0.84 |\n| 15 | Finished Loaf | 6.88 | 6.47 | 6.96 | 0.08 |\n| 16 | Finished Slice | 4.46 | 4.07 | 4.35 | ‐0.11 |\n| 17 | Finished Slice | 4.25 | 4.26 | 4.35 | 0.10 |\n| 18 | Finished Slice | 3.49 | 3.47 | 4.11 | 0.62 |\n| Min. absolute bias | | | | | 0.08 |\n| Max. absolute bias | | | | | 0.88 |"}
{"item_id": "item_0023", "chart_task_type": "paired_gap", "query": "Can you chart the difference in processing speed between POS tagging and full syntactic analysis for each language, making it easy to spot which one has the largest gap?", "table_markdown": "These results are quite good, compared to other NLP pro| Language | POS tagging | Syntactic analysis |\n|----------|-------------|--------------------|\n| eng | 12066 w/s | 4332 w/s |\n| fre | 8646 w/s | 6580 w/s |\n| spa | 23282 w/s | 11819 w/s |\n| ger | 4854 w/s | 4394 w/s |\n| ara | 436 w/s | 146 w/s |"}
{"item_id": "item_0024", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the total component quantities are dominated by a few items or spread out, so I can see at a glance how concentrated the demand is?", "table_markdown": "| Sl. No. | Description | Manufacturer Part. No. | Total Qty |\n|--------|-----------------------------------------------------------------------------|---------------------------------|-----------|\n| 1. | TE Connectivity Solistrand Series Uninsulated Crimp Ring Terminal, M3.5 Stud Size, 0.26mm² to 1.65mm² Wire Size | 34105 | 200 |\n| 2. | 200 piece Nuts and Washers, 3/8-32 UNEF 2B | HFBR-4411Z Broadcom | 25 |\n| 3. | 5MBd 820Nm Fiber Optic Receiver, Round, ST Connector | HFBR-2412TZ Broadcom | 25 |\n| 4. | 160MBd Fiber Optic Transmitter 865Nm, Round ST Connector, | HFBR-1414TZ Broadcom | 25 |\n| 5. | Device Driver Dual, 8-Pin, SOIC | SN75451BDR Texas Instruments | 20 |\n| 6. | Texas Instruments Hex CMOS Inverter, 4.5 → 5.5 V, 14-Pin SOIC | SN74HCT04D | 20 |\n| 7. | Line Transceiver, RS-2322-TX2-RX, 5V, 16-Pin SOIC | MAX232IDW Texas Instruments | 20 |\n| 8. | Quad Line Receiver, RS-422/ RS-422-A/ RS-423/ RS-423-A/ RS-485/ V.11, 5 V, 16-Pin SOIC | SN65LBC175DW Texas Instruments | 5 |\n| 9. | 4 (RS-422), 4 (RS-485)-TX ISO 8482, Line Transmitter Differential 5V, 16-Pin SOIC | SN75LBC174A16DW Texas Instruments | 5 |\n| 10. | Cap Panel Mount Fuse Holder for 5 x 20mm Fuse, PC1, IP66 | FX0345 Bulging 6.3A Slotted | 15 |\n| 11. | KEMET Paper Capacitor 4.7nF 250V ac )20% Tolerance PME271Y Through Hole +100 °C | PME271Y447MR30 | 50 |\n| 12. | 450V dc Aluminum Electrolytic Capacitor, Through Hole 16x29mm +105 °C 16mm 29mm | PEG124YF2100QL1 KEMET | 5 |"}
{"item_id": "item_0025", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the job changes were concentrated in a few key industries or spread out across the board, so I can see at a glance which sectors drove the majority of the shift?", "table_markdown": "| Oil and Gas Extraction | -4,900 | -9.7 |\n|---|---|---|\n| Support Activities for Mining | 3,600 | 10.3 |\n| Construction | -8,300 | -3.8 |\n| Manufacturing | 12,900 | 5.8 |\n| Durable Goods | 12,000 | 8.6 |\n| Non-Durable Goods | 900 | 1.1 |\n| Service Providing | 49,000 | 2.0 |\n| Trade, Transportation, and Utilities | -4,600 | -0.8 |\n| Wholesale Trade | -2,600 | -1.6 |\n| Retail Trade | -800 | -0.3 |\n| Transportation, Warehousing, Utilities | -1,200 | -0.9 |\n| Information | -700 | -2.1 |\n| Financial Activities | 1,700 | 1.1 |\n| Finance and Insurance | 2,100 | 2.1 |\n| Real Estate and Rental and Leasing | -400 | -0.7 |\n| Professional and Business Services | 16,600 | 3.5 |\n| Educational and Health Services | 13,000 | 3.4 |\n| Educational Services | 2,300 | 4.1 |\n| Health Care and Social Assistance | 10,700 | 3.3 |\n| Leisure and Hospitality | 11,400 | 3.6 |\n| Arts, Entertainment, and Recreation | 1,000 | 2.7 |\n| Accommodation and Food Services | 10,400 | 3.7 |\n| Other Services | 1,700 | 1.5 |\n| Government | 9,900 | 2.6 |\n| Source: Texas Workforce Commission | | |"}
{"item_id": "item_0026", "chart_task_type": "composition_compare", "query": "Can you chart the revenue mix for the General Fund versus the Special Fund so I can see at a glance how their funding structures differ?", "table_markdown": "| | General Fund | Special Fund | Totals |\n|--------------------------------|--------------|--------------|----------|\n| **REVENUES** | | | |\n| Property taxes | $361,719 | $-0- | $361,719 |\n| Income taxes | 79,073 | -0- | 79,073 |\n| Other local taxes | 809 | -0- | 809 |\n| Licenses and permits | 26,525 | -0- | 26,525 |\n| Intergovernmental | 83,417 | 11,262 | 94,679 |\n| Grants | -0- | 39,639 | 39,639 |\n| Miscellaneous | 28,653 | 30,239 | 58,892 |\n| **Total Revenues** | 580,196 | 81,140 | 661,336 |\n| **EXPENDITURES** | | | |\n| Current operating | | | |\n| General government | 561,897 | -0- | 561,897 |\n| Public safety | 67,330 | -0- | 67,330 |\n| Parks and recreation | 29,860 | -0- | 29,860 |\n| Capital outlay | 61,101 | 21,318 | 82,419 |\n| Special revenue fund | -0- | 164,918 | 164,918 |\n| **Total Expenditures** | 720,188 | 186,236 | 906,424 |\n| **EXCESS (DEFICIENCY) OF REVENUES OVER EXPENDITURES** | (139,992) | (105,096) | (245,088)|\n| **OTHER FINANCING SOURCES AND USES** | | | |\n| Reimbursements from other funds| 175,510 | -0- | 175,510 |\n| Loan proceeds | 2,922 | -0- | 2,922 |\n| **TOTAL OTHER FINANCING SOURCES AND USES** | 178,432 | -0- | 178,432 |\n| **NET CHANGE IN FUND BALANCES**| 38,440 | (105,096) | (66,656) |\n| **FUND BALANCES - BEGINNING** | 474,244 | 45,691 | 519,935 |\n| **FUND BALANCES - ENDING** | $512,684 | $(59,405) | $453,279 |"}
{"item_id": "item_0027", "chart_task_type": "momentum_turning", "query": "Can you chart the shifts in annual precipitation from 1965 to 1991? I want it to be obvious at a glance which year saw the biggest jump in rainfall.", "table_markdown": "| Year | July-Sept | Departure | Annual | Departure |\n|------|-----------|-----------|--------|-----------|\n| | (cm) | | | |\n| 1965 | 14.5 | −5.9 | 37.4 | −0.3 |\n| 1966 | 28.6 | +8.2 | 44.6 | +6.9 |\n| 1967 | 20.4 | 0.0 | 45.5 | +7.7 |\n| 1968 | 18.6 | −1.8 | 33.7 | −4.0 |\n| 1969 | 28.8 | +8.4 | 35.3 | −2.4 |\n| 1970 | 30.3 | +9.9 | 40.4 | +2.7 |\n| 1971 | 23.0 | +2.6 | 43.8 | +6.1 |\n| 1972 | 14.3 | −6.1 | 34.5 | −3.2 |\n| 1973 | 9.7 | −10.7 | 25.2 | −12.5 |\n| 1974 | 31.4 | +11.0 | 45.8 | +8.1 |\n| 1975 | 18.2 | −2.2 | 29.7 | −8.0 |\n| 1976 | 20.1 | −0.3 | 32.2 | −5.5 |\n| 1977 | 21.0 | +0.6 | 43.9 | +6.2 |\n| 1978 | 14.7 | −5.7 | 57.3 | +19.2 |\n| 1979 | 14.7 | −5.7 | 31.1 | −6.6 |\n| 1980 | 16.2 | −4.2 | 31.2 | −6.5 |\n| 1981 | 19.2 | −1.2 | 42.0 | +4.3 |\n| 1982 | 23.2 | +2.8 | 39.5 | +1.8 |\n| 1983 | 28.8 | +8.4 | 61.8 | +24.1 |\n| 1984 | 42.3 | +21.9 | 67.6 | +29.9 |\n| 1985 | 19.4 | −1.0 | 42.1 | +4.4 |\n| 1986 | 16.5 | −3.9 | 33.8 | −3.9 |\n| 1987 | 18.4 | −2.0 | 41.2 | +3.5 |\n| 1988 | 27.2 | +6.8 | 49.2 | +11.5 |\n| 1989 | 10.0 | −10.4 | 27.0 | −10.7 |\n| 1990 | 32.4 | +12.0 | 47.0 | +9.3 |\n| 1991 | 15.3 | −5.1 | 39.0 | +1.3 |\n| Mean 1936–2001 | 20.4 | | 37.1 | |"}
{"item_id": "item_0028", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of captured tax increment revenues over the plan's duration? I want it to be obvious at a glance exactly when the growth acceleration was sharpest.", "table_markdown": "| Year | Starting Value | Projected Increase | New Investment | Tax Day Value | Captured Value | Expenditure Period |\n|------|----------------|--------------------|----------------|---------------|----------------|-------------------|\n| 1998 | $6,934,497 | 110,952 | 2,515,990 | $9,561,439 | $2,626,942 | 1999-2000 |\n| 1999 | 9,561,439 | 191,229 | 250,000 | $10,002,668 | 3,068,171 | 2000-2001 |\n| 2000 | 10,002,668 | 200,053 | 250,000 | $10,452,721 | 3,518,224 | 2001-2002 |\n| 2001 | 10,452,721 | 209,054 | 250,000 | $10,911,776 | 3,977,279 | 2002-2003 |\n| 2002 | 10,911,776 | 218,236 | 250,000 | $11,380,011 | 4,445,514 | 2003-2004 |\n| 2003 | 11,380,011 | 227,600 | 500,000 | $12,107,611 | 5,173,114 | 2004-2005 |\n| 2004 | 12,107,611 | 242,152 | 500,000 | $12,849,763 | 5,915,266 | 2005-2006 |\n| 2005 | 12,849,763 | 256,995 | 500,000 | $13,606,759 | 6,672,262 | 2006-2007 |\n| 2006 | 13,606,759 | 272,135 | 500,000 | $14,378,894 | 7,444,397 | 2007-2008 |\n| 2007 | 14,378,894 | 287,578 | 500,000 | $15,166,472 | 8,231,975 | 2008-2009 |\n| 2008 | 15,166,472 | 303,329 | 500,000 | $15,969,801 | 9,035,304 | 2009-2010 |\n| 2009 | 15,969,801 | 319,396 | 500,000 | $16,789,197 | 9,854,700 | 2010-2011 |\n| 2010 | 16,789,197 | 335,784 | 500,000 | $17,624,981 | 10,690,484 | 2011-2012 |\n| 2011 | 17,624,981 | 352,500 | 500,000 | $18,477,481 | 11,542,984 | 2012-2013 |\n| 2012 | 18,477,481 | 369,550 | 500,000 | $19,347,030 | 12,412,533 | 2013-2014 |\n| 2013 | 19,347,030 | 386,941 | 750,000 | $20,483,971 | 13,549,474 | 2014-2015 |\n| 2014 | 20,483,971 | 409,679 | 750,000 | $21,643,650 | 14,709,153 | 2015-2016 |\n| 2015 | 21,643,650 | 432,873 | 750,000 | $22,826,523 | 15,892,026 | 2016-2017 |\n| 2016 | 22,826,523 | 456,530 | 750,000 | $24,033,054 | 17,098,557 | 2017-2018 |\n| 2017 | 24,033,054 | 480,661 | 750,000 | $25,263,715 | 18,329,218 | 2018-2019 |\n| 2018 | 25,263,715 | 505,274 | 750,000 | $26,518,989 | 19,584,492 | 2019-2020 |\n| 2019 | 26,518,989 | 530,380 | 750,000 | $27,799,369 | 20,864,872 | 2020-2021 |\n| 2020 | 27,799,369 | 555,987 | 750,000 | $29,105,356 | 22,170,859 | 2021-2022 |\n| 2021 | 29,105,356 | 582,107 | 750,000 | $30,437,464 | 23,502,967 | 2022-2023 |\n| 2022 | 30,437,464 | 608,749 | 750,000 | $31,796,213 | 24,861,716 | 2023-2024 |\n| 2023 | 31,796,213 | 635,924 | 1,000,000 | $33,432,137 | 26,497,640 | 2024-2025 |\n| 2024 | 33,432,137 | 668,643 | 1,000,000 | $35,100,780 | 28,166,283 | 2025-2026 |\n| 2025 | 35,100,780 | 702,016 | 1,000,000 | $36,802,795 | 29,868,298 | 2026-2027 |\n| 2026 | 36,802,795 | 736,056 | 1,000,000 | $38,538,851 | 31,604,354 | 2027-2028 |\n| 2027 | 38,538,851 | 770,777 | 1,000,000 | $40,309,628 | 33,375,131 | 2028-2029 |"}
{"item_id": "item_0029", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether employment in the Springfield Metro Area is concentrated among a few top employers or spread out, so I can see at a glance how dominant the largest companies are?", "table_markdown": "| LARGEST EMPLOYERS | # EMPLOYED |\n|------------------------------------------|------------|\n| CoxHealth | 12,178 |\n| Mercy Hospital Springfield | 9,214 |\n| State of Missouri | 5,411 |\n| Walmart and Sam’s Club | 4,981 |\n| Springfield Public Schools | 3,685 |\n| Bass Pro Shops | White River Marine Group (HQ) | 2,989 |\n| O’Reilly Auto Parts (HQ) | 2,631 |\n| United States Government | 2,425 |\n| Jack Henry & Associates, Inc. | 2,262 |\n| Citizens Memorial Healthcare | 2,038 |\n| Burrell Behavioral Health | 1,872 |\n| Missouri State University | 1,861 |\n| City of Springfield | 1,857 |\n| SRC Holdings (HQ) | 1,750 |\n| EFCO (HQ) | 1,600 |\n| Prime Inc. (HQ) | 1,425 |\n| Amazon | 1,400 |\n| RPS (Pyramid Foods) | 1,300 |\n| Ozarks Technical Community College | 1,125 |\n| Greene County | 1,113 |\n| Chase Card Services | 1,100 |\n| Expedia Inc. | 1,100 |\n| Great Southern Bank (HQ) | 1,100 |\n| American National Property & Casualty Co.| 1,034 |\n| Kraft Heinz Company | 997 |\n| Lowe’s (6 locations) | 969 |"}
{"item_id": "item_0030", "chart_task_type": "mix_trend", "query": "Can you chart how the reliance on donations, government grants, and other sources shifted as a share of total receipts from 2010 to 2014?", "table_markdown": "| Year | Donations | Govt Grants | Others |\n|------|-----------|-------------|--------|\n| 2010 | 1.8 | 4.8 | 4.1 |\n| 2011 | 2.0 | 5.4 | 3.8 |\n| 2012 | 2.3 | 6.0 | 4.3 |\n| 2013 | 2.5 | 6.4 | 5.0 |\n| 2014 | 2.5 | 7.0 | 5.1 |"}
{"item_id": "item_0031", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between RMSE and MAPE for the different experiments so I can easily identify which model configuration corresponds to each point?", "table_markdown": "| Exp | Model Inputs | RMSE [Mg/ha] | MAE [Mg/ha] | MAPE [%] |\n|-----|---------------------------------------------------|--------------|-------------|----------|\n| 1 | Sentinel-2 only | 82.4 | 37.7 | 86.6 |\n| 2 | Sentinel-2, DEM, geo-coordinates | **81.1** | **35.9** | 79.6 |\n| 3 | Sentinel-2, ESA WorldCover | 82.7 | 36.1 | **71.5** |\n| 4 | Sentinel-2, WTE | 83.2 | 37.95 | 87.4 |\n| 5 | Sentinel-2, Vegetation Indices | 83.59 | 37.05 | 83.7 |\n| 6 | Sentinel-2, Monte Carlo Dropout | 83.6 | 37.1 | 76.2 |"}
{"item_id": "item_0032", "chart_task_type": "baseline_index", "query": "I'm curious how the yields for Wheat, Maize, and Pulses changed from 2014 to 2018 when viewed relative to their 2014 starting points, so it's easy to spot which crop grew the most in proportion to where it began.", "table_markdown": "| Year | Crop | Yield (kg/ha) | Cropped Area (ha) |\n|---|---|---|---|\n| 2014 | Wheat | 2,500 | 100,000 |\n| | Maize | 3,200 | 80,000 |\n| | Pulses | 800 | 60,000 |\n| 2015 | Wheat | 2,700 | 110,000 |\n| | Maize | 3,100 | 85,000 |\n| | Pulses | 850 | 55,000 |\n| 2016 | Wheat | 2,400 | 95,000 |\n| | Maize | 2,900 | 75,000 |\n| | Pulses | 750 | 50,000 |\n| 2017 | Wheat | 2,800 | 105,000 |\n| | Maize | 3,300 | 90,000 |\n| | Pulses | 900 | 65,000 |\n| 2018 | Wheat | 2,600 | 100,000 |\n| | Maize | 3,400 | 95,000 |"}
{"item_id": "item_0033", "chart_task_type": "baseline_index", "query": "I'd like to see the percentage of hours earned for the seven student subpopulations from Fall 2010 to Fall 2012 shown as if they all began at the same level, so it's easy to spot which group climbed furthest from its own starting point.", "table_markdown": "| New Students | | | |\n|---|---|---|---|\n| | Fall 2010 | Fall 2011 | Fall 2012 |\n| Total New Students | 44.84% | 53.73% | 57.9% |\n| Full-time Freshmen | 41.72% | 50.65% | 55.4% |\n| Part-time Freshmen | 37.63% | 33.15% | 67.0% |\n| Full-Time Transfer | 57.77% | 66.27% | 49.3% |\n| Part-Time Transfer | 63.01% | 71.27% | 72.4% |\n| 25 and Older | 53.01% | 57.53% | 63.8% |\n| Pell Grant | 38.50% | 46.69% | 50.4% |\n| Learning Support | 25.29% | 29.55% | 34.0% |"}
{"item_id": "item_0034", "chart_task_type": "baseline_index", "query": "Could you show me the growth of colleges and universities from 2015-16 to 2019-20 relative to their own starting points, so it stands out which one gained the most ground from where it began?", "table_markdown": "| | 2015-16 | 2016-17 | 2017-18 | 2018-19 | 2019-20 |\n|---|---|---|---|---|---|\n| Colleges | 39071 | 40026 | 39050 | 39931 | 42343 |\n| Universities | 799 | 864 | 903 | 993 | 1043 |"}
{"item_id": "item_0035", "chart_task_type": "baseline_index", "query": "Show me the FRI scores for the PRP and control groups over the 8 weeks, with each group's values measured against its own baseline, so I can quickly tell which one moved furthest from its starting point.", "table_markdown": "| Outcome | Time | Control Mean | SD | PRP Mean | SD | P Value* |\n|--------------------------|--------|--------------|-------|----------|-------|----------|\n| FRI | Baseline | 45.37 | 15.61 | 51.47 | 15.62 | .027 |\n| | 1 wk | 45.99 | 15.74 | 49.83 | 15.72 | |\n| | 4 wk | 44.17 | 17.14 | 43.25 | 16.68 | |\n| | 8 wk | 44.45 | 19.60 | 37.99 | 19.60 | |\n| SF-36 Pain | Baseline | 47.92 | 21.13 | 43.28 | 21.11 | .079 |\n| | 1 wk | 47.22 | 21.76 | 40.52 | 21.76 | |\n| | 4 wk | 47.22 | 19.98 | 55.17 | 19.98 | |\n| | 8 wk | 52.78 | 22.19 | 61.29 | 22.19 | |\n| SF-36 Physical Function | Baseline | 56.11 | 18.54 | 56.40 | 18.52 | .435 |\n| | 1 wk | 51.28 | 20.04 | 51.63 | 20.46 | |\n| | 4 wk | 60.97 | 21.43 | 58.43 | 21.17 | |\n| | 8 wk | 57.08 | 22.91 | 61.70 | 22.89 | |\n| Current Pain | Baseline | 4.61 | 2.21 | 4.74 | 2.21 | .157 |\n| | 1 wk | 4.78 | 1.99 | 4.21 | 1.99 | |\n| | 4 wk | 4.61 | 2.21 | 4.00 | 2.21 | |\n| | 8 wk | 4.39 | 2.59 | 3.09 | 2.59 | |\n| Best Pain | Baseline | 2.08 | 1.74 | 2.81 | 1.78 | .015 |\n| | 1 wk | 2.44 | 1.82 | 2.88 | 1.83 | |\n| | 4 wk | 2.28 | 1.82 | 2.53 | 1.83 | |\n| | 8 wk | 2.72 | 2.12 | 2.00 | 2.06 | |\n| Worst Pain | Baseline | 7.72 | 1.53 | 7.98 | 1.56 | .086 |\n| | 1 wk | 7.39 | 1.95 | 6.86 | 1.94 | |\n| | 4 wk | 7.11 | 1.91 | 6.41 | 1.88 | |\n| | 8 wk | 6.83 | 2.33 | 5.82 | 2.33 | |"}
{"item_id": "item_0036", "chart_task_type": "baseline_index", "query": "Help me compare the diastolic blood pressure for Group I and Group II relative to where each one began, so it stands out which group fell the most compared with its own starting level throughout the procedure.", "table_markdown": "| Time | Group I (n-30) | Group II (n-30) | F | p-value |\n|---|---|---|---|---|\n| Baseline | 87.43±4.04 | 86.40±4.53 | 0.799 | 0.355 |\n| At 0 minute | 58.06±6.17 | 74.66±10.63 | 7.74 | 0 |\n| At 1 minute | 56.23±5.70 | 69.73±11.97 | 13.73 | 0 |\n| At 2 minute | 57.86±5.37 | 80.36±7.55 | 3.37 | 0 |\n| At 5 minute | 58.56±7.69 | 80.20±6.25 | 0.06 | 0 |\n| At 10 minute | 56.93±5.81 | 79.63±6.17 | 0.8 | 0 |\n| At 30 minute | 61.30±9.21 | 80.43±5.82 | 1.95 | 0 |\n| At 60 minute | 58.23±6.32 | 80.56±7.33 | 4.97 | 0 |"}
{"item_id": "item_0037", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between maximum head acceleration and HIC for the different impact scenarios, making it easy to spot how each padding type and impact point balances these two safety metrics?", "table_markdown": "| Impact point | Padding | $a_{\\text{max}}$ [g] | HIC | Limits |\n|--------------|---------|---------------------|-------|-----------------|\n| Point P | EPS | 215 | 1,612 | |\n| | MA | 344 | 2,909 | |\n| Point B | EPS | 192 | 2,409 | |\n| | MA | 333 | 2,899 | HIC < 2400 |\n| Point R | EPS | 195 | 866 | $a_{\\text{max}} < 275$ g |\n| | MA | 167 | 1,250 | |\n| Point X | EPS | 179 | 549 | |\n| | MA | 317 | 858 | |"}
{"item_id": "item_0038", "chart_task_type": "stability_volatility", "query": "Help me see the actual δ15N measurements for LUK 1459, KUW 16, and KUW 9 along with how scattered they were, so I can quickly tell which group was the most consistent.", "table_markdown": "| Method 1 | Sample and section number | δ15N | δ13C | Amt% C | Amt% N | C:N |\n|---------|---------------------------|------|------|--------|--------|-----|\n| * | LUK 1459 M1 1 | 13.1 | -19.8| 40.1 | 14.6 | 3.2 |\n| | LUK 1459 M1 2 | 12.5 | -20.1| 40.6 | 13.8 | 3.4 |\n| | LUK 1459 M1 3 | 12.3 | -19.9| 40.3 | 14.8 | 3.2 |\n| | LUK 1459 M1 4 | 12.1 | -20.0| 41.2 | 15.0 | 3.2 |\n| | LUK 1459 M1 5 | 12.4 | -19.7| 40.8 | 15.0 | 3.2 |\n| | LUK 1459 M1 6 | 11.9 | -19.5| 39.3 | 14.4 | 3.2 |\n| | LUK 1459 M1 7 | 11.7 | -19.4| 43.0 | 15.9 | 3.2 |\n| | LUK 1459 M1 8 | 11.7 | -19.5| 42.3 | 15.5 | 3.2 |\n| | LUK 1459 M1 9 | 11.7 | -19.4| 41.0 | 15.4 | 3.1 |\n| | LUK 1459 M1 10 | 11.6 | -19.6| 40.6 | 14.5 | 3.3 |\n| | LUK 1459 M1 11 | 11.4 | -19.6| 40.0 | 14.7 | 3.2 |\n| | LUK 1459 M1 12 | 11.3 | -19.7| 40.0 | 14.8 | 3.2 |\n| | LUK 1459 M1 13 | 11.1 | -19.8| 40.8 | 15.0 | 3.2 |\n| | LUK 1459 M1 14 | 11.1 | -19.8| 42.5 | 15.6 | 3.2 |\n| | LUK 1459 M1 15 | 11.4 | -19.9| 40.8 | 14.8 | 3.2 |\n| | LUK 1459 M1 16 | 11.3 | -20.0| 40.7 | 14.5 | 3.3 |\n| | LUK 1459 M1 17 | 11.4 | -19.7| 40.7 | 15.0 | 3.2 |\n| | LUK 1459 M1 18 | 11.3 | -19.9| 41.2 | 15.0 | 3.2 |\n| | KUW 16 M1 1 | 11.1 | -18.3| 31.3 | 11.7 | 3.1 |\n| | KUW 16 M1 2 | 10.8 | -19.6| 57.4 | 20.0 | 3.3 |\n| | KUW 16 M1 3 | 10.6 | -19.8| 55.0 | 20.4 | 3.1 |\n| | KUW 16 M1 4 | 10.6 | -20.3| 56.4 | 20.9 | 3.2 |\n| | KUW 16 M1 5 | 10.6 | -21.0| 55.1 | 19.5 | 3.3 |\n| | KUW 16 M1 6 | 10.7 | -20.6| 50.3 | 18.5 | 3.2 |\n| | KUW 16 M1 7 | 11.6 | -19.3| 52.5 | 19.2 | 3.2 |\n| | KUW 16 M1 8 | 11.8 | -19.5| 50.1 | 18.3 | 3.2 |\n| | KUW 9 M1 1 | 11.4 | -20.8| 11.2 | 4.5 | 2.9 |\n| | KUW 9 M1 2 | 11.3 | -20.7| 20.7 | 8.1 | 3.0 |\n| | KUW 9 M1 3 | 11.1 | -20.8| 26.8 | 10.5 | 3.0 |\n| | KUW 9 M1 4 | 11.0 | -20.8| 20.5 | 8.0 | 3.0 |\n| | KUW 9 M1 5 | 11.3 | -20.8| 6.6 | 2.6 | 3.0 |\n| | KUW 9 M1 6 | 11.5 | -20.7| 17.3 | 7.0 | 2.8 |\n| | KUW 9 M1 7 | 11.6 | -20.7| 16.7 | 6.7 | 2.9 |\n| | KUW 9 M1 8 | 11.5 | -20.6| 9.5 | 3.7 | 2.9 |\n| | KUW 9 M1 9 | 11.4 | -20.5| 25.6 | 10.1 | 3.0 |\n| | KUW 9 M1 10 | 11.0 | -20.4| 13.6 | 5.7 | 2.8 |\n| | KUW 9 M1 11 | 10.5 | -20.5| 16.5 | 6.6 | 2.9 |\n| | KUW 9 M1 12 | 10.0 | -20.6| 13.6 | 5.6 | 2.8 |\n| | KUW 9 M1 13 | 9.5 | -20.7| 7.9 | 3.4 | 2.7 |\n| | KUW 9 M1 14 | 10.2 | -20.4| 11.9 | 5.0 | 2.8 |"}
{"item_id": "item_0039", "chart_task_type": "stability_volatility", "query": "Could you show me the actual enrollment numbers for All Students, Graduate, and Undergraduate levels from Fall 2013 to Fall 2022 alongside how widely they varied, so it's easy to spot which group was the most stable?", "table_markdown": "| Semester | All Students | Graduate | Undergraduate |\n|------------|--------------|----------|---------------|\n| Fall 2013 | 5,953 | 3,492 | 2,461 |\n| Fall 2014 | 6,158 | 3,748 | 2,410 |\n| Fall 2015 | 7,468 | 4,473 | 2,995 |\n| Fall 2016 | 8,952 | 5,146 | 3,806 |\n| Fall 2017 | 10,229 | 5,568 | 4,661 |\n| Fall 2018 | 10,480 | 5,834 | 4,646 |\n| Fall 2019 | 10,425 | 6,012 | 4,413 |\n| Fall 2020 | 10,561 | 6,020 | 4,541 |\n| Fall 2021 | 10,386 | 5,867 | 4,519 |\n| Fall 2022 | 10,380 | 5,828 | 4,552 |"}
{"item_id": "item_0040", "chart_task_type": "tradeoff", "query": "Can you chart how IQ trades off against GDP per capita for these European countries, making it easy to spot the relationship between intelligence scores and economic prosperity for each nation?", "table_markdown": "| Country | IQ Nation | GDP per capita (1,000 USD) |\n|---|---|---|\n| Albania | 84 | 4.61 |\n| Andorra | 85 | 39.44 |\n| Austria | 92 | 47.42 |\n| Belarus | 95 | 6.66 |\n| Belgium | 93 | 43.32 |\n| Bosnia and Herzegovina | 82 | 4.48 |\n| Bulgaria | 91 | 6.93 |\n| Czech Republic | 91 | 18.64 |\n| Montenegro | 87 | 6.45 |\n| Denmark | 88 | 56.43 |\n| Estonia | 94 | 16.77 |\n| Finland | 89 | 45.86 |\n| France | 87 | 41.28 |\n| Greece | 84 | 23.02 |\n| Teh Netherlands | 87 | 45.87 |\n| Croatia | 88 | 13.28 |\n| Ireland | 87 | 32.93 |\n| Island | 93 | 42.9 |\n| Italy | 90 | 33.55 |\n| Cyprus | 96 | 20.37 |\n| Latvia | 91 | 13.98 |\n| Liechtenstein | 88 | 163.68 |\n| Lithuania | 92 | 11.67 |\n| Luxembourg | 102 | 103.69 |\n| Hungary | 88 | 12.86 |\n| Macedonia | 88 | 4.85 |\n| Malta | 91 | 21.79 |\n| Moldova | 78 | 1.97 |\n| Germany | 100 | 42.14 |\n| Norway | 85 | 97.77 |\n| Poland | 86 | 12.53 |\n| Portugal | 86 | 20.33 |\n| Romania | 87 | 8.4 |\n| Russia | 76 | 13.75 |\n| San Marino | 94 | 59.63 |\n| Slovakia | 87 | 16.99 |\n| Slovenia | 90 | 22.32 |\n| Spain | 71 | 29.01 |\n| Serbia | 87 | 5.15 |\n| Switzerland | 88 | 79.02 |\n| Sweden | 91 | 54.41 |\n| Turkey | 88 | 10.29 |\n| United Kingdom | 86 | 38.32 |\n| Ukraine | 82 | 3.88 |"}
{"item_id": "item_0041", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between airflow performance and horsepower for these compressor models so I can easily identify each unit's efficiency profile?", "table_markdown": "| Model | SCFM @ 100 PSIG* | HP | Phase |\n|---|---|---|---|\n| OTS110 | 3 | 1 | 1 |\n| OTS010 | 3 | 1 | 3 |\n| OTS115 | 4.2 | 1.5 | 1 |\n| OTS015 | 4.2 | 1.5 | 3 |\n| OTS120 | 7.2 | 2 | 1 |\n| OTS020 | 7.2 | 2 | 3 |\n| OTS130 | 10.1 | 3 | 1 |\n| OTS030 | 10.1 | 3 | 3 |\n| OTS151 | 18.2 | 5 | 1 |\n| OTS050 | 18.2 | 5 | 3 |\n| OTS075 | 28.4 | 7.5 | 3 |\n| OTS100 | 35.4 | 10 | 3 |\n| OTS150 | 45.2 | 15 | 3 |"}
{"item_id": "item_0042", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual trade share values for the Czech Republic, Hungary, Slovakia, and the EU27, along with how widely each one fluctuated, so it's obvious right away which was the most volatile.", "table_markdown": "| Czech Republic | 10.8 | 12.5 | 13.9 | 15.8 | 19.4 | 15.2 | 16.7 | 18.4 |\n|---|---|---|---|---|---|---|---|---|\n| Hungary | 9.8 | 9.8 | 13 | 15.2 | 16.6 | 16.8 | 15.6 | 13.4 |\n| Slovakia | 9.1 | 12.1 | 12.5 | 17 | 25.6 | 24.9 | 25.4 | 23.9 |\n| EU27 | 4.8 | 5.4 | 6.2 | 7.2 | 7.9 | 6 | 6.3 | 6.9 |\n| Imports | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 |\n| Czech Republic | 19.7 | 29.7 | 29.2 | 22.9 | 26.8 | 22.5 | 20.4 | 14.8 |\n| Hungary | 18.8 | 24.7 | 27.6 | 22.5 | 28.2 | 23.4 | 24.2 | 28.3 |\n| Slovakia | 43.5 | 48.2 | 45.2 | 35.8 | 38.9 | 34.6 | 34 | 40.4 |\n| EU27 | 8.2 | 9.5 | 10.3 | 10 | 11.3 | 9.6 | 10.5 | 11.6 |"}
{"item_id": "item_0043", "chart_task_type": "stability_volatility", "query": "Could you show me the monthly sales volume for each price band along with how much each one jumped around relative to its own typical level, so it's obvious right away which range was the most volatile?", "table_markdown": "| Price Range | Active Listings | Oct-23 | Nov-23 | Dec-23 | Jan-24 | Feb-24 | Mar-23 | Current Months of Inventory | Last 3 Month Trend Months of Inventory | Market Conditions |\n|----------------------|-----------------|--------|--------|--------|--------|--------|--------|-------------------------------|----------------------------------------|------------------|\n| $1 - 49,999 | 51 | 19 | 6 | 7 | 15 | 9 | 19 | 2.7 | 3.7 | Seller |\n| $50,000 - 74,999 | 67 | 4 | 3 | 4 | 7 | 6 | 5 | 13.4 | 12.4 | Buyer |\n| $75,000 - 99,999 | 90 | 7 | 7 | 10 | 6 | 9 | 8 | 11.3 | 11.7 | Buyer |\n| $100,000 - 124,999 | 42 | 13 | 3 | 5 | 8 | 5 | 5 | 8.4 | 6.9 | Slightly Buyer |\n| $125,000 - 149,999 | 48 | 4 | 4 | 6 | 1 | 2 | 7 | 6.9 | 15.1 | Buyer |\n| $150,000 - 174,999 | 47 | 2 | 7 | 2 | 1 | 4 | 4 | 11.8 | 13.6 | Buyer |\n| $175,000 - 199,999 | 53 | 2 | 5 | 7 | 3 | 2 | 1 | 53.0 | 27.2 | Buyer |\n| $200,000 - 224,999 | 40 | 2 | 2 | 2 | 4 | 0 | 2 | 20.0 | 18.2 | Buyer |\n| $225,000 - 249,999 | 39 | 5 | 1 | 0 | 4 | 1 | 2 | 19.5 | 14.9 | Buyer |\n| $250,000 - 274,999 | 30 | 1 | 0 | 3 | 1 | 1 | 0 | n/a | 46.5 | Buyer |\n| $275,000 - 299,999 | 17 | 1 | 2 | 2 | 2 | 0 | 1 | 170 | 19.7 | Buyer |\n| $300,000 - 349,999 | 33 | 1 | 1 | 2 | 0 | 0 | 4 | 8.3 | 25.3 | Buyer |\n| $350,000 - 399,999 | 41 | 1 | 1 | 0 | 2 | 1 | 0 | n/a | 39.7 | Buyer |\n| $400,000 - 499,999 | 41 | 1 | 0 | 4 | 1 | 1 | 1 | 41.0 | 38.0 | Buyer |\n| $500,000 - 599,999 | 20 | 0 | 0 | 0 | 0 | 2 | 0 | n/a | 28.0 | Buyer |\n| $600,000 - 699,999 | 14 | 0 | 0 | 1 | 0 | 0 | 0 | n/a | n/a | n/a |\n| $700,000 - 799,999 | 11 | 0 | 0 | 0 | 0 | 1 | 0 | n/a | 30.0 | Buyer |\n| $800,000 - 899,999 | 5 | 0 | 0 | 0 | 1 | 0 | 0 | n/a | 22.0 | Buyer |\n| $900,000 - 999,999 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | n/a | n/a | n/a |\n| $1,000,000 - and over| 22 | 0 | 2 | 1 | 0 | 0 | 0 | n/a | n/a | n/a |"}
{"item_id": "item_0044", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual figures for each education and financial wellness benefit over the years along with how widely they fluctuated relative to their typical level, so the most volatile one is obvious right away.", "table_markdown": "| Education and Financial Wellness | 2016 | 2017 | 2018 | 2019 | 2020 | 1 year change |\n|-----------------------------------------------------------------------|------|------|------|------|------|---------------|\n| Undergraduate or graduate tuition assistance | 71% | 74% | 77% | 81% | 71% | -10% |\n| Student loan repayment assistance | 6% | 15% | 8% | 16% | 16% | 0% |\n| 529 plan payroll deduction (tax-advantaged savings plan designed to encourage saving for future college costs) | 15% | 23% | 22% | 21% | 20% | -1% |\n| Non-retirement financial advice offered online, in a group/classroom, or one-on-one | 49% | 53% | 61% | 52% | 46% | -6% |\n| Credit counseling service (e.g., credit, debt consolidation, housing counseling) | 21% | 16% | 14% | 30% | 27% | -3% |\n| Paycards (payroll debit cards that enable employers to pay employees through payroll direct deposit even if they do not have bank accounts) | 28% | 46% | 32% | 34% | 24% | -10% |\n| Loans to employees for emergency/disaster assistance | 12% | 16% | 20% | 20% | 27% | 7% |\n| Employer contribution or match for 529 plan | -- | 2% | 6% | 5% | 3% | -2% |"}
{"item_id": "item_0045", "chart_task_type": "tradeoff", "query": "Can you plot how breath capture efficiency trades off against acetone recovery for the different samplers, so I can see at a glance which designs offer the best balance?", "table_markdown": "| Sampler description | CO (%) 2 | | H O (%) 2 | | Methanol ppbv | | Acetone ppbv | | Ammonia ppbv | |\n|---|---|---|---|---|---|---|---|---|---|---|\n| | 0h | 6h | 0h | 6h | 0h | 6h | 0h | 6h | 0h | 6h |\n| 1.Full snout sampler | 0.57 | 0.47 | * | 2.52 | * | 338 | * | 284 | * | 137 |\n| 2.Full snout sampler pre-heated | 0.6 | 0.57 | * | 2.73 | * | 307 | * | 229 | * | 176 |\n| 3.Full snout sampler with pre- flush of breath | 1.23 | 1.17 | * | 2.83 | * | 277 | * | 330 | * | 172 |\n| 4.Small sized snout sampler | 0.93 | 0.87 | * | 2.75 | * | 291 | * | 315 | * | 117 |\n| 5.Simple nostril sampler | 2.13 | 2.00 | * | 3.32 | * | 286 | * | 407 | * | 175 |\n| 6.Long narrow nostril sampler | 2.75 | 2.7 | 4.06 | 1.5 | 200 | 242 | 2808 | 2297 | 90 | 134 |\n| 7.Long thick nostril sampler | 2.85 | 2.7 | 3.3 | 1.52 | 212 | 268 | 2216 | 2073 | 95 | 159 |\n| 8.Short narrow nostril sampler | 2.45 | 2.15 | 3.66 | 1.65 | 216 | 296 | 2625 | 2343 | 94 | 163 |\n| 9.Short thick nostril sampler | 3.05 | 2.55 | 3.19 | 1.62 | 277 | 363 | 1961 | 1935 | 92 | 185 |"}
{"item_id": "item_0046", "chart_task_type": "concentration_long_tail", "query": "Can you chart the distribution of total royalty and fee income across the UC entities so I can see at a glance whether a few campuses dominate the revenue?", "table_markdown": "| | UCB | UCD | UCI | UCLA | UCM | UCR | UCSB | UCSC | UCSD | UCSF | LBNL | UC System | % change from FY14 |\n|----------------------|------|------|------|------|------|------|------|------|------|------|------|-----------|-------------------|\n| **Inventions** | | | | | | | | | | | | | |\n| Inventions Disclosed | 208 | 237 | 126 | 408 | 16 | 52 | 69 | 34 | 340 | 196 | 112 | 1,745 | -1.4% |\n| Total Active Inventions | 1,675 | 1,333 | 964 | 2,346 | 42 | 508 | 551 | 329 | 2,909 | 1,830 | ** | 12,203** | 2.0% |\n| **Patent Prosecution** | | | | | | | | | | | | | |\n| US Applications Filed| | | | | | | | | | | | | |\n| First U.S. Filings | 132 | 79 | 71 | 212 | 8 | 25 | 51 | 19 | 162 | 80 | 54 | 867 | -4.3% |\n| Secondary U.S. Filings| 93 | 98 | 85 | 249 | 2 | 45 | 57 | 19 | 109 | 68 | 100 | 889 | 2.9% |\n| U.S. Filings | 225 | 177 | 156 | 461 | 10 | 70 | 108 | 38 | 271 | 148 | 154 | 1,756 | -0.8% |\n| First Foreign Filings| 76 | 64 | 28 | 96 | 4 | 19 | 21 | 13 | 66 | 40 | 17 | 418 | -8.3% |\n| Patents Issued | | | | | | | | | | | | | |\n| U.S. Patents Issued | 56 | 36 | 49 | 120 | 2 | 11 | 53 | 13 | 97 | 49 | 48 | 520 | 4.8% |\n| Total Active U.S. Patents | 697 | 420 | 445 | 918 | 8 | 126 | 483 | 115 | 866 | 627 | ** | 4,621** | 3.9% |\n| Foreign Patents Issued| 80 | 85 | 25 | 242 | 3 | 13 | 70 | 7 | 192 | 74 | 19 | 795 | 17.1% |\n| Total Active Foreign Patents | 618 | 538 | 396 | 999 | 4 | 175 | 211 | 42 | 1,010 | 858 | ** | 4,833** | 11.1% |\n| **Licensing** | | | | | | | | | | | | | |\n| Letters of Intent (LOI) Issued | 30 | 36 | 24 | 54 | 1 | 2 | 16 | 0 | 30 | 23 | 1 | 215 | 8.6% |\n| Options Issued | 15 | 1 | 5 | 12 | 0 | 4 | 0 | 0 | 0 | 5 | 11 | 53 | -7.0% |\n| Total Active Options | 67 | 7 | 9 | 23 | 2 | 10 | 4 | 3 | 6 | 13 | 10 | 151 | 2.0% |\n| Utility Licenses Issued | 23 | 17 | 15 | 31 | 3 | 7 | 4 | 1 | 49 | 37 | 4 | 186 | 0.0% |\n| Total Active Utility Licenses | 343 | 152 | 102 | 293 | 5 | 44 | 65 | 21 | 352 | 403 | 53 | 1,749 | 2.8% |\n| Plant Licenses Issued| 0 | 40 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 53 | 29.3% |\n| Total Active Plant Licenses | 0 | 487 | 0 | 0 | 0 | 211 | 0 | 0 | 0 | 0 | 0 | 666 | 1.4% |\n| **Startup Companies**| | | | | | | | | | | | | |\n| Startup Companies Formed | 13 | 9 | 7 | 25 | 1 | 3 | 3 | 0 | 15 | 10 | 3 | 85 | -1.2% |\n| **Royalty & Fee Income**** (in thousands)** | | | | | | | | | | | | | |\n| Running Royalties | $1,698 | $11,316 | $2,028 | $48,820 | $0 | $3,193 | $1,263 | $7 | $14,195 | $19,273 | $774 | $105,181 | 41.3% |\n| Equity Income | $1,664 | $0 | $0 | $2,896 | $0 | $0 | $0 | $0 | $1,745 | $0 | $0 | $6,305 | 46.1% |\n| Other Royalty and Fee Income | $3,857 | $808 | $4,711 | $22,057 | $5 | $956 | $2,577 | $221 | $3,723 | $24,577 | $1,561 | $65,673 | 66.4% |\n| Total Royalty & Fee Income | $7,219 | $12,124 | $6,739 | $73,773 | $5 | $4,149 | $3,840 | $228 | $19,663 | $43,850 | $2,335 | $177,159 | 49.8% |\n| **Distributions (in thousands)** | | | | | | | | | | | | | |\n| Inventor Shares Distributed | $1,899 | $4,357 | $2,166 | $14,620 | $0 | $1,062 | $1,229 | $44 | $7,778 | $7,527 | $1,179 | $43,421 | 22.2% |"}
{"item_id": "item_0047", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the fastening screw quantities for the GRP short tail configuration are dominated by a few components or spread out evenly?", "table_markdown": "| GRP | | | |\n|---|---|---|---|\n| Short tail | R9911 | | |\n| Component | | Quantity | Position |\n| M8x30 screw | | 9 | A, B |\n| M8x50 screw | | 18 | C, D |\n| M8 body spacer | | 27 | A, B, C, D |\n| M8 locking nut | | 18 | C, D |\n| M8 spacer | | 18 | C, D |\n| Plastic screw | | 6 | E |\n| M8 grease nipple | | 1 | |\n| M6x16 screw | | 4 | F |\n| M6 spacer | | 4 | F |"}
{"item_id": "item_0048", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the cases are concentrated among a few lesion sites or spread out, so I can see at a glance which sites dominate the total?", "table_markdown": "| Site of lesion | No of cases | Cytologically Diagnosed cases | Histopathologically Correlated cases | Histopathological diagnosis | Histopathologically not Correlated cases | Histopathologically not done cases |\n|----------------|-------------|-------------------------------|--------------------------------------|-------------------------------------------------------------------------------------------|------------------------------------------|------------------------------------|\n| Liver | 20 | 18 | 16 | Hepatocellular carcinoma(16) Ileocecal TB(5) Stomach lymphoma(1) Stomach adenocarcinoma(1) Colon adenocarcinoma(2) Wilms tumor(4) Renal cell carcinoma(3) Gaucher disease(2) | 02 | 02 |\n| GIT | 13 | 11 | 09 | Pancreatic adenocarcinoma(1) Gall bladder adenocarcinoma (1) | 00 | 04 |\n| Kidney | 11 | 10 | 07 | Tuberculous lymphadenitis(1) | 01 | 03 |\n| Spleen | 02 | 02 | 02 | | 00 | 00 |\n| Pancreas | 02 | 01 | 01 | | 00 | 01 |\n| Gall bladder | 01 | 01 | 01 | | 00 | 00 |\n| Mesenteric LN | 01 | 01 | 00 | | 01 | 00 |\n| Total | 50 | 44 | 36 | | 04 | 10 |"}
{"item_id": "item_0049", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the respondents are concentrated among a few specific banks or if the distribution is more even across the options?", "table_markdown": "| S. No | Name of the Bank | No of Respondents |\n|---|---|---|\n| 1 | Indian Overseas Bank | 19 |\n| 2 | Indian Bank | 16 |\n| 3 | Bharath State Bank of India | 17 |\n| 4 | ICICI | 9 |\n| 5 | City Union Bank | 21 |\n| 6 | Others | 18 |\n| Total | | 100 |"}
{"item_id": "item_0050", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the respondents' basketball career goals are concentrated among a few specific levels, so I can see at a glance which targets dominate the group's ambitions?", "table_markdown": "| Basketball Career Goal | Number Hoping to Reach Goal | Respondents for Whom Nation Is Significant |\n|------------------------|------------------------------|---------------------------------------------|\n| | | Number | Percentage |\n| School team | 5 | 0 | 0 |\n| County team | 5 | 1 | 20 |\n| Provincial team | 6 | 1 | 17 |\n| (W)CBA team | 12 | 4 | 33 |\n| National team | 8 | 4 | 50 |\n| (W)NBA team | 20 | 10 | 50 |\n| No answer | 4 | 2 | 50 |\n| Total | 60 | 22 | 37 |"}
{"item_id": "item_0051", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the potential GDP boost is dominated by a few key geographies or spread more evenly across the regions?", "table_markdown": "| Geography | GDP Boost (%) | GDP Boost ($) |\n|-------------|---------------|-------------------|\n| UK | 0.59% | $16.579 billion |\n| Australia | 0.55% | $6.527 billion |\n| Canada | 0.55% | $8.961 billion |\n| US | 0.52% | $97.298 billion |\n| France | 0.49% | $13.583 billion |\n| DACH* | 0.48% | $23.848 billion |"}
{"item_id": "item_0052", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether fatalities were concentrated among a few accident categories or spread out, so I can see at a glance which types dominate the total death toll?", "table_markdown": "| Accident Category | Number of Accidents | Fatal Accidents | Number of Fatalities |\n|-----------------------------------|---------------------|-----------------|----------------------|\n| Loss of Control In-flight (LOC-I) | 30 | 27 | 949 |\n| Controlled Flight Into Terrain (CFIT) | 19 | 16 | 259 |\n| Other End State | 12 | 4 | 318 |\n| Inflight Damage | 35 | 3 | 86 |\n| Runway / Taxiway Excursion | 82 | 3 | 14 |\n| Undershoot | 12 | 1 | 7 |"}
{"item_id": "item_0053", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the training hours are concentrated in just a few course elements or spread out evenly?", "table_markdown": "| | | | Notional Training |\n|---|---|---|---|\n| S No. | | Course Element | |\n| | | | Hours |\n| 1. | Professional Skill (Trade Practical) | | 1260 |\n| 2. | Professional Knowledge (Trade Theory) | | 252 |\n| 3. | Employability Skills | | 110 |\n| 4. | Library & Extracurricular Activities | | 58 |\n| 5. | Project Work | | 160 |\n| 6. | Revision & Examination | | 240 |\n| | | Total | 2080 |"}
{"item_id": "item_0054", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether DX projects are concentrated in a few business units or spread out, so I can see at a glance which units dominate the total?", "table_markdown": "| Progress | RPA: 80 organizations | DX: 171 projects |\n|---------------------------------|-----------------------|------------------|\n| Corporate | 22 | 3 |\n| Metal Products | 11 | 20 |\n| Transportation & Construction Systems | 20 | 20 |\n| Infrastructure | 5 | 25 |\n| Media & Digital | 3 | 9 |\n| Living Related & Real Estate | 4 | 23 |\n| Mineral Resources, Energy, Chemical & Electronics | 15 | 11 |"}
{"item_id": "item_0055", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the abatement costs are concentrated among a few properties or spread out, so I can see at a glance which addresses drive the majority of the expense?", "table_markdown": "| Property Address | Cost of Abatement | Cost Billed to Property Owner (including 20% Inspection and Incidental Fee) |\n|------------------|-------------------|--------------------------------------------------------------------------------|\n| 625 Ranney St. | $320.07 | $386.48 |\n| 632 Pennington | $180.00 | $216.00 |\n| 625 Colorado | $135.00 | $162.00 |\n| 745 Taylor | $180.00 | $216.00 |\n| 888 Rose | $427.50 | $513.00 |\n| 700 Russell | $315.00 | $378.00 |\n| 614 School | $202.50 | $243.00 |\n| 343 Taylor | $101.25 | $121.50 |\n| 727 Tucker | $202.50 | $243.00 |\n| 682 Tucker | $225.00 | $270.00 |\n| 930 Taylor | $769.75 | $923.50 |"}
{"item_id": "item_0056", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether ICU admissions are dominated by a few specific clinical conditions or spread out across many?", "table_markdown": "| | Clinical conditions | No. of patients (%) |\n|---|---|---|\n| | Septicaemia | 49 (32.66) |\n| Post-surgery 25 (16.66) | | |\n| | Road traffic accident | 22 (14.66) |\n| Respiratory disease 16 (10.66) | | |\n| | Cardiovascular disease | 12 (08.00) |\n| Neurological disease 10 (06.66) | | |\n| | Obs & gyn disease | 10 (06.66) |\n| Others 06 (04.00) | | |\n| | Total | 150 (100) |"}
{"item_id": "item_0057", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the total acreage is dominated by a few land use categories or spread out, so I can see at a glance how concentrated the distribution is?", "table_markdown": "| Future Land Use & Character | Acres | % of Total |\n|-----------------------------|-------|------------|\n| Suburban Residential | 301 | 17.5% |\n| General Residential | 988 | 57.7% |\n| Small-Lot Residential | 141 | 8.2% |\n| Residential-Office Mix | 3 | 0.2% |\n| Corridor Mixed Use | 78 | 4.5% |\n| Urban Village | 40 | 2.3% |\n| Suburban Office | 41 | 2.4% |\n| North Bellaire Special Development Area | 33 | 1.9% |\n| Government | 15 | 0.9% |\n| Redevelopment Area | 3 | 0.2% |\n| Parks | 46 | 2.7% |\n| Transmission Lines | 26 | 1.6% |\n| **TOTAL** | **1,715** | **100%** |"}
{"item_id": "item_0058", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the solar entrepreneurs in the Western cluster are concentrated in just a few counties or spread out evenly?", "table_markdown": "| No. | County | Females | Males | Number of Entrepreneurs |\n|---|---|---|---|---|\n| 1 | Kakamega | 11 | 11 | 22 |\n| 2 | Bungoma B | 16 | 10 | 26 |\n| 3 | Bungoma A | 20 | 4 | 24 |\n| 4 | Vihiga | 10 | 3 | 13 |\n| 5 | Nandi | 9 | 7 | 16 |\n| 6 | Uasin Gishu | 5 | 2 | 7 |\n| 7 | Trans Nzoia | 1 | 1 | 2 |\n| 8 | Busia | 10 | 9 | 19 |\n| | Total | 82 | 47 | 129 |"}
{"item_id": "item_0059", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether WCHA scoring points are concentrated among a few top players or spread out, so I can see at a glance if the leaders dominate the total?", "table_markdown": "| Rank | Name/Position/Year/Team | G | A | Points |\n|------|--------------------------|---|---|--------|\n| 1 | David Hoogsteen/Fr/UND | 17| 18| 35 |\n| 2 | Brian Hooper/CUR/CC | 11| 24| 35 |\n| 3 | Chris Morrison/Sr/UND | 12| 20| 34 |\n| 4 | Jason Blasiu/CJ/UND | 12| 21| 33 |\n| 5 | Steve Reineprecht/CSr/W | 16| 17| 33 |\n| 6 | Will Hodge/CJ/UND | 18| 16| 32 |\n| 7 | Riley Nelson/CCSr/MTU | 16| 16| 32 |\n| 8 | Mike Rycroft/C-Wr/AMD | 18| 14| 32 |\n| 9 | Chris Bouchard/C/MTU | 15| 21| 31 |\n| 10 | Paul Comrie/WJ/URDU | 13| 18| 31 |\n| 11 | Brat Meyer/WJ/URDU | 15| 16| 31 |"}
{"item_id": "item_0060", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the NCIG funding is concentrated among a few top organisations or spread more evenly across the recipients?", "table_markdown": "| Project | Organisation | Funding |\n|---|---|---|\n| Early Supported Neurology Discharge Team (ESD) / Community Neurorehabilitation | Abertawe Bro Morgannwg University Health Board | £152,000 |\n| Community neuro- rehab service (CNRS) | Aneurin Bevan University Health Board | £206,000 |\n| Establishment of a level 2 neuro- rehab unit in N Wales | Betsi Cadwaladr University Health Board | £100,000 |\n| Community neuro- rehab service | Cardiff and the Vale University Health Board | £174,000 |\n| Multidisciplinary Community Neuro- rehab Team | Cwm Taf University Health Board | £117,000 |\n| Stratified, integrated community neuro- stroke rehab | Hywel Dda University Health Board | £145,000 |\n| Community neuro- rehab service | Powys Teaching Health Board | £96,000 |\n| Specialist physiotherapy service for adult patients with neuromuscular (NM) conditions and Family Care Advisor | Wales Neuromuscular Network | £120,000 |"}
{"item_id": "item_0061", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the Non-Treasury Management Investments are concentrated among a few large entities or spread across a long tail?", "table_markdown": "| Investment | Latest Balance 31 January 2024 | Notes |\n|---|---|---|\n| Long term loans to Keelman Homes | £15,661,864 | Loan Balance at 31/01/2024 |\n| SCAPE System Build Ltd | £784,000 | 17% Shareholding in SCAPE |\n| Newcastle International Airport | £11,661,242 | 13.33% shareholding in Newcastle Airport |\n| Loan to Citizens Advice Bureau | £425,807 | Loan Balance at 31/01/2024 |\n| Newcastle International Airport Long Term credit notes | £13,152,711 | Loan notes - interest paid bi-annual Principal repayment due 2032 |\n| Loans to North Music Trust | £76,070 | North Music Trust - Balance at 31/01/24 |\n| Loan to NE Credit Union | £107,303 | Loan Balance at 31/01/2024 |\n| Loans to Gateshead Energy Company | £5,500,000 | Loan Balance at 31/01/2024 |"}
{"item_id": "item_0062", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the scholarship funding was concentrated among a few top recipients or spread out, so I can see at a glance how much of the total is accounted for by the largest awards?", "table_markdown": "| Name | Amount | Department |\n|-----------------------|--------|-------------------------------------------------|\n| Richard Bach | $300 | Veterinary Medicine |\n| Anne M Bartlett | 300 | Art Education |\n| Douglas T Berg | 200 | College of Science Literature and the Arts |\n| James L Daley | 300 | Institute of Technology |\n| Duane F Dipprey | 300 | Mechanical Engineering |\n| Leo Theodore Hegstrom | 300 | Music |\n| Dorothy Jacobson | 400 | College of Science Literature and the Arts |\n| Hobart P Lundblade | 300 | College of Science Literature and the Arts |\n| Robert P Majner | 300 | Physical Education |\n| Rosalyn M Reeder | 300 | College of Science Literature and the Arts |\n| Joan O Saarela | 300 | Romance Languages |\n| Carol J Schad | 300 | Occupational Therapy |\n| Clayton E Schad | 200 | Dairy Husbandry |\n| Marjorie A Thompson | 300 | College of Science Literature and the Arts |\n| Richard B Zoller | 300 | Agriculture |"}
{"item_id": "item_0063", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether EV charging points in Spain are concentrated among a few specific site types, so I can see at a glance which locations dominate the infrastructure?", "table_markdown": "| Emplacements/Sites | Number of Charging Stations | Number of Charging Points |\n|--------------------------|-----------------------------|---------------------------|\n| Car dealers | 189 | 398 |\n| Hotels | 131 | 234 |\n| Restaurants | 85 | 172 |\n| Petro stations | 64 | 144 |\n| Shops/Malls | 31 | 143 |\n| Car repair garajes | 35 | 88 |\n| Campings | 14 | 30 |\n| Taxi stops | 5 | 9 |\n| Airports | 4 | 8 |\n| **Totals** | **1,659** | **4,547** |"}
{"item_id": "item_0064", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the acreage in Zaleski Forest is concentrated in just a few compartment groups, so I can see at a glance if a small number of areas dominate the total?", "table_markdown": "| Cruises Schedules | | |\n|---|---|---|\n| Forest | Compartments* | Acres |\n| Zaleski | B-11, B-12, B-13, B- 14, B-15 | 495 |\n| Zaleski | A-33, A-34, A-38, A- 41, C-4, C-5 | 637 |\n| Zaleski | A-2, A-3, A-6 | 259 |\n| Zaleski | E-1, E-2, E-4 | 182 |\n| Zaleski | D-5, D-17, D-18, D- 28, D-29 | 316 |\n| Zaleski | D-7, D-14, D-15, D- 16, D-17, D-19, D-25, D-26, D-27 | 614 |"}
{"item_id": "item_0065", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether capital request denials were concentrated among a few key reasons, so I can see at a glance which factors dominated the refusals?", "table_markdown": "| Reasons | Small (49 employees or fewer) | Other (Medium and large: 50 employees or over) | Total |\n|----------------------------------------------|-------------------------------|-----------------------------------------------|-------|\n| Biotechnology product/process not sufficiently developed | 37 | 5 | 42 |\n| Biotechnology product line or portfolio limited in scope | 13 | 0 | 13 |\n| Insufficient specific management skills/expertise | 12 | 0 | 12 |\n| Capital not available due to market conditions | 68 | 8 | 78 |\n| Further product development or proof of concept required | 37 | 6 | 43 |\n| Lender does not fund development project | 25 | x | 28 |\n| Other reason | 24 | x | 26 |"}
{"item_id": "item_0066", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether deposits in Wisconsin are concentrated among a few top institutions or spread out, so I can see at a glance how much of the market the biggest players hold?", "table_markdown": "| Rank | Institution | # of Branches | Deposits (US$B) | Market Share (%) |\n|------|------------------------------|---------------|-----------------|------------------|\n| 1 | Marshall & Ilsley Corp. | 195 | 23.7 | 19.1 |\n| 2 | U.S. Bancorp | 123 | 15.4 | 12.4 |\n| 3 | Associated Banc-Corp | 208 | 11.6 | 9.4 |\n| 4 | JPMorgan Chase & Co. | 77 | 5.2 | 4.2 |\n| 5 | Anchor BanCorp Wisconsin | 61 | 3.8 | 3.0 |\n| 11 | BMO Financial Group (Harris) | **51** | **1.2** | **1.0** |\n| 49 | AMCORE Financial Inc. | 14 | 0.4* | 0.3 |"}
{"item_id": "item_0067", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the bill payments in Abstract #12 are concentrated among a few funds or spread out, so I can see at a glance which entities dominate the total?", "table_markdown": "| General Fund | No. 296-337 | $142,250.72 |\n|---|---|---|\n| Street Lighting District | | 1,940.58 |\n| Refuse District | | 1,879.58 |\n| Debt District | | 0 |\n| Snow Removal | | 103.16 |\n| Highway Fund | No. 186-336 | 14,862.20 |\n| FEMA Repairs | No. | 1,000.00 |\n| Fire District | No. | 0 |\n| Sewer Fund | No. 78-82 | 1,189.49 |\n| Water Fund | No. 138-154 | 6,366.47 |"}
{"item_id": "item_0068", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the DISCOM dues are concentrated among a few states or spread out, so I can see at a glance how much of the total debt is held by the top contributors?", "table_markdown": "| | | Jan'21 : Rs Crs |\n|---|---|---|\n| Rajasthan | 41,914 | |\n| Tamil Nadu | 19,324 | |\n| Uttar Pradesh | 15,319 | |\n| Telangana | 7,437 | |\n| Karnataka | 6,162 | |\n| Jharkhand | 5,376 | |\n| Madhya Pradesh | 4,890 | |\n| Jammu & Kashmir | 4,824 | |\n| Andhra Pradesh | 4,426 | |\n| Maharashtra | 4,335 | |\n| Haryana | 3,609 | |\n| Odisha | 1,867 | |\n| Bihar | 1,834 | |\n| Delhi | 1,242 | |\n| Dadar & Nagar Haveli | 950 | |\n| Punjab | 801 | |"}
{"item_id": "item_0069", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the total number of cis-regulatory elements is dominated by a few species or spread evenly, so I can see at a glance how concentrated the data is?", "table_markdown": "| Family | Species | N. Analysed Promoters | N. CREsa | N. CREs/MLO |\n|-----------------|--------------------------|-----------------------|--------------------|-------------|\n| Volvocaceae | *Volvox carteri* | 3 (3)b | 1047 | 349 |\n| Chlamydomonadaeae | *Chlamydomonas reinhardtii* | 4 (4) | 941 | 235 |\n| Brassicaceae | *Arabidopsis thaliana* | 15 (15) | 7147 | 476 |\n| | *Brassica rapa* | 23 (23) | 10,212 | 444 |\n| | *Capsella rubella* | 16 (16) | 7288 | 456 |\n| Cucurbitaceae | *Citrullus lanatus* | 14 (14) | 6184 | 442 |\n| | *Cucumis melo* | 15 (16) | 6980 | 465 |\n| | *Cucumis sativus* | 13 (13) | 6105 | 470 |\n| Euphorbiaceae | *Manihot esculenta* | 19 (19) | 8899 | 468 |\n| Fabaceae | *Glycine max* | 41 (41) | 19,242 | 469 |\n| | *Medicago truncatula* | 16 (16) | 7170 | 448 |\n| | *Phaseolus vulgaris* | 20 (20) | 9418 | 471 |\n| Poaceae | *Brachypodium distachyon*| 13 (13) | 5157 | 397 |\n| | *Oryza sativa* | 12 (12) | 5282 | 440 |\n| | *Sorghum bicolor* | 15 (15) | 6145 | 410 |\n| | *Triticum aestivum* | 26 (55) | 10,595 | 408 |\n| Rosaceae | *Fragaria vesca* | 17 (17) | 7263 | 427 |\n| | *Malus domestica* | 20 (21) | 8533 | 427 |\n| | *Prunus persica* | 19 (19) | 8869 | 467 |\n| Solanaceae | *Solanum lycopersicum* | 17 (17) | 8051 | 474 |\n| | *Nicotiana tabacum* | 14 (14) | 6623 | 473 |\n| | *Capsicum annuum* | 15 (15) | 7139 | 476 |\n| | *Solanum melongena* | 16 (18) | 7489 | 416 |\n| | *Solanum tuberosum* | 12 (12) | 5573 | 464 |\n| Vitaceae | *Vitis vinifera* | 19 (19) | 8708 | 458 |\n| Total | | 414 (447) | 186,060 | 10,930 |"}
{"item_id": "item_0070", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the total payments are dominated by a few SWIS entities or spread out, so I can see at a glance how concentrated the contributions are?", "table_markdown": "| SWIS | Tax Paid | Penalty Paid | Total Paid | Unpaid Tax | Tax Levy | Total Levy |\n|--------|--------------|--------------|---------------|-------------|--------------|--------------|\n| 072200 | 1,476.23 | 0.00 | 1,476.23 | 1,601.19 | 3,077.42 | 3,077.42 |\n| 072400 | 10,454,396.66| 7,201.12 | 10,461,718.47 | 305,576.07 | 10,759,972.73| 10,759,972.73|\n| 072600 | 1,369,936.77 | 2,308.56 | 1,372,245.33 | 90,203.22 | 1,460,139.99 | 1,460,139.99 |\n| 073200 | 855,990.53 | 1,581.74 | 857,572.27 | 118,480.84 | 974,471.37 | 974,471.37 |\n| 073401 | 5,181,159.27 | 5,876.21 | 5,187,035.48 | 227,136.32 | 5,408,295.59 | 5,408,295.59 |\n| 073489 | 8,473,922.12 | 8,606.27 | 8,482,528.39 | 314,076.83 | 8,787,998.95 | 8,787,998.95 |\n| 074001 | 76,714.15 | 275.09 | 76,989.24 | 8,209.94 | 84,924.09 | 84,924.09 |\n| 074089 | 2,115,725.20 | 3,799.70 | 2,119,524.90 | 142,908.52 | 2,258,633.72 | 2,258,633.72 |\n| 442200 | 70,852.80 | 129.72 | 70,982.52 | 9,984.56 | 80,837.36 | 80,837.36 |\n| **Total:** | **28,600,173.73** | **29,778.41** | **28,630,072.83** | **1,218,177.49** | **29,818,351.22** | **29,818,351.22** |"}
{"item_id": "item_0071", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the Halifax 2006 labor force was concentrated in a few key industries or spread out, so I can see at a glance which sectors dominated the workforce?", "table_markdown": "| | # labour force | % of labour force |\n|---|---|---|\n| 23 Construction | 11,590 | 5.5% |\n| 31-33 Manufacturing | 11,015 | 5.2% |\n| 44-45 Retail trade | 25,045 | 11.9% |\n| 61 Educational services | 16,355 | 7.8% |\n| 62 Health care & social assist. | 24,485 | 11.7% |\n| 72 Accommodation & food service | 14,750 | 7.0% |\n| 91 Public administration | 23,375 | 11.1% |"}
{"item_id": "item_0072", "chart_task_type": "concentration_long_tail", "query": "Can you chart the distribution of available space across the lower-level units so I can see at a glance if a few units dominate the total inventory?", "table_markdown": "| Exposure | Unit Number | Available Space |\n|---|---|---|\n| Lower North | 004 | 687 SF |\n| | 005 | 270 SF |\n| | 006 | 270 SF |\n| | 008 | 1,065 SF |\n| | 011 | 649 SF |\n| | 025 | 1,289 SF |\n| Food Court | 016 | 606 SF |"}
{"item_id": "item_0073", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the project costs are concentrated among a few major initiatives or spread out, so I can see at a glance how much the top projects dominate the total budget?", "table_markdown": "| Project Name | Project Phasing | Estimated Cost |\n|--------------------------------------------------|----------------------------------|----------------|\n| Development Plan | Immediate (1999-2000) | $25,000 |\n| Zoning Overlay District Amendment | Immediate (1999-2000) | $7,500 |\n| District Welcome Signs | Immediate (1999-2000) | $5,000 |\n| Civic Center Improvements | Short-Term (1999-2003) | $770,000 |\n| Traffic Calming Study | Short-Term (1999-2003) | $25,000 |\n| Commercial Façade Design Guidelines | Short-Term (1999-2003) | $15,000 |\n| Intersection Improvements at Pardee | Long-Term (2004-2028) | $3,050,000 |\n| Intersection Improvements at Mortenview | Long-Term (2004-2028) | $3,050,000 |\n| Linear Park at Kilfoil | Long-Term (2004-2028) | $1,490,000 |\n| Traffic Calming Improvements | Long-Term (2004-2028) | $3,000,000 |\n| Street Trees and Landscaping | On-going (1999-2028) | $1,650,000 |\n| Street Banners | On-going (1999-2028) | $125,000 |\n| Business Recruitment & Retention | On-going (1999-2028) | $920,000 |\n| Commercial Façade Improvement Program | On-going (1999-2028) | $630,000 |\n| Pedestrian Amenities | On-going (1999-2028) | $800,000 |\n| Property Purchase | On-going (1999-2028) | $950,000 |\n| Contingencies and Administration | On-going (1999-2028) | $671,124 |"}
{"item_id": "item_0074", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the 1776 tributary population was concentrated in a few settlements or spread evenly, so I can see at a glance which places dominated the total?", "table_markdown": "| Place | T r i b u t a r i e s | | |\n|---|---|---|---|\n| | 1776 | 1781 | 1786 - 1801 |\n| | | (loss) | |\n| Jalapa | 16 | 11 | 15 - 36 |\n| Joconguera | 31 | 2 | 27 - 19 |\n| Piraera | 269 | 20 | 228 - 294 |\n| Corquin | 21 | 6 | 17 - 15 |\n| Guarabasque | 153 | ? | 121- 164 |\n| Yambalanguira | 174 | 26 | 134 - 222 |\n| Tambla | 25 | 3 | 19 - 36 |\n| Majatique | 52 | 12 | 39 - 49 |\n| Gualcha | 163 | 8 | 119 - 142 |\n| Xicaramani | 48 | 10 | 34 - 23 |\n| Celilaca | 56 | 11 | 39 - 97 |\n| Chucucuco | 71 | 13 | 49 - 35 |\n| Gualsinse | 232 | 49 | 159 - 254 |\n| Cururu | 19 | 3 | 13 - 12 |\n| Tiuma | 24 | 3 | 15 - 8 |\n| Sn Andres Ocotepeque | 275 | 4 | 156 - 110 |\n| Guajinlaca | 225 | ? | 122 - 237 |\n| Erandique | 125 | 26 | 66 - 101 |\n| Cucuiagua | 26 | 4 | 13 - 18 |\n| Sn Sebastian Hermita | 35 | ? | 17 - 16 |\n| Guancapla | 69 | 17 | 30 - 57 |\n| Macholoa | 45 | 26 | 16 - 63 |\n| Chinda | 29 | ? | 3 - 46 |\n| Totals | 2,183 | | 1,451 – 2,054 |"}
{"item_id": "item_0075", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the estimated DF cases were concentrated in just a few districts or spread out, so I can see at a glance which areas dominated the total?", "table_markdown": "| District | Affected area | Detected DF cases | Estimated DF cases | Remarks |\n|---|---|---|---|---|\n| Banke | Urban/suburban | 10 | 50 | India-2 |\n| Bardiya | Urban/suburban | 3 | 15 | Rajasthan-1 |\n| Dang | Urban/suburban | 6 | 30 | No |\n| Jhapa | Suburban | 1 | 5 | No |\n| Parsa | Urban | 4 | 20 | No |\n| Rupandehi | Urban | 2 | 10 | No |\n| Kapilvastu | Urban | 1 | 5 | Unknown |\n| Dhading | Urban | 1 | 1 | Unknown |\n| Kathmandu | Urban | 4 | 4 | India-1 |\n| Total districts-9 | Urban/suburban | Total detected-32 | Total estimated-140 | Imported cases-4 |"}
{"item_id": "item_0076", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the leased square footage in the top transactions is concentrated among a few tenants or spread out, so I can see at a glance if a small number of deals dominate the total volume?", "table_markdown": "| Tenant | Square Feet | Address | Transaction Type | Submarket |\n|---|---|---|---|---|\n| Boston Consulting Group | 250,000 | 360 N Green Street | Relocation | Far West Loop / Fulton Market |\n| Echo Global Logistics | 185,000 | 600 W Chicago Avenue | Renewal | River North |\n| IMC Financial Markets | 160,000 | 233 S Wacker Drive | Renewal / Expansion | West Loop |\n| Abbott Laboratories | 106,000 | 233 S Wacker Drive | New Location | West Loop |\n| Blue Cross Blue Shield Association | 95,070 | 200 E Randolph Street | Relocation | East Loop |\n| Microsoft | 63,888 | 200 E Randolph Street | Renewal | East Loop |\n| Amata Office Suites | 39,998 | 161 N Clark Street | Renewal / Expansion | Central Loop |\n| Snap | 38,000 | 800 W Fulton Street | Relocation | Far West Loop / Fulton Market |\n| The Scion Group | 34,500 | 401 N Michigan Avenue | Relocation | North Michigan Avenue |\n| CBIZ Gibraltar | 33,077 | 225 W Wacker Drive | Renewal | West Loop |"}
{"item_id": "item_0077", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the boat survey counts are concentrated in just a few areas or spread out, so I can see at a glance which locations dominate the total?", "table_markdown": "| Area | Boat | Helicopter | % Difference |\n|-----------------------|------|------------|--------------|\n| Elrington I. | 16 | 14 | + 14% |\n| Evans I. | 55 | 33 | + 66% |\n| Latouche I. | 51 | 12 | +325% |\n| Knight I. | 180 | 59 | +205% |\n| Keen I. area | 201 | 152 | + 32% |\n| Port Gravina-Sheep Bay| 480 | 299 | + 61% |\n| Simpson Bay | 0 | 0 | 0% |\n| Total | 983 | 569 | + 73% |"}
{"item_id": "item_0078", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether Actual Sales Amount in the Chicago market is concentrated among a few top products, so I can see at a glance how much revenue the biggest contributors account for?", "table_markdown": "| Ship-To Market | Product | Prod Std Cost Last Year | Actual Sales Amount | Actual Sales Units | Standard Cost Last Year |\n|----------------|--------------------------|-------------------------|---------------------|--------------------|-------------------------|\n| Chicago | Pear Hlvs LS 12 oz BR* 0A| 31.0209 | $248 | 6 | $171.24 |\n| | Applesauce 12oz BR* 0A | 33.1923 | $4,867 | 113 | $3,756.04 |\n| | Pear Slics LS 12 oz BR* 0A| 37.1205 | $1,224 | 28 | $1,024.53 |\n| | Peach Hlvs LS 12 oz BR* 0A| 31.3517 | $9,847 | 224 | $7,008.99 |\n| | Peach Slics LS 16 oz BR* 0A| 39.9241 | $3,194 | 58 | $2,314.00 |\n| | Pear 6oz LnchPk LS 0A | 65.8812 | $20,983 | 237 | $15,637.56 |\n| | Peach Slics HS 12 oz BR* 0A| 32.8256 | $29,312 | 671 | $22,015.47 |\n| | Peach Slics LS 12oz BR* 0A| 31.0008 | $14,937 | 339 | $10,524.15 |\n| | Pnappi Slics 12 oz BR* 0A| 33.0349 | $2,610 | 58 | $1,914.70 |\n| | Peachel Rites 12oz BR* 0A| 32.0240 | $4,600 | 105 | $1,454.67 |"}
{"item_id": "item_0079", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the fund allocations are concentrated among a few schools or spread evenly, so I can see at a glance how much the top recipients dominate the total?", "table_markdown": "| Sl. No. | SCHCD | SCHNAME | FUND_CD | BLOCK/MP/MC | CLRC | TOTAL |\n|--------|-----------|--------------------------------|------------------|---------------|--------------|-------|\n| 1 | 19092203303 | AKALIA CHANDIPUR F.P. SCHOOL | ELEC/P/17-18/0001 | MEMARI-II | SATGACHIA WEST | 17720 |\n| 2 | 19092207602 | TIKAIPUR (NO. 2) F.P. SCHOOL | ELEC/P/17-18/0002 | MEMARI-II | SATGACHIA | 14720 |\n| 3 | 19092209801 | MONDAL GRAM F.P. SCHOOL | ELEC/P/17-18/0003 | MEMARI-II | SATGACHIA | 17720 |\n| 4 | 19092403503 | KHARAMPUR, JHIKRA F.P. SCHOOL | ELEC/P/17-18/0004 | MONTESWAR | MONTESWAR-I | 7360 |\n| 5 | 19092404404 | KULI SWETPUR F.P. SCHOOL | ELEC/P/17-18/0005 | MONTESWAR | MONTESWAR-I | 7360 |\n| 6 | 19092404703 | BANUI KHARAMPUR F.P. SCHOOL | ELEC/P/17-18/0006 | MONTESWAR | MONTESWAR-I | 7360 |\n| 7 | 19092404801 | PASCHIM KHARAMPUR F.P. SCHOOL | ELEC/P/17-18/0007 | MONTESWAR | MONTESWAR-I | 7360 |\n| 8 | 19092404902 | RAJGACHI F.P. SCHOOL | ELEC/P/17-18/0008 | MONTESWAR | MONTESWAR-I | 7360 |\n| 9 | 19092405603 | KALU F.P. SCHOOL | ELEC/P/17-18/0009 | MONTESWAR | MONTESWAR-I | 7360 |\n| 10 | 19092406101 | KULUT GIRLS' F.P. SCHOOL | ELEC/P/17-18/0010 | MONTESWAR | MONTESWAR-I | 7360 |\n| 11 | 19092406203 | KULUT BALAK F.P. SCHOOL | ELEC/P/17-18/0011 | MONTESWAR | MONTESWAR-I | 7360 |\n| 12 | 19092406204 | KULUT DASKHINPARA F.P. SCHOOL | ELEC/P/17-18/0012 | MONTESWAR | MONTESWAR-I | 7360 |\n| 13 | 19092406304 | KANCHANDANGA F.P. SCHOOL | ELEC/P/17-18/0013 | MONTESWAR | MONTESWAR-I | 7360 |\n| 14 | 19092406502 | KUSUMGRAM NUTAN F.P. SCHOOL | ELEC/P/17-18/0014 | MONTESWAR | MONTESWAR-I | 7360 |\n| 15 | 19092406804 | KUSUMGRAM BALAK F.P. SCHOOL | ELEC/P/17-18/0015 | MONTESWAR | MONTESWAR-I | 7360 |\n| 16 | 19092406902 | DIRGHANAGAR F.P. SCHOOL | ELEC/P/17-18/0016 | MONTESWAR | MONTESWAR-I | 7360 |\n| 17 | 19092409801 | GULITA F.P. SCHOOL | ELEC/P/17-18/0017 | MONTESWAR | MONTESWAR-II | 7360 |\n| 18 | 19092413102 | MONTESWAR JR. BASIC SCHOOL | ELEC/P/17-18/0018 | MONTESWAR | MONTESWAR-I | 7360 |\n| 19 | 19092413504 | RUIGORIA F.P. SCHOOL | ELEC/P/17-18/0019 | MONTESWAR | MONTESWAR-I | 7360 |\n| 20 | 19092413701 | LOHAR F.P. SCHOOL | ELEC/P/17-18/0020 | MONTESWAR | MONTESWAR-I | 7360 |\n| 21 | 19092413802 | ASANPUR F.P. SCHOOL | ELEC/P/17-18/0021 | MONTESWAR | MONTESWAR-I | 7360 |\n| 22 | 19092413902 | BHARUCHA AKSHAY KUMARI F.P. SC| ELEC/P/17-18/0022 | MONTESWAR | MONTESWAR-I | 7360 |\n| 23 | 19092413903 | PATIKHEL DANGA F.P. SCHOOL | ELEC/P/17-18/0023 | MONTESWAR | MONTESWAR-I | 7360 |\n| 24 | 19092414702 | GHORADANGA F.P. SCHOOL | ELEC/P/17-18/0024 | MONTESWAR | MONTESWAR-I | 7360 |\n| 25 | 19092414802 | CHIOTO BAGHASON F.P. SCHOOL | ELEC/P/17-18/0025 | MONTESWAR | MONTESWAR-I | 7360 |\n| 26 | 19092414804 | MALADANGA F.P. SCHOOL | ELEC/P/17-18/0026 | MONTESWAR | MONTESWAR-I | 7360 |\n| 27 | 19092415002 | ATASHPUR F.P. SCHOOL | ELEC/P/17-18/0027 | MONTESWAR | MONTESWAR-I | 7360 |\n| 28 | 19092415101 | PASCHIM BAGHASON F.P. SCHOOL | ELEC/P/17-18/0028 | MONTESWAR | MONTESWAR-I | 7360 |\n| 29 | 19092415301 | BABUNPUR F.P. SCHOOL | ELEC/P/17-18/0029 | MONTESWAR | MONTESWAR-I | 7360 |\n| 30 | 19092415502 | JOYPUR F.P. SCHOOL | ELEC/P/17-18/0030 | MONTESWAR | MONTESWAR-I | 7360 |\n| 31 | 19093003201 | DAMRA COLLERY F.P. SCHOOL | ELEC/P/17-18/0031 | RANIGANJ | RANIGANJ | 14720 |\n| 32 | 19093500104 | PARULIA F.P. SCHOOL | ELEC/P/17-18/0032 | DURGAPUR MUNICIPAL C | DURGAPUR-II | 14720 |"}
{"item_id": "item_0080", "chart_task_type": "concentration_long_tail", "query": "Can you chart the concentration of contained copper reserves among the 10 largest undeveloped projects so I can see at a glance how much of the total is dominated by the top few?", "table_markdown": "| Project | Country | Operator | Contained Cu (Mt) | Contained Cu (B lbs) |\n|---------------|-----------|---------------------------|-------------------|----------------------|\n| Pebble | USA | Northern Dynasty | 37.2 | 82.0 |\n| Resolution | USA | Rio Tinto / BHP | 27.3 | 60.2 |\n| KSM | Canada | Seabridge | 25.0 | 55.1 |\n| Reko Diq | Pakistan | Barrick / Pakistan Gov. | 24.3 | 53.6 |\n| La Granja | Peru | Rio Tinto | 22.1 | 48.7 |\n| El Arco | Mexico | Southern Copper | 17.7 | 39.0 |\n| Hu’u / Onto | Indonesia | Vale / ANTAM | 17.2 | 37.9 |\n| Nueva Union | Chile | Teck / Newmont | 16.7 | 36.8 |\n| El Pachon | Argentina | Glencore | 15.5 | 34.2 |\n| Tampakan | Philippines | Sagittarius Mines | 15.3 | 33.7 |"}
{"item_id": "item_0081", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether total transactions are concentrated among a few top sources, so I can see at a glance how much volume the biggest partners account for?", "table_markdown": "| Source | Sub Sources | Total Transactions | Moderate Risk | Moderate Risk % | High Risk | High Risk % | Extreme Risk | Extreme Risk % | Total Risk | Total Risk % |\n|--------|-------------|--------------------|---------------|----------------|-----------|-------------|--------------|---------------|------------|--------------|\n| 1 | View Sub Sources | 123,210 | 4,831 | 3.9% | 1,124 | 0.9% | 18 | 0.0% | 5,973 | 4.8% |\n| 2 | View Sub Sources | 111,281 | 176 | 0.2% | 9 | 0.0% | 0 | 0.0% | 185 | 0.2% |\n| 3 | View Sub Sources | 108,245 | 954 | 0.9% | 1,449 | 1.3% | 1,429 | 1.3% | 3,832 | 3.5% |\n| 4 | View Sub Sources | 84,206 | 83 | 0.1% | 283 | 0.3% | 290 | 0.3% | 656 | 0.8% |\n| 5 | View Sub Sources | 81,129 | 0 | 0.0% | 0 | 0.0% | 0 | 0.0% | 0 | 0.0% |\n| 6 | View Sub Sources | 76,541 | 1,515 | 2.0% | 0 | 0.0% | 0 | 0.0% | 1,515 | 2.0% |\n| 7 | View Sub Sources | 61,999 | 4 | 0.0% | 317 | 0.5% | 0 | 0.0% | 321 | 0.5% |\n| 8 | View Sub Sources | 37,911 | 441 | 1.2% | 939 | 2.5% | 1 | 0.0% | 1,381 | 3.6% |\n| 9 | View Sub Sources | 33,872 | 569 | 1.7% | 46 | 0.1% | 0 | 0.0% | 615 | 1.8% |"}
{"item_id": "item_0082", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether hotel room supply in Bankstown is concentrated among a few large properties or spread across many smaller ones?", "table_markdown": "| Name | Address | Suburb | Postcode | LGA | No. of Rooms | Star Rating |\n|---|---|---|---|---|---|---|\n| Bankstown | | | | | | |\n| Rydges Bankstown (Bass Hill) | 874 Hume Highway | Bass Hill | 2197 | Bankstown | 120 | 4.0 |\n| Travelodge Hotel Bankstown Sydney | 8 Greenfield Parade | Bankstown | 2200 | Bankstown | 162 | 3.0 |\n| Bankstown Motel 10 | 217 Hume Highway | Greenacre | 2190 | Bankstown | 30 | 4.0 |\n| Gardenia Motor Inn | 850 Hume Highway | Bass Hill | 2197 | Bankstown | 42 | 4.0 |\n| Gardenview Hotel | 471 Chapel Rd | Bankstown | 2200 | Bankstown | 60 | 4.0 |\n| Sleep Express Motel | 97 Hume Highway | Chullora | 2190 | Bankstown | 88 | 3.0 |\n| Total | | | | | 502 | |"}
{"item_id": "item_0083", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the SIGLEC17P genotyping samples are concentrated among a few African ethnicities or spread out, so I can see at a glance if a small number of groups dominate the dataset?", "table_markdown": "| African Ethnicity | SIGLEC17P | SIGLEC13 |\n|-------------------|-----------|----------|\n| Bakola Pygmy | 17 | 8 |\n| Baniamer | 6 | 8 |\n| Boni | 8 | 18 |\n| Borana | 18 | 18 |\n| Bulala | 14 | 16 |\n| Datog | 15 | 17 |\n| Fulani | 17 | 12 |\n| Hadandawa | 6 | 10 |\n| Hadza | 19 | 15 |\n| Iraqw | 15 | 16 |\n| Lemande | 18 | 16 |\n| Luo | 11 | 19 |\n| Mada | 15 | 10 |\n| Sandawe | 18 | 18 |\n| Sengwer | 16 | 16 |\n| Yoruba | 15 | 13 |\n| **Total** | **228** | **230** |"}
{"item_id": "item_0084", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the Christmas presents were dominated by a few popular items or spread out evenly, so I can see at a glance which gifts accounted for the bulk of the total?", "table_markdown": "| | Item | Quantity |\n|---|---------------|----------|\n| A | Bike | 6 |\n| B | Books | 13 |\n| C | Connect 4 | 7 |\n| D | Cuddly toy | 8 |\n| E | Jigsaw | 11 |\n| F | Model racer | 5 |\n| G | Pens | 15 |\n| H | Shell suit | 8 |\n| I | Shoes | 11 |\n| J | Sindy Doll | 4 |\n| K | Skate Board | 8 |\n| L | Sweets | 16 |\n| M | Trainers | 6 |\n| N | Transformer | 9 |"}
{"item_id": "item_0085", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the General Cemetery Levies are concentrated among a few tax districts, so I can see at a glance which ones account for the majority of the total?", "table_markdown": "| Tax District Name | Tax District ID # | General |\n|-------------------|------------------|---------|\n| Attica | CM014 | 4,519 |\n| Blaine | CM302BA | 1,033 |\n| Burchfield | CM016 | 0,689 |\n| Danville | CM017 | 8,662 |\n| Eagle | CM018 | 1,991 |\n| Freeport | CM019 | 4,842 |\n| PI Hill | CM020 | 4,336 |\n| Sp Grove | CM021 | 14,573 |"}
{"item_id": "item_0086", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether fiscal revenue is concentrated among a few top cities or spread more evenly across the list?", "table_markdown": "| | Cities | Fiscal revenue (2013) | |\n|---|---|---|---|\n| | | RMB/Yuan (billions) | US$ (billions) |\n| 1 | Shanghai | 410.93 | 67.81 |\n| 2 | Beijing | 365.42 | 60.3 |\n| 3 | Tianjin | 207.37 | 34.22 |\n| 4 | Shenzhen | 172.71 | 28.5 |\n| 5 | Chongqing | 169.07 | 27.9 |\n| 6 | Suzhou | 132.71 | 21.9 |\n| 7 | Guangzhou | 113.81 | 18.78 |\n| 8 | Wuhan | 97.57 | 16.1 |\n| 9 | Hangzhou | 94.29 | 15.56 |\n| 10 | Chengdu | 89.69 | 14.8 |"}
{"item_id": "item_0087", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the fund's portfolio value is concentrated among a few top holdings or spread across a long tail?", "table_markdown": "| Company Name | Ticker | Shares | Market Value | % of Portfolio |\n|--------------------------------------------------|---------|--------|----------------|----------------|\n| CASH EQUIVALENTS & OTHER | | | | |\n| FACTSET RESEARCH SYSTEMS INC | FDS | 2,080 | $298,667.20 | 3.06% |\n| DOLLAR TREE INC | DLTR | 4,060 | $288,666.00 | 2.96% |\n| AMPHENOL CORP | APH | 5,180 | $278,217.80 | 2.85% |\n| HARMAN INTERNATIONAL INDUSTRIES INC | HAR | 2,110 | $273,519.30 | 2.80% |\n| MEAD JOHNSON NUTRITION CO | MJN | 2,690 | $264,938.10 | 2.72% |\n| AMERISOURCEBERGEN CORP | ABC | 2,700 | $256,635.00 | 2.63% |\n| STERICYCLE INC | SRCL | 1,920 | $252,076.80 | 2.58% |\n| WABTEC CORP/DE | WAB | 3,010 | $251,184.50 | 2.57% |\n| SIGNATURE BANK/NEW YORK NY | SBNY | 2,100 | $245,973.00 | 2.52% |\n| DUNKIN' BRANDS GROUP INC | DNKN | 5,150 | $243,646.50 | 2.50% |\n| O'REILLY AUTOMOTIVE INC | ORLY | 1,269 | $237,759.84 | 2.44% |\n| IHS INC | IHS | 2,060 | $237,167.80 | 2.43% |\n| WEX INC | WEX | 2,530 | $232,886.50 | 2.39% |\n| MEDNAX INC | MD | 3,350 | $227,431.50 | 2.33% |\n| PERRIGO CO PLC | PRGO | 1,491 | $226,244.34 | 2.32% |\n| MONSTER BEVERAGE CORP | MNST | 1,920 | $224,544.00 | 2.30% |\n| RAYMOND JAMES FINANCIAL INC | RJF | 4,190 | $220,477.80 | 2.26% |\n| HENRY SCHEIN INC | HSIC | 1,580 | $218,150.60 | 2.24% |\n| AMETEK INC | AME | 4,530 | $216,987.00 | 2.22% |\n| FASTENAL CO | FAST | 4,770 | $211,788.00 | 2.17% |\n| ROSS STORES INC | ROST | 2,280 | $209,098.80 | 2.14% |\n| LKQ CORP | LKQ | 8,050 | $207,770.50 | 2.13% |\n| INTERCONTINENTALEXCHANGE GROUP INC | ICE | 994 | $204,495.62 | 2.10% |\n| FIRST REPUBLIC BANK/CA | FRC | 3,940 | $200,624.80 | 2.06% |\n| CHURCH & DWIGHT CO INC | CHD | 2,460 | $199,063.20 | 2.04% |\n| PVH CORP | PVH | 1,680 | $185,236.80 | 1.90% |\n| VERISK ANALYTICS INC | VRSK | 2,810 | $180,823.50 | 1.85% |\n| NORDSON CORP | NDSN | 2,400 | $174,864.00 | 1.79% |\n| JB HUNT TRANSPORT SERVICES INC | JBHT | 2,150 | $171,161.50 | 1.75% |\n| DONALDSON CO INC | DCI | 4,590 | $167,810.40 | 1.72% |\n| COPART INC | CPRT | 4,560 | $166,896.00 | 1.71% |\n| IDEXX LABORATORIES INC | IDXX | 1,040 | $164,756.80 | 1.69% |\n| ANSYS INC | ANSS | 2,040 | $164,566.80 | 1.69% |\n| BORGWARNER INC | BWA | 2,880 | $155,548.80 | 1.59% |\n| TRACTOR SUPPLY CO | TSCO | 1,820 | $147,729.40 | 1.51% |\n| OCEANEERING INTERNATIONAL INC | OII | 2,790 | $146,084.40 | 1.50% |\n| ROPER INDUSTRIES INC | ROP | 914 | $141,066.76 | 1.44% |\n| ROBERT HALF INTERNATIONAL INC | RHI | 2,380 | $138,182.80 | 1.41% |\n| RALPH LAUREN CORP | RL | 802 | $133,845.78 | 1.37% |\n| RESMED INC | RMD | 2,090 | $130,562.30 | 1.34% |\n| QUINTILES TRANSNATIONAL HOLDINGS INC | Q | 2,090 | $126,445.00 | 1.29% |\n| MICHAEL KORS HOLDINGS LTD | KORS | 1,700 | $120,343.00 | 1.23% |\n| F5 NETWORKS INC | FFIV | 1,050 | $117,201.00 | 1.20% |\n| DISCOVERY COMMUNICATIONS INC | DISCA | 3,820 | $110,722.70 | 1.13% |"}
{"item_id": "item_0088", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the cost of the sweets is dominated by a few expensive items or spread out evenly?", "table_markdown": "| Price | Qty | Total |\n|-------|-----|-------|\n| EPIC Chocolate Chips Cookies | $42 | |\n| EPIC Brownies | $40 | |\n| Cookies & Brownies | $75 | |\n| Cotton Candy | $5 | |\n| Dippin Dots | $6.25 | |\n| ¼ Sheet Ice Cream Cake | $50 | |\n| Rita's Water Ice | $70 | |\n| Fudge | $8.75 | |\n| Big Vanilla IC Sandwich | $5.50 | |\n| Bunny Tracks Bar | $7.75 | |"}
{"item_id": "item_0089", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the 2014-15 completers were concentrated in just a few programs or spread out, so I can see at a glance if a small number of programs accounted for the majority of graduates?", "table_markdown": "| CIP | Program | AY 14-15 Completers | AY 14-15 On-Time Completers |\n|---|---|---|---|\n| 010102 | ADV AG/BUSINESS MANAGEMENT | 0 | 0 |\n| 010104 | AG/BUS FINANCIAL ANALYSIS | 10 | 10 |\n| 010105 | AG/BUS MARKETING & RISK MGMT | 16 | 16 |\n| 010106 | AG/BUS PLANNING & FINANCIAL RECS | 15 | 15 |\n| 010302 | LIVESTOCK PRODUCTION | 7 | ** |\n| 010304 | CROP PRODUCTION | 2 | ** |\n| 120401 | COSMETOLOGY | 17 | 13 |\n| 131210 | EARLY CHILDHOOD EDUCATION | 18 | 18 |\n| 419999 | APPLIED TECHNOLOGY | 0 | ** |\n| 430107 | LAW ENFORCEMENT ACADEMY | 21 | 21 |\n| 470604 | AUTOMOTIVE TECHNOLOGIES | 18 | 18 |\n| 510801 | MEDICAL OFFICE TECHNOLOGIES | 1 | ** |\n| 510904 | EMERGENCY MEDICAL SERVICES | 9 | ** |\n| 511004 | MEDICAL LABORATORY TECHNICIAN | 12 | 9 |\n| 511009 | PHLEBOTOMY | 0 | ** |\n| 511504 | HEALTH NAVIGATOR | 5 | ** |\n| 513801 | NURSING | 33 | 21 |\n| 513901 | PRACTICAL NURSING | 13 | 9 |\n| 513902 | NURSE AIDE | 53 | 53 |\n| 520201 | APPLIED BUSINESS TECHNOLOGY | 1 | ** |\n| 520213 | LEADERSHIP CERTIFICATE | 17 | 17 |\n| 520701 | RURAL BUSINESS ENTREPRENEURSHIP | 6 | ** |\n| 521501 | REAL ESTATE | 0 | ** |"}
{"item_id": "item_0090", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the goal scoring was dominated by a few top players or spread out, so I can see at a glance how much of the total was contributed by the leaders?", "table_markdown": "| Rank | Name & nation | Club | Goals |\n|---|---|---|---|\n| 1 | Ahlm Johanna (SWE) | Team Esbjerg (DEN) | 59 |\n| | Polman Estavana (NED) | Team Esbjerg (DEN) | 59 |\n| 3 | Tilinger Tamara (HUN) | Fehérvár KC (HUN) | 50 |\n| 4 | Davydenko Ekaterina (RUS) | Lada (RUS) | 46 |\n| 5 | Trehubova Tetyana (SVK) | IUVENTA Michalovce (SVK) | 45 |\n| 6 | Jovanovic Marija (MNE) | Astrakhanochka (RUS) | 42 |\n| | Martinkova Hana (CZE) | DHK Banik Most (CZE) | 42 |\n| 8 | Goos Michelle (NED) | Succes Schoonmaak (NED) | 41 |\n| | Ilina Ekaterina (RUS) | Lada (RUS) | 41 |\n| 10 | Forslund Susanne Kastrup (DEN) | Team Esbjerg (DEN) | 40 |\n| 11 | Augustesen Mie (DEN) | Kobenhavn Handbold (DEN) | 38 |\n| 12 | Kviesgaard Maibritt (DEN) | Team Esbjerg (DEN) | 37 |\n| 13 | Garanina Veronika (RUS) | Lada (RUS) | 35 |\n| | Yezhykava Karyna (BLR) | Astrakhanochka (RUS) | 35 |\n| 15 | Herr Anita (HUN) | Fehérvár KC (HUN) | 34 |\n| 16 | Farkas Veronika (HUN) | Ipress Center-Vac (HUN) | 33 |\n| | Gorshkova Polina (RUS) | Lada (RUS) | 33 |\n| | Popa Laura Petruta (ROU) | U Jolidon Cluj (ROU) | 33 |\n| | Szollosiova Terezia (SVK) | IUVENTA Michalovce (SVK) | 33 |\n| 20 | Chernoivanenko Olga (RUS) | Lada (RUS) | 32 |\n| | Naidzinavicius Kim (GER) | TSV Bayer 04 Leverkusen (GER) | 32 |\n| | Rebicova Marianna (SVK) | IUVENTA Michalovce (SVK) | 32 |"}
{"item_id": "item_0091", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether prion disease cases are concentrated in a few Australian states or spread evenly, so I can see at a glance which jurisdictions dominate the total?", "table_markdown": "| Jurisdiction | Cases | ASRc | Total cases | Long term average cases | Average ASRc |\n|--------------|-------|----------------|-------------|------------------------|-------------------------|\n| ACT | 0 | 0 | 14 | 0.5 | 1.27 |\n| NSW | 11 | 1.19 | 276 | 10 | 1.31 |\n| NT | 0 | 0 | 6 | 0.2 | 0.69 |\n| Qld. | 6 | 0.85 | 136 | 5 | 1.05 |\n| SA | 5 | 2.23 | 77 | 3 | 1.55 |\n| Tas. | 2 | 2.60 | 16 | 0.6 | 0.93 |\n| Vic. | 13 | 1.66 | 250 | 9 | 1.58 |\n| WA | 2 | 0.52 | 111 | 4 | 1.65 |\n| Australia | 39 | 1.24 | 886 | 32 | 1.36 |"}
{"item_id": "item_0092", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether freshman applications are concentrated among a few UC campuses, so I can see at a glance how much of the total volume the top schools account for?", "table_markdown": "| Campus | Applications | Admits | Selectivity (Admits/Applicants) | Enrollees | Yield (Enroll/Admit) |\n|--------------|--------------|----------|---------------------------------|-----------|----------------------|\n| Los Angeles | 55,431 | 12,667 | 23% | 4,740 | 37% |\n| Berkeley¹ | 48,478 | 12,689 | 26% | 5,138 | 40% |\n| San Diego¹ | 47,400 | 19,560 | 41% | 4,471 | 23% |\n| Santa Barbara| 47,078 | 23,188 | 49% | 4,387 | 19% |\n| Irvine | 42,426 | 20,672 | 49% | 4,584 | 22% |\n| Davis | 40,626 | 21,358 | 53% | 4,972 | 23% |\n| Santa Cruz | 27,840 | 19,962 | 72% | 3,964 | 20% |\n| Riverside² | 21,467 | 16,810 | 78% | 3,879 | 23% |\n| Merced² | 10,355 | 8,505 | 82% | 829 | 10% |"}
{"item_id": "item_0093", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the Total PL budget is concentrated among a few task components, so I can see at a glance how much of the total is accounted for by the top contributors?", "table_markdown": "| Task Component | Federal PL Fund | ODOT Match (Local) | Total PL |\n|---|---|---|---|\n| 210. SA Phase II: Scenario Analysis | SPR Fund* | SPR Fund* | $8,000 |\n| 220. Corvallis TSP | $15,672 | $1,793 | $17,466 |\n| 230. Philomath TSP | $14,461 | $1,655 | $16,116 |\n| 240. Benton County TSP | $14,564 | $1,667 | $16,231 |\n| 250. Preparatory Work on RTP | $12,110 | $1,386 | $13,496 |\n| 260. SRTS Contract with 509J | 509J contract | 509J contract | $2,000 |\n| 270. Travel Forecasting Model | $3,561 | $407 | $3,970 |\n| 280. City of Adair Village TSP | $1,895 | $217 | $2,112 |\n| Total | | | $79,391 |"}
{"item_id": "item_0094", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the home instruction costs are concentrated among a few students or spread out, so I can see at a glance how much of the total budget is driven by the top spenders?", "table_markdown": "| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ID# | # Hours Per Week | T o t a l # Weeks & Days | Cost Per Hr. | Total Cost | Account # |\n|---|---|---|---|---|---|---|\n| | 860023 | 5 | 10.5 | $37.00 | $1942.50 | 11-150-100-101-0000-400 |\n| | 1360065 | 5 | 5 | $37.00 | $925.00 | 11-150-100-101-0000-400 |\n| | 855055 | 5 | 8.6 | $37.00 | $1591.00 | 11-150-100-101-0000-401 |\n| | 1033815 | 5 | 1.2 | $37.00 | $222.00 | 11-150-100-101-0000-400 |\n| | 855115 | 5 | 1.2 | $37.00 | $222.00 | 11-150-100-101-0000-400 |\n| | 3005809 | 5 | 1.2 | $37.00 | $222.00 | 11-150-100-101-0000-400 |\n| | 3005742 | 5 | 1.2 | $37.00 | $222.00 | 11-150-100-101-0000-400 |\n| | 3000262 | 5 | 1.2 | $37.00 | $222.00 | 11-150-100-101-0000-400 |\n| | 1281069 | 5 | 1.2 | $37.00 | $222.00 | 11-150-100-101-0000-400 |\n| | 1085038 | 5 | 1.2 | $37.00 | $222.00 | 11-150-100-101-0000-400 |\n| | 1195004 | 5 | 1.2 | $37.00 | $222.00 | 11-150-100-101-0000-400 |\n| | 1480176 | 5 | 1.2 | $37.00 | $222.00 | 11-150-100-101-0000-400 |\n| | 1185036 | 5 | 1.2 | $37.00 | $222.00 | 11-150-100-101-0000-400 |\n| | 1280018 | 5 | 1.2 | $37.00 | $222.00 | 11-150-100-101-0000-400 |\n| | 1055233 | 10 | 1.2 | $37.00 | $444.00 | 11-150-100-101-0000-400 |\n| | 3006024 | 10 | 1.2 | $37.00 | $444.00 | 11-150-100-101-0000-400 |\n| | 995125 | 10 | 1.6 | $37.00 | $592.00 | 11-150-100-101-0000-400 |\n| | 955087 | 10 | 1.8 | $37.00 | $666.00 | 11-150-100-101-0000-400 |\n| | 960184 | 10 | 0.4 | $37.00 | $148.00 | 11-150-100-101-0000-400 |\n| | 1155029 | 10 | 1.6 | $37.00 | $592.00 | 11-150-100-101-0000-400 |\n| | 1155111 | 10 | 0.4 | $37.00 | $148.00 | 11-150-100-101-0000-400 |\n| | 1155074 | 10 | 1.2 | $37.00 | $444.00 | 11-150-100-101-0000-400 |\n| | 1060181 | 10 | 1.2 | $37.00 | $444.00 | 11-150-100-101-0000-400 |\n| | 3000035 | 10 | 1.2 | $37.00 | $444.00 | 11-150-100-101-0000-400 |\n| | 1080058 | 10 | 1.8 | $37.00 | $666.00 | 11-150-100-101-0000-400 |\n| | 3005745 | 10 | 1.2 | $37.00 | $444.00 | 11-150-100-101-0000-400 |\n| | 995085 | 10 | 1 | $37.00 | $370.00 | 11-150-100-101-0000-400 |\n| | 980225 | 10 | 1.2 | $37.00 | $444.00 | 11-150-100-101-0000-400 |\n| | 960022 | 10 | 1.2 | $37.00 | $444.00 | 11-150-100-101-0000-400 |"}
{"item_id": "item_0095", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether attendance is concentrated among a few top investors or spread out, so I can see at a glance how much the biggest firms dominate the total?", "table_markdown": "| Rank | Investor | Attendees |\n|------|---------------------------|-----------|\n| 1 | Lakestar | 105 |\n| 2 | Holtzbrinck Ventures | 85 |\n| 3 | Index Ventures | 77 |\n| 4 | Hellman & Friedman | 75 |\n| 5 | KKR | 69 |\n| 6 | SevenVentures | 69 |\n| 7 | Target Global | 67 |\n| 8 | Vitruvian Partners | 58 |\n| 9 | General Atlantic | 55 |\n| 10 | Accel Partners | 53 |\n| 11 | BCG Digital Ventures | 44 |\n| 12 | TA Associates | 42 |\n| 13 | EQT Partners | 41 |\n| 14 | Eight Roads | 40 |\n| 15 | Permira | 39 |\n| 16 | Acton Capital | 38 |\n| 17 | Summit Partners | 36 |\n| 18 | Partech Ventures | 35 |\n| 19 | Rocket Internet | 35 |\n| 20 | e.ventures | 33 |\n| 21 | MCI Capital | 33 |\n| 22 | Atlantic Labs | 31 |\n| 23 | btov Partners | 30 |\n| 24 | Macquarie Capital | 30 |\n| 25 | Insight Venture Partners | 29 |\n| 26 | Technology Crossover Ventures | 29 |\n| 27 | Maryland | 28 |\n| 28 | Oakley Capital | 28 |\n| 29 | Earlybird Venture Capital | 27 |\n| 30 | Project A Ventures | 27 |\n| 31 | NGP Capital | 26 |\n| 32 | Piton Capital | 26 |\n| 33 | Spectrum Equity Investors | 25 |\n| 34 | 10x.Group | 24 |\n| 35 | FinLeap | 24 |\n| 36 | HPE Growth Capital | 24 |\n| 37 | iLab Ventures | 24 |\n| 38 | HgCapital | 23 |\n| 39 | Atomico | 22 |\n| 40 | Coparion | 22 |\n| 41 | SpeedInvest | 22 |\n| 42 | Catalonia Trade & Investment | 21 |\n| 43 | Frog Capital | 21 |\n| 44 | Northzone | 21 |\n| 45 | Rheingau Founders | 21 |\n| 46 | DN Capital | 20 |\n| 47 | Scottish Equity Partners | 20 |\n| 48 | TPG Capital | 20 |\n| 49 | LetterOne Technology | 19 |\n| 50 | RTP Global | 19 |"}
{"item_id": "item_0096", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the body weight of the Standard Reference Man adult male is concentrated in a few major organs or spread out, so I can see at a glance which ones dominate the total mass?", "table_markdown": "| ORGAN | Weight: adult male (gr) | | Weight: adult female (gr) | |\n|----------------|-------------------------|-------|---------------------------|-------|\n| | SRM | ARM | SRFM | ARFM |\n| Adrenals | 16.3 | 14 | 14 | 13 |\n| Brain | 1420 | 1470 | 1200 | 1320 |\n| Breasts | 25 | 22 | 360 | 300 |\n| GB | 10.5 | 8 | 8 | 6 |\n| LLI | 167 | 150 | 160 | 120 |\n| SI | 677 | 590 | 600 | 450 |\n| Stomach | 158 | 140 | 140 | 110 |\n| ULI | 220 | 180 | 200 | 140 |\n| Heart | 316 | 380 | 240 | 320 |\n| Kidneys | 299 | 320 | 275 | 280 |\n| Liver | 1910 | 1600 | 1400 | 1400 |\n| Lungs | 1000 | 1200 | 800 | 910 |\n| Muscle | 28000 | 25000 | 17000 | 28000 |\n| Pancreas | 94.3 | 130 | 85 | 110 |\n| Red Marrow | 1120 | 1000 | 1300 | 780 |\n| Osteogenic cells | 120 | 120 | 90 | 90 |\n| Skin | 3010 | 2400 | 1790 | 1800 |\n| Spleen | 183 | 140 | 150 | 120 |\n| Testes | 39.1 | 37 | 0 | 0 |\n| Thymus | 20.9 | 30 | 20 | 29 |\n| Bladder | 47.6 | 40 | 35.9 | 30 |\n| Uterus / Prostate | 8 | 8 | 80 | 70 |\n| Fetus | 0 | 0 | 0 | 0 |\n| Placenta | 0 | 0 | 0 | 0 |\n| Total body | 73700 | 60000 | 56912 | 51000 |"}
{"item_id": "item_0097", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether annual zone page impressions are concentrated among a few top zones or spread out, so I can see at a glance which areas dominate the traffic?", "table_markdown": "| Zone Page Impressions | Monthly | Annual |\n|---------------------------|---------|---------|\n| Additive Manufacturing | 30,138 | 361,654 |\n| Cutting Tools | 27,961 | 335,533 |\n| Machining Centers & Milling Machines | 20,894 | 250,725 |\n| CAD-CAM Software | 12,279 | 147,344 |\n| Inspection & Measurement | 8,522 | 102,264 |\n| CNC & Machine Controls | 8,433 | 101,193 |\n| EDM | 6,279 | 75,348 |\n| Turning | 5,636 | 67,626 |\n| Aerospace | 4,748 | 56,973 |\n| Automation | 4,575 | 54,894 |\n| ERP Software | 3,285 | 39,415 |\n| Medical | 2,956 | 35,472 |\n| Micromachining | 2,745 | 32,939 |\n| Automotive | 2,633 | 31,592 |\n| Turn-Mill | 2,589 | 31,064 |\n| Parts Cleaning | 2,496 | 29,950 |\n| Next Generation | 1,863 | 22,359 |\n| Deburring | 1,279 | 15,347 |"}
{"item_id": "item_0098", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether volunteer participation is dominated by a few large teams or spread out, so I can see at a glance how much of the total comes from the top contributors?", "table_markdown": "| Volunteer Team | Build Location | No. of Volunteers |\n|-----------------------------------------------------|-------------------|-------------------|\n| 1 American International School | Chennai | 92 |\n| 2 Tim Pepper Team - US | Chennai | 16 |\n| 3 Golden Bytes team- Netherlands | Chennai | 13 |\n| 4 US team - Megan | Chennai | 12 |\n| 5 Sri Ram School | Delhi | 32 |\n| 6 ASK Wealth Advisors | Mumbai | 96 |\n| 7 AAA & US Consulate | Mumbai | 21 |\n| 8 IFC | Mumbai | 30 |\n| 9 Blackstone | Mumbai | 19 |\n| 10 Mercedes Benz International School | Mumbai | 26 |\n| 11 Cummins | Mumbai | 31 |\n| 12 Thomson Reuters | Bangalore | 226 |\n| 13 CISCO | Bangalore | 41 |\n| 14 Deshpande Foundation | Bangalore | 3 |\n| 15 JP Morgan | Bangalore | 3 |\n| 16 TIMKEN | Bangalore | 25 |\n| 17 International School of Kuala Lumpur | Bangalore | 34 |\n| 18 Individual Volunteers-Brett and Seran Vreskala | Bangalore | 2 |\n| **TOTAL** | **72** | **722** |"}
{"item_id": "item_0099", "chart_task_type": "concentration_long_tail", "query": "Can you chart how concentrated the prize money was among the top finishers in the Main Prize List, so I can see at a glance if a few winners took the bulk of the payout?", "table_markdown": "| DATE: February 16 & 17, 2008 HOUSE: Holiday Lanes - Albert Lea NO ENTRIES: 50 | | | | | | | | |\n|---|---|---|---|---|---|---|---|---|\n| | Main Prize List | | | | | | | |\n| # | NAME | CITY | QUAL | SEMI | BONUS | TOTAL | PTS | MONEY |\n| 1st | Tom Korth | Mankato | 2267 | 2899 | 270 | 3169 | 25 | 1000 |\n| 2nd | Chad Nelson | Owatonna | 2358 | 2943 | 210 | 3153 | 23 | 375 |\n| 3rd | Mark Luttchens | Rochester | 2267 | 2852 | 270 | 3122 | 21 | 300 |\n| 4th | Clark Poelzer | Arden Hills | 2281 | 2884 | 210 | 3094 | 19 | 250 |\n| 5th | Dan Bock | Albert Lea | 2339 | 2836 | 180 | 3016 | 17 | 225 |\n| 6th | Dave Langer | Brooklyn Park | 2267 | 2753 | 210 | 2963 | 15 | 185 |\n| 7th | Jason Barnhouse | Maple Grove | 2228 | 2759 | 165 | 2924 | 14 | 155 |\n| 8th | Rob Downer | Rochester | 2249 | 2736 | 165 | 2901 | 13 | 135 |\n| 9th | Arnie Gerdes | Eagan | 2274 | 2642 | 195 | 2837 | 12 | 125 |\n| 10th | John Kreyer Jr | Fridley | 2320 | 2669 | 120 | 2789 | 11 | 115 |\n| 11th | Dave Oulman | Owatonna | 2362 | 2594 | 180 | 2774 | 10 | 105 |\n| 12th | Luke Voaklander | St Bonifacious | 2287 | 2559 | 210 | 2769 | 9 | 100 |\n| 13th | Bob Hager | Owatonna | 2315 | 2561 | 180 | 2741 | 9 | 95 |\n| 14th | Doug Manhart | Roseville | 2218 | 2597 | 120 | 2717 | 9 | 90 |\n| 15th | Steve Barrett | Albert Lea | 2267 | 2526 | 135 | 2661 | 9 | 85 |\n| 16th | Brent Stearns | Carver | 2279 | 2533 | 60 | 2593 | 9 | 80 |\n| | Optional Qualifying Prize List | | | | | | | |\n| 1st | Dave Oulman | Owatonna | 2362 | | | | | 200 |\n| 2nd | Chad Nelson | Owatonna | 2358 | | | | | 150 |\n| 3rd | Dan Bock | Albert Lea | 2339 | | | | | 125 |\n| 4th | John Kreyer Jr | Fridley | 2320 | | | | | 100 |\n| 5th | Bob Hager | Owatonna | 2315 | | | | | 90 |\n| 6th | Clark Poelzer | Arden Hills | 2281 | | | | | 80 |\n| 7th | Brent Stearns | Carver | 2279 | | | | | 70 |\n| 8th | Arnie Gerdes | Eagan | 2274 | | | | | 60 |\n| | Bonus Senior Casher | | | | | | | |\n| | Ron Lindner | Mayer | 2174 | | | | | 80 |\n| QL | Qualifying Leader | Dave Oulman 2362 | | | | | 2 | 50 |\n| | \"ON TRACK CASHER\" | Rob Aimers Won 9th & 10th frame eliminator rolloff | | | | | | free entry |\n| Stepladder Finals | | | | | | | | |\n| | GAME 1: Poelzer def Bock 248-215 | | | | | | | |\n| | GAME 2: Luttchens def Poelzer 300-227 | | | | | | | |\n| | GAME 3: Nelson def Luttchens 206-194 | | | | | | | |\n| | GAME 4: Korth def Nelson 231-216 | | | | | | | |"}
{"item_id": "item_0100", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the acreage of these private and quasi-public facilities is concentrated among just a few sites, so I can see at a glance which ones dominate the total land area?", "table_markdown": "| Name | Acres | Facilities |\n|-----------------------|-------|---------------------------------------------------------------------------|\n| Jonesville District Library | <0.1 | Summer Reading Program; Video Rentals; & a 28,000 Volume Library |\n| Mill Race Golf Course | 72.3 | 9-Hole Course; Driving Range; Putting Green; Pro-Shop, Snack Bar, & Leagues |\n| Jonesville Eagles | 12.8 | 4 Softball Fields; Snack Bar; & Clubhouse |\n| Sauk Theater | 1.3 | Community Amateur Theater |\n| Grosvenor House Museum | 0.3 | Group Tours; Craft Shows; & Holiday Celebrations |\n| Way-Back-In Campground | 84.5 | Campsites (RV, Tent, Cabins); Playground; Basketball; Trails; and Canoe Rental |"}
{"item_id": "item_0101", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the total sales volume in Watson was dominated by a few high-priced properties, so I can see at a glance what percentage of the total is accounted for by the top sales?", "table_markdown": "| Date (2016) | Address | Price |\n|-------------|--------------------------|----------------|\n| 21/01/16 | 293 Antill St | $570,000 House |\n| 15/02/16 | 9/25 Aspinall St | $510,000 House |\n| 02/13/16 | 253 Antill St | $461,000 House |\n| 22/03/16 | 20/19 Aspinall Street | $447,500 Townhouse |\n| 08/03/16 | 160/395 Antill Unit | $405,000 |\n| 20/01/16 | 72 Ian Nicol St | $397,500 House |\n| 09/02/16 | 46/35 Tay St | $357,000 Apartment |\n| 01/02/16 | 54/23 Aspinall Apartment | $340,000 |\n| 17/03/16 | 41/25 Aspinall Apartment | $330,000 |\n| 29/01/16 | 27/21 Aspinall Apartment | $325,000 |\n| 15/02/16 | 44/21 Aspinall Apartment | $315,000 |"}
{"item_id": "item_0102", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the bill amounts are concentrated among a few entities, so I can see at a glance which vendors dominate the total expenditure?", "table_markdown": "| Vectra: | Amount | Description |\n|------------------|----------|--------------------------------------------------|\n| $ 425.00 | MBD&G, Monthly Accountant fees |\n| $ 500.00 | Banner and Bower, Attorney fees |\n| $ 127.08 | Banner and Bower, Advertising and E-filing fees |\n| $ 1,700.00 | Kidd Engineering, Administrator/Engineer fees |\n| $ 905.30 | Kidd Engineering, Public Outreach Presentations |\n| $ 375.00 | Kidd Engineering, Maintenance Fund Assessment Court Preparation and Attendance |\n| $ 200.00 | Bernard, Director’s Fee |\n| $ 200.00 | Cordova, Director’s Fee |\n| $ 200.00 | Koehler, Director’s Fee |"}
{"item_id": "item_0103", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the sample of banks is concentrated in just a few countries or spread out evenly, so I can see at a glance which nations dominate the dataset?", "table_markdown": "| Country | No of banks analyzed | % TA sample / TA banking system for large banks |\n|-----------|----------------------|-----------------------------------------------|\n| Germany | 4 | 6.30% |\n| Denmark | 12 | 1.21% |\n| Finland | 3 | - |\n| Greece | 8 | - |\n| Hungary | 1 | - |\n| Italy | 18 | 4.44% |\n| Poland | 12 | - |\n| Portugal | 4 | - |\n| Spain | 4 | 3,99% |\n| Sweden | 4 | 2.43% |\n| **Total** | **85** | **18.37%** |"}
{"item_id": "item_0104", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the total price value is concentrated among a few high-priced items or spread out across the long tail?", "table_markdown": "| Item Description | ID | Price |\n|------------------------------------------------------|-------------|--------|\n| 1- Candy cane | ID :065100012 | €0.30 |\n| 2- Milk chocolate Saint Nicholas filled with praline ± 11 cm | ID :094723374 | €1.80 |\n| 3- Dark chocolate Saint Nicholas filled with praline ± 11 cm | ID :094723373 | €1.80 |\n| 4- Saint Nicholas in marzipan ± 11 cm | ID :094723375 | €2.50 |\n| 5- Saint Nicholas in milk hallow chocolate ± 12 cm | ID :094723372 | €1.80 |\n| 6- Saint Nicholas in dark hallow chocolate ± 12 cm | ID :094723371 | €1.80 |\n| 7- Saint Nicholas Speculoos ± 15cm | ID :094723376 | €2.20 |\n| 8- Saint-Nicholas Speculoos with dark chocolate ± 15cm (L. Gerbaud) | ID :094723378 | €2.80 |\n| 9- Saint Nicholas Speculoos ± 20cm | ID :094723377 | €4.00 |\n| 10- Saint-Nicholas Speculoos with dark chocolate ± 20cm (L. Gerbaud) | ID :094723379 | €4.50 |\n| 11- Pain à la Grecque ± 15cm | ID :094717588 | €2.00 |\n| 12- Sablés chocolat 50g | ID :094723315 | €2.50 |\n| 13- Speculoos with milk chocolate (Laurent Gerbaud) | ID :094723385 | €1.80 |\n| 14- Speculoos with milk chocolate (Laurent Gerbaud) | ID :094723386 | €1.80 |\n| 15- Chocolat coin | ID :094723296 | €0.08 |\n| 16- Biscuit Florentin | ID :094723015 | €0.80 |"}
{"item_id": "item_0105", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the coastal sea bass fishers population is dominated by a few specific strata or spread out, so I can see at a glance how concentrated the user base is?", "table_markdown": "| Fishing frequency | Main fishing gear | Fishing mode | Coastal sea bass fishers population (2009 survey) | Non-coastal sea bass fishers population (estimation) |\n|---|---|---|---|---|\n| Occasional | All | Shore | 22,634 | 15,401 |\n| | All | Boat | 21,450 | 7,006 |\n| Regular | Spearfishing | Shore | 5,509 | 1,336 |\n| | Spearfishing | Boat | 4,738 | |\n| | Lines | Shore | 47,438 | 34,399 |\n| | Lines | Boat | 44,523 | 58,023 |\n| Highly regular | Spearfishing | Shore | 4,972 | 4,079 |\n| | Spearfishing | Boat | 5,377 | |\n| | Lines | Shore | 29,606 | 13,811 |\n| | Lines | Boat | 39,004 | 10,682 |\n| Total fishers population | | | 225,252 | 144,737 |"}
{"item_id": "item_0106", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether out-of-network costs are concentrated among a few specific procedures?", "table_markdown": "| Procedure | Out-of-Network Payment | Amount that Would Have Been Paid In-Network |\n|---|---|---|\n| Procedure A | $34,244,806 | $9,378,257 |\n| Procedure B | $6,214,917 | $2,021,191 |\n| Procedure C | $6,594,639 | $2,490,442 |\n| Procedure D | $7,483,563 | $3,934,019 |\n| Procedure E | $6,183,474 | $2,776,697 |\n| Procedure F | $3,982,245 | $596,463 |"}
{"item_id": "item_0107", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the stability requirements are concentrated among a few key locations, so I can see at a glance which sites dominate the total need?", "table_markdown": "| Location | Ref | Requirement (MVA) |\n|------------------------|-----|-------------------|\n| Spittal | 1 | 600 |\n| Blackhilllock | 2 | 1,300 |\n| Peterhead | 3 | 1,300 |\n| Longannet area | 4 | 600 |\n| Hunterslton | 5 | 1,200 |\n| Mark Hill/ Coylton area| 6 | 400 |\n| Moffat/ Elvanfoot area | 7 | 1,800 |\n| Eccles area | 8 | 1,200 |\n| **Total** | | **8,400** |"}
{"item_id": "item_0108", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the SOT amounts are concentrated among a few specific vehicles or spread out, so I can see at a glance which units dominate the total?", "table_markdown": "| VIN | Make | Model | SOT amount |\n|-------------|----------|-------------|------------|\n| 1FTSW21R48EE07057 | FORD | F-250 | $50,761.92 |\n| 1ZVHT80N985202593 | FORD | MUSTANG | $23,895.21 |\n| 3FAHP07Z58R270376 | FORD | FUSION | $21,623.05 |\n| 1D7HU18N13J622026 | DODGE | RAM | $6,822.00 |\n| 1FTNE14W09DA10125 | FORD | E-150 | $21,451.13 |\n| 1FTNE14W99DA10124 | FORD | E-150 | $21,451.13 |\n| 1FMCU04G79KA34376 | FORD | ESCAPE | $28,747.24 |\n| 2FMDK38C68BA09917 | FORD | EDGE | $29,006.05 |"}
{"item_id": "item_0109", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether Week 44 cholera cases were concentrated among a few LGAs, so I can see at a glance which areas dominated the total?", "table_markdown": "| Cholera Situation Report | State | Week 44 Cases | % Contribution | % Total |\n|--------------------------|-----------|---------------|----------------|---------|\n| Toro | Bauchi | 1911 | 2% | 30% |\n| Ganjuwa | Bauchi | 1311 | 1% | 32% |\n| Tafawa Balewa | Bauchi | 1081 | 1% | 33% |\n| Birnin Kudu | Jigawa | 970 | 1% | 34% |\n| Gwadabawa | Sokoto | 969 | 1% | 35% |\n| Dange-Shuni | Sokoto | 958 | 1% | 36% |\n| **Total** | | **35,597** | **36%** | |"}
{"item_id": "item_0110", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the revenue from these medical textbooks is concentrated among a few top titles or spread out, so I can see at a glance which books drive the majority of sales?", "table_markdown": "| Product | Description | Price | Qty | Dis. % | Dis.Amt. | Line Discount | Amount |\n|---------|--------------------------------------------------|-------|-----|--------|----------|---------------|----------|\n| 0340983558 | OBSTETRICS BY TEN TEACHERS 19ED | 1,800.00 | √2 | 10.00 | 0.00 | 360.00 | 3,240.00 |\n| 7340983566 | GYNAECOLOGY BY TEN TEACHERS 19ED | 1,800.00 | √2 | 10.00 | 0.00 | 360.00 | 3,240.00 |\n| 01311227049 | GUYTON AND HALL TEXTBOOK OF MEDICAL PHYSIOLOGY 12E | 4,465.00 | √2 | 10.00 | 0.00 | 893.00 | 8,037.00 |\n| 007181292X | JEWETZ MELNICK & ADELBERGS MEDICAL MICROBIOLOGY 26 | 4,160.00 | √2 | 10.00 | 0.00 | 832.00 | 7,488.00 |\n| 0071792775 | HARPER'S ILLUSTRATED BIOCHEMISTRY 29ED | 3,245.00 | √2 | 10.00 | 0.00 | 649.00 | 5,841.00 |\n| 1259027538 | GANONG'S REVIEW OF MEDICAL PHYSIOLOGY 24ED | 2,500.00 | √2 | 10.00 | 0.00 | 500.00 | 4,500.00 |\n| 0702045004 | KUMAR & CLARKS CLINICAL MEDICINE 8ED | 4,580.00 | √4 | 10.00 | 0.00 | 1832.00 | 16,488.00|"}
{"item_id": "item_0111", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the mass production of the top ten U.S. chemicals in 1997 was dominated by a few key substances, so I can see at a glance how much of the total is accounted for by the leaders?", "table_markdown": "| Rank | Chemical | Mass ($10^8$ lbs) |\n|------|------------------------|-------------------|\n| 1 | Sulfuric acid | 95.6 |\n| 2 | Nitrogen | 82.8 |\n| 3 | Oxygen | 64.8 |\n| 4 | Ethylene | 51.1 |\n| 5 | Lime | 42.5 |\n| 6 | Ammonia | 38.4 |\n| 7 | Phosphoric acid | 33.6 |\n| 8 | Propylene | 27.5 |\n| 9 | Ethylene dichloride | 26.3 |\n| 10 | Chlorine | 26.0 |"}
{"item_id": "item_0112", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether Potential Interactive Occurrences are concentrated among a few top metro areas, so I can see at a glance how much of the total is captured by the biggest players versus the long tail?", "table_markdown": "| Rank | Metro Area | Number of Metro Areas Within 300 Miles | PIO |\n|------|------------------|----------------------------------------|-------|\n| 1 | New York | 50 | 76832890 |\n| 2 | Philadelphia | 55 | 48836966 |\n| 3 | Chicago | 67 | 45354946 |\n| 4 | Washington | 57 | 34457878 |\n| 5 | Detroit | 53 | 30227846 |\n| 6 | Boston | 37 | 28645331 |\n| 7 | Nassausuffolk | 45 | 26469456 |\n| 8 | Newark | 51 | 21718720 |\n| 9 | Baltimore | 60 | 20750101 |\n| 10 | Pittsburg | 62 | 18517248 |\n| 11 | Cleveland | 61 | 15053368 |\n| 12 | Los Angeles | 10 | 13144040 |\n| 13 | Buffalo | 55 | 12654551 |\n| 14 | St. Louis | 47 | 11104376 |\n| 15 | Rochester, N. Y. | 52 | 10733400 |\n| 16 | Cincinnati | 63 | 9916230 |"}
{"item_id": "item_0113", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the planned Year 3 project mileage is concentrated among a few streets, so I can see at a glance which ones dominate the total?", "table_markdown": "| Street | From/To | Direction | Mileage | Status |\n|-----------------|--------------------------|-----------|---------|------------------------------------------------------------------------|\n| Cambridge Street| Oak St to Prospect St | eastbound | .08 miles | Community engagement will begin soon. Anticipated completion by April 30, 2023. |\n| Main Street | Portland St to Albany St | both | .20 miles | Plans related to Planning Board Special Permit 371 are complete. Construction anticipated to begin by April 30, 2023. |\n| Brattle Street | Mason St to Sparks St | both | .32 miles | Community engagement has begun. |\n| Garden Street | Huron Ave to Mason St | both | 1.25 miles | Community engagement has begun. |\n| Huron Avenue | Glaciken Field to Fresh Pond Pkwy | both | 1.06 miles | Glaciken Field Project plans are complete. Construction anticipated to begin by April 30, 2023. |\n| River Street | Memorial Dr to Mass Ave | eastbound | .68 miles | Bunker Street Reconstruction Project community engagement in process. Beginning of construction anticipated by April 30, 2023. |\n| Holworthy Street| Cambridge-Watertown Greenway to Belmont St | both | .08 miles | Belmont Street Reconstruction Project plans are complete. Beginning of construction anticipated by April 30, 2023. |"}
{"item_id": "item_0114", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether scoring points are concentrated among a few top players or spread out, so I can see at a glance who dominates the offense?", "table_markdown": "| Player | Goals | Assists | Pts. | Penalty |\n|------------|-------|---------|------|---------|\n| Yackel, wd | 12 | 14 | 26 | 7 |\n| Peterson, c | 2 | 12 | 14 | 26 |\n| Kelly, c | 2 | 10 | 12 | 9 |\n| Berg, w | 9 | 8 | 17 | 6 |\n| Simko, c | 4 | 8 | 12 | 3 |\n| Turk, w | 2 | 8 | 13 | 3 |\n| Simko, c | 4 | 4 | 8 | 3 |\n| Meredith, c| 7 | 8 | 15 | 10 |\n| Johnson, w | 5 | 1 | 6 | 4 |\n| Johnson, w | 5 | 1 | 6 | 4 |\n| Simko, c | 2 | 1 | 3 | 2 |\n| Rodda, wd | 2 | 1 | 3 | 16 |\n| Jorde, c | 1 | 1 | 2 | |\n| Anderson, c| 1 | 1 | 2 | |\n| Totals | 83 | 93 | 176 | 98 |"}
{"item_id": "item_0115", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the total project costs are dominated by a few large initiatives or spread more evenly across the list?", "table_markdown": "| Project Number | Project Description | Cost |\n|----------------|----------------------------------------------------------|----------|\n| 1 | Add Grass Soccer at South Park | $1,000,000 |\n| 2 | Replace Infrastructure/General at Cesar Chavez Neighborhood Park | $1,280,000 |\n| 3 | Add Multipurpose Room at Dr. Walter R. Tucker Park | $66,000 |\n| 4 | Add Pools/Aquatic Facilities at Wilson Park | $8,290,000 |\n| 5 | Repair Emergency lighting/call station at All Parks in Study Area | $70,750 |\n| 6-10 | Repair Playgrounds at Kelly, Wilson, Gonzales, Burrell McDonald & Lueders Parks | $250,000 |"}
{"item_id": "item_0116", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the project's budget is dominated by a few high-cost items or spread evenly across the list, so I can see at a glance how much of the total PhP 90,000 is accounted for by the top contributors?", "table_markdown": "| Item No. | Quantity | Unit | Description | Unit Cost | Total Cost |\n|----------|----------|--------|------------------------------------------------------------------------------|-----------|------------|\n| 1. | 30 | packs | Vinyl Inkjet Sticker, A4, 20pcs/pack, high quality | 400.00 | 12,000.00 |\n| 2. | 1 | pack | Laminating Film, A4, 125mic | 600.00 | 600.00 |\n| 3. | 1,000 | pcs | Folder, long, white | 4.00 | 4,000.00 |\n| 4. | 3 | packs | Photo Paper, A4, 10’s/pack, shiny, high quality | 50.00 | 150.00 |\n| 5. | 2 | pcs | External Drive, 1TB | 3,760.00 | 7,520.00 |\n| 6. | 12 | pcs | Flash Drive, 32GB | 550.00 | 6,600.00 |\n| 7. | 2 | pcs | PS, heavy duty | 3,000.00 | 6,000.00 |\n| 8. | 20 | packs | Battery, AAA, 2’s/pack | 70.00 | 1,400.00 |\n| 9. | 20 | packs | Battery, AAA, 2’s/pack | 70.00 | 1,400.00 |\n| 10. | 200 | pcs | Face Mask, washable | 50.00 | 10,000.00 |\n| 11. | 5 | boxes | Gloves, disposable | 570.00 | 2,850.00 |\n| 12. | 30 | bottles| Disinfectant Spray, lemon | 350.00 | 10,500.00 |\n| 13. | 30 | bottles| Disinfectant Liquid, 350mL | 350.00 | 10,500.00 |\n| 14. | 30 | bottles| Handsoap, 450mL, in bottle dispenser, 99.9% germ free, lemon | 200.00 | 6,000.00 |\n| 15. | 12 | pcs | Powder Detergent, approx.. 500g | 150.00 | 1,800.00 |\n| 16. | 12 | bottles| Bleaching Liquid, 1L | 40.00 | 480.00 |\n| 17. | 2 | pcs | Spray bottle, 1L capacity, heavy duty | 500.00 | 1,000.00 |\n| 18. | 6 | pcs | Tornado Mop, big, heavy duty | 1,200.00 | 7,200.00 |"}
{"item_id": "item_0117", "chart_task_type": "concentration_long_tail", "query": "Can you chart the distribution of outcomes for homes brought back into use in 18/19 so I can see at a glance whether a few categories dominate the results or if there is a long tail?", "table_markdown": "| Outcome | Number | % of total |\n|----------------------------------------------|--------|------------|\n| Sold via an estate agent | 270 | 33.4 |\n| Sold to RSL | 68 | 8.4 |\n| Sold at auction | 16 | 2 |\n| Privately sold | 6 | 0.7 |\n| Sold via MM | 2 | 0.2 |\n| Let privately | 156 | 19.3 |\n| Let through a Rent Deposit Guarantee Scheme | 31 | 3.8 |\n| Let at affordable rent | 11 | 1.4 |\n| Let through a Private Sector Leasing Scheme | 6 | 0.7 |\n| Let via agent | 3 | 0.4 |\n| Let through a Housing Association Management Scheme | 0 | 0.0 |\n| Owner occupation | 161 | 19.9 |\n| Became a holiday home | 45 | 5.6 |\n| Purchased by the council (including buy-backs) | 30 | 3.7 |\n| Demolished/closing order | 2 | 0.2 |\n| Combined with above property | 1 | 0.1 |\n| Repossessed | 1 | 0.1 |\n| Sweat equity arrangement | 0 | 0.0 |"}
{"item_id": "item_0118", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether sales volume is concentrated among a few top brokerages or spread out across the long tail?", "table_markdown": "| Brokerage | Price | Location |\n|-----------------------------------------------|-----------|------------------------|\n| LIV Sotheby’s International Realty | $17,250,000 | Colorado, USA |\n| Kuper Sotheby’s International Realty | $16,402,412 | Texas, USA |\n| Vista Sotheby’s International Realty | $16,000,000 | California, USA |\n| Sotheby’s International Realty – San Francisco Brokerage | $15,850,000 | California, USA |\n| LIV Sotheby’s International Realty | $15,454,000 | Colorado, USA |\n| Kuper Sotheby’s International Realty | $15,175,000 | Texas, USA |\n| Aspen Snowmass Sotheby’s International Realty | $15,000,000 | Colorado, USA |\n| Summit Sotheby’s International Realty | $14,267,000 | Utah, USA |\n| Sotheby’s International Realty – San Francisco Brokerage | $14,000,000 | California, USA |\n| Sotheby’s International Realty – San Francisco Brokerage | $13,480,000 | California, USA |\n| Premier Sotheby’s International Realty | $13,000,000 | Florida, USA |\n| One Sotheby’s International Realty | $13,000,000 | Florida, USA |\n| Telluride Sotheby’s International Realty | $12,750,000 | Colorado, USA |\n| Sotheby’s International Realty – Palm Beach Brokerage | $12,687,500 | Florida, USA |\n| Golden Gate Sotheby’s International Realty | $12,500,000 | California, USA |\n| Sotheby’s International Realty – San Francisco Brokerage | $12,450,000 | California, USA |\n| Pacific Sotheby’s International Realty | $12,300,000 | California, USA |\n| Aspen Snowmass Sotheby’s International Realty | $12,037,500 | Colorado, USA |\n| Russ Lyon Sotheby’s International Realty | $12,000,000 | Arizona, USA |\n| Realogics Sotheby’s International Realty | $12,000,000 | Washington, USA |\n| Maury People Sotheby’s International Realty | $11,670,000 | Massachusetts, USA |"}
{"item_id": "item_0119", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether camel bite cases are concentrated among specific age groups, so I can see at a glance which decades account for the majority of injuries?", "table_markdown": "| S no. | Age group | No. of cases |\n|---|---|---|\n| 1 | 1st decade | 0 |\n| 2 | 2nd decade | 3 |\n| 3 | 3rd decade | 6 |\n| 4 | 4th decade | 10 (6 thoracic wall injuries) |\n| 5 | 5th decade | 9 |\n| 6 | 6th decade | 3 |\n| | Total | 31 |"}
{"item_id": "item_0120", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the total publications are concentrated among a few Work Packages, so I can see at a glance which ones dominate the output?", "table_markdown": "| WP | No. of journal publications | No.of conference/ workshop proceedings | Other publications | Total |\n|-----|-----------------------------|----------------------------------------|--------------------|-------|\n| WP2 | 0 | 0 | 7 press articles | 16 |\n| | | | 9 \"On track\" newsletter issues | |\n| WP3 | 2 | 3 | 6 presentations | 14 |\n| | | | 3 scientific notes | |\n| WP4 | 0 | 0 | 1 poster | 1 |\n| WP5 | 0 | 2 | 6 presentations | 12 |\n| | | | 2 scientific notes | |\n| | | | 2 posters | |\n| WP6 | 10 | 2 | 1 poster | 13 |\n| WP7 | 12 | 8 | 2 presentations | 24 |\n| | | | 2 posters | |\n| WP8 | 2 | 2 | 0 | 4 |\n| WP9 | 1 | 0 | 5 presentations | 7 |\n| | | | 1 poster | |\n| WP13| 4 | 2 | 1 presentation | 10 |\n| | | | 3 posters | |\n| WP14| 4 | 7 | 1 scientific note | 12 |\n| WP15| 2 | 7 | 11 presentations | 32 |\n| | | | 5 scientific notes | |\n| | | | 7 posters | |\n| TOTAL | 37 | 33 | 75 | 145 |\n| TARGET | 60 | 50 | - | 180 |"}
{"item_id": "item_0121", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the total amount is dominated by a few units or spread evenly across all of them?", "table_markdown": "| SNO | DDO-NO | CODE | TAN-NO | UNIT-NAME | STATION | AMOUNT | BIN |\n|-----|--------|--------|--------|-----------------------------------------------|-------------|--------|------|\n| 1 | | 5906 | BLRA02207C | ASC CENTRE AND COLLEGE | BANGALORE | 95160 | 4000182|\n| 2 | | 8295 | BLRC17533F | CE COAST GUARD BAMBOIN GOA | GOA | 235061 | 4000182|\n| 3 | | 1512 | BLRC02472D | CWE AIR FORCE NORTH | BANGALORE | 707220 | 4000182|\n| 4 | | 1770 | BLRC05737A | CHIEF ENGINEER (AF) | BANGALORE | 637832 | 4000182|\n| 5 | | 1783 | BLRC03189E | CWE (NAVY) | GOA | 87360 | 4000182|\n| 6 | | 3201 | BLRA07962D | 51 ARM BASE WORKSHOP | BANGALORE | 297020 | 4000182|\n| 7 | | 3427 | BLRC05757C | CWE (ARMY) | BANGALORE | 109200 | 4000182|\n| 8 | | 3467 | BLRA08104F | DEO PANAJI GOA | GOA | 302 | 4000182|\n| 9 | | 3880 | BLRD04392D | DEFENCE ESTATE OFFICE,BLORE | BANGALORE | 81120 | 4000182|\n| 10 | | 4203 | BLRC00365B | COAL BANGALORE | BANGALORE | 954720 | 4000182|\n| 11 | | 4204 | BLRC00563D | COAE (WE) | BANGALORE | 137280 | 4000182|\n| 12 | | 4278 | BLRC00183B | OAE (WE) | BANGALORE | 58240 | 4000182|\n| 13 | | 4296 | BLRC00311E | COA RADAR | BANGALORE | 319280 | 4000182|\n| 14 | | 4316 | BLRD04352F | DICA | BANGALORE | 136240 | 4000182|\n| 15 | | 4529 | BLRM01063G | COA BELM | BANGALORE | 257825 | 4000182|\n| 16 | | 4537 | BLRD00184C | QAE (FP CELL) | BANGALORE | 520 | 4000182|\n| 17 | | 5352 | BLRS23817D | STATION HEALTH ORG | BANGALORE | 28784 | 4000182|\n| 18 | | 5865 | BLRM10464C | MILITARY SCHOOL | BANGALORE | 151216 | 4000182|\n| 19 | | 5867 | BLRD003194F | MLI REGIMENT CENTRE | BELGAUM | 11960 | 4000182|\n| 20 | | 5874 | BLRR04923C | RECORDS, MLI | BELGAUM | 29640 | 4000182|\n| 21 | | 5886 | BLRC00299F | CMP CENTRE & SCHOOL BLR | BANGALORE | 3120 | 4000182|\n| 22 | | 6200 | BLRH02948B | HQRS 2 SIGNAL TRG CENTRE | GOA | 10400 | 4000182|\n| 23 | | 7504 | BLRM09653A | MTCOY 2SIGTRGCENTRE | GOA | 18320 | 4000182|\n| 24 | | 7507 | BLRM09839E | 3MTR GOA | MARGAO | 5935 | 4000182|\n| 25 | | 7569 | BLRP11951C | PRO BANGALORE | BANGALORE | 4680 | 4000182|\n| 26 | | 7574 | BLRS23016A | SELECTION CENTRE SOUTH | BANGALORE | 353600 | 4000182|\n| 27 | | 7660 | BLRH02870A | HQJW BELGAUM | BELGAUM | 52884 | 4000182|\n| 28 | | 8058 | BLRC06572G | CWE(AF)SOUTH-BLORE | BANGALORE | 27144 | 4000182|\n| 29 | | 8122 | BLRN03489D | NCC DTE KAR & GOA, BANGALORE | BANGALORE | 27040 | 4000182|\n| 30 | | 8147 | BLRS21728A | SOAE(VEHICLES) | KOJAR | 67080 | 4000182|\n| 31 | | 5877 | BLRD07604C | MILITARY SCHOOL, BELGAUM | BELGAUM | 117520 | 4000182|\n| 32 | | 7501 | BLRT04690A | 3 TTR GOA | MARGAO | 15600 | 4000182|\n| 33 | | 3881 | BLRC01955C | CEO BELGAUM | BELGAUM | 5200 | 4000182|"}
{"item_id": "item_0122", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether trail mileage is concentrated among a few long trails or spread out, so I can see at a glance how much of the total length the top contributors account for?", "table_markdown": "| Number | Name | Address | Length |\n|--------|-----------------------------|----------------------------------|--------|\n| 1 | Conner Park | 408 Old Waco Dr. | 0.33 |\n| 2 | Echo Village Trail | 5030 Stonehaven Dr. | 0.50 |\n| 3 | FM 2305 Hike & Bike | W. Hwy. 2305 | 5.00 |\n| 4 | Friar's Creek Hike & Bike #1| 5000 S. 5th St. | 3.65 |\n| 5 | Friar's Creek Hike & Bike #2| 2264 S. 5th St. | 0.70 |\n| 6 | Jackson Park | 925 N. 4th St. | 0.50 |\n| 7 | Jaycee Park | 2302 W. Avenue Z | 0.43 |\n| 8 | Jefferson Park | 2310 Monticello Rd. | 0.25 |\n| 9 | Jones Park | 1102 W. Avenue H | 0.33 |\n| 10 | Lions Park | 4320 Lions Park Rd. | 2.00 |\n| 11 | Miller Park | 1919 N. 1st St. | 0.75 |\n| 12 | Oak Creek Park | 2304 Forest Trail | 0.25 |\n| 13 | Optimist Park | 820 W. Munroe Ave. | 0.25 |\n| 14 | Pepper Creek Hike & Bike | W. Hwy 2305 | 3.50 |\n| 15 | Silverstone Park | 404 Waters Dairy Rd. | 0.50 |\n| 16 | South Temple Park | 5000 S. 5th St. | 0.75 |\n| 17 | Terrace Gardens Park Trail | 2015 Linwood Rd. | 0.25 |\n| 18 | Valley Ranch Park | 7211 Dubose Rd. | 0.37 |\n| 19 | West Temple Park | 121 S. Montpark Rd. | 1.00 |\n| 20 | Western Hills Park | 4420 Gazelle Trail | 0.25 |\n| 21 | Wilson Park | 2205 Curtis B. Elliott Dr. | 1.75 |\n| 22 | Woodbridge Park | 3620 Whispering Oaks | 0.50 |\n| 23 | Wyndham Hills Trail | 708 Wyndham Hill Pkwy. | 0.65 |"}
{"item_id": "item_0123", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the 2021/2022 total compensation is concentrated among a few top executives, so I can see at a glance how much of the pool the highest earners account for?", "table_markdown": "| Name and Position | Salary | Holdback/Bonus/Incentive Plan Compensation | Benefits | Pension | All Other Compensation (expanded below) | 2021/2022 Total Compensation | Previous Two Years Totals |\n|----------------------------------------------------------------------------------|------------|---------------------------------------------|------------|------------|------------------------------------------|------------------------------|---------------------------|\n| Alan Davis, President & Vice Chancellor | $241,111 | - | $9,951 | $17,452 | $18,082 | $286,596 | $282,357 |\n| Steve Cardwell, Vice President, Students | $209,552 | - | $17,789 | $21,668 | $5,061 | $254,070 | $239,476 |\n| Tara Clowes, Vice President, Finance and Administration | $102,444 | - | $5,981 | $10,593 | $9,062 | $128,080 | $153,974 |\n| Barbara Marilyn Graziano, Vice President, External Affairs | $66,983 | - | $5,824 | $6,926 | $13,791 | $93,524 | $195,809 |\n| Randall Heidt, Vice President, External Affairs & CEO KPU Fund | $154,328 | - | $18,549 | $15,958 | $3,963 | $192,798 | |\n| Abdolreza Khakbaznejad, Interim Vice President, Administration & Chief Information Officer | $174,480 | - | $17,813 | $18,041 | $18,520 | $228,854 | |\n| Diane Purvey, Provost & Vice President, Academic, Pro Tem | $183,462 | - | $19,160 | $18,970 | $15,229 | $236,821 | |\n| Sandy Vanderburgh, Provost and Vice President Academic | $204,887 | - | $15,987 | $21,185 | $14,712 | $256,771 | $239,477 | $212,002 |"}
{"item_id": "item_0124", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the stationery costs are dominated by a few expensive items or spread evenly across the list?", "table_markdown": "| ITEMS | UNIT PRICE | QUANTITY | TOTAL |\n|----------------------------------------------------------------------|------------|----------|---------|\n| Oxford South African School Dictionary – ISBN 9780190731809, 4th Edition | R165.00 | 1 | R165.00 |\n| Nuwe Tweetalige Woordeboek – ISBN 9781868011111 | R210.00 | 1 | R210.00 |\n| Platinum Instamaths – Grade 4 CAPS – ISBN 9780636166189 | R85.00 | 1 | R85.00 |\n| Lees Sonder Grense – EAT Graad 4 Leesboek (Die Tarentaalboek) – ISBN 9780636138698 | R125.00 | 1 | R125.00 |\n| Platinum Life Skills – Grade 4 Learners Book – ISBN 9780536135727 | R141.00 | 1 | R141.00 |\n| A4 PVC Dividers 10 to View | R11.50 | 1 | R11.50 |\n| A4 Plastic Sleeves 10's | R6.00 | 2 | R12.00 |\n| A4 Firefly Flipfile 10pg | R9.80 | 1 | R9.80 |\n| A5 72pg Irish Exercise Books | R2.20 | 1 | R2.20 |\n| A4 72pg Irish Exercise Books | R4.00 | 8 | R32.00 |\n| A4 72pg Quad & Margin Books | R4.00 | 3 | R12.00 |\n| A5 128pg Hardcover Books | R7.00 | 1 | R7.00 |\n| A4 Exam Pads 100pg Punched | R11.00 | 1 | R11.00 |\n| A4 72pg Irish Nature Study Book | R6.00 | 1 | R6.00 |\n| A4 PVC Ring Binders 25mm | R21.00 | 1 | R21.00 |\n| A4 PVC Book Bag / BAG 1514 | R35.00 | 1 | R35.00 |\n| Treeline HB Pencils | R2.00 | 12 | R24.00 |\n| Erasers Large | R2.00 | 2 | R4.00 |\n| Bostik Glue Stick 40g | R20.00 | 4 | R80.00 |\n| Denim Pencil Bag 22cm | R15.00 | 1 | R15.00 |\n| Scissors Left/Right Handed 17cm | R15.50 | 1 | R15.50 |\n| Sharpener 1 Hole Metal | R2.00 | 2 | R4.00 |\n| I-write Pencil Crayons 12's | R24.00 | 2 | R48.00 |\n| Ruler 30cm | R2.00 | 2 | R4.00 |\n| Blue Border Labels 24's | R5.00 | 2 | R10.00 |\n| A4 Rotatrim Ream Copy Paper | R62.10 | 2 | R124.20 |\n| Highlighters Yellow | R5.00 | 1 | R5.00 |"}
{"item_id": "item_0125", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether panfacial fractures are concentrated in a few specific locations or spread out, so I can see at a glance which sites account for the majority of injuries?", "table_markdown": "| Fracture site | Fall (n = 31) | IPV (n = 3) | AM acc (n = 85) | B/E-B acc (n = 44) | SO (n = 17) | Work acc (n = 47) | Total (%) |\n|----------------------------------------------------|---------------|-------------|-----------------|--------------------|-------------|-------------------|-----------|\n| Frontal sinus/bone | 5 | 1 | 34 | 20 | 8 | 16 | 84 (37.0) |\n| Orbital roof | 6 | 1 | 21 | 10 | 7 | 14 | 59 (26.0) |\n| Lateral orbital wall | 24 | 2 | 58 | 36 | 14 | 36 | 170 (74.9) |\n| Medial orbital wall | 15 | 1 | 52 | 27 | 15 | 30 | 140 (61.7) |\n| Orbital floor | 22 | 1 | 54 | 34 | 11 | 35 | 157 (69.2) |\n| Nasal bone | 20 | 2 | 57 | 31 | 15 | 33 | 158 (69.6) |\n| Naso-orbito-ethmoid | 10 | 1 | 38 | 21 | 10 | 25 | 105 (46.3) |\n| Zygomatic arch | 24 | 2 | 63 | 35 | 10 | 37 | 171 (75.3) |\n| Maxillary sinus wall | 30 | 3 | 76 | 40 | 16 | 44 | 209 (92.1) |\n| Palatal bone | 7 | 1 | 29 | 9 | 5 | 18 | 69 (30.4) |\n| Le Fort I | 5 | 0 | 17 | 15 | 1 | 9 | 47 (20.7) |\n| Zygomatico-maxillary complex | 18 | 1 | 52 | 31 | 9 | 31 | 142 (62.6) |\n| Le Fort II | 3 | 0 | 16 | 10 | 2 | 7 | 38 (16.7) |\n| Le Fort III | 3 | 0 | 4 | 3 | 0 | 4 | 14 (6.2) |\n| Mandibular symphyseal/parasymphyseal | 22 | 0 | 44 | 18 | 8 | 28 | 120 (52.9) |\n| Mandibular body | 5 | 0 | 26 | 8 | 3 | 8 | 50 (22.0) |\n| Mandibular angle | 2 | 0 | 7 | 4 | 3 | 3 | 19 (8.4) |\n| Mandibular ramus | 1 | 1 | 14 | 5 | 0 | 10 | 31 (13.7) |\n| Mandibular sub-condyle | 4 | 0 | 6 | 2 | 0 | 10 | 22 (9.7) |\n| Mandibular condyle | 13 | 1 | 15 | 3 | 5 | 10 | 47 (20.7) |\n| Mandibular coronoid | 1 | 0 | 4 | 6 | 0 | 1 | 12 (5.3) |"}
{"item_id": "item_0126", "chart_task_type": "concentration_long_tail", "query": "Can you chart the spending breakdown for universal coverage to show if a few programs dominate the total cost?", "table_markdown": "| | Current | Optimal | Universal coverage |\n|----------------|---------|---------|--------------------|\n| PMTCT | 571 | 1037 | 1500 |\n| ART | 296 | 0 | 6100 |\n| HTC | 353 | 324 | 1300 |\n| PWID | 399 | 493 | 900 |\n| MSM | 181 | 224 | 400 |\n| FSW | 388 | 593 | 700 |\n| LRP | 483 | 0 | 2400 |"}
{"item_id": "item_0127", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the estimated costs for the SMECO Building purchase are dominated by a few large items or spread out, so I can see at a glance which expenses drive the total?", "table_markdown": "| Estimated Costs: | CORRESPONDING JP: | JP12xxx, DATED 07/01/15 |\n|------------------|-------------------|-------------------------|\n| Good Faith Deposit | $26,000 | |\n| Remainder of Purchase | $2,574,000 | |\n| Settlement fee | $295 | |\n| Title Search | $750 | |\n| Title insurance | $9,150 | |\n| Property survey | $2,000 | |\n| Other costs or overages | $2,805 | |\n| **TOTAL** | **$2,615,000** | |"}
{"item_id": "item_0128", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the 100% differential tuition scholarships are concentrated among a few institutions or spread out, so I can see at a glance which schools dominate the awards?", "table_markdown": "| Institution | Differential Tuition | Differential Tuition |\n|---|---|---|\n| | Scholarships (up to 100%) | Scholarships (up to 50%) |\n| University of Utah | 24 | 14 |\n| Utah State University | 330 | 112 |\n| Weber State University | 55 | 15 |\n| Southern Utah University | 96 | 25 |\n| Snow College | 2 | 7 |\n| Dixie State University | 161 | 44 |\n| Utah Valley University | 0 | 0 |\n| Salt Lake Community College | 7 | 8 |\n| USHE Total | 675 | 225 |"}
{"item_id": "item_0129", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of job satisfaction levels so I can see at a glance what share of respondents falls into each category?", "table_markdown": "| S. No | Job Satisfaction | No of Respondents |\n|---|---|---|\n| 1 | Highly Satisfied | 24 |\n| 2 | Satisfied | 58 |\n| 3 | Dissatisfied | 18 |\n| Total | | 100 |"}
{"item_id": "item_0130", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the injected crystals were concentrated in just a few channels or spread out, so I can see at a glance how much the top contributors dominate the total?", "table_markdown": "| Channel | Injected | Traps filled | Crystals trapped | Traps filled (%) | Crystals trapped (%) |\n|---------|----------|--------------|------------------|-----------------|---------------------|\n| 1 | 2 | 0 | 0 | 0.0 | 0.0 |\n| 2 | 10 | 7 | 8 | 70.0 | 80.0 |\n| 3 | 18 | 17 | 18 | 94.4 | 100.0 |\n| 4 | 42 | 30 | 35 | 71.4 | 83.3 |\n| 5 | 45 | 37 | 38 | 82.2 | 84.4 |\n| 6 | 18 | 17 | 18 | 94.4 | 100.0 |\n| 7 | 4 | 3 | 3 | 75.0 | 75.0 |\n| 8 | 0 | 0 | 0 | 0.0 | 0.0 |\n| Total | 139 | 111 | 120 | 79.9 | 86.3 |"}
{"item_id": "item_0131", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total number of individuals across the different districts and localities, so I can see at a glance how the population is distributed?", "table_markdown": "| District/Locality | Total number of Households: | Total number of Individuals: | Pregnant women | Ederly Person (60 years +) |\n|-----------------------------------------|-----------------------------|------------------------------|----------------|----------------------------|\n| Cidade De Nampula | 20 | 135 | 2 | 3 |\n| Bairro de militar | 1 | 7 | 0 | 0 |\n| Muatala | 6 | 39 | 2 | 2 |\n| Mutauanha | 1 | 6 | 0 | 0 |\n| Natikir | 12 | 83 | 0 | 1 |\n| Meconta | 10 | 30 | 3 | 1 |\n| Bairro de Reassentamento de Corrane | 10 | 30 | 3 | 1 |\n| Ngauma | 1 | 6 | 0 | 0 |\n| Massangulo sede | 1 | 6 | 0 | 0 |\n| **Grand Total** | **31** | **171** | **5** | **4** |"}
{"item_id": "item_0132", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of respondents by their level of education so I can see the share of each category?", "table_markdown": "| Level of Education | Frequency | Percent | Valid Percent | Cumulative Percent |\n|-----------------------------|-----------|---------|---------------|--------------------|\n| Primary Education | 24 | 55.8 | 58.5 | 58.5 |\n| O' level education | 13 | 30.2 | 31.7 | 90.2 |\n| A' Level Education | 2 | 4.7 | 4.9 | 95.1 |\n| Other Tertiary Education | 2 | 4.7 | 4.9 | 100.0 |\n| Total | 41 | 95.3 | 100.0 | |\n| Missing System | 2 | 4.7 | | |\n| Total | 43 | 100.0 | | |"}
{"item_id": "item_0133", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the outstanding debt for TIF 6 - Downtown West is concentrated among a few specific obligations, so I can see at a glance which ones dominate the total?", "table_markdown": "| District | Bonds/PAYGO | Original Bond/PAYGO Amount | Outstanding After 2/1/2021 | Term |\n|-------------------|------------------------------|----------------------------|----------------------------|------------|\n| TIF 6 - Downtown West | Hoyt PAYGO | $1,685,376 | $1,685,376 | 2/1/2046 |\n| | Zitzloff PAYGO | $1,000,000 | $1,000,000 | 2/1/2046 |\n| | Lothenbach PAYGO | $717,000 | $717,000 | 2/1/2046 |\n| | Hughes PAYGO | $1,700,000 | $1,700,000 | 2/1/2046 |\n| | 2020A GO TIF Bonds (In District) | $1,025,000 | $1,025,000 | 2/1/2031 |\n| | 2020A GO TIF Bonds (Pooling) | $440,000 | $440,000 | 2/1/2031 |\n| | Interfund Loan Parking Lot - Lake & Barry | $1,700,000 | $1,700,000 | TBD |"}
{"item_id": "item_0134", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the gross exposure value on the balance sheet as of 31 March 2019 by loan portfolio type, so I can see at a glance what share each category represents?", "table_markdown": "| Loans Portfolio | Gross exposure value - Average for the period | Gross exposure value on balance sheet as at 31/03/19 | Commitments – redraws, overdraft facilities undrawn | Past due facilities | Impaired facilities | Specific Provision as at end of quarter | Increase in specific provision and write offs in quarter |\n|---|---|---|---|---|---|---|---|\n| | $’000 | $’000 | $’000 | $’000 | $’000 | $’000 | $’000 |\n| Mortgage secured | 228,510 | 217,675 | 11,872 | - | - | - | - |\n| Personal | 11,389 | 10,758 | 638 | - | 112 | 91 | 18 |\n| Overdrafts & Credit cards | 5,597 | 1,862 | 3,696 | - | 5 | 3 | 1 |\n| Corporate borrowers | 14,113 | 12,755 | 1,382 | - | - | - | (2) |\n| Total | 259,609 | 243,050 | 17,588 | - | 117 | 94 | 17 |"}
{"item_id": "item_0135", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total expenditure by category so I can see at a glance what share each component represents?", "table_markdown": "| Expenditure | € | Income | € |\n|----------------------|-----------|------------|-----------|\n| Human Resources | 1,685,813.92 | Sport Ireland | 2,066,543.00 |\n| Performance Services | 148,723.55 | NGBs | 64,700.00 |\n| Capability & Expertise | 64,784.44 | HPC Usage | 56,188.50 |\n| Operations | 283,807.17 | PQAP | 580.00 |\n| **Total** | **2,183,129.08** | **2,188,011.50** |"}
{"item_id": "item_0136", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total QSOs by band so I can see at a glance which bands accounted for the largest share of the contacts?", "table_markdown": "| | CW | SSB | RTTY | PSK31 | SSTV | WSJT | Totals |\n|---|---|---|---|---|---|---|---|\n| 160M | 127 | 0 | 0 | 0 | 0 | 0 | 127 |\n| 80M | 1,721 | 0 | 0 | 0 | 0 | 0 | 1,721 |\n| 40M | 2,546 | 0 | 0 | 0 | 0 | 0 | 2,546 |\n| 30M | 2,781 | 0 | 660 | 0 | 0 | 0 | 3,441 |\n| 20M | 4,586 | 3,464 | 970 | 149 | 0 | 0 | 9,169 |\n| 17M | 4,777 | 2,471 | 741 | 200 | 50 | 0 | 8,239 |\n| 15M | 4,161 | 1,081 | 925 | 0 | 0 | 0 | 6,167 |\n| 12M | 1,435 | 117 | 0 | 0 | 0 | 0 | 1,552 |\n| 10M | 475 | 62 | 0 | 0 | 0 | 0 | 537 |\n| 6M | 0 | 0 | 0 | 0 | 0 | 5 | 5 |\n| Totals | 22,609 | 7,195 | 3,296 | 349 | 50 | 5 | 33,504 |"}
{"item_id": "item_0137", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total bid amount by item so I can see at a glance what share each component represents?", "table_markdown": "| ITEM NO. | ESTIMATED QUANTITY | UNIT | DESCRIPTION OF ITEM | UNIT PRICE BID DOLLARS | EXTENDED BID AMOUNT DOLLARS |\n|---------|--------------------|--------|----------------------------------------------------------|------------------------|-----------------------------|\n| 704-10 | 250 | LF | 10' High Vinyl Coated Chain Link Fence | 85.00 | 21,250.00 |\n| 1000 | 2,998 | LS + 10%| Additional Material | 1.10 | 3,297.25 |\n| 1002-01 | 26 | HR | Miscellaneous Labor - Laborer | 115.00 | 2,990.00 |\n| 1002-02 | 26 | HR | Miscellaneous Labor - Iron Worker | 135.00 | 3,510.00 |"}
{"item_id": "item_0138", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether December payments were concentrated among a few large vendors or spread across many small ones?", "table_markdown": "| Name | Business | Gross |\n|---|---|---|\n| K and M (DD) | Streetlights Maintenance | 29.06 |\n| Eon (DD) | Elec - November Charges | 64.23 |\n| CGM (DD) | Grounds Maintenance for Month | 97.67 |\n| Clerk (BACS) | Salary and Expenses – December | 834.64 |\n| The Ripper Hall | Meetings | 28 |\n| TESCO MOBILE (DD) | Parish Council Mobile Charge | 10.5 |\n| Norfolk Pensions (Bank Transfer) | Pension Contributions | 192.31 |"}
{"item_id": "item_0139", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the institutional investment (ISTI) observations are concentrated in a few ranges or spread out, so I can see at a glance if there is a long tail?", "table_markdown": "| Range | | | Frequency | Rel. |\n|---|---|---|---|---|\n| | < | 0.030497 | 1 | 0.32% |\n| 0.030497 | - | 0.091491 | 4 | 1.28% |\n| 0.091491 | - | 0.15248 | 5 | 1.60% |\n| 0.15248 | - | 0.21348 | 13 | 4.17% |\n| 0.21348 | - | 0.27447 | 14 | 4.49% |\n| 0.27447 | - | 0.33547 | 18 | 5.77% |\n| 0.33547 | - | 0.39646 | 28 | 8.97% |\n| 0.39646 | - | 0.45745 | 35 | 11.22% |\n| 0.45745 | - | 0.51845 | 34 | 10.90% |\n| 0.51845 | - | 0.57944 | 28 | 8.97% |\n| 0.57944 | - | 0.64043 | 43 | 13.78% |\n| 0.64043 | - | 0.70143 | 38 | 12.18% |\n| 0.70143 | - | 0.76242 | 14 | 4.49% |\n| 0.76242 | - | 0.82342 | 15 | 4.81% |\n| 0.82342 | - | 0.88441 | 10 | 3.21% |\n| 0.88441 | - | 0.9454 | 9 | 2.88% |\n| | >= | 0.9454 | 3 | 0.96% |"}
{"item_id": "item_0140", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of households by the five major non-English language groups so I can see at a glance which languages make up the largest shares?", "table_markdown": "| Language Groups | # of Households | % of Total Households |\n|-----------------|-----------------|-----------------------|\n| Vietnamese | 62 | 19% |\n| Spanish | 45 | 11% |\n| Chinese | 37 | 9% |\n| Portuguese | 29 | 8% |\n| Somali | 25 | 8% |\n| **Total** | **197** | **48%** |"}
{"item_id": "item_0141", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the 2022 baseline emissions by scope so I can see at a glance what share of the total each category represents?", "table_markdown": "| | Emissions 2022 | | Total tCO2e |\n|---|---|---|---|\n| Scope 1 | | 2,105* | |\n| Scope 2 | | 2,038 | |\n| Scope 3 (Across all 15 Categories) | | 204,657 | |\n| | Total Emissions | | 208,800 |"}
{"item_id": "item_0142", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total purchase amount for Section C by individual item, so I can see at a glance which items account for the largest share?", "table_markdown": "| Item | Qty | Tick | Price$ | Total$ |\n|----------------------------------------------------------------------|-----|------|--------|--------|\n| Staedtler Noris Club Jumbo Oil Pastels 24 Cols | 1 | | 7.90 | 7.90 |\n| Staedtler Norica Blacklead Pencils 2B | 1 | | 2.30 | 2.30 |\n| Staedtler Safety Scissor (UP: $2.10) | 1 | | 1.20 | 1.20 |\n| Chinese Flash / Character Card Holder (FOR CHINESE FLASH CARD) | 1 | | 3.90 | 3.90 |\n| Thermometer | 1 | | 4.50 | 4.50 |\n| A4 Luminous Expendng File with Handle | 1 | | 6.90 | 6.90 |\n| A3 Art Bag | 1 | | 7.20 | 7.20 |\n| PVC A5 Exercise Book Cover (10pcs) | 1 | | 2.30 | 2.30 |\n| PVC Book Wrapper 10 metres | 1 | | 3.90 | 3.90 |\n| PVC Textbook & Workbook Cover - P1 & P2 | 1 | | 8.00 | 8.00 |\n| **Total for Section C** | | | | 48.10 |"}
{"item_id": "item_0143", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total possible points by review component so I can easily see the percentage share of each part?", "table_markdown": "| Points Breakdown | Points | Weight (%) |\n|-----------------------------------|--------|------------|\n| Design Review (DR) | 36 | 20% |\n| Operational Rediness Review (ORR)| 36 | 20% |\n| Mission Readiness Review (MRR) | 12 | 10% |\n| Obstacles | 41 | 20% |\n| Tasks | 60 | 20% |\n| STEM Engagement | 12 | 10% |\n| **Total Possible Points** | **200**| **100%** |"}
{"item_id": "item_0144", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of library book holdings by branch so I can see at a glance which departments account for the largest share of the collection?", "table_markdown": "| S. No. | Branch | Quantity |\n|-------|--------------------------------------|----------|\n| 1. | Computer Science and engineering | 2340 |\n| 2. | Electronics and communication | 2779 |\n| | engineering | |\n| 3. | Electrical engineering | 2525 |\n| 4. | Mechanical engineering | 4383 |\n| 5. | Civil engineering | 2143 |\n| 6. | Physics | 620 |\n| 7. | Chemistry | 547 |\n| 8. | Mathematics | 991 |\n| 9. | Management | 130 |\n| 10. | General knowledge and aptitude | 1124 |\n| | **Total** | **17582**|"}
{"item_id": "item_0145", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the 321 electrocution deaths in 2007 by location so I can see at a glance what share of the total each place accounts for?", "table_markdown": "| 2007 | Scope | | | |\n|---|---|---|---|---|\n| Location | In | Out | Unknown | Total |\n| Farm | 0 | 9 | 0 | 9 |\n| Industrial | 0 | 82 | 0 | 82 |\n| Public Land | 1 | 43 | 6 | 50 |\n| Recreational | 2 | 5 | 1 | 8 |\n| Residential | 27 | 34 | 35 | 96 |\n| School | 0 | 3 | 0 | 3 |\n| Street | 0 | 40 | 5 | 45 |\n| Unknown | 1 | 16 | 11 | 28 |\n| Total | 31 | 232 | 58 | 321 |"}
{"item_id": "item_0146", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of Gross Capital Assets for Governmental Activities as of June 30, 2019, so I can see at a glance what share each category like Land, Buildings, and Vehicles represents of the total?", "table_markdown": "| Governmental Activities | Balance 07-01-18 | Additions | Deletions | Balance 06-30-19 |\n|-------------------------|------------------|-----------|-----------|------------------|\n| Capital Assets; not depreciated-Land | $10,055 | $0 | $0 | $10,055 |\n| Capital Assets; depreciated-Building | 56,088 | 0 | 0 | 56,088 |\n| Office Furniture, Fixtures & Equipment | 10,758 | 0 | 0 | 10,758 |\n| Outside Equipment & Walking Trail | 48,717 | 0 | 0 | 48,717 |\n| Pavilion/Walkway | 12,500 | 0 | 0 | 12,500 |\n| Vehicles | 1,500 | 0 | 0 | 1,500 |\n| Parking Lot | 7,624 | ___0 | 0 | 7,624 |\n| **Total Capital Assets** | $147,242 | $___0 | $0 | $147,242 |\n| Less, Accumulated Depreciation | | | | |\n| Building | $11,217 | $1,402 | $0 | $12,619 |\n| Office Furniture, Fixtures & Equipment | 6,606 | 0 | 0 | 6,606 |\n| Outside Equipment & Walking Trail | 45,739 | 3,146 | 0 | 48,885 |\n| Vehicles | 150 | 50 | 0 | 200 |\n| Parking Lot | 2,920 | 763 | 0 | 3,684 |\n| **Total Accumulated Depreciation** | $66,632 | $5,361 | $0 | $71,993 |\n| **Net Capital Assets** | $80,610 | $(5,361) | $0 | $75,249 |"}
{"item_id": "item_0147", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the Transportation expenses so I can see at a glance what share each item accounts for?", "table_markdown": "| Code | Description | Amount |\n|--------|--------------------------------------------------|------------|\n| 72710 | TRANSPORTATION | |\n| 72710 338 | Maintenance and Repair Services-Vehicle | $1,532.04 |\n| 72710 418 | Equipment & Machinery Parts | $1,500.00 |\n| 72710 425 | Gasoline | $10,000.00 |\n| 72710 450 | Tires and Tubes | $10,000.00 |\n| 72710 599 | Other Charges | $4,000.00 |\n| 72710 729 | Transportation Equipment | $24,030.63 |\n| TOTAL: | | $51,062.67 |"}
{"item_id": "item_0148", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the deferred outflows of resources as of June 30, 2016, so I can see at a glance which sources make up the largest shares of the total?", "table_markdown": "| | | Deferred Outflows | | Deferred Inflows of |\n|---|---|---|---|---|\n| | | of Resources | | Resources |\n| Differences between expected and actual experience | $180,723 | $180,723 | $2,225,598 | |\n| Changes in assumptions | $739,909 | | $0 | |\n| Net differences between projected and actual earnings on pension plan investments | $4,335,809 | | $0 | |\n| Changes in proportion and differences between employer contributions and proportionate share of contributions | $2,902 | | $0 | |\n| Employer contributions subsequent to the measurement date | $382,092 | | $0 | |\n| Total | $5,641,435 | | $2,225,598 | |"}
{"item_id": "item_0149", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of Current Liabilities for Governmental Activities so I can see at a glance which specific items make up the total?", "table_markdown": "| LIABILITIES | Governmental Activities | Component Units | Total |\n|---------------------------------------------|-------------------------|-----------------|---------|\n| **Current Liabilities** | | | |\n| Accounts payable | $40,578 | - | $40,578 |\n| Accrued liabilities | $36,237 | - | $36,237 |\n| Accrued interest on capital lease | $14,463 | - | $14,463 |\n| Accrued payroll liabilities | $57,095 | - | $57,095 |\n| Compensated absences payable | $127,431 | - | $127,431|\n| Leases payable | $66,189 | - | $66,189 |\n| Notes payable | $28,527 | - | $28,527 |\n| Bonds payable | $205,000 | - | $205,000|\n| **Total Current Liabilities** | $575,520 | - | $575,520|\n| **Noncurrent Liabilities** | | | |\n| Leases payable | $467,016 | - | $467,016|\n| Notes payable | $482,786 | - | $482,786|\n| Bonds payable | $3,770,000 | - | $3,770,000|\n| **Total Noncurrent Liabilities** | $4,719,802 | - | $4,719,802|\n| **TOTAL LIABILITIES** | $5,295,322 | - | $5,295,322|"}
{"item_id": "item_0150", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total credits across the four semesters so I can see at a glance what share each semester contributes to the overall 100 credits?", "table_markdown": "| Semester | Internal Credits | External Credits | Total Credits | Total Marks |\n|---|---|---|---|---|\n| Semester I | 02 | 22 | 24 | 1200 |\n| Semester II | - | 26 | 26 | 1300 |\n| Semester III | - | 25 | 25 | 1250 |\n| Semester IV | - | 25 | 25 | 1250 |\n| Total | 02 | 98 | 100 | 5000 |"}
{"item_id": "item_0151", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the Exchange Traded Funds category so I can see at a glance how the total value is split between the individual holdings?", "table_markdown": "| Shares | |\n|--------|-------|\n| United Kingdom — 2.8% | |\n| db x-trackers II – Harvest CSI China Sovereign Bond UCITS ETF | 38,558 | 797,572 |\n| db x-trackers II Iboxx USD Liquid Asia Ex-Japan Corporate Bond UCITS ETF | 3,778 | 444,029 |\n| **TOTAL EXCHANGE TRADED FUNDS** | | **1,241,601** |\n| (Cost $1,284,920) | | |"}
{"item_id": "item_0152", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total medium-cost estimates for all directly impacted vessels by installation type, so I can easily see what share each category represents?", "table_markdown": "| Required installation | Number of vessels | Low cost estimate | Medium cost estimate |\n|---|---|---|---|\n| No additional components needed. | 27 | $0 | $67,500 |\n| Additional camera(s) near existing cameras | 22 | $44,000 | $132,000 |\n| Additional camera(s) not near existing cameras | 12 | $36,000 | $96,000 |\n| New system | 7 | $28,000 | $73,500 |\n| Total | 68 | $108,000 | $369,000 |"}
{"item_id": "item_0153", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the unweighted interview volume was concentrated among a few key markets or spread more evenly across the 16 regions?", "table_markdown": "| Market | Interviews (unweighted) | Market | Interviews (unweighted) |\n|-----------------|-------------------------|-----------------|-------------------------|\n| Australia | 502 | Nordics | 402 |\n| Belgium | 201 | Peru | 203 |\n| Brazil | 307 | Poland | 303 |\n| Canada | 525 | Russia | 302 |\n| Chile | 312 | South Africa | 202 |\n| China | 523 | Southeast Asia | 321 |\n| Colombia | 305 | South Korea | 313 |\n| France | 507 | Spain | 300 |\n| Germany | 406 | Switzerland | 193 |\n| India | 309 | Netherlands | 312 |\n| Indonesia | 306 | Philippines | 311 |\n| Ireland | 202 | Turkey | 308 |\n| Italy | 306 | Argentina | 300 |\n| Japan | 429 | United Kingdom | 514 |\n| Mexico | 301 | United States | 521 |\n| New Zealand | 209 | | |"}
{"item_id": "item_0154", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of Total Assets as of 31 March 2024 so I can see at a glance what share each component represents?", "table_markdown": "| 31 March 2024 | QR\"000 | QR\"000 | QR\"000 |\n|---|---|---|---|\n| Cash and bank balances | 423,000 | 371,574 | 51,426 |\n| Receivables and prepayments | 96,544 | 99,960 | (3,416) |\n| Inventory | 14,260 | 14,592 | (332) |\n| Due from related parties | 467 | 473 | (6) |\n| Investment properties | 45,673,477 | 45,643,861 | 29,616 |\n| Property and equipment | 666,307 | 670,977 | (4,670) |\n| TOTAL ASSETS | 46,874,055 | 46,801,437 | 72,618 |\n| Payables and other liabilities | 387,342 | 889,175 | (501,833) |\n| Due to related parties | 1,559,017 | 1,589,257 | (30,240) |\n| Islamic financing borrowings | 11,503,739 | 10,995,266 | 508,473 |\n| TOTAL LIABILITIES | 13,450,098 | 13,473,698 | (23,600) |\n| Share capital | 26,524,967 | 26,524,967 | - |\n| Legal reserve | 1,706,526 | 1,706,526 | - |\n| Foreign currency translation reserve | 1,495 | 729 | 766 |\n| Retained earnings | 5,366,353 | 5,270,900 | 95,453 |\n| Equity Holders of the Parent | 33,599,341 | 33,503,122 | 96,219 |\n| Non-controlling interest | (175,384) | (175,383) | (1) |\n| Total Equity | 33,423,957 | 33,327,739 | 96,218 |\n| TOTAL LIABILITIES AND EQUITY | 46,874,055 | 46,801,437 | 72,618 |"}
{"item_id": "item_0155", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the Technical Score to show what percentage each of the five components contributes to the total?", "table_markdown": "| Item | Description | Scores | |\n|---|---|---|---|\n| I. | The company profile This include binding the documents, neat presentation , separation and arrangement of requested information and general purpose to all requirements………………………………………….5 Dully filled price quotes……………………………...5 | 10 | |\n| II. | Evidence of adequacy of working capital for this Contract Access to line(s) of credit ------------------------------- 10 Availability of other financial resources (cash in hand, overdraft facility e.t.c) ------------------------------------ 10 Bank statement for the last one year---------------------10 | 30 | |\n| III. | Information regarding any litigation, current or during the last seven years, in which the tenderer is involved, the parties concerned and disputed amount(if any) Without litigation history--------------------------- 10 points. With Litigation history------------------------------0 points | 10 | |\n| IV. | Proof of works of similar magnitude (Attach copies of previous orders and Contracts) | 20 | |\n| V. | Key Personnel | 10 | |\n| | TOTAL | | 80 |"}
{"item_id": "item_0156", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of Unrestricted expenses so I can see at a glance what share each category represents?", "table_markdown": "| Expenses | Unrestricted | Temporarily Restricted | Total |\n|------------------------------|--------------|------------------------|-------|\n| Grants and awards to orphanage | 316,763 | - | 316,763 |\n| Management and general | 95,561 | - | 95,561 |\n| Fundraising | 10,946 | - | 10,946 |\n| Unrealized net loss on investments | 851 | - | 851 |\n| | **424,121** | **-** | **424,121** |"}
{"item_id": "item_0157", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the 2015 forest harvest by municipality so I can see at a glance which areas accounted for the largest share of the total hectares?", "table_markdown": "| Municipality | Hectares | Acres | Approx. # of Trees Removed |\n|------------------|----------|---------|----------------------------|\n| Fort Erie | 8 | 19.7 | 187 |\n| Grimsby | 2.5 | 6.2 | 81 |\n| Lincoln | 12 | 30 | 728 |\n| Niagara Falls | 4.5 | 11.1 | 113 |\n| St. Catharines | 5.8 | 14.3 | 66 |\n| Thorold | 21 | 51.9 | 1096 |\n| Wainfleet | 13.3 | 38 | 949 |\n| West Lincoln | 4 | 9.9 | 117 |\n| **Total** | **71.1** | **175.7** | **3337** |"}
{"item_id": "item_0158", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of renewable energy options in the Malta Renewable Energy Action Plan so I can see at a glance what share of the total generation each source contributes?", "table_markdown": "| Renewable Energy Option | % | GWh/year |\n|----------------------------------|----|----------|\n| Offshore wind | 3.48 | 216 |\n| Biofuels | 2.40 | 149 |\n| Energy from waste –Electricity | 2.18 | 135 |\n| Solar PV | 0.69 | 43 |\n| Onshore wind | 0.61 | 38 |\n| Solar Water Heating | 0.52 | 32 |\n| Energy from waste – Heat | 0.22 | 20 |\n| **TOTAL:** | **10.20** | **634** |"}
{"item_id": "item_0159", "chart_task_type": "composition_snapshot", "query": "Can you chart the age breakdown of the professional workforce so I can see at a glance which age groups make up the largest shares of the total?", "table_markdown": "| Age Group | Male | Female | Total |\n|--------------------|------|--------|-------|\n| Below 25 | 8 | 17 | 25 |\n| 25 to below 35 | 32 | 112 | 144 |\n| 35 to below 45 | 82 | 133 | 215 |\n| 45 to below 55 | 117 | 139 | 256 |\n| 55 and over | 31 | 25 | 56 |\n| **Total** | **270** | **426** | **696** |"}
{"item_id": "item_0160", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the COB Group's total tenement area by individual license so I can see at a glance how much each one contributes to the whole?", "table_markdown": "| Title | Area (km$^2$) | Grant Date | Expiry Date |\n|---------|---------------|------------------|-----------------|\n| EL 6622 | 51 | 30 August 2006 | 30 August 2026 |\n| EL 8143 | 12 | 26 July 2013 | 26 July 2026 |\n| ML 86 | 2 | 5 November 1975 | 5 November 2022 |\n| ML 87 | 1 | 5 November 1975 | 5 November 2022 |\n| EL 8891 | 30 | 3 September 2019 | 3 September 2022|\n| EL 9139 | 67 | 15 April 2021 | 15 April 2027 |\n| EL 9254 | 58 | 26 July 2021 | 26 July 2027 |"}
{"item_id": "item_0161", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total working expenditure so I can see at a glance which components account for the largest shares?", "table_markdown": "| Description | Calculation | Amount |\n|------------------------------------------------------------------------------|----------------------|----------|\n| 1. Paddy straw @ Rs. 1,000 / t | 10 t x 1,500 | 10,000 |\n| 2. Spawn bags @ Rs. 15 / bag (20% more than the required to compensate contaminated bags) | 9,000 x 15 | 1,35,000 |\n| 3. Polythene bags for bed preparation and polythene bags for packing mushroom @ Rs. 60 per kg | 300 x Rs. 60 | 18,000 |\n| 4. Casing soil preparation | | 15,000 |\n| 5. Fungicides, insecticides, fumigants, etc. | | 5,000 |\n| 6. Women labour @ Rs. 40/day | 900 x 40 | 36,000 |\n| 7. Electricity charges and fuel | | 10,000 |\n| 8. Miscellaneous including jute thread | | 5,000 |\n| **Total** | | **2,34,000** |"}
{"item_id": "item_0162", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total capital costs by region so I can see at a glance which areas account for the largest share?", "table_markdown": "| Region | # of Rights | Estimated Number of Homes | Total Capital Cost (1) (millions) |\n|-------------------------|-------------|----------------------------|-----------------------------------|\n| New England | 6 | 1,388 | $474 |\n| Metro NY/NJ | 11 | 4,628 | 1,644 |\n| Mid-Atlantic | 3 | 998 | 314 |\n| Pacific Northwest | 3 | 911 | 238 |\n| Northern California | 4 | 904 | 458 |\n| Southern California | 1 | 475 | 245 |\n| **Total** | 28 | 9,304 | $3,373 |"}
{"item_id": "item_0163", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of tutorial assistance by level of study in 2020, so I can see at a glance what share of the total students assisted falls into each category?", "table_markdown": "| Level of study | Number of students assisted | Total hours of assistance9 |\n|---|---|---|\n| Enabling | 1 | 5 |\n| Undergraduate | 136 | 4232.95 |\n| Postgraduate | 7 | 122.25 |\n| Other | | |\n| Total | 144 | 4360.20 |"}
{"item_id": "item_0164", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the 'Other Expenses' to show what share each component represents of the total?", "table_markdown": "| 1. | Electricity | 10,000 |\n|---|---|---|\n| 2. | Water | 1000 |\n| 3. | Postage & Stamps | 500 |\n| 4. | Repairing & Maintenance | 1000 |\n| 5. | Adv. & Publicity | 500 |\n| 6. | Transportation | 5000 |\n| 7. | Insurance | 500 |\n| 8. | Misc. | 500 |\n| | Total : | 19,000 |"}
{"item_id": "item_0165", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of REP James 'Jim' Wright's total votes by voting method so I can see at a glance what share came from mail, early voting, and election day?", "table_markdown": "| Candidate | TOTAL | Ballots by Mail | Early Voting | Election Day |\n|------------------------------------------------|-------|-----------------|--------------|-------------|\n| REP James \"Jim\" Wright | 7,305 | 524 | 5,277 | 1,504 |\n| DEM Chrysta Castaneda | 2,812 | 439 | 1,542 | 331 |\n| LIB Matt Sterett | 125 | 8 | 93 | 24 |\n| GRN Kellie \"Kat\" Gruene | 60 | 6 | 36 | 18 |"}
{"item_id": "item_0166", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of Aramco's total sponsorship value by sport so I can see at a glance which sports account for the largest shares?", "table_markdown": "| Sport | No. of active deals | Annual value (estimates) | Total value (estimates) |\n|-----------|---------------------|--------------------------|-------------------------|\n| Motorsport| 4 | $81.5m | $495.7m |\n| Football | 3 | $216.3m | $757.6m |\n| Golf | 2 | $14.1m | $35.9m |\n| Cricket | 1 | $14m | $56m |\n| **Sum** | **10** | **$314.1m** | **$1,296m** |"}
{"item_id": "item_0167", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total labor budget by task, so I can see at a glance what share of the cost each task represents?", "table_markdown": "| TASK ACTIVITY | STAFF | HRS | HOURLY RATE | COST | % OF TOTAL COST |\n|---------------------------------------------------|---------|------|-------------|----------|-----------------|\n| **TASK 1: Commercial Outreach, Technical Assistance, & Evaluation** | | | | | |\n| D. Stitzhal | 30 | $190 | $5,700.00 | | |\n| Ellner | 146 | $155 | $22,630.00 | | |\n| L. Stitzhal | 25 | $135 | $3,375.00 | | |\n| Scales | 40 | $145 | $5,800.00 | | |\n| Oakley | 40 | $130 | $5,200.00 | | |\n| **Subtotal** | | | | $42,705.00 | 64% |\n| **TASK 2: OML Compliance & Program Assessment *** | | | | | |\n| D. Stitzhal | 20 | $190 | $3,800.00 | | |\n| Ellner | 75 | $155 | $11,625.00 | | |\n| L. Stitzhal | 3 | $135 | $405.00 | | |\n| Scales | 0 | $145 | $0.00 | | |\n| Oakley | 0 | $130 | $0.00 | | |\n| **Subtotal** | | | | $15,830.00 | 24% |\n| **TASK 3: Project Management, Invoicing, & Reporting** | | | | | |\n| D. Stitzhal | 10 | $190 | $1,900.00 | | |\n| Ellner | 30 | $155 | $4,650.00 | | |\n| L. Stitzhal | 0 | $135 | $0.00 | | |\n| Scales | 3 | $145 | $435.00 | | |\n| Oakley | 3 | $130 | $390.00 | | |\n| **Subtotal** | | | | $7,375.00 | 11% |\n| **TOTAL LABOR** | 425 | | | $65,910.00 | |"}
{"item_id": "item_0168", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of daily passenger train departures by route so I can see at a glance which routes account for the largest share of the total?", "table_markdown": "| Route | Departures | Arrivals |\n|----------------------------------------------------------------------|------------|----------|\n| 1. Oxted and London via Edenbridge Town | 18 | 18 |\n| 2. Oxted and London via East Grinstead | 13 | 15 |\n| 3. Three Bridges via East Grinstead (through trains)* | 2 | 2 |\n| 4. Uckfield, Lewes and Brighton | 12 | 13 |\n| 5. Heathfield, Hailsham and Eastbourne | 7 | 8 |\n| 6. Tonbridge and Sevenoaks via Central | 14 | 14 |\n| | **66** | **70** |"}
{"item_id": "item_0169", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of cumulative cholera cases by the top 10 states so I can see at a glance what share of the total each state represents?", "table_markdown": "| No | State | Cases | Percent of cumulative cases | Cumulative % of total cases |\n|----|---------|-----------|-----------------------------|----------------------------|\n| 1 | Bauchi | 19,470 | 19% | 19% |\n| 2 | Jigawa | 12,965 | 13% | 32% |\n| 3 | Kano | 12,116 | 12% | 45% |\n| 4 | Zamfara | 11,101 | 11% | 56% |\n| 5 | Katsina | 8,602 | 9% | 64% |\n| 6 | Sokoto | 8,477 | 8% | 73% |\n| 7 | Kebbi | 5,940 | 6% | 79% |\n| 8 | Borno | 3,938 | 4% | 83% |\n| 9 | Yobe | 3,750 | 4% | 86% |\n| 10 | Niger | 2,851 | 3% | 89% |\n| | Total | 89,180 | 89% | |"}
{"item_id": "item_0170", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total expenses by category so I can see at a glance what share each component represents?", "table_markdown": "| Expenses: | Unrestricted Net Assets | Temporarily Restricted Net Assets | Permanently Restricted Net Assets | Total |\n|---------------------------|-------------------------|----------------------------------|----------------------------------|-------|\n| Appreciation Dinner | $946 | $0 | $0 | $946 |\n| Depreciation | $272 | $0 | $0 | $272 |\n| Contract Services | $20,532 | $5,841 | $0 | $26,373 |\n| Office | $3,192 | $0 | $0 | $3,192 |\n| Telephone | $1,931 | $0 | $0 | $1,931 |\n| Uniforms | $520 | $0 | $0 | $520 |\n| Other | $4,641 | $0 | $0 | $4,641 |\n| **Total Expenses** | **$32,034** | **$5,841** | **$0** | **$37,875** |"}
{"item_id": "item_0171", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the FY2023 emergency funding by category so I can see the percentage share of each part?", "table_markdown": "| Category | Description | Amount |\n|--------------|--------------------------------------------------|---------|\n| Fuel | Fuel need for Vehicles | 16500 |\n| Repair | Repairs that come up | 17500 |\n| Supplies | Supplies that is needed | 7500 |\n| Septic | Assist in Septic tank cleaning | 22000 |\n| Wood pellets | Assist with members that need | 20000 |"}
{"item_id": "item_0172", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the intervention group's total satisfaction score into its constituent domains so I can see at a glance which areas contributed most to the overall result?", "table_markdown": "| Satisfaction Domains | Intervention group (n=53) | Control group (n=53) | |\n|---|---|---|---|\n| | Mean±SD | Mean±SD | t |\n| Health care providers attitude | 25.11± 3.63 | 19.55± 1.38 | 10.446 |\n| Information provided | 31.91 ± 3.50 | 14.21 ± .41 | 36.52 |\n| Intervention provided | 14.92± 1.60 | 4.62 ± .88 | 40.983 |\n| Discharge recommendation | 36.21± 3.96 | 8.92 ±1.17 | 48.101 |\n| Total satisfaction score | 108.15 ± 6.29 | 47.30 ± 1.88 | 67.496 |"}
{"item_id": "item_0173", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total word count in the Polish L1 subcorpus by speaking style, so I can see at a glance which styles contribute the most?", "table_markdown": "| speaking style (L1 Polish) | min. of speech | syllables | words |\n|---|---|---|---|\n| story | 67 | 17943 | 9397 |\n| sentences | 112 | 33856 | 13455 |\n| poems | 70 | 17703 | 10239 |\n| mini-dial. | 13 | 5098 | 2571 |\n| spontaneous | 39 | 12393 | 6341 |\n| total: | 301 | 86993 | 42023 |\n| mean/speaker | 16 | 4579 | 2212 |"}
{"item_id": "item_0174", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of ASTDD's 2020 income by funding source so I can see at a glance what share each source contributed to the total?", "table_markdown": "| Funding Source | Amount | What Funding Supports |\n|----------------------------------------------------|------------|---------------------------------------------------------------------------------------|\n| CDC Division of Oral Health | $395,723 | Multiple focus areas and SME for TA, training and resource documents |\n| Office of Head Start, Georgetown Univ, OHRC | $159,999 | National Center on Early Childhood Health and Wellness Dental Hygienist Liaison project |\n| HRSA MCHB, Georgetown Univ, OHRC | $94,414 | Center for Oral Health Systems Integration and Improvement SME for TA to states and territories |\n| Delta Dental of Michigan and GlaxoSmithKline | $96,000 | National Oral Health Data Portal |\n| Membership dues | $39,925 | Discretionary funds |\n| National Oral Health Conference registration | $22,500 | 2019 Virtual NOHC expenses; there was no profit from the 2020 NOHC as all funds are being applied towards the penalties from cancelling the 2021 in-person NOHC |\n| Merced County, CA | $19,995 | Fiscal agent for Matt Jacob to develop oral health messaging |\n| DentaQuest Foundation | $19,028 | ASTDD ED participation in OPEN National Oral Health Connection Team and one State Representative |\n| Gary and Mary West Foundation/Apple Tree Dental | $15,600 | Healthy Aging Toolkit |\n| Basic Screening Survey Sales and S/TOHP MOUs | $15,270 | BSS module sales and TA to states not funded by CDC |\n| DentaQuest Partnership for OH Advancement | $10,000 | School-Based Services During COVID Toolkit |\n| American Fluoridation Society | $2,500 | CWF Rollback Catalog |\n| ADA | $1,000 | Annual Fluoridation Awards |"}
{"item_id": "item_0175", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the 2020/2021 preliminary budget appropriations by fund type so I can see at a glance what share of the total each fund represents?", "table_markdown": "| Fund | Appropriations | Tax Levy |\n|-----------------------|----------------|------------|\n| General Fund | $2,816,415 | $2,129,519 |\n| Special Revenue Fund | $70,012 | |\n| Debt Service Fund | $110,894 | $110,894 |\n| Total Budget | $2,997,321 | $2,240,413 |"}
{"item_id": "item_0176", "chart_task_type": "concentration_long_tail", "query": "Can you chart whether the council funding allocations are concentrated among a few large authorities or spread more evenly, so I can see at a glance if a small number of councils receive the bulk of the money?", "table_markdown": "| Council | Allocations, £ millions | Allocations, £ per person |\n|--------------------------|-------------------------|---------------------------|\n| Aberdeen City | 450 | 2100 |\n| Aberdeenshire | 600 | 2300 |\n| Angus | 300 | 2500 |\n| Argyll & Bute | 200 | 3000 |\n| City of Edinburgh | 1200 | 2100 |\n| Clackmannanshire | 150 | 2700 |\n| Dumfries & Galloway | 400 | 2800 |\n| Dundee City | 400 | 2900 |\n| East Ayrshire | 300 | 2800 |\n| East Dunbartonshire | 300 | 2700 |\n| East Lothian | 200 | 2700 |\n| East Renfrewshire | 200 | 2700 |\n| Falkirk | 400 | 2700 |\n| Fife | 900 | 2700 |\n| Glasgow City | 1600 | 2700 |\n| Highland | 600 | 2800 |\n| Inverclyde | 200 | 2900 |\n| Midlothian | 200 | 2700 |\n| Moray | 200 | 2700 |\n| Na h-Eileanan Siar | 4500 | 4500 |\n| North Ayrshire | 400 | 2900 |\n| North Lanarkshire | 800 | 2700 |\n| Orkney | 4000 | 4000 |\n| Perth & Kinross | 400 | 2700 |\n| Renfrewshire | 400 | 2700 |\n| Scottish Borders | 300 | 2700 |\n| Shetland | 5000 | 4700 |\n| South Ayrshire | 300 | 2800 |\n| South Lanarkshire | 800 | 2700 |\n| Stirling | 200 | 2700 |\n| West Dunbartonshire | 200 | 2700 |\n| West Lothian | 400 | 2700 |"}
{"item_id": "item_0177", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total expenses by category so I can see at a glance which items make up the largest shares?", "table_markdown": "| DESCRIPTION | TOTAL | PROGRAM SERVICES | MANAGEMENT AND GENERAL |\n|---------------------|--------|------------------|-----------------------|\n| INSURANCE | 705 | | 705 |\n| ADVERTISING | 35 | | 35 |\n| SUBSCRIPTIONS | 407 | | 407 |\n| RESOURCE SERVICES | 171 | | 171 |\n| PAYROLL SERVICES | 565 | | 565 |\n| MISCELLANEOUS | 19,843 | | 19,843 |\n| PROPERTY TAX | 250 | | 250 |\n| FACILITY MAINTENANCE| 682 | | 682 |\n| UTILITIES | 1,076 | | 1,076 |\n| TOTALS | 23,734 | 21,021 | 2,713 |"}
{"item_id": "item_0178", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total assets as at September 30, 2020, into non-current and current assets, so I can see at a glance what share each category represents?", "table_markdown": "| Particulars | As at September 30, 2020 | As at March 31, 2020 |\n|--------------------------------------------------|---------------------------|----------------------|\n| | Unaudited | Audited |\n| **ASSETS** | | |\n| **Non-current assets** | | |\n| Property, plant and equipment | 166,768 | 169,659 |\n| Capital work-in-progress | 33,561 | 22,011 |\n| Goodwill | 125 | 125 |\n| Intangible assets | 76 | 86 |\n| Financial assets | | |\n| i. Investments | 1 | 1 |\n| ii. Other financial assets | 1,304 | 1,270 |\n| Current tax assets (net) | 1,637 | 2,589 |\n| Deferred tax assets (net) | 3,958 | 4,732 |\n| Other non current assets | 2,516 | 1,606 |\n| **Total non-current assets** | 209,946 | 202,079 |\n| **Current assets** | | |\n| Inventories | 2,653 | 4,211 |\n| Financial assets | | |\n| i. Investments | - | 730 |\n| ii. Trade receivables | 22,112 | 45,403 |\n| iii. Cash and cash equivalents | 28,458 | 22,247 |\n| iv. Bank balances other than (iii) above | 8,226 | 4,097 |\n| vi. Other financial assets | 10,954 | 6,467 |\n| Other current assets | 8,029 | 6,836 |\n| **Total current assets** | 80,432 | 89,991 |\n| **TOTAL ASSETS** | 290,378 | 292,070 |\n| **EQUITY AND LIABILITIES** | | |\n| **Equity** | | |\n| Equity Share capital | 3,454 | 3,397 |\n| Other equity | 172,182 | 162,064 |\n| **Equity attributable to owners of the Company** | 175,636 | 165,461 |\n| Non Controlling Interest | 10,485 | 9,060 |\n| **Total equity** | 186,121 | 174,521 |\n| **LIABILITIES** | | |\n| **Non-current liabilities** | | |\n| Financial liabilities | | |\n| i. Borrowings | 4,890 | 4,850 |\n| ii. Other financial liabilities | 32,197 | 31,646 |\n| Provisions | 1,468 | 1,391 |\n| Deferred tax liabilities (Net) | 3,502 | 3,090 |\n| Other non-current liabilities | 125 | 132 |\n| **Total Non-current liabilities** | 42,182 | 41,109 |\n| **Current liabilities** | | |\n| Financial liabilities | | |\n| i. Borrowings | 17,957 | 16,705 |\n| ii. Trade payables | | |\n| Total outstanding dues of creditors of micro enterprises and small enterprises | - | - |\n| Total outstanding dues of creditors other than micro enterprises and small enterprises | 18,698 | 40,155 |\n| iii. Other financial liabilities | 19,428 | 13,827 |\n| Other current liabilities | 3,811 | 3,702 |\n| Provisions | 402 | 402 |\n| Current tax liabilities (Net) | 1,779 | 1,649 |\n| **Total current liabilities** | 62,075 | 76,440 |\n| **TOTAL LIABILITIES** | 104,257 | 117,549 |\n| **TOTAL EQUITY AND LIABILITIES** | 290,378 | 292,070 |"}
{"item_id": "item_0179", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of weekday revenue hours by route for March 2021 so I can see at a glance what share of the total each route contributes?", "table_markdown": "| Route | Revenue Hours | Revenue Hours | Total Vehicle Hours |\n|---|---|---|---|\n| | Weekday | Saturday | Weekday |\n| East Pittsburgh | 11.53 | 5.07 | 12.00 |\n| McKeesport | 15.65 | 5.37 | 16.75 |\n| Monroeville | 13.78 | 4.80 | 14.75 |\n| Total | 40.96 | 15.24 | 43.50 |"}
{"item_id": "item_0180", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total 2016 expenditures by funder so I can see at a glance what share each source contributed?", "table_markdown": "| Docket(s) | Funder | Expensed |\n|---|---|---|\n| Chittenden, Franklin, Rutland, Washington | ADAP | $ 131,719.53 |\n| Franklin | Franklin Juvenile Drug Ct | $ 1,353.42 |\n| Windsor | GHSP DUI | $ 150,950.60 |\n| Chittenden | Samhsa | $ 273,469.95 |\n| Chittenden | BJA Joint | $ 191,032.47 |\n| | TOTAL for calendar year 2016 | $ 748,545.97 |"}
{"item_id": "item_0181", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total reshored jobs by state so I can see at a glance which states account for the largest shares?", "table_markdown": "| STATE | NUMBER OF JOBS | NUMBER OF COMPANIES |\n|---|---|---|\n| South Carolina | 7,780 | 7 |\n| Georgia | 3,005 | 7 |\n| Tennessee | 2,490 | 11 |\n| North Carolina | 1,020 | 14 |\n| Florida | 611 | 12 |\n| Mississippi | 540 | 5 |\n| Alabama | 397 | 4 |\n| TOTALS/AVG | 15,843 | 60 |"}
{"item_id": "item_0182", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the estimated sawlog tons by species so I can see at a glance what share each type contributes to the total?", "table_markdown": "| SPECIES | PP | DF | WL | LP | ES | GF | RC | AF | WP | WH | Dead | TOTAL or AVE |\n|------------------|------|------|------|------|------|------|------|------|------|------|------|--------------|\n| Minimum Bid per ton all species except PP | | | | | | | | | | | | $6.97 |\n| Minimum Bid per ton PP | | | | | | | | | | | | $2.88 |\n| Pounds per cubic foot | 62.0 | 61.7 | 58.3 | 56.0 | 57.0 | 60.0 | 44.0 | 55.0 | | | | |\n| Estimated CF | 125538.1 | 535406.3 | 23926.9 | 2840.7 | 4651.0 | 39272.3 | 25643.4 | 727.9 | 23469.2 | 781,476 CF | |\n| Estimated CF of non-sawlog material | 2030.5 | 7799.7 | 233.9 | 15.7 | 34.0 | 1000.0 | 500.2 | 15.5 | 303.0 | 11,932 CF | |\n| Estimated tons of sawlogs | 3739 | 15038 | 609 | 60 | 119 | 1014 | 525 | 17 | 413 | 21,533 tons | |\n| Estimated tons of non-sawlog material | 62 | 221 | 6 | 0 | 1 | 27 | 10 | 0 | 5 | 333 tons | |\n| Gross MBF | 566 | 2431 | 122 | 13 | 23 | 162 | 99 | 3 | 112 | 3,531 MBF | |\n| % Cull and Breakage | 4.3 | 9.0 | 12.9 | 23.9 | 10.0 | 14.6 | 7.0 | 15.0 | 12.2 | 8.7% | |\n| Net MBF | 542 | 2213 | 106 | 10 | 21 | 138 | 93 | 2 | 98 | 3,223 MBF | |\n| Average logs per MBF | 23.8 | 23.1 | 19.4 | 24.9 | 18.3 | 31.4 | 28.8 | 38.9 | 19.4 | 23.5 logs/MBF| |\n| Average DBH | 11.9 | 12.5 | 14.0 | 10.0 | 14.3 | 11.0 | 11.8 | 11.0 | 13.5 | 12.4 in. | |\n| Average tons to cut per acre | | | | | | | | | | 51 tons/ac | |\n| Average MBF to cut per acre | | | | | | | | | | 7.6 MBF/ac | |\n| Total Average TPA Cut | | | | | | | | | | 95 | |"}
{"item_id": "item_0183", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total library voucher amount by vendor so I can see at a glance what share each vendor accounts for?", "table_markdown": "| Voucher Number | Vendor Name | Description | Amount |\n|----------------|------------------------------|--------------------------------------------------|----------|\n| 405 | BAKER & TAYLOR | BOOKS, VIDEOS, AUDIOBOOKS | $2,069.22|\n| 412 | DEMCO, INC | OFFICE SUPPLIES | $102.11 |\n| 415 | EICHHORST, LORI | CLEANING | $325.00 |\n| 416 | ELKHART LAKE CHAMBER | MARKETING- LIL REV AD | $30.52 |\n| 418 | FRONTIER | PHONE | $75.91 |\n| 422 | JOURNAL SENTINEL INC. | SUBSCRIPTION | $462.48 |\n| 421 | MCCLONE | INSURANCE | $151.00 |\n| 423 | MONTES, RACHEL | PROGRAMS REIMBURSEMENT | $55.10 |\n| 400 | NATIONAL EXCHANGE BANK | PROGRAMS, EXPENSES, OFFICE SUPPLIES, FOL | $157.70 |\n| 430 | NORTHWOODS FIRE PROTECT | FIRE EXTINGUISHER MAINTENANCE | $13.00 |\n| 438 | SUN GRAPHICS | OFFICE SUPPLIES- ENVELOPES | $108.55 |\n| 402 | WE ENERGIES | ELECTRICITY | $343.43 |\n| 448 | WISCONSIN PUBLIC SERVICE | NATURAL GAS | $21.08 |"}
{"item_id": "item_0184", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the assets side of the balance sheet so I can see at a glance what share each group contributes to the total?", "table_markdown": "| Assets | $ million | Duration | Claims | $ million | Duration |\n|---|---|---|---|---|---|\n| Group I | 100 | 0,00 | Group I | 600 | 1,00 |\n| Group II | 200 | 2,00 | Group II | 100 | 2,00 |\n| Group III | 100 | 3,00 | Group III | 100 | 3,00 |\n| Group IV | 100 | 5,00 | Group IV | 100 | 4,00 |\n| Group V | 500 | 6,00 | Equity | 100 | 0,00 |\n| Total | 1000 | 4,20 | Total | 1000 | 1,50 |"}
{"item_id": "item_0185", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of labour time for the sample staircase so I can see at a glance which work items account for the largest share of the total effort?", "table_markdown": "| Base number | Description table | Unit | Circulation unit | The value of quantity survey | Time |\n|-------------|------------------------------------------------------------------------------------|------|------------------|------------------------------|--------|\n| DC-20 | TABLE 0319 - ceramic tiles suitable to be laid in the staircase (with certain allowance) | m | 1.45 | 150 | 217.50 |\n| DC-20 | TABLE 0318 - Plinths of non-absorbent ceramic tiles (landings) | m | 0.51 | 106.7 | 54.42 |\n| DC-20 | TABLE 0318 - Plinths of non-absorbent ceramic tiles (stairs) | m | 0.7 | 65.8 | 46.06 |\n| DC-20 | TABLE 0314 - Ceramic tiles of final non-absorbent surfaces | m² | 2.01 | 112.6 | 226.33 |\n| DC-19 | TABLE 0101 - Primer for absorbent substrates | m² | 0.15 | 200.35 | 30.05 |\n| DC-19 | TABLE 0212 - Filling the voids (reprofiling) in the surface of concrete grout (with certain allowance) | m² | 0.44 | 112.6 | 49.54 |\n| DC-19 | TABLE 0418 - Filling weight silicone joints | m | 0.14 | 322.5 | 45.15 |"}
{"item_id": "item_0186", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total annual carbon dioxide emissions by portfolio sector so I can see at a glance which programs contribute the most?", "table_markdown": "| Portfolio Sector | $ Loaned | tCO₂e Produced | tCO₂e/$1,000 |\n|-----------------------------------|----------------|----------------|--------------|\n| Affordable Housing | $15,823,308 | 1,098,101 | 69.398 |\n| Business | $6,580,178 | 24,997 | 3.799 |\n| Community Facilities | $5,355,935 | 1,090 | 0.204 |\n| Early Care & Learning | $3,242,095 | 623 | 0.192 |\n| TOTAL | $31,001,516 | 1,124,811 | 36.282 |"}
{"item_id": "item_0187", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the bank's total loan portfolio by location, showing the share of loans inside versus outside the Assessment Area so I can see the distribution at a glance?", "table_markdown": "| | Number of Loans | | | | | | | | |\n|---|---|---|---|---|---|---|---|---|---|\n| Loan Type | Inside | | Outside | | Total | Inside | | Outside | |\n| | # | % | # | % | | $ | % | $ | % |\n| Home Purchase | 8 | 89% | 1 | 11% | 9 | 828,751 | 96% | 30,413 | 4% |\n| Home Improvement | 4 | 80% | 1 | 20% | 5 | 68,492 | 68% | 32,273 | 32% |\n| Refinance | 26 | 76% | 8 | 24% | 34 | 1,664,691 | 78% | 477,334 | 22% |\n| Total | 38 | 78% | 10 | 22% | 48 | 2,561,934 | 83% | 540,020 | 17% |"}
{"item_id": "item_0188", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total records by collection type so I can see at a glance which categories make up the largest shares?", "table_markdown": "| Collections | Collections | Records |\n|-----------------------------------|-------------|------------------|\n| Images | 107 | 1,490,236 |\n| Text | 164 | 37,045,804 |\n| Finding Aids | 8 | 6,338 |\n| Bibliographic & Reference Collections | 13 | 4,197,968 |\n| Total | 292 | 42,740,346 |"}
{"item_id": "item_0189", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total contribution costs by employer size band so I can see at a glance which groups account for the largest shares?", "table_markdown": "| Number of employees | Contribution cost estimate £m | Percentage of labour cost percentage |\n|---------------------|------------------------------|--------------------------------------|\n| 1 | 80 | 0.8 |\n| 2 to 4 | 310 | |\n| 5 to 19 | 580 | 0.8 |\n| 20 to 49 | 440 | |\n| 50 to 249 | 590 | 0.6 |\n| 250 to 499 | 230 | |\n| 500+ | 1,020 | 0.4 |\n| **Total** | **3,240** | **0.5** |"}
{"item_id": "item_0190", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total community income by source so I can see at a glance what share each category contributes?", "table_markdown": "| Sources of income | Frequency | Mean income (US$) | Std. Err. | Total income (US$) | Share (%) |\n|-------------------------|-----------|-------------------|-----------|--------------------|-----------|\n| Agriculture | 141 | 50.4 | 5.48 | 7,104.8 | 3.51 |\n| Livestock herding | 106 | 156.2 | 19.30 | 16,562.2 | 8.19 |\n| Business | 52 | 456.4 | 105.85 | 23,732.9 | 11.74 |\n| Job | 8 | 544.0 | 200.85 | 4,352.4 | 2.15 |\n| Daily wage | 79 | 576.4 | 69.77 | 45,533.3 | 22.52 |\n| Forest resources | 139 | 54.9 | 1.91 | 7,635.9 | 3.78 |\n| NTFP/ Winter green | 126 | 2.5 | 0.32 | 311.6 | 0.15 |\n| Remittance | 38 | 2,461.8 | 406.41 | 93,547.6 | 46.26 |\n| Pension | 2 | 1,714.3 | 571.43 | 3,428.6 | 1.70 |\n| **Total Income** | | | | **202,209** | |\n| **Mean Income (HH/year)**| | | | **1,3945** | |"}
{"item_id": "item_0191", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of 2020 public sector emissions by source so I can see at a glance which categories account for the largest share of the total?", "table_markdown": "| | Bio COe 2 | Non-Bio COe 2 | Total COe 2 |\n|---|---|---|---|\n| | Emissions | Emissions | Emissions |\n| Scope 1 | | | |\n| Buildings (Direct Fuel Combustion) | 11,324 | 508,296 | 519,620 |\n| Fleet (Mobile Energy Use) | 7,520 | 135,865 | 143,384 |\n| Fugitive Emissions | | 1,071 | 1,071 |\n| Scope 2 | | | |\n| Buildings (Purchased Energy) | | 38,335 | 38,335 |\n| Scope 3 | | | |\n| Paper | | 12,116 | 12,116 |\n| Travel | 27 | 2,193 | 2,220 |\n| TOTAL | 18,869 | 697,876 | 716,745 |"}
{"item_id": "item_0192", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total Allocation by category so I can see at a glance what share each component represents?", "table_markdown": "| Category | Allocation | Disbursed Contracts | Cumulative disbursement |\n|-----------------------------------------------|----------------|---------------------|-------------------------|\n| Goods (Equipment and materials) | $148,900.00 | $0.00 | $14,162.03 |\n| Consulting services | $82,531.46 | $24,712.71 | $21,092.55 |\n| Workshops, training, seminars and conferences| $10,016.00 | $1,701.56 | $3,897.27 |\n| PIU Operations costs | $44,303.37 | $5,590.05 | $21,901.74 |\n| **Total** | **$285,750.83**| **$32,004.32** | **$61,053.59** |"}
{"item_id": "item_0193", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the UK space sector's total income in 2016/17 so I can see at a glance what share each category represents?", "table_markdown": "| Coverage | UK, 2016/17 (Size & Health) | World, 2018 (Bryce Space) | World, 2018 (Space Foundation) |\n|---------------------------|-----------------------------|---------------------------|---------------------------------|\n| Total | £14,792m | £217,620m | £325,370m |\n| Space manufacturing | £1,882m | £15,298m | |\n| Space operations | £2,179m | £4,864m | |\n| Space applications | £10,278m | £197,459m | |\n| Ancillary services | £453m | | |"}
{"item_id": "item_0194", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of annual operating expenditures so I can see at a glance what share each cost category represents?", "table_markdown": "Expenditure Item | Cost (ZMK, Millions) | % of Expenditures Made in Zambia\n---|---|---\nSite Services and Personnel | 250,000,000 | 100%\nReplacement Parts | 150,000,000 | 0%\nInsurance | 50,000,000 | 5%\nFees, Permits, Licenses | 25,000,000 | 100%\nOperating total | 475,000,000 | 58%"}
{"item_id": "item_0195", "chart_task_type": "composition_snapshot", "query": "Can you chart the age breakdown of people served in September so I can see at a glance what share of the total each group represents?", "table_markdown": "| Category | September | YTD 2024 | Grand Totals |\n|-----------------------------------|-----------|----------|--------------|\n| People served ages 0-18 | 70 | 1,888 | 64,685 |\n| People served ages 19-64 | 146 | 3,100 | 74,098 |\n| People served ages 65 & up | 197 | 4,078 | 50,242 |\n| Total People Served | 413 | 9,066 | 189,025 |\n| Total Meals Given | 8,187 | 182,083 | 3,946,241 |"}
{"item_id": "item_0196", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of Q1 power production by technology so I can see at a glance what share each source contributes to the total?", "table_markdown": "| Technology | TWh | Change in TWh |\n|----------------|-----|---------------|\n| Hydropower | 18.6| +3.7 |\n| Wind power | 0.6 | - |\n| Gas power | 0.2 | - |\n| Bio power | 0.1 | - |\n| Total | 19.4| +3.7 |"}
{"item_id": "item_0197", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total project repayment by source so I can see at a glance how much each contributor accounts for?", "table_markdown": "| TOTAL PROJECT REPAYMENT CHART | | |\n|---|---|---|\n| Amount | Source | ($’s in thousands) |\n| $ 49,000,000 | Irrigators’ Repayment | $ 49,000 |\n| 80,000,000 | M&I Users’ Repayment | 80,000 |\n| 170,000,000 | Power Users’ Repayment | 170,000 |\n| 283,000,000 | Non-reimbursable | 283,000 |\n| 200,000,000 | Basin Fund (Power Revenues) | 200,000 |\n| 55,000,000 | Other Credits | 55,000 |\n| 837,000,000 | Total | 837,000 |"}
{"item_id": "item_0198", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of closing security positions by holding sector so I can see at a glance what share each sector holds?", "table_markdown": "| Security | Opening positions | Transactions | Price changes | FX changes | Other volume changes | Closing positions | Sector ratio | Volume changes total | Change % |\n|-----------------------------------------------|-------------------|--------------|---------------|------------|----------------------|-------------------|--------------|----------------------|----------|\n| **Total security** | 61 782 | 2 430 | -262 | 430 | 3 | 64 382 | 100,0% | 2 600 | 4,2% |\n| **Nonfinancial corporations** | 2 707 | -12 | 45 | 14 | 1 | 2 756 | 4,3% | 48 | 1,8% |\n| **Financial corporations** | 25 039 | 579 | -259 | 46 | 1 | 25 405 | 39,5% | 366 | 1,5% |\n| **General government** | 1 395 | 24 | -2 | 0 | 0 | 1 418 | 2,2% | 23 | 1,7% |\n| **Households and nonprofit institutions** | 15 391 | 137 | 43 | 44 | 0 | 15 615 | 24,3% | 223 | 1,5% |\n| **Rest of the world** | 17 248 | 1 701 | -89 | 327 | 1 | 19 188 | 29,8% | 1 939 | 11,2% |"}
{"item_id": "item_0199", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total number of fires by class for 1930-39 so I can see at a glance what share each class represents?", "table_markdown": "| Class of fire | Number | Percent of total |\n|-------------------------------|--------|------------------|\n| Class A (less than $\\frac{1}{4}$ acre in size) | 2,150 | 56 |\n| Class B (between $\\frac{1}{4}$ and 10 acres in size) | 1,179 | 30 |\n| Class C (over 10 acres in size) | 559 | 14 |\n| Total of all classes | 3,888 | 100 |"}
{"item_id": "item_0200", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of annual estimated energy consumption by device type for the Preschool Institution, so I can see at a glance what share of the total each device accounts for?", "table_markdown": "| Type | No. | Nomin | Installed | Annual | Estimated |\n|---|---|---|---|---|---|\n| | | al | Capacity | Hours | Energy |\n| | | Rating | (kW) | Used | Consumpti |\n| | | (kW) | | | on (kWh) |\n| Washing Machines | 2 | 1 | 2 | 1000 | 2.000 |\n| Electric Ovens | 1 | 3 | 3 | 1200 | 3600 |\n| Boilers 80 lt | 2 | 2 | 4 | 800 | 3,200 |\n| Deep Freezers | 1 | 1 | 1 | 3000 | 3,000 |\n| Refrigerators | 1 | 0.75 | 0.75 | 4000 | 3,000 |\n| Electric heaters | 5 | 2 | 10 | 400 | 4,000 |\n| Air Conditioners | 5 | 3 | 15 | 800 | 12.000 |\n| Small Kitchen | 4 | 1 | 4 | 200 | 800 |\n| Commuters and TV | 11 | 0.2 | 2.2 | 440 | 968 |\n| TOTAL | | | | | 32568 |"}
{"item_id": "item_0201", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the train's total length by individual car so I can see at a glance what share each car contributes?", "table_markdown": "| NUMBER | POSITION | CAR | CAR TYPE | LENGTH | MASS |\n|----------|----------|-----|-----------------------------------------------|------------|---------|\n| XP2017 | Leading | H | Driving/power | 17.335 m | 76 t |\n| XAM2178 | 2nd | G | Sleeping with attendant’s station | 24.2 m | 48.3 t |\n| XL2230 | 3rd | F | First class seating and luggage | 24.2 m | 39.6 t |\n| XBR2154 | 4th | E | Economy class seating & buffet | 23.254 m | 43.6 t |\n| XF2218 | 5th | D | Economy seating | 24.2 m | 41.6 t |\n| XF2200 | 6th | C | Economy seating | 24.2 m | 41.6 t |\n| XFH2106 | 7th | B | Economy seating, supervisor’s station & luggage | 24.2 m | 40.1 t |\n| XP2011 | Trailing | A | Driving/power | 17.335 m | 76 t |"}
{"item_id": "item_0202", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the 2019 total direct GHG costs by category so I can see what share each component represents?", "table_markdown": "| GHG Cost Category | ERRABA | PABA | LGBA | Total |\n|------------------------------------|--------|------|------|-------|\n| UOG | $0 | $30 | $0 | $30 |\n| Imported (out-of-state) UOG | $0 | $18 | $0 | $18 |\n| Tolling Contracts | $0 | $2 | $10 | $12 |\n| Total | $0 | $50 | $10 | $60 |"}
{"item_id": "item_0203", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total loan amount by size category so I can see at a glance which ranges account for the largest shares?", "table_markdown": "| Size of the Loan | Guarantees approved | Loan Amount Rs. Crore | Average Loan Size Rs. Lakhs |\n|---------------------------|---------------------|-----------------------|-----------------------------|\n| Upto Rs. 1 lakh | 85638 | 4215.61 | 0.49 |\n| Rs. 1 lakh to Rs. 2 lakh | 52363 | 8105.56 | 1.55 |\n| Rs. 2 lakh to Rs. 5 lakh | 474802 | 17231.96 | 3.63 |\n| Rs. 5 lakh to Rs. 10 lakh | 238885 | 18274.7 | 7.66 |\n| Rs. 10 lakh to Rs. 25 lakh| 163878 | 28224.76 | 17.22 |\n| Rs. 25 lakh to Rs. 50 lakh| 46180 | 17516.3 | 37.93 |\n| Rs. 50 lakh to Rs. 1 crore| 20127 | 15421.96 | 76.62 |\n| Total | 2323673 | 108990.85 | 4.69 |"}
{"item_id": "item_0204", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total shares held by these four public shareholders so I can see at a glance what percentage of the total each one accounts for?", "table_markdown": "| Sr. No. | Name of the shareholder | Number of shares held | Shares as a percentage of total number of shares {i.e., Grand Total (A)+(B)+(C) indicated in Statement at para (I)(a) above} | Details of warrants | | Details of convertible securities | |\n|---|---|---|---|---|---|---|---|\n| | | | | Number of warrants held | As a % total no. of warrants of the same class | Number of convertible securities held | % w.r.t total no. of convertible securities of the same class |\n| 1 | COLLEGE RETIREMENT EQUITIES FUND - STOCK ACCOUNT | 91780547 | 3.51 | 0 | 0 | 0 | 0 |\n| 2 | HSBC GLOBAL INVESTMENT FUNDS A/C HSBC GIF MAURITIUS LIMITED | 49226897 | 1.88 | 0 | 0 | 0 | 0 |\n| 3 | DIMENSIONAL EMERGING MARKETS VALUE FUND | 37045083 | 1.42 | 0 | 0 | 0 | 0 |\n| 4 | COLLEGE RETIREMENT EQUITIES FUND - GLOBAL EQUITIES ACCOUNT | 26406156 | 1.01 | 0 | 0 | 0 | 0 |\n| TOTAL | | 204458683 | 7.81 | 0 | 0 | 0 | 0 |"}
{"item_id": "item_0205", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the Colorado State Patrol's FY 2004-05 operating appropriation by funding source so I can see at a glance what share each component contributes to the total?", "table_markdown": "| COLORADO STATE PATROL FY 2004-05 OPERATING APPROPRIATION | | |\n|---|---|---|\n| General Fund | | $1,242,352 |\n| Cash Funds | | $2,848,109 |\n| | HUTF | $73,071,846 |\n| Cash Funds Exempt | | $25,454,311 |\n| Federal Funds | | $3,861,150 |\n| TOTAL | | $107,610,973 |\n| | | 937.0 FTE |\n| Sworn Officers | | 681.4 FTE |\n| Communications Branch | | 136.1 FTE |\n| Other Civilians | | 119.5 FTE |"}
{"item_id": "item_0206", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the Minutes Board budget by expense category so I can see at a glance what share each part represents?", "table_markdown": "| | Clerk | Board | Total |\n|----------------------|-----------|---------|----------|\n| **Full Time Equivalent Positions** | - | 3.0 | 3.0 |\n| **Salaries** | $- | $119,252| $119,252 |\n| **Employee Benefits**| $- | $73,807 | $73,807 |\n| **Purchased Services**| $- | $34,150 | $34,150 |\n| **Materials & Supplies**| $- | $2,700 | $2,700 |\n| **Total Minutes** | $- | $229,909| $229,909 |"}
{"item_id": "item_0207", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total Amberley Beach reserve area by its seven constituent parts, so I can easily see the percentage share each reserve contributes to the whole?", "table_markdown": "| Map Ref | Reserve | Land Parcel | Area (ha) | Facilities |\n|---------|--------------------------------|--------------------------------------|-----------|-----------------------------------------------------------------------------|\n| 1 | Golf Links Road Plantation | Golf Links Reserve 41907, 42285 & 42287 | 5.7271 | Mature pine plantation |\n| 2 | Grierson Plantation | Grierson Rural Sections 42280-42281 | 4.1325 | Beach Lagoon Walkway Loop |\n| 3 | Amberley Beach Coastal Reserve/Lookout | Coastal Reserve Rural Section 42017 | 3.4785 | Car park, walking tracks, rubbish bins, picnic tables |\n| 4 | Holton Road Reserve | Holton Rural Section 42016 | 0.5319 | Children's roundabout & rubbish bin |\n| 5 | Amberley Beach Reserve | Amberley Beach Rural Section 42018 | 1.7158 | Community meeting room, camping ground, tennis court, backboards, children's play area, toilet block, picnic tables |\n| 6 | South Crescent Camping Reserve | South Crescent Rural Section 42019 | 0.7843 | Toilet block and picnic tables |\n| 7 | Mimimoto Lagoon | Mimimoto Rural Sections 42282-42283 | 3.8748 | Seating |"}
{"item_id": "item_0208", "chart_task_type": "composition_snapshot", "query": "Can you chart the product breakdown for the 122 growing-stock walnut trees so I can see at a glance what share of the total each category represents?", "table_markdown": "| Products cut from tree | Number of trees |\n|------------------------|-----------------|\n| | Growing stock | Nongrowing stock |\n| Saw logs only | 61 | 13 |\n| Saw logs major,\\(^1\\) veneer | 35 | 2 |\n| Veneer logs only | 1 | 1 |\n| Veneer logs major, saw logs minor | 25 | 2 |\n| Total | 122 | 18 |"}
{"item_id": "item_0209", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of interrupted PPP projects by geographical area so I can see at a glance which region accounts for the largest share?", "table_markdown": "| | Number | | | Amount (Mn €) | | |\n|---|---|---|---|---|---|---|\n| Geograph- | | | | | | |\n| | Interrupted | Activated pro- | | Interrupted | Activated pro- | |\n| ical area | | | (a/b) | | | (a/b) |\n| | projects(a) | jects (b) | | projects(a) | jects (b) | |\n| North | 2,046 | 15,226 | 13.4% | 14,338 | 63,022 | 22.8% |\n| Centre | 891 | 6,286 | 14.2% | 6,608 | 23,295 | 28.4% |\n| South | 1,492 | 11,566 | 12.9% | 27,366 | 50,824 | 53.8% |\n| Subdivision | | | | | | |\n| | - | 86 | - | - | - | - |\n| not possible | | | | | | |\n| Total | 4,429 | 33,164 | 13.4% | 48,312 | 137,142 | 35.2% |"}
{"item_id": "item_0210", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the non-assignable area categories so I can see at a glance what share of the total space each component represents?", "table_markdown": "| Category | Area (SF) | Range (SF) |\n|-----------------------------------------------|-----------|------------|\n| MAIN ENTRANCE | 170 | 350 |\n| ENTRANCE LOBBY | 855 | 1,385 |\n| DATA CENTER/ TELECOMMUNICATIONS ROOM | 160 | 450 |\n| RESTROOMS | 760 | 1,385 |\n| CUSTODIAN'S WORK AREA | 140 | 275 |\n| GENERAL LIBRARY STORAGE | 1,110 | 1,850 |\n| STAFF ENTRANCE | 115 | 230 |\n| WALL THICKNESS | 2,770 | 3,500 |\n| CIRCULATION | 1,455 | 2,300 |\n| MECH | 1,155 | 1,850 |\n| **TOTAL** | **8,690** | **13,575** |"}
{"item_id": "item_0211", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total book sales by branch so I can see at a glance which departments account for the largest share?", "table_markdown": "| Sr. No. | Branch | Total Book Sold |\n|---------|----------|-----------------|\n| 1 | Mechanical | 23 |\n| 2 | Civil | 35 |\n| 3 | Computer | 14 |\n| 4 | Electrical| 18 |\n| 5 | Chemical | 43 |\n| 6 | EC | 30 |\n| 7 | First Year| 7 |\n| | Total | 170 |"}
{"item_id": "item_0212", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total infrastructure costs by specific item so I can see at a glance which expenses make up the largest shares?", "table_markdown": "| Category | Description | Reoccurring Costs |\n|-------------------|------------------------------------|-------------------|\n| Communication | Telephone | $4,368 |\n| Communication | Data Lines | $17,220 |\n| | **Subtotal** | **$21,588** |\n| Facilities | General Bldg. Maintenance | $5,000 |\n| Facilities | Furniture | $5,000 |\n| Facilities | Janitorial | $60,000 |\n| Facilities | Security Alarm | $570 |\n| Facilities | Pest Control | $1,800 |\n| Facilities | Brivo - Access Control | $7,176 |\n| Facilities | InformaCast – Emergency System | $1,908 |\n| Facilities | Security Guard | $70,000 |\n| Facilities | Office Space - Rent | $1,200,000 |\n| Facilities | Utilities | $80,000 |\n| | **Subtotal** | **$1,431,454** |\n| | **Grand Total** | **$1,453,042** |"}
{"item_id": "item_0213", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total income to show what share comes from the levy, additional funding, and carry over?", "table_markdown": "| Description | Amount | Notes |\n|------------------------------------|----------|-------|\n| Levy | £243,763 | 1 |\n| Additional Funding | £13,227 | 2 |\n| Carry over year 1 | £38,735 | |\n| **Total** | **£295,725** | |"}
{"item_id": "item_0214", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total assets as of 30.04.2024 by valuation level, so I can see at a glance what share each level represents?", "table_markdown": "| | 30.04.2024 | | |\n|---|---|---|---|\n| Valuation Technique | Assets (£000's) | Liabilities (£000's) | Assets (£000's) |\n| Level 1: Unadjusted quoted price in an active market for an identical instrument | 133,412 | - | 141,701 |\n| Level 2: Valuation techniques using observable inputs other than quoted prices within level 1 | 33,706 | ( 21) | 60,457 |\n| Level 3: Valuation techniques using unobservable inputs | 603 | - | 1,766 |\n| Total | 167,721 | ( 21) | 203,924 |"}
{"item_id": "item_0215", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total Vehicle Maintenance costs so I can see at a glance what share each item represents?", "table_markdown": "| Item | Vendor | Quantity | Unit Price | Amount |\n|-------------------------------------------|----------------------|----------|------------|----------|\n| Vehicle registration | TX DMV | 0.5 | 80.00 | 40.00 |\n| Vehicle inspection | Hopps | 0.5 | 25.50 | 12.75 |\n| Oil changes | City of Keene PW | 4 | 12.50 | 50.00 |\n| Misc Maintenance or Repairs | Chevy Dealership | 1 | 500.00 | 500.00 |\n| Fuel | City of Keene PW | 12 | 40.00 | 480.00 |"}
{"item_id": "item_0216", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of PhD results declared in 2017-18 by institution, so I can see at a glance what share each center contributed to the total?", "table_markdown": "| Discipline | BARC | IGCAR | RRCAT | VECC | SINP | NISER | IPR | IOP | HRI | TMC | IMSc | Total |\n|------------|------|-------|-------|------|------|-------|------|-----|------|------|------|-------|\n| Chem | 25 | 10 | | | | 10 | | 1 | | | | 46 |\n| Engg | 24 | 11 | 1 | 2 | | | | | | | | 38 |\n| Health | | | | | | | | | | | 2 | 2 |\n| Life | 4 | | | | | 5 | | | | | 13 | 22 |\n| Math | | | | | | | | | | | 9 | 13 |\n| Physical | 18 | 6 | 9 | 9 | 18 | 1 | 13 | 7 | 12 | 11 | | 104 |\n| **Total** | **71** | **27** | **10** | **11** | **18** | **16** | **13** | **8** | **16** | **15** | **20** | **225** |"}
{"item_id": "item_0217", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total required equipment cost by individual item so I can see at a glance which pieces account for the largest share of the budget?", "table_markdown": "| Equipment | Quantity | Objective | Costo |\n|------------------------------------------------|----------|-------------------------------------------------|--------|\n| Interactive panels | 6 | Tactic, Visual and Hearing Stimulation | $90,000|\n| Cushioned walls | 4 | Tactic Stimulation and Patient Safety | $10,000|\n| Fiber optic | 2 | Visual Stimulation and Fine Coordination | $18,000|\n| Bubble lamp with control box | 2 | Visual Stimulation | $32,000|\n| Hand texture set | 4 | Motor Stimulation | $15,000|\n| Mirrored sphere for effects | 1 | Visual Stimulation | $8,000 |\n| Proyection canyon with rotating base and effect balls | 1 canyon, 1 base, 6 balls | Visual Stimulation | $41,000 |\n| Aroma diffuser with aroma set | 1 | Smell Stimulation | $8,000 |\n| Cushioned couch | 4 | Patient Positioning | $9,000 |\n| Texture panel | 3 | Tactic Stimulation | $9,000 |\n| Black light with electronic regulator | 1 | Visual Stimulation | $18,000|\n| Luminous carpet | 4 | Visual and Tactic Stimulation | $13,000|\n| Ambiance audio equipment | 1 | Auditory Stimulation | $20,000|"}
{"item_id": "item_0218", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total estimated annual savings by each of the five audit reports so I can see at a glance which audits contributed the most?", "table_markdown": "| Audit Report | Audit Overpayment | Restitution Paid | Estimated Annual Savings After Provider Joined the Network |\n|---|---|---|---|\n| 2007-S-72, 2010-F-6 | $2,412,416 | $2,225,015 | $1,478,084 |\n| 2007-S-87, 2010-F-7 | $1,456,947 | $1,332,120 | $677,760 |\n| 2007-S-120, 2010-F-8 | $1,461,856 | $1,162,232 | $362,917 |\n| 2007-S-86, 2010-F-9 | $1,413,548 | $1,165,000 | $2,096,823 |\n| 2007-S-73, 2010-F-10 | $2,686,856 | $3,135,834 | $996,272 |\n| Total annual savings | | | $5,611,856 |\n| Total cumulative savings | | | |"}
{"item_id": "item_0219", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total FFY2014 Available Federal funding by cluster type so I can see the relative share of each category?", "table_markdown": "| | FFY2014 Available Federal | FFY2014 Programmed Federal | FFY2014 Balance Federal | #1 Santa Fe River Trail - Pedestrian Improvements | #2 Cerro Gordo Road - Pedestrian Improvements | #3 Santa Fe River Trail Connections and Improvements | #4 Santa Fe Rail Trail Segment 4 |\n|----------------------|---------------------------|---------------------------|------------------------|--------------------------------------------------|---------------------------------------------|---------------------------------------------------|---------------------------------|\n| Rural/Sm Cluster | $36,432 | $36,432 | $- | $- | $- | $- | $36,432 |\n| Lg Cluster/Sm UZA | $120,021 | $120,021 | $- | $- | $- | $60,213 | $- |\n| Flexible | $211,322 | $211,322 | $- | $- | $- | $211,322 | $- |\n| **Total** | **$367,775** | **$367,775** | **$-** | **$59,808** | **$-** | **$271,535** | **$36,432** |"}
{"item_id": "item_0220", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total annual financial benefits to show the relative share of savings from reducing coal, mazout, and CO2 emissions?", "table_markdown": "| Item | Unit price | Reduction of consumption/emissions | Financial benefits |\n|-------------------------------------------|------------|-----------------------------------|--------------------|\n| | PLN/Mg | Mg/year | PLN mln /year |\n| Benefits of reducing coal consumption | 300 | 45,019 | 13.51 |\n| Benefits of reducing mazout consumption | 1,800.00 | 4,371 | 7.87 |\n| Benefits of reducing CO$_2$ emissions | 103.63 | 103,707 | 10.75 |\n| TOTAL | | | 32.12 |"}
{"item_id": "item_0221", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total bid amount by cost component so I can see at a glance which items account for the largest share?", "table_markdown": "| Description | Quantity | Unit Price | Price |\n|------------------------------------------------------------------------------|-----------|------------|-----------|\n| Furnish and install approximately 250' LF of 10' black chain link fence with privacy slats, tapering to 4' H fence near the north side of Stop & Shop. | | | |\n| **Town of Huntington - General Requirements Contract ES-2022-04/O-E** | | | |\n| Item #704-10 - 10' High Vinyl Coated Chain Link Fence | 250.00 | $85.00 | $21,250.00|\n| Item #1000 - Additional Materials - Privacy Slats | 2,997.50 | $1.10 | $3,297.25 |\n| Item #1002-01 - Miscellaneous Labor - Laborer | 26.00 | $115.00 | $2,990.00 |\n| Item #1002-02 - Miscellaneous Labor - Iron Worker | 26.00 | $135.00 | $3,510.00 |\n| **Subtotal:** | | | $31,047.25|\n| **Total:** | | | $31,047.25|"}
{"item_id": "item_0222", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of revenue contributions by conference, excluding the union total, so I can see at a glance which regions account for the largest shares?", "table_markdown": "| | Rec. | Per cent |\n|----------------|-------|----------|\n| Tasmania | £570 | 142½ |\n| Victoria | 2,911 | 116½ |\n| South New Zealand | 1,262 | 105 |\n| W. Australia | 787 | 98 |\n| South N. S. Wales | 2,421 | 97 |\n| North New Zealand | 3,125 | 89 |\n| North N. S. Wales | 738 | 82 |\n| S. Australia | 755 | 75½ |\n| Queensland | 977 | 65 |\n| Total for Union | £13,546 | 97 |"}
{"item_id": "item_0223", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of NAS cases by region so I can see at a glance what share of the total each county contributes?", "table_markdown": "| Maternal County of Residence (By Health Department Region) | # Cases | Rate per 1,000 births |\n|---|---|---|\n| Davidson | 63 | 6.3 |\n| East | 218 | 27.6 |\n| Hamilton | 25 | 5.8 |\n| Jackson/Madison | 8 | 6.6 |\n| Knox | 103 | 19.6 |\n| Mid-Cumberland | 100 | 6.3 |\n| North East | 163 | 48.6 |\n| Shelby | 31 | 2.3 |\n| South Central | 63 | 13.2 |\n| South East | 30 | 8.3 |\n| Sullivan | 78 | 52.6 |\n| Upper Cumberland | 101 | 26.9 |\n| West | 31 | 5.2 |\n| Unknown | 1 | -- |\n| Total | 1015 | 12.6 |"}
{"item_id": "item_0224", "chart_task_type": "growth_speed", "query": "Can you chart the growth for the PreK-12 education categories so I can see at a glance which one expanded the fastest?", "table_markdown": "| | FY 2016-17 Appropriations | FY 2017-18 Appropriations | Change | Percent Change |\n|--------------------------------|---------------------------|---------------------------|------------|----------------|\n| Office of Early Learning | $1,047,596,261 | $1,061,594,741 | $13,998,480| 1.34% |\n| K12 - FEFP¹ | 20,186,770,414 | 20,644,233,449 | 457,463,035| 2.27% |\n| K12 - Non-FEFP/Education Media | 291,009,222 | 611,088,398 | 320,079,176| 109.99% |\n| State Board of Education | 241,667,470 | 254,844,272 | 13,176,802 | 5.45% |\n| Federal Programs | 1,656,703,052 | 1,688,629,022 | 31,925,970 | 1.93% |\n| Total PreK-12 Education | 23,423,746,419 | 24,260,389,882 | 836,643,463| 3.57% |"}
{"item_id": "item_0225", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of total expenses by category so I can see the relative share of distributions, program costs, management, and fundraising?", "table_markdown": "| EXPENSES | | | |\n|------------------------------|--------------|------------------|-------|\n| Program | | | |\n| Distributions to Charities | 299,115 | | 299,115 |\n| Other Program Expenses | 87,960 | | 87,960 |\n| **Total Program** | **387,075** | | **387,075** |\n| Supporting Services | | | |\n| Management & General | 33,390 | | 33,390 |\n| Fundraising | 68,408 | | 68,408 |\n| **Total Supporting Services** | **101,798** | | **101,798** |\n| **Total Expenses** | **488,873** | | **488,873** |"}
{"item_id": "item_0226", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of student AIS/2021/334's total score by subject, so I can see at a glance which subjects contribute the most to their overall performance?", "table_markdown": "| ID No | Student Name | Mathematics | English | Quantitative Reasoning | Verbal reasoning | Social studies | Civic Education | Security | BASIC SCIENCE AND TECH | P.H.E | C.C.A | Home Economics | Total | Average | Remark | Position |\n|-----------|--------------|-------------|---------|------------------------|-----------------|---------------|----------------|----------|------------------------|-------|-------|----------------|-------|---------|--------|----------|\n| AIS/2021/334 | | 93.0 | 78.0 | 91.0 | 80.0 | 74.0 | 74.0 | 76.0 | 87.0 | 97.0 | 82.0 | 91.0 | 923.0 | 84 | Excellent | 1st |\n| AIS/2021/335 | | 72.0 | 64.0 | 87.0 | 66.0 | 64.0 | 64.0 | 62.0 | 83.0 | 82.0 | 72.0 | 80.0 | 796.0 | 72 | Credit | 4th |\n| AIS/2021/338 | | 53.0 | 65.0 | 73.0 | 66.0 | 52.0 | 52.0 | 52.0 | 65.0 | 72.0 | 74.0 | 61.0 | 685.0 | 62 | Credit | 10th |\n| AIS/2021/339 | | 76.0 | 64.0 | 76.0 | 70.0 | 58.0 | 56.0 | 56.0 | 58.0 | 67.0 | 67.0 | 72.0 | 720.0 | 65 | Credit | 8th |\n| AIS/2021/340 | | 77.0 | 69.0 | 78.0 | 77.0 | 68.0 | 71.0 | 68.0 | 80.0 | 85.0 | 84.0 | 83.0 | 840.0 | 76 | Excellent | 3rd |\n| AIS/2021/341 | | 63.0 | 63.0 | 88.0 | 79.0 | 58.0 | 58.0 | 58.0 | 77.0 | 81.0 | 71.0 | 82.0 | 778.0 | 71 | Credit | 6th |\n| AIS/2021/343 | | 71.0 | 65.0 | 87.0 | 86.0 | 68.0 | 72.0 | 70.0 | 82.0 | 90.0 | 82.0 | 92.0 | 865.0 | 79 | Excellent | 2nd |\n| AIS/2021/344 | | 55.0 | 58.0 | 52.0 | 52.0 | 58.0 | 56.0 | 58.0 | 59.0 | 61.0 | 66.0 | 68.0 | 643.0 | 58 | Credit | 13th |\n| AIS/2021/347 | | 57.0 | 52.0 | 66.0 | 52.0 | 67.0 | 64.0 | 66.0 | 60.0 | 65.0 | 61.0 | 73.0 | 683.0 | 62 | Credit | 12th |\n| AIS/2021/350 | | 60.0 | 67.0 | 93.0 | 70.0 | 61.0 | 58.0 | 60.0 | 61.0 | 57.0 | 74.0 | 77.0 | 738.0 | 67 | Credit | 7th |\n| AIS/2021/353 | | 74.0 | 66.0 | 88.0 | 70.0 | 52.0 | 50.0 | 50.0 | 53.0 | 68.0 | 65.0 | 72.0 | 708.0 | 64 | Credit | 9th |\n| AIS/2021/381 | | 55.0 | 56.0 | 73.0 | 64.0 | 50.0 | 48.0 | 64.0 | 63.0 | 70.0 | 75.0 | 67.0 | 685.0 | 62 | Credit | 10th |\n| AIS/2018/189 | | 79.0 | 57.0 | 72.0 | 81.0 | 67.0 | 67.0 | 62.0 | 75.0 | 78.0 | 66.0 | 78.0 | 782.0 | 71 | Credit | 17th5th |"}
{"item_id": "item_0227", "chart_task_type": "growth_speed", "query": "Can you chart the growth in net absorption for these markets from the pre-pandemic average to 2021? I want it to be obvious which one expanded the fastest.", "table_markdown": "| Market | Avg. 2015-2019 | 2021 |\n|--------|---------------|------|\n| AUS | 2,000 | 6,000|\n| SAY | 3,000 | 7,000|\n| CHR | 4,000 | 7,000|\n| SAV | 4,000 | 8,000|\n| NAH | 4,000 | 8,000|\n| GRV | 5,000 | 9,000|\n| PHO | 9,000 | 16,000|"}
{"item_id": "item_0228", "chart_task_type": "growth_speed", "query": "Can you chart the growth in employee numbers by age group from 2021 to 2022? I want it to be obvious which group grew the fastest or declined the least.", "table_markdown": "| Age Group | 2021 | 2022 |\n|--------------|------|------|\n| Under 30 years| 299 | 297 |\n| Over 50 years | 1,140| 1,096|\n| 30–49 years | 1,814| 1,692|"}
{"item_id": "item_0229", "chart_task_type": "growth_speed", "query": "Can you chart the growth in net returns for the crops grown with Eucalyptus between 2014-15 and 2015-16? I want it to be obvious which crop saw the fastest growth.", "table_markdown": "| Treatments | Net Return (Profit) in Rs. /ha | | | |\n|---|---|---|---|---|\n| | 2014-15 | | 2015-16 | |\n| | With Eucalyptus | Control | With Eucalyptus | Control |\n| | tereticornis | (without tree) | tereticornis | (without tree) |\n| Mustard | 6067 | 8233 | 6889 | 21820 |\n| Wheat | 10925 | 15550 | 4545 | 18068 |\n| Berseem | 8693 | 10318 | 7086 | 19239 |\n| Oat | 26535 | 29785 | 14580 | 34587 |"}
{"item_id": "item_0230", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the 2014-15 Recurring EIA Base budget so I can see at a glance what share each line item represents?", "table_markdown": "| EIA Budget Recommendations | | Recurring EIA Base |\n|---|---|---|\n| | | 2014-15 |\n| | EIA Recurring Base Appropriation | $534,133,953 |\n| | Technology | |\n| K-12 Technology Initiative | | $10,171,826 |\n| | Early Literacy | |\n| Full-Day 4K (SCDE) | | $34,324,437 |\n| | Assessment | $27,261,400 |\n| | Science and Industry | |\n| Modernize Vocational Equipment | | $6,682,406 |\n| Science PLUS | | $503,406 |\n| | Classroom Support | |\n| Teacher Supplies | | $13,596,000 |\n| Instructional Materials | | $20,922,839 |\n| TOTAL: | | $647,596,267 |\n| EIA Non-Recurring | | |\n| Instructional Materials | | |\n| Reach Out and Read | | |\n| Total Non-Recurring: | | |"}
{"item_id": "item_0231", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each individual bank account so I can see at a glance which one expanded the fastest?", "table_markdown": "| Bank Accounts | 7/1/2014 Balance | 6/30/2015 Balance | INCREASE or DECREASE |\n|-------------------------------|------------------|-------------------|----------------------|\n| Checking | $22,486.85 | $49,928.93 | $27,442.08 |\n| EMERGENCY FUND | $21,155.94 | $21,166.53 | $10.59 |\n| MORELAND 2 | $88,997.98 | $88,402.01 | $595.97 |\n| TOTAL Bank Accounts | $132,640.77 | $159,497.47 | $26,856.70 |"}
{"item_id": "item_0232", "chart_task_type": "composition_snapshot", "query": "Can you chart the breakdown of the total estimated project cost by item, so I can see at a glance what share each component represents?", "table_markdown": "| S/N | ITEM | Estimated Cost (₦) |\n|-----|----------------------------------------------------------------------|--------------------|\n| 1 | Customized Office furniture (Provost, Bursary, staff, c.t.c.) | 5,000,000 |\n| 2 | Basic science Laboratory Equipment’s | 20,000,000 |\n| 3 | Library books and equipment’s | 50,000,000 |\n| 4 | ICT Center equipment’s | 15,000,000 |\n| 5 | 100KVA Generator | 5,000,000 |\n| 6 | Electrification/ Wiring of the entire college | 20,000,000 |\n| 7 | Class rooms furniture | 10,000,000 |\n| 8 | Teaching Aids / Equipment’s for demonstration | 30,000,000 |\n| 9 | Completion and furnishing of chapel auditorium | 25,000,000 |\n| 10 | Completion and furnishing of male/females student’s hostels | 40,000,000 |\n| 11 | Completion and furnishing of school Clinic (which is at the foundation level) | 40,000,000 |\n| 12 | Construction and furnishing of a 250 seating capacity auditorium | 100,000,000 |\n| 13 | Sport facility | 10,000,000 |\n| 14 | Furnishing of the Matrons house | 5,000,000 |\n| 15 | Fencing of the college | 50,000,000 |\n| 16 | Toyota Coaster Bus (32 Seater) | 25,000,000 |\n| | **TOTAL** | **495,000,000** |"}
{"item_id": "item_0233", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each revenue source from 2014-2015 to 2016-2017 so I can see at a glance which one expanded the fastest?", "table_markdown": "| Source | 2014-2015 | 2015-2016 | 2016-2017 |\n|---------------------------------------------|-----------|-----------|-----------|\n| **1— Operating Transfers In** | | | |\n| Taxes | 2,391,487.00 | 2,389,720.00 | 2,481,511.00 |\n| Non-Capital Sales | 1,697.65 | 1,780.00 | 1,550.00 |\n| School Activity Income | 10,079.00 | 9,099.75 | 9,000.00 |\n| Investment Earnings | 1,255.39 | 2,932.29 | 2,500.00 |\n| Other Revenue from Local Sources | 26,285.70 | 25,839.44 | 23,700.00 |\n| **Total Local** | 2,430,918.74 | 2,432,482.41 | 2,481,281.00 |\n| **2— Total Revenue from Local Sources** | | | |\n| Non-Open Enrollment Tuition | 0.00 | 0.00 | 0.00 |\n| Open Enrollment Tuition | 699,471.00 | 701,443.50 | 820,005.00 |\n| **3— Total Interdistrict Payments in Wisconsin** | | | |\n| Transit of Aids | 2,803.00 | 2,084.00 | 2,000.00 |\n| Payments for Services from CESAs | 0.00 | 0.00 | 0.00 |\n| **Total Intermediate Sources** | 2,803.00 | 2,084.00 | 2,000.00 |\n| Transportation State Aid | 15,304.08 | 14,955.45 | 14,950.00 |\n| Library (Common School Fund) | 17,871.00 | 20,300.00 | 18,000.00 |\n| Other than Categorical Aid | 72,200.00 | 72,340.00 | -120,580.00|\n| Equalization Aid | 3,033,028.00 | 3,039,984.00 | 3,064,454.00 |\n| Special Aid | 0.00 | 0.00 | 288.00 |\n| Special Project Grants | 10,320.00 | 7,862.00 | 7,880.00 |\n| State Revenue from State Sources | 40,082.81 | 38,792.58 | 39,000.00 |\n| State Aid for Exempt Computers | 2,982.00 | 1,442.00 | 888.00 |\n| Sparsity Aid | 0.00 | 149,940.00 | 145,280.00 |\n| **Total Revenue from State Sources** | 3,189,348.29 | 3,346,673.88 | 3,430,938.00 |\n| Federal Special Projects Aid Through DPI | 15,762.00 | 14,510.00 | 15,385.00 |\n| ESEA | 78,440.72 | 61,560.00 | 53,708.00 |\n| Federal Aid Through State Agencies other than ESEA | 2,297.00 | 3,373.04 | 2,000.00 |\n| Other Revenue from Federal Sources | 44,277.00 | 44,238.00 | 44,000.00 |\n| **Federal Sources** | 140,785.72 | 123,988.04 | 115,088.00 |\n| Compensation for Sale or Loss of Fixed Assets | 92,000.00 | 92,000.00 | 92,000.00 |\n| Total Financing Sources | 103,219.87 | 82,448.83 | 82,000.00 |\n| Refund of Disbursement | 0.00 | 0.00 | 0.00 |\n| Other Miscellaneous Revenues | 8,045.19 | 8,504.40 | 7,400.00 |\n| **Total Miscellaneous Revenues** | 4,076.68 | 7,673.77 | 4,000.00 |\n| **TOTAL FUND 10 REVENUES** | 6,549,288.49 | 6,715,958.64 | 6,809,687.00 |"}
{"item_id": "item_0234", "chart_task_type": "growth_speed", "query": "Can you chart the growth of these four trading channels from Dec-01 to Jun-03 so I can see at a glance which one expanded the fastest?", "table_markdown": "| | Dec-01 | Jun-02 | Dec-02 | Jun-03 |\n|---|---|---|---|---|\n| ATS | 28 | 39 | 48 | 51 |\n| anonymous ATS | 16 | 21 | 35 | 31 |\n| voice-brokers | 43 | 67 | 65 | 61 |\n| tri-party repo | 22 | 29 | 32 | 34 |"}
{"item_id": "item_0235", "chart_task_type": "growth_speed", "query": "Can you chart the price growth for each region from the start of the data to 2010? I want it to be obvious which area saw the fastest increase.", "table_markdown": "| Year | 2005 | 2006 | 2007 | 2008 | 2009 | 201028 |\n|---|---|---|---|---|---|---|\n| MONTENEGRO | 813 | 1.087 | 1.332 | 1.53 | 1.25 | 1.272 |\n| PODGORICA | 798 | 868 | 1.072 | 1.355 | 1.168 | 1.137 |\n| BAR | 798 | 986 | 1.486 | 1.188 | 1.516 | 1.479 |\n| BUDVA | 932 | 1.377 | 1.527 | 2.165 | 1.334 | 1.714 |\n| NIKŠIĆ | - | - | 907 | 1.003 | 802 | 752 |\n| OTHERS | 659 | 2.082 | 2.769 | 2.806 | 1.537 | 1.296 |"}
{"item_id": "item_0236", "chart_task_type": "growth_speed", "query": "Can you chart the order growth for each business segment between the nine months ended October 31, 2014, and 2015, so I can see at a glance which one expanded the fastest?", "table_markdown": "| | Nine months ended October 31, 2014 | | Nine months ended October 31, 2015 | | |\n|---|---|---|---|---|---|\n| | Orders | Accumulated Orders | Orders | Accumulated Orders | Orders |\n| Custom detached houses | 291,247 | 225,431 | 291,737 | 222,526 | 388,648 |\n| Rental housing | 303,208 | 331,167 | 321,099 | 362,117 | 408,525 |\n| Remodeling | 94,496 | 30,703 | 102,584 | 30,892 | 125,647 |\n| Real estate management fees | 319,890 | - | 335,692 | - | 428,227 |\n| Houses for sale | 88,675 | 42,197 | 100,373 | 39,611 | 122,260 |\n| Condominiums | 50,093 | 69,528 | 55,464 | 50,828 | 65,771 |\n| Urban redevelopment | 164,862 | 129,170 | 76,485 | - | 176,114 |\n| Overseas business | 58,292 | 63,258 | 100,777 | 116,618 | 94,539 |\n| Other Businesses | 58,026 | 46,364 | 55,657 | 45,851 | 82,884 |\n| Total | 1,428,793 | 937,821 | 1,439,873 | 868,445 | 1,892,619 |"}
{"item_id": "item_0237", "chart_task_type": "growth_speed", "query": "Can you chart the growth in balances for each fund between the two dates? I want it to be obvious which fund grew the fastest, rather than just seeing the final amounts.", "table_markdown": "| Fund | Balance as of 12/29/21 | Balance as of 1/12/21 |\n|--------------------|------------------------|-----------------------|\n| General | $16,368,858.91 | $17,854,763.53 |\n| Ambulance | $5,661,112.50 | $6,154,685.22 |\n| Dispatch | $687,963.36 | $779,084.40 |\n| Pension | $635,203.36 | $823,804.20 |\n| Capital Projects | | |\n| 2019 | $7,506,760.75 | $7,493,996.59 |\n| Debt Services | $2,415,154.07 | $2,799,526.43 |\n| HRA | $2,653.29 | $2,653.31 |\n| FSA | $4,957.92 | $6,106.08 |"}
{"item_id": "item_0238", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each contingent liability category from 2012 to 2013? I want it to be obvious which one expanded the fastest.", "table_markdown": "| Particulars | As on 31.03.2013 | As on 31.03.2012 |\n|--------------------------------------------------|------------------|------------------|\n| Outstanding amount of Bank Guarantee | 16.44 | 296.96 |\n| Corporate Guarantees against Credit Facilities extended to the Subsidiary Company | 6102.89 | 6832.00 |\n| Claims against the Company not acknowledged as Debts | 2.56 | 2.56 |\n| Others | 1079.56 | 503.89 |"}
{"item_id": "item_0239", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each individual asset category from mid-2011 to mid-2012? I want it to be obvious which one expanded the fastest.", "table_markdown": "| Notes | 30 June 2012 (Unaudited) SR’000 | 31 December 2011 (Audited) SR’000 | 30 June 2011 (Unaudited) SR’000 |\n|-------|---------------------------------|----------------------------------|---------------------------------|\n| **ASSETS** | | | |\n| Cash and balances with SAMA | 5,649,161 | 4,379,043 | 3,542,210 |\n| Due from banks and other financial institutions | 4,036,416 | 4,331,024 | 4,293,412 |\n| Investments | 8,841,209 | 5,396,915 | 5,299,471 |\n| Loans and advances, net | 27,231,791 | 23,307,451 | 22,034,472 |\n| Other real estate, net | 674,778 | 680,778 | 678,450 |\n| Property and equipment, net | 456,622 | 446,829 | 451,565 |\n| Other assets | 377,140 | 356,210 | 442,215 |\n| **Total assets** | **47,267,117** | **38,898,250** | **36,741,795** |"}
{"item_id": "item_0240", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each revenue source from 2013 to 2014 so I can see at a glance which one expanded the fastest?", "table_markdown": "| INDICATOR | 2014 | 2013 |\n|------------------------------------------------|---------------|---------------|\n| Total Revenue | $5,906,507 | $5,743,166 |\n| State & Other Grants | $1,679,475 (28%) | $1,868,338 (32.5%) |\n| Federal Grants | $1,203,457 (20%) | $1,078,482 (19%) |\n| Patient Revenue | $2,698,048 (46%) | $2,465,411 (43%) |\n| Cash Donations | $30,123 (1%) | $17,646 (0.25%) |\n| Contributed Goods & Services | $263,713 (4%) | $297,533 (5%) |\n| Interest & Other Revenue | $31,691 (1%) | $15,706 (0.25%) |"}
{"item_id": "item_0241", "chart_task_type": "growth_speed", "query": "Can you chart the growth of T&E replacement costs for the three non-public school scenarios so I can see at a glance which one expanded the fastest?", "table_markdown": "| Fiscal Year | Number of ESAs | If 25% Currently in Non-Public School | If 50% Currently in Non-Public School | If 75% Currently in Non-Public School |\n|-------------|----------------|----------------------------------------|----------------------------------------|----------------------------------------|\n| 2019 | 4,366 | $7.0 | $4.7 | $2.3 |\n| 2020 | 8,791 | $14.2 | $9.5 | $4.7 |\n| 2021 | 13,247 | $21.4 | $14.3 | $7.1 |\n| 2022 | 17,734 | $28.6 | $19.1 | $9.5 |"}
{"item_id": "item_0242", "chart_task_type": "growth_speed", "query": "Can you chart the sales growth for each product line from Q2 2009 to Q2 2010? I want it to be obvious which one expanded the fastest.", "table_markdown": "| | Sales Revenue | | Q2 2010 | | Q2 2009 | | Q2 2010/Q2 2009 | | HY 2010 | | S1 2010/HY 2009 |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| | (unaudited) | | M€ | | M€ | | Growth** | | M€ | | Growth** |\n| Document Process Automation* | | 6.47 | | 4.52 | | +37% | | 12.06 | | +27% | |\n| Fax Servers | | 1.35 | | 1.42 | | -12% | | 2.65 | | -5% | |\n| Host Access | | 0.71 | | 0.54 | | +25% | | 1.23 | | +5% | |\n| Total | | 8.53 | | 6.48 | | +25% | | 15.94 | | +18% | |"}
{"item_id": "item_0243", "chart_task_type": "growth_speed", "query": "Can you chart the growth for each individual crime category from 2015 to 2016? I want it to be obvious which one grew the fastest or declined the least.", "table_markdown": "| crime | 2015 | 2016 |\n|---|---|---|\n| rape | 5 | 3 |\n| robbery | 12 | 16 |\n| aggravated assault | 22 | 16 |\n| burglary | 28 | 21 |\n| theft / larceny | 84 | 57 |\n| motor vehicle theft | 10 | 11 |\n| total | 161 | 124 |\n| total violent crime | 39 | 35 |"}
{"item_id": "item_0244", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each individual business unit from Q3 2009 to Q3 2010? I want it to be obvious which one expanded the fastest.", "table_markdown": "| Business | 3rd Quarter 2010 | 3rd Quarter 2009 | Total difference (% ) | Price-Quantity effect (%) |\n|---|---|---|---|---|\n| Lamps Electronic Devices Vacuum Systems and Thermal Insulation Semiconductors | 3,297 6,770 4,052 7,707 | 2,628 5,096 2,622 3,199 | 25.5% 32.8% 54.5% 140.9% | 18.0% 24.3% 41.0% 117.6% |\n| Subtotal Industrial Applications | 21,826 | 13,545 | 61.1% | 48.3% |\n| Subtotal Shape Memory Alloys | 11,292 | 6,417 | 76.0% | 60.0% |\n| Liquid Crystal Displays Cathode Ray Tubes | 1,241 865 | 7,968 1,124 | -84.4% -23.0% | -86.2% -30.7% |\n| Subtotal Information Displays | 2,106 | 9,092 | -76.8% | -79.3% |\n| Subtotal Advanced Materials | 102 | 110 | -7.3% | -12.5% |\n| Total Net Sales | 35,326 | 29,164 | 21.1% | 10.8% |"}
{"item_id": "item_0245", "chart_task_type": "growth_speed", "query": "Can you chart the cotton production growth for these provinces from 1972 to 1973? I want it to be obvious at a glance which one grew the fastest, not just which had the highest total.", "table_markdown": "| No. | Province | 1972/MT | 1973/MT |\n|-----|--------------|---------|---------|\n| 1. | Kunduz | 27,246 | 56,000 |\n| 2. | Mazaresharif | 15,000 | 25,000 |\n| 3. | Herat | 5,150 | 10,000 |\n| 4. | Helmand | 3,200 | 11,000 |"}
{"item_id": "item_0246", "chart_task_type": "growth_speed", "query": "Can you chart the population growth for each of the 12 counties in the Deep East Texas Region between 2010 and 2013? I want it to be obvious which county grew the fastest.", "table_markdown": "| 2010 to 2013 Population Change | | DETCOG&EDD Region | | | | | | |\n|---|---|---|---|---|---|---|---|---|\n| | | 2010 | | 2013 | | # Change | | % |\n| | | | 2010 | | 2013 | | # Change | Change |\n| | Angelina County | | 86,771 | | 87,441 | | 670 | 0.8% |\n| | Houston County | | 23,732 | | 22,911 | | -821 | -3.5% |\n| | Jasper County | | 35,710 | | 35,649 | | -61 | -0.2% |\n| | Nacogdoches County | | 64,524 | | 65,330 | | 806 | 1.2% |\n| | Newton County | | 14,445 | | 14,140 | | -305 | -2.1% |\n| | Polk County | | 45,413 | | 45,790 | | 377 | 0.8% |\n| | Sabine County | | 10,834 | | 10,361 | | -473 | -4.4% |\n| | San Augustine County | | 8,865 | | 8,769 | | -96 | -1.1% |\n| | San Jacinto County | | 26,384 | | 26,856 | | 472 | 1.8% |\n| | Shelby County | | 25,448 | | 25,972 | | 524 | 2.1% |\n| | Trinity County | | 14,585 | | 14,393 | | -192 | 1.3% |\n| | Tyler County | | 21,766 | | 21,464 | | -302 | -1.4% |\n| | DETCOG&EDD Totals: | | 378,477 | | 379,076 | | 599 | 0.2% |\n| | Texas | | 25,145,561 | | 26,448,193 | | 1,302,632 | 5.2% |\n| | United States | | 308,745,538 | | 316,128,839 | | 7,383,301 | 2.4% |"}
{"item_id": "item_0247", "chart_task_type": "growth_speed", "query": "Can you chart the growth in food-grain stocks from February 2019 to February 2020 so I can see at a glance which category expanded the fastest?", "table_markdown": "| Crops | February 1, 2019 | February 1, 2020 |\n|---|---|---|\n| 1. Rice | 22.8 | 27.5 |\n| 2. Unmilled Paddy# | 26.7 | 25.9 |\n| 3. Converted Unmilled Paddy in terms of Rice | 17.9 | 17.4 |\n| 4. Wheat | 23.9 | 30.4 |"}
{"item_id": "item_0248", "chart_task_type": "growth_speed", "query": "Can you chart the growth in the 2022 Standard Deduction for each filing status? I want it to be obvious which category grew the fastest.", "table_markdown": "| Filing Status | 2022 | 2021 |\n|----------------------------------------------------|-------|-------|\n| Married, filing jointly and qualifying widow(er)s | $25,900 | $25,100 |\n| Single or married, filing separately | $12,950 | $12,550 |\n| Head of household | $19,400 | $18,800 |\n| Dependent filing own tax return | $1,150* | $1,100* |"}
{"item_id": "item_0249", "chart_task_type": "growth_speed", "query": "Can you chart the growth of net profit and retained earnings from 2017 to 2018 so I can see at a glance which metric expanded the fastest?", "table_markdown": "| | 2018 | 2017 |\n|--------------------------------|----------|----------|\n| Net profit | $185,527 | $45,223 |\n| Retained earnings at the beginning of the financial year | $109,018 | $63,795 |\n| Retained earnings at the end of the financial year | $294,545 | $109,018 |"}
{"item_id": "item_0250", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each performance component from FY19/20 to FY 20/21 so I can see at a glance which one improved the fastest?", "table_markdown": "| | Performance & Quality Management Key Component | FY19/20 | FY 20/21 % | |\n|---|---|---|---|---|\n| | | % | | |\n| 1. | Access to Care | 96% | | 100% |\n| 2. | Timeliness of Services | 67% | | 88% |\n| 3. | Quality of Care | 57% | | 99% |\n| 4. | Beneficiary Progress/Outcomes | 73% | | 97% |\n| 5. | Structure and Operations | 80% | | 99% |\n| 6. | Information System (IS) Capability | n/a* | | n/a* |\n| | OVERALL TOTAL | 83% | | 96% |"}
{"item_id": "item_0251", "chart_task_type": "growth_speed", "query": "Can you chart the growth of portfolio yields for each scenario from 1982 to 1985? I want it to be obvious which scenario expanded the fastest, rather than just which one ended up highest.", "table_markdown": "| Scenario | 1982 | 1983 | 1984 | 1985 |\n|------------|------|------|------|------|\n| Pessimistic| 9.8 | 10.6 | 11.4 | 12.2 |\n| Optimistic | 9.4 | 9.8 | 10.2 | 10.6 |\n| Cyclic | 9.75 | 10.5 | 11.25| 12.0 |"}
{"item_id": "item_0252", "chart_task_type": "growth_speed", "query": "Can you chart the growth of the four base student categories from FY15 to FY17 so I can see at a glance which one expanded the fastest?", "table_markdown": "| LOWELL | | | | | |\n|---|---|---|---|---|---|\n| Student FTEs | FY15 | FY16 | FY17 | # Change | % Change |\n| UG - In State | 9,302 | 9,585 | 9,892 | | |\n| UG - Out State | 1,319 | 1,379 | 1,423 | | |\n| UG Total | 10,621 | 10,964 | 11,315 | 351 | 3.2% |\n| G - In State | 1,605 | 1,592 | 1,643 | | |\n| G - Out State | 1,144 | 1,064 | 1,098 | | |\n| G Total | 2,749 | 2,656 | 2,741 | 85 | 3.2% |\n| TOTAL ALL | 13,370 | 13,620 | 14,056 | 436 | 3.2% |"}
{"item_id": "item_0253", "chart_task_type": "growth_speed", "query": "Can you chart the growth of the EBL e-book metrics from FY2009-2010 to FY2010-2011? I want it to be obvious which metric expanded the fastest.", "table_markdown": "| EBL E-Books | FY2009-2010 | FY2010-2011 |\n|-------------|------------|------------|\n| E-books owned | 445 | 550 |\n| Searches | 17,305 | 17,233 |\n| Browse online | 1,948 | 3,345 |\n| Downloads | 280 | 363 |\n| Read online | 159 | 273 |"}
{"item_id": "item_0254", "chart_task_type": "growth_speed", "query": "Can you chart the growth in average prices from 2019 Winter to 2020 Summer so I can see at a glance which region grew the fastest?", "table_markdown": "| Region | | 2019 Winter | | 2019 Shoulder 2 | | 2020 Summer |\n|---|---|---|---|---|---|---|\n| NSW | $58.03 | | $56.93 | | $59.57 | $59.57 |\n| QLD | $53.25 | | $52.49 | | $71.79 | |\n| SA | $71.61 | | $60.27 | | $79.52 | |\n| TAS | $51.83 | | $60.69 | | $71.38 | |"}
{"item_id": "item_0255", "chart_task_type": "growth_speed", "query": "Can you chart the growth speed of R&D costs, capital investment, and depreciation from FY 2013 to FY 2016? I want it to be obvious which category expanded the fastest.", "table_markdown": "| | | FY 2013 | | FY 2014 | | FY 2015 | | FY 2016 | Year-on-year |\n|---|---|---|---|---|---|---|---|---|---|\n| | | | | | | | | (Estimate) | comparison |\n| R & D costs | 1,840 | | 1,945 | | 2,456 | | 2,766 | | |\n| Capital investment | 693 | | 839 | | 1,226 | | 4,181 | | |\n| Depreciation | 1,040 | | 1,114 | | 1,253 | | 1,700 | | |"}
{"item_id": "item_0256", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each household type from 2016 to 2041 so I can see at a glance which one expanded the fastest?", "table_markdown": "| | Households 2016 | Households 2041 | % Households 2016 | % Households 2041 | Change 2016-2041 Number | Change 2016-2041 % |\n|------------------------|-----------------|-----------------|-------------------|-------------------|-------------------------|-------------------|\n| Single | 27,206 | 33,994 | 31% | 33% | 6,788 | 25% |\n| Households with one dependent child | 11,529 | 12,553 | 13% | 12% | 1,024 | 9% |\n| Households with two dependent children | 10,383 | 11,014 | 12% | 11% | 631 | 6% |\n| Households with three or more dependent children | 3,705 | 3,748 | 4% | 4% | 43 | 1% |\n| Other households with two or more adults | 35,846 | 41,395 | 40% | 40% | 5,549 | 15% |\n| Total | 88,669 | 102,704 | | | 14,035 | 16% |"}
{"item_id": "item_0257", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each education program type from 2016 to 2017 so I can see at a glance which one expanded the fastest?", "table_markdown": "| | Totals | |\n|---|---|---|\n| | Prior Year 2016 | Budget Year 2017 |\n| Schoolwide | 3,495,121 | 4,157,943 |\n| Classroom Site Projects | 133,011 | 155,952 |\n| Instructional Improvement | 18,100 | 19,697 |\n| ELL Structured English Immersion | 0 | 0 |\n| ELL Compensatory Instruction | 0 | 0 |\n| Federal Projects | 409,021 | 376,283 |\n| State Projects | 0 | 0 |\n| Capital Acquisitions | 50,000 | 50,000 |\n| Total Expenses | 4,105,253 | 4,759,875 |"}
{"item_id": "item_0258", "chart_task_type": "growth_speed", "query": "Can you chart the growth of Casinos, Ticket Lottery, and Video Lottery from the 2019-20 estimate to the 2020-21 estimate? I want it to be obvious which source expanded the fastest.", "table_markdown": "| | Estimate 2019-20 ($'000) | Forecast 2019-20 ($'000) | Estimate 2020-21 ($'000) |\n|--------------------------|---------------------------|---------------------------|---------------------------|\n| **Sales** | | | |\n| Casinos | $85,400 | $88,600 | $89,300 |\n| Ticket Lottery | 249,200 | 236,800 | 255,700 |\n| Video Lottery | 132,300 | 131,200 | 130,500 |\n| **Total Sales** | 466,900 | 456,600 | 475,500 |\n| **Cost of Sales** | 321,500 | 314,000 | 325,800 |\n| **Gross Profit** | 145,400 | 142,600 | 149,700 |\n| **Expenses** | | | |\n| Responsible Gambling Programs | 7,500 | 7,300 | 7,500 |\n| Commitments to Community Programs | 9,900 | 9,900 | 11,200 |\n| **Total Expenses** | 17,400 | 17,200 | 18,700 |\n| **Net Income** | 128,000 | 125,400 | 131,000 |\n| Casino Win Tax | 15,100 | 15,800 | 15,900 |\n| **Payment to Province** | $143,100 | $141,200 | $146,900 |"}
{"item_id": "item_0259", "chart_task_type": "growth_speed", "query": "Can you chart the growth speed of each business segment from 2017 to 2024 so I can see at a glance which one expanded the fastest?", "table_markdown": "| Year | Transmission | Electric Distribution and MA Solar | Natural Gas Distribution | Water | IT and Facilities |\n|------|--------------|-----------------------------------|-------------------------|-------|------------------|\n| 2017A | $889 | $1,082 | $335 | $152 | $2,472 |\n| 2018A | $994 | $1,145 | $404 | $102 | $2,830 |\n| 2019A | $1,030 | $1,221 | $453 | $110 | $3,053 |\n| 2020E | $910 | $1,347 | $470 | $124 | $3,071 |\n| 2021E | $832 | $1,208 | $498 | $127 | $2,834 |\n| 2022E | $855 | $1,162 | $501 | $134 | $2,817 |\n| 2023E | $711 | $1,170 | $507 | $147 | $2,706 |\n| 2024E | $668 | $1,234 | $537 | $153 | $2,763 |"}
{"item_id": "item_0260", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each email metric from 2015 to 2019 so I can see at a glance which one expanded the fastest?", "table_markdown": "| Business Email | 2015 | 2016 | 2017 | 2018 | 2019 |\n|---------------|------|------|------|------|------|\n| Average Number of Emails Sent/Received per | 122 | 123 | 124 | 125 | 126 |\n| Average Number of Emails Received | 88 | 90 | 92 | 94 | 96 |\n| Average Number of Legitimate Emails | 76 | 76 | 76 | 76 | 77 |\n| Average Number of Spam Emails | 12 | 14 | 16 | 18 | 19 |\n| Average Number of Emails Sent | 34 | 33 | 32 | 31 | 30 |"}
{"item_id": "item_0261", "chart_task_type": "growth_speed", "query": "Can you chart the growth trends in these four liability categories so I can see at a glance which one grew the fastest?", "table_markdown": "| Description | 30-Sep-17 | 31-Dec-16 |\n|-------------|-----------|-----------|\n| Mark-up/return/interest payable | 82,704,482 | 112,222,868 |\n| Bills payable | 91,678,033 | 112,450,292 |\n| Accrued Expenses | 1,899,129 | 1,691,888 |\n| Others | 65,655,513 | 69,365,833 |"}
{"item_id": "item_0262", "chart_task_type": "growth_speed", "query": "Can you chart the growth of the survey variables from July to December 2020 so I can see at a glance which one expanded the fastest?", "table_markdown": "| | Activity | New Business | Suppliers' Delivery Times | Future Activity | Employment | Outstanding Business | Input Prices | Prices Charged |\n|----------------|----------|--------------|---------------------------|-----------------|------------|----------------------|--------------|----------------|\n| Jul '20 | 53.3 | 50.0 | 39.9 | 65.2 | 44.0 | 43.4 | 57.0 | 48.5 |\n| Aug '20 | 50.6 | 49.9 | 39.5 | 63.1 | 44.5 | 42.6 | 56.0 | 50.1 |\n| Sep '20 | 51.8 | 51.1 | 38.1 | 62.0 | 47.1 | 48.1 | 55.5 | 49.4 |\n| Oct '20 | 47.0 | 46.8 | 35.4 | 61.0 | 46.8 | 46.5 | 56.3 | 50.1 |\n| Nov '20 | 47.4 | 49.1 | 34.5 | 66.9 | 45.7 | 47.9 | 56.1 | 49.9 |\n| Dec '20 | 48.8 | 48.2 | 30.9 | 68.0 | 45.3 | 47.7 | 59.3 | 50.3 |"}
{"item_id": "item_0263", "chart_task_type": "growth_speed", "query": "Can you chart the growth of these economic indicators from 1985 to 1994 so I can see at a glance which one expanded the fastest?", "table_markdown": "| obs | LMQ | LFDI | LGVT_EXP | LL | LK | LLOAN | LINF |\n|---|---|---|---|---|---|---|---|\n| 1985 | 5.53184 | 17.5361 | 0.491079 | 4.19268 | 6.91968 | 6.06739 | 2.21706 |\n| 1986 | 5.68856 | 18.0294 | 0.502758 | 4.22244 | 6.9754 | 6.02184 | 2.65386 |\n| 1987 | 5.35609 | 18.1421 | 2.70136 | 4.15419 | 7.07036 | 6.05709 | 2.47434 |\n| 1988 | 5.91179 | 18.1766 | 2.80336 | 4.09933 | 7.11185 | 5.96762 | 1.95698 |\n| 1989 | 5.62019 | 18.3526 | 2.72785 | 3.9982 | 7.05416 | 6.27836 | 2.45153 |\n| 1990 | 5.84148 | 17.2095 | 2.00148 | 3.99452 | 7.40212 | 6.32826 | 2.74084 |\n| 1991 | 5.95112 | 17.9644 | 2.70805 | 4.00551 | 7.6023 | 6.45239 | 3.14845 |\n| 1992 | 5.96128 | 17.6842 | 2.94444 | 4.03777 | 7.59555 | 6.35889 | 3.73987 |\n| 1993 | 5.81312 | 17.9839 | 2.7213 | 4.05526 | 7.6539 | 6.41509 | 3.31874 |\n| 1994 | 6.08467 | 19.1731 | 3.08191 | 4.01998 | 7.65112 | 6.29592 | 3.10333 |"}
{"item_id": "item_0264", "chart_task_type": "growth_speed", "query": "Can you chart the application types growth so I can see at a glance which one grew the fastest between January and July 2015?", "table_markdown": "| Type | January 2015 | February 2015 | March 2015 | April 2015 | May 2015 | June 2015 | July 2015 |\n|---|---|---|---|---|---|---|---|\n| DA’s | 26 | 22 | 31 | 35 | 21 | 29 | 27 |\n| SA’s | 2 | 1 | 3 | 4 | 5 | 0 | 3 |\n| PSA’s | 0 | 1 | 0 | | 0 | 0 | 0 |\n| STR | 0 | 1 | 2 | | 2 | | 0 |\n| ADH | 1 | | 0 | | 0 | 1 | 0 |"}
{"item_id": "item_0265", "chart_task_type": "growth_speed", "query": "Can you chart the growth of these cash accounts from 2016 to 2017 so I can see at a glance which one expanded the fastest?", "table_markdown": "| Account | 2017 | 2016 |\n|----------------------------------------------|--------|--------|\n| Baptist Investment – Trading Account | 1,752 | 5,223 |\n| Westpac - Community Solutions Account | 7,063 | 591 |\n| Westpac - Community Cheque Account | 8,762 | 5,100 |\n| Westpac - Community Cash Reserve | 53,239 | 79,776 |\n| **Total** | **70,816** | **90,690** |"}
{"item_id": "item_0266", "chart_task_type": "growth_speed", "query": "Can you chart the growth speed of Exploration, Development, and Acquisitions from 1999 to 2001? I want it to be obvious which category expanded the fastest.", "table_markdown": "| Year | Exploration | Development | Acquisitions |\n|------|-------------|-------------|--------------|\n| 1999 | $54 | $66 | $512 |\n| 2000 | $388 | $232 | $133 |\n| 2001 | $341 | $224 | $632 |"}
{"item_id": "item_0267", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each revenue and support source from 2018 to 2019 so I can see at a glance which one expanded the fastest?", "table_markdown": "| | 2019 | 2018 |\n|----------------------|---------------|---------------|\n| Contributions | $49,827,480 | $58,261,723 |\n| Grants | 220,665 | 116,338 |\n| Administrative costs | (50,000) | 64,723 |\n| Transfers from NIH | 500,000 | 2,000,000* |\n| Investment income | 5,197,124 | 917,698 |\n| In-kind contributions| 270,780 | 256,859 |\n| Donated services | 50,000 | 60,000 |\n| Fundraising event | 401,000 | 368,156 |\n| **Total revenue and support** | **$56,417,049** | **$62,045,497** |"}
{"item_id": "item_0268", "chart_task_type": "growth_speed", "query": "Can you chart the growth trends for each month from 2013 to 2017 so I can see at a glance which month experienced the fastest increase in HIV/AIDS and STDs cases?", "table_markdown": "| Months | 2013 | 2014 | 2015 | 2016 | 2017 |\n|--------|------|------|------|------|------|\n| January| 17 | 25 | 8 | 19 | 0 |\n| February| 12 | 9 | 24 | 14 | 1 |\n| March | 15 | 9 | 22 | 26 | 7 |\n| April | 21 | 18 | 25 | 18 | 4 |\n| May | 17 | 17 | 21 | 8 | 17 |\n| June | 13 | 19 | 36 | 10 | 30 |\n| July | 14 | 30 | 14 | 0 | 18 |\n| August | 13 | 10 | 30 | 19 | 19 |\n| September| 9 | 15 | 22 | 9 | 12 |\n| October| 18 | 17 | 12 | 6 | 16 |\n| November| 18 | 13 | 18 | 5 | 7 |\n| December| 12 | 17 | 17 | 0 | 8 |\n| Total | 179 | 199 | 249 | 134 | 139 |"}
{"item_id": "item_0269", "chart_task_type": "growth_speed", "query": "Can you chart the growth of total remuneration for Fokion Karavias, Stavros Ioannou, Konstantinos Vassilou, and Theodoros Kalantoun from 2016 to 2020? I want it to be obvious at a glance which director's pay grew the fastest.", "table_markdown": "| Name of Director, Position | Financial Year | Fixed Remuneration | Variable Remuneration | Pension Expense | Total Remuneration | % vs Previous Year (*) | Proportion of Variable / Fixed Remuneration |\n|----------------------------|----------------|--------------------|----------------------|----------------|-------------------|------------------------|------------------------------------------|\n| Fokion Karavias | 2016 | 301,601 | 0,000 | 21,693 | 323,294 | -6% | 0% |\n| Chief Executive Officer | 2017 | 300,131 | 0,000 | 21,693 | 321,824 | 0% | 0% |\n| (CEO since June 2014) | 2018 | 300,901 | 0,000 | 21,693 | 322,594 | 0% | 0% |\n| | 2019 | 295,921 | 0,000 | 34,170 | 330,091 | 4% | 0% |\n| | 2020 | 239,451 | 0,000 | 40,079 | 338,531 | 1% | 0% |\n| Stavros Ioannou | 2016 | 307,520 | 0,000 | 22,273 | 329,793 | 0% | 0% |\n| Deputy Chief Executive Officer (CEO) | 2017 | 151,380 | 0,000 | 25,753 | 177,133 | 14% | 0% |\n| Group Chief Operating Officer (COO) & International Activities | 2018 | 310,900 | 0,000 | 35,763 | 346,663 | 0% | 0% |\n| (Deputy CEO since April 2015)| 2019 | 368,283 | 0,000 | 42,867 | 411,150 | 9% | 0% |\n| | 2020 | 374,903 | 0,000 | 51,433 | 426,336 | 4% | 0% |\n| Konstantinos Vassilou | 2016 | 307,100 | 0,000 | 22,273 | 329,373 | -2% | 0% |\n| Deputy Chief Executive Officer (CEO) | 2017 | 350,680 | 0,000 | 25,753 | 376,433 | 14% | 0% |\n| Head of Group Corporate & Investment Banking (Deputy CEO since June 2018) | 2018 | 350,190 | 0,000 | 25,753 | 375,943 | 0% | 0% |\n| | 2019 | 364,883 | 0,000 | 42,867 | 407,750 | 9% | 0% |\n| | 2020 | 316,145 | 0,000 | 18,514 | 334,663 | -48% | 0% |\n| Theodoros Kalantoun | 2016 | 307,100 | 0,000 | 22,273 | 329,373 | -2% | 0% |\n| Deputy Chief Executive Officer (CEO) | 2017 | 350,680 | 0,000 | 25,753 | 376,433 | 14% | 0% |\n| Head of Troubled Asset Group (Deputy CEO since April 2020) | 2018 | 350,190 | 0,000 | 25,753 | 375,943 | 0% | 0% |\n| | 2019 | 364,883 | 0,000 | 42,867 | 407,750 | 9% | 0% |\n| | 2020 | 32,324 | 0,000 | 2,389 | 34,713 | 0% | 0% |"}
{"item_id": "item_0270", "chart_task_type": "growth_speed", "query": "Can you chart the growth speed for Part I, Part II, Burglaries, GTA's, and Narco arrests from 2011 to 2015? I want it to be obvious which category expanded the fastest.", "table_markdown": "| Category | 2015 | 2014 | 2013 | 2012 | 2011 |\n|--------------|------|------|------|------|------|\n| Part I | 7 | 12 | 13 | 7 | 19 |\n| Part II | 69 | 79 | 42 | 47 | 44 |\n| **Total Arrests** | 76 | 91 | 55 | 54 | 63 |\n| Burglaries | 2 | 7 | 7 | 1 | 11 |\n| GTA's | 0 | 2 | 0 | 1 | 3 |\n| Narco | 6 | 17 | 5 | 8 | 9 |"}
{"item_id": "item_0271", "chart_task_type": "growth_speed", "query": "Can you chart the net sales growth for each region so I can see at a glance which one expanded the fastest?", "table_markdown": "| | First three-month period of consolidated FY 2018 (January 1, 2018 to March 31, 2018) | First three-month period of consolidated FY 2019 (January 1, 2019 to March 31, 2019) |\n|---|---|---|\n| Japan | 41,015 | 40,159 |\n| China | 10,394 | 11,708 |\n| Other Asia | 4,904 | 3,086 |\n| North America | 4,697 | 5,076 |\n| Europe | 10,620 | 9,745 |\n| Other areas | 324 | 499 |"}
{"item_id": "item_0272", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each inventory category from late 2020 to early 2021 so I can see at a glance which one expanded the fastest?", "table_markdown": "| Vineyard in process | $ | 404,400 | $ | 286,491 |\n|---|---|---|---|---|\n| Wine in process | | 547,112 | | 576,801 |\n| Finished wine | | 35,571 | | 39,549 |\n| Clothes and accessories | | 210,892 | | 215,951 |\n| Other | | 60,625 | | 53,983 |\n| Total | $ | 1,258,600 | $ | 1,172,775 |"}
{"item_id": "item_0273", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each small business subcategory from 2016 to 2017 so I can see at a glance which one expanded the fastest?", "table_markdown": "| Fiscal year | Small Disadvantaged Businesses | Women-Owned Small Businesses | Service-Disabled Veteran-Owned Small Businesses | Historically Underutilized Business Zones |\n|-------------|--------------------------------|------------------------------|-----------------------------------------------|------------------------------------------|\n| 2016 | 36,821 | 26,612 | 11,334 | 5,962 |\n| 2017 | 37,848 | 26,149 | 12,128 | 6,264 |\n| Difference | +1,027 | -463 | +794 | +302 |"}
{"item_id": "item_0274", "chart_task_type": "growth_speed", "query": "Can you chart the growth of the four main expenditure categories from FY 2002 to FY 2003 so I can see at a glance which one expanded the fastest?", "table_markdown": "| Description of Expenditures | FY 2002 | FY 2003 |\n|-------------------------------------|---------------|---------------|\n| Personnel Expenses | $1,879,631 | $2,090,781 |\n| Other Current Expenses | | |\n| Commission* | 805,586 | 810,900 |\n| Consumer Advocate | 2,355,529 | 2,453,275 |\n| Total Other | 3,161,115 | 3,264,175 |\n| Equipment | 43,705 | 14,434 |\n| **Total** | **$5,084,451**| **$5,369,390**|"}
{"item_id": "item_0275", "chart_task_type": "growth_speed", "query": "Can you chart the growth in staff numbers by disability status from 2018-19 to 2019-20? I want it to be obvious which category expanded the fastest.", "table_markdown": "| Workforce | 2018-19 | | |\n|---|---|---|---|\n| | Number | % | Number |\n| Disabled | 117 | 3% | 130 |\n| Non-disabled | 2974 | 84% | 2995 |\n| Not disclosed | 470 | 13% | 543 |"}
{"item_id": "item_0276", "chart_task_type": "growth_speed", "query": "Can you chart the growth of the three Net Asset categories from 2011 to 2012 so I can see at a glance which one expanded the fastest?", "table_markdown": "| Net Assets | 2012 | 2011 |\n|---------------------------------------------|------------|------------|\n| Invested in Capital Assets, net of Related Debt | 5,657 | 6,044 |\n| Restricted for Educational Assistance Programs | 518,930 | 516,080 |\n| Unrestricted for Student Aid Programs | 61,476 | 58,935 |\n| **Total Net Assets** | 586,063 | 581,059 |\n| **Total Liabilities and Net Assets** | $4,558,070 | $4,694,121 |"}
{"item_id": "item_0277", "chart_task_type": "growth_speed", "query": "Can you chart the proficiency growth for these schools so I can see at a glance which one improved the fastest?", "table_markdown": "| | 2015 % Proficient or Above | 2016 % Proficient or Above |\n|---|---|---|\n| Concord | 38 | 45 |\n| Abbot-Downing | 40 | 42 |\n| Beaver Meadow | 46 | 39 |\n| Broken Ground | 35 | 52 |\n| Christa McAuliffe | 33 | 41 |"}
{"item_id": "item_0278", "chart_task_type": "growth_speed", "query": "Can you chart the drop in informal workers' earnings reduction by region so I can see at a glance which area suffered the steepest decline?", "table_markdown": "| By region | Median earnings of informal workers pre COVID-19 (2016 PPP$) | Expected median earnings of informal workers in the first month of the crisis (2016 PPP$) |\n|-----------|-------------------------------------------------------------|----------------------------------------------------------------------------------------|\n| Africa | 518 | 96 |\n| Americas | 1298 | 244 |\n| Asia and the Pacific | 549 | 430 |\n| Europe and Central Asia | 1253 | 387 |"}
{"item_id": "item_0279", "chart_task_type": "growth_speed", "query": "Can you chart the growth in gas supply sources from 2015 to 2035 so I can see at a glance which one grew the fastest?", "table_markdown": "| Year | Russia | LNG Northern Africa | Norway | EU |\n|------|--------|---------------------|--------|----|\n| 2015 | 481 bcm | 35 bcm | 119 bcm | 141 bcm |\n| 2035 | 472 bcm | 288 bcm | 10 bcm | 72 bcm |"}
{"item_id": "item_0280", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each direct expenditure category from 2010 to 2011? I want it to be obvious which one expanded the fastest.", "table_markdown": "| Direct expenditure | 2011 | 2010 |\n|---------------------------------------------|------------|------------|\n| Membership (including other related expenditure) | 56,450 | 45,326 |\n| Conference, SIGs and regional meetings | 460,512 | 493,932 |\n| Training events | 569,755 | 580,480 |\n| Legal affairs | 16,374 | 12,196 |\n| Publishing and communications | 119,675 | 96,110 |\n| **Total** | **1,222,766** | **1,228,044** |"}
{"item_id": "item_0281", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each individual product segment from FY2008 to FY2012 so I can see at a glance which one expanded the fastest?", "table_markdown": "| (Rs. crore) | Segment | FY2008 | FY2012 |\n|---|---|---|---|\n| Electronics | Stabilizers | 93.72 | 200.96 |\n| | Standalone UPS | 16.87 | 42.08 |\n| | Invertor & Digital UPS | - | 72.69 |\n| | Segment Total | 110.59 | 315.73 |\n| Electricals | Pumps | 56.09 | 151.87 |\n| | House wiring cable | 60.02 | 282.55 |\n| | LT cable | 1.0 | 58.19 |\n| | Electric water heater | 27.22 | 86.3 |\n| | Fan | 8.62 | 63.8 |\n| | Segment Total | 152.95 | 642.68 |\n| Others | Solar water heater | 12.57 | 25.95 |\n| | Gas water heater + Windmill | 2.01 | 9.28 |\n| | Segment Total | 14.58 | 35.23 |\n| | TOTAL | 278.12 | 993.64 |"}
{"item_id": "item_0282", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each trade receivable age bucket from late 2011 to mid-2012 so I can see at a glance which category expanded the fastest?", "table_markdown": "| | As at 30 June 2012 (HK$’000) (Unaudited) | As at 31 December 2011 (HK$’000) (Audited) |\n|------------------------|------------------------------------------|-------------------------------------------|\n| Current to 30 days | 3,449 | 933 |\n| 31 to 60 days | 278 | 150 |\n| 61 to 90 days | 50 | 80 |\n| Over 90 days | 8 | 925 |\n| **Total** | **3,785** | **2,088** |"}
{"item_id": "item_0283", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each expense category from 2020 to 2021 so I can see at a glance which one expanded the fastest?", "table_markdown": "| | 2021 | 2020 |\n|--------------------------------|----------|----------|\n| **Revenues** | | |\n| Measure W sales tax | $93,198 | $88,345 |\n| **Total operating revenues** | 93,198 | 88,345 |\n| **Expenses** | | |\n| District | | |\n| Transit operations | 4,115 | 15,046 |\n| Disbursements to Transportation Authority | | |\n| Highway | 20,980 | 19,868 |\n| Major arterial and local roadway improvements | 11,655 | 11,038 |\n| Bicycle, pedestrian, and active transportation projects | 4,662 | 4,415 |\n| Infrastructure and services designed to improve transit connectivity | 9,324 | 8,830 |\n| **Total expenses** | 50,736 | 59,197 |\n| **Operating Income** | 42,462 | 29,148 |\n| **Nonoperating Revenues (Expenses)** | | |\n| Interest income | 1,033 | - |\n| **Total nonoperating revenues (expenses)** | 1,033 | - |\n| **Change in Net Position** | 43,495 | 29,148 |\n| **Net Position - Beginning** | 29,148 | - |\n| **Net Position - Ending** | $72,643 | $29,148 |"}
{"item_id": "item_0284", "chart_task_type": "growth_speed", "query": "Can you chart the growth in the four key financial metrics from the first quarter of 2019 to 2020? I want it to be obvious which one declined the fastest.", "table_markdown": "| | Three months ended March 31, 2019 | Three months ended March 31, 2020 |\n|---|---|---|\n| Net sales | 67.8 | 64.9 |\n| Operating profit | 5 | 4 |\n| Ordinary profit | 6.3 | 3.5 |\n| Profit attributable to owners of parent | 3.8 | 2.2 |"}
{"item_id": "item_0285", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each CO2 emission source from 1986 to 1991 so I can see at a glance which one expanded the fastest?", "table_markdown": "| Year | On-farm fuel use | Soil carbon flux | N-fertilizer manufacture | Total |\n|------|------------------|------------------|--------------------------|-------|\n| 1986 | 9.2 | 7.3 | 3.2 | 19.7 |\n| 1991 | 10.4 | 7.2 | 3.2 | 20.8 |"}
{"item_id": "item_0286", "chart_task_type": "growth_speed", "query": "Can you chart the growth for each administration expense category so I can see at a glance which one grew the fastest?", "table_markdown": "| Description | 2016 | 2015 |\n|--------------------------------------------------|----------|----------|\n| Amortization | $8,065 | $9,724 |\n| Audit | 3,135 | 3,547 |\n| Bank charges | 327 | 284 |\n| Contract fees | 29,633 | 29,583 |\n| Insurance | 922 | 114 |\n| Office administration | 3,325 | 2,027 |\n| Summer student wage | 311 | 355 |\n| **Total** | **$45,718** | **$45,634** |"}
{"item_id": "item_0287", "chart_task_type": "growth_speed", "query": "Can you chart the growth in sickness fund turnover by age group from 1999 to 2000? I want it to be obvious which age group saw the sharpest increase.", "table_markdown": "| Age group | 1999 | | | 2000 | | | N (000) |\n|-----------|------|-------|-------|------|-------|-------|---------|\n| | Total | Men | Women | Total | Men | Women | (Nov 1999) |\n| Total | 1.01 | 0.99 | 1.02 | 1.04 | 1.03 | 1.05 | 6281 |\n| 0-4 | 1.39 | 1.37 | 1.41 | 1.46 | 1.44 | 1.47 | 641 |\n| 5-14 | 0.85 | 0.86 | 0.85 | 0.91 | 0.92 | 0.91 | 1182 |\n| 15-24 | 1.88 | 1.73 | 2.02 | 1.71 | 1.53 | 1.86 | 964 |\n| 25-34 | 1.37 | 1.40 | 1.34 | 1.45 | 1.52 | 1.39 | 941 |\n| 35-44 | 0.73 | 0.73 | 0.74 | 0.82 | 0.86 | 0.79 | 791 |\n| 45-54 | 0.49 | 0.44 | 0.53 | 0.57 | 0.54 | 0.60 | 713 |\n| 55-64 | 0.45 | 0.41 | 0.49 | 0.52 | 0.45 | 0.57 | 422 |\n| 65-74 | 0.37 | 0.35 | 0.38 | 0.42 | 0.40 | 0.43 | 352 |\n| 75+ | 0.23 | 0.22 | 0.23 | 0.28 | 0.25 | 0.30 | 275 |"}
{"item_id": "item_0288", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each inmate-on-inmate assault type from October to December? I want it to be obvious which category expanded the fastest.", "table_markdown": "| | October | November | December |\n|---|---|---|---|\n| Bodily Force | 231 | 237 | 243 |\n| Weapon Used | 6 | 1 | 12 |\n| Liquid | 6 | 3 | 14 |"}
{"item_id": "item_0289", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each offence type in Croxley Green North from 2013-2014 to 2014-2015? I want it to be obvious which one increased the fastest.", "table_markdown": "| Offence | Croxley Green North 2013-2014 | Croxley Green North 2014-2015 | Up-down | Croxley Green South 2013-2014 | Croxley Green South 2014-2015 | Up-down |\n|-------------------------|-------------------------------|-------------------------------|---------|-------------------------------|-------------------------------|---------|\n| Violent Crime | 3 | 9 | 6 | 6 | 23 | 17 |\n| Robbery | 0 | 0 | 1 | 0 | -1 | -1 |\n| Burglary Dwelling | 3 | 1 | -2 | 11 | 5 | -6 |\n| Burglary Other | 2 | 0 | -2 | 8 | 0 | -8 |\n| Theft of a Vehicle | 2 | 1 | -1 | 1 | 0 | -1 |\n| Theft From a Vehicle | 4 | 2 | -2 | 5 | 3 | -2 |\n| Vehicle Interference | 1 | 0 | -1 | 0 | 1 | 1 |\n| Theft From Person | 0 | 0 | 1 | 1 | 0 | 1 |\n| Theft Other | 5 | 3 | -2 | 5 | 13 | 8 |\n| Arson | 0 | 0 | 0 | 0 | 0 | 0 |\n| Criminal Damage | 7 | 3 | -4 | 17 | 15 | -2 |\n| Other Offences | 6 | 8 | 2 | 5 | 12 | 7 |\n| Other Offences | 1 | 1 | 0 | 5 | 13 | 8 |"}
{"item_id": "item_0290", "chart_task_type": "growth_speed", "query": "Can you chart the growth for Revenue, Operating profit, Profit before income taxes, and Profit attributable to owners of parent? I want it to be obvious which metric expanded the fastest.", "table_markdown": "| | Previous consolidated fiscal year (From October 1, 2016 to September 30, 2017) | Current consolidated fiscal year (From October 1, 2017 to September 30, 2018) |\n|---|---|---|\n| Revenue | 21,054,421 | 26,417,320 |\n| Operating profit | 3,948,395 | 6,550,904 |\n| Profit before income taxes | 3,587,058 | 6,700,079 |\n| Profit attributable to owners of parent | 2,420,851 | 4,255,069 |"}
{"item_id": "item_0291", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each donation source from 2015 to 2016 so I can see at a glance which one expanded the fastest?", "table_markdown": "| | 2016 | 2015 |\n|----------------------|--------|--------|\n| Donations | 394,565| 410,638|\n| Golak | 324,904| 303,632|\n| Gift Aid | 53,319 | 74,308 |\n| Grant from West Thames College | 7,049 | 7,049 |\n| | 779,837| 795,627|"}
{"item_id": "item_0292", "chart_task_type": "growth_speed", "query": "Can you chart the growth rates for Subsidence, Infill, Beyond Shoreline, X-Section Balance, and Erosion from 1976 to 1995? I want it to be obvious which category expanded the fastest.", "table_markdown": "| YEAR | 1976 | 1980 | 1984 | 1987 | 1988 | 1995 |\n|--------------|------------|------------|------------|------------|------------|------------|\n| SUBSIDENCE | 2,040,411 | 3,956,207 | 4,671,553 | 5,465,636 | 5,950,297 | 6,396,982 |\n| INFILL | 1,994,248 | 2,060,989 | 2,819,481 | 2,742,001 | 2,630,278 | 2,250,303 |\n| BEYOND SHORELINE | 497,963 | 538,364 | 805,798 | 801,922 | 716,374 | 562,603 |\n| X-SECTION BALANCE | (451,799) | 1,356,855 | 1,046,273 | 1,921,713 | 2,603,645 | 3,584,076 |\n| 10% BULKING OF 22,480,000 C.Y. SLIDE | 2,250,000 | 2,250,000 | 2,250,000 | 2,250,000 | 2,250,000 | 2,250,000 |\n| EROSION | 1,798,201 | 3,606,855 | 3,296,273 | 4,171,713 | 4,853,645 | 5,834,076 |"}
{"item_id": "item_0293", "chart_task_type": "growth_speed", "query": "Can you chart the growth of White, Bench Colour, and Start Colour from 2007 to 2010 so I can see at a glance which category expanded the fastest?", "table_markdown": "| Year | White | Bench Colour | Start Colour |\n|------|-------|--------------|-------------|\n| 2010 | 16 | 2 | 4 |\n| 2009 | 17 | 2 | 3 |\n| 2008 | 17 | 2 | 3 |\n| 2007 | 18 | 2 | 2 |"}
{"item_id": "item_0294", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each asset category from April 2022 to April 2023? I want it to be obvious which one expanded the fastest.", "table_markdown": "| | 30 April 2023 | 30 April 2022 | 31 Dec. 2022 |\n|--------------------------------|---------------|---------------|--------------|\n| **NON-CURRENT ASSETS** | | | |\n| Intangible assets | 386,600 | 401,293 | 391,275 |\n| Tangible assets | 600,819 | 597,386 | 603,694 |\n| Investments | 55,825 | 43,283 | 52,096 |\n| **Total** | 1,164,255 | 1,181,109 | 1,148,740 |\n| **CURRENT ASSETS** | | | |\n| Inventories | 7,604 | 6,385 | 8,661 |\n| Financial assets | 113,407 | 132,762 | 93,013 |\n| **Total** | 1,164,255 | 1,181,109 | 1,148,740 |"}
{"item_id": "item_0295", "chart_task_type": "growth_speed", "query": "Can you chart the seat growth for each reservation and gender category so I can see at a glance which one grew the fastest?", "table_markdown": "| Institution | Course | Qualification | Category | Gender | Seats (2023) | Seats (2024) |\n|-------------------------------------------------|------------------------------------------------------------------------|---------------|------------|-------------------------|--------------|--------------|\n| Motilal Nehru National Institute of Technology | Electronics and Communication Engineering (4 Years, Bachelor of | HS | OPEN (PwD) | Female-only (including Supernumerary) | 1469 | 1469 |\n| Allahabad | Technology) | | | | | |\n| Motilal Nehru National Institute of Technology | Electronics and Communication Engineering (4 Years, Bachelor of | HS | EWS | Gender-Neutral | 1289 | 1430 |\n| Allahabad | Technology) | | | | | |\n| Motilal Nehru National Institute of Technology | Electronics and Communication Engineering (4 Years, Bachelor of | HS | EWS | Female-only (including Supernumerary) | 2650 | 3126 |\n| Allahabad | Technology) | | | | | |\n| Motilal Nehru National Institute of Technology | Electronics and Communication Engineering (4 Years, Bachelor of | HS | OBC-NCL | Gender-Neutral | 2662 | 3616 |\n| Allahabad | Technology) | | | | | |\n| Motilal Nehru National Institute of Technology | Electronics and Communication Engineering (4 Years, Bachelor of | HS | OBC-NCL | Female-only (including Supernumerary) | 5248 | 6445 |\n| Allahabad | Technology) | | | | | |\n| Motilal Nehru National Institute of Technology | Electronics and Communication Engineering (4 Years, Bachelor of | HS | OBC-NCL (PwD) | Gender-Neutral | 183 | 183 |\n| Allahabad | Technology) | | | | | |\n| Motilal Nehru National Institute of Technology | Electronics and Communication Engineering (4 Years, Bachelor of | HS | SC | Gender-Neutral | 1111 | 2050 |\n| Allahabad | Technology) | | | | | |\n| Motilal Nehru National Institute of Technology | Electronics and Communication Engineering (4 Years, Bachelor of | HS | SC | Female-only (including Supernumerary) | 3461 | 3883 |\n| Allahabad | Technology) | | | | | |\n| Motilal Nehru National Institute of Technology | Electronics and Communication Engineering (4 Years, Bachelor of | HS | ST | Gender-Neutral | 986 | 1294 |\n| Allahabad | Technology) | | | | | |\n| Motilal Nehru National Institute of Technology | Electronics and Communication Engineering (4 Years, Bachelor of | HS | ST | Female-only (including Supernumerary) | 3457 | 3457 |\n| Allahabad | Technology) | | | | | |\n| Motilal Nehru National Institute | Electronics and Communication Engineering (4 Years, Bachelor of | OS | OPEN | Gender-Neutral | 5392 | 7404 |\n| | Technology) | | | | | |"}
{"item_id": "item_0296", "chart_task_type": "growth_speed", "query": "Can you chart the growth of the three price indices from 2011-12 to 2016-17 so I can see at a glance which one expanded the fastest?", "table_markdown": "| S.N. | Item | Unit | 2011-12 | 2015-16 | 2016-17 |\n|------|-------------------------------------------|--------|---------|---------|---------|\n| (5) | Price Index | | | | |\n| | (2004-05=100) | | | | |\n| 1 | Whole Sale Price | Percent| 159.0 | 204 | 137.9 |\n| 2 | Rural Consumer Price Index | Percent| 173.7 | 244.1 | 259.0 |\n| 3 | Urban Consumer Price Index | Percent| 166.9 | 228.2 | 239.5 |"}
{"item_id": "item_0297", "chart_task_type": "growth_speed", "query": "Can you chart the growth of these occupations from 2015 to 2016 so I can see at a glance which one expanded the fastest?", "table_markdown": "| | | | | | Median | |\n|---|---|---|---|---|---|---|\n| Occupation | | 2015 | 2016 | Net Job | Hourly | |\n| | | Jobs | Jobs | Growth | Earnings | Openings* |\n| 1 | Health Diagnosing and Treating Practitioners | 41,632 | 42,602 | 970 | $44.09 | |\n| 2 | Other Personal Care and Service Workers | 27,812 | 28,430 | 618 | $9.91 | |\n| 3 | Nursing, Psychiatric, and Home Health Aides | 17,029 | 17,632 | 603 | $11.18 | |\n| 4 | Food and Beverage Serving Workers | 51,490 | 52,008 | 518 | $8.89 | |\n| 5 | Health Technologists and Technicians | 22,555 | 23,058 | 503 | $20.12 | |\n| 6 | Computer Occupations | 35,561 | 36,061 | 500 | $34.68 | |\n| 7 | Preschool, Primary, Secondary, and Special Education School Teachers | 30,558 | 31,005 | 447 | $21.85 | |\n| 8 | Secretaries and Administrative Assistants | 31,147 | 31,493 | 346 | $17.49 | |\n| 9 | Information and Record Clerks | 46,639 | 46,939 | 300 | $15.54 | |\n| 10 | Financial Specialists | 25,186 | 25,481 | 295 | $28.92 | |\n| 11 | Business Operations Specialists | 36,474 | 36,742 | 268 | $30.56 | |\n| 12 | Financial Clerks | 28,015 | 28,275 | 260 | $16.90 | |\n| 13 | Other Healthcare Support Occupations | 10,587 | 10,834 | 247 | $14.93 | |\n| 14 | Counselors, Social Workers, and Other Community and Social Service Specialists | 11,965 | 12,202 | 237 | $19.27 | |\n| 15 | Sales Representatives, Services | 21,108 | 21,343 | 235 | $26.01 | |\n| 16 | Building Cleaning and Pest Control Workers | 25,033 | 25,258 | 225 | $10.61 | |\n| 17 | Cooks and Food Preparation Workers | 22,143 | 22,366 | 223 | $9.98 | |\n| 18 | Engineers | 10,764 | 10,971 | 207 | $39.17 | |\n| 19 | Other Education, Training, and Library Occupations | 9,955 | 10,106 | 151 | $13.22 | |\n| 20 | Postsecondary Teachers | 6,228 | 6,369 | 141 | $29.32 | |\n| 21 | Top Executives | 21,521 | 21,658 | 137 | $47.99 | |\n| 22 | Other Teachers and Instructors | 9,110 | 9,246 | 136 | $15.85 | |\n| 23 | Material Moving Workers | 31,494 | 31,629 | 135 | $12.33 | |\n| 24 | Operations Specialties Managers | 12,129 | 12,234 | 105 | $46.05 | |\n| 25 | Sales Representatives, Wholesale and Manufacturing | 15,171 | 15,274 | 103 | $30.21 | |\n| 26 | Supervisors of Office and Administrative Support Workers | 9,733 | 9,834 | 101 | $24.06 | |\n| 27 | Grounds Maintenance Workers | 8,752 | 8,853 | 101 | $12.01 | |\n| 28 | Retail Sales Workers | 54,365 | 54,464 | 99 | $9.78 | |\n| 29 | Supervisors of Food Preparation and Serving Workers | 7,348 | 7,442 | 94 | $13.92 | |\n| 30 | Motor Vehicle Operators | 32,398 | 32,483 | 85 | $16.56 | |"}
{"item_id": "item_0298", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of annual operating expenses for each share class so I can see at a glance how the cost structure differs between them?", "table_markdown": "| Annual Fund Operating Expenses (expenses that you pay each year as a percentage of the value of your investment): | Class AAA Shares | Class A Shares | Class C Shares | Class I Shares |\n|-----------------------------------------------------------------------------------------------------------------|------------------|---------------|---------------|---------------|\n| Management Fees | 1.00% | 1.00% | 1.00% | 1.00% |\n| Distribution and Service (Rule 12b-1) Fees | 0.25% | 0.25% | 1.00% | None |\n| Other Expenses | 0.14% | 0.14% | 0.14% | 0.14% |\n| Total Annual Fund Operating Expenses | 1.39% | 1.39% | 2.14% | 1.14% |"}
{"item_id": "item_0299", "chart_task_type": "growth_speed", "query": "Can you chart the growth for each creditor category so I can see at a glance which one grew or declined the fastest?", "table_markdown": "| | 2006 | 2005 |\n|----------------------|--------|--------|\n| Corporation Tax | 767 | 698 |\n| Accruals | 3,564 | 4,033 |\n| Other Creditors | 950 | 6,180 |\n| | 5,281 | 10,911 |"}
{"item_id": "item_0300", "chart_task_type": "growth_speed", "query": "Can you chart the employment growth for Wyoming State Government, Laramie County, and Natrona County so I can see at a glance which one declined the least?", "table_markdown": "| Wyoming Total Nonfarm Employment | Feb 2020 (p) | Jan 2020 (r) | Feb 2019 (b) | Percent Change Month | Year |\n|----------------------------------|-------------|-------------|-------------|----------------------|------|\n| Wyoming State Government | 46,400 | 46,400 | 47,000 | 0.4 | -0.9 |\n| Laramie County Nonfarm Employment| 46,600 | 46,400 | 47,000 | 0.4 | -0.9 |\n| Natrona County Nonfarm Employment| 38,300 | 38,300 | 38,900 | 0.0 | -1.5 |"}
{"item_id": "item_0301", "chart_task_type": "growth_speed", "query": "Can you chart the growth speed of each asset category so I can see at a glance which one expanded the fastest?", "table_markdown": "| Assets | 30.06.2022 in m CHF | 31.12.2021 in m CHF | Difference in m CHF |\n|---------------------------------------------|---------------------|---------------------|---------------------|\n| Cash and cash equivalents | 76.3 | 51.8 | 24.5 |\n| Trade and other receivables | 126.5 | 95.2 | 31.3 |\n| Inventories and net contract assets | 155.8 | 102.7 | 53.1 |\n| Prepaid expenses and accrued income | 6.9 | 3.5 | 3.5 |\n| Total current assets | 365.5 | 253.1 | 112.4 |\n| Property, plant and equipment | 412.9 | 331.2 | 81.8 |\n| Other non-current assets | 161.2 | 100.2 | 61.0 |\n| Total non-current assets | 574.1 | 431.3 | 142.8 |\n| Total Assets | 939.6 | 684.4 | 255.2 |"}
{"item_id": "item_0302", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of who benefits from the U.S.-Turkey alliance for each voter group, so I can see at a glance how the opinion mix differs between AKP, CHP, MHP, and HDP supporters?", "table_markdown": "| | AKP voters | CHP voters | MHP voters | HDP voters |\n|----------------------|------------|------------|------------|------------|\n| The U.S. | 33 | 42 | 37 | 40 |\n| Turkey | 26 | 16 | 26 | 9 |\n| Both benefit equally | 23 | 27 | 19 | 31 |\n| Neither benefits at all | 4 | 6 | 6 | 4 |\n| Don't know/no response | 13 | 9 | 12 | 15 |"}
{"item_id": "item_0303", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of time frame opinions for each respondent group so I can see at a glance how the mix of responses differs between those who have and haven't been through the process?", "table_markdown": "| | Yes | No | Unsure | Total |\n|------------------------|-----|----|--------|-------|\n| Too Short | 1 | 0 | 0 | 1 |\n| Moderate/Adequate | 3 | 2 | 0 | 5 |\n| Too Long | 0 | 2 | 0 | 2 |\n| Unsure | 1 | 7 | 1 | 9 |\n| **Total** | 5 | 11 | 1 | 17 |"}
{"item_id": "item_0304", "chart_task_type": "growth_speed", "query": "Can you chart the growth of the individual risky asset classes from FY 2019 to H1 2020? I want it to be obvious which one grew the fastest or declined the least.", "table_markdown": "| Risky assets (€m) | FY 2019 | H1 2020 |\n|-------------------------------------------------------|-----------|-----------|\n| Equities | 2,607 | 2,377 |\n| Real estate\\(^1\\) | 2,615 | 2,414 |\n| BB bonds or below | 341 | 375 |\n| Preference shares | 320 | 312 |\n| Fixed income funds (not rated & high yield) | 265 | 254 |\n| Mortgages with LtMV >110% | 52 | 35 |\n| **Total risky assets** | **6,200** | **5,766** |\n| Unrestricted Tier 1 | 5,789 | 6,136 |\n| **Asset leverage** | **107%** | **94%** |"}
{"item_id": "item_0305", "chart_task_type": "composition_compare", "query": "Can you chart the asset mix for each of the five fund categories so I can see at a glance how their internal structures differ?", "table_markdown": "| Assets | Total | General/Operating | Restricted | Endowments | Scholarships | Plant & Equipment |\n|-------------------------------|-------------|-------------------|------------|--------------|--------------|------------------|\n| **Current Assets** | | | | | | |\n| Pooled Cash | 16,026,871.80 | 1,440,281.67 | 2,632,444.75 | 10,124,310.23 | 1,548,865.30 | 280,969.85 |\n| Accounts Receivable | 108,852.75 | 0.00 | 670.98 | 92,821.77 | 15,360.00 | 0.00 |\n| **Total Current Assets** | 16,135,724.55 | 1,440,281.67 | 2,633,155.73 | 10,217,132.00 | 1,564,225.30 | 280,969.85 |\n| **Fixed Assets** | | | | | | |\n| Land | 630,382.99 | 0.00 | 0.00 | 54,537.76 | 0.00 | 575,845.23 |\n| Buildings; net | 121,997.57 | 0.00 | 0.00 | 110,569.29 | 0.00 | 11,428.28 |\n| Equipment & Furniture; net | 11,925.50 | 2,310.30 | 9,615.20 | 0.00 | 0.00 | 0.00 |\n| FMW Adjustment | 559,800.00 | 0.00 | 0.00 | 0.00 | 0.00 | 559,800.00 |\n| **Total Fixed Assets** | 1,324,106.06 | 2,310.30 | 9,615.20 | 165,107.05 | 0.00 | 1,147,073.51 |\n| **Total Assets** | 17,459,830.61 | 1,442,591.97 | 2,642,730.93 | 10,382,239.05 | 1,564,225.30 | 1,428,043.36 |"}
{"item_id": "item_0306", "chart_task_type": "growth_speed", "query": "Can you chart the growth speed for each of the five regions from their first to last recorded year? I want it to be obvious which one expanded the fastest.", "table_markdown": "| Region | 1970 | 1971 | 1972 | 1973 | 1974 | 1975 |\n|-----------------|------|------|------|------|------|------|\n| Poverty Bay | 11.2 | 12.8 | 11.7 | 12.9 | 13.2 | 13.8 |\n| Waikato | 12.6 | 12.4 | 13.1 | 12.2 | 13.0 |\n| Hawkes Bay | 11.5 | 11.5 | 10.8 | 12.9 | 12.5*| 12.2 |\n| Wairarapa | | | 9.2 | 9.2 | 10.4*| 11.4*|\n| Manawatu | | | | | 12.0*| 12.2 |\n| Top N.Z. entry | 13.0 | 14.0 | 13.8 | 14.9 | 14.3 | 14.4 |"}
{"item_id": "item_0307", "chart_task_type": "composition_compare", "query": "Can you chart the species mix of sea turtle nests for each of the 8 locations so I can see at a glance which areas rely more heavily on Loggerheads versus Greens or Leatherbacks?", "table_markdown": "| Sea Turtle Species | Washington Oaks State Park (.6 miles) | Flagler County Beaches (15 miles) | Flagler Beach (3.5 miles) | Gamble Rogers SRA (1.1 miles) | North Peninsula SRA (2.7 miles) | Volusia Turtle Patrol (25 miles) | Volusia Sea Turtle Society (11 miles) | Canaveral Nat'l Seashore (11.2 miles) |\n|--------------------|----------------------------------------|-----------------------------------|---------------------------|-------------------------------|---------------------------------|----------------------------------|-------------------------------------|----------------------------------------|\n| Loggerhead | 15 | 217 | 76 | 30 | 84 | 212 | 223 | 1340 |\n| Green | 3 | 33 | 8 | 3 | 19 | 13 | 8 | 352 |\n| Leatherback | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 |\n| **Total Nests** | **18** | **250** | **84** | **33** | **103** | **226** | **231** | **1695** |"}
{"item_id": "item_0308", "chart_task_type": "growth_speed", "query": "Can you chart the shareholding growth from Mar-22 to Jun-22 so I can see at a glance which entity grew the fastest?", "table_markdown": "| SHP | Jun - 22 (%) | Mar - 22 (%) | Change |\n|-----------|--------------|--------------|--------|\n| Promoters | 72.53 | 72.92 | -0.39 |\n| FPI | 0.60 | 0.34 | 0.26 |\n| DII | 3.22 | 3.39 | -0.17 |\n| Public & Others | 23.66 | 23.35 | 0.31 |\n| Pledged | 0.00 | 0.00 | 0.00 |"}
{"item_id": "item_0309", "chart_task_type": "growth_speed", "query": "Can you chart the subscriber trends so I can see at a glance which service type grew or declined the fastest between Q2 2015 and Q3 2016?", "table_markdown": "| | Q2 2015 | Q3 2015 | Q4 2015 | Q1 2016 | Q2 2016 | Q3 2016 |\n|----------------|---------|---------|---------|---------|---------|---------|\n| Fixed telephony| 3,571 | 3,540 | 3,503 | 3,472 | 3,439 | 3,410 |\n| Internet | 2,486 | 2,537 | 2,630 | 2,680 | 2,720 | 2,770 |\n| TV | 2,730 | 2,741 | 2,745 | 2,745 | 2,750 | 2,750 |\n| GPON | 1,279 | 1,365 | 1,504 | 1,598 | 1,620 | 1,625 |"}
{"item_id": "item_0310", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each income category from 2003 to 2004 so I can see at a glance which one expanded the fastest?", "table_markdown": "| Income | 2003 | 2004 |\n|---------------------------|--------|--------|\n| Admissions | $2,151 | $2,108 |\n| Vendor Fees | $2,220 | $2,405 |\n| Table/Chair Rental | $215 | $262 |\n| Book Sales | $2,002 | $2,674 |\n| Banquet Fees | $670 | $960 |\n| Raffle Sales | $923 | $741 |\n| **Total Income** | $8,181 | $9,140 |"}
{"item_id": "item_0311", "chart_task_type": "growth_speed", "query": "Can you chart the enrollment growth for each student status from Fall 2007-2008 to Fall 2008-2009? I want it to be obvious which group grew the fastest.", "table_markdown": "| Student Status | 2007-2008 | | |\n|---|---|---|---|\n| | Fall | Spring | Fall |\n| Undergraduates | 20,846 | 19,908 | 21,372 |\n| Graduates | 6,425 | 6,294 | 6,583 |\n| Professional (School of Law and Doctor of Pharmacy) | 919 | 882 | 925 |\n| Medicine – Students | 323 | 323 | 331 |\n| Medicine – Other (1) | 585 | 585 | 585 |\n| Dental – Students | 164 | 164 | 172 |\n| Dental – Other (1) | 109 | 109 | 121 |"}
{"item_id": "item_0312", "chart_task_type": "composition_compare", "query": "Can you chart how the expense mix across departments shifts between the Unaudited, Year To Date, 11 Month, and 2021-2022 periods, so I can easily spot which categories dominate the budget in each timeframe?", "table_markdown": "| | Unaudited | Year To Date | 11 Month | 2021-2022 |\n|----------------------|-----------|--------------|----------|-----------|\n| **Department Expenses** | | | | |\n| General Government Support | 307,666.95 | 307,693.83 | 318,580.43 | 335,666.00 |\n| Public Safety | 495,312.49 | 476,518.17 | 458,593.91 | 519,838.00 |\n| Public Health | 600.00 | 687.50 | 150.00 | 750.00 |\n| Transportation | 677,282.02 | 507,191.67 | 579,638.08 | 553,500.00 |\n| Culture & Recreation | 32,159.65 | 35,750.00 | 14,398.21 | 39,000.00 |\n| Home & Community Services | 198,613.11 | 183,680.75 | 311,613.68 | 200,379.00 |\n| Employee Benefits | 84,517.04 | 650,446.50 | 618,107.97 | 709,578.00 |\n| Debt Service** | 101,821.88 | 93,336.83 | 101,100.01 | 101,822.00 |\n| Interfund Transfers | 83,000.00 | 117,333.33 | 10,000.00 | 128,000.00 |\n| **Total Department Expenses** | 2,534,978.14 | 2,372,638.58 | 2,412,142.27 | |"}
{"item_id": "item_0313", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of drug-related death causes for Portugal and Sweden so I can easily compare how the composition differs between the two countries?", "table_markdown": "| | EMCDDA sel B | HIV/AIDS (see above) | Hepatitis (Eurostat, 30%) | Unknown (Eurostat, 30%) | Total |\n|----------------|--------------|----------------------|---------------------------|-------------------------|-------|\n| **Portugal** | 30 | 246 | 40 | 809 | 1,125 |\n| **Sweden** | 590 | 1 | 15 | 305 | 911 |"}
{"item_id": "item_0314", "chart_task_type": "growth_speed", "query": "Can you chart the growth for each country between the late 80s and late 90s so I can see at a glance which one expanded the fastest?", "table_markdown": "| Country | 1987-89 | 1997-99 |\n|---|---|---|\n| Czech Republic | 0.20 | 0.26 |\n| Hungary | 0.23 | 0.25 |\n| Poland | 0.28 | 0.33 |\n| Slovakia | 0.19 | 0.25 |\n| Slovenia | 0.21 | 0.25 |\n| Central-East Europe Mean | 0.25 | 0.3 |\n| Estonia | 0.28 | 0.36 |\n| Russia | 0.27 | 0.47 |\n| Ukraine | 0.23 | 0.32 |\n| Belarus | 0.23 | 0.24 |\n| Former CIS Mean | 0.26 | 0.43 |\n| OECD Mean | | 0.31 |"}
{"item_id": "item_0315", "chart_task_type": "growth_speed", "query": "Can you chart the growth of monthly mean AOD from August to October for the four algorithm-surface combinations? I want it to be obvious which one expanded the fastest.", "table_markdown": "| AOD | C6 Land | C6 Ocean | Research Land | Research Ocean |\n|-------|---------|----------|---------------|----------------|\n| August| 0.41 | 0.30 | 0.41 | 0.30 |\n| September| 0.99 | 0.61 | 1.17 | 0.63 |\n| October| 1.26 | 0.80 | 1.45 | 0.80 |"}
{"item_id": "item_0316", "chart_task_type": "growth_speed", "query": "Can you chart the growth of the four individual asset categories from 2020 to 2021 so I can see at a glance which one expanded the fastest?", "table_markdown": "| Assets | 2021 | 2020 |\n|---------------------------------------------|------------|------------|\n| Cash and cash equivalents | $521,742 | $508,271 |\n| Accrued interest receivable | 45,372 | 52,107 |\n| Prepaid expenses and other assets | 56,247 | 48,088 |\n| Investments | 38,464,571 | 29,760,300 |\n| **Total assets** | **$39,087,932** | **$30,368,766** |"}
{"item_id": "item_0317", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each asset category from 2015 to 2016 so I can see at a glance which one expanded the fastest?", "table_markdown": "| ASSETS | 6/30/2016 | 6/30/2015 | Inc/(Dec) |\n|---------------------------------------------|-----------|-----------|-----------|\n| Cash & Cash Equivalents | 779 | 878 | (99) |\n| Pledges Receivable, net | 794 | 736 | 58 |\n| Other Receivables | 9 | 42 | (33) |\n| Prepaid & Deferred Expenses | 35 | 23 | 12 |\n| Investments | 6,885 | 7,097 | (212) |\n| Life Insurance-Cash Value | 187 | 179 | 8 |\n| Fixed Assets | 233 | 250 | (17) |\n| Pooled Income Fund | 56 | 57 | (1) |\n| **TOTAL ASSETS** | | | $(284) |"}
{"item_id": "item_0318", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of egg counts per beetle for the July 9 and July 19 collections so I can see how the composition of parasitism differed between the two dates?", "table_markdown": "| Date collected | Number of beetles collected | Number of beetles with one egg | Number of beetles with two eggs | Number of beetles with three eggs | Number of beetles with four eggs |\n|----------------|----------------------------|--------------------------------|---------------------------------|----------------------------------|---------------------------------|\n| July 9 | 207 | 141 | 45 | 18 | 3 |\n| July 19 | 152 | 136 | 16 | 0 | 0 |"}
{"item_id": "item_0319", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of enforcement categories for 2014 and 2015 so I can see at a glance how the mix of issues shifted between the two years?", "table_markdown": "| Categories | 2014 | 2015 |\n|---------------------|------|------|\n| chimney | 2 | 1 |\n| Exterior Surfaces | 67 | 81 |\n| Grass/Weeds | 27 | 32 |\n| Clean Up Orders | 40 | 46 |\n| Cross connection | 25 | |\n| Drainage | 5 | 5 |\n| General | 5 | 1 |\n| Structure | 7 | 3 |\n| Interior | 7 | 13 |\n| Multiple | 5 | 2 |\n| Parking | 9 | 3 |\n| Permit required | 9 | 15 |\n| Equipment | 1 | 1 |\n| Fence | 5 | 14 |\n| Windows/Doors | 1 | 4 |\n| Signage | 1 | 2 |\n| Total Orders | 191 | 248 |"}
{"item_id": "item_0320", "chart_task_type": "composition_compare", "query": "Can you chart the composition of debtors for 2018 and 2017 so I can see at a glance how the mix of components changed between the two years?", "table_markdown": "| 2018 | 2017 |\n|------|------|\n| Trade debtors | 74,741 | 9,555 |\n| Amounts owed by group undertakings | 194,986 | 175,075 |\n| VAT recoverable | 190,210 | 50,331 |\n| Other debtors | 193,763 | 116,524 |\n| Prepayments and accrued income | 76,412 | 39,007 |\n| **Total** | **730,112** | **390,492** |"}
{"item_id": "item_0321", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each contact type from May to September 2021 so I can see at a glance which one expanded the fastest?", "table_markdown": "| MONTH | F2F IN HOUSE | F2F COMMUNITY | VIDEO |\n|---|---|---|---|\n| MAY 2021 | 175 | 0 | 129 |\n| JUNE 2021 | 216 | 5 | 104 |\n| JULY 2021 | 218 | 15 | 86 |\n| AUGUST 2021 | 177 | 19 | 63 |\n| SEPTEMBER 2021 | 189 | 14 | 67 |"}
{"item_id": "item_0322", "chart_task_type": "growth_speed", "query": "Can you chart which mineral element grew the fastest between 2002 and 2005? I want it to be obvious at a glance which one had the strongest growth rate.", "table_markdown": "| Year – Rok | N-NO$_3^-$ | P | K | Ca | Mg |\n|------------|-------------|-----|-----|-----|-----|\n| 2002 | 7 | 33 | 70 | 480 | 38 |\n| 2003 | 1 | 114 | 110 | 470 | 52 |\n| 2005 | 11 | 153 | 150 | 796 | 77 |"}
{"item_id": "item_0323", "chart_task_type": "composition_compare", "query": "Can you chart the cost structure breakdown for a leather seat cover in Mexico versus Thailand so I can see at a glance how the mix of expenses differs between the two locations?", "table_markdown": "| Cost per seat cover, complete set (US$) | Mexico | Thailand |\n|---|---|---|\n| Labor | 88 | 15 |\n| Leather | 199 | 185 |\n| Other materials | 67 | 67 |\n| Packaging | 2 | 6 |\n| Outbound logistics | 4 | 21 |\n| Inbound logistics | 2 | 8 |\n| Cost of inventory | Not Applicable | 3 |\n| Obsolescence allowance | Not Applicable | 9 |\n| Total Costs | 362 | 314 |"}
{"item_id": "item_0324", "chart_task_type": "composition_compare", "query": "Can you chart how the distribution of the normalized coefficients across the six components shifts as the parameter p varies from 0.125 to 0.875, so I can see at a glance which component dominates for each value?", "table_markdown": "| $p$ \\textbackslash $i$ | 1 | 2 | 3 | 4 | 5 | 6 |\n|------------------------|-------|-------|-------|-------|-------|-------|\n| 0.125 | .039523862 | .27061578 | .41912956 | .21403574 | .05076996 | .005925096 |\n| 0.250 | .027542704 | .23035554 | .41485439 | .24660750 | .07057252 | .010067335 |\n| 0.375 | .019690081 | .19707889 | .40283629 | .27219189 | .09219174 | .016011103 |\n| 0.500 | .014759958 | .17165881 | .38730827 | .28930842 | .11318550 | .023779039 |\n| 0.625 | .011739323 | .15345823 | .37195956 | .29861487 | .13118609 | .033041933 |\n| 0.750 | .009890500 | .14100071 | .35895153 | .30222981 | .14473280 | .043194647 |\n| 0.875 | .008723362 | .13263367 | .34886712 | .30260013 | .15363133 | .053544390 |"}
{"item_id": "item_0325", "chart_task_type": "growth_speed", "query": "Can you chart the growth of each cash equivalent category from 2012 to 2013 so I can see at a glance which one expanded the fastest?", "table_markdown": "| Interest Rate | 2013 Balance | 2012 Balance |\n|---------------|--------------|--------------|\n| Checking account: Home Federal Bank | - | $186,403 | $157,574 |\n| Money market account: Five Points Bank | 0.42% | 850,902 | 832,339 |\n| Ticket office change box | - | 5,100 | 5,100 |\n| Concession change bags | - | 22,800 | 22,800 |\n| **Total** | **$1,065,205** | **$1,017,813** |"}
{"item_id": "item_0326", "chart_task_type": "composition_compare", "query": "Can you chart the sectoral cost structure for each Australian state and territory so I can easily spot which regions rely most heavily on specific industries?", "table_markdown": "| Sector/Region | NSW | VIC | QLD | SA | WA | TAS | NT | ACT | Sum |\n|-------------------------------------|-------|-------|-------|-------|-------|-------|-------|-------|-------|\n| Mining | 11.9 | 3.2 | 30.5 | 2.3 | 67.9 | 0.7 | 4.3 | 0.0 | 120.9 |\n| Manufacturing | 29.2 | 23.4 | 19.0 | 4.9 | 10.3 | 1.8 | 1.1 | 0.4 | 89.9 |\n| Electricity, gas, water, waste | 6.9 | 4.5 | 5.3 | 1.8 | 2.6 | 0.9 | 0.3 | 0.3 | 22.5 |\n| Construction | 31.7 | 26.2 | 19.3 | 5.7 | 14.4 | 1.5 | 3.3 | 1.8 | 103.9 |\n| Wholesale trade | 12.0 | 9.6 | 7.6 | 2.3 | 3.5 | 0.5 | 0.2 | 0.3 | 36.0 |\n| Retail trade | 8.4 | 7.0 | 5.2 | 1.8 | 2.7 | 0.6 | 0.3 | 0.5 | 26.5 |\n| Accom & food services | 6.5 | 3.9 | 3.0 | 0.9 | 1.6 | 0.3 | 0.1 | 0.7 | 16.9 |\n| Transport, postal, warehouse | 10.5 | 9.8 | 8.4 | 2.5 | 6.8 | 0.9 | 0.7 | 0.5 | 40.2 |\n| Infor media & telecom | 10.3 | 6.6 | 3.4 | 1.5 | 1.9 | 0.4 | 0.2 | 0.4 | 24.7 |\n| Financial & insurance | 45.0 | 28.0 | 13.3 | 4.9 | 6.7 | 1.1 | 0.4 | 0.9 | 100.4 |\n| Rental, hiring, real estate | 13.1 | 7.1 | 7.1 | 1.6 | 2.7 | 0.6 | 1.0 | 0.8 | 34.0 |\n| Professional, scientific & tech | 34.4 | 24.8 | 13.2 | 4.0 | 8.9 | 0.7 | 0.8 | 2.4 | 89.2 |\n| Administrative services | 17.6 | 11.4 | 9.6 | 2.8 | 5.4 | 0.4 | 0.5 | 0.6 | 48.3 |\n| Education & training | 26.4 | 21.1 | 14.6 | 5.0 | 7.0 | 1.6 | 0.8 | 4.4 | 81.0 |\n| Health care, social assistance | 22.5 | 17.6 | 14.6 | 5.7 | 8.5 | 1.4 | 0.9 | 1.6 | 72.7 |\n| Arts & recreation services | 2.7 | 1.8 | 1.1 | 0.3 | 0.5 | 0.1 | 0.1 | 0.4 | 7.0 |\n| Other services | 4.5 | 3.7 | 2.8 | 0.8 | 1.6 | 0.2 | 0.1 | 0.2 | 14.0 |\n| **Sum** | **293.6** | **209.6** | **177.8** | **48.8** | **153.2** | **13.8** | **15.1** | **16.3** | **928.2** |"}
{"item_id": "item_0327", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of turn-taking preferences for each participant group so I can see at a glance how the mix differs between Sweden, USA 34, and USA 2?", "table_markdown": "| Group | GA at end of Line | Same Time | No Preference |\n|---|---|---|---|\n| Sweden 13 | 2 | 6 | 5 |\n| USA 34 | 9 | 21 | 4 |\n| USA 2 | 1 | 1 | 0 |\n| Total | 12 | 28 | 9 |"}
{"item_id": "item_0328", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of eye fixations by Macro TU type for both Source Text and Target Text AOIs so I can see at a glance how their internal compositions differ?", "table_markdown": "| Type of Macro TU | Po | P1 | P2 | P3 |\n|------------------|------|------|------|------|\n| Source Text (ST) | 1800 | 1387 | 101 | 129 |\n| | (64.9%) | (51.2%) | (22.0%) | (16.9%) |\n| Target Text (TT) | 973 | 1324 | 358 | 635 |\n| | (35.1%) | (48.8%) | (78.0%) | (83.1%) |\n| TOTAL | 2773 | 2711 | 459 | 764 |"}
{"item_id": "item_0329", "chart_task_type": "growth_speed", "query": "Can you chart the income growth for each decile and Cook County from 1940 to 1977? I want it to be obvious which group saw the fastest expansion, not just which one ended up with the highest income.", "table_markdown": "| Deciles* | Per Cap Tax 1940 | Per Cap Tax 1977 | Per Cap Income 1940 | Per Cap Income 1977 | Tax as % of Income 1940 | Tax as % of Income 1977 |\n|----------|------------------|------------------|---------------------|---------------------|-------------------------|-------------------------|\n| 1 | $35 | $384 | $729 | $8,543 | 4.76% | 4.49% |\n| Cook | 53 | 393 | 958 | 8,268 | 5.55 | 4.76 |\n| 2 | 33 | 325 | 554 | 7,519 | 5.93 | 4.35 |\n| 3 | 33 | 318 | 599 | 7,171 | 5.46 | 4.43 |\n| 4 | 29 | 288 | 523 | 6,918 | 5.60 | 4.16 |\n| 5 | 32 | 292 | 489 | 6,735 | 6.50 | 4.34 |\n| 6 | 27 | 246 | 446 | 6,371 | 5.97 | 3.87 |\n| 7 | 29 | 208 | 507 | 6,096 | 5.77 | 3.41 |\n| 8 | 22 | 230 | 400 | 5,865 | 5.32 | 4.00 |\n| 9 | 24 | 231 | 361 | 5,486 | 6.70 | 4.14 |\n| 10 | 21 | 214 | 328 | 4,916 | 6.49 | 4.36 |"}
{"item_id": "item_0330", "chart_task_type": "composition_compare", "query": "Can you chart the funding mix for FY 2002, FY 2003, and FY 2003-II so I can see at a glance how the proportion of spending on equipment, training, and other categories differs across these periods?", "table_markdown": "| FISCAL YEAR | EQUIPMENT ALLOCATION | TRAINING ALLOCATION | EXERCISE ALLOCATION | PLANNING ALLOCATION | ADMIN ALLOCATION | TOTAL ALLOCATION |\n|-------------|----------------------|---------------------|---------------------|---------------------|-----------------|-----------------|\n| FY 2002 | 125920 | | 4369 | | 3257 | 130289 |\n| FY 2003 | 17301 | 933 | 3112 | 1244 | | 22590 |\n| FY 2003-II | 86443 | | | | 2673 | 89116 |\n| | 229664 | 933 | 7481 | 1244 | 5930 | 241995 |"}
{"item_id": "item_0331", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of the initial payment for each aircraft type so I can see how the cost structure differs between them?", "table_markdown": "| | Cessna 152 | Warrior PA-28 | Cessna 172SP |\n|---|---|---|---|\n| Aircraft Deposit | $300 | $600 | $900 |\n| Non-refundable Application Fee | $200 | $200 | $200 |\n| One Month’s Dues | $15 | $15 | $15 |\n| Total Initial Payment for Member | $515 | $815 | $1115 |"}
{"item_id": "item_0332", "chart_task_type": "composition_compare", "query": "Can you chart the inventory mix for 2010 and 2011 so I can see at a glance how the proportion of each category changed between the two years?", "table_markdown": "| Item | As at 31st December, 2011 (Nu) | As at 31st December, 2010 (Nu) |\n|-------------------------------------------|-------------------------------|-------------------------------|\n| Raw Material | 27,163,230 | 22,053,249 |\n| Work In Progress | 16,745,911 | 24,761,850 |\n| Finished Goods | 3,291,592 | 4,202,933 |\n| Stores & Spares, Fuel & Loose Tools | 13,788,203 | 6,990,812 |\n| **Total** | **60,988,936** | **58,008,844** |"}
{"item_id": "item_0333", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of tree planting volumes for each river basin, excluding non-participants, so I can see at a glance how the engagement mix differs between them?", "table_markdown": "| Tree Planting Engagement in Each River Basin | Nyamindi (%) | Rupingazi (%) | Thiba (%) | Thuci (%) |\n|---------------------------------------------|--------------|---------------|-----------|----------|\n| 1-20 | 28.4 | 33.3 | 37.0 | 33.3 |\n| 21-50 | 21.6 | 23.4 | 21.0 | 25.3 |\n| 51-100 | 22.4 | 11.6 | 16.3 | 12.0 |\n| 101-200 | 9.8 | 5.0 | 6.5 | 10.7 |\n| 201 & above | 6.7 | 13.3 | 5.8 | 6.7 |\n| Total Percentage of Engagement in Tree Planting per River Basin | 88.9 | 86.6 | 86.6 | 88.0 |\n| Total Percentage of No Engagement in Tree Planting per River Basin | 11.1 | 13.4 | 13.4 | 12.0 |"}
{"item_id": "item_0334", "chart_task_type": "composition_compare", "query": "Can you chart the raptor species mix for the breeding and autumn migration seasons so I can easily spot which species dominate each period?", "table_markdown": "| Species | Breeding season | Autumn migration season |\n|--------------------------|-----------------|-------------------------|\n| Black Kite | 21.7 | 1.4 |\n| Buzzard | 46.5 | 47.3 |\n| Golden Eagle | 5.4 | 7.5 |\n| Falcon | 3.1 | 2.7 |\n| Common Kestrel | 16.3 | 21.2 |\n| Red Kite | 5.4 | 16.4 |\n| Sparrow Hawk | 0.8 | 3.4 |\n| Raptor unidentified | 0.8 | 0.0 |"}
{"item_id": "item_0335", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of emergency call types for Town versus County calls so I can see at a glance how their service mixes differ?", "table_markdown": "| Smoky Lake Fire Department: 2015 Fire Call | Town Calls | County Calls |\n|-------------------------------------------|------------|-------------|\n| Fires | 0 | 1 |\n| Medical Assists | 7 | 1 |\n| Motor Vehicle Collisions | 0 | 7 |\n| Fire Alarms | 1 | 4 |\n| **TOTAL** | **8** | **13** |"}
{"item_id": "item_0336", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of response times for each platform so I can easily compare their internal composition?", "table_markdown": "| | Facebook | Instagram | Twitter | TikTok | Youtube | Others |\n|---|---|---|---|---|---|---|\n| Within 24H | 1667 | 370 | 2051 | 807 | 82 | 0 |\n| After 24H | 2308 | 490 | 1961 | 980 | 33 | 9 |\n| No response | 2826 | 830 | 3538 | 460 | 1287 | 58 |\n| Total | 6801 | 1690 | 7550 | 2247 | 1402 | 67 |"}
{"item_id": "item_0337", "chart_task_type": "composition_compare", "query": "Can you chart the revenue mix for direct, indirect, and induced impacts so I can see at a glance how the composition of funding sources differs across these categories?", "table_markdown": "| IMPACT TYPE | CONSTRUCTION IN SALES TAX | EMPLOYEES SPENDING | PROPERTY SALES TAX | STATE SHARED REVENUE | TOTAL REVENUE |\n|-------------|---------------------------|--------------------|--------------------|----------------------|---------------|\n| DIRECT | 11,091,600 | 912,000 | 739,800 | 1,114,000 | 13,857,400 |\n| REVENUE | | | | | |\n| INDIRECT | N/A | 635,700 | 529,300 | 427,300 | 1,592,300 |\n| REVENUE | | | | | |\n| INDUCED | N/A | 507,100 | 451,600 | 338,800 | 1,297,500 |\n| REVENUE | | | | | |\n| TOTAL | 11,091,600 | 2,054,800 | 1,720,700 | 1,880,100 | 16,747,200 |"}
{"item_id": "item_0338", "chart_task_type": "composition_compare", "query": "Can you chart the racial mix for the City Auditor's office, Dallas, and the metro area so I can easily spot how their demographic structures differ?", "table_markdown": "| Race/Category | Office of the City Auditor – City of Dallas | Dallas, Texas (censusreporter.org) | Dallas, Fort-Worth, Arlington Metropolitan Area (censusreporter.org) |\n|------------------------|---------------------------------------------|------------------------------------|---------------------------------------------------------------------|\n| Non-Hispanic White | 45 | 27 | 42 |\n| Non-Hispanic Black | 25 | 23 | 16 |\n| Hispanic | 15 | 43 | 30 |\n| Race-Other | 15 | 7 | 12 |"}
{"item_id": "item_0339", "chart_task_type": "composition_compare", "query": "Can you chart the composition of Net Assets for 2014 and 2013 so I can see how the mix of Capital, Discipline, and General assets differs between the two years?", "table_markdown": "| Net Assets | 2014 | 2013 |\n|------------|------|------|\n| Capital assets | 14,502 | 6,801 |\n| Discipline | 73,106 | 69,062 |\n| General | 382,141 | 353,177 |\n| **Total Net Assets** | 469,749 | 429,040 |"}
{"item_id": "item_0340", "chart_task_type": "composition_compare", "query": "Can you chart the chemical composition of Streams 19 through 28 so I can easily compare the relative mix of components across each stream?", "table_markdown": "| Stream: | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 |\n|---------|------|------|------|------|------|------|------|------|------|------|\n| N₂ | -- | 509.4| 509.4| 509.4| 529.7| 529.7| 529.7| 550.0| 550.0| 550.0|\n| O₂ | -- | 135.4| -- | -- | -- | -- | -- | -- | -- | -- |\n| H₂S | 292.4| -- | 85.2 | 85.2 | 85.2 | 27.2 | 27.2 | 27.2 | 11.2 | 11.2 |\n| SO₂ | -- | -- | 31.8 | 31.8 | 35.4 | 6.4 | 6.4 | 10.0 | 2.0 | 2.0 |\n| H₂O | 20.9 | 11.4 | 238.9| 238.9| 243.2| 301.2| 301.2| 305.5| 321.5| 321.5|\n| S (vapor or liquid)* | -- | -- | 175.4| 3.1 | 3.1 | 90.1 | 3.1 | 3.1 | 27.1 | 3.2 |\n| Total lb-mol/hr | 313.3| 656.2| 1040.7| 868.4| 896.6| 954.6| 867.6| 897.0| 911.8| 887.9|"}
{"item_id": "item_0341", "chart_task_type": "composition_compare", "query": "Can you chart the distribution of agencies by how many subcontracting subcategory goals they met in 2016 versus 2017, so I can see at a glance how the performance mix shifted between the two years?", "table_markdown": "| Fiscal year | n/a | n/a | Number of agencies that met overall goal | Met 0 subcategory goals | Met 1 subcategory goal | Met 2 subcategory goals | Met 3 subcategory goals | Met all 4 subcategory goals |\n|-------------|-----|-----|----------------------------------------|-------------------------|------------------------|-------------------------|--------------------------|----------------------------|\n| 2016 | | | 16 | 2 | 3 | 9 | 7 | 3 |\n| 2017 | | | 15 | 3 | 1 | 6 | 7 | 7 |"}
{"item_id": "item_0342", "chart_task_type": "composition_compare", "query": "Can you chart the revenue mix for the previous and current fiscal years so I can see at a glance how the composition of business models has shifted?", "table_markdown": "| Business model | Previous consolidated fiscal year (From October 1, 2016 to September 30, 2017) | Current consolidated fiscal year (From October 1, 2017 to September 30, 2018) |\n|---|---|---|\n| Initial (Initial revenue) | 1,368,868 | 1,501,377 |\n| Stock (Monthly revenue) | 2,885,230 | 3,445,115 |\n| Fee (Transaction processing revenue) | 7,527,812 | 10,364,887 |\n| Spread (Merchant acquiring service revenue) | 9,272,510 | 11,105,940 |\n| Total | 21,054,421 | 26,417,320 |"}
{"item_id": "item_0343", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of non-residential space types (Retail, Office, Hotel) for the Existing, Pipeline, and Proposed New Development stages so I can see how the composition differs across them?", "table_markdown": "| | Existing² | Pipeline | Proposed New Development | TOTAL STATION AREA/DOWNTOWN SPECIFIC PLAN | TOTAL NEW DEVELOPMENT (INCLUDING PIPELINE) | TOTAL DEVELOPMENT (EXISTING + PROPOSED NEW DEVELOPMENT) 2035 |\n|--------------------------|-----------|----------|--------------------------|------------------------------------------|--------------------------------------------|-------------------------------------------------------------|\n| **Residential (Units)** | | | | | | |\n| Multifamily | 190 | 520 | 270 | 80 | 350 | 870 |\n| Single Family Attached | 510 | 100 | – | 170 | 170 | 270 |\n| Single Family Detached | 290 | 50 | – | 40 | 40 | 90 |\n| **TOTAL** | 990 | 670 | 270 | 290 | 560 | 1,230 |\n| **Non-Residential (Square Feet)** | | | | | | |\n| Retail | 112,500 | 65,000 | 303,300 | 80,200 | 383,500 | 448,500 |\n| Office | 403,00 | 12,000 | 163,300 | 73,300 | 236,600 | 248,600 |\n| Hotel³ | 0 | – | 62,000 | – | 62,000 | 62,000 |\n| **TOTAL** | 152,800 | 77,000 | 528,600 | 153,500 | 682,100 | 759,100 |\n| **JOBS⁴** | 430 | 210 | 1,480 | 470 | 1,950 | 2,160 |\n| **POPULATION⁵** | 2,800 | 1,300 | 480 | 640 | 1,120 | 2,420 |"}
{"item_id": "item_0344", "chart_task_type": "composition_compare", "query": "Can you chart the chemical composition mix for Portland cement, Silica fume, and Fly ash so I can see at a glance how their internal structures differ?", "table_markdown": "| Ingredient (%) | CaO | SiO₂ | Al₂O₃ | Fe₂O₃ | K₂O | MgO | Na₂O | SO₃ | P₂O₅ | MnO | ZnO | SrO |\n|----------------|-------|-------|-------|-------|-------|-------|-------|------|------|------|------|------|\n| Portland cement | 64.94 | 19.58 | 4.5 | 3.119 | 0.75 | 2.64 | 0.079 | 2.14 | 0.128| 0.127| 0.024| 0.148|\n| Silica fume | 0.213 | 92.87 | 0.354 | 0.113 | 0.332 | 0.224 | 0.068 | 1.26 | 0.11 | 0.008| 0.019| 0.005|\n| Fly ash | 2.44 | 48.74 | 30.63 | 2.611 | 1.25 | 0.575 | 0.552 | 0.706| 0.247| 0.016| 0.013| 0.06 |"}
{"item_id": "item_0345", "chart_task_type": "composition_compare", "query": "Can you chart how the mix of household investment decisionmakers (sole male, sole female, or joint) shifted between 1999, 2002, and 2005, so I can see at a glance which group's share changed the most?", "table_markdown": "| | 1999 Equity Investors | 2002 Equity Investors | 2005 Equity Investors | Individual Stock | Stock Mutual Fund |\n|--------------------------|-----------------------|-----------------------|-----------------------|------------------|------------------|\n| Male is sole decisionmaker | 26 | 24 | 25 | 28 | 24 |\n| Female is sole decisionmaker | 20 | 19 | 21 | 18 | 21 |\n| Co-decisionmakers | 54 | 57 | 54 | 54 | 55 |\n| Number of respondents | 2,336 | 2,165 | 2,414 | 1,259 | 2,172 |"}
{"item_id": "item_0346", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of loss types for each Gate so I can easily compare how the composition of misconduct differs between them?", "table_markdown": "| Type of Loss | Gate A | Gate A+ | Gate B | Total |\n|-------------------------------|--------|---------|--------|-------|\n| Reputational | 130 | 20 | 140 | 290 |\n| (blank) | 125 | 6 | 17 | 148 |\n| Reputational & Financial | 42 | 1 | 19 | 62 |\n| Financial | 22 | 0 | 0 | 22 |\n| **Total** | **319**| **27** | **176**| **522**|"}
{"item_id": "item_0347", "chart_task_type": "composition_compare", "query": "Can you chart the energy output mix for each run to show how the proportion of heat in products, steam, and losses compares across the different trials?", "table_markdown": "| RUN NO. | HEAT CONTENT OF FEED | HEAT OF REACTION | TOTAL INPUT | HEAT CONTENT OF PRODUCTS | HEAT IN STEAM | TOTAL OUTPUT | HEAT LOST | UNACCOUNTED FOR |\n|---------|----------------------|------------------|-------------|--------------------------|---------------|-------------|-----------|-----------------|\n| 3B | 199 | 280 | 479 | 179 | 224 | 403 | 76 | -2 |\n| 4A | 304 | 310 | 615 | 186 | 248 | 434 | 181 | 83 |\n| 4E | 262 | 326 | 588 | 187 | 248 | 435 | 153 | 75 |\n| 5A | 194 | 294 | 489 | 168 | 148 | 316 | 173 | 95 |\n| 5B | 198 | 362 | 560 | 165 | 240 | 405 | 155 | 77 |\n| 7A | 238 | 283 | 521 | 162 | 206 | 368 | 153 | 75 |\n| 7B | 25 | 374 | 399 | 224 | 0 | 224 | 175 | 97 |\n| 9A | 270 | 495 | 765 | 186 | 507 | 694 | 71 | -6 |\n| 9B | 358 | 504 | 862 | 250 | 507 | 757 | 105 | 27 |\n| 11A | 370 | 417 | 787 | 204 | 475 | 679 | 108 | 30 |\n| 12A | 351 | 414 | 768 | 243 | 437 | 680 | 88 | 10 |\n| 12B | 358 | 435 | 793 | 247 | 497 | 744 | 49 | -29 |\n| 12C | 357 | 458 | 815 | 241 | 490 | 731 | 84 | 6 |\n| 12D | 334 | 449 | 783 | 237 | 490 | 727 | 56 | -22 |\n| 12E | 359 | 415 | 774 | 238 | 420 | 678 | 96 | 18 |"}
{"item_id": "item_0348", "chart_task_type": "composition_compare", "query": "Can you chart the investment mix for 2017 and 2018 so I can see at a glance how the allocation across asset classes shifted between the two years?", "table_markdown": "| | 2018 | 2017 |\n|--------------------------------|------------|------------|\n| Guaranteed investment certificates, measured at amortized cost | $1,529,776 | $1,524,803 |\n| Provincial bonds and debentures, measured at amortized cost | 307,729 | 456,217 |\n| Common and preferred shares, measured at fair value | 5,201,331 | 5,130,526 |\n| **Total** | **$7,038,836** | **$7,111,546** |"}
{"item_id": "item_0349", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of budget adjustment types for each funding source so I can easily compare their structural differences?", "table_markdown": "| Funding Type | Appropriations | Deappropriations | Transfers | Total |\n|-------------------------------|----------------------|----------------------|--------------------|------------------|\n| TransNet | $229,373.00 | $- | $482,058.00 | $711,431.00 |\n| Impact Fees | 20,621,585.28 | - | - | 20,621,585.28 |\n| Redevelopment Bond Proceeds | 100,000.00 | - | 14,511,000.00 | 14,611,000.00 |\n| Water/Sewer Funds | 6,811,460.00 | 16,000,000.00 | 39,870,029.00 | 62,681,489.00 |\n| Other Funds | 4,916,610.69 | 3,628,990.91 | 7,804,305.85 | 16,349,907.45 |\n| **Total** | **$32,679,028.97** | **$19,628,990.91** | **$62,667,392.85** | **$114,975,412.73** |"}
{"item_id": "item_0350", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of transport modes for daily commuters from India to Nepal by nationality, so I can see at a glance how the travel mix differs between Nepali and Indian passengers?", "table_markdown": "| National | on foot, bicycle, ricksha | by truck | by small vehicle | by Indian bus | By Nepali bus | Total passengers daily | Total persons, annually |\n|---|---|---|---|---|---|---|---|\n| Nepali | 300 | 40 | 120 | 50 | 250 | 760 | 273,600 |\n| Indian | 400 | 800 | 1,200 | 350 | 50 | 2,800 | 1,008,000 |\n| | Total= 1,281,600 | | | | | | |"}
{"item_id": "item_0351", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of elephant population estimates by certainty level for each of the seven countries, so I can see at a glance how the mix of definite, probable, possible, and speculative counts differs across them?", "table_markdown": "| COUNTRY | DEFINITE | PROBABLE | POSSIBLE | SPECULATIVE | RANGE AREA (km²) | % OF REGIONAL RANGE | % OF RANGE ASSESSED | IQI¹ | PFS² |\n|-------------------------------|----------|----------|----------|-------------|------------------|---------------------|---------------------|------|------|\n| Cameroon | 179 | 726 | 4,965 | 9,517 | 118,571 | 12 | 45 | 0.03 | 1 |\n| Central African Republic | 109 | 1,689 | 1,036 | 500 | 73,453 | 8 | 95 | 0.51 | 2 |\n| Chad | 3,885 | 0 | 2,000 | 550 | 149,443 | 15 | 26 | 0.15 | 1 |\n| Congo | 402 | 16,947 | 4,024 | 729 | 135,918 | 14 | 23 | 0.18 | 1 |\n| Democratic Republic of Congo | 2,447 | 7,955 | 8,855 | 4,457 | 263,700 | 27 | 40 | 0.18 | 1 |\n| Equatorial Guinea | 0 | 0 | 700 | 630 | 15,008 | 2 | 13 | 0.00 | 2 |\n| Gabon | 1,523 | 23,457 | 27,911 | 17,746 | 218,985 | 22 | 94 | 0.33 | 1 |\n| **TOTAL** | **10,383** | **48,936** | **43,098** | **34,129** | **975,079** | **29** | **52** | **0.22** | **1** |"}
{"item_id": "item_0352", "chart_task_type": "composition_compare", "query": "Can you chart the cost structure breakdown for the Clerk and Board groups within Information Technology so I can easily compare their relative spending on salaries, benefits, and services?", "table_markdown": "| | Clerk | Board | Total |\n|----------------------|---------|--------|----------|\n| **Full Time Equivalent Positions** | 1.0 | 2.2 | 3.2 |\n| **Salaries** | $42,203 | $216,393 | $258,596 |\n| **Employee Benefits**| $21,924 | $113,183 | $135,107 |\n| **Contract/Professional Services**| $7,812 | $17,188 | $25,000 |\n| **Purchased Services**| $128,095 | $281,809 | $409,904 |\n| **Materials & Supplies**| $15,061 | $33,139 | $48,200 |\n| **Capital Outlay** | - | - | - |\n| **Total Information Technology** | $215,095 | $661,712 | $876,807 |"}
{"item_id": "item_0353", "chart_task_type": "composition_compare", "query": "Can you chart how the breakdown of execution time shifts across the four mesh sizes, so I can see which processing stage dominates the total duration for each?", "table_markdown": "| # of faces | 40M | 95M | 125M | 224M |\n|------------|-----|-----|------|------|\n| Halo Sync (MPI) | 0.20 | 0.19 | 0.20 | 0.22 |\n| Marching Cube (CUDA) | 0.58 | 1.93 | 3.69 | 19.75 |\n| Rendering (OpenGL) | 0.13 | 0.14 | 0.15 | 0.15 |\n| Compositing (CUDA + MPI) | 0.50 | 0.53 | 0.52 | 0.53 |\n| Spark System Overhead | 0.37 | 0.37 | 0.37 | 0.38 |\n| Total | 1.79 | 3.16 | 4.92 | 21.03 |"}
{"item_id": "item_0354", "chart_task_type": "composition_compare", "query": "Can you chart the expense mix for 2013 and 2014 so I can see at a glance how the spending structure shifted between the two years?", "table_markdown": "| | 2014 (Reviewed) | 2013 (Audited) |\n|--------------------------------|-----------------|----------------|\n| **Revenues** | | |\n| Member assessments | $365,637 | $366,475 |\n| Parking lease income | 2,440 | 1,240 |\n| Late penalty and other | 718 | 1,040 |\n| **Total Revenues** | 368,795 | 368,755 |\n| **Expenses** | | |\n| Administrative costs | 52,896 | 48,923 |\n| Common property maintenance | 139,394 | 107,867 |\n| Insurance-property and liability| 14,846 | 14,741 |\n| Landscaping | 60,904 | 52,148 |\n| Legal fees | 2,680 | 1,290 |\n| Major repairs | 13,909 | 14,866 |\n| Pool service and repairs | 11,604 | 10,131 |\n| Tennis court renovation | - | 8,643 |\n| Theme tower renovation | 3,050 | - |\n| Trash removal | 17,359 | 17,298 |\n| Utilities-electricity | 14,522 | 15,926 |\n| Utilities-water and sewer | 47,985 | 43,378 |\n| **Total Expenses** | 379,150 | 335,211 |\n| **Excess (Deficit) Revenues Over Expenses** | (10,355) | 33,544 |\n| **Fund Deficit Balances, Beginning** | (27,740) | (61,284) |\n| **Fund Deficit Balances, Ending** | $ (38,095) | $ (27,740) |"}
{"item_id": "item_0355", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of metadata standards for repositories with and without educational metadata, so I can see at a glance how the composition differs between the two groups?", "table_markdown": "| ROER | DC | Educational Standard (LOM, SCORM & IMS) | Free tags | METS | Not identified | Total | % |\n|---------------------------|----|----------------------------------------|-----------|------|----------------|-------|-----|\n| With educational metadata | 18 | 18 | 5 | 3 | 3 | 47 | 42.7|\n| Without educational metadata | 38 | 4 | 5 | | 16 | 63 | 57.3|\n| **Total** | **56** | **22** | **10** | **3** | **19** | **110** | |"}
{"item_id": "item_0356", "chart_task_type": "composition_compare", "query": "Can you chart the composition of Equity and Liabilities for 2015 and 2016 so I can easily spot how the funding mix shifted between the two years?", "table_markdown": "| Particulars | Notes | 31/03/2016 (₹) | 31/03/2015 (₹) |\n|--------------------------------------------------|-------|----------------|----------------|\n| **I) EQUITY AND LIABILITIES** | | | |\n| (1) SHARE HOLDERS FUND | | | |\n| (a) Share capital | 3 | 18,001,400 | 18,001,400 |\n| (b) Reserves and Surplus | 4 | 67,687,482 | 50,845,432 |\n| (2) SHARE APPLICATION MONEY | | | |\n| PENDING ALLOTMENT | | | |\n| (3) NON-CURRENT LIABILITIES | | | |\n| (a) Long-Term Borrowings | 5 | 7,551,079 | 10,597,487 |\n| (b) Deferred Tax Liabilities (Net) | 6 | 7,033,992 | 6,415,850 |\n| (c) Other Long Term Liabilities | 7 | - | 2,500,000 |\n| (d) Long-Term Provisions | 8 | 182,556 | 2,038,552 |\n| (4) CURRENT LIABILITIES | | | |\n| (a) Short-Term Borrowings | 9 | 19,273,466 | 13,915,728 |\n| (b) Trade Payables | 10 | 12,540,122 | 22,033,191 |\n| (c) Other Current Liabilities | 11 | 12,026,295 | 9,427,173 |\n| (d) Short-Term Provisions | 12 | 7,141,653 | 5,076,653 |\n| **TOTAL** | | 151,438,045 | 140,851,466 |"}
{"item_id": "item_0357", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of collection sources for Mildura, Merbein, and Red Cliffs so I can see at a glance how the funding mix differs between the locations?", "table_markdown": "| Last week’s collections | Mildura | Merbein | Red Cliffs |\n|-------------------------|---------|---------|------------|\n| 1st Collection (supporting Priests & Presbytery) | $1043.55 | $83.70 | $169.20 |\n| 2nd Collection (includes all EFT, DD & CC) | $679.50 | $185.00 | $205.00 |\n| Loose Plate | $258.50 | $16.00 | $25.15 |"}
{"item_id": "item_0358", "chart_task_type": "composition_compare", "query": "Can you chart the distribution channel mix for the D & P market in the UK versus Ireland so I can see at a glance how their structures differ?", "table_markdown": "| | UK | Ireland |\n|---------------------|------|---------|\n| % Volume | | |\n| Pharmacists | 49 | 65 |\n| Mail Order | 39 | 25 |\n| Express Service | 6 | 5 |\n| Specialist Shops | 6 | 5 |\n| | 100 | 100 |"}
{"item_id": "item_0359", "chart_task_type": "composition_compare", "query": "Can you chart the operating expense structure for each service type so I can easily spot which services have a different cost mix?", "table_markdown": "| | | | | | | |\n|--------------------------------|--------------|-------------|-------------|--------------|-------------|--------------|\n| Electricity, gas and water purchased for resale | $61,039,093 | $12,423,623 | $1,448,856 | – | – | $74,911,572 |\n| Operation and maintenance | $11,582,318 | $2,208,148 | $9,995,476 | $2,393,930 | $705,608 | $26,885,480 |\n| Administrative and general | $9,165,983 | $1,008,420 | $3,329,429 | $844,396 | $106,726 | $14,454,954 |\n| Customer accounts and sales | $1,412,149 | $223,192 | $954,446 | $293,074 | $22,216 | $2,895,077 |\n| Provision for depreciation and amortization | $22,488,052 | $1,827,108 | $4,813,472 | $1,460,916 | $1,452,690 | $32,042,238 |\n| **Total operating expenses** | $105,687,595 | $17,690,491 | $20,541,679 | $4,952,316 | $2,287,240 | $151,159,321 |\n| **Operating income (loss)** | $29,658,107 | $3,432,176 | $2,546,019 | $139,601 | $(1,894,299)| $33,881,604 |"}
{"item_id": "item_0360", "chart_task_type": "composition_compare", "query": "Can you chart how the mix of library circulation categories shifted from 1980 to 1981, so I can see at a glance which types made up a larger share of the total in each year?", "table_markdown": "| Category | 1980 | 1981 | % Change |\n|------------------|------|------|----------|\n| CRC items | 2,227| 2,633| +18.2 |\n| Juvenile | 560 | 638 | +13.9 |\n| LC non-print | 147 | 225 | +53.0 |\n| Records | 388 | 720 | +85.5 |\n| Reserve | 574 | 82 | -85.7 |\n| Tapes | 136 | 178 | +30.8 |\n| Total | 4,032| 4,476| +11.0 |"}
{"item_id": "item_0361", "chart_task_type": "composition_compare", "query": "Can you chart the composition of the two estate retirement groups so I can see at a glance how their internal structures differ?", "table_markdown": "| | Operating Margins | Plains G&T | Investment in Associated Organizations | Cushion of Credit & Other Non-Ops | Tri-State | Gains | Unpaid Balance | Total | Total without Tri-State |\n|----------------|-------------------|------------|----------------------------------------|----------------------------------|-----------|-------|----------------|-------|-------------------------|\n| EM-Dec-22-A | $1,163.57 | $16.75 | $94.61 | $70.35 | $473.72 | $128.80| $0.00 | $1,947.80 | $1,474.08 |\n| EM-Dec-22-B | $143.21 | $0.32 | $16.69 | $14.43 | $64.39 | $0.00 | $0.00 | $239.04 | $174.65 |"}
{"item_id": "item_0362", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of outcomes for processed applications in the Home Child Care Pilot versus the Home Support Worker Pilot, so I can see at a glance how their approval and refusal rates differ?", "table_markdown": "| Caregiver pilot program and year of application | Total received | Processed | | | |\n|---|---|---|---|---|---|\n| | | Approved | Refused | Withdrawn | Total |\n| Home Child Care Pilot | 25,017 | 2,075 | 1,335 | 1,005 | 4,415 |\n| 2019 | 2,292 | 753 | 299 | 91 | 1,143 |\n| 2020 | 8,165 | 607 | 732 | 496 | 1,835 |\n| 2021 | 6,470 | 584 | 254 | 351 | 1,189 |\n| 2022 | 6,219 | 131 | 50 | 62 | 243 |\n| 2023 (January) | 1,871 | | | 5 | 5 |\n| Home Support Worker Pilot | 12,551 | 506 | 636 | 568 | 1,710 |\n| 2019 | 749 | 209 | 167 | 47 | 423 |\n| 2020 | 3,049 | 179 | 364 | 364 | 882 |\n| 2021 | 3,890 | 118 | 103 | 103 | 366 |\n| 2022 | 4,685 | | 2 | 2 | 33 |\n| 2023 (January) | 133 | | | 6 | 6 |\n| Grand Total | 37,568 | 2,581 | 1,971 | 1,573 | 6,125 |"}
{"item_id": "item_0363", "chart_task_type": "composition_compare", "query": "Can you chart how the mix of income sources differs between the 2020 and 2019 budgets, so I can see at a glance which year relied more heavily on each component?", "table_markdown": "| | 2020 BUDGET | 2019 BUDGET (UNAUDITED) |\n|----------------------|-------------|-------------------------|\n| Earned Income | 116,000 | 102,753 |\n| Fees for Services | 1,693,678 | 1,629,278 |\n| Grants | 142,240 | 144,440 |\n| Investment | 26,000 | 90,475 |\n| Other | 1,250 | 472 |\n| **TOTAL INCOME** | **$1,979,168** | **$1,967,418** |"}
{"item_id": "item_0364", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of the salary package components for the minimum SRF 1 level versus the maximum SRF 6 level, so I can easily see how the composition of the total package differs between the two?", "table_markdown": "| Senior Research Fellow - SRF1 to SRF6 | | |\n|---|---|---|\n| PACKAGE COMPONENT | Minimum Value SRF 1 ($) | Maximum Value SRF 6 ($) |\n| Gross Salary (position advertised as Academic Level C, SRF1 - SRF6) | 128,399 | 146,717 |\n| Superannuation (14% superannuation contribution depends on employee contributing 3% of pre-tax salary) | 17,976 | 20,540 |\n| Salary Packaging Grossed Up (Based on utilising the full $15,900 salary packaging component plus the $2,650 Meal Entertainment Card.) | 8,469 | 9,657 |\n| Leave Loading (Payable on the last pay before Christmas (first year will be a pro rata payment) | 1,676 | 1,676 |\n| Total Salary Package | 156,519 | 178,591 |"}
{"item_id": "item_0365", "chart_task_type": "composition_compare", "query": "Can you chart the asset composition for 2012 and 2013 so I can see at a glance how the mix of fixed assets, investments, current assets, loans, and miscellaneous expenditure differs between the two periods?", "table_markdown": "| Assets | As at 31.03.2013 | As at 31.03.2012 |\n|------------------------------|------------------|------------------|\n| Fixed Assets | 7073.21 | 5683.34 |\n| Investments | 2500.85 | 2501.94 |\n| Current Assets | 17482.60 | 13739.46 |\n| Loans & Advances | 7786.60 | 8846.04 |\n| Misc. Expenditure (to the extent not written off or adjusted) | - | 10.77 |\n| **Total** | **34843.35** | **28780.55** |"}
{"item_id": "item_0366", "chart_task_type": "composition_compare", "query": "Can you chart the income mix for the Town of Johnstown versus Fulton County so I can see how the distribution of households across income brackets differs between them?", "table_markdown": "| | Town of Johnstown | Fulton County |\n|--------------------------------|-------------------|---------------|\n| **Population and Households** | | |\n| Population (2015 Estimate) | 7,360 | 54,000 |\n| Households | 2,640 | 22,300 |\n| Avg. Household Size | 2.5 | 2.4 |\n| **Households by Income (2015 Estimate)** | | |\n| Median Household Income | $52,700 | $47,000 |\n| <$25,000 | 561 | 5,860 |\n| $25,000-$50,000 | 652 | 5,940 |\n| $50,000-$75,000 | 584 | 4,590 |\n| $75,000-$100,000 | 244 | 2,370 |\n| $100,000-$150,000 | 390 | 2,670 |\n| $150,000+ | 204 | 856 |\n| **Housing** | | |\n| Housing Units (2010) | 2,910 | 28,600 |\n| Pct. Owner-Occupied (2011-2015)| 92% | 70% |\n| Median Home Value | $117,200 | $108,200 |\n| Median Gross Rent | $963 | $711 |\n| **Education** | | |\n| Pct. High School Graduate or Higher | 86% | 86% |\n| Pct. Bachelor’s Degree or Higher | 16.5% | 16.2% |\n| **Economy** | | |\n| Mean Travel Time to Work (minutes) | 27.8 | 24.1 |\n| Retail Sales per Household (2012) | $30,000 | $30,000 |\n| Unemployment Rate¹ | N/A | 5.7% |\n| Average Wage in Manufacturing | N/A | $19.30 |\n| Average Wholesale Wage | N/A | $22.20 |\n| Average Wage in Transportation & Warehousing | N/A | $18.00 |"}
{"item_id": "item_0367", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of professional fees for 2013 and 2012 so I can see how the mix of contract, engineering, accounting, and legal costs shifted between the two years?", "table_markdown": "| | 2013 | 2012 |\n|----------------------|----------|----------|\n| Contract services | $59,220 | $58,351 |\n| Engineering fees | 30,688 | 26,428 |\n| Accounting fees | 20,392 | 19,577 |\n| Legal fees | 10,581 | 22,833 |\n| **Total professional fees** | **$120,881** | **$127,189** |"}
{"item_id": "item_0368", "chart_task_type": "composition_compare", "query": "Can you chart the cost structure breakdown for each of the four fuel cycle strategies so I can easily spot which approach relies most heavily on capital, operating, or fuel cycle expenses?", "table_markdown": "| Storage of discharge fuel mill/kwh | Reprocessing discharge fuel, recycle U and Pu mill/kwh | Reprocessing discharge fuel, U recycle, 10-yr Pu storage and recycle mill/kwh | 10-yr. storage of discharge fuel, U-Pu recycle mill/kwh |\n|-------------------------------------|--------------------------------------------------------|-----------------------------------------------------------------------------|----------------------------------------------------------|\n| Capital cost | 26.1 | 26.1 | 26.1 | 26.1 |\n| Operating cost | 2 | 2 | 2 | 2 |\n| Fuel cycle cost | 5.2 | 4.8 | 5.3 | 5.0 |\n| Total cost of electrical energy | 33.3 | 32.9 | 33.4 | 33.1 |\n| Percentage difference | 0 | -1.2 | +0.3 | -0.6 |"}
{"item_id": "item_0369", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of maintenance costs for 2015 and 2016 so I can see at a glance how the cost structure shifted between the two years?", "table_markdown": "| Year ended | December 31, 2016 | December 31, 2015 |\n|------------|-------------------|-------------------|\n| Costs of maintenance of computer equipment | 1,040 | 1,457 |\n| Costs of maintenance vehicles | 1,441 | 3,370 |\n| Costs of maintenance of office furniture | 958 | 278 |\n| Maintenance costs – business premises | 2,559 | 3,255 |\n| Maintenance costs of the building | 2,164 | 1,478 |\n| Other costs of maintenance | 1,281 | 6,506 |\n| **Total** | **9,443** | **16,344** |"}
{"item_id": "item_0370", "chart_task_type": "composition_compare", "query": "Can you chart the income mix for 2005 and 2006 so I can see at a glance how the relative contribution of each source changed between the two years?", "table_markdown": "| Category | 2006 $Am | 2005 $Am | % Δ |\n|-----------------------------------------------|----------|----------|-----|\n| Funds Management | 768 | 700 | 10 |\n| M&A, Advisory and Underwriting | 913 | 571 | 60 |\n| Brokerage and Commissions | 441 | 329 | 34 |\n| Wrap and Other Administration Fee Income | 99 | 68 | 46 |\n| Financial Products | 65 | 75 | (13)|\n| Banking, Lending & Securitisation | 37 | 17 | 118 |\n| Income From Business Alliances | 52 | 35 | 49 |\n| Other | 65 | 26 | 150 |\n| **TOTAL** | **2,440**| **1,821**| **34**|"}
{"item_id": "item_0371", "chart_task_type": "composition_compare", "query": "Can you chart the diagnostic mix for each follow-up status group so I can see at a glance how the composition of conditions differs between those who were interviewed, refused, lost, or deceased?", "table_markdown": "| Subjects' Follow-Up Status | Schizophrenia | Schizoaffective Disorder | Affective Disorders | Atypical Psychosis | Other | Organic Disorders | Total |\n|----------------------------|---------------|--------------------------|---------------------|--------------------|-------|-------------------|-------|\n| Alive and interviewed | 82 | 25 | 29 | 13 | 19 | 10 | 178 |\n| Alive; refused participation | 4 | 1 | 1 | 0 | 5 | 2 | 13 |\n| Could not be located | 4 | 1 | 1 | 0 | 1 | 0 | 7 |\n| Deceased | 28 | 4 | 16 | 5 | 7 | 10 | 70 |\n| **Total** | **118** | **31** | **47** | **18** | **32**| **22** | **268**|"}
{"item_id": "item_0372", "chart_task_type": "composition_compare", "query": "Can you chart how the breakdown of system overhead across the different modules shifts as the bit-width changes, so I can see which components dominate the cost structure at each level?", "table_markdown": "| # Bits | SGQ | CAG’s FP | CAG’s BP | GC | Total |\n|--------------|-------|----------|----------|-------|-------|\n| 8 bits | 31.93 | 1.09 | 1.88 | 8.67 | 43.57 |\n| 6 bits | 14.86 | 1.06 | 1.76 | 4.51 | 22.19 |\n| **4 bits (Suggested)** | **5.51** | **1.05** | **1.75** | **1.62** | **9.89** |\n| 2 bits | 4.12 | 1.04 | 1.73 | 1.24 | 8.14 |"}
{"item_id": "item_0373", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of raw biomass sources by region so I can see at a glance how the supply mix differs across BC, Prairies, ON, QC, and Maritimes?", "table_markdown": "| | BC | Prairies | ON | QC | Maritimes |\n|------------------|----------|-----------|-----------|-----------|-----------|\n| Hardwood Roadside| 403,902 | 1,735,900 | 896,329 | 1,366,950 | 196,094 |\n| Softwood Roadside| 13,331,800 | 2,704,032 | 3,484,810 | 5,446,830 | 1,275,246 |\n| Hardwood Mill | 86,727 | 124,886 | 392,389 | 626,017 | 98,896 |\n| Softwood Mill | 2,722,930 | 572,896 | 929,515 | 1,807,200 | 475,028 |\n| Urban Waste | 1,302,787 | 1,572,707 | 3,900,176 | 2,489,721 | 368,003 |\n| **Total** | **17,848,146** | **6,710,421** | **9,603,219** | **11,736,718** | **2,413,267** |"}
{"item_id": "item_0374", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of gross postmortem findings for each wapiti status group so I can see at a glance whether the lesion profile differs between reactors, nonreactors, and untested animals?", "table_markdown": "| Status | Number | GVLa | LNPb | NVLc |\n|--------------|--------|------|------|------|\n| Nonreactor | 62 | 1 | 4 | 57 |\n| Reactors | 32 | 13 | 7 | 12 |\n| Not tested | 24 | 0 | 6 | 18 |\n| TOTAL | 118 | 14 | 17 | 87 |"}
{"item_id": "item_0375", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of council membership by policy maker category for each of the 10 areas, so I can easily compare how the composition differs between them?", "table_markdown": "| Policy Maker Category | | | | | | | | | | | | |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| | | Mash East | Mash West | Mash Central | Manicaland | Masvingo | Midlands | Mat South | Mat North | Harare | Bulawayo | |\n| | | | | | | | | | | | | Totals |\n| 1. Senators | | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 60 |\n| 2. Senator Chiefs | | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 16 |\n| 3. President & Deputy Presidents of Chiefs50 | | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | |\n| 4. Members of National Assembly | | 23 | 23 | 18 | 26 | 26 | 27 | 13 | 13 | 29 | 12 | |\n| 5. Women members of the National Assembly51 | | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | |\n| 6. Mayors and Chairpersons | | 10 | 14 | 10 | 10 | 9 | 14 | 10 | 9 | 5 | 1 | |\n| 7. Proportional Reps | | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 100 |\n| 8. Total Council Membership 2013-2023 | | 57 | 61 | 52 | 60 | 60 | 65 | 47 | 47 | 56 | 35 | |\n| | 8. Total Council | | | | | | | | | | | |\n| | Membership 2013-2023 | | | | | | | | | | | |\n| 9. Numbers after 2023 | | 51 | 55 | 46 | 54 | 54 | 59 | 41 | 41 | 50 | 29 | |"}
{"item_id": "item_0376", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of Pari-Mutuel Handle sources for Fiscal Years 2022 and 2023 so I can see at a glance if the mix of Live, Simulcast, and Teletracking races has shifted?", "table_markdown": "| Handle By Source | Advanced Deposit Wagering | Commercial Horse | Commercial Dog | County Fair | Fiscal Year 2023 | Fiscal Year 2022 |\n|---------------------------|---------------------------|------------------|----------------|-------------|-----------------|-----------------|\n| Live Races | $2,050,607 | $7,709,117 | $0 | $243,818 | $10,003,542 | $9,497,821 |\n| Simulcast Races | $59,991,747 | $80,669,795 | $9,877,686 | $40,652 | $150,579,880 | $142,055,406 |\n| Teletracking | $0 | $692,927 | $0 | $9,622 | $702,549 | $775,569 |\n| Total Pari-Mutuel Handle | $62,042,354 | $89,071,839 | $9,877,686 | $294,092 | $161,285,971 | $152,328,796 |"}
{"item_id": "item_0377", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of county economic classifications for each state so I can easily compare how the mix of distressed, transitional, and attainment counties differs across them?", "table_markdown": "| SCRC States | # of Distressed Counties | % of SCRC Distressed Counties | # of Transitional Counties | % of SCRC Transitional Counties | # of Attainment Counties | % of SCRC Attainment Counties |\n|----------------------|--------------------------|-------------------------------|---------------------------|---------------------------------|-------------------------|-------------------------------|\n| Alabama | 2 | 1.2% | 10 | 5.6% | 1 | 1.2% |\n| Florida | 21 | 12.5% | 34 | 19.1% | 12 | 15.0% |\n| Georgia | 79 | 47.0% | 33 | 18.5% | 10 | 12.5% |\n| Mississippi | 14 | 8.3% | 4 | 2.2% | 0 | 0.0% |\n| North Carolina | 26 | 15.5% | 35 | 19.7% | 8 | 10.0% |\n| South Carolina | 18 | 10.7% | 17 | 9.6% | 4 | 5.0% |\n| Virginia | 8 | 4.8% | 45 | 25.3% | 45 | 56.3% |\n| Southeast Crescent Region | 168 | 100.0% | 178 | 100.0% | 80 | 100.0% |"}
{"item_id": "item_0378", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of work hours by task type for each staircase so I can see how the composition of labor differs across them?", "table_markdown": "| staircase number | preparatory works [w-h] | execution of the floor covering [w-h] | preparation of landings [w-h] | laying the terrazzo flooring on landings [w-h] | installation of stair plinths [w-h] | installation of plinths on landings [w-h] | grouting [w-h] | silicone sealing [w-h] | total [w-h] |\n|------------------|------------------------|--------------------------------------|-------------------------------|-----------------------------------------------|-------------------------------------|------------------------------------------|----------------|------------------------|------------|\n| A1 | 67.06 | 207.76 | 83.91 | 126.71 | 23.50 | 26.07 | 41.39 | 22.68 | 599.08 |\n| A2 | 65.78 | 136.12 | 71.02 | 108.30 | 24.30 | 23.64 | 35.76 | 21.56 | 486.48 |\n| E1 | 49.07 | 119.22 | 62.70 | 89.24 | 23.40 | 27.56 | 34.42 | 20.03 | 425.65 |\n| E2 | 53.52 | 121.38 | 75.41 | 97.37 | 25.10 | 24.76 | 36.02 | 22.79 | 456.35 |\n| B1 | 42.80 | 135.50 | 54.00 | 98.50 | 25.00 | 26.20 | 39.20 | 23.60 | 444.80 |\n| B2 | 45.60 | 116.80 | 53.40 | 81.50 | 24.10 | 23.50 | 36.70 | 22.30 | 403.90 |\n| D1 | 50.30 | 111.80 | 64.70 | 82.10 | 25.00 | 25.10 | 36.40 | 24.50 | 419.90 |\n| C1 | 54.15 | 100.54 | 55.72 | 86.95 | 27.30 | 23.19 | 26.57 | 20.48 | 394.90 |\n| C2 | 58.46 | 113.31 | 61.55 | 87.28 | 23.71 | 20.56 | 34.77 | 20.13 | 419.78 |\n| D2 | 60.50 | 116.50 | 65.10 | 88.60 | 24.30 | 24.30 | 45.60 | 29.00 | 453.90 |"}
{"item_id": "item_0379", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of adaptation measures for Term C and Term M so I can see at a glance how their internal compositions differ?", "table_markdown": "| CATEGORY | Total | Term C | Term M |\n|-----------------------------------------------|-------|--------|--------|\n| Legal-institutional framework | 9 | 5 | 4 |\n| Spaces for participation | 6 | 6 | |\n| Capacity building | 10 | 4 | 6 |\n| Watershed and water source management | 5 | 4 | 1 |\n| Infrastructure | 4 | 2 | 2 |\n| Risk management | 5 | 4 | 1 |\n| Demand management | 9 | 9 | |\n| TOTAL | 48 | 34 | 14 |"}
{"item_id": "item_0380", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of bikeway facility types for the Study Area versus Ventura County so I can see at a glance how their infrastructure mixes differ?", "table_markdown": "| BICYCLE FACILITY TYPE | STUDY AREA | VENTURA COUNTY |\n|-----------------------|------------|---------------|\n| Class I | 33 | 80 |\n| Class II | 217 | 361 |\n| Class III | 48 | 70 |\n| Class IV | 1 | 1 |\n| **Total** | **299** | **512** |"}
{"item_id": "item_0381", "chart_task_type": "composition_compare", "query": "Can you chart the cost structure for each of the five student status groups so I can see at a glance how the mix of expenses differs between them?", "table_markdown": "| | Dependent | $9025.00 | $740.00 | $4,665.00 | $2,420.00 | $450.00 | $60.00 | $17,360.00 |\n|---|---|---|---|---|---|---|---|---|\n| FLHCON | Part-Time Inde- pendent | $9740.00 | $900.00 | $3,332.00 | $5,940.00 | $680.00 | $115.00 | $20,707.00 |\n| | Part-Time Depend- ent | $9,740.00 | $900.00 | $3,332.00 | $1,485.00 | $345.00 | $70.00 | $15,872.00 |\n| | Full-Time Inde- pendent | $11,540.00 | $900.00 | $3,332.00 | $7,920.00 | $900.00 | $115.00 | $24,707.00 |\n| | Full-time Dependent | $11,540.00 | $900.00 | $3,332.00 | $1,980.00 | $450.00 | $70.00 | $18,272.00 |\n| | | Tuition & Fees | Books & Supplies* | Transportation* | Living Allowance* | Personal/Misc. Expenses* | Student Loan Fees | TOTAL |"}
{"item_id": "item_0382", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of voucher types for each housing authority so I can see at a glance which program drives the majority of purchases for each group?", "table_markdown": "| HA Name | HVO | FSS | MTW | Totals |\n|--------------------------|-----|-----|-----|--------|\n| San Francisco HA | 2 | 0 | 0 | 2 |\n| Oakland HA | 0 | 0 | 1 | 1 |\n| Fresno City Housing Authority | 0 | 2 | 0 | 2 |\n| Richmond Housing Authority | 1 | 0 | 0 | 1 |\n| San Mateo County | 0 | 0 | 5 | 5 |\n| Monterey | 2 | 0 | 0 | 2 |\n| Marin Housing | 4 | 1 | 0 | 5 |\n| Santa Clara | 0 | 0 | 2 | 2 |\n| Pittsburg | 1 | 0 | 0 | 1 |\n| Alameda County Hsg Auth | 3 | 1 | 0 | 4 |\n| Madera | 0 | 1 | 0 | 1 |\n| Vacaville | 3 | 0 | 0 | 3 |\n| Southern Nevada Regional HA | 3 | 2 | 0 | 5 |\n| **Total** | **19** | **7** | **8** | **34** |"}
{"item_id": "item_0383", "chart_task_type": "composition_compare", "query": "Can you chart how the breakdown of the Student Services Fee changed between the 2022-23 and 2023-24 cohorts, so I can see at a glance which components make up a larger share of the total in each period?", "table_markdown": "| | 2022-23 SSF | 2023-24 SSF Incoming Cohort | Incremental Increase Incoming Cohort |\n|----------------------|-------------|-----------------------------|-------------------------------------|\n| RTA | $98 | $122 | $24 |\n| Mental Health Fee | $126 | $141 | $15 |\n| Discretionary SSF | $952 | $967 | $15 |\n| TOTAL | $1,176 | $1,230 | $54 |"}
{"item_id": "item_0384", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of ungulate types (pigs, deer, goats) for each location so I can see at a glance how the hunting effort mix differs across sites?", "table_markdown": "| Location | Pigs | Deer | Goats | Total |\n|---------------------------------|------|------|-------|-------|\n| Akatarawa | 8 | 3 | 79 | 90 |\n| Baring Head/Ōrua-pouanui | 0 | 0 | 3 | 3 |\n| Belmont Regional Park | 0 | 0 | 1 | 1 |\n| East Harbour Regional Park | 7 | 7 | 0 | 14 |\n| Hutt City Council | 10 | 66 | 0 | 76 |\n| Hutt Water Collection Area | 2 | 40 | 27 | 69 |\n| Kaitoke Regional Park | 0 | 25 | 10 | 35 |\n| Keith George Memorial Park | 0 | 0 | 28 | 28 |\n| Pākuratahi | 4 | 8 | 94 | 106 |\n| Parangarahu Lakes | 1 | 1 | 94 | 96 |\n| Wainuiomata-Ōrongorongo | 23 | 10 | 51 | 84 |\n| Wainuiomata Mainland Island | 81 | 5 | 3 | 89 |\n| Other | 3 | 113 | 19 | 135 |\n| **Total** | **134** | **278** | **409** | **826** |"}
{"item_id": "item_0385", "chart_task_type": "composition_compare", "query": "Can you chart the relative chemical makeup of Wood, Peat, Lignite, Bituminous Coal, and Anthracite Coal so I can easily compare how the proportion of Carbon, Hydrogen, Oxygen, and Nitrogen shifts across these fuel types?", "table_markdown": "| | Wood. | Peat. | Lignite. | Bituminous Coal. | Anthracite Coal. |\n|----------------|-------|-------|----------|------------------|-----------------|\n| Carbon | 50 | 60 | 70 | 82 | 94 |\n| Hydrogen | 6 | 6 | 5 | 5 | 3 |\n| Oxygen | 43 | 32 | 24 | 12 | 3 |\n| Nitrogen | 1 | 2 | 1 | 1 | Trace. |"}
{"item_id": "item_0386", "chart_task_type": "composition_compare", "query": "Can you chart the relative mix of the seven atmospheric patterns for both present and future conditions, so I can easily see how the composition of each scenario differs?", "table_markdown": "| Cluster No | 1 | 2 | 3 | 4 | 5 | 6 | 7 | Total |\n|------------|-----|-----|-----|-----|-----|-----|-----|-------|\n| Present condition | 21.5% | 10.0% | 6.81% | 14.9% | 6.41% | 6.00% | 2.32% | 67.9% |\n| Future condition | 11.9% | 4.33% | 6.97% | 13.2% | 4.64% | 10.3% | 5.47% | 56.8% |"}
{"item_id": "item_0387", "chart_task_type": "composition_compare", "query": "Can you chart the expense mix for both the APS and Staff proposed budgets so I can easily see how the cost structure differs between them?", "table_markdown": "| | APS’ Proposed Budget | Staff’s Proposed Budget | Difference |\n|--------------------------------|----------------------|-------------------------|------------|\n| Non Capital-Related Expenses\\(^{(1)}\\) | $2,835,000 | $2,498,000 | $337,000 |\n| Capital-Related Carrying Costs\\(^{(2)}\\) | $721,015 | $199,639 | $521,376 |\n| Consultant Expenses\\(^{(3)}\\) | $125,000 | $0 | $125,000 |\n| Total | $3,681,015 | $2,697,639 | $983,376 |"}
{"item_id": "item_0388", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of Calls for Service from 2015 to 2019? I want it to be obvious at a glance when the growth first started to weaken.", "table_markdown": "| Year | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sept | Oct | Nov | Dec | Total |\n|------|------|------|------|------|------|------|------|------|------|------|------|------|-------|\n| 2015 | 899 | 777 | 1081 | 950 | 1015 | 1160 | 1193 | 1066 | 1120 | 966 | 980 | 12320 |\n| 2016 | 939 | 940 | 929 | 998 | 1265 | 1302 | 1213 | 1257 | 1183 | 1035 | 947 | 845 | 12853 |\n| 2017 | 936 | 937 | 1231 | 1243 | 1291 | 1283 | 1243 | 1324 | 1067 | 1123 | 1098 | 1004 | 13780 |\n| 2018 | 884 | 845 | 933 | 1031 | 1138 | 1221 | 1305 | 1286 | 1167 | 1105 | 882 | 962 | 12759 |\n| 2019 | 941 | 839 | 952 | 986 | 1149 | 1109 | 1168 | | | | | | 8268 |"}
{"item_id": "item_0389", "chart_task_type": "momentum_turning", "query": "Can you plot the momentum of the portfolio balance so I can spot at a glance which year saw the biggest jump?", "table_markdown": "| Year | Loans Issued | Amount | Repayments | Portfolio Balance |\n|------|--------------|----------|------------|------------------|\n| 1997 | 19 | $311,826 | $80,595 | $1,074,220 |\n| 1998 | 19 | $512,810 | $142,285 | $1,307,769 |\n| 1999 | 25 | $783,816 | $199,300 | $1,598,725 |\n| 2000 | 15 | $470,013 | $109,947 | $1,794,593 |\n| 2001 | 28 | $884,862 | $253,154 | $2,837,062 |\n| 2002 | 26 | $550,434 | $182,942 | $3,040,681 |\n| 2003 | 32 | $790,538 | $234,412 | $3,560,527 |\n| 2004 | 39 | $722,462 | $591,362 | $3,977,548 |\n| 2005 | 20 | $484,928 | $252,834 | $3,783,889 |"}
{"item_id": "item_0390", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of depreciation expenses for 2020 and 2019 so I can see at a glance how the cost structure shifted between the two years?", "table_markdown": "| | 2020 | 2019 |\n|--------------------------------|------------|------------|\n| Depreciation expense | | |\n| Depreciation - buildings | 315,052 | 328,857 |\n| Depreciation - gaming machines | 937,062 | 988,936 |\n| Depreciation - office equipment| 296,163 | 293,925 |\n| Depreciation - landscaping and carpark | 7,572 | 7,547 |\n| Depreciation - grounds | 52,944 | 21,121 |\n| Depreciation - investment properties | 19,272 | 18,646 |\n| Depreciation – right of use asset | 266,254 | - |\n| Total depreciation expense | 1,894,319 | 1,659,032 |"}
{"item_id": "item_0391", "chart_task_type": "composition_compare", "query": "Can you chart the age mix of older adults across the six Hamilton communities so I can easily spot which areas have a relatively higher concentration of the oldest residents?", "table_markdown": "| Age Group | Ancaster | Dundas | Flamborough | Glanbrook | Hamilton | Stoney Creek | City of Hamilton |\n|-----------------|----------|--------|-------------|-----------|----------|--------------|------------------|\n| 55-64 years | 5,780 | 3,745 | 6,160 | 3,220 | 44,595 | 9,810 | 73,310 |\n| 65-74 years | 3,710 | 3,120 | 4,040 | 2,935 | 29,005 | 6,755 | 49,565 |\n| 75-84 years | 1,830 | 1,730 | 1,590 | 1,595 | 16,850 | 3,305 | 26,900 |\n| 85+ years | 700 | 660 | 585 | 480 | 6,800 | 1,170 | 10,395 |\n| Total 55 years and older | 12,020 | 9,255 | 12,375 | 8,230 | 97,250 | 21,040 | 160,170 |\n| % of total population in 2016 | 30% | 38% | 29% | 28% | 29% | 30% | 30% |\n| % growth from 2006-2016 | 39% | 20% | 35% | 69% | 14% | 33% | 22% |"}
{"item_id": "item_0392", "chart_task_type": "momentum_turning", "query": "Can you chart the NCLEX-PN pass rate trends from 2005 to 2010? I want it to be obvious at a glance where the momentum shifted most sharply downward.", "table_markdown": "| Exam Year | NCLEX- PN® Pass Rate | Number of First Time Candidates (Passed/Total) | BON Approval Status |\n|---|---|---|---|\n| 2010 | 78.28% | 209/267 | Pending |\n| 2009 | 79.55% | 210/264 | Full |\n| 2008 | 81.67% | 196/240 | Full |\n| 2007 | 85.52% | 254/297 | Full |\n| 2006 | 86.70% | 163/188 | Full |\n| 2005 | 88.69% | 149/168 | Full |"}
{"item_id": "item_0393", "chart_task_type": "momentum_turning", "query": "Can you chart the shifts in the mean unemployment rate from 1961 to 1973? I want it to be obvious at a glance which year saw the sharpest spike.", "table_markdown": "| Year | Mean Unemployment Rate (1) | Standard deviation of Employment rates (2) | Coefficient of variation (1)-(2) |\n|------|---------------------------|------------------------------------------|-------------------------------|\n| 1961 | 6.34 | .981 | .155 |\n| 1962 | 5.33 | .894 | .168 |\n| 1963 | 5.64 | .872 | .154 |\n| 1964 | 5.21 | .769 | .148 |\n| 1965 | 4.65 | 1.015 | .218 |\n| 1966 | 3.89 | .831 | .214 |\n| 1967 | 3.89 | .953 | .245 |\n| 1968 | 3.64 | .823 | .226 |\n| 1969 | 3.54 | .772 | .218 |\n| 1970 | 5.00 | 1.065 | .213 |\n| 1971 | 5.88 | 1.341 | .228 |\n| 1972 | 5.53 | 1.192 | .215 |\n| 1973 | 4.93 | 1.041 | .211 |"}
{"item_id": "item_0394", "chart_task_type": "composition_compare", "query": "Can you chart the composition of creditors falling due within one year for 2015 and 2014 so I can easily spot how the mix of debt types shifted between the two years?", "table_markdown": "| Description | 2015 | 2014 |\n|--------------------------------------------------|----------|----------|\n| Bank credit card | 3,242 | 1,332 |\n| Trade creditors | 138,781 | 98,479 |\n| Amounts owed to group undertakings | 19,824 | 30,924 |\n| Corporation tax | 40 | 20 |\n| Other taxation and social security (see below) | 53,845 | 39,812 |\n| Director's loan account | 11,988 | - |\n| Accruals | 29,762 | 29,776 |\n| Other creditors | 125,714 | 58,924 |\n| **Total** | **383,196** | **259,267** |"}
{"item_id": "item_0395", "chart_task_type": "momentum_turning", "query": "Can you plot the monthly inflation trend for urban areas from late 2015 to mid-2017? I want it to be obvious at a glance exactly when the upward momentum stopped and prices started to ease.", "table_markdown": "| Source No | Time Period | Sub Population | Geographical Area | Data Value |\n|-----------|-------------|----------------|-------------------|------------|\n| 1 | 2015 Dec | Urban | National | 2.2 |\n| 2 | 2016 Jan | Urban | National | 2.2 |\n| 3 | 2016 Feb | Urban | National | 2.1 |\n| 4 | 2016 Mar | Urban | National | 2.2 |\n| 5 | 2016 Apr | Urban | National | 2.6 |\n| 6 | 2016 May | Urban | National | 2.7 |\n| 7 | 2016 Jun | Urban | National | 3 |\n| 8 | 2016 Jul | Urban | National | 3.4 |\n| 9 | 2016 Aug | Urban | National | 3.7 |\n| 10 | 2016 Sep | Urban | National | 3.9 |\n| 11 | 2016 Oct | Urban | National | 4 |\n| 12 | 2016 Nov | Urban | National | 4 |\n| 13 | 2016 Dec | Urban | National | 4 |\n| 14 | 2017 Jan | Urban | National | 4.3 |\n| 15 | 2017 Feb | Urban | National | 4.8 |\n| 16 | 2017 Mar | Urban | National | 5 |\n| 17 | 2017 Apr | Urban | National | 5.3 |\n| 18 | 2017 May | Urban | National | 5.6 |\n| 19 | 2017 Jun | Urban | National | 5.5 |"}
{"item_id": "item_0396", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of producer returns from 1988 to 2005? I want it to be obvious at a glance when the growth hit its steepest drop.", "table_markdown": "| Year | Checkoff funding $ | Returns in mil.2 $ | Net Import or Export3 % | Return per cwt. $ |\n|---|---|---|---|---|\n| 2005 | 4,755,263 | 1,608.7 | 7.91 net exp. | 5.83 |\n| 2004 | 4,365,761 | 1,654.2 | 5.27 net exp. | 6.05 |\n| 2003 | 4,675,003 | - 46.3 | 2.67 net exp. | - 0.17 |\n| 2002 | 4,842,222 | - 199.9 | 2.75 net exp. | - 0.76 |\n| 2001 | 5,159,989 | 810.7 | 3.18 net exp. | 3.18 |\n| 2000 | 5,245,321 | - 377.7 | 1.69 net exp. | - 1.49 |\n| 1999 | 4,896,000 | - 129.2 | 2.33 net exp. | - 0.49 |\n| 1998 | 5,700,000 | 114.7 | 2.76 net exp. | 0.45 |\n| 1997 | 4,800,000 | 136.0 | 2.37 net exp. | 0.58 |\n| 1996 | 2,498,000 | 363.4 | 2.05 net exp. | 2.45 |\n| 1995 | 1,754,000 | 597.4 | 0.69 net exp. | 2.49 |\n| 1994 | 1,267,800 | 200.3 | 1.11 net imp. | 0.84 |\n| 1993 | 1,150,000 | - 149.9 | 1.73 net imp. | - 0.65 |\n| 1992 | 1,079,000 | 545.9 | 1.31 net imp. | 2.35 |\n| 1991 | 996,268 | 419.5 | 3.04 net imp. | 1.94 |\n| 1990 | 651,595 | - 67.9 | 4.28 net imp. | - 0.33 |\n| 1989 | 466,024 | 368.1 | 3.98 net imp. | 1.72 |\n| 1988 | 280,577 | 277.5 | 6.03 net imp. | 1.31 |\n| 1987 | 125,834 | | 7.59 net imp. | |\n| Total | 54,657,668 | 6,325.5 | | avg. 1.40 |"}
{"item_id": "item_0397", "chart_task_type": "momentum_turning", "query": "Can you plot the sales volume trend from 2011 to 2023? I want it to be obvious at a glance when the growth momentum first turned negative.", "table_markdown": "| Year | Sales Volume (ktonnes) | Adjusted Operating Profit (SEK million) |\n|------|------------------------|----------------------------------------|\n| 2011 | 209 | 149 |\n| 2012 | 362 | 152 |\n| 2013 | 371 | 159 |\n| 2014 | 463 | 160 |\n| 2015 | 541 | 164 |\n| 2016 | 687 | 239 |\n| 2017 | 933 | 373 |\n| 2018 | 1,005 | 375 |\n| 2019 | 866 | 347 |\n| 2020 | 648 | 351 |\n| 2021 | 1,008 | 489 |\n| 2022 | 1,150 | 479 |\n| 2023 | 463 | 1,536 |"}
{"item_id": "item_0398", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of sales growth over the years so I can spot at a glance exactly when it first started to weaken?", "table_markdown": "| Date | Gross Margin | Sales Growth | Pretax Profit |\n|------------|--------------|--------------|---------------|\n| 12/31/2006 | 60.2% | 11.0% | 10.9% |\n| 12/31/2007 | 59.8% | 13.7% | 15.8% |\n| 12/31/2008 | 64.6% | 5.2% | 12.5% |\n| 12/31/2009 | 59.5% | -6.3% | 7.6% |\n| 12/31/2010 | 54.5% | 7.6% | 7.2% |\n| 12/31/2011 | 57.5% | 10.3% | 7.5% |\n| 12/31/2012 | 54.5% | 14.3% | 7.7% |\n| 12/31/2013 | 56.00% | 12.80% | 7.30% |"}
{"item_id": "item_0399", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of ongoing administrative costs by activity for each firm size, so I can see at a glance how the cost structure differs across groups?", "table_markdown": "| Activity | 250+ | 50-249 | 20-49 | 5-19 | 2-4 | 1 | Total |\n|---------------------------|------|--------|-------|-------|-------|-------|-------|\n| Prepare for start-up | 0 | 0 | 2 | 0 | 6 | 2 | 10 |\n| Registration | 0 | 0 | 0 | 0 | 2 | 0 | 3 |\n| Enrolment | 7 | 3 | 2 | 3 | 3 | 1 | 18 |\n| Collection and Administration | 5 | 7 | 8 | 26 | 37 | 12 | 96 |\n| **Total** | **11** | **10** | **12** | **30** | **47** | **16** | **127** |"}
{"item_id": "item_0400", "chart_task_type": "composition_compare", "query": "Can you chart the expense mix for the CURRENT and 2019 periods so I can see at a glance if the cost structure has shifted?", "table_markdown": "| | Per Unit | CURRENT | Per Unit | 2019 |\n|--------------------------------|----------|-----------|----------|----------|\n| Real Estate Taxes | $928 | $31,535 | $928 | $31,535 |\n| Insurance | $30 | $1,003 | $30 | $1,003 |\n| Management Fee (mowing & plowing) | $36 | $1,210 | $36 | $1,210 |\n| Repairs & Maintenance | $234 | $7,971 | $234 | $7,971 |\n| Water / Sewer Expenses | $98 | $3,345 | $98 | $3,345 |\n| Professional Services (accounting & legal) | $339 | $11,529 | $339 | $11,529 |\n| **Total Operating Expense** | $1,665 | $56,593 | $1,665 | $56,593 |\n| % of EGI | 54.20 % | | 54.20 % | |"}
{"item_id": "item_0401", "chart_task_type": "composition_compare", "query": "Can you chart the ingredient mix for each of the 9 formulations so I can see at a glance how the composition differs between them?", "table_markdown": "| Ingredient (mg) | FC c | FC 1 | FC 2 | FC 3 | FS 1 | FS 2 | FS 3 | FCA 1 | FCA 2 |\n|---|---|---|---|---|---|---|---|---|---|\n| Ritonavir | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 |\n| Lopinavir | 80 | 80 | 80 | 80 | 80 | 80 | 80 | 80 | 80 |\n| CP | - | 5 | 10 | 15 | - | - | - | - | - |\n| SSG | - | - | - | - | 5 | 10 | 15 | - | - |\n| CA | - | - | - | - | - | - | - | 5 | 10 |\n| MCC | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 |\n| Aspartame | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |\n| Flavour | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |\n| Talc | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |\n| Mannitol | 100 | 95 | 90 | 85 | 95 | 90 | 85 | 95 | 90 |\n| Total Weight | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 |"}
{"item_id": "item_0402", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of inmate release types for 2009 and 2010 so I can see at a glance how the composition of releases shifted between the two years?", "table_markdown": "| Type of Release | Jan - Dec 2009 | Jan - Dec 2010 | |\n|---|---|---|---|\n| Total Releases | 26,939 | 26,394 | 2,245 |\n| Parole | 9,569 | 9,240 | 797 |\n| Cond. Release | 11,418 | 11,077 | 952 |\n| Alt Program Cases | 965 | 1,087 | 80 |\n| Max. Exp. | 2,796 | 2,935 | 233 |\n| Max. Exp. PRS | 1,322 | 1,291 | 110 |\n| Other2/ | 869 | 764 | 72 |"}
{"item_id": "item_0403", "chart_task_type": "momentum_turning", "query": "Can you plot the momentum in the number of learners writing Maths from 2008 to 2016? I want it to be obvious at a glance when the decline stopped and numbers started rising again.", "table_markdown": "| | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 |\n|---|---|---|---|---|---|---|---|---|---|\n| Wrote Maths | 300008 | 290630 | 263034 | 224635 | 225874 | 241509 | 225458 | 263903 | 265810 |\n| Pass +30% | 136503 | 133505 | 124749 | 104033 | 121970 | 142666 | 120523 | 129481 | 135958 |\n| % | 45.5 | 45.9 | 47.4 | 46.3 | 54 | 59 | 53.5 | 49.1 | 51.1 |\n| Pass +40% | 89788 | 85356 | 81506 | 67592 | 81000 | 97748 | 79037 | 84273 | 89084 |\n| % | 29.9 | 29.4 | 30 | 30.1 | 35.9 | 40.5 | 35.1 | 31.9 | 33.5 |"}
{"item_id": "item_0404", "chart_task_type": "momentum_turning", "query": "Can you chart the changes in total costs from Sep-16 to Dec-17? I want it to be obvious at a glance which month saw the sharpest drop.", "table_markdown": "| DATE | Direct Labor | Fringe | Direct Materials | Direct Consultants | Direct Equipment | Direct Travel | Other Direct Costs | Subtotal | F&A | Total Costs | Fixed Fee | Total | Funding Balance |\n|------|--------------|--------|------------------|--------------------|-----------------|--------------|-------------------|----------|-----|-------------|-----------|-------|-----------------|\n| Sep-16 | 8,407 | 2,186 | 102 | - | - | - | 7,250 | 17,945 | 5,384 | 23,329 | 1,633 | 24,962 | 715,473 |\n| Oct-16 | 12,779 | 3,323 | 1,009 | - | 9,347 | 940 | 16,011 | 43,410 | 13,023 | 56,433 | 3,950 | 60,383 | 655,090 |\n| Nov-16 | 19,867 | 5,165 | - | 20,000 | - | - | 8,729 | 53,761 | 16,128 | 69,890 | 4,892 | 74,782 | 580,308 |\n| Dec-16 | 8,376 | 2,178 | - | 8,358 | - | - | 18,563 | 37,474 | 11,242 | 48,717 | 3,410 | 52,127 | 524,181 |\n| Jan-17 | 14,753 | 3,836 | - | - | - | 362 | 6,115 | 25,065 | 7,520 | 32,585 | 2,281 | 34,866 | 460,236 |\n| Feb-17 | 14,947 | 3,885 | - | - | - | 211 | 10,737 | 29,773 | 8,932 | 38,705 | 2,709 | 41,415 | 418,822 |\n| Mar-17 | 17,590 | 4,574 | - | 6,000 | 2,287 | - | 17,260 | 47,711 | 14,313 | 62,024 | 4,342 | 66,366 | 352,456 |\n| Apr-17 | 31,215 | 8,116 | - | 7,750 | - | - | 25,548 | 72,629 | 21,789 | 94,417 | 6,609 | 101,027 | 251,429 |\n| May-17 | 23,528 | 6,117 | - | 8,500 | 1,583 | 648 | 16,851 | 57,228 | 17,168 | 74,396 | 5,208 | 79,604 | 171,825 |\n| Jun-17 | 34,177 | 8,886 | - | 11,540 | 4,488 | 466 | 21,968 | 81,526 | 24,458 | 105,984 | 7,419 | 113,403 | 58,423 |\n| Jul-17 | 20,222 | 5,257 | - | 9,250 | - | 2,793 | 19,710 | 57,231 | 14,880 | 72,111 | 5,048 | 77,159 | 707,293 |\n| Aug-17 | 16,514 | 4,294 | - | 33,450 | - | 215 | 36,290 | 90,762 | 23,598 | 114,361 | 8,005 | 122,366 | 584,927 |\n| Sep-17 | 13,864 | 3,605 | - | 12,000 | 1,974 | 557 | 28,762 | 60,762 | 15,798 | 76,561 | 5,359 | 81,920 | 503,008 |\n| Oct-17 | 5,747 | 1,494 | - | - | 10,625 | 436 | 79,037 | 97,339 | 25,308 | 122,647 | 8,585 | 131,232 | 371,776 |\n| Nov-17 | 8,330 | 2,166 | - | 3,000 | 157 | - | 4,437 | 18,090 | 4,704 | 22,794 | 1,596 | 24,389 | 347,386 |\n| Dec-17 | 7,141 | 1,000 | - | - | - | - | 35,700 | 49,701 | 12,921 | 62,625 | 4,164 | 67,088 | 280,778 |"}
{"item_id": "item_0405", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of Common Equity Tier 1 capital over the last five quarters? I want it to be obvious at a glance when the growth trend first turned negative.", "table_markdown": "| (in billions, except ratio) | Estimated Mar 31, 2017 | Dec 31, 2016 | Sep 30, 2016 | Jun 30, 2016 | Mar 31, 2016 |\n|-----------------------------|------------------------|--------------|--------------|--------------|--------------|\n| Total equity | $202.5 | 200.5 | 204.0 | 202.7 | 198.5 |\n| Adjustments: | | | | | |\n| Preferred stock | (25.5) | (24.6) | (24.6) | (24.8) | (24.1) |\n| Additional paid-in capital on ESOP preferred stock | (0.2) | (0.1) | (0.1) | (0.2) | (0.2) |\n| Unearned ESOP shares | 2.5 | 1.6 | 1.6 | 1.9 | 2.3 |\n| Noncontrolling interests | (1.0) | (0.9) | (1.0) | (1.0) | (1.0) |\n| **Total common stockholders’ equity** | **178.3** | 176.5 | 179.9 | 178.6 | 175.5 |\n| Adjustments: | | | | | |\n| Goodwill | (26.7) | (26.7) | (26.7) | (27.0) | (27.0) |\n| Certain identifiable intangible assets (other than MSRs) | (2.4) | (2.7) | (3.0) | (3.4) | (3.8) |\n| Other assets (2) | (2.1) | (2.1) | (2.2) | (2.0) | (2.1) |\n| Applicable deferred taxes (3) | 1.7 | 1.8 | 1.8 | 1.9 | 2.0 |\n| Investment in certain subsidiaries and other | (0.1) | (0.4) | (2.0) | (2.5) | (1.9) |\n| **Common Equity Tier 1 (Fully Phased-In) under Basel III** | **(A)** | 148.7 | 146.4 | 147.8 | 145.6 | 142.7 |\n| Total risk-weighted assets (RWAs) anticipated under Basel III (4)(5) | **(B)** | $1,327.4 | 1,358.9 | 1,380.0 | 1,372.9 | 1,345.1 |\n| Common Equity Tier 1 to total RWAs anticipated under Basel III (Fully Phased-In) (5) | **(A)/(B)** | 11.2% | 10.8 | 10.7 | 10.6 | 10.6 |"}
{"item_id": "item_0406", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of Ohio's average UC tax rate as a percentage of total wages from 1988 to 2002? I want it to be obvious at a glance which year saw the sharpest acceleration in that rate.", "table_markdown": "| Year | Yr. End Trust Fund Balance- Millions $$ | Trust Fund Revenue- Millions $$ | Avg. Taxes as % of Total Wages | Avg. Taxes as % of Taxable Wages |\n|---|---|---|---|---|\n| 1988 | $443.9 | $761.1 | 1.09 | 2.96 |\n| 1989 | $778.5 | $816.3 | 0.98 | 2.71 |\n| 1990 | $886.6 | $735.1 | 0.86 | 2.44 |\n| 1991 | $647.4 | $709.7 | 0.82 | 2.35 |\n| 1992 | $602.5 | $852.3 | 0.95 | 2.8 |\n| 1993 | $845.1 | $897.7 | 0.96 | 2.82 |\n| 1994 | $1167 | $955.8 | 0.95 | 2.77 |\n| 1995 | $1601 | $982.9 | 0.91 | 2.66 |\n| 1996 | $1751 | $806.6 | 0.76 | 2.27 |\n| 1997 | $1875 | $661.5 | 0.54 | 1.68 |\n| 1998 | $2028 | $669.1 | 0.51 | 1.64 |\n| 1999 | $2152 | $631.9 | 0.47 | 1.53 |\n| 2000 | $2236 | $640.5 | 0.44 | 1.47 |\n| 2001 | $1904 | $594.1 | 0.42 | 1.42 |\n| 2002 | $1537 | $685.9 | 0.46 | 1.61 |"}
{"item_id": "item_0407", "chart_task_type": "composition_compare", "query": "Can you chart the change in the asset mix between August and December 2009 so I can see at a glance how the composition of total assets shifted?", "table_markdown": "| DKKm | 10 August 2009 | 31 December 2009 |\n|-----------------------|----------------|------------------|\n| **ASSETS** | | |\n| Cash in hand and demand deposits with central banks | 650 | 535 |\n| Due from credit institutions and central banks | 234 | 149 |\n| Loans, advances and other receivables at amortised cost | 14,523 | 12,602 |\n| Bonds and shares at fair value | 2,005 | 1,970 |\n| Land and buildings | 433 | 871 |\n| Assets held temporarily | 68 | 45 |\n| Other assets | 181 | 89 |\n| **TOTAL ASSETS** | **18,094** | **16,261** |\n| **EQUITY AND LIABILITIES** | | |\n| Due to credit institutions and central banks | 14,355 | 249 |\n| Debt to Finansiel Stabilitet A/S | - | 10,578 |\n| Deposits and other payables | 1,577 | 1,353 |\n| Other liabilities | 316 | 314 |\n| Other provisions | 941 | 663 |\n| Subordinated debt | 1,000 | 1,000 |\n| Equity | (95) | 2,104 |\n| **TOTAL EQUITY AND LIABILITIES** | **18,094** | **16,261** |"}
{"item_id": "item_0408", "chart_task_type": "composition_compare", "query": "Can you chart the expense structure for 2018 versus 2017 so I can see at a glance how the mix of costs shifted between the two years?", "table_markdown": "| Expenses | 2018 | 2017 |\n|---------------------------------------------|----------|----------|\n| Accounting/Bookeeping Fees | - | 1,150 |\n| Assets < $20,000 | 2,002 | - |\n| Audit Fee Expense | 1,417 | 5,500 |\n| Bank Charges | 1,061 | 717 |\n| Blue Card Expenses | 698 | - |\n| Computers & Software | 4,034 | 1,224 |\n| Directors Development | 6,140 | - |\n| Fees & Subscriptions | 1,158 | 1,656 |\n| Fundraising - Commission | 14,358 | - |\n| Fundraising Event - Expenses | 13,391 | - |\n| Gifts | 356 | - |\n| Government Fees | 2,522 | 900 |\n| Insurance Expense | 4,896 | 2,615 |\n| Marketing Expense | 9,881 | 5,601 |\n| Motor Vehicle Expenses | 45 | - |\n| Non-Clinical Contractors | 5,120 | 1,148 |\n| Non-Clinical Materials | 11 | 890 |\n| Office Supplies | 1,589 | 634 |\n| Operational Expenses | 132,175 | 38,782 |\n| Postage | 415 | 139 |\n| Staff Training Expenses | 1,763 | 84 |\n| Superannuation | 22,203 | 9,459 |\n| Travelling Expenses | 102,302 | 24,673 |\n| Uniforms | 683 | 132 |\n| Wages & Salaries Expenses | 230,645 | 100,695 |\n| Work Cover Premiums | 549 | 689 |\n| **Total Expenses** | **559,411** | **196,687** |"}
{"item_id": "item_0409", "chart_task_type": "composition_compare", "query": "Can you chart the incident type mix for each jurisdiction so I can easily spot which cities rely most heavily on EMS calls versus fires?", "table_markdown": "| CITY/JURISDICTION | FIRE | OVER-PRESSURE RUPTURE | EMS/RESCUE CALL | HAZMAT | SERVICE CALL | GOOD INTENT CALL | FALSE CALL | NATURAL | OTHER | TOTAL |\n|----------------------------|------|-----------------------|-----------------|--------|--------------|------------------|------------|---------|-------|-------|\n| ALISO VIEJO | 0 | 0 | 24 | 0 | 0 | 0 | 2 | 0 | 5 | 31 |\n| BUENA PARK | 4 | 0 | 104 | 0 | 5 | 10 | 5 | 0 | 7 | 135 |\n| CYPRESS | 0 | 0 | 50 | 0 | 2 | 4 | 4 | 0 | 7 | 67 |\n| DANA POINT | 2 | 0 | 48 | 1 | 5 | 5 | 1 | 0 | 5 | 67 |\n| IRVINE | 3 | 0 | 171 | 2 | 6 | 16 | 16 | 0 | 32 | 246 |\n| IRVINE - MCE | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| IRVINE - UCI | 0 | 0 | 5 | 0 | 0 | 3 | 3 | 0 | 4 | 15 |\n| LA PALMA | 0 | 0 | 17 | 0 | 1 | 3 | 1 | 0 | 0 | 22 |\n| LAGUNA HILLS | 1 | 0 | 41 | 0 | 2 | 3 | 1 | 0 | 3 | 51 |\n| LAGUNA NIGUEL | 0 | 0 | 45 | 0 | 6 | 8 | 1 | 0 | 12 | 72 |\n| LAGUNA WOODS | 1 | 1 | 74 | 0 | 3 | 4 | 1 | 0 | 14 | 98 |\n| LAKE FOREST CITY | 1 | 0 | 81 | 2 | 5 | 2 | 6 | 0 | 9 | 106 |\n| LOS ALAMITOS | 1 | 0 | 24 | 0 | 0 | 1 | 0 | 0 | 0 | 26 |\n| MISSION VIEJO | 0 | 0 | 104 | 1 | 4 | 5 | 4 | 0 | 7 | 125 |\n| PLACENTIA | 0 | 0 | 35 | 1 | 2 | 1 | 0 | 0 | 17 | 56 |\n| RANCHO SANTA MARGARITA | 2 | 0 | 37 | 0 | 0 | 2 | 2 | 0 | 1 | 44 |\n| SAN CLEMENTE | 4 | 0 | 78 | 1 | 8 | 8 | 3 | 0 | 9 | 111 |\n| SAN JUAN CAPISTRANO | 0 | 0 | 41 | 0 | 2 | 4 | 0 | 0 | 11 | 58 |\n| SANTA ANA | 4 | 0 | 304 | 4 | 9 | 33 | 18 | 0 | 47 | 419 |\n| SEAL BEACH | 2 | 0 | 54 | 1 | 4 | 3 | 0 | 0 | 12 | 76 |\n| STANTON | 1 | 0 | 52 | 0 | 2 | 3 | 0 | 0 | 2 | 60 |\n| TUSTIN | 1 | 1 | 69 | 2 | 5 | 4 | 2 | 0 | 6 | 90 |\n| VILLA PARK | 0 | 0 | 3 | 0 | 0 | 2 | 1 | 0 | 1 | 7 |\n| WESTMINSTER | 2 | 1 | 127 | 1 | 8 | 10 | 3 | 0 | 13 | 165 |\n| YORBA LINDA | 1 | 0 | 43 | 0 | 6 | 7 | 0 | 0 | 16 | 73 |\n| UNINCORPORATED | 0 | 1 | 76 | 3 | 9 | 15 | 4 | 0 | 17 | 125 |\n| **TOTAL** | **30** | **4** | **1708** | **19** | **94** | **156** | **78** | **0** | **257** | **2346** |"}
{"item_id": "item_0410", "chart_task_type": "composition_compare", "query": "Can you chart the breakdown of expenditure categories for 2023 and 2022 so I can see at a glance how the spending structure shifted between the two years?", "table_markdown": "| | 2023 | 2022 |\n|--------------------------------|------------|------------|\n| **EXPENDITURES** | | |\n| Awards night | 4,309 | - |\n| Benevolence | 8,750 | 7,200 |\n| Consulting fees and benefits (Note 10) | 88,700 | 65,207 |\n| Dental care program | 5,294 | 6,024 |\n| Donation | 300 | - |\n| Dues - The H.B.P.A. of Canada | 9,030 | 8,500 |\n| Election | 632 | - |\n| Equipment maintenance and rental | 17,381 | 17,083 |\n| Insurance | 37,785 | 28,724 |\n| Jockey recruitment program | 6,245 | 11,413 |\n| Learning centre program | 2,500 | - |\n| Meals - backstretch personnel | 4,510 | - |\n| Meetings - general membership | 1,135 | - |\n| Miscellaneous | 7,363 | 7,396 |\n| Office, telephone and sundry | 7,250 | 8,953 |\n| Political initiatives | - | 10,000 |\n| Post-mortem program | 500 | 2,700 |\n| Professional fees | 8,000 | 6,700 |\n| Scholarships | 6,150 | 10,550 |\n| **Total Expenditures** | **215,834**| **190,450**|"}
{"item_id": "item_0411", "chart_task_type": "composition_compare", "query": "Can you chart the liability mix for June 2022 versus December 2021 so I can see at a glance how the composition of debt and payables shifted between the two periods?", "table_markdown": "| Liabilities | June 30, 2022 (Unaudited) | December 31, 2021 |\n|----------------------------------------------|----------------------------|-------------------|\n| Mortgage and other notes payable, net | $919,525 | $929,811 |\n| Due to related party | 3,769 | 7,338 |\n| Escrow deposits payable | 1,113 | 1,171 |\n| Accounts payable and accrued expenses | 17,472 | 24,671 |\n| Other liabilities | 2,854 | 3,064 |\n| **Total liabilities** | **944,733** | **966,055** |"}
{"item_id": "item_0412", "chart_task_type": "momentum_turning", "query": "Can you plot the trend in the monthly import volume for Green gram from January to June 2016? I want it to be obvious at a glance when the upward momentum first stalled.", "table_markdown": "| Month | Quantity (mt) | Value (Rs.mn) | CIF price (Rs/kg) | Retail Price (Rs/kg) | Gross Margin (Rs/kg) |\n|---|---|---|---|---|---|\n| June 2016 | 1,284 | 215.62 | 167.99 | 243.5 | 75.51 |\n| May 2016 | 1,751 | 291.42 | 166.44 | 248.05 | 81.61 |\n| Apr 2016 | 1,405 | 229.68 | 163.52 | 248.41 | 84.89 |\n| Mar 2016 | 2,497 | 422.19 | 169.08 | 246.73 | 77.65 |\n| Feb 2016 | 805 | 140.96 | 175.17 | 243.85 | 68.68 |\n| Jan 2016 | 768 | 133.05 | 173.24 | 240.43 | 67.19 |"}
{"item_id": "item_0413", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of free cash flow from 2020 to 2024e? I want it to be obvious at a glance when the trend first turned from negative to positive.", "table_markdown": "| Income statement | 2020 | 2021 | 2022 | 2023e | 2024e | Per share data | 2020 | 2021 | 2022 | 2023e |\n|---|---|---|---|---|---|---|---|---|---|---|\n| Revenue | 160.1 | 179.3 | 204.4 | 213.6 | 209.2 | EPS (reported) | 0.36 | 0.22 | 0.22 | 0.23 |\n| EBITDA | 26.3 | 25.8 | 23.8 | 25.2 | 28.1 | EPS (adj.) | 0.33 | 0.29 | 0.34 | 0.23 |\n| EBIT | 18.3 | 16.4 | 13.2 | 14.2 | 17.9 | OCF / share | 0.79 | 0.50 | 0.50 | 0.60 |\n| PTP | 15.9 | 10.3 | 10.3 | 10.3 | 14.1 | FCF / share | -0.15 | -0.23 | -0.51 | 0.37 |\n| Net Income | 12.7 | 7.8 | 7.8 | 8.2 | 11.1 | Book value / share | 1.90 | 3.24 | 3.29 | 3.41 |\n| Extraordinary items | 1.1 | -2.6 | -4.3 | -0.1 | 0.0 | Dividend / share | 0.00 | 0.10 | 0.10 | 0.12 |\n| Balance sheet | 2020 | 2021 | 2022 | 2023e | 2024e | Growth and profitability | 2020 | 2021 | 2022 | 2023e |\n| Balance sheet total | 221.5 | 249.8 | 280.7 | 284.5 | 290.2 | Revenue growth-% | 12% | 12% | 14% | 4% |\n| Equity capital | 66.9 | 114.9 | 116.9 | 121.5 | 128.4 | EBITDA growth-% | 36% | -2% | -8% | 6% |\n| Goodwill | 118.1 | 135.2 | 157.6 | 157.6 | 157.6 | EBIT (adj.) growth-% | 130% | 10% | -8% | -18% |\n| Net debt | 86.5 | 59.3 | 85.6 | 79.0 | 69.8 | EPS (adj.) growth-% | 144% | -10% | 16% | -32% |\n| | | | | | | EBITDA-% | 16.4 % | 14.4 % | 11.6 % | 11.8 % |\n| Cash flow | 2020 | 2021 | 2022 | 2023e | 2024e | EBIT (adj.)-% | 10.7 % | 10.6 % | 8.5 % | 6.7 % |\n| EBITDA | 26.3 | 25.8 | 23.8 | 25.2 | 28.1 | EBIT-% | 11.4 % | 9.1 % | 6.4 % | 6.7 % |\n| Change in working capital | 2.1 | -3.2 | -3.0 | -1.0 | 0.1 | ROE-% | 20.4 % | 8.6 % | 6.8 % | 6.9 % |\n| Operating cash flow | 27.9 | 17.7 | 17.9 | 21.3 | 24.5 | ROI-% | 11.7 % | 9.0 % | 6.4 % | 6.5 % |\n| CAPEX | -33.1 | -25.8 | -36.1 | -8.0 | -8.0 | Equity ratio | 30.2 % | 46.0 % | 41.6 % | 42.7 % |\n| Free cash flow | -5.1 | -8.2 | -18.2 | 13.3 | 16.5 | Gearing | 129.3 % | 51.6 % | 73.2 % | 65.0 % |\n| Valuation multiples | 2020 | 2021 | 2022 | 2023e | 2024e | | | | | |\n| EV/S | 0.6 | 1.9 | 1.3 | 0.9 | 0.9 | | | | | |\n| EV/EBITDA (adj.) | 3.8 | 13.3 | 11.3 | 7.9 | 6.8 | | | | | |\n| EV/EBIT (adj.) | 5.9 | 18.2 | 15.4 | 13.9 | 10.6 | | | | | |\n| P/E (adj.) | 0.0 | 27.4 | 15.0 | 14.4 | 10.8 | | | | | |\n| P/B | 0.0 | 2.5 | 1.6 | 1.0 | 0.9 | | | | | |\n| Dividend-% | | 1.2 % | 1.9 % | 3.6 % | 4.1 % | | | | | |"}
{"item_id": "item_0414", "chart_task_type": "composition_compare", "query": "Can you chart the cost structure for Felony versus Misdemeanor cases so I can easily see how the mix of expenses like arrest, prosecution, and legal fees differs between them?", "table_markdown": "| New Criminal Activity While on Bond (NCA) for Travis & Tarrant Counties19 | | | |\n|---|---|---|---|\n| Cost Element per Case | Felony Cost | Misdemeanor Cost | Cost Detail |\n| Arrest | $2,027.27 | $2,027.27 | C-4d |\n| Prosecution | $873.09 | $217.48 | C-4a |\n| Court Costs | $200.29 | $135.95 | C-4b |\n| Legal Representation for Indigent Defendants20 | $632.31 | $213.73 | C-4c |\n| TOTAL | $3,732.96 | $2,594.43 | |"}
{"item_id": "item_0415", "chart_task_type": "momentum_turning", "query": "Can you plot the trend of the General Obligation Debt trend so I can spot at a glance the exact year the decline reversed and started climbing again?", "table_markdown": "| FY Ended September 30 | General Obligation Debt | General Obligation Debt to Assessed Value |\n|-----------------------|-------------------------|------------------------------------------|\n| 2013 | $8,560,000 | 6.68% |\n| 2012 | 7,900,000 | 6.57 |\n| 2011 | 8,330,000 | 7.15 |\n| 2010 | 8,740,000 | 7.44 |\n| 2009 | 9,140,000 | 7.79 |"}
{"item_id": "item_0416", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of private investment from 2008 to 2013? I want it to be obvious at a glance when the growth first started to decline.", "table_markdown": "| Year | # of Projects | Private Investment | New Jobs | Retained Jobs |\n|------|---------------|--------------------------|----------|---------------|\n| 2008 | 10 | $29,031,000.00 | 159 | 60 |\n| 2009 | 9 | $35,425,000.00 | 449 | 257 |\n| 2010 | 6 | $34,649,000.00 | 444 | 648 |\n| 2011 | 13 | $32,085,736.00 | 132 | 970 |\n| 2012 | 8 | $20,644,130.00 | 166 | 1,313 |\n| 2013 | 12 | $37,997,400.00 | 250 | 909 |\n| TOTALS | 58 | $189,832,266.00 | 1,600 | 4,157 |"}
{"item_id": "item_0417", "chart_task_type": "momentum_turning", "query": "Can you plot the momentum in interest costs so I can spot at a glance which year saw the sharpest drop?", "table_markdown": "| Year | Outstanding Principal Beginning of Year | Annual Payments End of Year | Interest Costs For The Year | Principal Reduction | PV AVOIDED COSTS |\n|------|----------------------------------------|-----------------------------|----------------------------|---------------------|------------------|\n| 1995 | | | | | |\n| 1996 | $10,850,000 | $1,481,160 | $661,850 | $819,310 | $3,127,949 |\n| 1997 | $10,030,690 | $1,481,160 | $611,872 | $869,288 | |\n| 1998 | $9,161,402 | $1,481,160 | $558,846 | $922,314 | |\n| 1999 | $8,239,088 | $1,481,160 | $502,584 | $978,576 | |\n| 2000 | $7,260,512 | $1,481,160 | $442,891 | $1,038,269 | |\n| 2001 | $6,222,243 | $1,481,160 | $379,557 | $1,101,603 | |\n| 2002 | $5,120,640 | $1,481,160 | $312,359 | $1,168,801 | |\n| 2003 | $3,951,839 | $1,481,160 | $241,062 | $1,240,098 | |\n| 2004 | $2,711,741 | $1,481,160 | $165,416 | $1,315,744 | |\n| 2005 | $1,395,997 | $1,481,160 | $85,156 | $1,396,004 | |"}
{"item_id": "item_0418", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of the projected youth population (ages 5-19) from 2005 to 2025? I want it to be obvious at a glance when the decline stopped and growth resumed.", "table_markdown": "| Year | Total | White Alone | Black Alone | All Other Races Alone | Two or More Races | Hispanic |\n|------|---------|-------------|-------------|-----------------------|-------------------|----------|\n| | Number | Percent | Number | Percent | Number | Percent |\n| 2005 | 22,854 | 100% | 11,987 | 52.5% | 7,538 | 33.0% |\n| 2010 | 22,372 | 100% | 11,551 | 51.6% | 6,969 | 31.2% |\n| 2015 | 22,573 | 100% | 11,567 | 51.2% | 6,902 | 30.6% |\n| 2020 | 22,755 | 100% | 11,470 | 50.4% | 6,948 | 30.5% |\n| 2025 | 22,795 | 100% | 11,361 | 49.8% | 6,833 | 30.0% |"}
{"item_id": "item_0419", "chart_task_type": "momentum_turning", "query": "Can you chart the trend of the beta correction coefficient over the five fiscal years? I want it to be obvious at a glance where the downward momentum stopped and it started to rise again.", "table_markdown": "| | Change in current revenues (yoy) (billions) | Discretionary current revenue measures (billions) | Nominal GDP growth assumptions (%) | Change in output gap | Current revenues in year t-1 (billions) | Revenue gap (billions)* | Nominal GDP | Correction coefficient β (% of nominal GDP) |\n|----------------|---------------------------------------------|--------------------------------------------------|-----------------------------------|----------------------|----------------------------------------|-------------------------|-------------|---------------------------------------------|\n| | assessment | assessment | assessment | assessment | assessment | (6)=(1)-(2)-(3)+(4)-(5) | (7) | (8)=100*(6)/(7) |\n| 2010-11 | 37.0 | 1.6 | 5.1% | 1.6% | 575.9 | 3.3 | 3576.3 | 0.2 |\n| 2011-12 | 18.0 | 13.9 | 3.2% | 0.4% | 612.9 | -16.0 | 3626.2 | -1.0 |\n| 2012-13 | 13.7 | 2.9 | 2.3% | -0.3% | 631.0 | -3.0 | 3663.1 | -0.2 |\n| 2013-14 | 31.4 | 2.1 | 4.2% | 1.0% | 644.6 | 0.2 | 3732.8 | 0.0 |\n| 2014-15 | 21.2 | -0.7 | 4.2% | 1.4% | 676.0 | -9.7 | 3806.1 | -0.5 |\n| | Average | | | | | | | -0.3 |"}
{"item_id": "item_0420", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of merger cases handled by CCC from 2013 to 2017? I want it to be obvious at a glance when the growth rate slowed down the most.", "table_markdown": "| Approved Mergers/Year | | 2013 | | 2014 | | 2015 | | 2016 | | 2017 | | | 2013-2017 |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| | | | | | | | | | | | | | Aggregate |\n| | Comfort Letter Granted | | 2 | | 16 | | 3 | | 5 | | 5 | | 31 |\n| Unconditional Approval | | 16 | | 23 | | 16 | | 19 | | 18 | | 92 | |\n| | Approved with Conditions | | 0 | | 1 | | 2 | | 7 | | 3 | | 13 |\n| Cases referred to Member States | | 0 | | 1 | | 1 | | 1 | | 0 | | 3 | |\n| | Non-merger Transactions | | 3 | | 2 | | 0 | | 1 | | 0 | | 6 |\n| Ongoing Transactions | | 0 | | 0 | | 0 | | 0 | | 8 | | 8 | |\n| | Totals | | 21 | | 42 | | 21 | | 32 | | 34 | | 150 |"}
{"item_id": "item_0421", "chart_task_type": "momentum_turning", "query": "Can you plot the momentum in the World Risk Index from 2011 to 2016 so I can spot at a glance the first year it actually went down?", "table_markdown": "| Year | Rank | World Risk Index | Exposure | Vulnerability | Susceptibility | Lack of Coping Capacities | Lack of Adaptive Capacities |\n|------|------|------------------|----------|---------------|-----------------|---------------------------|----------------------------|\n| 2011 | 3 | 24.32% | 45.09% | 53.93% | 34.99% | 82.78% | 44.01% |\n| 2012 | 3 | 27.98% | 52.46% | 53.35% | 33.92% | 83.09% | 43.03% |\n| 2013 | 3 | 27.52% | 52.46% | 52.46% | 33.74% | 80.47% | 43.16% |\n| 2014 | 2 | 28.25% | 52.46% | 53.85% | 33.35% | 80.03% | 48.17% |\n| 2015 | 3 | 27.98% | 52.46% | 53.33% | 32.00% | 80.06% | 47.94% |\n| 2016 | 3 | 26.70% | 52.46% | 50.90% | 31.83% | 80.92% | 39.96% |"}
{"item_id": "item_0422", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum in shrimp catch from 1988 to 2007? I want it to be obvious at a glance which year saw the steepest drop.", "table_markdown": "| Year | Number of shrimp trawlers | Number of Fishing days | Shrimp Catch (in Kg) |\n|------|--------------------------|------------------------|----------------------|\n| 1988 | 13 | 1,476 | 650,929 |\n| 1989 | 13 | 2,166 | 688,837 |\n| 1990 | 9 | 1,574 | 960,686 |\n| 1991 | 13 | 1,315 | 669,016 |\n| 1992 | 15 | 1,560 | 663,852 |\n| 1993 | 10 | 1,462 | 597,211 |\n| 1994 | 16 | 2,513 | 1,014,087 |\n| 1995 | 18 | 2,108 | 795,436 |\n| 1996 | 12 | 1,779 | 769,651 |\n| 1997 | 16 | 2,091 | 699,059 |\n| 1998 | 17 | 2,778 | 995,564 |\n| 1999 | 17 | 2,252 | 688,006 |\n| 2000 | 20 | 3,352 | 909,715 |\n| 2001 | 20 | 3,882 | 1,193,685 |\n| 2002 | 23 | 2,521 | 926,079 |\n| 2003 | 25 | 3,664 | 1,320,056 |\n| 2004 | 25 | 3,037 | 661,062 |\n| 2005 | 14 | 1,528 | 467,037 |\n| 2006 | 13 | 1,082 | 312,076 |\n| 2007 | 10 | 666 | 202,455 |"}
{"item_id": "item_0423", "chart_task_type": "momentum_turning", "query": "Can you plot the trend in investment gains and losses from 2016 to 2020? I want it to be obvious at a glance where the downward trend first reversed.", "table_markdown": "| Year | (Gains) and Losses | Percent Recognized | Percent Deferred | Deferred Amount |\n|------|--------------------|--------------------|------------------|-----------------|\n| 2016 | $128,478 | 100% | 0% | $0 |\n| 2017 | (868,609) | 80% | 20% | ($173,722) |\n| 2018 | 1,556,814 | 60% | 40% | $622,726 |\n| 2019 | (1,530,035) | 40% | 60% | ($918,021) |\n| 2020 | (847,760) | 20% | 80% | ($678,208) |"}
{"item_id": "item_0424", "chart_task_type": "momentum_turning", "query": "Can you chart the daily changes in river flow for April? I want it to be obvious at a glance which day saw the biggest surge.", "table_markdown": "| April | Air (F) Max | Air (F) Min | Water (F) Max | Water (F) Min | Weather | Flow cfs | Gallo Fish Trapped Adults | Gallo Fish Trapped Grilse | MRFF Fish Trapped Adults | MRFF Fish Trapped Grilse |\n|-------|-------------|-------------|---------------|---------------|---------|----------|---------------------------|--------------------------|--------------------------|--------------------------|\n| 1 | 81 | 45 | 54 | 52 | Clear | 139 | | | | |\n| 2 | 80 | 45 | 56 | 52 | Clear | 149 | | | | |\n| 3 | 81 | 46 | 56 | 52 | Clear | 153 | | | | |\n| 4 | 83 | 47 | 54 | 51 | Overcast| 118 | | | | |\n| 5 | 87 | 48 | 56 | 52 | Clear | 208 | | | | |\n| 6 | 77 | 49 | 55 | 53 | Clear | 208 | | | | |\n| 7 | 75 | 45 | 54 | 51 | Clear | 156 | | | | |\n| 8 | 84 | 49 | 55 | 50 | Clear | 198 | | | | |\n| 9 | 88 | 50 | 57 | 51 | Clear | 164 | | | | |\n| 10 | 75 | 50 | 55 | 51 | Clear | 156 | | | | |\n| 11 | 76 | 40 | 55 | 51 | Clear | 194 | | | | |\n| 12 | 81 | 44 | 56 | 51 | Clear | 252 | | | | |\n| 13 | 84 | 48 | 58 | 52 | Clear | 208 | | | | |\n| 14 | 96 | 52 | 57 | 52 | Clear | 180 | | | | |\n| 15 | 80 | 56 | 56 | 52 | Clear | 218 | | | | |\n| 16 | 68 | 48 | 54 | 50 | Overcast| 203 | | | | |\n| 17 | 79 | 50 | 55 | 50 | Rain | 168 | | | | |\n| 18 | 79 | 45 | 54 | 50 | Cloudy | 189 | | | | |\n| 19 | 67 | 46 | 53 | 50 | Cloudy | 203 | | | | |\n| 20 | 61 | 45 | 53 | 50 | Overcast| 180 | | | | |\n| 21 | 70 | 49 | 54 | 50 | Overcast| 185 | | | | |\n| 22 | 79 | 45 | 55 | 50 | Cloudy | 189 | | | | |\n| 23 | 79 | 46 | 56 | 50 | Clear | 189 | | | | |\n| 24 | 77 | 43 | 54 | 50 | Clear | 203 | | | | |\n| 25 | 69 | 46 | 54 | 50 | Clear | 229 | | | | |\n| 26 | 79 | 37 | 55 | 50 | Clear | 246 | | | | |\n| 27 | 83 | 47 | 56 | 53 | Clear | 224 | | | | |\n| 28 | 86 | 49 | 56 | 50 | Clear | 224 | | | | |\n| 29 | 83 | 48 | 56 | 50 | Clear | 189 | | | | |\n| 30 | 89 | 46 | 56 | 50 | Clear | 234 | | | | |"}
{"item_id": "item_0425", "chart_task_type": "momentum_turning", "query": "Can you plot the EBIT trend from 2002 to 2006 so I can spot at a glance exactly when the growth momentum turned negative?", "table_markdown": "| DKKm | 2006 | 2005 | 2004 | 2003 | 2002 |\n|------------|--------|--------|--------|--------|--------|\n| **Revenue by segment:** | | | | | |\n| Generation | 7,620 | 114 | 116 | 56 | 3 |\n| Exploration & Production | 5,556 | 4,346 | 3,565 | 3,632 | 4,090 |\n| Distribution | 2,560 | 857 | 861 | 1,806 | |\n| Markets | 24,115 | 13,885 | 10,022 | 9,988 | 9,650 |\n| Other (including eliminations) | (4,190)| (709) | (355) | (1,215)| (14) |\n| **EBITDA\\(^1\\) by segment:** | | | | | |\n| Generation | 2,695 | 47 | 59 | 23 | 0 |\n| Exploration & Production | 3,499 | 2,692 | 1,995 | 2,079 | 2,328 |\n| Distribution | 1,012 | 565 | 596 | 1,139 | |\n| Markets | 1,601 | 2,921 | 1,907 | 2,147 | |\n| Other (including eliminations) | (14) | 89 | 130 | 159 | (74) |\n| **EBIT** | 5,534 | 4,099 | 2,421 | 3,168 | 2,546 |\n| Financial items, net | (592) | (152) | 171 | 56 | 154 |\n| **Profit after tax** | 4,917 | 2,687 | 2,074 | 1,941 | 1,476 |\n| EBITDA margin (%) | 25 | 34 | 33 | 39 | 36 |\n| EBIT margin (operating margin) (%) | 16 | 22 | 17 | 22 | 19 |\n| Free cash flow to equity (with acquisitions)\\(^2\\) | 360 | (4,262)| (1,061)| 1,517 | 500 |\n| Free cash flow to equity (without acquisitions)\\(^3\\) | 14,302 | 3,325 | 1,653 | 1,592 | 1,063 |\n| Assets | 105,586| 46,854 | 31,436 | 33,230 | 28,930 |\n| Cash and cash equivalents\\(^4\\) | 9,981 | 7,356 | 145 | 3,448 | 3,195 |\n| Interest-bearing debt\\(^5\\) | 27,760 | 7,148 | 3,331 | 5,890 | 6,459 |\n| Net interest-bearing debt | 17,779 | (208) | 3,186 | 2,442 | 3,264 |\n| Equity | 42,268 | 26,278 | 16,360 | 16,794 | 14,655 |\n| Capital employed\\(^6\\) | 59,237 | 26,611 | 19,791 | 19,519 | 17,731 |\n| Financial gearing\\(^7\\) | 0.42 | (0.01) | 0.19 | 0.14 | 0.22 |"}
{"item_id": "item_0426", "chart_task_type": "momentum_turning", "query": "Can you plot the trend in Carroll County's average household size so it's obvious at a glance when the decline stopped and numbers started to tick back up?", "table_markdown": "| | Carroll County: Average Household Size | | | | | | | | | | |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| Category | | 1980 | 1985 | 1990 | 1995 | 2000 | 2005 | 2010 | 2015 | 2020 | 2025 |\n| Persons per Household | | 2.85 | 2.75 | 2.71 | 2.69 | 2.66 | 2.62 | 2.6 | 2.59 | 2.6 | 2.62 |"}
{"item_id": "item_0427", "chart_task_type": "momentum_turning", "query": "Can you plot the trend in the age- and sex-adjusted veteran suicide rate from 2005 to 2017? I want it to be obvious at a glance when the upward momentum shifted and the rate actually decreased.", "table_markdown": "| Year | Suicide Deaths | Average per Day | Veteran Population | Age-and-Sex-Adjusted Suicide Rate |\n|------|----------------|-----------------|--------------------|----------------------------------|\n| 2005 | 5,787 | 15.9 | 24,240,000 | 18.5 |\n| 2006 | 5,688 | 15.6 | 23,731,000 | 17.6 |\n| 2007 | 5,893 | 16.1 | 23,291,000 | 18.8 |\n| 2008 | 6,216 | 17.0 | 22,996,000 | 20.6 |\n| 2009 | 6,172 | 16.9 | 22,603,000 | 20.8 |\n| 2010 | 6,158 | 16.9 | 22,411,000 | 21.4 |\n| 2011 | 6,116 | 16.8 | 22,061,000 | 22.3 |\n| 2012 | 6,065 | 16.6 | 21,765,000 | 22.4 |\n| 2013 | 6,132 | 16.8 | 21,415,000 | 23.6 |\n| 2014 | 6,272 | 17.2 | 21,029,000 | 25.0 |\n| 2015 | 6,227 | 17.1 | 20,560,000 | 26.3 |\n| 2016 | 6,010 | 16.4 | 20,170,000 | 25.7 |\n| 2017 | 6,139 | 16.8 | 19,803,000 | 27.7 |"}
{"item_id": "item_0428", "chart_task_type": "momentum_turning", "query": "Can you chart the trend in scholarship numbers so I can spot at a glance when the growth momentum peaked?", "table_markdown": "| Year | Number of Scholarships | Amount |\n|------|------------------------|--------|\n| 2013 | 42 | $229,000 |\n| 2014 | 45 | $247,000 |\n| 2015 | 79 | $320,000 |\n| 2016 | 145 | $404,400 |\n| 2017 | 200 | $544,000 |\n| 2018 | 219 | $554,250 |"}
{"item_id": "item_0429", "chart_task_type": "momentum_turning", "query": "Can you chart the quarterly loss trends so I can spot at a glance which quarter saw the sharpest spike in losses?", "table_markdown": "| Quarter ending (unaudited) | Total revenues | Loss ($000s) | Loss per share ($) |\n|----------------------------|----------------|--------------|--------------------|\n| June 30, 2009 | nil | 2,918 | 0.05 |\n| March 31, 2009 | nil | 2,700 | 0.05 |\n| December 31, 2008 | nil | 61,069 | 1.13 |\n| September 30, 2008 | nil | 7,515 | 0.14 |\n| June 30, 2008 | nil | 1,918 | 0.04 |\n| March 31, 2008 | nil | 1,818 | 0.03 |\n| December 31, 2007 | nil | 1,490 | 0.03 |\n| September 30, 2007 | nil | 863 | 0.02 |"}
{"item_id": "item_0430", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of Bahraini FDI inflows into India from 2015 to 2019? I want it to be obvious at a glance which year saw the biggest surge in growth.", "table_markdown": "| | March | March | March | March | March |\n|---|---|---|---|---|---|\n| Financial Year | 2015 | 2016 | 2017 | 2018 | 2019 |\n| Cumulative FDI equity | | | | | |\n| inflows from Bahrain into | 48.93 | 64.65 | 144.92 | 164.60 | 173.38 |"}
{"item_id": "item_0431", "chart_task_type": "momentum_turning", "query": "Can you plot the momentum of average AUM per branch over the five years? I want it to be obvious at a glance which year saw the strongest acceleration in growth.", "table_markdown": "| Year | No of Branches | Average AUM per Branch (INR Crores) |\n|---|---|---|\n| 2014-15 | 3699 | 2.26 |\n| 2015-16 | 3645 | 2.44 |\n| 2016-17 | 3483 | 2.97 |\n| 2017-18 | 3572 | 3.2 |\n| 2018-19** | 3530 | 3.21 |"}
{"item_id": "item_0432", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of new construction growth from 2009 to 2019? I want it to be obvious at a glance which year saw the biggest surge in that acceleration.", "table_markdown": "| Year | Appreciation | New Construction |\n|------|--------------|-----------------|\n| 2009 | 0.59% | 1.69% |\n| 2010 | 0.79% | -1.55% |\n| 2011 | 0.63% | 0.00% |\n| 2012 | 0.56% | -1.78% |\n| 2013 | 0.78% | 0.61% |\n| 2014 | 0.63% | 2.03% |\n| 2015 | 0.78% | 2.00% |\n| 2016 | 1.27% | 3.22% |\n| 2017 | 1.06% | 14.13% |\n| 2018 | 0.85% | 4.87% |\n| 2019 | 1.66% | 6.94% |\n| Avg. | 0.94% | 2.92% |"}
{"item_id": "item_0433", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of total funds under management so I can spot at a glance when the decline reversed and growth started to pick up again?", "table_markdown": "| £’million | 28 Feb 2013 | 30 Sep 2012 | 31 Mar 2012 | 30 Sep 2011 | 31 Mar 2011 |\n|---|---|---|---|---|---|\n| Total IWI | | | | | |\n| - Discretionary | 15,962 | 14,605 | 14,187 | 8,924 | 9,571 |\n| - Non-discretionary | 5,686 | 5,128 | 5,316 | 2,826 | 3,164 |\n| - Other* | 348 | 407 | 453 | - | - |\n| TTo tt all | 2211, 999966 | 2200, 114400 | 1199, 995566 | 1111, 775500 | 1122, 773355 |\n| NCB | 1,555 | 1,423 | - | - | - |\n| Other UK and Australia funds under advice^ | 925 | 929 | 1,013 | 2,706 | 2,117 |\n| Total | 24,476 | 22,492 | 20,969 | 14,456 | 14,852 |"}
{"item_id": "item_0434", "chart_task_type": "momentum_turning", "query": "Can you plot the shifts in the cuckoo parasitism rate so I can immediately spot when the trend took its steepest dive?", "table_markdown": "| Year | Nests followed | Predateda | Nests used | Parasitised (%) |\n|------|----------------|-----------|------------|-----------------|\n| 2006 | 38 | 7 | 31 | 10 (32.3) |\n| 2007 | 37 | 3 | 34 | 13 (38.2) |\n| 2008 | 42 | 7 | 35 | 11 (31.4) |\n| 2009 | 25 | 4 | 21 | 7 (33.3) |\n| 2010 | 34 | 4 | 30 | 15 (50.0) |\n| 2011 | 27 | 1 | 26 | 7 (25.9) |\n| 2012 | 58 | 1 | 57 | 15 (26.3) |\n| 2013 | 75 | 4 | 71 | 12 (16.9) |\n| 2014 | 100 | 4 | 96 | 27 (27.6) |\n| 2015 | 84 | 2 | 82 | 27 (32.9) |\n| 2016 | 91 | 5 | 86 | 41 (47.7) |\n| | Total | | 569 | 185 (32.5) |\n| | Effectively parasitisedb | | 73 | 12.8 |"}
{"item_id": "item_0435", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of internship matches over the years? I want it to be obvious at a glance exactly when the growth trend peaked and started to decline.", "table_markdown": "| Year | 2008-09 | 2009-10 | 2010-11 | 2011-12 | 2012-13 | 2013-14 | 2014-15 | 2015-16 | 2016-17 | 2017-18 |\n|------------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|\n| Total Matches | 557 | 529 | 518 | 443 | 466 | 420 | 377 | 224 | 160 | 111 |\n| Full-Time | 167 | 159 | 114 | 122 | 224 | 248 | 222 | 132 | 90 | 64 |\n| Half-Time | 390 | 370 | 404 | 321 | 242 | 172 | 155 | 92 | 70 | 47 |"}
{"item_id": "item_0436", "chart_task_type": "momentum_turning", "query": "Can you plot the momentum of M&A value growth so I can spot at a glance when the surge peaked?", "table_markdown": "| Year | Number of M&A | Value of M&A (USD billion) | GDP of GCC (USD billion) |\n|---|---|---|---|\n| 2000 | 120 | 4.14 | 524.66 |\n| 2001 | 109 | 2.74 | 531.74 |\n| 2002 | 104 | 3.5 | 538.83 |\n| 2003 | 89 | 6.06 | 582.46 |\n| 2004 | 101 | 4.14 | 638.58 |\n| 2005 | 163 | 3 | 681.42 |\n| 2006 | 252 | 25.81 | 737.95 |\n| 2007 | 475 | 125.63 | 783.00 |\n| 2008 | 802 | 89.13 | 838.84 |\n| 2009 | 480 | 42.38 | 878.00 |\n| 2010 | 564 | 50.13 | 903.00 |\n| 2011 | 604 | 45.38 | 953.00 |\n| 2012 | 572 | 36.53 | 1,004.00 |\n| 2013 | 487 | 34.74 | 1,040.00 |\n| 2014 | 527 | 32.43 | 1,104.00 |\n| 2015YTD* | 367 | 24.5 | 1,160.00 |\n| TOTAL | 5816 | 530.24 | 12899.482 |"}
{"item_id": "item_0437", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum shifts in the share of work-eligible individuals with zero participation hours from 2001 to 2015? I want it to be obvious at a glance which year saw the sharpest spike in that percentage.", "table_markdown": "| Table 1: TANF Adults (pre-FY 2007)/Work Eligible Individuals (FY 2007 on) with Zero Hours of Countable Participation (FY 2000-FY 2015) | | | | | | | | | | | | | | | | |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |\n| % w/0 hrs. | 60.3 | 56.8 | 58.3 | 58.8 | 57.5 | 56.6 | 55.3 | 62.1 | 60.5 | 58.2 | 59 | 58 | 55.1 | 56.7 | 54.7 | 48.3 |"}
{"item_id": "item_0438", "chart_task_type": "momentum_turning", "query": "Can you plot the trend in juvenile gulf menhaden catch rates from 1972 to 1978? I want it to be obvious at a glance exactly when the decline stopped and the numbers started to recover.", "table_markdown": "| Year class | No. of streams | No. of tows | \\( y_{st} \\) | 95% CI |\n|------------|----------------|-------------|---------------|----------|\n| 1972 | 39 | 200 | 3215.8 | <0.0–4738.8 |\n| 1973 | 39 | 200 | 1578.2 | 639.4–2516.6 |\n| 1974 | 39 | 200 | 1417.4 | 741.4–2093.4 |\n| 1975 | 39 | 200 | 653.3 | 392.4–914.2 |\n| 1976 | 39 | 200 | 2753.5 | 830.4–4676.3 |\n| 1977 | 36 | 183 | 5248.5 | 951.4–9545.6 |\n| 1978 | 39 | 200 | 1850.4 | 442.8–3258.0 |"}
{"item_id": "item_0439", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum in net income from 2018 to 2022? I want it to be obvious at a glance which year saw the sharpest slowdown in growth momentum.", "table_markdown": "| | 2022 | 2021 | 2020 | 2019 | 2018 |\n|--------------------------------|------------|------------|------------|------------|------------|\n| Interest income | $27,921,536| $25,479,656| $25,778,366| $25,128,193| $22,847,491|\n| Interest expense | $2,245,749 | $2,107,277 | $3,785,991 | $4,419,637 | $3,333,160 |\n| Net interest income | $25,675,787| $23,372,379| $21,992,375| $20,708,556| $19,514,331|\n| Provision for loan losses | $(1,574,783)| $(1,185,500)| $(1,525,414)| $(724,077) | $(1,968,546)|\n| Net interest income after provision for loan losses | $27,250,570 | $22,186,879 | $20,466,961 | $19,984,479 | $17,545,785 |\n| Noninterest income | $4,986,295 | $5,167,058 | $4,056,768 | $4,225,882 | $3,756,583 |\n| Noninterest expense | $15,810,592| $15,936,398| $14,917,148| $14,942,396| $14,509,970|\n| Income before income taxes | $16,426,273| $11,417,539| $9,606,581 | $9,267,965 | $6,792,398 |\n| Income tax expense | $3,389,962 | $2,350,617 | $1,964,089 | $1,890,101 | $1,367,342 |\n| Net income | $13,036,311| $9,066,922 | $7,642,492 | $7,377,864 | $5,425,056 |\n| Dividends | $2,109,881 | $1,874,224 | $1,493,437 | $1,447,565 | $1,168,873 |"}
{"item_id": "item_0440", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum in the number of UHNIs so I can spot at a glance when the expansion hit its peak?", "table_markdown": "| Year | Size of list | >Rs16bn | >Rs18bn |\n|------|--------------|---------|---------|\n| 2012 | 100 | | |\n| 2013 | 141 | | |\n| 2014 | 230 | | |\n| 2015 | 296 | | |\n| 2016 | 339 | | |\n| 2017 | 617 | 680 | 750 |\n| 2018 | 680 | 750 | 828 |\n| 2019 | 750 | 828 | 1,007 |\n| 2020 | 828 | 1,007 | 1,103 |\n| 2021 | 1,007 | 1,103 | 1,319 |\n| 2022 | 1,103 | 1,319 | |\n| 2023 | 1,319 | | |"}
{"item_id": "item_0441", "chart_task_type": "momentum_turning", "query": "Can you plot the momentum of childhood cancer funding's share of the NCI budget from 2008 to 2018? I want it to be obvious at a glance when the downward trend reversed and started to pick up again.", "table_markdown": "| Year | Total Budget NCI Funding | Childhood Cancers Funding | Percent |\n|------|--------------------------|---------------------------|---------|\n| 2008 | $4,827,552,152 | $189,672,374 | 3.93% |\n| 2009 | $4,966,926,530 | $192,844,826 | 3.88% |\n| 2010 | $5,098,146,876 | $197,126,947 | 3.87% |\n| 2011 | $5,058,104,978 | $195,529,112 | 3.87% |\n| 2012 | $5,066,969,036 | $208,070,156 | 4.11% |\n| 2013 | $4,787,897,881 | $185,134,664 | 3.87% |\n| 2014 | $4,932,807,990 | $203,716,485 | 4.13% |\n| 2015 | $4,951,675,428 | $205,060,620 | 4.14% |\n| 2016 | $5,206,169,249 | $206,767,589 | 3.97% |\n| 2017 | $5,636,393,224 | $220,273,687 | 3.91% |\n| 2018 | $5,937,729,104 | $302,325,670 | 5.09% |\n| Total| $56,470,372,448 | $2,306,522,130 | 4.08% |"}
{"item_id": "item_0442", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of INDOT's total capital spending from 2006 to 2026? I want it to be obvious at a glance which year saw the sharpest drop in spending growth.", "table_markdown": "| | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |\n|---|---|---|---|---|---|---|---|---|---|---|\n| Preservation | $ 491 | $ 534 | $ 451 | $ 613 | $ 442 | $ 361 | $ 351 | $ 370 | $ 440 | $ 514 |\n| Major New Capital Improvements | $ 297 | $ 411 | $ 383 | $ 766 | $ 643 | $ 1,111 | $ 804 | $ 703 | $ 293 | $ 321 |\n| P3s (2013 Ohio River Bridge, I-69, Evansville) | | | | | | | | $ 763 | | $ 325 |\n| Total | $ 788 | $ 945 | $ 834 | $ 1,379 | $ 1,085 | $ 1,472 | $ 1,155 | $ 1,836 | $ 733 | $ 1,160 |\n| | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 |\n| Preservation | $ 961 | $ 839 | $ 1,038 | $ 1,520 | $ 1,440 | $ 1,605 | $ 2,703 | $ 1,662 | $ 1,631 | $ 1,894 |\n| Major New Capital Improvements | $ 180 | $ 330 | $ 62 | $ 119 | $ 244 | $ 525 | $ 133 | $ 92 | $ 144 | $ 44 |\n| P3s (2013 Ohio River Bridge, I-69, Evansville) | $ 149 | $ 23 | $ 18 | $ 19 | $ 18 | $ 171 | $ 10 | $ 6 | $ 1 | $ 3 |"}
{"item_id": "item_0443", "chart_task_type": "momentum_turning", "query": "Can you chart the weekly attendance trend for July so I can spot at a glance when the momentum first turned negative?", "table_markdown": "| Date | Attendance | Offerings |\n|---|---|---|\n| July 1 | 59 | $3999.88 |\n| July 8 | 70 | $3,456.51 |\n| July 15 | 67 | $1,651.00 |\n| July 22 | 65 | $2,242.10 |\n| July 29 | 44 | $6,774.00 |"}
{"item_id": "item_0444", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of pooled returns for each vintage year so I can spot at a glance when the sharpest drop in performance occurred?", "table_markdown": "| Vintage Year | Pooled Return (%) | Arithmetic Mean (%) | Median (%) | Equal-Weighted Pooled Return (%) | Upper Quartile (%) | Lower Quartile (%) | Standard Deviation (%) | DPI | RVPI | TVPI | Number of Funds |\n|--------------|-------------------|---------------------|-----------|----------------------------------|--------------------|--------------------|-----------------------|-----|------|------|-----------------|\n| 1981 | 8.47 | 9.01 | 7.87 | 9.03 | 13.24 | 5.94 | 5.59 | 1.76| 0.00 | 1.76 | 9 |\n| 1982 | 7.38 | 7.20 | 7.90 | 7.36 | 9.11 | 4.87 | 3.29 | 1.79| 0.00 | 1.79 | 11 |\n| 1983 | 10.23 | 9.55 | 8.72 | 10.09 | 12.46 | 7.10 | 5.73 | 2.01| 0.00 | 2.01 | 28 |\n| 1984 | 8.65 | 7.76 | 6.27 | 8.11 | 12.92 | 3.78 | 8.82 | 1.77| 0.00 | 1.77 | 32 |\n| 1985 | 12.91 | 11.70 | 12.86 | 12.88 | 17.35 | 5.49 | 8.21 | 2.69| 0.00 | 2.69 | 26 |\n| 1986 | 14.52 | 8.81 | 9.43 | 9.11 | 12.90 | 5.27 | 5.13 | 2.89| 0.00 | 2.90 | 30 |\n| 1987 | 18.26 | 14.53 | 15.65 | 15.82 | 22.18 | 8.70 | 10.64 | 2.72| 0.00 | 2.72 | 34 |\n| 1988 | 18.89 | 14.31 | 11.87 | 14.70 | 21.65 | 6.60 | 13.77 | 2.41| 0.09 | 2.51 | 26 |\n| 1989 | 19.16 | 17.05 | 13.31 | 18.88 | 28.80 | 7.75 | 14.46 | 2.59| 0.00 | 2.59 | 37 |\n| 1990 | 33.11 | 24.07 | 21.54 | 26.28 | 31.19 | 14.28 | 19.60 | 3.15| 0.00 | 3.15 | 17 |\n| 1991 | 27.89 | 24.02 | 18.56 | 25.64 | 27.86 | 11.64 | 20.33 | 3.17| 0.00 | 3.17 | 17 |\n| 1992 | 32.60 | 28.23 | 19.65 | 37.29 | 35.86 | 10.85 | 30.55 | 3.09| 0.00 | 3.09 | 22 |\n| 1993 | 46.71 | 30.44 | 18.83 | 41.09 | 46.49 | 12.19 | 31.10 | 4.13| 0.00 | 4.13 | 36 |\n| 1994 | 59.26 | 34.24 | 26.45 | 44.87 | 46.45 | 6.73 | 47.15 | 5.40| 0.01 | 5.40 | 42 |\n| 1995 | 88.48 | 56.60 | 41.65 | 77.43 | 80.62 | 21.54 | 58.50 | 6.07| 0.00 | 6.07 | 35 |\n| 1996 | 100.73 | 60.60 | 37.06 | 87.55 | 81.49 | 7.18 | 77.87 | 4.89| 0.02 | 4.91 | 42 |\n| 1997 | 91.80 | 53.70 | 9.64 | 73.71 | 63.32 | -2.60 | 102.09 | 3.08| 0.02 | 3.10 | 71 |\n| 1998 | 11.94 | 16.80 | -0.27 | 15.71 | 15.19 | -6.15 | 71.44 | 1.47| 0.03 | 1.50 | 81 |\n| 1999 | -0.56 | -3.03 | -2.49 | -1.05 | 3.70 | -10.90 | 17.78 | 0.89| 0.08 | 0.96 | 113 |\n| 2000 | 0.89 | -2.19 | -0.69 | 0.46 | 4.61 | -6.66 | 12.51 | 0.86| 0.21 | 1.06 | 158 |\n| 2001 | 3.00 | 0.25 | 1.66 | 4.15 | 7.79 | -5.26 | 19.03 | 0.93| 0.26 | 1.19 | 55 |\n| 2002 | 0.50 | 0.96 | 0.70 | 2.71 | 6.56 | -3.88 | 8.85 | 0.74| 0.29 | 1.03 | 34 |\n| 2003 | 9.30 | -1.39 | 1.83 | 6.03 | 7.32 | -4.16 | 25.42 | 1.18| 0.49 | 1.68 | 40 |\n| 2004 | 8.95 | 3.05 | 1.22 | 9.50 | 8.92 | -4.59 | 20.88 | 0.93| 0.68 | 1.61 | 67 |\n| 2005 | 8.05 | 3.37 | 4.98 | 7.10 | 10.93 | -0.40 | 17.79 | 0.56| 0.91 | 1.48 | 63 |\n| 2006 | 9.66 | 4.89 | 6.65 | 7.81 | 13.81 | -1.22 | 13.75 | 0.62| 0.93 | 1.55 | 85 |\n| 2007 | 17.55 | 15.74 | 15.40 | 18.26 | 22.80 | 5.95 | 17.30 | 0.73| 1.18 | 1.91 | 66 |\n| 2008 | 17.62 | 13.29 | 11.81 | 15.88 | 21.13 | 5.90 | 14.33 | 0.43| 1.27 | 1.70 | 65 |\n| 2009 | 20.63 | 15.01 | 15.52 | 17.47 | 27.33 | 2.68 | 16.10 | 0.35| 1.37 | 1.72 | 23 |\n| 2010 | 39.40 | 27.08 | 21.33 | 32.79 | 34.37 | 14.84 | 29.58 | 0.45| 1.88 | 2.34 | 23 |\n| 2011 | 30.28 | 16.27 | 11.00 | 18.58 | 36.50 | -4.37 | 31.37 | 0.08| 1.49 | 1.57 | 40 |\n| 2012 | 30.50 | 14.50 | 12.30 | 16.99 | 29.15 | -3.68 | 30.95 | 0.08| 1.33 | 1.41 | 50 |\n| 2013 | 9.09 | -4.89 | -9.03 | -5.77 | 10.83 | -21.77 | 32.24 | 0.03| 1.03 | 1.06 | 39 |"}
{"item_id": "item_0445", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of Korea Post's net income from 2000 to 2004? I want it to be obvious at a glance when the growth trend first reversed.", "table_markdown": "| Classification | 2000 | 2001 | 2002 | 2003 | 2004 |\n|---------------------------------|--------|--------|--------|--------|--------|\n| Mail Volume (unit: million pieces) | 4,517 | 5,056 | 5,537 | 5,256 | 4,975 |\n| Total Revenue (unit: billion won) | 12,542 | 14,171 | 17,082 | 17,154 | 17,342 |\n| Net Income (unit: billion won) | 19.1 | 35.8 | 77.9 | -46.1 | -62.2 |"}
{"item_id": "item_0446", "chart_task_type": "momentum_turning", "query": "Can you chart the trend in OTA publication volume from 1990 to 1995? I want it to be obvious at a glance which year saw the biggest jump in output.", "table_markdown": "| YEAR | NUMBER OF PUBLICATIONS | MOST COMMON TOPICS |\n|---|---|---|\n| 1990 | 45 | • Biological Research & Technology • Defense Technology • Cancer • Health & Health Technology • Space |\n| 1991 | 43 | • Defense Technology • Biological Research & Technology • Children’s Health • Transportation |\n| 1992 | 42 | • Defense Technology • Biological Research & Technology • Business & Industry • Environmental Protection • Health & Health Technology |\n| 1993 | 50 | • Health & Health Technology • Defense Technology • Business & Industry • Energy Efficiency • Remote Sensing |\n| 1994 | 41 | • Health & Health Technology • Defense Technology |\n| 1995 | 64 | • Defense Technology • Education • Health & Health Technology • Environmental Protection • Fishing Industry • Research & Development • Space |"}
{"item_id": "item_0447", "chart_task_type": "momentum_turning", "query": "Can you chart the month-over-month shifts in viewer count so I can spot at a glance which month saw the biggest surge?", "table_markdown": "| Month | Viewers | Videos Viewed | Hours Watched | New Subscribers | Total Impressions |\n|---------|----------|---------------|---------------|-----------------|-------------------|\n| January | 23,800 | 38,487 | 3,620.2 | 132 | 532,400 |\n| February| 21,198 | 34,307 | 3,201 | 103 | 507,655 |\n| March | 26,738 | 46,359 | 5,147 | 145 | 668,404 |\n| April | 20,378 | 28,623 | 1,653 | 84 | 462,844 |\n| May | 26,205 | 39,801 | 4,364 | 141 | 559,725 |\n| June | 78,020 | 98,880 | 16,114 | 335 | 2,187,848 |\n| July | 19,290 | 25,592 | 1,848.5 | 61 | 401,947 |\n| **TOTAL:** | **215,629** | **312,049** | **35,947.7** | **1,001** | **5,320,823** |"}
{"item_id": "item_0448", "chart_task_type": "momentum_turning", "query": "Can you chart the trend in Lost Time Incident Frequency over the five financial years? I want it to be obvious at a glance where the safety improvements stalled and the rate started to rise again.", "table_markdown": "| Year | Fatalities | Lost time injuries (LTI) | Restricted work day cases (RWDC) | Medical treatment cases | Lost time incident frequency (LTIF) | TRCF |\n|------------|------------|--------------------------|----------------------------------|-------------------------|-------------------------------------|------|\n| 2011 – 2012| 0 | 8 | 11 | 1 | 0.46 | 1.37 |\n| 2012 – 2013| 0 | 5 | 19 | 10 | 0.36 | 2.45 |\n| 2013 – 2014| 0 | 3 | 17 | 3 | 0.21 | 1.60 |\n| 2014 – 2015| 0 | 4 | 13 | 2 | 0.30 | 1.40 |\n| 2015 – 2016| 0 | 3 | 5 | 2 | 0.28 | 0.95 |"}
{"item_id": "item_0449", "chart_task_type": "momentum_turning", "query": "Can you plot the trend in average handle time so I can spot at a glance when the momentum shifted from increasing to decreasing?", "table_markdown": "| Year | Actual | Proj. | Plan |\n|--------|--------|-------|------|\n| 2015 | 276 | | |\n| 2016 | 267 | | |\n| 2017 | 280 | | |\n| 2018 | 299 | | |\n| 2019 | 310 | | |\n| 2020 | 270 | | |\n| 2021 | 270 | | |\n| 2022 | 270 | | |"}
{"item_id": "item_0450", "chart_task_type": "momentum_turning", "query": "Can you plot the momentum of vehicle registration growth so I can spot at a glance the year it accelerated the most?", "table_markdown": "| Year | Registered Vehicles | Deaths | Injury | Total Casualties |\n|------|---------------------|--------|--------|------------------|\n| 2000 | 28764 | 3430 | 3211 | 6641 |\n| 2001 | 42510 | 3109 | 3172 | 6281 |\n| 2002 | 54877 | 3398 | 3770 | 7168 |\n| 2003 | 59248 | 3289 | 3818 | 7107 |\n| 2004 | 61202 | 2748 | 1080 | 5621 |\n| 2005 | 65878 | 3187 | 2754 | 5941 |\n| 2006 | 80305 | 3193 | 2409 | 5602 |\n| 2007 | 121272 | 3749 | 3273 | 7022 |\n| 2008 | 144419 | 3765 | 3284 | 7049 |\n| 2009 | 145243 | 2958 | 2686 | 5644 |\n| 2010 | 161178 | 2847 | 1803 | 4449 |\n| 2011 | 172484 | 2467 | 1631 | 3858 |"}
{"item_id": "item_0451", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of net profit from 2013 to 2017? I want it to be obvious at a glance which year saw the sharpest acceleration.", "table_markdown": "| | 2017 | 2016 | 2015 | 2014 | 2013 |\n|----------------------|--------|--------|--------|--------|--------|\n| **Revenue** | 14,531 | 14,142 | 14,002 | 12,459 | 11,746 |\n| **Gross profit** | 8,413 | 8,126 | 8,129 | 7,149 | 6,716 |\n| **EBITDA** | 5,114 | 4,960 | 5,011 | 4,400 | 3,639 |\n| **Operating profit / EBIT** | 4,047 | 3,946 | 3,884 | 3,384 | 2,901 |\n| **Financial items, net** | (157) | (34) | (257) | (84) | (134) |\n| **Net profit** | 3,120 | 3,050 | 2,825 | 2,525 | 2,201 |"}
{"item_id": "item_0452", "chart_task_type": "momentum_turning", "query": "Can you chart the monthly balance changes for 2018 so I can spot at a glance which month saw the biggest jump?", "table_markdown": "| Monthly Tax Payment | Best Western | Foster Inn | Zybell House | Tax Expended | Balance |\n|---------------------|-------------|------------|--------------|--------------|---------|\n| Balance brought forward from 12-31-2017 | | | | | 9,420.13 |\n| Jan-18 | 1407.67 | 381.49 | | | 11,209.29 |\n| Feb-18 | 2,811.39 | 212.75 | | | 12,444.27 |\n| Mar-18 | 304.00 | | | | 12,748.27 |\n| Apr-18 | 820.46 | 186.86 | | | 13,755.59 |\n| May-18 | 326.46 | 166.89 | | | 14,082.05 |\n| Jun-18 | 401.31 | | | 9,750.00 | 5,220.36 |\n| Jul-18 | 487.00 | | | | 5,758.33 |\n| Aug-18 | 537.97 | | | | 5,758.33 |\n| Sep-18 | 3,151.30 | 285.59 | 50.64 | | 9,195.22 |\n| Oct-18 | 282.24 | 128.13 | | 9,750.00 | -272.54 |\n| Nov-18 | 400.54 | 285.18 | | | 128.00 |\n| Dec-18 | 6,792.68 | 334.72 | 268.32 | | 7,255.40 |"}
{"item_id": "item_0453", "chart_task_type": "momentum_turning", "query": "Can you plot the momentum of net interest income from 2013 to 2017? I want it to be obvious at a glance which year saw the sharpest acceleration in growth.", "table_markdown": "| | 2017 | 2016 | 2015 | 2014 | 2013 |\n|--------------------------------|------------|------------|------------|------------|------------|\n| **Net interest income** | 12,496,193 | 10,661,784 | 10,085,543 | 9,618,645 | 9,106,152 |\n| (Recapture of) Provision for loan losses | 110,400 | 3,600 | (646,400) | 408,600 | 163,745 |\n| **Net interest income after provision for loan losses** | 12,385,793 | 10,658,184 | 10,731,943 | 9,210,045 | 8,942,407 |"}
{"item_id": "item_0454", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of Turkey's total exports so I can spot at a glance when the growth accelerated the most?", "table_markdown": "| Year s | Tot.Exp . ($ mil.)(1) | Tot.Imp . ($ mil.)(2) | Exp.of Agr. Pro.($ mil.)(3) | Imp.of Agr. Pro.($ mil.)(4) | 1/2*1 00 (%) | 3/1*1 00 (%) | 4/2*1 00 (%) | 3/4*1 00 (%) | Share of fru. &veg. In Agr. Exp.(%) |\n|---|---|---|---|---|---|---|---|---|---|\n| 1996 | 23224 | 43627 | 4949 | 4866 | 53.2 | 21.3 | 11.2 | 101.7 | 47.1 |\n| 1997 | 26261 | 48559 | 5470 | 4926 | 54.1 | 20.8 | 10.1 | 111 | 47.4 |\n| 1998 | 26974 | 45921 | 5053 | 4321 | 58.7 | 18.7 | 9.4 | 116.9 | 49 |\n| 1999 | 26587 | 40671 | 4442 | 3398 | 65.3 | 16.7 | 8.4 | 130.7 | 50.7 |\n| 2000 | 27775 | 54503 | 3855 | 4156 | 50.9 | 13.9 | 7.6 | 92.8 | 50.2 |\n| 2001 | 31334 | 41399 | 4349 | 3079 | 75.6 | 13.9 | 7.4 | 141.2 | 52.3 |\n| 2002 | 36059 | 51554 | 4052 | 3995 | 69.9 | 11.2 | 7.7 | 101.4 | 55.5 |\n| 2003 | 47253 | 69340 | 5257 | 5265 | 68.1 | 11.1 | 7.6 | 99.8 | 52.8 |\n| 2004 | 63121 | 97540 | 6501 | 6059 | 64.7 | 10.3 | 6.2 | 107.3 | 57.2 |"}
{"item_id": "item_0455", "chart_task_type": "momentum_turning", "query": "Can you plot the wheat yield trend so I can spot at a glance the exact moment the growth momentum turned negative?", "table_markdown": "| Year | Bushels | Precipitation |\n|------|---------|---------------|\n| 1905 | 29 | 22.18 |\n| 1906 | 30 | 21.80 |\n| 1907 | 30 | 16.57 |\n| 1908 | 36½ | 25.23 |\n| 1909 | 25 | 18.95 |\n| 1910 | 28 2-3 | 12.88 |\n| 1911 | 14½ | 16.05 |\n| 1912 | 35½ | 20.44 |"}
{"item_id": "item_0456", "chart_task_type": "momentum_turning", "query": "Can you chart the market value trend from 2013 to 2017 so I can spot at a glance exactly when the growth momentum turned negative?", "table_markdown": "| Calendar Year | | | Market Value as of |\n|---|---|---|---|\n| | | | 12/31/xx |\n| January 31 - December 31, 2013 | Year 1 | $33,153,077.91 | |\n| January 31 - December 31, 2014 | Year 2 | $34,600,580.37 | |\n| January 31 - December 31, 2015 | Year 3 | $34,052,161.12 | |\n| January 31 - December 31, 2016 | Year 4 | $35,296,332.08 | |\n| January 31 - December 31, 2017 | Year 5 | $38,776,234.09 | |\n| | Total | $175,878,385.57 | |\n| Average Market Value* | | $ 35,175,677.11 | |"}
{"item_id": "item_0457", "chart_task_type": "momentum_turning", "query": "Can you plot the revenue growth from 2006 to 2010 so I can spot at a glance which year saw the biggest surge in momentum?", "table_markdown": "| Year Ended December 31, | 2010 | 2009 | 2008 | 2007 | 2006 |\n|-------------------------|--------|--------|--------|--------|--------|\n| **Revenues:** | | | | | |\n| Premium | $4,192,172 | $3,786,525 | $3,199,360 | $2,611,953 | $1,707,439 |\n| Service | 91,661 | 91,758 | 74,953 | 80,508 | 79,159 |\n| **Premium and service revenues** | 4,283,833 | 3,878,283 | 3,274,313 | 2,692,461 | 1,786,598 |\n| **Premium tax** | 164,490 | 224,581 | 90,202 | 76,567 | 35,848 |\n| **Total revenues** | 4,448,323 | 4,102,864 | 3,364,515 | 2,769,028 | 1,822,446 |"}
{"item_id": "item_0458", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of amended filings from 2014 to 2019? I want it to be obvious at a glance which year saw the biggest surge in activity.", "table_markdown": "| △ | Data Year | Amended Filings or Refilings | Total Filings | % Amended Filings or Refilings to Total Filings |\n|----|-----------|------------------------------|---------------|-------------------------------------------------|\n| ↑ | 2019 * | 4,535 | 40,566 | 11.18% |\n| ↓ | 2018 | 5,488 | 38,607 | 14.22% |\n| ↑ | 2017 | 4,325 | 36,749 | 11.77% |\n| ↓ | 2016 | 5,608 | 36,676 | 15.29% |\n| ↓ | 2015 | 4,063 | 34,130 | 11.90% |\n| — | 2014 | 3,543 | 33,761 | 10.49% |"}
{"item_id": "item_0459", "chart_task_type": "momentum_turning", "query": "Can you plot the trend in the number of waterbodies sampled from 1991 to 1999? I want it to be obvious at a glance which year saw the sharpest drop in sampling activity.", "table_markdown": "| Year | Number of waterbodies sampled | Number which contained cyanobacteria (and expressed as a percentage of the total) | Number where warning threshold exceeded (and expressed as percentage of those sampled) | Number where bloom/scum was present (and expressed as percentage of those sampled) |\n|------|-------------------------------|----------------------------------------------------------------------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------|\n| 1991 | 137 | 101 (74%) | 71 (70%) | 44 (44%) |\n| 1992 | 69 | 43 (62%) | 31 (72%) | 23 (53%) |\n| 1993 | 80 | 61 (76%) | 60 (98%) | 40 (66%) |\n| 1994 | 70 | 50 (72%) | 36 (72%) | 46 (92%) |\n| 1995 | 44 | 35 (80%) | 22 (63%) | 16 (46%) |\n| 1996 | 39 | 26 (67%) | 12 (46%) | 20 (77%) |\n| 1997 | 33 | 27 (82%) | 19 (70%) | 21 (78%) |\n| 1998 | 21 | 18 (86%) | 12 (66%) | 6 (33%) |\n| 1999 | 15 | 8 (53%) | 4 (50%) | 5 (62%) |"}
{"item_id": "item_0460", "chart_task_type": "momentum_turning", "query": "Can you chart the trend in natural gas transportation revenues from 2018 to 2023? I want it to be obvious at a glance when the decline stopped and growth resumed.", "table_markdown": "| Year | Revenues | EBITDA EM |\n|------|----------|-----------|\n| 2018 | 410 | 287 |\n| 2019 | 381 | 256 |\n| 2020 | 282 | 197 |\n| 2021 | 210 | 112 |\n| 2022 | 225 | 107 |\n| 2023 | 185 | 53 |\n| LTM* | 318 | 169 |"}
{"item_id": "item_0461", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of Peakload prices from 2007 to 2014? I want it to be obvious at a glance which year saw the biggest surge in growth.", "table_markdown": "| Year | Peakload | Baseload |\n|------|----------|----------|\n| 2007 | 79,36 €/MWh | 55,83 €/MWh |\n| 2008 | 99,40 €/MWh | 70,33 €/MWh |\n| 2009 | 69,84 €/MWh | 49,20 €/MWh |\n| 2010 | 64,48 €/MWh | 49,90 €/MWh |\n| 2011 | 59,02 €/MWh | 56,07 €/MWh |\n| 2012 | 60,85 €/MWh | 49,30 €/MWh |\n| 2013 | 49,67 €/MWh | 39,08 €/MWh |\n| 2014 | 44,53 €/MWh | 35,13 €/MWh |"}
{"item_id": "item_0462", "chart_task_type": "momentum_turning", "query": "Can you plot the trend in the Gonadosomatic Index so I can spot at a glance when the sharpest drop occurred?", "table_markdown": "| Month (First year) | GSR | Month (Second year) |\n|---|---|---|\n| Apr-11 | 2.5557 | Apr-12 |\n| May-11 | 0.7785 | May-12 |\n| Jun-11 | 0.4157 | Jun-12 |\n| Jul-11 | 0.8719 | Jul-12 |\n| Aug-11 | 1.5577 | Aug-12 |\n| Sep-11 | 2.4392 | Sep-12 |\n| Oct-11 | 4.7425 | Oct-12 |\n| Nov-11 | 4.3341 | Nov-12 |\n| Dec-11 | 5.3775 | Dec-12 |\n| Jan-12 | 8.438 | Jan-13 |\n| Feb-12 | 1.5191 | Feb-13 |\n| Mar-12 | 4.8781 | Mar-13 |"}
{"item_id": "item_0463", "chart_task_type": "momentum_turning", "query": "Can you plot the trend in engine production units so I can spot at a glance the exact moment growth turned negative?", "table_markdown": "| Year | Units | HP |\n|------|-------|------|\n| FY2008 | 214 units | 4.7 mil. HP |\n| FY2009 | 218 units | 4.37 mil. HP |\n| FY2010 | 221 units | 4.18 mil. HP |\n| FY2011 | 220 units | 4.31 mil. HP |\n| FY2012 | 187 units | 3.83 mil. HP |\n| FY2013 | 164 units | 3.57 mil. HP |\n| FY2014 | 181 units | 3.54 mil. HP |\n| FY2015 | 181 units | 3.28 mil. HP |\n| FY2016 | 180 units | 3.8 mil. HP |"}
{"item_id": "item_0464", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of Total Revenues across these quarters? I want it to be obvious at a glance when the growth first started to weaken.", "table_markdown": "| Profit & Loss Statement (Rs. million) | Q1 FY13 | Q2 FY13 | Q3 FY13 | Q4 FY13 | Q1 FY14 |\n|---|---|---|---|---|---|\n| Revenues Net Sales / Income from Operations (Net of Excise Duty) Other Operating Income | 2,725 18 | 2,661 97 | 2,721 23 | 2,981 (19) | 3,408 32 |\n| Total Revenues | 2,743 | 2,758 | 2,744 | 2,961 | 3,439 |\n| Expenses Cost of Raw Materials Consumed Other Expenditure Changes in Inventories of Finished Goods, WIP and Stock in Trade Employee Benefit Expenses | | | | | |\n| | 1,397 882 (297) 142 | 1,250 793 52 124 | 1,399 885 (5) 93 | 1,329 1,029 8 144 | 1,504 1,020 272 156 |\n| Total expenses | 2,124 | 2,219 | 2,372 | 2,509 | 2,951 |\n| Operating Profit (EBITDA) | 619 | 539 | 371 | 452 | 488 |\n| Depreciation and Amortisation Expenses | 141 | 141 | 143 | 163 | 147 |\n| EBIT | 478 | 398 | 229 | 289 | 342 |\n| Finance Costs | 159 | 108 | 98 | 50 | 99 |\n| PBT | 319 | 291 | 130 | 239 | 243 |\n| Tax Expenses | 58 | 35 | (39) | 89 | 43 |\n| PAT | 261 | 255 | 169 | 150 | 200 |\n| Basic EPS (Rs) | 18.75 | 18.33 | 12.15 | 9.33 | 12.44 |\n| Margins (%) | | | | | |\n| EBITDA margins PAT margins | 22.6% | 19.5% | 13.5% | 15.3% | 14.2% |\n| | 9.5% | 9.3% | 6.2% | 5.1% | 5.8% |\n| Q-o-Q Growth (%) | | | | | |\n| Total Revenues EBITDA PAT Effective Tax Rate | 8.9% | 0.5% | (0.5)% | 7.9% | 16.1% |\n| | 31.3% | (13.0)% | (31.1)% | 21.7% | 8.0% |\n| | 17.2% | (2.3)% | (33.7)% | (11.5)% | 33.3% |\n| | 18.1% | 12.1% | (30.0)% | 37.2% | 17.8% |"}
{"item_id": "item_0465", "chart_task_type": "momentum_turning", "query": "Can you plot the momentum of groundnut meal exports so I can spot at a glance when the sharpest drop occurred?", "table_markdown": "| Year | Groundnut Meal | Total Oil Meal Export | % of total meal Exports |\n|--------|----------------|-----------------------|-------------------------|\n| 2012-13| 2883 | 48,46,013 | 0.06 |\n| 2013-14| 3290 | 43,81,994 | 0.08 |\n| 2014-15| 3013 | 24,65,663 | 0.12 |\n| 2015-16| 1102 | 15,29,115 | 0.07 |\n| 2016-17| 2918 | 18,85,480 | 0.15 |"}
{"item_id": "item_0466", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of Total Revenue over these fiscal years? I want it to be obvious at a glance when the growth first started to weaken.", "table_markdown": "| Year | Total Revenue | Simplot | AVIKO | RDO | Total Processing Revenue | Fresh/Seed Revenue | Total CWT |\n|------------|---------------|----------|---------|---------|--------------------------|--------------------|-----------|\n| 1994-95 | 558,789 | 218,375 | 0 | 0 | 218,375 | 340,394 | 3,911,346 |\n| 1995-96 | 622,915 | 241,751 | 0 | 18,070 | 259,821 | 363,094 | 3,723,097 |\n| 1996-97 | 804,213 | 278,994 | 67,980 | 21,440 | 388,394 | 235,819 | 2,694,800 Diversion |\n| 1997-98 | 872,950 | 282,802 | 90,650 | 8,133 | 381,585 | 291,366 | 2,595,000 Diversion |\n| 1998-99 | 210,663 | 23,020 | 0 | 13,505 | 36,525 | 174,138 | 2,286,400 |\n| 1999-2000 | 202,172 | 3,915 | 6,690 | 12,893 | 23,498 | 178,675 | 2,291,900 |\n| 2000-2001 | 211,935 | 0 | 0 | 10,175 | 10,175 | 201,780 | 2,152,956 Diversion |\n| 2001-2002 | 190,072 | 0 | 0 | 5,775 | 5,775 | 184,297 | 2,258,285 |"}
{"item_id": "item_0467", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of the total amount due over the years? I want it to be obvious at a glance when the decline was sharpest.", "table_markdown": "| Year Of Offense | Age In Years | Amount Turned Over | Total Dollar Amount Due | Total Dollar Amount Paid | Percent Paid | Total Dollar Amount Non Cash | Percent Non Cash | Total Dollar Amount Liquidated | Percent Liquidated |\n|-----------------|--------------|--------------------|-------------------------|--------------------------|--------------|-------------------------------|------------------|--------------------------------|---------------------|\n| 2015 | 0 | $120,589.43 | $112,597.93 | $5,740.30 | 4.76% | $2,251.20 | 1.87% | $7,991.50 | 6.63% |\n| 2014 | 1 | $261,571.18 | $184,653.28 | $51,884.35 | 19.84% | $25,033.55 | 9.57% | $76,917.90 | 29.41% |\n| 2013 | 2 | $167,468.36 | $138,955.41 | $19,848.94 | 11.85% | $8,664.01 | 5.17% | $28,512.95 | 17.03% |\n| 2012 | 3 | $140,364.18 | $120,805.88 | $14,152.71 | 10.08% | $5,405.59 | 3.85% | $19,558.30 | 13.93% |\n| 2011 | 4 | $143,366.79 | $117,640.68 | $20,910.71 | 14.59% | $4,815.40 | 3.36% | $25,726.11 | 17.94% |\n| 2010 | 5 | $193,648.35 | $169,447.54 | $14,164.20 | 7.31% | $10,036.61 | 5.18% | $24,200.81 | 12.50% |\n| 2009 | 6 | $113,680.70 | $101,754.40 | $8,801.00 | 7.74% | $3,125.30 | 2.75% | $11,926.30 | 10.49% |\n| 2008 | 7 | $179,315.50 | $156,646.60 | $10,136.70 | 5.65% | $12,532.20 | 6.39% | $22,668.50 | 12.64% |\n| 2007 | 8 | $112,820.81 | $100,504.00 | $8,984.16 | 7.96% | $3,332.65 | 2.95% | $12,316.81 | 10.92% |\n| 2006 | 9 | $101,307.05 | $96,112.90 | $3,201.45 | 3.16% | $1,992.70 | 1.97% | $5,194.15 | 5.13% |\n| 2005 | 10 | $127,712.56 | $123,282.16 | $2,173.60 | 1.70% | $2,256.80 | 1.77% | $4,430.40 | 3.47% |\n| 2004 | 11 | $68,165.11 | $59,577.45 | $3,320.65 | 4.87% | $5,267.01 | 7.73% | $8,587.66 | 12.60% |\n| 2003 | 12 | $6,905.60 | $6,905.60 | $0.00 | 0.00% | $0.00 | 0.00% | $0.00 | 0.00% |\n| 2002 | 13 | $1,490.20 | $1,490.20 | $0.00 | 0.00% | $0.00 | 0.00% | $0.00 | 0.00% |\n| 2001 | 14 | $362.25 | $362.25 | $0.00 | 0.00% | $0.00 | 0.00% | $0.00 | 0.00% |"}
{"item_id": "item_0468", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of total waste tonnage over these five years? I want it to be obvious at a glance when the decline stopped and the trend started to turn upward.", "table_markdown": "| Period (per annum) | Tonnages | Kilo Per Household |\n|--------------------|----------|-------------------|\n| July 1994 - June 1995 | 31,759 | 945 |\n| July 1995 - June 1996 | 31,139 | 895 |\n| July 1996 - June 1997 | 29,547 | 832 |\n| July 1997 - June 1998 | 30,164 | 846 |\n| July 1998 - June 1999 | 32,145 | 892 |"}
{"item_id": "item_0469", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum in diversion volumes from 1925 to 1976? I want it to be obvious at a glance which year saw the sharpest drop.", "table_markdown": "| YEAR | ACRE-FEET | TWO-YEAR TOTAL IN ACRE-FEET |\n|------|-----------|-----------------------------|\n| 1925 | 11,600 | -- |\n| 1926 | 30,100 | 41,700 |\n| 1927 | 8,900 | 39,000 |\n| 1928 | 15,700 | 24,600 |\n| 1929 | 15,000 | 30,700 |\n| 1930 | 27,800 | 42,800 |\n| 1931 | 15,600 | 43,400 |\n| 1932 | 6,300 | 21,900 |\n| 1933 | 13,600 | 19,900 |\n| 1934 | 2,200 | 15,800 |\n| 1935 | 5,300 | 7,500 |\n| 1936 | 6,100 | 11,400 |\n| 1937 | 9,200 | 15,300 |\n| 1938 | 8,500 | 17,700 |\n| 1939 | 5,300 | 13,800 |\n| 1940 | 3,100 | 8,400 |\n| 1941 | 20,800 | 23,900 |\n| 1942 | 6,700 | 27,500 |\n| 1943 | 4,500 | 11,200 |\n| 1944 | 20,200 | 24,700 |\n| 1945 | 11,000 | 31,200 |\n| 1946 | 5,700 | 16,700 |\n| 1947 | 12,900 | 28,600 |\n| 1948 | 16,500 | 29,400 |\n| 1949 | 11,500 | 28,000 |\n| 1950 | 2,200 | 13,700 |\n| 1951 | 1,400 | 3,600 |\n| 1952 | 5,100 | 6,500 |\n| 1953 | 5,500 | 10,600 |\n| 1954 | 3,700 | 9,200 |\n| 1955 | 2,000 | 5,700 |\n| 1956 | 4,800 | 6,800 |\n| 1957 | 25,700 | 30,500 |\n| 1958 | 17,400 | 43,110 |\n| 1959 | 7,900 | 25,300 |\n| 1960 | 14,500 | 22,400 |\n| 1961 | 25,100 | 39,600 |\n| 1962 | 10,300 | 35,400 |\n| 1963 | 7,100 | 17,400 |\n| 1964 | 4,900 | 12,000 |\n| 1965 | 6,400 | 11,300 |\n| 1966 | 11,700 | 18,100 |\n| 1967 | 3,800 | 15,500 |\n| 1968 | 12,200 | 16,000 |\n| 1969 | 4,700 | 16,900 |\n| 1970 | 6,800 | 11,500 |\n| 1971 | 2,900 | 9,700 |\n| 1972 | 2,000 | 4,900 |\n| 1973 | 11,600 | 13,600 |\n| 1974 | 2,200 | 13,800 |\n| 1975 | 800 | 3,000 |\n| 1976 | 2,700 | 3,500 |"}
{"item_id": "item_0470", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of our quarterly profit from 2020 Q1 to 2021 Q3? I want it to be obvious at a glance which quarter saw the biggest single-period improvement.", "table_markdown": "| SEK Unit | 2021 Q3 | 2021 Q2 | 2021 Q1 | 2020 Q4 | 2020 Q3 | 2020 Q2 | 2020 Q1 | Full Year 2020 | Full Year 2019 |\n|----------|---------|---------|---------|---------|---------|---------|---------|---------------|---------------|\n| Number of shares kNo. | 7 433 | 7 433 | 7 433 | 7 433 | 7 433 | 7 433 | 7 433 | 7 433 | 7 433 |\n| Profit for the period SEK | -1.3 | -0.4 | -0.9 | -0.6 | -1.3 | -0.4 | -0.4 | -2.7 | -2.9 |\n| Equity SEK | 3.4 | 4.6 | 5.1 | 5.7 | 6.7 | 8.0 | 8.7 | 5.7 | 8.9 |"}
{"item_id": "item_0471", "chart_task_type": "momentum_turning", "query": "Can you chart the trend in credit losses so I can spot at a glance the exact quarter where the upward momentum first reversed?", "table_markdown": "| Quarter | Credit Losses | % of Avg. Managed Assets |\n|---------|---------------|--------------------------|\n| 2Q 11 | $60 | 0.41% |\n| 3Q 11 | $71 | 0.48% |\n| 4Q 11 | $78 | 0.51% |\n| 1Q 12 | $73 | 0.44% |\n| 2Q 12 | $70 | 0.41% |"}
{"item_id": "item_0472", "chart_task_type": "momentum_turning", "query": "Can you plot the trend in consultation volume for the Army Ocular Teleconsultation Program so I can spot at a glance when the growth momentum first shifted?", "table_markdown": "| Program Year | Different Volunteer Consultants | No. of Consultations | Total Consultant Responses | Average Responses per Consultation | Average Response Time (h:min) |\n|--------------|---------------------------------|----------------------|----------------------------|-----------------------------------|-------------------------------|\n| 2004 | 5 | 11 | 11 | 1.0 | 6:45 |\n| 2005 | 10 | 59 | 83 | 1.4 | 7:31 |\n| 2006 | 10 | 37 | 46 | 1.2 | 6:55 |\n| 2007 | 16 | 54 | 64 | 1.2 | 4:53 |\n| 2008 | 19 | 77 | 141 | 1.8 | 5:02 |\n| 2009 | 28 | 63 | 97 | 1.5 | 4:34 |\n| **Total** | | **301** | **97** | **1.5** | **5:41** |"}
{"item_id": "item_0473", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum in the total street maintenance budget so I can spot at a glance which fiscal year saw the biggest jump in growth?", "table_markdown": "| Year | Operating Budget(1) (1,000's) | Overlays in CIP (1,000's) | Total (1,000's) | Road Mileage | Caltrans Highway Cost Index | Budget Per Mile Adjusted for Inflation | Adequate Budget(2) (1,000's) |\n|------|-----------------------------|--------------------------|----------------|--------------|-----------------------------|----------------------------------------|-------------------------------|\n| 73-74| $1,171 | $ | $1,171 | $930 | 100.0 | $1,259 | $1,171 |\n| 74-75| 1,439 | | 1,439 | 934 | 147.2 | 1,047 | 1,731 |\n| 75-76| 1,724 | | 1,724 | 937 | 175.1 | 1,051 | 2,066 |\n| 76-77| 1,753 | | 1,753 | 943 | 152.6 | 1,218 | 1,812 |\n| 77-78| 1,820 | | 1,820 | 962 | 177.6 | 1,065 | 2,151 |\n| 78-79| 1,543 | | 1,543 | 988 | 202.3 | 772 | 2,517 |\n| 79-80| 1,788 | 220 | 2,008 | 1,035 | 259.9 | 746 | 3,387 |\n| 80-81| 2,157 | 400 | 2,557 | 1,051 | 273.7 | 889 | 3,622 |\n| 81-82| 2,316 | 500 | 2,816 | 1,068 | 313.0 | 842 | 4,209 |\n| 82-83| 2,387 | 500 | 2,887 | 1,067 | 275.1 | 984 | 3,696 |\n| 83-84| 2,655 | 500 | 3,155 | 1,078 | 283.3 | 1,033 | 3,845 |\n| 84-85| 3,139 | 1,170 | 4,309 | 1,090 | 294.6 | 1,342 | 4,043 |"}
{"item_id": "item_0474", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of global cotton support so I can spot at a glance when the growth trend first turned negative?", "table_markdown": "| Marketing Season | Production 000 m/t | Ave. Support US c/kg | Total Support US $m |\n|------------------|--------------------|----------------------|---------------------|\n| 2010/11 | 25,453 | 9 | 1,477 |\n| 2011/12 | 27,847 | 8 | 4,886 |\n| 2012/13 | 26,667 | 13 | 7,351 |\n| 2013/14 | 26,283 | 8 | 6,525 |\n| 2014/15 | 26,209 | 18 | 10,353 |"}
{"item_id": "item_0475", "chart_task_type": "momentum_turning", "query": "Can you chart the monthly fish biomass so I can spot at a glance when the growth momentum first turned negative?", "table_markdown": "| lbs. fish | lbs. food |\n|-----------|-----------|\n| January | 1,800,000 | 260,000 |\n| February | 1,800,000 | 260,000 |\n| March | 2,000,000 | 280,000 |\n| April | 2,400,000 | 320,000 |\n| May | 2,600,000 | 340,000 |\n| June | 2,800,000 | 380,000 |\n| July | 2,800,000 | 380,000 |\n| August | 3,100,000 | 450,000 |\n| September | 2,600,000 | 340,000 |\n| October | 1,600,000 | 240,000 |\n| November | 1,400,000 | 220,000 |\n| December | 1,000,000 | 200,000 |"}
{"item_id": "item_0476", "chart_task_type": "momentum_turning", "query": "Can you plot the trend of the daily mean temperature for Berlin throughout the year? I want it to be obvious at a glance when the warming trend stops and the temperatures start to drop.", "table_markdown": "| Month | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Year |\n|-------|------|------|------|------|------|------|------|------|------|------|------|------|------|\n| **Record high °C (°F)** | 15.5 (59.9) | 18.7 (65.7) | 24.8 (76.6) | 31.3 (88.3) | 35.5 (95.9) | 35.9 (96.6) | 38.1 (100.6) | 38.0 (100.4) | 34.2 (93.6) | 28.1 (82.6) | 20.5 (68.9) | 16.0 (60.8) | 38.1 (100.6) |\n| **Average high °C (°F)** | 3.3 (37.9) | 5.0 (41) | 9.0 (48.2) | 15.0 (59) | 19.6 (67.3) | 22.3 (72.1) | 25.0 (77) | 24.5 (76.1) | 19.3 (66.7) | 13.9 (57) | 7.7 (45.9) | 3.7 (38.7) | 14 (57.2) |\n| **Daily mean °C (°F)** | 0.6 (33.1) | 1.4 (34.5) | 4.8 (40.6) | 8.9 (48) | 14.3 (57.7) | 17.1 (62.8) | 19.2 (66.6) | 18.9 (66) | 14.5 (58.1) | 9.7 (49.5) | 4.7 (40.5) | 2.0 (35.6) | 9.7 (49.4) |\n| **Average low °C (°F)** | −1.9 (28.6) | −1.5 (29.3) | 1.3 (34.3) | 4.2 (39.6) | 9.0 (48.2) | 12.3 (54.1) | 14.3 (57.7) | 14.1 (57.4) | 10.6 (51.1) | 6.4 (43.5) | 2.2 (36) | −0.4 (31.3) | 5.9 (42.6) |"}
{"item_id": "item_0477", "chart_task_type": "momentum_turning", "query": "Can you chart the trend in affiliation fees so I can spot at a glance when the growth momentum slowed down the most?", "table_markdown": "| Year | Affiliation to June | Fees. | No. of Leagues |\n|------|---------------------|-------|----------------|\n| 1931 | 86 | | 32 |\n| 1932 | 142 | | 38 |\n| 1933 | 169 | | 55 |\n| 1934 | 233 | | 71 |\n| 1935 | 303 | | 99 |\n| 1936 | 446 | | 121 |"}
{"item_id": "item_0478", "chart_task_type": "momentum_turning", "query": "Can you plot the changes in sanctioned amounts over the years so I can spot at a glance when the momentum first shifted?", "table_markdown": "| SNo. | Year | No. of DEPM Projects Sanctioned | No. of Exhibitions Participated | Amount Sanctioned (Rs. in lakh) |\n|------|--------------------|---------------------------------|---------------------------------|---------------------------------|\n| 1. | 1996-1997 | 10 | 8 | 250.00 |\n| 2. | 1997-1998 | 19 | 2 | 469.00 |\n| 3. | 1998-1999 | 8 | 4 | 335.00 |\n| 4. | 1999-2000 | 26 | 5 | 400.00 |\n| 5. | 2000-2001 | 15 | 9 | 400.00 |\n| 6. | 2001-2002 (up to 31.12.2001) | 11 | 7 | 223.08 |\n| | Total | 89 | 35 | 2077.08 |"}
{"item_id": "item_0479", "chart_task_type": "momentum_turning", "query": "Can you plot the rice price trend from 1912 to 1920? I want it to be obvious at a glance which year saw the steepest drop in value.", "table_markdown": "| Year | Acreage devoted to rice cultivation | Bushels of rice produced | Average price per bushel produced |\n|------|-----------------------------------|--------------------------|----------------------------------|\n| 1912 | 1,400 | 70,000 | $1.09 |\n| 1913 | 6,100 | 293,000 | $1.00 |\n| 1914 | 15,000 | 800,000 | $1.00 |\n| 1915 | 34,000 | 2,268,000 | $1.11 |\n| 1916 | 53,000 | 3,263,000 | $1.28 |\n| 1917 | 80,000 | 5,600,000 | $1.75 |\n| 1918 | 106,220 | 7,011,000 | $1.90 |\n| 1919 | 142,000 | 7,881,000 | $2.67 |\n| 1920 | 162,000 | 9,720,000 | $1.21 |"}
{"item_id": "item_0480", "chart_task_type": "momentum_turning", "query": "Can you chart the trend in total enrollment so I can spot at a glance which year saw the biggest jump?", "table_markdown": "| Year | Total Enrollment | Ave. Att. | Agg. Att. |\n|----------|------------------|-----------|-----------|\n| 1912-13 | 85 | 70.8 | 11,818 |\n| 1913-14 | 86 | 76.28 | 13,274 |\n| 1914-15 | 95 | 88.7 | 15,151 |\n| 1915-16 | 113 | 101.85 | 17,723 |\n| 1916-17 | 114 | | |"}
{"item_id": "item_0481", "chart_task_type": "momentum_turning", "query": "Can you plot the trend in defence export values so I can spot at a glance the first year the growth momentum actually reversed?", "table_markdown": "| Year | Total Export (in Rs. Crs) | No. of Authorizations issued |\n|------------|---------------------------|------------------------------|\n| 2014-2015 | 1941 | 42 |\n| 2015-16 | 2059 | 241 |\n| 2016-17 | 1522 | 254 |\n| 2017-18 | 4682 | 288 |\n| 2018-19 | 10746 | 668 |\n| 2019-20 | 9116 | 829 |\n| 2020-21 | 5711 | 633 |"}
{"item_id": "item_0482", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of GAAP net interest income from 2017 to 2022? I want it to be obvious at a glance when the decline stopped and growth resumed.", "table_markdown": "| Year Ended | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 |\n|------------|------|------|------|------|------|------|\n| **GAAP net interest income** | $243,616 | $247,969 | $195,199 | $161,940 | $167,406 | $173,107 |\n| **Net (gain) loss from fair value adjustments on qualifying hedges** | $(775) | $(2,079) | $1,185 | $1,678 | — | — |\n| **Net amortization of purchase accounting adjustments** | $(2,542) | $(3,049) | $(11) | — | — | — |\n| **Tax equivalent adjustment** | $461 | $450 | $508 | $542 | $895 | — |\n| **Core net interest income FTE** | $240,760 | $243,291 | $196,881 | $164,160 | $168,301 | $173,107 |\n| **Total average interest-earning assets** | $7,841,407 | $7,681,441 | $6,863,219 | $6,582,473 | $6,194,248 | $5,916,073 |\n| **Core net interest margin FTE** | 3.07% | 3.17% | 2.87% | 2.49% | 2.72% | 2.93% |\n| **GAAP interest income on total loans, net** | $293,287 | $274,331 | $248,153 | $251,744 | $232,719 | $209,283 |\n| **Net (gain) loss from fair value adjustments on qualifying hedges** | $(775) | $(2,079) | $1,185 | $1,678 | — | — |\n| **Net amortization of purchase accounting adjustments** | $(2,628) | $(3,013) | $(356) | — | — | — |\n| **Core interest income on total loans, net** | $289,884 | $269,239 | $248,982 | $253,422 | $232,719 | $209,283 |\n| **Average total loans, net** | $6,748,165 | $6,653,980 | $6,006,931 | $5,621,033 | $5,316,968 | $4,988,613 |\n| **Core yield on total loans** | 4.30% | 4.05% | 4.14% | 4.51% | 4.38% | 4.20% |"}
{"item_id": "item_0483", "chart_task_type": "paired_gap", "query": "Can you chart the net change in admissions for the four major correctional institutions between the 1992 and 1993 trimesters, making it easy to spot which facility experienced the largest swing?", "table_markdown": "| MAJOR CORRECTIONAL INSTITUTIONS | BY TRIMESTERS ENDING ON Oct 31, 1992 | Net Change | Oct 31, 1993 | THIS MONTH VS. PAST THREE MONTHS | Avg. Per Mo/ Past 3 Months | Net Change | Oct 31, 1993 |\n|---------------------------------|--------------------------------------|------------|-------------|---------------------------------|---------------------------|------------|-------------|\n| ADMISSIONS a/ | | | | | | | |\n| Prisons Complex | 3425 | -159 | -5% | 3266 | 1128 | -100 | -9% | 1028 |\n| E. Mahan (Women) | 2807 | -176 | -6% | 2631 | 913 | -84 | -9% | 829 |\n| Youth Complex | 225 | -3 | -1% | 222 | 81 | -16 | -20% | 65 |\n| Juvenile Complex | 155 | -51 | -33% | 104 | 36 | -8 | -22% | 28 |\n| PAROLE RELEASES b/ | | | | | | | |\n| Prisons Complex | 238 | 71 | 30% | 309 | 98 | 8 | 8% | 106 |\n| E. Mahan (Women) | 3064 | -983 | -32% | 2081 | 745 | -124 | -17% | 621 |\n| Youth Complex | 1911 | -617 | -32% | 1294 | 462 | -71 | -15% | 391 |\n| Juvenile Complex | 220 | -88 | -40% | 132 | 49 | -10 | -20% | 39 |\n| RESIDENT COUNT c/ | | | | | | | |\n| BY CUSTODY PROVIDED | | | | | | | |\n| In Cnty Facilities | 23145 | 718 | 3% | 23863 | 23646 | 217 | 1% | 23863 |\n| Medium/Maximum d/ | 3985 | 346 | 9% | 4331 | 4221 | 110 | 3% | 4331 |\n| Minimum Custody | 13947 | 289 | 2% | 14236 | 14151 | 85 | 1% | 14236 |\n| Training Schools | 4766 | 65 | 1% | 4831 | 4812 | 19 | 0% | 4831 |\n| BY INSTITUTION | | | | | | | |\n| In Cnty Facilities | 23145 | 718 | 3% | 23863 | 23646 | 217 | 1% | 23863 |\n| Prison Complex | 3985 | 346 | 9% | 4331 | 4221 | 110 | 3% | 4331 |\n| Youth Complex | 13995 | 101 | 1% | 14096 | 14013 | 83 | 1% | 14096 |\n| Juvenile Facilities | 4596 | 251 | 5% | 4847 | 4829 | 18 | 0% | 4847 |\n| TOTAL | 0* | 622 | ERR | 622 | 637 | -15 | -2% | 622 |"}
{"item_id": "item_0484", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of passenger miles traveled from January to September 2020? I want it to be obvious at a glance which month saw the sharpest drop in growth.", "table_markdown": "| Month | Number of Trips | Vehicle Miles Travelled (VMT) | Passenger Miles Travelled | VMT Savings |\n|-------|-----------------|-------------------------------|---------------------------|-------------|\n| Jan-20| 2,505 | 36,053 | 120,096 | 84,043 |\n| Feb-20| 2,230 | 30,358 | 107,628 | 77,270 |\n| Mar-20| 1,692 | 24,831 | 79,703 | 54,872 |\n| Apr-20| 346 | 8,647 | 14,006 | 5,359 |\n| May-20| 484 | 9,587 | 19,897 | 10,310 |\n| Jun-20| 718 | 11,829 | 29,314 | 17,485 |\n| Jul-20| 634 | 9,878 | 26,567 | 16,689 |\n| Aug-20| 672 | 8,929 | 26,897 | 17,968 |\n| Sep-20| 648 | 8,695 | 26,793 | 18,098 |"}
{"item_id": "item_0485", "chart_task_type": "paired_gap", "query": "Can you chart the wage premium for these Office & Clerical roles compared to the national average, making it easy to spot which position has the biggest pay advantage?", "table_markdown": "| Position | National Springfield | Springfield |\n|---------------------------------|----------------------|-------------|\n| Executive Admin Assistant | $24.85 | $32.15 |\n| General Office Clerk | $16.83 | $18.75 |\n| Receptionist | $13.23 | $15.82 |\n| Office Manager | $26.59 | $30.47 |"}
{"item_id": "item_0486", "chart_task_type": "paired_gap", "query": "Can you chart the difference in cycle life between varied and constant temperatures for each fleet, making it easy to spot which one has the largest gain?", "table_markdown": "| Fleet | Cycle number | Number of replacements | Cycle number |\n|---|---|---|---|\n| | (Constant temperature) | (Constant temperature) | (Varied temperatures) |\n| 1 | 2,126 | 10 | 2,366 |\n| 2 | 1,523 | 10 | 1,689 |\n| 3 | 1,267 | 20 | 1,411 |\n| 4 | 1,178 | 20 | 1,305 |\n| 5 | 1,087 | 20 | 1,204 |"}
{"item_id": "item_0487", "chart_task_type": "paired_gap", "query": "Can you chart the acreage shift for each land cover type between 2001 and 2006, making it easy to spot which category experienced the biggest change?", "table_markdown": "| Land Cover | 2001 Acres | 2001 Percent | 2006 Acres | 2006 Percent | % Change 2001 to 2006 |\n|------------------|------------|--------------|------------|--------------|----------------------|\n| Agriculture | 7 | 0.30 | 7 | 0.32 | No Change |\n| Forest | 520 | 22.15 | 519 | 22.10 | 0.2% Decrease |\n| Grass/Shrub/Wetland | 844 | 35.92 | 863 | 36.74 | 2.3% Increase |\n| Water | 933 | 39.69 | 913 | 38.87 | 2.1% Decrease |\n| Urban | 47 | 2.00 | 46 | 1.98 | 1.3% Decrease |"}
{"item_id": "item_0488", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum in the monthly rainwater harvest so I can spot at a glance when the collection volume took its biggest drop?", "table_markdown": "| Month | Rainwater Harvested (L) | Irrigation Demand (L) | WC Demand (L) | Bin Washout (L) |\n|-------|--------------------------|------------------------|---------------|-----------------|\n| Jan | 37,828 | 61,887 | 171,740 | - |\n| Feb | 46,442 | 56,062 | 155,120 | - |\n| Mar | 42,750 | 28,758 | 171,740 | - |\n| Apr | 51,252 | 27,531 | 166,200 | - |\n| May | 45,597 | 28,407 | 171,740 | - |\n| Jun | 49,084 | 12,945 | 166,200 | - |\n| Jul | 37,415 | 13,189 | 171,740 | - |\n| Aug | 47,807 | 13,189 | 171,740 | - |\n| Sep | 49,918 | 37,839 | 166,200 | - |\n| Oct | 51,292 | 38,553 | 171,740 | - |\n| Nov | 71,501 | 37,601 | 166,200 | - |\n| Dec | 50,824 | 62,366 | 171,740 | - |\n| **Total** | **581,709** | **418,327** | **2,022,100** | **-** |\n| **STORM tool** | **57.3** | **0.0** | | |"}
{"item_id": "item_0489", "chart_task_type": "momentum_turning", "query": "Can you plot the momentum in the daily steelhead counts for this year so I can spot at a glance exactly when the momentum shifted downward the most sharply?", "table_markdown": "| Dates | Species | Daily Count Current Year | Daily Count Last Year | Ten Year Average Daily Count |\n|---------|---------------|--------------------------|-----------------------|------------------------------|\n| 15-Sep | Adult Steelhead | 593 | 592 | 866 |\n| 16-Sep | Adult Steelhead | 724 | 1237 | 1104 |\n| 17-Sep | Adult Steelhead | 1590 | 818 | 1138 |\n| 18-Sep | Adult Steelhead | 1055 | 716 | 1404 |\n| 19-Sep | Adult Steelhead | 905 | 726 | 1531 |\n| 20-Sep | Adult Steelhead | 960 | 1266 | 2196 |\n| 21-Sep | Adult Steelhead | 1259 | 827 | 1935 |\n| 22-Sep | Adult Steelhead | 1501 | 1062 | 1833 |\n| 23-Sep | Adult Steelhead | 1782 | 1351 | 1872 |\n| 24-Sep | Adult Steelhead | 891 | 788 | 1568 |\n| 25-Sep | Adult Steelhead | 1329 | 909 | 2437 |\n| 26-Sep | Adult Steelhead | 1567 | 931 | 1887 |\n| 27-Sep | Adult Steelhead | 1674 | 983 | 2371 |\n| 28-Sep | Adult Steelhead | 1209 | 796 | 1898 |"}
{"item_id": "item_0490", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of total energy sales from 2017 to 2021? I want it to be obvious at a glance when the growth trend first started to weaken.", "table_markdown": "| | 2017 | 2018 | 2019 | 2020 | 2021 |\n|----------------------|--------|--------|--------|--------|--------|\n| **Margins for Interest - MFI (Required 1.10)** | 2.21 | 1.64 | 3.25 | 2.40 | 1.97 |\n| **Debt Service Coverage - DSC (Required 1.00)** | 3.14 | 1.96 | 3.75 | 3.61 | 3.70 |\n| **Energy Sales - MWh** | | | | | |\n| Member REC Sales | 2,718,070 | 3,066,455 | 2,948,336 | 2,859,040 | 2,928,591 |\n| Other | 277,160 | 254,388 | 252,139 | 246,148 | 244,957 |\n| **Total Energy Sales** | 2,995,230 | 3,320,843 | 3,200,475 | 3,105,188 | 3,173,548 |\n| **Systems Peaks - MW** | | | | | |\n| Winter | 797 | 874 | 783 | 751 | 943 |\n| Summer | 680 | 659 | 643 | 633 | 658 |"}
{"item_id": "item_0491", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of cremation figures over the fiscal years? I want it to be obvious at a glance when the growth first started to decline.", "table_markdown": "| | 2012- 2013 | 2013- 2014 | 2014- 2015 | 2015- 2016 | 2016- 2017 | 2017- 2018 | 2018- 2019 | 2019- 2020 | 2020- 2021 |\n|---|---|---|---|---|---|---|---|---|---|\n| April | 102 | 161 | 125 | 139 | 163 | 117 | 150 | 121 | 174 |\n| May | 123 | 117 | 135 | 118 | 126 | 138 | 136 | 137 | 136 |\n| June | 116 | 102 | 123 | 143 | 125 | 129 | 127 | 127 | 126 |\n| July | 111 | 121 | 106 | 111 | 126 | 142 | 133 | 128 | 136 |\n| Aug | 106 | 117 | 100 | 109 | 114 | 120 | 106 | 118 | 118 |\n| Sept | 104 | 107 | 122 | 127 | 121 | 127 | 106 | 114 | 121 |\n| Oct | 101 | 98 | 125 | 140 | 127 | 137 | 132 | 129 | 120 |\n| Nov | 129 | 96 | 120 | 108 | 157 | 135 | 127 | 115 | 149 |\n| Dec | 100 | 98 | 120 | 128 | 123 | 131 | 119 | 113 | 141 |\n| Jan | 159 | 164 | 142 | 131 | 158 | 182 | 169 | 147 | 176 |\n| Feb | 121 | 116 | 154 | 136 | 137 | 175 | 143 | 133 | 136 |\n| Mar | 144 | 127 | 157 | 151 | 162 | 164 | 145 | 143 | 141 |\n| Total | 1416 | 1424 | 1529 | 1541 | 1639 | 1697 | 1593 | 1525 | 1674 |"}
{"item_id": "item_0492", "chart_task_type": "paired_gap", "query": "Can you chart the change in absorbance for each masking agent between the 0.25 M and 0.5 M concentrations, making it easy to spot which one shows the biggest drop?", "table_markdown": "| Masking agents 0.01M | | |\n|---|---|---|\n| | 0.25 | 0.5 |\n| 1-10-phenanthroline | 0.149 | 0.138 |\n| Ammonium acetate | 0.151 | 0.149 |\n| Ascorbic acid | 0.15 | 0.137 |\n| NTA | 0.147 | 0.134 |\n| Citric acid | 0.151 | 0.149 |\n| Tartaric acid | 0.148 | 0.138 |\n| NH OH.Hcl 2 | 0.149 | 0.145 |\n| Thiourea | 0.15 | 0.147 |\n| NaF | 0.145 | 0.138 |\n| KI | 0.151 | 0.15 |"}
{"item_id": "item_0493", "chart_task_type": "paired_gap", "query": "Can you chart the gap between Aboriginal and TDSB students across the different close-friend categories, making it easy to spot which group has the biggest disparity?", "table_markdown": "| | Aboriginal | TDSB |\n|----------------------|------------|------|\n| Three or more | 72% | 78% |\n| Two | 15% | 12% |\n| One | 8% | 5% |\n| None | 6% | 5% |"}
{"item_id": "item_0494", "chart_task_type": "momentum_turning", "query": "Can you plot the trend in premiums so I can spot at a glance the exact moment growth lost momentum and started to decline?", "table_markdown": "| Year | Premiums (in USD) | Enrollments | Claims (in USD) |\n|------|------------------|-------------|-----------------|\n| 2008 | 0.1 | 0.03 | 0 |\n| 2009 | 0.26 | 0.06 | 0 |\n| 2010 | 0.6 | 0.1 | 0 |\n| 2011 | 1.1 | 0.26 | 0 |\n| 2012 | 2.8 | 0.6 | 2.6 |\n| 2013 | 3.97 | 0.77 | 2.1 |\n| 2014 | 5.2 | 0.95 | 7.5 |\n| 2015 | 10.9 | 7.8 | 3.8 |\n| 2016 | 18.5 | 18 | 4.5 |\n| 2017 | 24.3 | 16.6 | 4.9 |\n| 2018 | 31.8 | 19.8 | 6.9 |\n| 2019 | 39.4 | 20.5 | 10.1 |\n| 2020 | 28.9 | 15.2 | 12.9 |\n| 2021 | 35.4 | 18.5 | 12.5 |"}
{"item_id": "item_0495", "chart_task_type": "paired_gap", "query": "Can you chart the spread between the maximum and minimum temperature anomalies for each station, making it easy to spot which location has the widest gap?", "table_markdown": "| Jun-Jul-Aug 2023 | Min T | Max T |\n|------------------|-------|-------|\n| Castlepoint | 0.3 | 0.4 |\n| Kelburn | -0.1 | 0.5 |\n| Masterton | -0.3 | 0.2 |\n| Ngawi | 0.2 | 0.9 |\n| Paraparaumu | -0.5 | 0.9 |\n| Wellington Airport | 0.0 | 0.7 |\n| Martinborough | 0.0 | 0.8 |\n| Mana Island | 0.4 | 0.9 |\n| Upper Hutt | 0.2 | 0.8 |\n| Greta Point | 0.5 | 0.8 |"}
{"item_id": "item_0496", "chart_task_type": "paired_gap", "query": "Can you chart the drop in plaque index scores for each study group so it's immediately obvious which one showed the biggest improvement?", "table_markdown": "| Time | Group | N | Mean | Std. Deviation | Std. Error | 95% Confidence Interval for Mean | | f-value | p-value |\n|---|---|---|---|---|---|---|---|---|---|\n| | | | | | | Lower Bound | Upper Bound | | |\n| Baseline | Chlorhexidine | 20 | 1.44 | 0.31 | 0.07 | 1.29 | 1.58 | 7.63 | 0.079 |\n| | Hiora | 20 | 1.34 | 0.32 | 0.07 | 0.99 | 1.29 | | |\n| | Perioaid | 20 | 1.42 | 0.32 | 0.07 | 1.27 | 1.58 | | |\n| | Control | 20 | 1.59 | 0.27 | 0.06 | 1.46 | 1.72 | | |\n| 15 days | Chlorhexidine | 20 | 0.33 | 0.30 | 0.07 | 0.19 | 0.47 | 15.93 | <0.001* |\n| | Hiora | 20 | 0.69 | 0.37 | 0.08 | 0.51 | 0.86 | | |\n| | Perioaid | 20 | 0.80 | 0.25 | 0.06 | 0.68 | 0.92 | | |\n| | Control | 20 | 1.07 | 0.41 | 0.09 | 0.87 | 1.26 | | |"}
{"item_id": "item_0497", "chart_task_type": "momentum_turning", "query": "Can you chart the trend in employer payroll so I can spot at a glance when the growth momentum slowed the most?", "table_markdown": "| | 2019 | 2018 | 2017 | 2016 | 2015 |\n|--------------------------------|----------|----------|----------|----------|----------|\n| Statutorily required contribution | $85,705 | $81,032 | $78,071 | $73,467 | $68,026 |\n| Contributions in relation to the statutorily required contribution | (85,705) | (81,032) | (78,071) | (73,084) | (66,468) |\n| Contribution (deficiency) excess | - | - | - | 383 | 1,558 |\n| Employer’s covered-employee payroll | $749,388 | $716,265 | $689,299 | $641,855 | $594,199 |\n| Contributions as a percentage of covered-employee payroll | 11.44% | 11.31% | 11.33% | 11.39% | 11.19% |"}
{"item_id": "item_0498", "chart_task_type": "momentum_turning", "query": "Can you chart the Greater Sage-grouse population trend in Montana from 2002 to 2023? I want it to be obvious at a glance when the growth momentum first shifted to a decline.", "table_markdown": "| Year | Population Estimate | Standard Error | Confidence Interval |\n|------|---------------------|----------------|---------------------|\n| | | | Lower Bound | Upper Bound |\n| 2002 | 87893 | 10520 | 67275 | 108511 |\n| 2003 | 98026 | 11599 | 75291 | 120760 |\n| 2004 | 90509 | 10761 | 69417 | 111601 |\n| 2005 | 89571 | 10570 | 68854 | 110287 |\n| 2006 | 114356 | 13566 | 87767 | 140946 |\n| 2007 | 92876 | 10902 | 71508 | 114244 |\n| 2008 | 66682 | 7867 | 51262 | 82102 |\n| 2009 | 68027 | 8012 | 52323 | 83732 |\n| 2010 | 63727 | 7498 | 49031 | 78424 |\n| 2011 | 56441 | 6668 | 43372 | 69510 |\n| 2012 | 57848 | 6824 | 44473 | 71223 |\n| 2013 | 41037 | 4840 | 31551 | 50523 |\n| 2014 | 36933 | 4382 | 28345 | 45521 |\n| 2015 | 58893 | 6932 | 45306 | 72480 |\n| 2016 | 85491 | 10047 | 65799 | 105184 |\n| 2017 | 78088 | 9140 | 60173 | 96003 |\n| 2018 | 62592 | 7373 | 48141 | 77043 |\n| 2019 | 47052 | 5539 | 36194 | 57909 |\n| 2020 | 73360 | 8654 | 56399 | 90321 |\n| 2021 | 70287 | 8266 | 54086 | 86488 |\n| 2022 | 53758 | 6341 | 41329 | 66186 |\n| 2023 | 51087 | 6127 | 39078 | 63096 |"}
{"item_id": "item_0499", "chart_task_type": "paired_gap", "query": "Can you chart the year-over-year change for Net Patient Service Revenue, Other Revenue, Salaries and Benefits, Other Expenses, and Operating Income, making it easy to spot which line item has the biggest swing?", "table_markdown": "| | 2007 | 2006 |\n|--------------------------------|------------|------------|\n| Net Patient Service Revenue | $296,957 | $265,034 |\n| Other Revenue | $9,440 | $8,567 |\n| **Total Operating Revenue** | **$306,397** | **$273,601** |\n| Salaries and Benefits | $185,147 | $164,792 |\n| Other Expenses | $119,230 | $107,563 |\n| **Total Operating Expenses** | **$304,377** | **$272,355** |\n| Operating Income | $2,020 | $1,246 |"}
{"item_id": "item_0500", "chart_task_type": "paired_gap", "query": "Can you chart the gap in sub-band counts between 3D-DWT and 2D-DWT for each decomposition level, making it easy to spot which level has the largest disparity?", "table_markdown": "| Decomposition Levels | 2D-DWT sub band | 3D-DWT sub band |\n|----------------------|-----------------|-----------------|\n| 1 | 4 | 16 |\n| 2 | 7 | 22 |\n| 3 | 10 | 29 |\n| 4 | 13 | 36 |\n| 5 | 16 | 43 |"}
{"item_id": "item_0501", "chart_task_type": "paired_gap", "query": "Can you chart the disparity in prediction variability between the indirect and direct QSPR models for each compound class, making it easy to spot which group has the widest gap?", "table_markdown": "| Class of compounds | SD indirect QSPR | SD direct QSPR |\n|-------------------------------------------|------------------|----------------|\n| All reaction products | 2.43 | 1.31 |\n| Methyl alkanes | 1.52 | 1.66 |\n| Alkanes without C3/C4 side chains | 1.08 | 0.59 |\n| Alkanes with C3/C4 side chains | 2.75 | 0.57 |"}
{"item_id": "item_0502", "chart_task_type": "paired_gap", "query": "Can you chart the performance gap between the personalized and unified compositional models across the different domain subsets, making it easy to spot which category shows the biggest advantage for the personalized approach?", "table_markdown": "| | #Utterances | Default component | Compositional (unified entity) | Compositional (personal entity) |\n|----------------------|-------------|-------------------|-------------------------------|---------------------------------|\n| All domains | 138,094 | - | -0.3% | 0.1% |\n| Communications | 14,943 | - | -8.9% | -22.2% |\n| only w/ contact names| 6,574 | - | -20.6% | -43.6% |\n| only w/ personal entities | 4,181 | - | -19.3% | -51.8% |"}
{"item_id": "item_0503", "chart_task_type": "paired_gap", "query": "Can you chart the performance gap between the proposed algorithm and the Cosine similarity baseline across different neighbor counts, making it easy to spot where the improvement is largest?", "table_markdown": "| Number of Neighbors | RMSE_c | RMSE_p | RMSE_ps | RMSE_ps-c | RMSE_ps-p |\n|---------------------|----------|----------|-----------|-----------|-----------|\n| 10 | 1.0417 | 0.9835 | 0.9793 | 0.0624 | 0.0042 |\n| 20 | 1.0248 | 0.9643 | 0.9541 | 0.0707 | 0.0102 |\n| 30 | 1.0189 | 0.9574 | 0.9453 | 0.0736 | 0.0121 |\n| 40 | 1.0163 | 0.9558 | 0.9427 | 0.0736 | 0.0131 |\n| 50 | 1.0162 | 0.9556 | 0.9393 | 0.0769 | 0.0163 |\n| 60 | **1.0154** | 0.9547 | 0.9389 | 0.0765 | 0.0158 |\n| 70 | 1.0162 | 0.9544 | 0.9383 | 0.0779 | 0.0161 |\n| 80 | 1.0170 | 0.9543 | 0.9381 | 0.0789 | 0.0162 |\n| 90 | 1.0173 | 0.9542 | 0.9364 | 0.0809 | 0.0178 |\n| 100 | 1.0176 | **0.9538** | **0.9359** | **0.0817** | **0.0179** |\n| average | 1.0201 | 0.9588 | 0.9448 | 0.0753 | 0.0140 |"}
{"item_id": "item_0504", "chart_task_type": "paired_gap", "query": "Can you chart the gap in annual leave days between 12-month and 10-month employees for each service tenure category, making it easy to spot which group has the widest disparity?", "table_markdown": "| Employees With: | 10-Month Employees | 12-Month Employees |\n|------------------------------------------------------|--------------------|--------------------|\n| Less than 2 years of service | 10 | 12 |\n| 2 years but less than 5 years of service | 11.5 | 13.8 |\n| 5 years but less than 10 years of service | 14 | 16.8 |\n| 10 years but less than 15 years of service | 16.5 | 19.8 |\n| 15 years but less than 20 years of service | 19 | 22.8 |\n| 20 or more years of service | 21.5 | 25.8 |"}
{"item_id": "item_0505", "chart_task_type": "paired_gap", "query": "Can you chart the year-over-year shift in average exchange rates for these currencies, making it easy to spot at a glance which one experienced the biggest change?", "table_markdown": "| Average exchange rates | 2015 | 2014 |\n|---|---|---|\n| Euro - revenue | €1.24 | €1.19 |\n| Euro - costs | €1.30 | €1.20 |\n| US dollar | $1.60 | $1.58 |\n| Swiss franc | CHF 1.50 | CHF 1.45 |"}
{"item_id": "item_0506", "chart_task_type": "paired_gap", "query": "Can you chart the disparity in distraction factors between doctors and nurses so the biggest gap jumps out?", "table_markdown": "| Distractions | Total |\n|-------------------------------|-------|\n| | Doctors | Nurses |\n| Other Medical Officers | 29% | 30% |\n| Nurses | 43% | 42% |\n| Other Staff | 22% | 28% |\n| The Patient | 14% | 19% |\n| Other Patients | 31% | 25% |\n| Relations | 4% | 17% |\n| Phone Calls | 90% | 81% |\n| Bleeps | 0% | 3% |\n| External Noise (TV/Audio/Alarms etc) | 29% | 28% |"}
{"item_id": "item_0507", "chart_task_type": "paired_gap", "query": "Can you chart the gap in neurological deficits between the CT/TT and CC genotype groups so I can see at a glance which variable has the biggest disparity?", "table_markdown": "| Variables | CT/TT | CC |\n|---|---|---|\n| Consciousness | 0% | 0% |\n| Speech impairment | 0% | 0% |\n| Motor function | 4% | 0% |\n| Sensory function | 46% | 31% |\n| Balance and Gait | 4% | 0% |\n| Reflexes | 65% | 44% |\n| Cranial Nerves | 4% | 0% |"}
{"item_id": "item_0508", "chart_task_type": "momentum_turning", "query": "Can you chart the momentum of total receivables over the past year? I want it to be obvious at a glance when the growth first started to weaken.", "table_markdown": "| Date | Total Receivable | Total 30 Day | Total 60 Day | Total 90 Day | Total 120+ |\n|--------|------------------|--------------|--------------|--------------|------------|\n| Jun-20 | $1,488,042.85 | $161,656.90 | $51,852.97 | $20,881.26 | $547,222.85|\n| Jul-20 | $1,583,837.79 | $205,305.34 | $59,602.81 | $28,489.38 | $566,565.69 |\n| Aug-20 | $1,684,021.42 | $192,624.46 | $76,918.05 | $30,574.26 | $570,116.80 |\n| Sep-20 | $1,840,863.30 | $191,779.18 | $82,660.00 | $42,641.43 | $575,925.87 |\n| Oct-20 | $1,950,141.76 | $203,947.29 | $98,625.44 | $49,360.25 | $599,167.07 |\n| Nov-20 | $1,729,935.26 | $171,058.62 | $106,151.43 | $69,194.84 | $629,952.81 |\n| Dec-20 | $1,770,411.66 | $207,852.94 | $108,237.68 | $71,753.10 | $681,133.00 |\n| Jan-21 | $1,882,882.11 | $177,835.92 | $101,402.21 | $60,325.89 | $726,213.52 |\n| Feb-21 | $1,722,490.80 | $214,525.76 | $82,699.58 | $53,887.48 | $885,301.95 |\n| Mar-21 | $1,766,831.02 | $207,653.81 | $91,445.80 | $55,281.36 | $751,717.52 |\n| Apr-21 | $1,638,972.28 | $171,883.21 | $75,000.39 | $49,451.65 | $759,714.51 |\n| May-21 | $1,873,653.31 | $221,883.33 | $72,858.73 | $33,999.47 | $717,874.78 |\n| Jun-21 | $1,987,389.25 | $333,419.13 | $84,947.48 | $25,810.47 | $687,246.17 |"}
{"item_id": "item_0509", "chart_task_type": "paired_gap", "query": "Can you chart the difference in student evaluation scores between ENGL 160 and ENGL 299 for each criterion, making it easy to spot which area has the biggest disparity?", "table_markdown": "| Course Number | ENGL 160 | ENGL 299 |\n|---|---|---|\n| Course Title | The Literary Imagination | Literatures of the Middle East and North Africa |\n| Section Enrollment | 17 | 7 |\n| # of Completed Evaluations | 16 | 9 |\n| The instructor has high standards for achievement. | 4.81 | 4.7 |\n| The instructor clearly explained concepts. | 4.63 | 4.42 |\n| The instructor was organized and prepared. | 4.63 | 5.0 |\n| The instructor challenged me to think. | 4.75 | 4.86 |\n| The instructor was available to me outside of class. | 4.87 | 4.7 |\n| The instructor showed respect for students. | 4.87 | 4.86 |\n| The assignments in this course have enhanced my learning. | 4.62 | 4.86 |\n| The instructor’s feedback enhanced my learning. | 4.75 | 4.57 |\n| The instructor used teaching methods that enhanced my learning. | 4.69 | 4.7 |\n| Overall, the instructor has been an effective teacher. | 4.87 | 4.86 |\n| Overall, this course has advanced my learning. | 4.75 | 4.86 |"}
{"item_id": "item_0510", "chart_task_type": "paired_gap", "query": "Can you chart the gap between the 5.00 mm and 2.50 mm CBR values for each sample so I can see at a glance which one has the largest difference?", "table_markdown": "| Sample No. | CBR Value 2.50 mm | CBR Value 5.00 mm | MDD in g/cc | OMC in % |\n|------------|-------------------|-------------------|-------------|----------|\n| RSC-I | 6.95% | 7.28% | 1.752 | 15.70 |\n| RSC-II | 6.70% | 6.62% | 1.750 | 18.60 |\n| RSC-III | 5.07% | 5.56% | 1.745 | 18.60 |\n| RSC-IV | 5.56% | 5.79% | 1.726 | 15.90 |"}
{"item_id": "item_0511", "chart_task_type": "paired_gap", "query": "Can you chart the availability gap between all properties and those with under 50,000 SF available for each submarket, so the submarket with the widest disparity jumps out?", "table_markdown": "| PROPERTY | ALL PROPERTIES' AVAILABILITIES | PROPERTIES WITH <50,000 SF AVAILABLE | PERCENT DIFFERENCE | ALL PROPERTIES' VACANCIES | PROPERTIES WITH <50,000 SF VACANT |\n|---|---|---|---|---|---|\n| Suburban All | 20.41% | 10.04% | 10.37% | 16.52% | 8.22% |\n| Suburban A | 21.36% | 8.10% | 13.26% | 17.65% | 6.92% |\n| Suburban B | 18.96% | 12.22% | 6.74% | 14.81% | 9.68% |\n| East-West Corridor | 17.39% | 10.76% | 6.63% | 13.83% | 8.72% |\n| North | 18.45% | 8.44% | 10.01% | 15.23% | 6.92% |\n| Northwest | 28.09% | 10.04% | 18.05% | 23.58% | 8.22% |\n| O'Hare | 18.01% | 10.04% | 7.97% | 12.80% | 8.22% |"}
{"item_id": "item_0512", "chart_task_type": "paired_gap", "query": "Can you chart the margin between the Associate price and the Suggested Retail for each product, making it easy to spot at a glance which item has the widest gap?", "table_markdown": "| PRODUCTS | Associate | Sug. Retail |\n|---|---|---|\n| Ageless Actives™ - 60 softgels (30 servings) | $43 | $58 |\n| Ageless Essentials™ Daily Pack for Men - 60 packets (30 A.M. and 30 P.M.) | $77 | $99 |\n| Ageless Essentials™ Daily Pack for Women - 60 packets (30 A.M. and 30 P.M.) | $77 | $99 |\n| Ageless Essentials with Product B® IsaGenesis® for Men - 60 packets (30 A.M. and 30 P.M.) | $156 | $202 |\n| Ageless Essentials with Product B® IsaGenesis® for Women - 60 packets (30 A.M. and 30 P.M.) | $156 | $202 |\n| AMPED™ Power | $34.95 | $39.95 |\n| Boost & Renewal™ - 90 capsules (30 servings) | $49.95 | $67 |\n| Brain and Sleep Support System™ - 90 capsules and 2 oz. spray bottle | $54 | $72 |\n| Cleanse for Life® - Natural Rich Berry Flavor Powder - 96 g (3.4 oz.) canister | $35 | $48 |\n| Cleanse for Life® - Natural Rich Berry Liquid - 32 oz. bottle | $35 | $48 |\n| Cleanse for Life® - Natural Rich Berry Liquid - 2oz. bottle, 16 count | $41.95 | $54 |\n| Cleanse Support Bundle - includes IsaComfort®, Natural Accelerator and Isagenix Snacks | $43 | $56 |\n| C-Lyte® - 30 capsules (30 servings) | $16 | $24 |\n| e+™ Natural Balanced Energy Shot - 2oz. bottle, 6count | $21 | $27 |\n| e+™ Natural Balanced Energy Shot - 2 units of 6 count | $40 | $51 |\n| Essentials for Men™ - 120 tablets (60 servings) | $30 | $42 |\n| Essentials for Women™ - 120 tablets (60 servings) | $30 | $42 |\n| Fiber Snacks™ - Honey Almond Crisp - 12 bars | $26.95 | $32.95 |"}
{"item_id": "item_0513", "chart_task_type": "paired_gap", "query": "Can you chart the salary shortfall between Giles Schools and the lowest District B benchmark across all experience levels, so I can see at a glance which tenure has the biggest gap?", "table_markdown": "| | District B Salary Ranking (BA)* | | | | | | |\n|---|---|---|---|---|---|---|---|\n| Years Experience | | Low | High | Giles Schools | Current | 2% Raise | 6% Raise |\n| 0 | | $32,300 | $38,569 | $34,310 | 9 | 8 | 8 |\n| 5 | | $33,931 | $40,802 | $36,780 | 8 | 8 | 7 |\n| 10 | | $35,396 | $42,697 | $39,700 | 8 | 7 | 3 |\n| 15 | | $39,678 | $46,223 | $43,064 | 6 | 3 | 2 |\n| 20 | | $43,024 | $50,190 | $46,897 | 3 | 3 | 2 |\n| 25 | | $47,015 | $54,603 | $51,147 | 6 | 3 | 2 |\n| 30 | | $43,616 | $64,028 | $55,337 | 7 | 7 | 4 |"}
{"item_id": "item_0514", "chart_task_type": "paired_gap", "query": "Can you chart the cost gap between acetic acid and ethanol for each plant size, making it easy to spot which size has the widest disparity?", "table_markdown": "| Pulp Mill Size (Tonne per Day) | Ethanol ($/gallon) | Acetic Acid ($/gallon) |\n|---|---|---|\n| 550 | 1.89 | 2.51 |\n| 750 | 1.66 | 2.21 |\n| 1000 | 1.49 | 1.98 |\n| 1500 | 1.32 | 1.75 |"}
{"item_id": "item_0515", "chart_task_type": "paired_gap", "query": "Can you chart the preference gap between those who want life-sustaining care and those who don't for each scenario, so the scenario with the widest disparity jumps out?", "table_markdown": "| Scenario | All (n = 202) mean$^a$ (SD) | Those not wanting life-sustaining care (n = 177) mean$^a$ (SD) | Those wanting life-sustaining care (n = 25) mean$^a$ (SD) | Point biserial correlation coefficient$^b$ |\n|-----------------------------------------------|-----------------------------|---------------------------------------------------------------|----------------------------------------------------------|-------------------------------------------|\n| Shortness of breath | 2.44 (1.46) | 2.28 (1.49) | 3.49 (0.64) | 0.272 |\n| Moderately severe stroke/arm and leg paralyzed| 1.64 (1.60) | 1.47 (1.54) | 2.88 (1.53) | 0.396 |\n| Colon cancer that has spread to the liver, no pain | 1.49 (1.59) | 1.29 (1.51) | 2.78 (1.56) | 0.307 |\n| Alzheimer’s disease | 1.44 (1.56) | 1.25 (1.49) | 2.84 (1.37) | 0.337 |\n| Colon cancer that has spread to the liver, with pain | 0.94 (1.44) | 0.75 (1.30) | 2.34 (1.68) | 0.366 |\n| Severe stroke, in coma | 0.55 (1.16) | 0.34 (0.92) | 2.04 (1.55) | 0.484 |"}
{"item_id": "item_0516", "chart_task_type": "paired_gap", "query": "Can you chart the extra cost of sponsoring both events versus just one for each level, so the biggest price jump is immediately obvious?", "table_markdown": "| W Awards and/or Summit | Sponsor Level | Single Event | Both Events |\n|---|---|---|---|\n| | Platinum Sponsor | $3,000 | $5,000 |\n| | Gold Sponsor | $1,500 | $2,500 |\n| | Silver Sponsor | $750 | $1,250 |\n| | Bronze Sponsor | $295 | $500 |"}
{"item_id": "item_0517", "chart_task_type": "paired_gap", "query": "Can you chart the performance gap between LWSD and the State for SpEd students on the SBA Math test across grades 3 through 8, so I can see at a glance which grade has the largest advantage?", "table_markdown": "Grade | LWSD | State\n------|------|------\n3rd | 39 | 28\n4th | 36 | 24\n5th | 29 | 18\n6th | 28 | 14\n7th | 24 | 13\n8th | 24 | 11"}
{"item_id": "item_0518", "chart_task_type": "paired_gap", "query": "Can you chart the disparity between the uraninite and coffinite saturation indexes for each site, making it easy to spot which location has the widest gap?", "table_markdown": "| Site number | Source | pH | Specific conductance | Water type | Uranium μg/L | Dominant uranium species | Calculated Eh, volts | SI for uraninite | SI for coffinite |\n|-------------|-------------------------|-----|----------------------|------------|--------------|-----------------------------------|---------------------|-----------------|-----------------|\n| 26 | irrig. well | 7.76| 420 | Ca, HCO₃ | 12 | UO₂(CO₃)₂²⁻UO₂(CO₃)₃⁴⁻ | .098 | -3.041 | -1.496 |\n| 27 | stream, North Ck. | 7.32| 145 | ---do--- | 1.4 | UO₂(CO₃)₂²⁻UO₂CO₃ | .136 | -3.293 | -1.893 |\n| 28 | stream, Indian Ck. | 7.96| 92 | ---do--- | 1.2 | --------do----------- | .021 | -0.078 | 1.338 |\n| 29 | irrig. well | 7.60| 275 | ---do--- | 1.2 | UO₂(CO₃)₂²⁻UO₂(CO₃)₃⁴⁻ | .158 | -5.677 | -3.837 |\n| 30 | spring | 7.33| 410 | ---do--- | 2.1 | --------do----------- | .144 | -5.240 | -3.505 |\n| 31 | domestic well | 7.09| 450 | ---do--- | 5.2 | UO₂(CO₃)₂²⁻UO₂CO₃ | .159 | -4.339 | -2.544 |\n| 32 | stream, Birch Ck. | 8.13| 122 | ---do--- | <.20 | UO₂(CO₃)₂²⁻UO₂(CO₃)₃⁴⁻ | .004 | -1.617 | 0.088 |\n| 33 | stream, South Ck. | 7.26| 130 | ---do--- | 20 | UO₂(CO₃)₂²⁻UO₂(CO₃)₃⁴⁻ | .004 | -1.617 | 0.088 |"}
{"item_id": "item_0519", "chart_task_type": "paired_gap", "query": "Can you chart the gap between fair value and carrying value for the four specific asset and liability line items from the September 30, 2017 table, so I can see at a glance which one has the largest discrepancy?", "table_markdown": "| SEPTEMBER 30, 2017 | Level 1 | Level 2 | Level 3 | Total Fair Value | Carrying Value |\n|---------------------|---------|---------|---------|-----------------|---------------|\n| **ASSETS** | | | | | |\n| Held-to-maturity bonds (1) | - | 741 | - | 741 | 670 |\n| Mortgages and loans (2) | - | 38,455 | - | 38,455 | 36,821 |\n| Policy loans | - | 81 | - | 81 | 81 |\n| **TOTAL ASSETS DISCLOSED AT FAIR VALUE** | - | 39,277 | - | 39,277 | 37,572 |\n| **LIABILITIES** | | | | | |\n| Investment contract liabilities (3) | - | 230,787 | - | 230,787 | 237,483 |\n| **TOTAL LIABILITIES DISCLOSED AT FAIR VALUE** | - | 230,787 | - | 230,787 | 237,483 |"}
{"item_id": "item_0520", "chart_task_type": "paired_gap", "query": "Can you chart the difference in adverse experience rates between the 5 mg Cetirizine group and the Placebo group, making it easy to spot which side effect shows the largest increase?", "table_markdown": "| Adverse Experiences | Placebo (N=309) | 5 mg (N=161) |\n|---|---|---|\n| Headache | 12.3% | 11.0% |\n| Pharyngitis | 2.9% | 6.2% |\n| Abdominal pain | 1.9% | 4.4% |\n| Coughing | 3.9% | 4.4% |\n| Somnolence | 1.3% | 1.9% |\n| Diarrhea | 1.3% | 3.1% |\n| Epistaxis | 2.9% | 3.7% |\n| Bronchospasm | 1.9% | 3.1% |\n| Nausea | 1.9% | 1.9% |\n| Vomiting | 1.0% | 2.5% |"}
{"item_id": "item_0521", "chart_task_type": "paired_gap", "query": "Can you chart the particle size imbalance for each mine spoil so I can see at a glance which sample has the widest gap between the fine and coarse fractions?", "table_markdown": "| Lab-ID | Material | Geologic Description | Geologic formation | Coal Seam | Particle size % |\n|--------|-------------------|--------------------------------------------------------------------------------------|-------------------------------------|----------------------|-----------------|\n| | | | | | < 1 cm | > 1 cm |\n| OSM 1 | Mine spoil Unweathered | 98% unweathered, gray and orange, medium to coarse grained, feldspathic sandstone; 2% unweathered gray silty mudstone. (No coal apparent.) | Norton | Raven 1 | 23 | 77 |\n| OSM 2 | Mine spoil Unweathered | 93% dark gray carbonaceous silty mudstone; 6% unweathered, gray, fine-grained sandstone/siltstone; 1% coal. | Four Corners Formation; Breathitt Group | Hazard #7 and Hazard #8 | 60 | 40 |\n| OSM 3 | Mine spoil Partially-Weathered | 50% highly weathered, gray and orange, fine grained and medium to coarse grained, feldspathic sandstone; 30% unweathered gray silty mudstone ; 10% unweathered feldspathic sandstone; 8% unweathered gray silty mudstone; 2% coal. | Middle Wise | Kelly/Imboden | 87 | 13 |\n| OSM 4 | Mine spoil Weathered | 98% weathered, reddish-brown silty mudstone; 1% weathered sandstone; 1% coal. | Lower Wise | Clintwood/Blair | 85 | 15 |\n| OSM 6 | Mine spoil Unweathered | 90% minimally weathered gray clayey siltstone; 10% brown silty mudstone; trace coal. | Lower Wise | Clintwood/Blair | 79 | 21 |\n| OSM 7 | Mine spoil Weathered | 85% weathered brown-gray silty mudstone; 13% unweathered gray silty mudstone; 2% weathered sandstone. (No coal apparent.) | Middle Wise | Kelly/Imboden | 62 | 38 |\n| OSM 10 | Mine spoil Unweathered | 97% unweathered gray silty mudstone; 2% coal; <1% unweathered sandstone; <1% brown shale. | Upper-middle Wise | Phillips | 72 | 28 |\n| OSM 11 | Mine spoil Weathered | 99% weathered sandstone; 1% silty mudstone; trace coal. | Upper-middle Wise | Taggart | 68 | 32 |\n| OSM 12 | Mine spoil Unweathered | 98% unweathered, gray, medium grained sandstone; 1% weathered sandstone; 1% silty mudstone. | Upper-middle Wise | Taggart | 45 | 55 |\n| OSM 14 | Mine spoil Weathered | 80% weathered, gray and orange, feldspathic sandstone; 20% gray silty mudstone. (No coal apparent.) | Lower Wise | Clintwood/Blair | 65 | 35 |"}
{"item_id": "item_0522", "chart_task_type": "paired_gap", "query": "Can you chart the gap between the two GA Index values for each generation so I can see at a glance which one has the largest disparity?", "table_markdown": "| n | GA Index of NS [ n ] 1 1 | GA Index of NS [ n ] 1 2 |\n|---|---|---|\n| 1 | 33.9525 | 20.65 |\n| 2 | 96.0862 | 52.1788 |\n| 3 | 220.354 | 115.236 |\n| 4 | 468.889 | 241.351 |\n| 5 | 965.958 | 493.582 |\n| 6 | 1960.1 | 998.042 |\n| 7 | 3948.4 | 2007 |\n| 8 | 7924.9 | 4024.8 |\n| 9 | 15878 | 8060.5 |\n| 10 | 31784 | 16132 |"}
{"item_id": "item_0523", "chart_task_type": "paired_gap", "query": "Can you chart the disparity between on-peak and off-peak emission factors for each jurisdiction so I can see at a glance which one has the widest gap?", "table_markdown": "| g/kWh | Off-Peak | On-Peak |\n|-------|----------|---------|\n| ISO-NE| 344 | 480 |\n| NYISO| 352 | 510 |\n| PJM| 812 | 605 |\n| MISO| 965 | 789 |\n| Manitoba | 0 | 0 |"}
{"item_id": "item_0524", "chart_task_type": "paired_gap", "query": "Can you chart the disparity between credit-granting and non-credit-granting private schools for each school type, making it easy to spot which category has the widest gap?", "table_markdown": "| | OSSD | | |\n|---|---|---|---|\n| | Credit- | Non-credit- | |\n| | granting | granting | |\n| | Schools | Schools | Total |\n| Elementary | — | 545 | 545 |\n| Combined elementary and secondary | 167 | 88 | 255 |\n| Secondary | 279 | 13 | 292 |\n| Total | 446 | 646 | 1,092 |"}
{"item_id": "item_0525", "chart_task_type": "paired_gap", "query": "Can you chart the disparity between interview and focus group participation for each intervention, making it easy to spot which one has the widest gap?", "table_markdown": "| | Interview | Focus group | Total |\n|---|---|---|---|\n| Intervention A | 8 | - | 8 |\n| Intervention B | 4 | - | 4 |\n| Intervention C | 8 | 7 | 15 |\n| Intervention D | 8 | - | 8 |\n| Total | 28 | 7 | 35 |"}
{"item_id": "item_0526", "chart_task_type": "paired_gap", "query": "Can you chart the shift in instrument depth for each hydrophone between the two periods, making it easy to spot which one experienced the largest change?", "table_markdown": "| Hydrophone | Approximate mooring location | Approximate bottom depth (m) | 1999–2000 instrument depth (m) | 2000–2001 instrument depth (m) |\n|------------|------------------------------|-----------------------------|-------------------------------|-------------------------------|\n| SE | 16°N 43°W | 4565 | 865 | 767 |\n| SW | 16°N 49°W | 4715 | 815 | 746 |\n| CE | 26°N 40°W | 5105 | 905 | 866 |\n| CW | 26°N 50°W | 5182 | 982 | 874 |\n| NE | 32°N 35°W | 3927 | 927 | 925 |\n| NW | 35°N 43°W | 4179 | 679 | 686 |"}
{"item_id": "item_0527", "chart_task_type": "paired_gap", "query": "Can you chart the difference between the transgenic and non-transgenic values for each property so I can see at a glance which one has the biggest gap?", "table_markdown": "| Property | Transgenic | Non-transgenic | T-test (5%) |\n|---------------------------|------------|----------------|-------------|\n| Dry matter (%) | 34.2 | 34.4 | NS |\n| Starch content (%) | 77.5 | 83.9 | 5.30 |\n| Ash (%) | 2.7 | 2.8 | NS |\n| Crude fibre (5%) | 2.9 | 3.6 | 0.41 |\n| Total sugar (%) | 3.4 | 3.4 | NS |\n| Pasting clarity (%) | 65.7 | 58.7 | NS |\n| Pasting temperature (°C) | 64.3 | 67.5 | NS |\n| Amylose (%) | 23.3 | 22.4 | NS |\n| Pasting viscosity (m Pa s)| 621.0 | 783.2 | 20.37 |\n| Breakdown (m Pa s) | 133.1 | 284.2 | 43.75 |\n| Consistency (m Pa s) | 166.7 | 163.3 | NS |\n| Solubility (%), db | -136.3 | -143.2 | NS |\n| Swelling power (%), g/g | 37.9 | 41.7 | NS |"}
{"item_id": "item_0528", "chart_task_type": "paired_gap", "query": "Can you chart the disparity between the NPO and PO average percentiles for each STAXI2 scale, making it easy to spot which scale has the largest gap where NPO exceeds PO?", "table_markdown": "| STAXI2 scales | PO | NPO |\n|--------------------------------------|------|------|\n| Anger expression index | 55.7 | 92.5 |\n| = F (1, 13) 6.622, p = .023 | | |\n| Anger control in | 47.1 | 40 |\n| = F (1, 13) 0.988, p = .388 | | |\n| Anger control out | 57.1 | 4 |\n| = F (1, 13) 21.54, p = .000* | | |\n| Anger expression in | 68.5 | 82.5 |\n| = F (1, 13) 0.953, p = .347 | | |\n| Anger expression out | 28.7 | 84 |\n| = F (1, 13) 5.970, p = .030 | | |\n| Angry reaction | 49.5 | 62.5 |\n| = F (1, 13) 0.552, p = .471 | | |\n| Angry temperament | 37.7 | 84.5 |\n| = F (1, 13) 1.384, p = .260 | | |\n| Trait anger | 42.8 | 84.5 |\n| = F (1, 13) 5.75, p = .462 | | |\n| Feel like expressing anger physically| 55 | 73.5 |\n| = F (1, 13) 0.887, p = .369 | | |\n| Feel like expressing anger verbally | 58.5 | 74 |\n| = F (1, 13) 0.339, p = .570 | | |\n| Feeling angry | 53.5 | 57.5 |\n| = F (1, 13) 1.92, p = .189 | | |\n| State Anger | 56.4 | 65 |\n| = F (1, 13) 0.027, p = .872 | | |"}
{"item_id": "item_0529", "chart_task_type": "paired_gap", "query": "Can you chart the gap between undergraduate and graduate enrollment growth for each school, making it easy to spot which institution has the widest disparity?", "table_markdown": "| Institution | Graduate enrollment CAGR, 2008-2012 | Undergraduate enrollment CAGR, 2008-2012 |\n|---------------|-------------------------------------|----------------------------------------|\n| Tulane | 3.9% | 6.2% |\n| NE | 12.9% | 1.2% |\n| Syracuse | 2.1% | 5.1% |\n| Georgetown | 5.6% | 2.1% |\n| American | -1.4% | 3.7% |\n| Wake Forest | -0.2% | 1.6% |\n| BU | 1.0% | 0.3% |\n| SMU | 0.2% | 0.6% |\n| Lehigh | -0.6% | 0.8% |\n| GWU | 0.0% | -2.7% |"}
{"item_id": "item_0530", "chart_task_type": "paired_gap", "query": "Can you chart the awareness gap for each financial asset so I can see at a glance which one has the widest difference between those who have heard of it and those who haven't?", "table_markdown": "| | I heard (%) | I didn't hear (%) |\n|------------------------|-------------|-------------------|\n| Credit card | 97.10 | 2.90 |\n| Checking account | 94.30 | 5.70 |\n| Deposit account | 93.80 | 6.20 |\n| Insurance | 93.50 | 6.50 |\n| Stock | 90.80 | 9.20 |\n| Pension fund | 85.70 | 14.30 |\n| Mobile payment | 84.50 | 15.50 |\n| Bill of exchange | 79.80 | 20.20 |\n| Treasury bill | 79.20 | 20.80 |\n| Capital account | 78.30 | 21.70 |\n| Mortgage | 75.20 | 24.80 |\n| Junior debt | 42.00 | 58.00 |"}
{"item_id": "item_0531", "chart_task_type": "paired_gap", "query": "Can you chart the disparity between the SSW and ACE Study percentages for each ACE score category, making it easy to spot which category has the biggest gap?", "table_markdown": "| Prevalence of Adverse Childhood Experiences (ACE Score) | SSW % (N = 180) | ACE Study % (N = 17,337) |\n|--------------------------------------------------------|-----------------|--------------------------|\n| 0 | 22.2 | 32.7 |\n| 1 | 16.1 | 25.6 |\n| 2 | 18.3 | 15.5 |\n| 3 | 12.2 | 9.9 |\n| 4 | 11.7 | 5.9 |\n| 5 or more | 19.5 | 10.5 |"}
{"item_id": "item_0532", "chart_task_type": "paired_gap", "query": "Can you chart the improvement in symptom scores for each questionnaire item so it's easy to spot which one showed the biggest change after treatment?", "table_markdown": "| Questionnaire item | Mean ± SD score before treatment | Mean ± SD score after Treatment | Mean difefrencea | ESIb |\n|---|---|---|---|---|\n| (1) Emptying | 2.0±1.7 | 1.1±1.1 | 0.9 | 0.57 |\n| (2) Frequency | 2.0±1.5 | 0.8±0. 9 | 1.2 | 0.77 |\n| (3) Intermittency | 1.9±1.9 | 0.9±1.2 | 1 | 0.54 |\n| (4) Urgency | 1.6±1.6 | 0.6±0.8 | 0.9 | 0.58 |\n| (5) Weak stream | 2.3±1.7 | 0.9±1.0 | 1.4 | 0.81 |\n| (6) Hesitancy | 1.6±1.7 | 0.7±0.9 | 1 | 0.58 |\n| (7) Nocturia | 2.5±1.3 | 1.2±1.0 | 1.3 | 0.99 |\n| sIPSS | 14.1±8.3 | 6.2±4. 9 | 7.9 | 0.94 |\n| (8) QoL | 3.7±1.8 | 1.5±1.1 | 2.21 | 1.22 |"}
{"item_id": "item_0533", "chart_task_type": "paired_gap", "query": "Can you chart the CPU time gap between NCG and CG-DESCENT for each application so I can see at a glance which one has the largest performance difference?", "table_markdown": "| NCG #iter #fg cpu | | | | CG-DESCENT | | |\n|---|---|---|---|---|---|---|\n| | | #fg | cpu | #iter | #fg | cpu |\n| A1 | 1113 | 2257 | 351.62 | 1145 | 2291 | 474.64 |\n| A2 | 2843 | 5714 | 1143.97 | 3370 | 6741 | 1835.51 |\n| A3 | 4725 | 9494 | 2754.26 | 4814 | 9630 | 3949.71 |\n| A4 | 1413 | 2864 | 2014.17 | 1802 | 3605 | 3786.25 |\n| A5 | 1270 | 2566 | 571.45 | 1225 | 2451 | 753.75 |\n| TOTAL | 11364 | 22895 | 6835.47 | 12356 | 24718 | 10799.86 |"}
{"item_id": "item_0534", "chart_task_type": "paired_gap", "query": "Can you chart the yield disparity between tar-to-pitch and coal-to-pitch for each mine, making it easy to spot which one has the biggest advantage?", "table_markdown": "| State | Mine | Tar to Pitch | Coal to Pitch | Quinoline Insoluble Content (wt%) | Anisotropy (%) |\n|-------|---------------|--------------|---------------|----------------------------------|----------------|\n| UT | Sufco | 31.9 | 21.3 | 64.7 | 75.4 |\n| IL | Illinois #6 | 26.7 | 24.3 | 12.9 | 8.2 |\n| WY | PRB Black Thunder | 22.8 | 26.6 | 43.5 | 32.7 |\n| WV | Flying Eagle | 34.5 | 22.1 | 71.5 | 88.5 |"}
{"item_id": "item_0535", "chart_task_type": "paired_gap", "query": "Can you chart the point disparity between N-S and E-W for each team so I can see at a glance which one has the biggest gap?", "table_markdown": "| N/S No. | Con- tract | By | Made | N/S | E/W | E-W No. | N-S Pts. | E-W Pts. |\n|---|---|---|---|---|---|---|---|---|\n| 1 | 3D | E | 3 | (-110) | 110 | 2 | 1 | 7 |\n| 2 | 3D | E | -1 | 100 | | 4 | 4 | 4 |\n| 3 | 4S | N | 4 | 420 | | 6 | 7 | 1 |\n| 4 | 3NT | S | -1 | (-50) | 50 | 8 | 3 | 5 |\n| 5 | 3NT | S | 4 | 430 | | 1 | 8 | 0 |\n| 6 | 2H | W | -2 | 200 | | 3 | 5 | 3 |\n| 7 | 3D | E | 4 | (-130) | 130 | 5 | 0 | 8 |\n| 8 | 3NT | S | 3 | 400 | | 7 | 6 | 2 |\n| 9 | 3NT | S | -2 | (-100) | 100 | 9 | 2 | 8 |\n| TOTAL | | | | | | | 36 | 36 |"}
{"item_id": "item_0536", "chart_task_type": "paired_gap", "query": "Can you chart the disparity in larval response between the control and each dye treatment, making it easy to spot at a glance which color causes the biggest shift in preference?", "table_markdown": "| Treatment | Mean percentage of responding larva ± SE | t | 2-tailed $P$ |\n|-----------------|----------------------------------------|-----|--------------|\n| SC vs SCLG | $12.00 \\pm 5.83$ vs $50.00 \\pm 7.07$ | -3.413 | 0.027* |\n| SC vs SCLY | $20.00 \\pm 3.16$ vs $58.00 \\pm 3.74$ | -7.757 | 0.001* |\n| SC vs SCOR | $18.00 \\pm 4.89$ vs $26.00 \\pm 5.09$ | -0.930 | 0.405 |\n| SC vs SCC | $34.00 \\pm 7.48$ vs $30.00 \\pm 7.07$ | 0.286 | 0.789 |\n| SC vs SCCR | $22.00 \\pm 5.83$ vs $18.00 \\pm 3.74$ | -0.667 | 0.541 |"}
{"item_id": "item_0537", "chart_task_type": "paired_gap", "query": "Can you chart the month-by-month change in enforcement cases between 2017/18 and 2016/17, making it easy to spot which month saw the biggest increase?", "table_markdown": "| Month | 2017/18 | 2016/17 |\n|-------|---------|---------|\n| Apr | 5 | 4 |\n| May | 5 | 5 |\n| Jun | 10 | 5 |\n| Jul | 15 | 5 |\n| Aug | 15 | 5 |\n| Sep | 15 | 2 |\n| Oct | 20 | 2 |\n| Nov | 25 | 5 |\n| Dec | 20 | 10 |\n| Jan | 20 | 5 |\n| Feb | 20 | 2 |\n| Mar | 15 | 5 |"}
{"item_id": "item_0538", "chart_task_type": "paired_gap", "query": "Can you chart the gender imbalance across the specific age groups so it's immediately obvious which bracket has the widest gap between males and females?", "table_markdown": "| Age groups | Persons | Males (%) | Females (%) |\n|------------|-----------|-----------|-------------|\n| 0-4 | 52269 | 51.25 | 48.74 |\n| 5-9 | 64970 | 50.26 | 49.74 |\n| 10-14 | 71668 | 50.95 | 49.05 |\n| 15-19 | 63444 | 51.45 | 48.55 |\n| 20-24 | 55171 | 53.59 | 46.41 |\n| 25-29 | 47552 | 52.50 | 47.50 |\n| 30-34 | 39153 | 55.30 | 44.70 |\n| 35-39 | 36384 | 56.02 | 43.98 |\n| 40-44 | 28129 | 57.64 | 42.36 |\n| 45-49 | 22177 | 57.86 | 42.14 |\n| 50-54 | 16919 | 57.75 | 42.25 |\n| 55-59 | 12037 | 58.74 | 41.26 |\n| 60-64 | 10469 | 55.52 | 44.48 |\n| 65-69 | 7593 | 55.97 | 44.03 |\n| 70-74 | 5523 | 58.54 | 41.46 |\n| 75-79 | 2749 | 56.46 | 43.54 |\n| 80+ | 2706 | 57.06 | 42.94 |\n| Age not stated | 1938 | 53.77 | 46.23 |\n| All ages | 540851 | 53.34 | 46.66 |"}
{"item_id": "item_0539", "chart_task_type": "paired_gap", "query": "Can you chart the GPU performance gap between Sorting and Hashing for all data sets, making it easy to spot which one has the largest disadvantage for Sorting?", "table_markdown": "| Data set | CPU | | GPU | |\n|------------|-----|--------|-----|--------|\n| | Sorting | Hashing | Sorting | Hashing |\n| Enzo-10M | 0.9 | 0.9 | 0.7 | 0.4 |\n| Nek-50M | 4.3 | 4.3 | 3.3 | 2.1 |\n| Enzo-80M | 7.4 | 7.3 | 7.4 | 7.3 |\n| Re-Enzo-10M| 1.2 | 0.9 | 1.0 | 0.4 |\n| Re-Nek-50M | 5.5 | 4.5 | 5.3 | 2.2 |\n| Re-Enzo-80M| 9.2 | 7.7 | 10.1 | 6.5 |"}
{"item_id": "item_0540", "chart_task_type": "paired_gap", "query": "Can you chart the gap between viewable and downloadable data percentages for each theme, making it easy to spot which one has the biggest discrepancy?", "table_markdown": "| Data theme | Downloadable data | Viewable data |\n|---|---|---|\n| AM | 8% | 6% |\n| PS | 19% | 18% |\n| BR | 11% | 11% |\n| HB | 10% | 17% |\n| SD | 3% | 11% |"}
{"item_id": "item_0541", "chart_task_type": "paired_gap", "query": "Can you chart the increase in fire service charges for each pipe size so it's easy to spot which one has the biggest price jump?", "table_markdown": "| Service Size | Current Cost | Proposed Costs |\n|--------------|--------------|----------------|\n| 1 1/2-inch | $24.34 | $24.59 |\n| 2-inch | $35.37 | $37.51 |\n| 2 1/2-inch | $35.37 | $54.77 |\n| 3-inch | $64.82 | $71.97 |\n| 4-inch | $97.92 | $110.74 |\n| 6-inch | $189.91 | $218.43 |\n| 8-inch | $336.92 | $347.65 |\n| 10-inch | $539.18 | $498.41 |\n| 12-inch | $796.54 | $929.15 |"}
{"item_id": "item_0542", "chart_task_type": "paired_gap", "query": "Can you chart the poverty gap between rural and urban areas for Argentina, El Salvador, and Perú so I can see at a glance which country has the widest disparity?", "table_markdown": "| | Total Population | | | Population below the poverty line | | | | |\n|---|---|---|---|---|---|---|---|---|\n| Countries | Total population in thousands of inhabitants | Urban | Rural | Country total | Urban | Rural | Country total | Urban |\n| Argentina | 37032 | 90% | 10% | 27% | 27% | 30% | 7% | 7% |\n| El Salvador | 6276 | 55% | 45% | 50% | 38% | 64% | 22% | 13% |\n| Perú | 25939 | 72% | 28% | 49% | 38% | 76% | 22% | 12% |\n| LA&C total | 488547 | 76% | 24% | 42% | 37% | 59% | 17% | 12% |"}
{"item_id": "item_0543", "chart_task_type": "paired_gap", "query": "Can you chart the gap between new and renewal licensing fees for each class, making it easy to spot which one has the biggest price difference?", "table_markdown": "| LICENSING FEES | Class | Class Description | New License | Renewal License |\n|----------------|-------|-------------------|-------------|-----------------|\n| Manufacturer | M-9A | Manufacturer of Factory-Build Buildings (FBBs) | $1,125.00 | $563.00 |\n| | M-9C | Manufacturer of Manufactured Homes | $1,125.00 | $563.00 |\n| | M-9E | Master, includes License Scopes of M-9A and M-9C | $2,000.00 | $1,000.00 |\n| Retailer/Dealer/Broker | D8 | Retailer of Mobile/MFG Homes | $750.00 | $375.00 |\n| | D8-B | Broker Mobile/MFG Homes | $562.00 | $281.00 |\n| | D-10 | Retailer FBB | $750.00 | $375.00 |\n| | D12 | Master, includes License Scopes of D8, D8-B, and D10 | $1,500.00 | $750.00 |\n| Installers | I-10C | General Installer | $750.00 | $375.00 |\n| | I-10D | Installer of attached accessory structures | $562.00 | $281.00 |\n| | I-10G | Master, includes I-10C and I-10D | $1,350.00 | $675.00 |\n| Sales Person | N/A | Employee/Agent of a licensed Retailer/Dealer/Broker | $270.00 | $135.00 |"}
{"item_id": "item_0544", "chart_task_type": "paired_gap", "query": "Can you chart the point gap between the semifinal and qualification scores for each finishing position, making it easy to spot which position has the largest advantage in the semifinals?", "table_markdown": "| Finishing Position | Qual. | Semi. |\n|---|---|---|\n| 1st | 0 | 0 |\n| 2nd | 2 | 3 |\n| 3rd | 3 | 4.5 |\n| 4th | 4 | 6 |\n| 5th | 5 | 7.5 |\n| 6th | 6 | 9 |\n| 7th | 7 | 10.5 |\n| 8th | 8 | 12 |"}
{"item_id": "item_0545", "chart_task_type": "paired_gap", "query": "Can you chart the gender disparity across the five alcohol use categories so it's easy to spot where the gap between males and females is widest?", "table_markdown": "| 2021 prevalence of: | Full-Time College Students |\n|--------------------------------------|----------------------------|\n| | Male | Female |\n| Annual alcohol use | 73.4% | 77.5% |\n| Past month use | 59.3% | 59.4% |\n| Daily drinking (past month) | 3.3% | 1.6% |\n| Binge drinking (past 2 weeks) | 33.9% | 28.8% |\n| High intensity drinking (past 2 weeks)| 18% | 6.1% |"}
{"item_id": "item_0546", "chart_task_type": "paired_gap", "query": "Can you chart the gap between the Met Results and In Situ CuEq values for each drill interval, making it easy to spot which one has the largest positive difference?", "table_markdown": "| Hole ID | From (m) | To (m) | Interval (m) | Cu (%) | Au (g/t) | Ag (g/t) | Mo (g/t) | CuEq MRS (%) | CuEq In Situ (%) | CuEq Met Results (%) |\n|---------|----------|--------|--------------|--------|----------|----------|----------|--------------|------------------|---------------------|\n| ATXD12A | 864.00 | 1,986.00 | 1,122.00 | 0.37 | 0.14 | 0.97 | 57 | 0.48 | 0.50 | 0.50 |\n| | incl. | 1,500.00 | 1,986.00 | 486.00 | 0.36 | 0.17 | 1.40 | 21 | 0.49 | 0.53 | 0.52 |\n| | Also incl.| 1,648.00 | 1,682.00 | 34.00 | 0.48 | 0.22 | 2.60 | 44 | 0.65 | 0.70 | 0.69 |\n| | and | 1,890.00 | 1,924.00 | 34.00 | 0.48 | 0.25 | 2.02 | 5 | 0.65 | 0.71 | 0.70 |\n| ATXD16A | 950.00 | 1,802.00 | 852.00 | 0.60 | 0.28 | 0.98 | 72 | 0.82 | 0.89 | 0.88 |\n| | incl. | 1,168.00 | 1,762.00 | 594.00 | 0.67 | 0.32 | 1.13 | 71 | 0.92 | 1.00 | 0.99 |\n| | incl. | 1,616.00 | 1,728.00 | 112.00 | 1.01 | 0.57 | 2.06 | 46 | 1.42 | 1.53 | 1.52 |\n| ATXD17A | 1,052.00 | 1,976.00 | 924.00 | 0.45 | 0.17 | 0.88 | 99 | 0.61 | 0.66 | 0.65 |\n| | incl. | 1,062.00 | 1,555.00 | 493.00 | 0.50 | 0.21 | 0.82 | 113 | 0.69 | 0.75 | 0.74 |\n| | incl. | 1,216.00 | 1,314.00 | 98.00 | 0.56 | 0.28 | 0.90 | 103 | 0.79 | 0.87 | 0.85 |\n| ATXD25 | 1,346.00 | 2,208.20 | 862.20 | 0.42 | 0.27 | 1.72 | 26 | 0.62 | 0.68 | 0.68 |\n| | incl. | 1,550.00 | 2,208.20 | 658.20 | 0.42 | 0.33 | 2.09 | 7 | 0.66 | 0.73 | 0.72 |\n| | And incl.| 1,858.00 | 2,208.20 | 350.20 | 0.45 | 0.42 | 2.60 | 3 | 0.75 | 0.83 | 0.82 |\n| | And incl.| 2,084.00 | 2,198.00 | 114.00 | 0.54 | 0.48 | 2.95 | 6 | 0.88 | 0.97 | 0.97 |\n| ATXD17B | 750.00 | 1,254.00 | 504.00 | 0.42 | 0.17 | 0.96 | 51 | 0.56 | 0.61 | 0.60 |\n| ATXD26 | 586.00 | 1,564.00 | 978.00 | 0.54 | 0.21 | 1.26 | 145 | 0.75 | 0.82 | 0.81 |\n| | incl. | 1,010.00 | 1,366.00 | 356.00 | 0.70 | 0.29 | 1.49 | 180 | 0.98 | 1.07 | 1.05 |\n| | And incl.| 1,086.00 | 1,208.00 | 122.00 | 1.11 | 0.49 | 2.71 | 348 | 1.60 | 1.77 | 1.73 |\n| | And incl.| 1,100.00 | 1,168.00 | 68.00 | 1.39 | 0.60 | 3.81 | 473 | 2.02 | 2.23 | 2.19 |\n| ATXD25A | 1,230.00 | 1,454.20 | 224.20 | 0.37 | 0.07 | 0.57 | 112 | 0.47 | 0.51 | 0.50 |\n| ATXD26A | 791.85 | 823.30 | 31.45 | 0.45 | 0.13 | 1.31 | 175 | 0.62 | 0.68 | 0.66 |"}
{"item_id": "item_0547", "chart_task_type": "paired_gap", "query": "Can you chart the wear rate gap between dry and lubricated sliding for each austempering temperature, making it easy to spot which temperature has the largest difference?", "table_markdown": "| Austempering Temperature, °C | Lubricated sliding with SAE 30 grade base oil | Dry sliding |\n|-----------------------------|-----------------------------------------------|------------|\n| | Wear rate * $10^{-5}$ g/ cm | |\n| 850 | 79 | 91 |\n| 900 | 48 | 70 |\n| 1000 | 43 | 63 |\n| 1050 | 42 | 62 |"}
{"item_id": "item_0548", "chart_task_type": "paired_gap", "query": "Can you chart the difference between the total project value and the federal funding for each sponsor, so I can see at a glance which one has the largest gap?", "table_markdown": "| Sponsor | Concept/ Type of Improvement/ Location | Funding Amount | Total Project Value |\n|----------------------------------------------|-------------------------------------------------------------------------------------------------------|----------------|--------------------|\n| Non-Motorized | | | |\n| Salt Lake Climbers Alliance | Gate Buttress Infrastructure Phase II: Rehabilitation of user created trails and have them built by professional trail crews on the 140 acres of leased land from the LDS Church as well as replacing old fixed anchors with stainless steel hardware. Phase two of this project focuses on stewardship of the recreational resources. | $50,000 | $206,000 |\n| Salt Lake City Corporation | Foothills Trail System Phase I: Trail construction of approximately 6 miles of trail along with corresponding wayfinding signage. | $100,000 | $250,000 |\n| UWC NF & Salt Lake RD | Adams Canyon Trail Work: Construct retaining walls, rock stairs, rebuilding tread, rock removal and causeways on four eroded sections of side-hill along the creek to make it safer and more sustainable. This project will also involve the installation of three new directional signs at intersecting paths along the trail. | $24,914 | $49,831 |\n| Trails Utah | Hardlick Trails: Creating a network of downhill trails and an uphill mountain bike climbing trail totaling 3 miles. These trails will eventually connect to a newly completed 7-mile Eric’s trail as well as new sections of the Bonneville Shoreline trail. | $30,000 | $64,000 |\n| Salt Lake County Parks & Recreation | Parleys Trail - 900 W to Jordan River Trail: Completing the final gap of the Parley’s Trail from 900 West to the Jordan River Trail by installing a half mile, 10’ wide concrete paved pedestrian/bicycle trail which will include a ramp and bridge. Once completed, this trail will link to a larger network of regional trails connecting users to Utah and Davis County. | $100,000 | $5,210,789 |"}
{"item_id": "item_0549", "chart_task_type": "paired_gap", "query": "Can you chart the tax savings for each town when switching from the standard rate to the Ch. 61B program, making it easy to spot which town benefits the most?", "table_markdown": "| Town | Number of Enrolled Acres | Land Assessment | FY 2014 Tax Rate | Ch. 59 (no program) | Ch. 61B | Ch. 61/61A |\n|----------|--------------------------|-------------------|------------------------|---------------------|---------|------------|\n| Boxford | 6.45 | $288,400 | $15.47 | $4,462 | $1,115 | $5* |\n| Taunton | 18.00 | $145,600 | $14.61 (Res) $31.19 (Com) | $2,127 | $532 | $13 |\n| Falmouth | 9.46 | $289,300 | $8.15 | $2,358 | $589 | $4* |\n| Phillipston | 72.16 | $179,700 | $16.29 | $2,927 | $732 | $58 |\n| Sterling | 45.87 | $293,300 | $16.93 | $4,966 | $1,241 | $38 |\n| Charlton | 13.68 | $105,500 | $12.66 | $1,336 | $334 | $8 |\n| Hawley | 41.00 | $70,400 | $16.05 | $1,130 | $282 | $43 |\n| Montague | 14.64 | $75,300 | $16.34 (Res) $24.85 (Com) | $1,230 | $308 | $12 |\n| Williamsburg | 67.63 | $263,400 | $17.37 | $4,575 | $1,144 | $76 |\n| Chester | 130.25 | $107,300 | $20.88 | $2,240 | $560 | $177 |\n| Southwick | 24.00 | $93,700 | $17.06 | $1,599 | $400 | $26 |\n| Richmond | 22.23 | $549,500 | $10.29 | $5,654 | $1,414 | $15 |\n| Monterey | 92.00 | $746,900 | $6.08 | $4,541 | $1,135 | $36 |"}
{"item_id": "item_0550", "chart_task_type": "paired_gap", "query": "Can you chart the gap between house staff agreement and actual patient risk factor prevalence for each item, sorted so the largest disparity jumps out immediately?", "table_markdown": "| Risk Factor | Response to Hazards of Hospitalization Questionnaire | Patients With Risk Factor or Hazard, % (n=105) | Agreement, % (n=173) | κ Value |\n|------------------------------|------------------------------------------------------|-----------------------------------------------|----------------------|---------|\n| Cognitive impairment | Not oriented to location | 17.1 | 75.7 | 0.11 |\n| | Not oriented to duration of hospitalization | 22.8 | 68.8 | 0.19 |\n| | Not alert | 12.4 | 86.1 | 0.29 |\n| Medication adverse effects | ≥4 Medications ordered | 91.4 | 75.7 | 0.20 |\n| | Antipsychotics or anxiolytics ordered | 14.3 | 82.8 | 0.35 |\n| Insomnia | Did not sleep well | 40.0 | 39.9 | −0.25a |\n| Pain | Had pain | 21.9 | 64.2 | 0.14 |\n| Sensory impairment | Uses a hearing aid | 17.1 | 72.2 | 0.15 |\n| | Uses glasses | 86.7 | 43.4 | −0.04a |\n| Mobility | Uses a cane or walker | 55.2 | 56.0 | 0.14 |\n| | Fell in the past year | 43.8 | 41.0 | −0.18a |\n| | Path to the bathroom obstructed | 10.5 | 83.2 | 0.08 |\n| | Lower side rails raised | 1.0 | 73.4 | 0.02 |\n| | Has not ambulated in the hospital | 35.2 | 65.3 | 0.31 |\n| | Physical restraints present | 0 | 91.3 | NC |\n| Depression | Often feels sad | 39.0 | 32.9 | −0.37a |\n| Discontinuity of care | House staff unaware of primary physician’s name | 98.1b | 46.8 | 0.02 |\n| Incontinence | Incontinent of urine | 22.8 | 74.6 | 0.27 |\n| | Incontinent of stool | 21.0 | 80.3 | 0.35 |\n| | Foley catheter present | 15.2 | 85.5 | 0.44 |\n| Poor nutrition | Unable to self-feed | 13.3 | 86.1 | 0.44 |\n| | Currently NPO | 12.4 | 85.5 | 0.28 |\n| | Eating less than half of food on tray | 21.9 | 45.7 | −0.04a |\n| Skin integrity | Pressure ulcers present | 10.5 | 87.9 | 0.42 |"}
{"item_id": "item_0551", "chart_task_type": "paired_gap", "query": "Can you chart the price gap between Amazon and Sanity for these music items so the biggest disparity jumps out immediately?", "table_markdown": "| Music Item | Amazon CD $US | Sanity CD* AS | Differential | JB Hi-Fi CD A$ | Differential |\n|-------------------------------------------------|---------------|---------------|--------------|----------------|--------------|\n| Adele: Adele21 | 9.99 | 21.99 | 120% | 19.99 | 100% |\n| Alison Krauss and Union Station: Paper Airplane | 11.88 | 29.99 | 152% | 19.99 | 68% |\n| Emmylou Harris: Hard Bargain | 11.88 | 26.99 | 127% | 23.99 | 102% |\n| Paul Simon: So Beautiful or So What | 9.99 | 21.99 | 120% | 19.99 | 100% |\n| Foo Fighters: Wasting Light | 9.99 | 21.99 | 120% | 19.99 | 100% |\n| Glee Cast: Glee: The Music presents the Warblers| 9.99 | 21.99 | 120% | 19.99 | 100% |\n| Mumford & Sons: Sigh No More | 9 | 26.99 | 200% | 19.99 | 122% |\n| Fleet Foxes: Helplessness Blues (pre-release) | 8.99 | 21.99 | 145% | 19.99 | 122% |\n| Adele: Adele | 9.99 | 24.99 | 150% | 12.99 | 30% |\n| Beastie Boys: Hot Sauce Committee Part 2 | 9.99 | 24.99 | 150% | 19.99 | 100% |\n| Stevie Nicks: In Your Dreams | 11.88 | 24.99 | 110% | # | 0% |\n| Explosions in the Sky: Take Care, Take Care, Take Care | 7.99 | 24.99 | 213% | 24.99 | 213% |"}
{"item_id": "item_0552", "chart_task_type": "paired_gap", "query": "Can you chart the profit range for each DEMcpt entity so I can see at a glance which one has the widest gap between its lower and upper bounds?", "table_markdown": "| Rank | Name | Lower | Upper |\n|---|---|---|---|\n| 1 | DEMcpt331 | 1,322,086 | 1,329,111 |\n| 2 | DEMcpt111 | 1,323,168 | 1,329,879 |\n| 3 | DEMcpt231 | 1,322,298 | 1,328,756 |\n| 4 | DEMcpt332 | 1,322,034 | 1,328,018 |\n| 5 | DEMcpt211 | 1,323,217 | 1,329,045 |\n| 6 | DEMcpt311 | 1,323,764 | 1,329,580 |\n| 7 | DEMcpt132 | 1,323,817 | 1,329,543 |\n| 8 | DEMcpt213 | 1,323,196 | 1,328,903 |\n| 9 | DEMcpt112 | 1,323,957 | 1,329,662 |\n| 10 | DEMcpt222 | 1,322,587 | 1,328,275 |"}
{"item_id": "item_0553", "chart_task_type": "paired_gap", "query": "Can you plot the difference between the max DC and AC frequencies for each S1/S0 combination, making it easy to spot which configuration has the largest gap?", "table_markdown": "| $S_1$ | $S_0$ | Max Frequency for DC Inputs (Hz) | Max Frequency for AC Inputs (Hz) |\n|-------|-------|----------------------------------|----------------------------------|\n| 0 | 0 | 0.68 | 0.34 |\n| 0 | 1 | 1.36 | 0.68 |\n| 1 | 0 | 2.72 | 1.36 |\n| 1 | 1 | 5.44 | 2.72 |"}
{"item_id": "item_0554", "chart_task_type": "paired_gap", "query": "Can you chart the gap between the cross-over and cusp angles for each lens, making it easy to spot which one has the widest disparity?", "table_markdown": "| LENS | Cusp angle $\\theta_2$ | Cross-over angle $\\theta_1$ | shrinking $\\langle \\beta \\rangle$ at decoupling |\n|---------------|-----------------------|----------------------------|--------------------------------------------------|\n| +H1413+117 | 0.6 | 0.9 | 3 |\n| 2345+007 | 3 | 6 | 3 |\n| +MG2016+112 | 5 | 8 | 3 |\n| 1635+267 | 6 | 10 | 3 |\n| +MG1131+0456 | 0.6 | 1.0 | 3 |"}
{"item_id": "item_0555", "chart_task_type": "paired_gap", "query": "Can you plot the motorcycle and rickshaw traffic volume for each road link so I can see at a glance which one has the highest share?", "table_markdown": "| Code | Link No. | Road | AADT | AADT excl. MC Rickshaw | AADT in PCU | Pavement type | Actual Length (IRI) |\n|------|----------|-----------------------------|------|------------------------|-------------|---------------|--------------------|\n| 21 | F01102 | Gorusinge-Patharkot | 637 | 296 | 743 | STGB | 11.01 |\n| 34 | F01401 | Pyuthan-Chakchake | 188 | 88 | 207 | | 25 |\n| 103 | F04004 | Basantapur-Tehrathum | 224 | 181 | 326 | | 2 |\n| 111 | F04206 | Baglung-Myagdi district border | 234 | 169 | 227 | | 1.63 |\n| 127 | F04801 | Surkhet-Dailekh district border | 296 | 174 | 400 | | 23 |\n| 153 | F05203 | Katari-Harkapur (Sunkoshi) | 324 | 238 | 512 | | 49 |"}
{"item_id": "item_0556", "chart_task_type": "paired_gap", "query": "Can you chart the week-on-week percentage change for all the currencies so I can see at a glance which one experienced the most extreme movement?", "table_markdown": "| Currency | 01/04/19 | 01/11/19 | | Wk-on- |\n|---|---|---|---|---|\n| | | | | Wk % |\n| | | | | Change |\n| Indian Rupee (INR)RBI ref rate | 69.87 | 70.47 | 0.87 | |\n| Euro (EUR) | 1.14 | 1.47 | 28.76 | |\n| Japanese Yen (JPY) | 108.52 | 108.54 | 0.02 | |\n| Brazilian Real (BRL) | 3.72 | 3.71 | -0.12 | |\n| Chinese Yuan (CNY) | 6.87 | 6.76 | -1.55 | |\n| Singapore Dollar (SGD) | 1.36 | 1.35 | -0.46 | |\n| Tanzanian Shilling (TZS) | 2300.00 | 2325.50 | 1.11 | |\n| Thai Baht (THB) | 31.98 | 31.89 | -0.28 | |\n| Mozambique New Metical (MZN) | 61.40 | 61.65 | 0.41 | |\n| Vietnam Dong (VND) | 23198.00 | 23198.00 | 0.00 | |\n| Indonesian Rupiah (IDR) | 14265.00 | 14040.00 | -1.58 | |\n| Benin CFA Franc BCEAO (XOF) | 573.45 | 565.45 | -1.40 | |\n| Ghanaian New Cedi (GHS) | 4.87 | 4.95 | 1.77 | |"}
{"item_id": "item_0557", "chart_task_type": "paired_gap", "query": "Can you chart the yield gap between the two trial columns for each alfalfa variety so I can see at a glance which one has the biggest advantage?", "table_markdown": "| SW 6330 | 4.79 | 5.84 | 11 |\n|---|---|---|---|\n| AmeriStand 815T | 10.31 | 12.55 | 5 |\n| DKA 65-10RR | 9.69 | 11.72 | 3 |\n| AmeriLeaf 721 | 5.04 | 5.74 | 4 |\n| Archer II | 5.03 | 5.74 | 4 |\n| WL 440HQ | 5.88 | 6.59 | 32 |\n| AmeriStand 855T | 11.27 | 12.37 | 8 |\n| DKA 84-10RR | 11.46 | 12.55 | 5 |\n| WL 442 | 3.46 | 3.74 | 8 |\n| SW 9720 | 9.53 | 10.02 | 13 |\n| Artesian Sunrise | 8.07 | 8.43 | 18 |\n| SW 7410 | 7.79 | 8.09 | 50 |\n| UC Cibola | 7.75 | 8.04 | 11 |\n| SW 9573 | 11.68 | 12.07 | 2 |\n| WL 660 RR | 12.23 | 12.55 | 5 |\n| Saltana | 8.74 | 8.71 | 20 |\n| Desert Sun 8.10RR | 12.06 | 11.89 | 6 |"}
{"item_id": "item_0558", "chart_task_type": "paired_gap", "query": "Can you chart the change in incident counts from June 2009 to this month for each type, making it easy to spot at a glance which category has the biggest swing?", "table_markdown": "| Incident Type | JUNE 2009 | THIS MONTH |\n|---|---|---|\n| Anti Social Behaviour | 32 | 34 |\n| Burglary Dwelling (Houses) | 4 | 2 |\n| Burglary Other (Out buildings) | 9 | 9 |\n| Criminal Damage | 15 | 13 |\n| Theft form Motor Vehicle | 8 | 7 |\n| Theft of Motor Vehicle | 4 | 4 |"}
{"item_id": "item_0559", "chart_task_type": "paired_gap", "query": "Can you chart the gap between top growth and mastery rates for each student subgroup, making it easy to spot which group has the widest disparity?", "table_markdown": "| Subgroup | % Mastery+ | % Top Growth |\n|---------------------------|------------|--------------|\n| All | 35 | 46 |\n| Black | 21 | 43 |\n| Economically Disadvantaged| 27 | 45 |\n| English Learners | 13 | 46 |\n| Students with Disabilities| 10 | 43 |"}
{"item_id": "item_0560", "chart_task_type": "paired_gap", "query": "Can you chart the disparity between chloramphenicol and florfenicol MICs for each strain so the combination with the widest gap jumps out?", "table_markdown": "| Strain (vector) | Clone (mutation) | MIC (µg/ml)$^a$ |\n|-----------------|------------------|-----------------|\n| | | Chl | Fli |\n| DH5α(pCC1FOS$^b$) | Ak20-3 | 64 | 16 |\n| DH5α(pCC1FOS) | Ak20-3 (EZ-Tn5 ) | 64 | 2 |\n| DH5α(pCC1FOS) | | 64 | 2 |\n| DH5α(pDrive) | *pexA* | 16 | 16 |\n| DH5α | | 4 | 2 |\n| DH5α(pDrive) | | 4 | 2 |"}
{"item_id": "item_0561", "chart_task_type": "paired_gap", "query": "Can you chart the disparity in standard error between public and private counsel for each inmate category, making it easy to spot at a glance which group has the widest gap?", "table_markdown": "| Standard error of the esti- mates for type of counsel | | | | |\n|---|---|---|---|---|\n| | Public | | Private | |\n| All jail inmates | 0.83 | % | 0.63 | % |\n| Those charged with a & | | | | |\n| Felony | 1.04 | % | 0.97 | % |\n| Misdemeanor | 1.84 | | 1.21 | |\n| Type of offense | | | | |\n| Violent | 1.38 | % | 1.24 | % |\n| Property | 1.33 | | 0.91 | |\n| Drug | 1.47 | | 1.23 | |\n| Public-order | 1.81 | | 1.27 | |"}
{"item_id": "item_0562", "chart_task_type": "paired_gap", "query": "Can you chart the availability gap for each body motion instrument so I can see at a glance which one has the biggest difference between being available and not?", "table_markdown": "| S. no | Basic Instruments | N | Available | | Mean | Std. D | Result | | |\n|---|---|---|---|---|---|---|---|---|---|\n| | | | Yes (%) | NO (%) | | | Respond | % | Av. |\n| 1 | Stop watch | 65 | 43(66.2) | 22(33.8) | 1.33 | .47 | available | 66 | 62% |\n| 2 | Tread mill | 65 | 42(64.6) | 23(35.4) | 1.35 | .48 | available | 65 | |\n| 3 | Cycle ergometer | 65 | 35(53.8) | 30(46.2) | 1.46 | .50 | available | 54 | |\n| 4 | Dynamometer | 65 | 1(1.5) | 64(98.5) | 1.96 | .12 | not available | 99 | 99% |\n| 5 | Pedometer | 65 | 2(3.1) | 63(96.9) | 1.96 | .17 | not available | 97 | |\n| 6 | Inclinometer | 65 | 0(0) | 65(100) | 1.96 | .24 | not available | 100 | |\n| 7 | Goniometer | 65 | 0(0) | 65(100) | 1.96 | .24 | not available | 100 | |"}
{"item_id": "item_0563", "chart_task_type": "paired_gap", "query": "Can you chart the difference in employment decline between Scenario 2 and Scenario 1 for each sector, making it easy to spot which one has the largest gap?", "table_markdown": "| Sector | Scenario 1 | Scenario 2 |\n|---------------------------------------------|------------|------------|\n| Building Materials, Ceramics and Glass Industry | -17.3 | -23.5 |\n| Mechanical and Electrical Industry | -16.59 | -22.5 |\n| Textile, Clothing and Leather Industry | -21.25 | -28.8 |\n| Miscellaneous Industries | -15.21 | -20.6 |\n| Building and Civil Engineering | -21.47 | -64.22 |\n| Commerce | -4.63 | -7.7 |\n| Transport | -19.1 | -31.7 |\n| Hotels, Cafés and Restaurants | -28.63 | -47.5 |\n| Various Merchant Services | -9.99 | -16.6 |"}
{"item_id": "item_0564", "chart_task_type": "paired_gap", "query": "Can you chart the runoff increase caused by existing impermeable surfaces for each drainage catchment, making it easy to spot which one has the largest gap between its current and greenfield rates?", "table_markdown": "| Drainage Catchment | Receiving Watercourse | Total Development Area (Ha) | Existing Impermeable Area (Ha) | Greenfield Runoff Rates (QMED) (l/s) | Existing Runoff Rate (QMED) (l/s) |\n|--------------------|-----------------------|-----------------------------|---------------------------------|-------------------------------------|----------------------------------|\n| A1 | River Tay | 4.65 | 0.73 | 26.0 | 31.8 |\n| A2 | River Tay | 2.16 | 0.41 | 10.3 | 13.2 |\n| B | River Tay | 8.00 | 1.81 | 38.1 | 50.9 |\n| C | River Tay | 3.84 | 0.72 | 16.2 | 20.6 |\n| D1 | WF38 | 1.88 | 0.70 | 5.2 | 8.3 |\n| D2 | River Tay | 4.31 | 0.64 | 15.5 | 18.8 |\n| E | River Tay | 1.77 | 0.77 | 7.1 | 12.1 |\n| F1 | WF42 | 1.68 | 0.61 | 6.0 | 9.3 |\n| F2 | WF42 | 1.01 | 0.22 | 2.9 | 3.8 |\n| G1 | WF50 | 3.86 | 0.38 | 10.6 | 12.0 |\n| G2 | WF50 | 1.45 | 0.47 | 4.0 | 6.0 |\n| H | WF55 | 1.68 | 0.66 | 4.1 | 6.6 |"}
{"item_id": "item_0565", "chart_task_type": "paired_gap", "query": "Can you chart the percentage point gap between patients and controls for each haplotype so I can see at a glance which one has the largest positive difference?", "table_markdown": "| Haplotype | Patients N (%) Total Number=338 | Controls N (%) Total Number=142 | X2 | P value |\n|---|---|---|---|---|\n| CAG | 146 (43.1%) | 72 (50.70%) | 2.27 | 0.131 |\n| TAA | 47 (13.9%) | 11 (7.74%) | 3.57 | 0.058 |\n| TCG | 23 (6.8%) | 0 (0%) | 10.15 | 0.001** |\n| TCA | 18 (5.32%) | 17 (11.97%) | 6.53 | 0.010 |\n| CCG | 19 (5.62%) | 4 (2.81%) | 1.72 | 0.189* |\n| TAG | 47 (13.9%) | 27 (19.01%) | 2 | 0.157 |\n| CAA | 34 (10.05%) | 6 (4.22%) | 4.45 | 0.034 |\n| CCA | 4 (1.18%) | 5 (3.52%) | 3.04 | 0.095* |"}
{"item_id": "item_0566", "chart_task_type": "paired_gap", "query": "Can you chart the savings for each ARC package item so I can see at a glance which one offers the biggest discount?", "table_markdown": "| Item Num | Description | MSRP | Coda Price |\n|---|---|---|---|\n| KC-62352 | For use with 60606: 42\" ARC | $715.00 | $643.50 |\n| KC-62355 | For use with 60609: 60\" ARC | $820.00 | $738.00 |\n| KC-62356 | For use with 60613: 80\" ARC | $885.00 | $796.50 |\n| KC-62358 | For use with 60611: 100\" ARC | $1,030.00 | $927.00 |\n| KC-62359 | For use with 60612: 120\" ARC | $1,095.00 | $985.50 |"}
{"item_id": "item_0567", "chart_task_type": "paired_gap", "query": "Can you chart the strength gap between the left and right sides for each muscle, making it easy to spot which one has the worst deficit?", "table_markdown": "| MUSCLE | ROOT/NERVE | LEFT | RIGHT |\n|-----------------|-----------------------|------|-------|\n| Deltoid | C5/6, Axillary | 5 | 0 |\n| Supraspinatus | C5/6, Suprascap | 5 | 4 |\n| Infraspinatus | C5/6 Suprascap | 5 | 2 |\n| Biceps | C5/6 Musculocut | 5 | 5 |\n| Triceps | C6/7 Radial | 5 | 5 |\n| Wrist Flex | C6/7 Median | 5 | 5 |\n| Wrist Ext | C6/7 Radial | 5 | 5 |\n| Flexor Digitorum| C8 Median | 5 | 5 |\n| Interossei | T1 Ulnar | 5 | 5 |\n| Opponens Pollicus| C8/T1 Median | 5 | 5 |"}
{"item_id": "item_0568", "chart_task_type": "paired_gap", "query": "Can you chart the extra adjustment needed when using pipe and gravel compared to the hole diameter alone, so I can see at a glance which hole size has the biggest gap?", "table_markdown": "| Hole diameter | Adjustment factor for hole diameter (hole diameter) | Adjustment factor plus pipe & gravel |\n|---------------|-----------------------------------------------------|--------------------------------------|\n| 4 inches | 2.5 | 3.61 |\n| 6 inches | 1.8 | 2.32 |\n| 12 inches | 1.1 | 1.43 |\n| 14 inches | 1.0 | 1.24 |"}
{"item_id": "item_0569", "chart_task_type": "paired_gap", "query": "Can you chart the rate disparity between Good/High Use and Standard categories for each location, making it easy to spot at a glance which area has the widest gap?", "table_markdown": "| Location | Good/High Use | Standard |\n|---|---|---|\n| Main urban centre | 500 | 335 |\n| Tourist area | 400 | 265 |\n| Scenic area | 265 | 200 |\n| Rural | 200 | 165 |\n| Remote | 135 | 100 |"}
{"item_id": "item_0570", "chart_task_type": "paired_gap", "query": "Can you chart the gap between multiple and single shifts for each story so I can see at a glance which one has the largest surplus of multiple shifts?", "table_markdown": "| The Stories | Single Shifts | Multiple Shifts |\n|-------------|---------------|-----------------|\n| Story 1 | 2 | 6 |\n| Story 2 | 2 | 8 |\n| Story 3 | 5 | 3 |\n| Story 4 | 3 | 5 |\n| Story 5 | 5 | 3 |\n| Story 6 | 4 | 4 |\n| **Total** | **21** | **29** |"}
{"item_id": "item_0571", "chart_task_type": "paired_gap", "query": "Can you chart the gap between each company's 12-month high and final price so I can see at a glance which one has suffered the biggest drop?", "table_markdown": "| Company | Market Cap $m | 12 month high c | *Final price c | Total return % |\n|-----------------------|---------------|-----------------|----------------|----------------|\n| General Mining Corp | 80.8 | 27.5 | 25.5 | 5442.9 |\n| SB Barbara | 693.1 | 153.5 | 142.5 | 12571 |\n| Pilbara Minerals | 248.5 | 40.5 | 32 | 661.9 |\n| Galaxy Resources | 151.7 | 13.5 | 11.5 | 360.0 |\n| Blackham Resources | 46.4 | 28 | 23 | 351.0 |\n| Neometals | 57.8 | 22.5 | 17 | 347.4 |\n| Lithium Australia NL | 23.1 | 23 | 16.5 | 292.5 |\n| Lodestar Minerals | 12.0 | 5.5 | 3.5 | 288.9 |\n| Reserves Resources | 92.4 | 24.5 | 19.5 | 282.4 |\n| European Metals | 14.8 | 26.5 | 17 | 277.8 |"}
{"item_id": "item_0572", "chart_task_type": "paired_gap", "query": "Can you chart the unused embryo capacity for each licence so I can see at a glance which one has the largest gap between what was authorised and what was actually used?", "table_markdown": "| Licence number | Licence holder | Licence title | Embryos authorised to be used under licence | Embryos used in licensed activity up to 31 August 2015 |\n|---|---|---|---|---|\n| 309702B | Genea Limited | Development of methods for pre-implantation genetic and metabolic evaluation of human embryos | 220 | 50 (plus 8 embryos first used in 309701 and then transferred to 309702B) |\n| 309703 | Genea Limited | Development of human embryonic stem (ES) cells | 300 (plus up to 20 inner cell masses which may be transferred from 309702A or 309702B) | 228 (including 12 embryos first used in 309702A and then transferred to 309703) |\n| 309710 | Genea Limited | Derivation of human embryonic stem cells from embryos identified through preimplantation genetic diagnosis to be affected by known genetic conditions | 500 | 304 |\n| 309718 | Genea Limited | Use of excess ART embryos and clinically unusable eggs for validation of an IVF device | 345 | 259 |\n| 309719 | Genea Limited | Use of excess ART embryos for the development of improved IVF culture media | 640 | 0 |\n| 309722 | Monash IVF Pty Ltd | Optimising embryo- endometrial interactions to improve pregnancy success during IVF | 200 | 22 |\n| 309723 | Melbourne IVF Pty Ltd | Use of excess ART embryos for blastocyst-stage biopsy training | 150 | 0 |"}
{"item_id": "item_0573", "chart_task_type": "paired_gap", "query": "Can you chart the disparity between $c_1$ and $c_2$ for each row so I can see at a glance which case has the largest gap?", "table_markdown": "| $\\rho_{AB}^g$ | $\\rho_{AB}^w$ | $\\bar{f}_c$ | $v_{\\text{min}}$ | $v_{\\text{max}}$ | $c_1$ | $c_2$ |\n|---------------|---------------|-------------|-----------------|-----------------|------|------|\n| HBS | 0.10 | 1.00 | 1.05 | 1.00 | 2.00 | 0.17 | 2.12 |\n| | | | 1.05 | 1.40 | 1.60 | 0.17 | 2.12 |\n| | | | 1.05 | 1.00 | 1.60 | 0.14 | 1.84 |\n| | | | 1.05 | 1.20 | 1.40 | 0.14 | 1.84 |\n| | | | 1.05 | 0.71 | 1.62 | 0.13 | 1.59 |\n| | | | 1.10 | 1.00 | 2.00 | 0.17 | 1.50 |\n| | | | 1.01 | 1.00 | 2.00 | 0.17 | 4.74 |\n| Fruin | 0.31 | 0.83 | 1.05 | 1.00 | 2.00 | 0.89 | 4.09 |\n| | | | 1.05 | 1.40 | 1.60 | 0.89 | 4.09 |\n| | | | 1.05 | 1.00 | 1.60 | 0.77 | 3.54 |\n| | | | 1.05 | 1.20 | 1.40 | 0.77 | 3.54 |\n| | | | 1.05 | 0.71 | 1.62 | 0.67 | 3.06 |\n| | | | 1.10 | 1.00 | 2.00 | 0.89 | 2.89 |\n| | | | 1.01 | 1.00 | 2.00 | 0.89 | 9.14 |\n| HCM | 0.18 | 0.83 | 1.05 | 1.00 | 2.00 | 0.41 | 3.11 |\n| | | | 1.05 | 1.40 | 1.60 | 0.41 | 3.11 |\n| | | | 1.05 | 1.00 | 1.60 | 0.36 | 2.69 |\n| | | | 1.05 | 1.20 | 1.40 | 0.35 | 2.69 |\n| | | | 1.05 | 0.71 | 1.62 | 0.31 | 2.33 |\n| | | | 1.10 | 1.00 | 2.00 | 0.41 | 2.20 |\n| | | | 1.01 | 1.00 | 2.00 | 0.41 | 6.95 |"}
{"item_id": "item_0574", "chart_task_type": "paired_gap", "query": "Can you chart the year-over-year shift for each financial ratio so I can see at a glance which one changed the most?", "table_markdown": "| | As of June 30, 2019 | As of Jun 30, 2018 |\n|--------------------------------|---------------------|--------------------|\n| Return on average assets | 0.72% | 0.70% |\n| Return on average equity | 8.32% | 8.19% |\n| Net interest margin | 3.90% | 3.97% |\n| Efficiency ratio | 80.16% | 77.50% |\n| Loans to deposits | 77.24% | 76.56% |\n| Allowance for loan losses to loans | 1.23% | 1.31% |"}
{"item_id": "item_0575", "chart_task_type": "mix_trend", "query": "Can you chart how the market mix of cars, SUVs, and trucks shifted from 2010 to 2020?", "table_markdown": "| Year | Car | SUV | Truck | Van |\n|------|------|------|-------|-----|\n| 2010 | 49% | 30% | 14% | |\n| 2011 | 48% | 32% | 14% | |\n| 2012 | 50% | 31% | 13% | |\n| 2013 | 49% | 32% | 14% | |\n| 2014 | 47% | 34% | 14% | |\n| 2015 | 43% | 37% | 15% | |\n| 2016 | 39% | 40% | 15% | |\n| 2017 | 35% | 43% | 16% | |\n| 2018 | 31% | 47% | 17% | |\n| 2019 | 28% | 48% | 18% | |\n| 2020 | 25% | 51% | 19% | |"}
{"item_id": "item_0576", "chart_task_type": "paired_gap", "query": "Can you chart the drop in row correlation for each image after encryption, making it easy to spot which one has the biggest reduction?", "table_markdown": "| | Rows | Columns | Rows | Columns | | |\n|---|---|---|---|---|---|---|\n| Lena Row :239 Column :143 | 0.8556 | 0.7217 | 0.3984 | 0.2763 | 0 | 1.39 |\n| Baboon Row: 233 Column: 53 | 0.7351 | 0.7068 | 0.2709 | 0.2344 | 0 | 1.48 |\n| Pepper Row : 59 Column: 111 | 0.5995 | 0.6281 | 0.2425 | 0.2287 | 0 | 5.72 |\n| Mahalakshmi Row: 145 Column: 141 | 0.319 | 0.3419 | 0.186 | 0.186 | 0 | 1.35 |\n| RadhaKrishna Row: 13 Column: 71 | 0.4774 | 0.5316 | 0.2261 | 0.206 | 0 | 1.52 |"}
{"item_id": "item_0577", "chart_task_type": "paired_gap", "query": "Can you chart the difference in antibacterial activity between S. aureus and E. coli for each enzyme concentration, making it easy to spot which condition has the largest gap?", "table_markdown": "| Enzyme concentration (%) | Temperature (°C) | pH | S. aureus | E.coli |\n|--------------------------|------------------|----|-----------|--------|\n| - | - | - | - | - |\n| - | - | - | 20 | 21 |\n| 2 | 40 | 4.2| 31 | 32 |\n| 3 | 45 | 4.8| 33 | 34 |\n| 4 | 50 | 5.1| 38 | 40 |\n| 5 | 55 | 5.3| 36 | 37 |\n| 6 | 60 | 5.5| 35 | 38 |"}
{"item_id": "item_0578", "chart_task_type": "paired_gap", "query": "Can you chart the divergence between the two factor loadings for each barrier item so I can see at a glance which one has the biggest gap?", "table_markdown": "| Item | Factor 1* | Factor 2† |\n|----------------------------------------------------------------------|-----------|-----------|\n| Did not have medication with you | 0.20 | 0.46 |\n| Busy with other things | 0.24 | 0.68‡ |\n| Simply forgot | 0.04 | 0.73‡ |\n| Had not had medication prescription refilled | 0.24 | −0.22 |\n| Had too many pills to take | 0.53‡ | 0.34 |\n| Wanted to avoid adverse effects | 0.67‡ | −0.08 |\n| Did not want others to notice medication | 0.57‡ | 0.23 |\n| Change in daily routine | 0.01 | 0.72‡ |\n| Felt like the drug was toxic or harmful | 0.64‡ | −0.06 |\n| Fell asleep or slept through dose time | 0.11 | 0.67‡ |\n| Felt sick or ill | 0.64‡ | 0.21 |\n| Felt depressed or overwhelmed | 0.63‡ | 0.34 |\n| Problem with special instructions | 0.51‡ | 0.38 |\n| Confused about what and/or when to take drug(s) | 0.46 | 0.20 |\n| Felt healthy, so did not take | 0.71‡ | 0.17 |\n| Already missed medications—blew it for the day | 0.29 | 0.62‡ |\n| Felt like medications had no positive effect on health | 0.72‡ | 0.02 |\n| Reminder that you have HIV | 0.67‡ | 0.25 |\n| Did not like the taste | 0.55‡ | 0.38 |"}
{"item_id": "item_0579", "chart_task_type": "paired_gap", "query": "Can you chart the discrepancy between a predictor's contribution when removed from the full model versus its standalone power, making it easy to spot which variable has the biggest difference?", "table_markdown": "| Predictor | ROC Diff: Leave out Predictor | RankDiff: Leave out Predictor | ROC Diff: Single Predictor | RankDiff: Single Predictor |\n|------------------------------------------------|-------------------------------|-------------------------------|---------------------------|---------------------------|\n| Age | 2.91 | 1 | 1 | 1 |\n| LV dysfunction (mod) | 1.46 | 2 | 3 | 7 |\n| Chronic pulmonary disease | 1.35 | 3 | 4 | 4 |\n| Critical preoperative state | 1.21 | 4 | 5 | 5 |\n| LV dysfunction (poor) | 1.08 | 5 | 5 | 6 |\n| Active endocarditis | 0.58 | 6 | 7 | 7 |\n| Extracardiac arteriopathy | 0.49 | 7 | 8 | 8 |\n| Other than isolated CABG | 0.37 | 8 | 9 | 9 |\n| Pulmonary hypertension | 0.24 | 9 | 10 | 10 |\n| Neurological dysfunction disease | 0.22 | 10 | 11 | 11 |\n| Emergency | 0.20 | 11 | 12 | 12 |\n| Serum creatinine | 0.13 | 12 | 13 | 13 |\n| Recent myocardial infarct | 0.07 | 13 | 14 | 14 |\n| Postinfarct septal rupture | 0.06 | 14 | 15 | 15 |\n| Thoracic aorta | 0.05 | 15 | 16 | 16 |\n| Unstable angina | 0.05 | 16 | 17 | 17 |\n| Acute myocardial infarct | 0.02 | 17 | 18 | 18 |\n| New Time Seru Rec Thor mon rolo rgen ment | 0.02 | 18 | 19 | 19 |\n| Acic infar able | 0.01 | 19 | 20 | 20 |"}
{"item_id": "item_0580", "chart_task_type": "mix_trend", "query": "Can you chart how the split between external and domestic debt service shifted over the five quarters from Q2-2023 to Q2-2024, so I can see at a glance how the composition of total payments changed?", "table_markdown": "| Debt Service Payments | Q2-2023 | Q3-2023 | Q4-2023 | Q1-2024 | Q2-2024 |\n|-----------------------|---------|---------|---------|---------|---------|\n| Total Debt Service | 12.75 | 12.44 | 12.99 | 12.86 | 12.78 |\n| Total Principal Repayments | 8.90 | 8.92 | 9.55 | 9.52 | 9.53 |\n| Total Interest Payments | 3.85 | 3.52 | 3.44 | 3.34 | 3.25 |\n| External Debt Service | 7.08 | 6.80 | 7.38 | 7.27 | 7.22 |\n| Principal Repayments | 4.80 | 4.83 | 5.46 | 5.43 | 5.44 |\n| Interest Payments | 2.28 | 1.97 | 1.92 | 1.85 | 1.78 |\n| Domestic Debt Service | 5.67 | 5.64 | 5.62 | 5.59 | 5.56 |\n| Principal Repayments | 4.10 | 4.10 | 4.10 | 4.10 | 4.10 |\n| Interest Payments | 1.57 | 1.55 | 1.52 | 1.49 | 1.46 |"}
{"item_id": "item_0581", "chart_task_type": "paired_gap", "query": "Can you chart the disparity between Error 1 and Error 2 for each training/testing group combination, making it easy to spot at a glance which pair has the widest gap?", "table_markdown": "| Groups in Training | Groups in Testing | Error 1 (%) | Error 2 (%) | Total Error (%) | Average Error (%) |\n|-------------------|------------------|-------------|-------------|----------------|------------------|\n| B | A | 1.7 | 4.1 | 5.8 | |\n| C | A | 13.3 | 0.3 | 13.6 | 8.7 |\n| D | A | 6.7 | 0.0 | 6.7 | |\n| BC | A | 1.9 | 1.6 | 3.5 | |\n| BD | A | 2.3 | 0.1 | 2.4 | 5.3 |\n| CD | A | 9.8 | 0.2 | 10.0 | |\n| BCD | A | 2.4 | 0.3 | 2.6 | 2.63 |\n| A | B | 0.0 | 12.5 | 12.6 | |\n| C | B | 14.2 | 5.7 | 19.9 | 14.6 |\n| D | B | 5.4 | 6.0 | 11.4 | |\n| AC | B | 1.5 | 6.6 | 8.1 | |\n| AD | B | 9.8 | 5.4 | 15.2 | 10.1 |\n| CD | B | 0.4 | 6.8 | 7.2 | |\n| ACD | B | 1.4 | 6.0 | 7.4 | 7.4 |\n| A | C | 0.0 | 24.6 | 24.7 | |\n| B | C | 2.3 | 16.7 | 19.0 | 21.0 |\n| D | C | 4.8 | 14.5 | 19.3 | |\n| AB | C | 0.9 | 17.5 | 18.4 | |\n| AD | C | 0.3 | 15.9 | 16.2 | 17.4 |\n| BD | C | 2.4 | 15.3 | 17.6 | |\n| ABD | C | 1.2 | 14.5 | 15.6 | 15.6 |\n| A | D | 0.3 | 6.4 | 6.7 | |\n| B | D | 0.6 | 9.5 | 10.1 | 8.8 |\n| C | D | 7.7 | 1.8 | 9.5 | |\n| AB | D | 0.2 | 8.5 | 8.7 | |\n| AC | D | 15.9 | 2.3 | 18.1 | 14.3 |\n| BC | D | 11.0 | 5.1 | 16.1 | |\n| ABC | D | 10.3 | 3.0 | 13.3 | 13.3 |"}
{"item_id": "item_0582", "chart_task_type": "mix_trend", "query": "Can you chart how the year-level mix of student enrolment shifted from 2011 to 2014, so I can see at a glance how the relative proportion of each grade changed over time?", "table_markdown": "| | 2011 | 2012 | 2013 | 2014 |\n|---|---|---|---|---|\n| Foundaiton | 74 | 57 | 66 | 79 |\n| Year 1 | 65 | 99 | 70 | 78 |\n| Year 2 | 59 | 61 | 100 | 72 |\n| Year 3 | 65 | 60 | 62 | 101 |\n| Year 4 | 46 | 61 | 62 | 58 |\n| Year 5 | 57 | 49 | 55 | 61 |\n| Year 6 | 54 | 53 | 44 | 51 |\n| Year 7 | 38 | 45 | 47 | 47 |\n| Total | 458 | 485 | 506 | 547 |"}
{"item_id": "item_0583", "chart_task_type": "mix_trend", "query": "Can you chart how the relative contribution of Agriculture, Industry, and Service to Gross National Product shifted between 1929 and 1965, using constant dollar values so I can see at a glance how the economic structure evolved?", "table_markdown": "| | 1929 | 1947 | 1956 | 1965 |\n|----------------------|------|------|------|------|\n| **Constant (1958) dollars** | | | | |\n| Agriculture | 8.4 | 5.7 | 4.9 | 4.1 |\n| Industry | 43.2 | 47.2 | 48.1 | 47.6 |\n| Service | 48.4 | 47.0 | 47.0 | 48.3 |\n| Service subsector | 29.6 | 27.4 | 26.3 | 27.1 |\n| **Current dollars** | | | | |\n| Agriculture | 9.2 | 9.1 | 4.7 | 3.7 |\n| Industry | 43.9 | 46.0 | 48.4 | 45.7 |\n| Service | 46.9 | 45.0 | 46.9 | 50.5 |\n| Service subsector | 26.6 | 27.8 | 26.4 | 27.3 |\n| **Service as share of nonagricultural output** | | | | |\n| Constant (1958) dollars | 52.9 | 49.9 | 49.5 | 50.4 |\n| Current dollars | 51.7 | 49.4 | 49.2 | 52.5 |"}
{"item_id": "item_0584", "chart_task_type": "paired_gap", "query": "Can you chart the performance gap between the 1982-1999 bull market and the 1966-1981 bear market for each asset class, making it easy to spot which one had the biggest swing in returns?", "table_markdown": "| Annualized Return | | |\n|---|---|---|\n| Time Period: | 1966-1981 | 1982-1999 |\n| Type of Market: | Secular Bear | Secular Bull |\n| Length in Years: | 16 Years | 18 Years |\n| Annualized Returns by Asset Class: | | |\n| S & P 500 | 6.00% | 18.50% |\n| Small Cap Value | 14.80% | 18.40% |\n| Small Cap Growth | 10.50% | 13.70% |\n| Large Cap Value | 11.00% | 17.40% |\n| Large Cap Growth | 5.10% | 17.70% |\n| Long Term Govt Bonds | 2.50% | 12.20% |\n| Long Term Corp Bonds | 2.90% | 12.00% |\n| 30 Day T Bill | 6.80% | 6.20% |\n| Inflation Index | 7.00% | 3.30% |"}
{"item_id": "item_0585", "chart_task_type": "mix_trend", "query": "Can you chart how the funding mix for the Multi-County Branch Office shifted between State and County shares from FY 2021 to FY 2023, so I can see at a glance how the reliance on each source changed?", "table_markdown": "| Fund/ALI | FY 2021 Actual | FY 2022 Appropriation | FY 2023 Appropriation |\n|-----------------------------------------------|----------------|-----------------------|-----------------------|\n| GRF ALI 019403, Multi-County: State Share | $2,957,225 | $4,881,554 | $5,076,816 |\n| % change | -- | 65.1% | 4.0% |\n| DPF Fund 4C70 ALI 019601, Multi-County: County Share | $1,185,641 | $149,879 | $272,016 |\n| % change | -- | -87.4% | 81.5% |\n| Total: | $4,142,865 | $5,031,433 | $5,348,832 |\n| % change | -- | 21.4% | 6.3% |"}
{"item_id": "item_0586", "chart_task_type": "mix_trend", "query": "Can you chart how the composition of Treasury Assets shifted from 2018 through 1H 2022, so I can see at a glance how the reliance on bank balances versus available-for-sale securities changed over time?", "table_markdown": "| Treasury Assets | 2018 | 2019 | 2020 | 2021 | 1H 2022 |\n|----------------------------------------|------|------|------|------|---------|\n| Bank balances & placements with FI | 48.6 | 81.3 | 34.3 | 170.2| 286.2 |\n| Available-for-sale | 255.3| 336.3| 541.2| 619.4| 268.4 |\n| Held-to-maturity | - | - | - | - | - |\n| **Total** | 303.9| 417.6| 575.5| 789.6| 554.6 |"}
{"item_id": "item_0587", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of prison service complaint outcomes shifted across the four quarters from 2013 to 2014, so I can see at a glance how the relative proportion of upheld versus not upheld cases changed over time?", "table_markdown": "| Number of | Q1 (Apr-Jun 2013) | Q2 (Jul-Sept 2013) | Q3 (Oct- Dec) | Q4 (Jan-Mar 2014) | Annual |\n|---|---|---|---|---|---|\n| Complaints | | | | | Total |\n| Upheld | 2 | 1 | 1 | 2 | 6 (11%) |\n| Partially upheld | 2 | 0 | 0 | 1 | 3 (6%) |\n| Not upheld | 10 | 8 | 12 | 11 | 41 (77%) |\n| Withdrawn | 0 | 1 | 1 | 1 | 3 (6%) |"}
{"item_id": "item_0588", "chart_task_type": "paired_gap", "query": "Can you chart the gap between TRC and DART for each contractor so I can see at a glance which one has the widest disparity?", "table_markdown": "| Contractor | TRC | DART |\n|-------------------------------------------------|-------|------|\n| CH2M Hill/B&W West Valley - WVDP | 0.00 | 0.00 |\n| CH2M-Washington Group Idaho - ICP | 0.27 | 0.18 |\n| Savannah River Nuclear Solutions - SRS | 0.28 | 0.02 |\n| Washington Closure Hanford - RCCP | 0.28 | 0.00 |\n| Washington River Protection Solutions - TOC | 0.31 | 0.04 |\n| CH2M Plateau Remediation - Hanford | 0.42 | 0.14 |\n| Savannah River Remediation - LWDP | 0.43 | 0.22 |\n| LATA Environmental of Kentucky | 0.62 | 0.00 |\n| DOE-EM Complex Average | 0.63 | 0.27 |\n| Mission Support Alliance - Hanford | 0.65 | 0.30 |\n| URS-CH2M Hill-Oak Ridge - ETTP | 0.90 | 0.49 |\n| Nuclear Waste Partnership - WIPP | 1.00 | 0.62 |\n| B&W Conversion Services - DUF6 | 1.05 | 0.42 |\n| Fluor-B&W - Portsmouth | 1.49 | 0.53 |\n| Idaho Treatment Group - AMWTP | 1.56 | 1.09 |"}
{"item_id": "item_0589", "chart_task_type": "mix_trend", "query": "Can you chart how the income segment mix shifted from 2008 to 2030 so I can see at a glance which groups gained or lost relative share?", "table_markdown": "| Income segment (USD) | 2008 | 2020 | 2030 |\n|----------------------|------|------|------|\n| Globals (>22065.3) | 50% | 26% | 15% |\n| Seekers (4413.1 - 11032.7) | 35% | 40% | 32% |\n| Aspirers (1985.9 - 4413.1) | 12% | 25% | 29% |\n| Strivers (11032.7 - 22065.3) | 2% | 6% | 17% |\n| Deprived (<1985.9) | 1% | 3% | 7% |"}
{"item_id": "item_0590", "chart_task_type": "mix_trend", "query": "Can you chart how the revenue mix from Merchandise, Intermodal, and Coal shifted across the four reported periods so I can see at a glance how the company's reliance on each segment changed?", "table_markdown": "| | Third Quarter 2017 | Third Quarter 2016 | First Nine Months 2017 | First Nine Months 2016 |\n|--------------------------------|--------------------|--------------------|------------------------|------------------------|\n| **Railway operating revenues** | | | | |\n| Merchandise | $1,600 | $1,552 | $4,781 | $4,678 |\n| Intermodal | 621 | 575 | 1,785 | 1,635 |\n| Coal | 449 | 397 | 1,316 | 1,085 |\n| **Total railway operating revenues** | 2,670 | 2,524 | 7,882 | 7,398 |\n| **Railway operating expenses** | | | | |\n| Compensation and benefits | 755 | 691 | 2,201 | 2,081 |\n| Purchased services and rents | 377 | 386 | 1,146 | 1,149 |\n| Fuel | 198 | 181 | 601 | 504 |\n| Depreciation | 265 | 258 | 788 | 767 |\n| Materials and other | 164 | 188 | 574 | 584 |\n| **Total railway operating expenses** | 1,759 | 1,704 | 5,310 | 5,085 |\n| **Income from railway operations** | 911 | 820 | 2,572 | 2,313 |\n| Other income – net | 23 | 29 | 79 | 49 |\n| Interest expense on debt | 134 | 144 | 416 | 421 |\n| **Income before income taxes** | 800 | 705 | 2,235 | 1,941 |\n| Provision for income taxes | | | | |\n| Current | 189 | 169 | 580 | 512 |\n| Deferred | 105 | 76 | 219 | 177 |\n| **Total income taxes** | 294 | 245 | 799 | 689 |\n| **Net income** | $506 | $460 | $1,436 | $1,252 |\n| **Earnings per share** | | | | |\n| Basic | $1.76 | $1.56 | $4.96 | $4.23 |\n| Diluted | 1.75 | 1.55 | 4.93 | 4.21 |\n| **Weighted average shares outstanding** | | | | |\n| Basic | 287.1 | 292.7 | 288.8 | 294.9 |\n| Diluted | 289.5 | 294.7 | 291.2 | 296.7 |"}
{"item_id": "item_0591", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of site development permit types shifted across the six time periods, so I can easily see which categories dominated the total in each period?", "table_markdown": "| Permits Issued | 2015 Oct | 2015 YTD | 2014 Oct | 2014YTD | 2013 Oct | 2013 YTD |\n|-------------------------|----------|----------|----------|---------|----------|----------|\n| New Residence | 1 | 4 | 1 | 9 | 2 | 9 |\n| Second Unit | 1 | 2 | 1 | 9 | 2 | 9 |\n| Addition | 1 | 7 | 0 | 10 | 2 | 17 |\n| Fence/Gates | 1 | 12 | 9 | 20 | 4 | 19 |\n| Landscape | 1 | 7 | 1 | 9 | 0 | 5 |\n| Pool | 0 | 6 | 0 | 5 | 3 | 4 |\n| Misc. (Hardscape, Gra) | 4 | 17 | 7 | 24 | 6 | 16 |\n| **Total Permits Issued** | **9** | **55** | **18** | **77** | **17** | **70** |"}
{"item_id": "item_0592", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of parents in state versus federal prisons shifted from 1991 to 2007, so I can see at a glance which system held the larger share over time?", "table_markdown": "| Number of parents | Total | State | Federal |\n|---|---|---|---|\n| 2007b | 809,800 | 686,000 | 123,800 |\n| 2004c | 754,900 | 644,100 | 110,800 |\n| 1999 | 721,500 | 642,300 | 79,200 |\n| 1997 | 649,500 | 578,100 | 62,500 |\n| 1991 | 452,500 | 413,100 | 39,400 |\n| Number of Children | | | |\n| 2007b | 1,706,600 | 1,427,500 | 279,100 |\n| 2004c | 1,590,100 | 1,340,300 | 249,800 |\n| 1999d | 1,515,200 | 1,338,900 | 176,300 |\n| 1997d | 1,362,900 | 1,223,800 | 139,100 |\n| 1991d | 945,600 | 860,300 | 85,100 |"}
{"item_id": "item_0593", "chart_task_type": "mix_trend", "query": "Can you chart how the composition of UK employment across private, public, and voluntary sectors shifted between 1995 and 2004, so I can see at a glance how the relative weight of each sector changed over time?", "table_markdown": "| | 1995 | 2000 | 2004 |\n|---|---|---|---|\n| Private sector | 19,095 | 20,711 | 20,270 |\n| Public sector | 6,042 | 6,246 | 6,842 |\n| Voluntary sector | 478 | 563 | 608 |\n| Total | 25,616 | 27,520 | 27,720 |"}
{"item_id": "item_0594", "chart_task_type": "mix_trend", "query": "Can you chart how the racial mix of CDC reported MSSA cases shifted from 2016 to 2019, so I can see at a glance how the relative contribution of each group changed over time?", "table_markdown": "| Table 7: CDC reported cases of MSSA by race and year22-25 | | | | |\n|---|---|---|---|---|\n| | White - n (%) | Black - n (%) | Other - n (%) | Total |\n| 2016 | 1,551 (63) | 584 (24) | 311 (13) | 2,446 |\n| 2017 | 2,254 (66) | 858 (25) | 314 (9) | 3,426 |\n| 2018 | 2,344 (63) | 1,024 (27) | 380 (10) | 3,748 |\n| 2019 | 2,396 (63) | 984 (25.9) | 420 (11.1) | 3,800 |"}
{"item_id": "item_0595", "chart_task_type": "mix_trend", "query": "Can you chart how the programming mix between commissioned and sponsored shows shifted over the last three quarters, so I can see at a glance how the reliance on each type changed?", "table_markdown": "| Show Type | Hours for Q Ending | | | Percentage | | |\n|---|---|---|---|---|---|---|\n| | Dec‐10 | Sep‐10 | Dec‐09 | Dec‐10 | Sep‐10 | Dec‐09 |\n| Commissioned* | 162 | 158.5 | 217 | 46% | 50% | 55% |\n| Sponsored | 187 | 159 | 177 | 54% | 50% | 45% |\n| Total | 349 | 317.5 | 394 | 100% | 100% | 100% |"}
{"item_id": "item_0596", "chart_task_type": "mix_trend", "query": "Can you chart how the revenue mix across the three market areas shifted over the four periods, so I can see at a glance which segments gained or lost relative share?", "table_markdown": "| in EUR m | Q2 2022 | Share | Q2 2021 | H1 2022 | Share | H1 2021 |\n|-----------------------------------------------|---------|-------|---------|---------|-------|---------|\n| Passenger Cars & Light Commercial Vehicles | 100.5 | 73.8% | 103.4 | 210.8 | 74.0% | 215.2 |\n| Commercial Vehicles | 24.7 | 18.2% | 24.6 | 46.6 | 16.3% | 49.2 |\n| Smart Plastics & Industrial Applications | 10.9 | 8.0% | 15.9 | 27.6 | 9.7% | 27.3 |\n| **POLYTEC GROUP** | **136.1** | **100%** | **143.9** | **285.0** | **100%** | **291.7** |"}
{"item_id": "item_0597", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of public opinion on whether current Middle Eastern migrants differ from 1990s Yugoslav refugees shifted over time?", "table_markdown": "| | Mar 2016. | Sep 2016. | 2017. | 2019. |\n|-----------------------------------------------------------------|-----------|-----------|-------|-------|\n| There is a difference between current migrants/refugees from the Middle East and refugees from the former Yugoslavia in 1990s | 36 | 39 | 34 | 38 |\n| There is no difference between current migrants/refugees from the Middle East and refugees from former Yugoslavia in 1990s | 50 | 45 | 48 | 41 |\n| I don’t know, no answer | 14 | 16 | 18 | 21 |"}
{"item_id": "item_0598", "chart_task_type": "mix_trend", "query": "Can you chart how the spending mix across the four major projects shifted over the four time periods, so I can easily see which projects dominated the budget at each stage?", "table_markdown": "| Project | 2010/11 | Qtr 1 11/12 | Qtr 2 11/12 | Qtr 3 11/12 | YTD |\n|------------------|-------------|-------------|-------------|-------------|------------|\n| BSF | £267,802 | £41,636 | £54,693 | £63,767 | £160,096 |\n| Entertainment Venue | £250,489 | £54,160 | £27,529 | £41,192 | £122,881 |\n| Waste | £478,141 | £59,029 | £71,365 | £16,968 | £147,362 |\n| The Pods | £200,728 | £45,947 | £15,766 | £1,605 | £63,319 |\n| **Total** | **£1.197m** | **£200,772**| **£169,353**| **£123,533**| **£493,658**|"}
{"item_id": "item_0599", "chart_task_type": "mix_trend", "query": "Can you chart how the weight status mix for ANOKA: BLAINE shifted from 2011 to 2021, so I can easily see the changing proportion of each category over time?", "table_markdown": "| CITY OF 1 RESIDENCE | 2 Total | 3 Underweight | 4 Normal | 5 Overweight | 6 Obese |\n|---|---|---|---|---|---|\n| ANOKA: Anoka | Total | Underweight | Nor ma l | Ov er w eig ht | Obese |\n| 2021 | 89 | 2 (2.2) | 23 (25.8) | 22 (24.7) | 21 (23.6) |\n| 2020 | 101 | 0 | 26 (25.7) | 33 (32.7) | 19 (18.8) |\n| 2019 | 111 | 2 (1.8) | 38 (34.2) | 23 (20.7) | 20 (18.0) |\n| 2018 | 76 | 2 (2.6) | 24 (31.6) | 17 (22.4) | 13 (17.1) |\n| 2017 | 85 | 6 (7.1) | 27 (31.8) | 25 (29.4) | 15 (17.6) |\n| 2016 | 108 | 3 (2.8) | 30 (27.8) | 36 (33.3) | 15 (13.9) |\n| 2015 | 87 | 6 (6.9) | 25 (28.7) | 20 (23.0) | 17 (19.5) |\n| 2014 | 98 | 4 (4.1) | 44 (44.9) | 20 (20.4) | 18 (18.4) |\n| 2013 | 85 | 6 (7.1) | 33 (38.8) | 16 (18.8) | 18 (21.2) |\n| 2012 | 111 | 1 (<1) | 47 (42.3) | 29 (26.1) | 21 (18.9) |\n| 2011 | 117 | 7 (6.0) | 42 (35.9) | 30 (25.6) | 21 (17.9) |\n| ANOKA: Bethel/ East Bethel/ Ham Lake/ St. Francis | Total | Underweight | Nor ma l | Ov er w eig ht | Obese |\n| 2021 | 76 | 3 (3.9) | 23 (30.3) | 16 (21.1) | 14 (18.4) |\n| 2020 | 105 | 5 (4.8) | 34 (32.4) | 25 (23.8) | 18 (17.1) |\n| 2019 | 64 | 0 | 21 (32.8) | 12 (18.8) | 15 (23.4) |\n| 2018 | 68 | 1 (1.5) | 29 (42.6) | 17 (25.0) | 12 (17.6) |\n| 2017 | 71 | 2 (2.8) | 26 (36.6) | 17 (23.9) | 17 (23.9) |\n| 2016 | 95 | 1 (1.1) | 31 (32.6) | 25 (26.3) | 19 (20.0) |\n| 2015 | 71 | 1 (1.4) | 24 (33.8) | 18 (25.4) | 18 (25.4) |\n| 2014 | 87 | 7 (8.0) | 34 (39.1) | 26 (29.9) | 15 (17.2) |\n| 2013 | 82 | 5 (6.1) | 34 (41.5) | 19 (23.2) | 8 (9.8) |\n| 2012 | 87 | 5 (5.7) | 43 (49.4) | 21 (24.1) | 10 (11.5) |\n| 2011 | 87 | 5 (5.7) | 29 (33.3) | 26 (29.9) | 11 (12.6) |\n| ANOKA: BLAINE | Total | Underweight | Nor ma l | Ov er w eig ht | Obese |\n| 2021 | 195 | 6 (3.1) | 56 (28.7) | 74 (37.9) | 31 (15.9) |\n| 2020 | 220 | 10 (4.5) | 67 (30.5) | 69 (31.4) | 39 (17.7) |\n| 2019 | 213 | 5 (2.3) | 70 (32.9) | 57 (26.8) | 47 (22.1) |\n| 2018 | 144 | 4 (2.8) | 49 (34.0) | 46 (31.9) | 24 (16.7) |\n| 2017 | 159 | 7 (4.4) | 59 (37.1) | 42 (26.4) | 29 (18.2) |\n| 2016 | 220 | 6 (2.7) | 70 (31.8) | 58 (26.4) | 51 (23.2) |\n| 2015 | 180 | 2 (1.1) | 85 (47.2) | 41 (22.8) | 25 (13.9) |\n| 2014 | 159 | 5 (3.1) | 75 (47.2) | 39 (24.5) | 20 (12.6) |\n| 2013 | 190 | 10 (5.3) | 86 (45.3) | 40 (21.1) | 25 (13.2) |\n| 2012 | 190 | 9 (4.7) | 81 (42.6) | 47 (24.7) | 32 (16.8) |\n| 2011 | 197 | 7 (3.6) | 81 (41.1) | 44 (22.3) | 33 (16.8) |"}
{"item_id": "item_0600", "chart_task_type": "mix_trend", "query": "Can you chart how the composition of wild garlic populations shifted between 1983 and 1985, so I can see at a glance how the reliance on dormant, sprouted, and emerged stages changed over time?", "table_markdown": "| | 1983 | 1984 | 1985 |\n|------------------|------|------|------|\n| Dormant bulbs | 2 | 19 | 1 |\n| Sprouted bulbs | 8 | 17 | 3 |\n| Emerged plants | 90 | 64 | 96 |"}
{"item_id": "item_0601", "chart_task_type": "mix_trend", "query": "Can you chart how the spending mix within the Other Special Revenue Funds shifted from 2013-14 through 2016-17, so I can see at a glance how the relative reliance on personal services, other expenses, and capital expenditures changed over time?", "table_markdown": "| | History 2013-14 | History 2014-15 | 2015-16 | 2016-17 |\n|----------------------|-----------------|-----------------|---------|---------|\n| POSITIONS - LEGISLATIVE COUNT | 107,000 | 107,000 | 98,000 | 98,000 |\n| POSITIONS - FTE COUNT | 0.924 | 0.924 | 0.416 | 0.416 |\n| Personal Services | $9,954,024 | $10,293,649 | $9,864,208| $9,725,799|\n| All Other | $18,068,762 | $18,067,362 | $19,056,090| $19,056,232|\n| Capital Expenditures | $362,200 | $372,700 | $271,500| $188,000|\n| **OTHER SPECIAL REVENUE FUNDS TOTAL** | **$28,384,986** | **$28,733,711** | **$29,191,798** | **$28,970,031** |"}
{"item_id": "item_0602", "chart_task_type": "mix_trend", "query": "Can you chart how the revenue mix between electric and telecom services shifted from 2017 to 2021, so I can see at a glance which segment drove the total operating revenue?", "table_markdown": "| | 2017 | 2018 | 2019 | 2020 | 2021 |\n|----------------------|----------|----------|----------|----------|----------|\n| **Operating Revenue:** | | | | | |\n| Electric Revenue | $176,868 | $190,613 | $185,134 | $171,836 | $176,952 |\n| Telecom Revenue | 33,627 | 34,564 | 35,135 | 36,119 | 37,355 |\n| **Total Operating Revenue** | 210,495 | 225,177 | 220,269 | 207,955 | 214,307 |\n| **Operating Expenses:** | | | | | |\n| Purchased Power, Net of Pooling Credits | 143,751 | 157,128 | 145,772 | 137,734 | 143,657 |\n| Other Operating Expenses | 66,011 | 73,350 | 72,868 | 72,158 | 74,481 |\n| **Total Operating Expenses** | 209,762 | 230,478 | 218,640 | 209,892 | 218,138 |\n| **Operating Margins** | 733 | (5,301) | 1,629 | (1,937) | (3,831) |\n| **Non-Operating Margins** | 2,025 | 2,095 | 2,080 | 1,348 | 1,119 |\n| **Margins Before G&T Capital Credits** | 2,758 | (3,206) | 3,709 | (589) | (2,712) |\n| G&T Capital Credits | 7,531 | 8,871 | 8,337 | 8,560 | 8,591 |\n| **Margins Before Income Taxes** | 10,289 | 5,665 | 12,046 | 7,971 | 5,879 |\n| Income Tax Expense | (5,567) | 179 | (1,808) | (2,529) | (2,001) |\n| **Net Margins** | $15,856 | $5,486 | $13,854 | $10,500 | $7,880 |"}
{"item_id": "item_0603", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of operation costs shifted across the four periods, so I can see at a glance which categories became more or less dominant?", "table_markdown": "| Costs of Operations | 2005-06 | 2006-07 | 2006-07 | 2007-08 |\n|---------------------|---------|---------|---------|---------|\n| Administration | 2,226 | 3,065 | 0 | 5,750 |\n| Info & Education | 4,958 | 3,247 | 0 | 7,500 |\n| Lake Management | 5,920 | 2,880 | 0 | 4,000 |\n| Aquatic Plants | 8,402 | 7,939 | 14,000 | 26,500 |\n| Total Expenditures: | 31,505 | 27,130 | 14,000 | 43,750 |"}
{"item_id": "item_0604", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of Performance Assessment types shifted over the three years, so I can see at a glance whether the reliance on Standards versus Risk assessments changed?", "table_markdown": "| Period | Standards | Risk | Total |\n|--------------|-----------|------|-------|\n| 2010 - 2011 | 15 | 5 | 20 |\n| 2009 - 2010 | 10 | 5 | 15 |\n| 2008 - 2009 | 5 | 3 | 8 |"}
{"item_id": "item_0605", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of transfer types shifted over the 12-month period, so I can see at a glance which category dominated the total volume each month?", "table_markdown": "| | Metrics | YearMonth | Nov-18 | Dec-18 | Jan-19 | Feb-19 | Mar-19 | Apr-19 | May-19 | Jun-19 | Jul-19 | Aug-19 | Sep-19 | Oct-19 | Avg |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| Inter-Unit Transfers | | | 18 | 29 | 15 | 18 | 44 | 12 | 10 | 11 | 26 | 29 | 18 | 21 | 21 |\n| Emergency Medical Leaves | | | 13 | 15 | 17 | 17 | 10 | 12 | 9 | 10 | 7 | 7 | 16 | 12 | 12 |\n| Non-Emergency Medical Leaves | | | 19 | 9 | 17 | 13 | 25 | 22 | 14 | 20 | 22 | 17 | 17 | 23 | 18 |\n| Total | | | 50 | 53 | 49 | 48 | 79 | 46 | 33 | 41 | 55 | 53 | 51 | 56 | -- |"}
{"item_id": "item_0606", "chart_task_type": "mix_trend", "query": "Can you chart how the racial composition of U.S. team members shifted from 2019 to 2022?", "table_markdown": "| | POC | Non-POC | NR |\n|-------|-----|---------|----|\n| 2022 | 30% | 66% | 4% |\n| 2021 | 29% | 67% | 4% |\n| 2020 | 28% | 68% | 4% |\n| 2019 | 28% | 72% | 0% |"}
{"item_id": "item_0607", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of inpatient versus emergency department admissions for prescription drug poisonings shifted from 2009 to 2013?", "table_markdown": "| | Admission Type | | Total by Year |\n|---|---|---|---|\n| | IP | ED | |\n| Discharge Year | | | |\n| 2009 | 471 | 897 | 1368 |\n| 2010 | 469 | 999 | 1468 |\n| 2011 | 602 | 1007 | 1609 |\n| 2012 | 611 | 908 | 1519 |\n| 2013 | 589 | 861 | 1450 |\n| Total by Admission Type | 2742 | 4672 | 7414 |"}
{"item_id": "item_0608", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of Existing versus Additional IDR Reductions shifted over the seven quarters, so I can see at a glance how the relative contribution of each component changed?", "table_markdown": "| Quarter Ending | Existing IDR Reduction | Additional IDR Reduction | Total IDR Reduction |\n|----------------------|------------------------|--------------------------|--------------------|\n| June 30, 2016 | $34,250,000 | $75,000,000.00 | $109,250,000 |\n| September 30, 2016 | $34,250,000 | $85,000,000.00 | $119,250,000 |\n| December 31, 2016 | $34,250,000 | $95,000,000.00 | $129,250,000 |\n| March 31, 2017 | $36,250,000 | $105,000,000.00 | $141,250,000 |\n| June 30, 2017 | $36,250,000 | $110,000,000.00 | $146,250,000 |\n| September 30, 2017 | $27,500,000 | $120,000,000.00 | $147,500,000 |\n| December 31, 2017 | $27,500,000 | $130,000,000.00 | $157,500,000 |"}
{"item_id": "item_0609", "chart_task_type": "mix_trend", "query": "Can you chart how the composition of ASCIA's total income shifted across the 2013-2014 to 2015-2016 periods, so I can see at a glance which revenue sources became more or less dominant?", "table_markdown": "| Income | 2015-2016 | 2014-2015 | 2013-2014 |\n|---|---|---|---|\n| Sponsorship | $282,424 | $303,805 | $314,085 |\n| Membership Fees | $129,351 | $113,859 | $112,384 |\n| Donations | $14,993 | $13,374 | $129,609 |\n| Conference Income | $452,565 | $480,172 | $458,251 |\n| Interest | $31,633 | $45,749 | $47,421 |\n| Other Income | $3,022 | $3,890 | $32 |\n| Total Income | $913,988 | $960,849 | $1,061,782 |"}
{"item_id": "item_0610", "chart_task_type": "mix_trend", "query": "Can you chart how the reliance on solar versus auxiliary energy shifted month by month, so I can see at a glance which source dominated the mix over the year?", "table_markdown": "| Month | Load, KBTU/mo. | Solar, KBTU/mo. | Auxiliary, KBTU/mo. |\n|-------|----------------|-----------------|---------------------|\n| Sept. | 15.01 | 17.13 | .70 |\n| Oct. | 15.51 | 11.61 | 5.30 |\n| Nov. | 15.01 | 12.98 | 4.16 |\n| Dec. | 15.51 | 12.10 | 5.13 |\n| Jan. | 15.51 | 14.88 | 2.70 |\n| Feb. | 14.01 | 12.39 | 3.72 |\n| Mar. | 15.51 | 12.27 | 5.01 |\n| Apr. | 15.01 | 14.32 | 2.61 |\n| May | 15.51 | 13.25 | 4.21 |\n| June | 15.01 | 16.04 | 1.54 |\n| July | 15.51 | 14.45 | 2.73 |\n| Aug. | 15.51 | 17.27 | .80 |\n| TOTAL | 182.6 | 168.7 | 38.61 |"}
{"item_id": "item_0611", "chart_task_type": "mix_trend", "query": "Can you chart how the balance between endemic and exotic plant cover shifted across the four time periods, so I can see at a glance which group dominated the total cover at each stage?", "table_markdown": "| Species | Before 3 May 1975 | After Nov. 1975 (6 mo) | After May 1976 (12 mo) | After Nov. 1976 (18 mo) |\n|-------------------------------|------------------|------------------------|------------------------|------------------------|\n| **ENDEMIC SPECIES** | | | | |\n| *Styphelia tameiameiae* | 12.7 | 10.5 | 10.3 | 12.2 |\n| *Dodonaea sandwicensis* | 4.5 | 4.9 | 4.6 | 5.3 |\n| *Carex wahuensis* | 5.8 | 4.6 | 5.3 | 4.9 |\n| *Railliardia ciliolata* | 3.4 | 3.9 | 4.3 | 4.0 |\n| *Metrosideros collina (<1 m)* | 1.6 | 1.9 | 1.6 | 1.3 |\n| *Vaccinium reticulatum* | 1.0 | 0.6 | 1.0 | 0.6 |\n| **TOTAL ENDEMIC SPP.** | 29.0 | 26.4 | 27.1 | 28.3 |\n| **EXOTIC SPECIES** | | | | |\n| *Andropogon spp.* | 20.5 | 20.3 | 21.0 | 21.7 |\n| *Bulbostylis capillaris* | 2.7 | 2.6 | 2.3 | 2.0 |\n| *Melinis minutiflora* | 1.0 | 1.3 | 0.6 | 0.6 |\n| Other | 4.1 | 3.3 | 0.9 | 0.6 |\n| **TOTAL EXOTIC SPP.** | 28.3 | 27.5 | 24.8 | 24.9 |\n| **TOTAL PLANT COVER** | 57.3 | 53.9 | 51.9 | 53.2 |"}
{"item_id": "item_0612", "chart_task_type": "mix_trend", "query": "Can you chart how Country Club Bank's reliance on solo versus joint underwriting roles shifted in terms of par value from 2010 to 2019?", "table_markdown": "| YEAR | CCB ALONE OR MANAGER | | CCB MEMBER OR JOINT MGR. | | TOTAL | |\n|---|---|---|---|---|---|---|\n| | # ISSUES | PAR VALUE | # ISSUES | PAR VALUE | # ISSUES | PAR VALUE |\n| 2010 | 61 | $123,050,000 | 69 | $846,860,000 | 130 | $969,910,000 |\n| 2011 | 56 | $112,743,000 | 36 | $410,493,000 | 92 | $523,236,000 |\n| 2012 | 49 | $145,385,000 | 96 | $588,315,000 | 145 | $733,700,000 |\n| 2013 | 53 | $157,450,000 | 119 | $752,917,000 | 172 | $910,367,000 |\n| 2014 | 41 | $80,420,000 | 122 | $721,605,000 | 163 | $802,025,000 |\n| 2015 | 65 | $145,550,000 | 154 | $1,741,171,827 | 219 | $1,886,721,827 |\n| 2016 | 51 | $147,625,000 | 258 | $3,556,953,014 | 309 | $3,704,578,014 |\n| 2017 | 38 | $127,340,000 | 259 | $3,152,006,000 | 297 | $3,279,346,000 |\n| 2018 | 58 | $159,974,000 | 243 | $2,469,496,000 | 301 | $2,629,470,000 |\n| 2019 | 65 | $189,220,000 | 350 | $4,235,700,000 | 415 | $4,424,920,000 |\n| TOTAL | 537 | $1,388,757,000 | 1,706 | $18,475,516,841 | 2,243 | $19,864,273,841 |"}
{"item_id": "item_0613", "chart_task_type": "mix_trend", "query": "Can you chart how the spending mix across Administration, O&M, Opportunity Fund, and Capital Projects shifted from 2021 to 2027, so I can see at a glance which areas drove the changes in total expenditure composition?", "table_markdown": "| | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | 2027 | Total 2022-2027 |\n|---|---|---|---|---|---|---|---|---|\n| Beginning 154-00 Fund Balance | $ 25,253,000 | $ 16,644,319 | $ 15,941,248 | $ 15,446,635 | $ 15,581,671 | $ 17,733,550 | $ 20,937,963 | |\n| Revenues | | | | | | | | |\n| Levy Assessment | $ 1 3,802,699 | $ 1 5,450,000 | $ 1 5,604,500 | $ 1 5,760,545 | $ 1 5,918,150 | $ 1 6,077,332 | $ 1 6,238,105 | $ 9 2,613,226 |\n| Opportunity Fund Annual Allocation (154-01) | $ 1,380,270 | $ 1,545,000 | $ 1,560,450 | $ 1,576,055 | $ 1,591,815 | $ 1,607,733 | $ 1,623,811 | $ 9,261,323 |\n| Interest | $ 60,000 | $ 60,000 | $ 60,000 | $ 60,000 | $ 60,000 | $ 60,000 | $ 60,000 | $ 360,000 |\n| TOTAL REVENUE | $ 1 3,862,699 | $ 1 5,510,000 | $ 1 5,664,500 | $ 1 5,820,545 | $ 1 5,978,150 | $ 1 6,137,332 | $ 1 6,298,105 | $ 95,408,633 |\n| Expenditures | | | | | | | | |\n| Administration | $ 511,691 | $ 514,066 | $ 529,488 | $ 545,373 | $ 561,734 | $ 578,586 | $ 595,943 | $ 3,325,190 |\n| Operations and Maintenance | $ 2,067,888 | $ 2,317,500 | $ 2,340,675 | $ 2,364,082 | $ 2,387,723 | $ 2,411,600 | $ 2,435,716 | $ 1 4,257,295 |\n| Opportunity Fund Expenditures (154- 01) | $ 4,825,511 | $ 1,545,000 | $ 1,560,450 | $ 1,576,055 | $ 1,591,815 | $ 1,607,733 | $ 1,623,811 | $ 9,504,863 |\n| Capital Projects* | $ 15,066,290 | $ 11,836,505 | $ 11,728,500 | $ 11,200,000 | $ 9,285,000 | $ 8,335,000 | $ 7,900,000 | $ 6 0,285,005 |\n| TOTAL EXPENDITURES | $ 2 2,471,380 | $ 1 6,213,071 | $ 1 6,159,113 | $ 1 5,685,509 | $ 1 3,826,271 | $ 1 2,932,919 | $ 1 2,555,470 | $ 87,372,353 |\n| Ending Fund Balance | $ 1 6,644,319 | $ 1 5,941,248 | $ 1 5,446,635 | $ 1 5,581,671 | $ 1 7,733,550 | $ 2 0,937,963 | $ 2 4,680,599 | |\n| (Includes Strategic Reserve and Large Capital Project) | | | | | | | | |"}
{"item_id": "item_0614", "chart_task_type": "mix_trend", "query": "Can you chart how the composition of our waste generation shifted across 2021, 2022, and 2023, so I can see at a glance which categories drove the changes in the overall mix?", "table_markdown": "| Annual Waste Generation Totals [short tons] | 2021 | 2022 | 2023 |\n|-------------------------------------------|--------|--------|--------|\n| Total Waste Generation | 62,422 | 59,687 | 62,928 |\n| General Trash | 6,590 | 7,187 | 7,806 |\n| Hazardous Waste | 636 | 1,171 | 1,821 |\n| Non-hazardous/chemical/process waste | 12,887 | 11,835 | 10,632 |\n| Wastewater trucked off-site | 26,351 | 22,895 | 28,549 |\n| Recycling | 15,956 | 16,599 | 14,120 |"}
{"item_id": "item_0615", "chart_task_type": "mix_trend", "query": "Can you chart how the breakdown of our emissions by scope shifted from 2019 to 2021?", "table_markdown": "| Year | Scope 1 | Scope 2 | Scope 3.6 |\n|------|---------|---------|-----------|\n| 2019 | 3% | 31% | 66% |\n| 2020 | 23.4% | 25.1% | 55.5% |\n| 2021 | 28.9% | 31% | 40.4% |"}
{"item_id": "item_0616", "chart_task_type": "mix_trend", "query": "Can you chart how the revenue mix between 'Machine Tools' and 'Hard Metal and Hard Metal Products' shifted across the six periods, so I can see at a glance how their relative contributions changed over time?", "table_markdown": "| Particulars | 3 months ended (21/12/2012) | Previous 3 months ended (10/09/2012) | Corresponding 3 months ended in the previous year (21/12/2011) | Year to date figures for the current period ended (31/12/2012) | Year to date figures for the previous period ended (31/12/2011) | Previous year ended (20/06/2012) |\n|-------------------------------------------------|-----------------------------|--------------------------------------|---------------------------------------------------------------|-------------------------------------------------------------|-------------------------------------------------------------|----------------------------------|\n| **1 Segment Revenue (Sales / Income)** | | | | | | |\n| Net Sales | (Unaudited) | (Unaudited) | (Unaudited) | (Unaudited) | (Unaudited) | (Audited) |\n| Machine Tools | 3092 | 3077 | 2256 | 6189 | 4171 | 813/10000 |\n| Hard Metal and Hard Metal Products | 10000 | 9253 | 11533 | 10362 | 23668 | 48006 |\n| Net Sales / Income from Operations | 13191 | 12330 | 13799 | 25521 | 27739 | 56233 |\n| **2 Segment Results** | | | | | | |\n| Machine Tools | 524 | 647 | 310 | 1171 | 605 | 1158 |\n| Hard Metal and Hard Metal Products | 934 | 829 | 2471 | 1763 | 6346 | 10324 |\n| Total | 1458 | 1476 | 2781 | 2934 | 6951 | 11482 |\n| Interest paid | - | - | - | - | - | - |\n| Un allocable expenditure (net of income) | 489 | 597 | 347 | 1086 | 751 | 1588 |\n| Total Profit before Tax | 969 | 879 | 2434 | 1848 | 6200 | 9894 |\n| **3 Capital Employed (Segment Assets less Segment Liabilities)** | | | | | | |\n| Machine Tools | 1858 | 1422 | (494) | 1858 | (494) | 692 |\n| Hard Metal and Hard Metal Products | 22305 | 23292 | 23154 | 22305 | 23154 | 22838 |\n| Un allocable | 7464 | 6046 | 11209 | 7464 | 11209 | 6618 |\n| Total | 31421 | 30780 | 33959 | 31421 | 33959 | 30148 |"}
{"item_id": "item_0617", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of new versus returning website visitors changed over the three fiscal years?", "table_markdown": "| Date Range | New Visitors | Returning Visitors | Total Visitors |\n|---------------------|--------------|--------------------|---------------|\n| July 1 2016 – June 30 2017 | 48,789 | 22,522 | 71,311 |\n| July 1 2017 – June 30 2018 | 57,222 | 27,948 | 85,170 |\n| July 1 2018 – June 30 2019 | 62,111 | 10,631 | 72,742 |"}
{"item_id": "item_0618", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of separation allowance and assigned pay shifted over these periods, so I can see at a glance which component dominated the total payments?", "table_markdown": "| Date | Cheque No. | Amount S/A | Amount A/P | Total |\n|--------|------------|------------|------------|-------|\n| Dec 31 | | 440 | 160 | 600 |\n| Jan 18 | X 69620 | 30 | 20 | 50 |\n| July | F 69360 | 25 | 20 | 45 |\n| Marh | R 93446 | 25 | 20 | 45 |\n| Apr | R 11225 | 25 | 20 | 45 |\n| May | V 13777 | 25 | 20 | 45 |\n| June | S 24620 | 25 | 20 | 45 |\n| July | F 25821 | 25 | 20 | 45 |\n| Aug | S 39031 | 25 | 20 | 45 |\n| Sept | O 48896 | 25 | 20 | 45 |\n| Oct | H 53886 | 25 | 20 | 45 |\n| Nov | W 59267 | 25 | 20 | 45 |\n| Dec | H 66490 | 145 | 20 | 65 |"}
{"item_id": "item_0619", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of outage causes shifted from 2017 to 2019, so I can see at a glance which factors became more or less dominant over time?", "table_markdown": "| | 2017 | 2018 | 2019 |\n|--------|------|------|------|\n| Wildlife | 23 | 24 | 18 |\n| Equipment | 20 | 25 | 19 |\n| Weather | 20 | 20 | 7 |\n| Vegetation | 12 | 12 | 5 |\n| Other | 6 | 6 | 2 |"}
{"item_id": "item_0620", "chart_task_type": "mix_trend", "query": "Can you chart how the revenue mix shifted from 2023 to 2027 so I can see at a glance which sources became more or less dominant over time?", "table_markdown": "| For the year ended December 31 | 2023 | 2024 | 2025 | 2026 | 2027 |\n|-------------------------------|--------|--------|--------|--------|--------|\n| **Revenue** | | | | | |\n| Property Value Tax | 75,715 | 78,124 | 80,605 | 83,153 | 85,772 |\n| Levies (Storm and Eco) | 4,143 | 4,447 | 4,773 | 5,171 | 5,533 |\n| Revenue from Fees and Services| 49,976 | 53,142 | 56,556 | 60,286 | 63,466 |\n| Revenue from Other Sources | 3,929 | 3,804 | 3,842 | 3,880 | 3,919 |\n| **Total Revenues** | 133,763| 139,517| 145,776| 152,490| 158,690|"}
{"item_id": "item_0621", "chart_task_type": "mix_trend", "query": "Can you chart how the reliance on SWP versus other water sources shifted across the three water years?", "table_markdown": "| Year | SWP Allocation* | SWP Water Available (AF)** | Other Water Available (AF) | Source | Total Water (AF) |\n|-----------------------|-----------------|----------------------------|----------------------------|--------------|------------------|\n| 2017-2018 | 35% (2013) | 6055 | 1200 | Exchange | 7255 |\n| 2018-2019 | 5% (2014) | 865 | 300 | Yuba | 1165 |\n| 2019-2020 | 20% (2015) | 3460 | 300 | Yuba | 3760 |"}
{"item_id": "item_0622", "chart_task_type": "mix_trend", "query": "Can you chart how the split between domestic and export sales shifted across September 2018, August 2018, and September 2017, so I can see at a glance how the reliance on each channel changed?", "table_markdown": "| Brazil: CS Ethanol Sales (mln litres; Apr/Mar season) | | | | | | | | |\n|---|---|---|---|---|---|---|---|---|\n| | Sep 2018 | Aug 2018 | Sep 2017 | Change y/y in % | Cumulative 2018/19 | Cumulative 2017/18 | Change y/y in % | 2017/18 |\n| Domestic market | 2,567 | 2,764 | 2,175 | 18 | 14,057 | 11,942 | 18 | 25,400 |\n| Anhydrous | 678 | 799 | 783 | -13 | 4,158 | 4,786 | -13 | 9,527 |\n| Hydrous | 1,889 | 1,965 | 1,392 | 36 | 9,899 | 7,156 | 38 | 15,873 |\n| Export | 165 | 180 | 157 | 5 | 858 | 920 | -7 | 1,511 |\n| Anhydrous | 76 | 108 | 116 | -34 | 491 | 632 | -22 | 1,099 |\n| Hydrous | 89 | 72 | 41 | 117 | 367 | 288 | 27 | 412 |\n| Total | 2,732 | 2,944 | 2,333 | 17 | 14,915 | 12,862 | 16 | 26,911 |\n| Anhydrous | 753 | 906 | 900 | -16 | 4,649 | 5,418 | -14 | 10,626 |\n| Hydrous | 1,978 | 2,037 | 1,433 | 38 | 10,266 | 7,444 | 38 | 16,286 |"}
{"item_id": "item_0623", "chart_task_type": "mix_trend", "query": "Can you chart how the composition of NGEU payments shifted from 2021 to 2026, so I can see at a glance how the relative reliance on grants versus loans changed over time?", "table_markdown": "| €bn | | committed | | | paid | | | | | |\n|---|---|---|---|---|---|---|---|---|---|---|\n| | total | 2021 | 2022 | 2023 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 |\n| MS grants | 313 | 109 | 109 | 94 | 27 | 55 | 78 | 78 | 51 | 23 |\n| EU grants | 77.5 | 26 | 26 | 26 | 6 | 13 | 19 | 19 | 13 | 6 |\n| MS loans | 360 | 165 | 120 | 75 | 41 | 71 | 90 | 90 | 49 | 19 |\n| total | 750 | 300 | 255 | 195 | 75 | 139 | 188 | 188 | 113 | 49 |"}
{"item_id": "item_0624", "chart_task_type": "mix_trend", "query": "Can you chart how the composition of Infrastructure Appropriations shifted from 2012 to 2016, so I can see at a glance how the reliance on Public Works, MVH Subsidy, and Transfer to Parks Cuml Bldg changed over time?", "table_markdown": "| Infrastructure Appropriations | 2012 | 2013 | 2014 | 2015 | 2016 |\n|-----------------------------------|------|------|------|------|------|\n| Public Works | 2,000,000 | 2,000,000 | 2,000,000 | 2,000,000 | 2,000,000 |\n| MVH Subsidy | 800,000 | - | - | - | - |\n| Transfer to Parks Cuml Bldg | 300,000 | 300,000 | 500,000 | 500,000 | 500,000 |\n| **Infrastructure Total** | 3,100,000 | 2,300,000 | 2,500,000 | 2,500,000 | 2,500,000 |"}
{"item_id": "item_0625", "chart_task_type": "mix_trend", "query": "Can you chart how the spending mix for the Mountain View Campus Master Plan projects shifted from 2014 to 2018, so I can see which initiatives drove the relative composition of costs over time?", "table_markdown": "| Project | 2014 | 2015 | 2016 | 2017 | 2018 |\n|----------------------------------------------|--------|--------|--------|--------|--------|\n| 1245 - Behavioral Health Bldg Replace | 1,257 | 3,775 | 1,389 | 10,323 | 28,676 |\n| 1413 - North Drive Parking Structure Exp | - | 167 | 1,266 | 18,120 | 4,670 |\n| 1414 - Integrated MOB | - | 2,009 | 8,875 | 32,805 | 75,319 |\n| 1422 - CUP Upgrade | - | - | 896 | 1,245 | 5,428 |\n| **Sub-Total Mountain View Campus Master Plan** | 1,257 | 5,950 | 12,426 | 62,493 | 114,093|"}
{"item_id": "item_0626", "chart_task_type": "mix_trend", "query": "Can you chart how the composition of permanent staff shifted over time, so I can see at a glance how the reliance on different roles like research versus administration changed?", "table_markdown": "| Year | DG/executive | Technical | Research | Administration |\n|------|--------------|-----------|----------|---------------|\n| 1970 | 20 | 30 | 50 | 10 |\n| 1975 | 20 | 40 | 60 | 10 |\n| 1983 | 20 | 50 | 70 | 10 |\n| 1993 | 20 | 60 | 80 | 10 |\n| 2002 | 20 | 60 | 80 | 10 |\n| 2009 | 20 | 60 | 80 | 10 |"}
{"item_id": "item_0627", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of pension assets between private and government sectors shifted from 1950 to 1987, so I can see at a glance which sector became more dominant over time?", "table_markdown": "| Year | Total | Private | State and Local Gov't |\n|------|---------|---------|-----------------------|\n| 1950 | $70 | $49 | $21 |\n| 1955 | 147 | 105 | 42 |\n| 1960 | 255 | 186 | 69 |\n| 1965 | 427 | 315 | 112 |\n| 1970 | 547 | 386 | 161 |\n| 1975 | 738 | 536 | 202 |\n| 1980 | 1,033 | 783 | 250 |\n| 1985 | 1,523 | 1,133 | 390 |\n| 1987 | 1,836* | 1,358 | 478 |"}
{"item_id": "item_0628", "chart_task_type": "mix_trend", "query": "Can you chart how the distribution of beef cattle across Arizona counties shifted between 1940 and 1954, so I can see at a glance which areas gained or lost relative importance in the state's total herd?", "table_markdown": "| County | 1940 | 1945 | 1950 | 1954 |\n|------------|------|------|------|------|\n| Apache | 31 | 29 | 37 | 63 |\n| Cochise | 122 | 89 | 80 | 72 |\n| Coconino | 46 | 34 | 56 | 56 |\n| Gila | 57 | 48 | 50 | 43 |\n| Graham | 44 | 69 | 62 | 57 |\n| Greenlee | 17 | 21 | 18 | 16 |\n| Maricopa | 117 | 152 | 130 | 201 |\n| Mohave | 43 | 52 | 35 | 37 |\n| Navajo | 37 | 60 | 47 | 41 |\n| Pima | 76 | 71 | 51 | 64 |\n| Pinal | 70 | 92 | 46 | 66 |\n| Santa Cruz | 35 | 29 | 33 | 28 |\n| Yavapai | 73 | 85 | 82 | 91 |\n| Yuma | 25 | 19 | 18 | 46 |\n| **Total** | 793 | 850 | 745 | 881 |"}
{"item_id": "item_0629", "chart_task_type": "mix_trend", "query": "Can you chart how the reliance on Grid, DG, and PV power sources shifted over the 12 months, so I can see at a glance how the energy mix changed throughout the year?", "table_markdown": "| Month | Utilization of sources (Operational Time) | | | % saving by PV |\n|---|---|---|---|---|\n| | Grid | Dg | PV | |\n| January | 45% | 5% | 50% | 50% |\n| February | 44% | 4% | 52% | 52% |\n| March | 40% | 6% | 54% | 54% |\n| April | 40% | 10% | 50% | 50% |\n| May | 45% | 5% | 50% | 50% |\n| June | 40% | 9% | 51% | 51% |\n| July | 41% | 7% | 52% | 52% |\n| August | 35% | 5% | 60% | 60% |\n| September | 40% | 5% | 55% | 55% |\n| October | 42% | 6% | 52% | 52% |\n| November | 45% | 5% | 50% | 50% |\n| December | 40% | 6% | 54% | 54% |"}
{"item_id": "item_0630", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of 911 versus non-emergency police calls shifted from January through October, so I can see at a glance how the relative reliance on each type changed over time?", "table_markdown": "| Month | 911 | Non-emergency | Total Calls |\n|--------|-------|---------------|-------------|\n| January| 3,791 | 7,180 | 10,971 |\n| February| 3,421 | 6,371 | 9,792 |\n| March | 3,662 | 7,449 | 11,111 |\n| April | 3,623 | 6,639 | 10,262 |\n| May | 4,202 | 7,585 | 11,787 |\n| June | 4,150 | 7,966 | 12,116 |\n| July | 4,050 | 8,107 | 12,157 |\n| August | 4,157 | 8,130 | 12,287 |\n| September| 3,873 | 7,111 | 10,984 |\n| October| 4,045 | 7,319 | 11,364 |\n| November| - | - | - |\n| December| - | - | - |\n| TOTAL | 38,974| 73,857 | 112,831 |"}
{"item_id": "item_0631", "chart_task_type": "mix_trend", "query": "Can you chart how the reliance on residential versus public utility property for millage rates shifted from 2014 to 2018, so I can see at a glance which sector became more dominant?", "table_markdown": "| Collection Year | Residential/Agricultural and Other Real Estate | Public Utility | Total |\n|-----------------|-----------------------------------------------|----------------|-------|\n| 2018 | $973,054,020 | $97,689,820 | $1,070,743,840 |\n| 2017 | 944,483,070 | 73,624,360 | 1,018,107,430 |\n| 2016 | 859,121,079 | 73,092,980 | 932,214,059 |\n| 2015 | 840,645,460 | 72,531,280 | 913,176,740 |\n| 2014 | 829,350,990 | 72,301,530 | 901,652,520 |"}
{"item_id": "item_0632", "chart_task_type": "mix_trend", "query": "Can you chart how the composition of O&M expenses shifted across the four periods, so I can see at a glance how the relative reliance on A&G, R&M, and Employee costs changed?", "table_markdown": "| O&M Expenses | Provisional FY 2011-12 | Previous Petition Submission for FY 2012-13 | Approved in the last Tariff Order | Revised Estimates-FY 2012-13 |\n|------------------|------------------------|---------------------------------------------|-----------------------------------|-------------------------------|\n| A&G Expenses | 3.46 | 5.95 | 5.91 | 3.76 |\n| R&M Expenses | 10.11 | 49.14 | 13.56 | 12.01 |\n| Employee Expenses| 28.57 | 45.26 | 28.42 | 31.04 |\n| **Total** | **42.15** | **100.35** | **47.89** | **46.81** |"}
{"item_id": "item_0633", "chart_task_type": "mix_trend", "query": "Can you chart how the General Fund's revenue mix shifted across the fiscal years from 2007–08 to 2010–11, so I can see at a glance how reliance on property taxes, external revenues, internal revenues, and fund balance changed?", "table_markdown": "| Resources | Actual FY 2007–08 | Actual FY 2008–09 | Revised FY 2009–10 | Proposed FY 2010–11 | Approved FY 2010–11 | Adopted FY 2010–11 |\n|-----------|------------------|------------------|-------------------|--------------------|--------------------|-------------------|\n| Current Property Taxes | 171,602,287 | 176,440,072 | 181,020,159 | 187,334,419 | 187,334,419 | 187,334,419 |\n| Prior Year Property Taxes | 3,570,184 | 3,729,161 | 3,933,572 | 3,432,812 | 3,432,812 | 3,432,812 |\n| Payment in Lieu of Taxes | 1,434,002 | 758,737 | 1,175,317 | 1,192,725 | 1,192,725 | 1,192,725 |\n| **Total Property Taxes** | 176,606,473 | 180,927,970 | 186,129,048 | 191,959,956 | 191,959,956 | 191,959,956 |\n| Licenses & Permits | 131,969,659 | 124,976,822 | 113,518,670 | 115,233,295 | 115,233,295 | 115,233,295 |\n| Lodging Taxes | 16,372,997 | 19,643,852 | 15,674,351 | 14,524,258 | 14,524,258 | 14,524,258 |\n| Charges for Services | 19,274,986 | 17,667,024 | 17,957,275 | 18,152,649 | 18,152,649 | 18,152,649 |\n| Intergovernmental | 26,233,145 | 29,288,132 | 31,410,258 | 25,417,495 | 25,739,495 | 25,893,139 |\n| Miscellaneous | 8,434,525 | 7,487,887 | 6,061,516 | 4,164,636 | 4,264,636 | 4,264,754 |\n| **Total External Revenues** | 202,285,312 | 199,063,717 | 184,622,070 | 177,492,333 | 177,914,333 | 178,068,095 |\n| General Fund Discretionary | 0 | 0 | 0 | 0 | 0 | 0 |\n| Fund Transfers - Revenue | 54,542,185 | 50,140,959 | 51,137,653 | 46,024,739 | 46,242,631 | 46,553,158 |\n| Interagency Revenue | 40,368,867 | 29,569,803 | 22,346,417 | 23,220,864 | 23,565,916 | 23,624,162 |\n| **Total Internal Revenues** | 94,911,052 | 79,710,762 | 73,484,070 | 69,245,603 | 69,808,547 | 70,177,320 |\n| Beginning Fund Balance | 81,658,081 | 75,205,021 | 48,268,516 | 11,607,626 | 11,607,626 | 11,607,626 |\n| **TOTAL RESOURCES** | $555,460,918 | $534,907,470 | $492,503,704 | $450,305,518 | $451,290,462 | $451,812,997 |"}
{"item_id": "item_0634", "chart_task_type": "mix_trend", "query": "Can you chart how the expenditure mix between General Schemes and BRGM Projects shifted over the three years, so I can see at a glance how the relative reliance on each changed?", "table_markdown": "| | General Schemes | BRGM (Projects) | Total (Rs. Crore) |\n|---|---|---|---|\n| 2002-03 | 14.1 | 1.68 | 15.78 |\n| 2003-04 | 14.67 | 1.5 | 16.17 |\n| 2004-05 | 14.72 | 3.21 | 17.93 |\n| Total | 43.49 | 6.39 | 49.88 |"}
{"item_id": "item_0635", "chart_task_type": "mix_trend", "query": "Can you chart how the composition of MRW waste shifted from 1999 to 2006, so I can see at a glance how the relative contribution of HHW, Used Oil, and CESQG changed over time?", "table_markdown": "| Collection Year | HHW lbs (no UO) | Used Oil lbs | CESQG lbs | Total MRW lbs |\n|-----------------|-----------------|--------------|-----------|---------------|\n| 1999 | 9.9M | 9.3M | 637K | 20.4M |\n| 2000 | 10.5M | 8.3M | 1.1M | 19.8M |\n| 2001 | 15.6M | 11.3M | 1.0M | 27.9M |\n| 2002 | 13.5M | 9.2M | 1.4M | 24.1M |\n| 2003 | 16.0M | 11.7M | 1.3M | 29.0M |\n| 2004 | 15.3M* | 12.4M | 2.4M | 30.1M* |\n| 2005 | 14.7M | 11.3M | 6.3M | 32.3M |\n| 2006 | 15.2M | 10.0M | 7.1M | 32.3M |"}
{"item_id": "item_0636", "chart_task_type": "mix_trend", "query": "Can you chart how the geographic mix of net sales shifted across the three periods, so I can see at a glance which countries drove the relative contribution?", "table_markdown": "| | 1-6/2019 | 1-6/2018 | 1-12/2018 |\n|----------------|----------|----------|-----------|\n| Finland | 42.2 | 39.0 | 86.1 |\n| Great Britain | 28.4 | 33.8 | 61.5 |\n| Other | 42.0 | 36.7 | 80.6 |\n| Total net sales| 112.6 | 109.5 | 228.2 |"}
{"item_id": "item_0637", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of flat recoveries shifted between voluntary surrenders and tenancy-related recoveries from 1997 to 2007?", "table_markdown": "| Year | Voluntary Surrender (including surrender of flat after purchase of HOS/PSPS flats or HOS/PSPS secondary market scheme flats, or obtaining a loan under HPLS/HALS) | Tenancy-related flat recovery (including various kinds of transfers) | Total |\n|------------|---------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------|-------|\n| 1997 | 14,762 | 5,603 | 20,365|\n| 1998 | 13,688 | 8,866 | 22,554|\n| 1999 | 17,097 | 8,909 | 26,006|\n| 2000 | 15,901 | 7,739 | 23,640|\n| 2001 | 15,506 | 9,412 | 24,918|\n| 2002 | 7,944 | 10,153 | 18,097|\n| 2003 | 7,523 | 8,183 | 15,706|\n| 2004 | 6,309 | 7,562 | 13,871|\n| 2005 | 6,652 | 11,072 | 17,724|\n| 2006 | 6,787 | 9,132 | 15,919|\n| 2007 (January to October) | 7,512 | 6,171 | 13,683|"}
{"item_id": "item_0638", "chart_task_type": "mix_trend", "query": "Can you chart how the composition of additional sustainable development expenditures shifted from 1990 to 2000, so I can see at a glance which components dominated the spending mix over time?", "table_markdown": "| Year | Raising Energy Efficiency | Developing Renewable Energy | Reforesting the Earth | Protecting Topsoil on Cropland | Slowing Population Growth | Total |\n|------|---------------------------|-----------------------------|-----------------------|--------------------------------|--------------------------|-------|\n| 1990 | 1 | 1 | 2 | 3 | 13 | 20 |\n| 1991 | 2 | 2 | 3 | 6 | 18 | 31 |\n| 1992 | 3 | 4 | 4 | 9 | 22 | 42 |\n| 1993 | 4 | 5 | 5 | 12 | 26 | 52 |\n| 1994 | 5 | 6 | 6 | 16 | 28 | 61 |\n| 1995 | 6 | 7 | 6 | 16 | 30 | 65 |\n| 1996 | 7 | 9 | 6 | 16 | 31 | 69 |\n| 1997 | 8 | 10 | 6 | 16 | 32 | 72 |\n| 1998 | 9 | 12 | 7 | 16 | 32 | 76 |\n| 1999 | 10 | 13 | 7 | 16 | 32 | 78 |\n| 2000 | 11 | 15 | 7 | 16 | 33 | 82 |\n| Total| | | | | | 648 |"}
{"item_id": "item_0639", "chart_task_type": "mix_trend", "query": "Can you chart how the species mix of the fish harvest shifted from May through September, so I can see at a glance which species dominated the catch in each month?", "table_markdown": "| Month | Chinook | Sockeye | Coho | Pink | Chum | Total |\n|-------|---------|---------|------|------|------|-------|\n| May | 302 | 573 | 1 | 0 | 0 | 876 |\n| June | 802 | 3,962 | 51 | 79 | 40 | 4,934 |\n| July | 799 | 72,332 | 5,915| 1,513| 1,564| 82,123|\n| August| 100 | 8,229 | 5,806| 375 | 796 | 15,306|\n| September | 21 | 573 | 3,213| 69 | 98 | 3,975 |\n| Total.| 2,024 | 85,669 | 14,987| 2,036| 2,498| 107,215|"}
{"item_id": "item_0640", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of emergency versus administrative calls shifted for the Seattle Fire Department from 2013 to 2017?", "table_markdown": "| Year | Emergency Calls | % Increase | Administrative Calls | Total Calls Received | % Increase |\n|------|-----------------|------------|----------------------|----------------------|------------|\n| 2017 | 150,919 | -1% | 40,557 | 191,476 | 2% |\n| 2016 | 151,912 | 8.7% | 32,329 | 187,709 | 6.2% |\n| 2015 | 145,189 | 9.9% | 31,578 | 176,767 | 10.9% |\n| 2014 | 132,071 | 4.3% | 27,185 | 159,256 | 6.0% |\n| 2013 | 126,610 | 4.2% | 23,558 | 150,168 | 7.4% |"}
{"item_id": "item_0641", "chart_task_type": "mix_trend", "query": "Can you chart how the gender and program mix of graduates shifted over the three academic years, so I can see at a glance how the relative contribution of each group changed?", "table_markdown": "| Year | Female Pre-Licensure | Male Pre-Licensure | Total Pre-Licensure | Female Post-Licensure | Male Post-Licensure | Total Post-Licensure |\n|------------|----------------------|--------------------|---------------------|-----------------------|---------------------|----------------------|\n| 2016 - 2017| 108 | 41 | 156 | 7 | 0 | 7 |\n| 2017 - 2018| 99 | 43 | 153 | 9 | 2 | 11 |\n| 2018 - 2019| 94 | 40 | 148 | 11 | 3 | 14 |"}
{"item_id": "item_0642", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of finished flats between residential buildings and private houses shifted from 2005 to 2011, so I can see at a glance which type dominated the market over time?", "table_markdown": "| Number of finished flats | | | | | | | |\n|---|---|---|---|---|---|---|---|\n| YEAR | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 |\n| TOTAL | 2.114 | 2.679 | 3.448 | 4.650 | 5.825 | 4.093 | 4.343 |\n| Flats in residential buildings | 614 | 749 | 995 | 877 | 1.637 | 890 | 931 |\n| Flats in private houses | 1.500 | 1.930 | 2.453 | 3.773 | 4.188 | 3.203 | 3.412 |\n| Area of completed units, in thousand m2 | | | | | | | |\n| TOTAL | 139 | 168 | 244 | 302 | 402 | 282 | 366 |\n| Flats in residential buildings | 34 | 40 | 73 | 58 | 106 | 54 | 64 |\n| Flats in private houses | 105 | 128 | 171 | 244 | 296 | 228 | 302 |\n| Number of unfinished flats | | | | | | | |\n| TOTAL | 11.570 | 11.932 | 13.344 | 15.688 | 15.135 | 14.169 | 8.075 |\n| Flats in residential buildings | 2.063 | 1.515 | 1.673 | 2.409 | 2.447 | 1.873 | 1.682 |\n| Flats in private houses | 9.507 | 10.417 | 11.671 | 13.279 | 12.688 | 12.296 | 6.393 |\n| Area of uncompleted units, in thousand m2 | | | | | | | |\n| TOTAL | 856 | 898 | 1.006 | 1.196 | 1.191 | 1.139 | 631 |\n| Flats in residential buildings | 135 | 95 | 111 | 158 | 176 | 136 | 115 |\n| Flats in private houses | 721 | 803 | 895 | 1.038 | 1.015 | 1.003 | 516 |"}
{"item_id": "item_0643", "chart_task_type": "mix_trend", "query": "Can you chart how the expenditure mix shifted across the five components from 2022/23 to 2025/26, so I can easily spot which areas are taking up a larger share of the total budget over time?", "table_markdown": "| | 2022/23 | 2023/24 | 2024/25 | 2025/26 |\n|---|---|---|---|---|\n| Income | | | | |\n| Levies | 1,100,685 | 1,122,699 | 1,145,143 | 1,168,056 |\n| “New Burden” | 394,145 | | | |\n| Other | 70,000 | 80,000 | 80,000 | 80,000 |\n| Total Income | 1,564,830 | 1,202,699 | 1,225,143 | 1,248,056 |\n| Expenditure | | | | |\n| Staff cost | 1,207,000 | 1,231,140 | 1,250,000 | 1,270,000 |\n| Administration | 207,450 | 211,500 | 212,500 | 215,000 |\n| Operations | 38,500 | 39,000 | 39,500 | 40,000 |\n| Vessels | 126,850 | 105,000 | 107,000 | 110,000 |\n| Vehicles | 31,250 | 32,500 | 34,500 | 35,500 |\n| Total | 1,572,550 | 1,619,140 | 1,643,500 | 1,670,500 |\n| Surplus/Shortfall | (7,720) | (416,441) | (418,357) | (422,444) |\n| New burden alt. | | 394,145 | 394,145 | 394,145 |"}
{"item_id": "item_0644", "chart_task_type": "mix_trend", "query": "Can you chart how the enrollment mix between elementary and secondary students shifted from 1929 to 1935, so I can see at a glance how the relative reliance on each level changed over time?", "table_markdown": "| Year | Elementary | Secondary | Total |\n|------|------------|-----------|-------|\n| 1929 | 110 | 26 | 136 |\n| 1930 | 108 | 24 | 132 |\n| 1931 | 103 | 28 | 131 |\n| 1932 | 106 | 31 | 137 |\n| 1933 | 105 | 31 | 136 |\n| 1934 | 97 | 41 | 138 |\n| 1935 | 93 | 38 | 131 |"}
{"item_id": "item_0645", "chart_task_type": "mix_trend", "query": "Can you chart how the expense mix shifted from 2021 through 2025 so I can see at a glance how the relative weight of salaries, supplies, travel, and other operating costs changed over time?", "table_markdown": "| OBJECT OF EXPENSE | Exp 2021 | Est 2022 | Bud 2023 | BL 2024 | BL 2025 |\n|---------------------------|------------|------------|------------|-----------|-----------|\n| 1001 SALARIES AND WAGES | $6,920,158 | $6,920,158 | $6,920,158 | $188,250 | $188,250 |\n| 2003 CONSUMABLE SUPPLIES | $85,000 | $85,000 | $85,000 | $85,000 | $85,000 |\n| 2005 TRAVEL | $7,500 | $7,500 | $7,500 | $7,500 | $7,500 |\n| 2009 OTHER OPERATING EXPENSE | $22,490 | $22,490 | $22,490 | $22,490 | $22,490 |\n| OOE Total (Excluding Riders) | $7,035,148 | $7,035,148 | $7,035,148 | $303,240 | $303,240 |\n| OOE Total (Riders) | | | | | |\n| Grand Total | $7,035,148 | $7,035,148 | $7,035,148 | $303,240 | $303,240 |"}
{"item_id": "item_0646", "chart_task_type": "mix_trend", "query": "Can you chart how the gender mix of first professional degrees shifted from 1970 to 2005, so I can easily see the changing balance between men and women over time?", "table_markdown": "| Year | Total number of degrees | Percent earned by men | Percent earned by women |\n|---------------|-------------------------|-----------------------|-------------------------|\n| 1970-1971 | 37,946 | 94% | 6% |\n| 1974-1975 | 55,916 | 88% | 12% |\n| 1979-1980 | 70,131 | 75% | 25% |\n| 1984-1985 | 75,063 | 67% | 33% |\n| 1989-1990 | 70,988 | 62% | 38% |\n| 1994-1995 | 75,800 | 59% | 41% |\n| 1999-2000 | 80,057 | 55% | 45% |\n| 2004-2005 | 87,289 | 50% | 50% |"}
{"item_id": "item_0647", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of new Provincial Court cases shifted from 1993/94 to 1999/00, so I can see at a glance which categories drove the changes in overall volume?", "table_markdown": "| | 1993/94 | 1994/95 | 1995/96 | 1996/97 | 1997/98 | 1998/99 | 1999/00 |\n|---|---|---|---|---|---|---|---|\n| Prov. Crim Adult New Cases | 101,563 | 105,583 | 109,758 | 107,236 | 112,316 | 110,161 | 107,458 |\n| Prov. Crim Youth New Cases | 19,614 | 19,940 | 20,937 | 18,938 | 19,308 | 18,258 | 17,728 |\n| Prov. Crim New Traffic cases | 49,682 | 56,119 | 50,984 | 57,725 | 56,813 | 70,164 | 72,832 |\n| Prov. Crim New Bylaw Cases | 15,163 | 13,445 | 13,596 | 15,495 | 17,731 | 23,839 | 24,721 |\n| Prov. Civil Fam New Cases/Apps | 18,295 | 18,024 | 20,644 | 23,305 | 23,959 | 25,854 | 28,130 |\n| Prov. Civil Small Clm New Cases | 39,277 | 38,218 | 39,678 | 34,800 | 33,598 | 29,056 | 28,556 |\n| Total Provincial Crt New Cases | 243,594 | 251,329 | 255,597 | 257,499 | 264,075 | 277,332 | 279,425 |"}
{"item_id": "item_0648", "chart_task_type": "mix_trend", "query": "Can you chart how the geographic mix of Selkirk College's domestic students shifted from 2018 to 2022, so I can see at a glance how reliance on the local region changed relative to other areas?", "table_markdown": "| Year | Selkirk College Region | Rest of BC | Unknown Region | Alberta | From Across Canada |\n|------|------------------------|------------|----------------|---------|---------------------|\n| 2018 | 52% | 15% | 4% | 8% | 12% |\n| 2019 | 59% | 16% | 3% | 4% | 4% |\n| 2020 | 72% | 17% | 6% | 2% | 2% |\n| 2021 | 69% | 17% | 8% | 3% | 3% |\n| 2022 | 74% | 14% | 6% | 3% | 3% |"}
{"item_id": "item_0649", "chart_task_type": "mix_trend", "query": "Can you chart how the geographic mix of case studies in neoclassical realist works shifted across the three decades, so I can see at a glance which continents gained or lost relative prominence over time?", "table_markdown": "| Continent | 1990-1999 | 2000-2009 | 2010-2019 | Total |\n|-----------------|-----------|-----------|-----------|-------|\n| North America | 4 | 15 | 32 | 51 |\n| Europe | 5 | 31 | 71 | 107 |\n| Asia | 5 | 7 | 32 | 44 |\n| Africa | 0 | 1 | 2 | 3 |\n| Latin America | 0 | 3 | 0 | 3 |\n| Oceania | 0 | 0 | 2 | 2 |"}
{"item_id": "item_0650", "chart_task_type": "mix_trend", "query": "Can you chart how the age composition of Nevada's 60+ population shifted from FY12 to FY17, so I can see at a glance which age groups made up a larger share of the total over time?", "table_markdown": "| Age Group | FY12 - Est. | FY13 - Est. | FY14 - Proj. | FY15 - Proj. | FY16 - Proj. | FY17 - Proj. |\n|----------------------------|-------------|-------------|--------------|--------------|--------------|--------------|\n| 60 to 64 Years of Age | 150,842 | 154,972 | 158,976 | 162,991 | 167,051 | 171,481 |\n| 65 to 69 Years of Age | 128,620 | 132,954 | 135,379 | 138,241 | 141,865 | 143,335 |\n| 70 to 74 Years of Age | 94,477 | 100,742 | 105,532 | 108,724 | 111,132 | 115,607 |\n| 75 to 79 Years of Age | 61,904 | 64,193 | 67,072 | 70,248 | 73,188 | 78,286 |\n| 80 to 84 Years of Age | 39,837 | 40,083 | 41,208 | 42,851 | 45,054 | 45,554 |\n| 85 Years of Age and Over | 34,642 | 35,982 | 37,007 | 38,108 | 39,236 | 40,190 |\n| TOTAL - Age 60 and Older | 510,322 | 528,926 | 545,174 | 561,163 | 577,526 | 594,453 |"}
{"item_id": "item_0651", "chart_task_type": "mix_trend", "query": "Can you chart how the revenue mix across the three business segments shifted from FY2017 to FY2022, so I can easily see which areas drove the changing composition over time?", "table_markdown": "| | Press Business | Automation/FA Business | Maintenance/Modernization Business | Total |\n|------------------|----------------|------------------------|-------------------------------------|-------|\n| **Results** | | | | |\n| FY2017 | 462 | 112 | 165 | 739 |\n| | 29.7 | ▲2.0 | 35.5 | 63 |\n| | 6% | ▲2% | 22% | 8.5% |\n| FY2018 | 524 | 122 | 194 | 841 |\n| | 25.7 | ▲8.0 | 37.9 | 56 |\n| | 5% | ▲7% | 20% | 6.6% |\n| FY2019 | 385 | 115 | 191 | 692 |\n| | 15.2 | 2.9 | 43.7 | 62 |\n| | 4% | 2% | 23% | 8.9% |\n| **Plan** | | | | |\n| FY2020 | 350 | 90 | 170 | 610 |\n| | 3.6 | 0.9 | 28.1 | 33 |\n| | 1% | 1% | 17% | 5.3% |\n| FY2021 | 330 | 80 | 180 | 590 |\n| | 3.3 | 1.6 | 30.6 | 36 |\n| | 1% | 2% | 17% | 6.0% |\n| FY2022 | 360 | 110 | 190 | 660 |\n| | 18.0 | 5.5 | 34.2 | 58 |\n| | 5% | 5% | 18% | 8.7% |"}
{"item_id": "item_0652", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between WOC time and 24-hour compressive strength for all the experimental configurations, making it easy to identify which additive type and conditions correspond to each data point?", "table_markdown": "| Additive | Mole ratio | % Additive by weight of dry cement | Temp (°F) (°C) | Initial set time (hr:min) | WOC Time (hr:min) | 24 Hour compressive strength (psi) (MPa) |\n|----------------|------------|-----------------------------------|----------------|---------------------------|-------------------|------------------------------------------|\n| NNDMA/AMPS | 1:1.5 | 0.6% | 80 (27) | 9:25 | 14:57 | 940 (6.5) |\n| NNDMA/AMPS | 1:1.5 | 0.8% | 80 (27) | 8:57 | 14:26 | 960 (6.8) |\n| AA/AMPS | 4:1 | 0.8% | 80 (27) | 12:32 | 22:40 | 590 (4.1) |\n| NNDMA/AMPS | 1:1.5 | 0.6% | 100 (38) | 7:01 | 10:12 | 1410 (9.7) |\n| NNDMA/AMPS | 1:1.5 | 0.8% | 100 (38) | 6:49 | 10:30 | 1390 (9.6) |\n| AA/AMPS | 4:1 | 0.8% | 100 (38) | 7:00 | 12:06 | 1270 (8.8) |\n| NNDMA/AMPS | 1:1.5 | 0.6% | 120 (49) | 3:58 | 6:10 | 2300 (15.8) |\n| NNDMA/AMPS | 1:1.5 | 0.8% | 120 (49) | 4:24 | 7:06 | 2000 (13.8) |\n| AA/AMPS | 4:1 | 0.8% | 120 (49) | 5:48 | 8:38 | 1760 (12.1) |\n| NNDMA/AMPS | 1:1.5 | 0.6% | 140 (60) | 3:11 | 5:07 | 2550 (17.6) |\n| NNDMA/AMPS | 1:1.5 | 0.8% | 140 (60) | 3:40 | 6:10 | 2080 (14.3) |\n| AA/AMPS | 4:1 | 0.8% | 140 (60) | 6:36 | 9:58 | 1420 (9.8) |"}
{"item_id": "item_0653", "chart_task_type": "mix_trend", "query": "Can you chart how the occupational mix in Bellevue shifted from 1990 to 2019, so I can see at a glance which groups gained or lost share of the workforce?", "table_markdown": "| Occupation Group | 1990 | 2000 | 2010 | 2019 |\n|-----------------------------------------|------|------|------|------|\n| Management, business, science, and arts occupations | 40% | 53% | 60% | 67% |\n| Service occupations | 9% | 10% | 11% | 9% |\n| Sales and office | 38% | 26% | 19% | 15% |\n| Natural resources, construction, and maintenance | 6% | 4% | 4% | 3% |\n| Production, transportation, and material moving | 6% | 6% | 6% | 6% |"}
{"item_id": "item_0654", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of course evaluation forms shifted across the Fall '14, Winter '15, and Spring '15 terms so I can see at a glance which forms dominated the responses in each period?", "table_markdown": "| | Fall ‘14 | Winter ‘15 | Spring ‘15 | Total | % |\n|---|---|---|---|---|---|\n| Form A - Lecture | 10,032 | 9,829 | 7,830 | 27,691 | 65% |\n| Form C – Skills Acquisition | 1,396 | 802 | 756 | 2,954 | 7% |\n| Form D - Lab | 931 | 694 | 451 | 2,076 | 5% |\n| Form E - Arts | 521 | 412 | 358 | 1,291 | 3% |\n| Form F – Field Experience | 100 | 58 | 83 | 241 | 1% |\n| Form W - Online | 2,508 | 3,034 | 2,938 | 8,480 | 20% |\n| Term Totals | 15,488 | 14,829 | 12,416 | 42,733 | 100% |\n| Percent of 2013/14 | 36% | 35% | 29% | | |"}
{"item_id": "item_0655", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between production volume and market value for these fish species so I can easily identify which ones are high-yield versus high-value?", "table_markdown": "| Top 15 cultured fish species by weight in millions of tonnes, according to FAO statistics for 2013 [1] | | | |\n|---|---|---|---|\n| Species | Environment | Tonnage (millions) | Value (US$, billion) |\n| Grass carp | freshwater | 5.23 | 6.69 |\n| Silver carp | freshwater | 4.59 | 6.13 |\n| Common carp | freshwater | 3.76 | 5.19 |\n| Nile tilapia | freshwater | 3.26 | 5.39 |\n| Bighead carp | freshwater | 2.90 | 3.72 |\n| Catla (Indian carp) | freshwater | 2.76 | 5.49 |\n| Crucian carp | freshwater | 2.45 | 2.67 |\n| Atlantic salmon | marine | 2.07 | 10.10 |\n| Roho labeo | freshwater | 1.57 | 2.54 |\n| Milkfish | freshwater | 0.94 | 1.71 |\n| Rainbow trout | freshwater, brackish | 0.88 | 3.80 |\n| Wuchang bream | freshwater | 0.71 | 1.16 |\n| Black carp | freshwater | 0.50 | 1.15 |\n| Northern snakehead | freshwater | 0.48 | 0.59 |\n| Amur catfish | freshwater | 0.41 | 0.55 |"}
{"item_id": "item_0656", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of new orders across the business segments shifted from FY21 to FY23, so I can see at a glance which segments drove the changes in composition?", "table_markdown": "| New orders received (cumulative) | FY21 | | | | FY22 | | | | FY23 | | | |\n|----------------------------------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|\n| | 1Q | 2Q | 3Q | 4Q | 1Q | 2Q | 3Q | 4Q | 1Q | 2Q | 3Q | 4Q |\n| AP-related business | 4,519 | 9,048 | 13,072| 18,180| 5,094 | 8,115 | 12,705| 17,614| 5,227 | | | |\n| BP-related business | 3,142 | 5,646 | 8,442 | 12,086| 2,799 | 5,102 | 7,921 | 11,461| 2,502 | | | |\n| Environmental- and energy-related business | 523 | 1,286 | 2,025 | 3,014 | 804 | 1,215 | 1,921 | 2,456 | 1,002 | | | |\n| Other business | 1,568 | 2,820 | 4,606 | 6,572 | 1,719 | 4,515 | 6,669 | 9,316 | 2,044 | | | |\n| Total | 9,753 | 18,802| 28,146| 39,853| 10,217| 18,949| 29,217| 40,849| 10,777| | | |"}
{"item_id": "item_0657", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between venue size and banquet capacity so I can see at a glance how space relates to the number of tables for each location?", "table_markdown": "| 場地 | 面積 (平方米) | *宴會（席） |\n|------|-------------|------------|\n| Canton | 170 | 9 |\n| Portas do Sol | 470 | 15 |\n| Taipa I+II+III | 300 | 13 |\n| Taipa I | 105 | 5 |\n| Taipa II | 60 | 4 |\n| Taipa III | 135 | 4 |\n| Mandarin | 125 | 5 |\n| Hong Kong I+II | 106 | 3 |\n| Hong Kong I | 40 | 1 |\n| Hong Kong II | 66 | 2 |"}
{"item_id": "item_0658", "chart_task_type": "mix_trend", "query": "Can you chart how the living arrangement mix for adults aged 25-34 shifted from 1960 to 1992, so I can see at a glance how their reliance on different household types changed over time?", "table_markdown": "| Age 25-34: | 1960 | 1970 | 1980 | 1990 | 1992 |\n|------------|------|------|------|------|------|\n| Living with Parents | 9.1% | 8.0% | 8.7% | 11.5% | 12.0% |\n| Head of Household | 43.4% | 47.5% | 50.2% | 47.3% | 47.1% |\n| Family | 40.6% | 42.6% | 38.2% | 34.4% | 33.8% |\n| Nonfamily | 2.9% | 4.9% | 12.0% | 13.0% | 13.2% |"}
{"item_id": "item_0659", "chart_task_type": "mix_trend", "query": "Can you chart how the mix of Supreme Court income sources shifted from 2010/11 to 2022/23, so I can see at a glance how the reliance on taxpayer contributions, court fees, and other income changed over time?", "table_markdown": "| | | | | | Supreme | Other |\n|---|---|---|---|---|---|---|\n| | | Supreme | | Taxpayer | Court fees | income |\n| | Taxpayer | Court | Other | contributions | to total | to total |\n| | contributions* | fees | income** | to total income | income | income |\n| Year | (£) (000) | (£) (000) | (£) (000) | (%) | (%) | (%) |\n| 2010/11 | 5,970 | 934 | 206 | 84 | 13 | |\n| 2011/12 | 5,970 | 727 | 241 | 86 | 10 | 3 |\n| 2012/13 | 6,415 | 851 | 201 | 86 | 11 | |\n| 2013/14 | 6,440 | 849 | 273 | 85 | 11 | 4 |\n| 2014/15 | 6,631 | 966 | 382 | 83 | 12 | |\n| 2015/16 | 6,632 | 940 | 402 | 83 | 12 | 5 |\n| 2016/17 | 6,632 | 761 | 320 | 86 | 10 | |\n| 2017/18 | 6,781 | 850 | 409 | 84 | 11 | 5 |\n| 2018/19 | 6,781 | 809 | 412 | 85 | 10 | |\n| 2019/20 | 6,949 | 867 | 374 | 85 | 11 | 5 |\n| 2020/21 | 6,632 | 751 | 294 | 86 | 10 | |\n| 2021/22 | 6,632 | 533 | 455 | 87 | 7 | 6 |\n| 2022/23 | 6,792 | 783 | 512 | 84 | 10 | |"}
{"item_id": "item_0660", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between accuracy and runtime for the models in Table 1, so I can easily see which ones offer the best performance relative to their computational cost?", "table_markdown": "| Model | Accuracy (%) | Runtime (s) |\n|-------|--------------|-------------|\n| DT | 53.9 | 5,583.156 |\n| KNN | 55.6 | 640.574 |\n| RF | 77.5 | 49,686.936 |\n| SVM | 79.4 | 45,639.071 |\n| LR | 82.9 | 6,066.966 |\n| MNB | 84.1 | 15.320 |\n| BC (11)| 87.3 | 0.043 |"}
{"item_id": "item_0661", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between average annual yields and losses for each sub-area, so I can easily see which locations produce the most while losing the least?", "table_markdown": "| Sub Area | Area Dedicated (acres) | Average Packs produced per acre/ harvest | Harvest Times a year (average) | Harvest times loss a year (average) | Average Annual Yields (packs) | Average Annual Yields (Tons) | Average Annual Loses (Tons) | Estimated Annual Gross Income |\n|-----------------------|------------------------|------------------------------------------|-------------------------------|------------------------------------|------------------------------|-------------------------------|-------------------------------|--------------------------------|\n| El Anegado | 2,232 | 107 | 2.2 | 0.83 | 239 | 9,530 | 3,466 | $1,905,278 |\n| North of El Anegado | 106 | 117 | 2 | 1 | 233 | 384 | 192 | $76,711 |\n| West of El Anegado | 2,189 | 118 | 2.6 | 0.5 | 319 | 12,541 | 2,036 | 2,508,152 |\n| Lagoon | 166 | 155 | 3 | 1 | 420 | 1,198 | 500 | $219,873 |\n| North of Lagoon | 641 | 114 | 2.1 | 1 | 235 | 2,649 | 1,280 | $529,711 |\n| South of Lagoon | 210 | 200 | 2.5 | 1 | 500 | 1,700 | 735 | $294,050 |\n| All Lagoon Area | 1,017 | 128 | 2.2 | 1 | 285 | 5,546 | 2,516 | $1,043,634 |\n| Total Area Surveyed | 5,545 | 117 | 2.4 | 0.74 | 282 | 28,000 | 8,209 | $5,533,776 |"}
{"item_id": "item_0662", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between portfolio weight and discount for these funds, making it easy to spot which specific holdings offer the best balance?", "table_markdown": "| Rank | Fund Name | Fund (%) | Discount* (%) |\n|------|-----------------------------------------------|----------|---------------|\n| 1 | Templeton Emerging Markets Investment Trust | 8.8 | 12.0 |\n| 2 | Asia Dragon Trust | 7.7 | 10.7 |\n| 3 | Taiwan Fund Inc | 6.5 | 17.8 |\n| 4 | JPMorgan Emerging Markets Investment Trust | 5.9 | 12.0 |\n| 5 | abrdn Emerging Markets Equity Income Fund Inc | 5.4 | 15.0 |\n| 6 | Fidelity China Special Situations | 5.4 | 12.4 |\n| 7 | JPMorgan Indian Investment Trust | 4.6 | 17.6 |\n| 8 | Schroder AsiaPacific Fund | 4.6 | 12.4 |\n| 9 | abrdn Asia Focus | 4.2 | 17.4 |\n| 10 | Utilico Emerging Markets Trust | 3.8 | 18.4 |"}
{"item_id": "item_0663", "chart_task_type": "tradeoff", "query": "Can you plot the trade-off between energy and structural ratings for the ranked organisms so I can easily identify each one by its rank?", "table_markdown": "| Rank | area shadow (m²) | % | area patio (m²) | Difference (m²) | Ratio | % | max length (m) | energy ratings | structural ratings | total ratings | total radiation (kWh/year) |\n|------|-----------------|---|----------------|-----------------|-------|---|---------------|----------------|------------------|--------------|--------------------------|\n| Zero | 60,00 | | | | | | | | | | |\n| 1 | 71,5 | 19,2 | 116,4 | 44,9 | -58,2 | 0,63 | -64,9 | 7,50 | 19 | 94 | 1008417 |\n| 2 | 83,7 | 39,5 | 142,2 | 58,4 | -45,6 | 0,70 | -61,0 | 6,75 | 65 | 28 | 1217178 |\n| 3 | 80,1 | 33,5 | 138,2 | 58,2 | -45,9 | 0,73 | -59,5 | 7,42 | 62 | 22 | 1045807 |\n| 4 | 66,1 | 10,2 | 112,4 | 46,2 | -56,9 | 0,70 | -60,9 | 7,83 | 67 | 10 | 773916 |\n| 5 | 62,7 | 4,57 | 115,4 | 52,7 | -51,0 | 0,84 | -53,2 | 6,95 | 50 | 25 | 75 |\n| 6 | 65,1 | 8,63 | 107,3 | 42,1 | -60,8 | 0,65 | -63,9 | 8,45 | 69 | 3 | 72 |\n| 7 | 64,9 | 8,30 | 115,2 | 50,2 | -53,3 | 0,77 | -56,9 | 7,53 | 56 | 16 | 72 |\n| 8 | 67,4 | 12,4 | 121,9 | 54,4 | -49,4 | 0,81 | -55,0 | 7,60 | 56 | 14 | 70 |\n| 9 | 90,8 | 51,3 | 170,9 | 80,2 | -25,5 | 0,88 | -50,7 | 7,51 | 52 | 18 | 70 |\n| 10 | 79,4 | 32,4 | 161,0 | 81,5 | -24,2 | 1,03 | -42,7 | 6,94 | 42 | 26 | 68 |\n| 11 | 72,8 | 21,3 | 125,4 | 52,6 | -51,1 | 0,72 | -59,6 | 9,75 | 66 | 1 | 67 |\n| 12 | 87,5 | 45,9 | 162,3 | 74,7 | -30,5 | 0,85 | -52,4 | 6,88 | 54 | 11 | 65 |\n| 13 | 44,0 | -26,6 | 90,6 | 46,6 | -56,7 | 1,06 | -40,9 | 6,97 | 37 | 24 | 61 |\n| 14 | 59,9 | -0,12 | 117,2 | 57,3 | -46,7 | 0,96 | -46,7 | 7,49 | 38 | 20 | 58 |\n| 15 | 62,4 | 4,08 | 118,5 | 56,1 | -47,8 | 0,90 | -49,9 | 7,61 | 43 | 13 | 56 |\n| 16 | 60,1 | 0,10 | 136,4 | 76,3 | -29,0 | 1,27 | -29,1 | 7,48 | 26 | 21 | 47 |\n| 17 | 65,1 | 8,42 | 136,4 | 71,3 | -33,6 | 1,10 | -38,8 | 7,88 | 35 | 9 | 44 |\n| 18 | 92,8 | 54,6 | 198,9 | 106,1 | -1,38 | 1,14 | -36,2 | 8,76 | 39 | 2 | 41 |\n| 19 | 55,7 | -7,13 | 120,8 | 65,1 | -39,4 | 1,17 | -34,8 | 7,54 | 26 | 15 | 41 |\n| 20 | 77,6 | 29,3 | 177,5 | 99,9 | -7,10 | 1,29 | -28,1 | 7,99 | 31 | 8 | 39 |"}
{"item_id": "item_0664", "chart_task_type": "tradeoff", "query": "Can you plot the trade-off between iteration count and CPU time for these methods, making it easy to see which variants are efficient in terms of steps versus total cost?", "table_markdown": "| Method | Iter. | LU fact. | CPU [s] | nlin.sol. [s] |\n|-------------------------------|-------|----------|---------|---------------|\n| Arnoldi | 144 | 2 | 707.0 | 469.9 |\n| Arnoldi, restarted | 139 | 5 | 199.6 | 25.0 |\n| Jacobi–Davidson | 111 | 9 | 1050.5 | 161.2 |\n| Jacobi–Davidson, restarted | 109 | 12 | 914.4 | 18.9 |\n| rational Krylov | 147 | 3 | 1107.1 | 465.3 |\n| rational Krylov, restarted | 147 | 4 | 647.8 | 28.5 |"}
{"item_id": "item_0665", "chart_task_type": "tradeoff", "query": "Can you plot the trade-off between pressure and flow for the different plunger sizes so I can see how they compare?", "table_markdown": "| Pump Series | Plunger Size | Pressure (PSI) | Flow (GPM) |\n|---|---|---|---|\n| 3015 | 11 | 7,500 | 23.3 |\n| | 10 | 10,000 | 18.4 |\n| | 9 | 12,500 | 14.9 |\n| | 8 | 15,000 | 11.8 |\n| 3020 | 7 | 20,000 | 10 |"}
{"item_id": "item_0666", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between daily milk yield and lactation length for the different genetic groups so I can see how they compare?", "table_markdown": "| Effect | Number of observations | DMY LSM ± SE (kg) | | | | Lactation length LSM ± SE (days) | | | | |\n|---|---|---|---|---|---|---|---|---|---|---|\n| Overall | 2186 | | 6.88 | ± 0.05 | | 326.69 ± 2.03 | | | | |\n| Genetic group | | | | | | | | | | |\n| 50% F1 | 1543 | | 6.69b | ± 0 | .083 | | 343.62b | ± | 3.56 | |\n| 50% F2 | 234 | | 5.66c | ± | 0.16 | | 319.42c | ± | 6.68 | |\n| 50% F3 | 139 | | 5.02d | ± | 0.19 | | 319.25c | ± | 8.37 | |\n| 75% first generation | 236 | | 8.70a | ± | 0.17 | | 374.05a | ± | 7.24 | |\n| 75% second generation | 34 | | 6.72b | ± | 0.37 | | 303.12c | ± | 15.73 | |\n| Calving period | | | | | | | | | | |\n| 1977-1982 | 23 | | 6.18cd | ± | 0.44 | 3 | 16.06cd | ± | 18.9 | 8 |\n| 1983-1987 | 75 | | 5.88d | ± | 0.26 | | 407.35a | ± | 11.13 | |\n| 1988-1992 | 167 | | 4.60e | ± | 0.18 | | 374.99b | ± | 7.50 | |\n| 1993-1997 | 183 | | 5.55d | ± | 0.16 | | 369.94b | ± | 6.94 | |\n| 1998-2002 | 272 | | 7.58b | ± | 0.16 | | 314.78c | ± | 6.95 | |\n| 2003-2007 | 370 | | 6.99c | ± | 0.15 | | 316.77c | ± | 6.50 | |\n| 2008-2012 | 565 | | 7.65b | ± | 0.14 | | 286.66d | ± | 5.85 | |\n| 2013-2017 | 531 | | 8.08a | ± | 0.14 | | 268.56e | ± | 5.80 | |\n| Calving season | | | | | | ns | | | | |\n| Dry season | 1027 | | 6.67a | ± | 0.12 | | 333.26 | ± | 5.30 | |\n| Short rain | 585 | | 6.40b | ± | 0.13 | | 329.03 | ± | 5.90 | |\n| Main rain | 574 | | 6.61 a | ± | 0.14 | | 333.39 | ± | 5.96 | |\n| Parity | | | | | | | | | | |\n| 1 | 653 | | 5.24e | ± | 0.12 | | 348.67a | ± | 5.22 | |\n| 2 | 454 | | 5.83d | ± | 0.14 | | 347.73a | ± | 5.92 | |\n| 3 | 318 | | 6.52c | ± | 0.15 | | 330.45b | ± | 6.50 | |\n| 4 | 234 | | 6.72bc | ± | 0.17 | | 341.44ab | ± | 7.23 | |\n| 5 | 181 | | 6.99ab | ± | 0.18 | | 332.22b | ± | 7.85 | |\n| 6 | 147 | | 6.79bc | ± | 0.20 | | 328.12b | ± | 8.62 | |\n| 7 | 94 | | 7.04ab | ± | 0.24 | 3 | 22.03bc | ± | 10.3 | 4 |\n| 8 | 105 | | 7.37a | ± | 0.24 | | 304.46c | ± | 10.14 | |"}
{"item_id": "item_0667", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between scoring output and turnovers for the individual players, so I can easily spot who balances high production with ball security?", "table_markdown": "| No. | Player | | | | Total | | 3-Point | | F-Throw | | Rebounds | | | | | | | | | | | |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| | | GP-GS | MIN | AVG | FG-FGA | FG% | 3FG-3FGA | 3FG% | FT-FTA | FT% | OFF | DEF | TOT | AVG | PF | DQ | A | TO | BLK | STL | PTS | AVG |\n| 00 | LATSON, Ta'Niya | 25-25 | 739:24 | 29.6 | 191-407 | .469 | 32-87 | .368 | 149-172 | .866 | 37 | 81 | 118 | 4.7 | 51 | 2 | 78 | 74 | 20 | 44 | 563 | 22.5 |\n| 21 | TIMPSON, Makayla | 25-25 | 635:22 | 25.4 | 137-229 | .598 | 0-0 | .000 | 65-94 | .691 | 91 | 134 | 225 | 9.0 | 68 | 2 | 8 | 33 | 60 | 29 | 339 | 13.6 |\n| 4 | BEJEDI, Sara | 25-24 | 654:44 | 26.2 | 85-256 | .332 | 26-113 | .230 | 86-110 | .782 | 6 | 49 | 55 | 2.2 | 74 | 2 | 63 | 70 | 0 | 23 | 282 | 11.3 |\n| 23 | HOWARD, Erin | 25-25 | 604:48 | 24.2 | 68-184 | .370 | 39-125 | .312 | 11-18 | .611 | 45 | 104 | 149 | 6.0 | 36 | 0 | 35 | 30 | 14 | 35 | 186 | 7.4 |\n| 3 | GORDON, O'Mariah | 23-1 | 405:47 | 17.6 | 52-134 | .388 | 20-55 | .364 | 37-47 | .787 | 15 | 34 | 49 | 2.1 | 43 | 0 | 47 | 37 | 3 | 18 | 161 | 7.0 |\n| 5 | VALENZUELA, Mariana | 25-0 | 455:02 | 18.2 | 63-123 | .512 | 34-62 | .548 | 11-20 | .550 | 29 | 78 | 107 | 4.3 | 30 | 0 | 15 | 25 | 15 | 6 | 171 | 6.8 |\n| 11 | O'BRIEN, Taylor | 15-3 | 314:07 | 20.9 | 37-111 | .333 | 9-35 | .257 | 19-23 | .826 | 8 | 21 | 29 | 1.9 | 32 | 0 | 21 | 21 | 2 | 26 | 102 | 6.8 |\n| 1 | MASSENGILL, Jazmine | 23-22 | 657:09 | 28.6 | 48-123 | .390 | 15-44 | .341 | 7-16 | .438 | 2 | 96 | 98 | 4.3 | 29 | 1 | 84 | 32 | 11 | 19 | 118 | 5.1 |\n| 32 | MYERS, Valencia | 23-0 | 289:17 | 12.6 | 32-64 | .500 | 0-1 | .000 | 43-58 | .741 | 35 | 72 | 107 | 4.7 | 55 | 1 | 5 | 18 | 18 | 9 | 107 | 4.7 |\n| 2 | TURNAGE, Brianna | 21-0 | 294:20 | 14.0 | 17-57 | .298 | 8-36 | .222 | 6-8 | .750 | 13 | 55 | 68 | 3.2 | 7 | 0 | 19 | 8 | 18 | 7 | 48 | 2.3 |\n| Team | | | | | | | | | | | 56 | 63 | 119 | | | | | 9 | | | | |\n| Total | | 25 | 5050 | | 730-1688 | .432 | 183-558 | .328 | 434-566 | .767 | 337 | 787 | 1124 | 45.0 | 425 | 8 | 375 | 357 | 161 | 216 | 2077 | 83.1 |\n| Opponents | | 25 | 5050 | | 606-1731 | .350 | 161-546 | .295 | 270-389 | .694 | 364 | 657 | 1021 | 40.8 | 483 | 12 | 337 | 428 | 101 | 156 | 1643 | 65.7 |"}
{"item_id": "item_0668", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between junction-to-ambient and junction-to-case thermal resistance for these packages, so I can easily see how they compare?", "table_markdown": "| Package Type | $\\theta_{JA}$ | $\\theta_{JC}$ | Unit |\n|-------------------------------|---------------|---------------|------|\n| 5-Lead SC70 (KS) | 376 | 126 | °C/W |\n| 5-Lead SOT-23 (RT) | 230 | 146 | °C/W |\n| 8-Lead SOIC (R) | 158 | 43 | °C/W |\n| 8-Lead MSOP (RM) | 210 | 45 | °C/W |\n| 8-Lead TSSOP (RU) | 240 | 43 | °C/W |\n| 14-Lead SOIC (R) | 120 | 36 | °C/W |\n| 14-Lead TSSOP (RU) | 240 | 43 | °C/W |"}
{"item_id": "item_0669", "chart_task_type": "tradeoff", "query": "Can you plot the trade-off between the number of features and overall accuracy for these feature sets, making it easy to identify which specific set corresponds to each point?", "table_markdown": "| Feature Set | $N_F$ | OA | $\\kappa$ | mF$_1$ | mIoU |\n|---------------------|-------|------|----------|--------|------|\n| $\\mathcal{S}_{RGB}$ | 3 | 51.31| 43.02 | 39.60 | 26.82|\n| $\\mathcal{S}_{RGB,\\text{norm}}$ | 3 | 62.73| 55.14 | 49.62 | 35.52|\n| $\\mathcal{S}_{c_1 \\times c_2 \\times c_3}$ | 3 | 61.94| 54.27 | 51.06 | 36.70|\n| $\\mathcal{S}_{l_1 \\times l_2 \\times l_3}$ | 3 | 37.17| 25.82 | 21.53 | 13.98|\n| $\\mathcal{S}_{CCIN}$ | 3 | 61.16| 53.37 | 47.69 | 33.76|\n| $\\mathcal{S}_{GW}$ | 3 | 54.66| 45.83 | 40.47 | 27.53|\n| $\\mathcal{S}_{EBCC}$ | 3 | 55.70| 47.07 | 41.75 | 28.67|\n| $\\mathcal{S}_{HSI}$ | 64 | 68.96| 61.04 | 54.82 | 40.36|\n| $\\mathcal{S}_{HSIPCA}$ | 15 | 76.05| 69.40 | 64.71 | 50.14|\n| $\\mathcal{S}_{HSILCFS}$ | 11 | 74.31| 67.77 | 63.83 | 48.32|\n| $\\mathcal{S}_{HSI \\rightarrow S_2}$ | 7 | 67.80| 59.78 | 54.29 | 39.99|\n| $\\mathcal{S}_{3D}$ | 14 | 49.76| 36.94 | 26.69 | 18.44|\n| $\\mathcal{S}_{RGB + 3D}$ | 17 | 69.54| 61.32 | 51.34 | 38.14|\n| $\\mathcal{S}_{RGB,\\text{norm} + 3D}$ | 17 | 74.59| 67.70 | 59.02 | 44.92|\n| $\\mathcal{S}_{c_1 \\times c_2 \\times c_3 + 3D}$ | 17 | 73.88| 66.40 | 56.69 | 42.85|\n| $\\mathcal{S}_{l_1 \\times l_2 \\times l_3 + 3D}$ | 17 | 60.94| 50.47 | 38.92 | 27.60|\n| $\\mathcal{S}_{CCIN + 3D}$ | 17 | 72.46| 64.99 | 58.05 | 43.61|\n| $\\mathcal{S}_{GW + 3D}$ | 17 | 69.41| 61.12 | 52.51 | 38.97|\n| $\\mathcal{S}_{EBCC + 3D}$ | 17 | 69.70| 61.59 | 51.56 | 38.03|\n| $\\mathcal{S}_{HSI + 3D}$ | 78 | 74.88| 68.36 | 61.91 | 48.57|\n| $\\mathcal{S}_{HSIPCA + 3D}$ | 29 | 79.24| 73.17 | 69.93 | 57.08|\n| $\\mathcal{S}_{HSILCFS + 3D}$ | 25 | 79.04| 73.24 | 65.87 | 51.65|\n| $\\mathcal{S}_{HSI \\rightarrow S_2) + 3D}$ | 21 | 75.34| 68.52 | 60.23 | 46.82|"}
{"item_id": "item_0670", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between maximum output current and efficiency for the TEL 5 Series models, making it easy to identify each specific order code?", "table_markdown": "| Models | Ordercode | Input voltage range | Output voltage | Output current max. | Efficiency typ. |\n|--------|-----------|---------------------|----------------|--------------------|----------------|\n| | TEL 5-1210| 9 – 18 VDC | 3.3 VDC | 1200 mA | 77 % |\n| | TEL 5-1211| | 5 VDC | 1000 mA | 81 % |\n| | TEL 5-1212| | 12 VDC | 500 mA | 84 % |\n| | TEL 5-1222| | ±12 VDC | ±250 mA | 84 % |\n| | TEL 5-1223| | ±15 VDC | ±200 mA | 84 % |\n| | TEL 5-2410| 18 – 36 VDC | 3.3 VDC | 1200 mA | 79 % |\n| | TEL 5-2411| | 5 VDC | 1000 mA | 83 % |\n| | TEL 5-2412| | 12 VDC | 500 mA | 86 % |\n| | TEL 5-2422| | ±12 VDC | ±250 mA | 86 % |\n| | TEL 5-2423| | ±15 VDC | ±200 mA | 86 % |"}
{"item_id": "item_0671", "chart_task_type": "tradeoff", "query": "Can you plot the tradeoff between operating current and resistance for the different coil versions, making it easy to spot which voltage rating corresponds to each point?", "table_markdown": "| Nominal voltage Vdc | Operating current mA | Nominal power consumption Vdc | Resistance mW | British Telecom Code Ω / ± 10 % | Relay code | Tyco part number |\n|---------------------|----------------------|------------------------------|---------------|---------------------------------|------------|------------------|\n| 5 | 80.0 | 695 | 36 | 47 W / 5 | V23105A5475A201 | 1-1393793-2 |\n| 10 | 32.5 | 500 | 200 | 47 W / 9 | V23105A5479A201 | 3-1393794-0 |\n| 12 | 27.0 | 515 | 280 | 47 W / 6 | V23105A5476A201 | 1-1393793-3 |\n| 24 | 14.0 | 550 | 1050 | 47 W / 7 | V23105A5477A201 | 1-1393793-4 |\n| 48 | 7.0 | 575 | 4000 | 47 W / 8 | V23105A5478A201 | 1-1393793-5 |"}
{"item_id": "item_0672", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between average processing time and the number of successful instances for these methods, so I can easily see how speed compares to coverage?", "table_markdown": "| method | average time yes | # yes instances |\n|-------------------------------|------------------|----------------|\n| no compression | 11.9 | 584 |\n| compression with MCTreeRePair | 12.2 | 628 |\n| naive compression with TreeRePair | 11.9 | 571 |\n| dependency pairs (DP) | 1.85 | 681 |\n| DP and compression | 4.10 | 709 |"}
{"item_id": "item_0673", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between resistance and current rating for the recommended inductors so I can easily identify which specific parts offer the best balance?", "table_markdown": "| PART | L (µH) | MAX DCR (Ω) | CURRENT RATING (A) | VENDOR |\n|---------------|--------|-------------|--------------------|-----------------|\n| MSS1038 | 100 | 0.3 | 1.46 | Coilcraft |\n| | 220 | 0.76 | 0.99 | www.coilcraft.com |\n| | 470 | 0.935 | 1.0 | |\n| CDRH10D68 | 100 | 0.205 | 1.5 | Sumida |\n| | 220 | 0.362 | 1.0 | www.sumida.com |\n| | 470 | 0.67 | 1.01 | |\n| DS1262C2 | 100 | 0.17 | 1.5 | Toko |\n| | 220 | 0.35 | 1.0 | www.toko.com |\n| VLF10040 | 100 | 0.22 | 1.3 | TDK |\n| | 220 | 0.47 | 0.9 | www.tdk.com |\n| DR124 | 100 | 0.26 | 1.79 | Coiltronics |\n| | 220 | 0.56 | 1.15 | www.cooperet.com|\n| DR127 | 470 | 0.861 | 1.6 | |\n| DR74 | 100 | 0.383 | 0.99 | |\n| 744771220 | 220 | 0.40 | 1.2 | Würth Elektronik|\n| | | | | www.we-online.com|"}
{"item_id": "item_0674", "chart_task_type": "tradeoff", "query": "Can you chart how nematode control effectiveness trades off against crop yield for the different chisel spacing treatments, so I can see the balance between pest reduction and production?", "table_markdown": "| Spacing (centimeters) | Larvae per 50 cm³ Soil | Yield (Kgs/ha) |\n|-----------------------|------------------------|----------------|\n| Control | 14.5 | 3505 |\n| Single Chisel | | |\n| 15.2 | 0.6 | 4562 |\n| 20.3 | 0.2 | 4599 |\n| 25.4 | 0.5 | 4497 |\n| 30.5 | 3.9 | 4017 |\n| 35.6 | 5.0 | 4271 |\n| Soilbrom 90 EC | | |\n| (18.70 L/ha) | 3.2 | 4470 |\n| LSD (p: 0.05): | 8.9 | 390 |\n| LSD (p: 0.05): | 11.8 | 521 |"}
{"item_id": "item_0675", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between yield and ash content for these samples so I can easily see how they compare?", "table_markdown": "| Sample | Yield (%) | Ash (%) | pH | EC (dS m$^{-1}$) |\n|----------|-----------|---------|-------|------------------|\n| RHB4 | 46.8 | 32.8 | 8.2 | 0.07 |\n| RHB6 | 40.1 | 41.1 | 9 | 0.12 |\n| RHB8 | 35.4 | 43.1 | 10.1 | 0.14 |\n| RHBS | 32.6 | 46.8 | 10.1 | 0.21 |\n| RHBK | 28.2 | 11.5 | 9.2 | 0.26 |\n| RHBKS | 24.7 | 3.7 | 9.7 | 0.24 |"}
{"item_id": "item_0676", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between individual bean weight and fat content for the cocoa clones, making it easy to spot which varieties offer the best balance of size and richness?", "table_markdown": "| Clones | Wet bean weight/pod (grams) | Dry bean weight/pod (grams) | Weight of one dry bean (grams) | Fat content (%) |\n|-----------------|-----------------------------|-----------------------------|-------------------------------|-----------------|\n| K1 (Sulawesi 1) | 111.72 c | 31.91 a | 1.33 a | 50.92 |\n| K2 (M01) | 128.31 b | 25.73 d | 1.24 a | 52.72 |\n| K3 (ICCR1 03) | 119.20 c | 30.43 c | 0.87 b | 50.70 |\n| K4 (ICCR1 04) | 92.41 c | 22.04 d | 0.88 a | 51.75 |\n| K5 (PT. Ladongi)| 138.44 b | 33.18 b | 1.36 a | 49.90 |\n| K6 (M04) | 163.89 a | 38.85 a | 1.64 a | 50.76 |\n| K7 (Amirudin) | 110.54 c | 30.75 c | 0.96 b | 51.52 |\n| K8 (Sulawesi 2) | 110.81 c | 30.50 c | 0.52 b | 52.16 |\n| K9 (Lambadia 01)| 132.38 b | 38.36 a | 1.08 b | 49.29 |\n| K10 (BAL 209) | 106.63 c | 26.71 d | 1.02 b | 49.02 |\n| K11 (KKM 22) | 132.06 b | 34.20 b | 1.14 b | 32.90 |\n| K12 (MT) | 169.90 a | 40.66 a | 1.55 a | 53.36 |\n| Average | 126.36 | 31.94 | 1.13 | 49.58 |\n| Standard deviation | 23.01 | 5.57 | 0.31 | 5.41 |"}
{"item_id": "item_0677", "chart_task_type": "tradeoff", "query": "Can you plot the trade-off between the number of rooms and the cost for these hotel projects, making it easy to spot which specific hotels offer the best balance?", "table_markdown": "| Project | No. of rooms | Cost (Rs. in crores) | Year of completion |\n|---|---|---|---|\n| Windsor Manor | 600 | 275 | 1999 |\n| Leela Hotels | 310 | 235 | 1999 |\n| Mumbai Heights | 250 | 250 | 1998 |\n| Royal Holidays | 536 | 225 | 1998 |\n| Majestic Holiday | 500 | 250 | 1999 |\n| Supremo Hotel | 300 | 300 | 1999 |\n| Hyatt Regency | 500 | 250 | 2000 |"}
{"item_id": "item_0678", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between segmentation accuracy and computational cost for these models, so I can easily spot which variants offer the best efficiency?", "table_markdown": "| Model | $N_c$ | Dice (%) | L-Dice (%) | L-F1 (%) | L-PPV (%) | L-TPR (%) | M Ratio | F Ratio | PS Ratio |\n|-----------|-------|----------|------------|----------|-----------|-----------|---------|---------|----------|\n| Base | | 67.41 | 51.53 | 57.53 | 58.48 | 56.62 | 1.00 | 1.00 | 1.00 |\n| Base F | 128 | 27.94 | 24.41 | 31.91 | 45.25 | 24.64 | 1.06 | 1.02 | 1.00 |\n| Base B | | 40.95 | 31.44 | 38.24 | 90.95 | 24.21 | 1.17 | 1.02 | 1.00 |\n| SCP (ours)| | **74.47**| **61.30** | **66.39**| 67.28 | **65.53** | 1.78 | 1.42 | 1.02 |\n| Base | | 75.29 | 64.90 | 69.40 | 71.09 | 67.78 | 2.13 | 3.96 | 4.02 |\n| Base F | 256 | 59.39 | 39.67 | 47.22 | 44.37 | 50.45 | 2.19 | 4.00 | 4.02 |\n| Base B | | 67.82 | 56.74 | 64.02 | **83.41** | 51.95 | 2.43 | 4.00 | 4.02 |\n| SCP (ours)| | **77.63**| **69.55** | **72.27**| 69.78 | **74.95** | 3.69 | 5.51 | 4.06 |\n| Base | | 75.39 | 65.61 | 70.50 | 67.74 | **73.50** | 4.84 | 15.71 | 16.06 |\n| Base F | 512 | 62.63 | 45.85 | 54.10 | 54.48 | 53.73 | 4.90 | 15.80 | 16.08 |\n| Base B | | 74.90 | 63.89 | 69.97 | **81.67** | 61.20 | 5.39 | 15.82 | 16.08 |\n| SCP (ours)| | **77.28**| **70.34** | **73.89**| 75.89 | 72.00 | 7.94 | 21.76 | 16.22 |"}
{"item_id": "item_0679", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between GDP per person and poverty rates for these six countries so I can easily see how economic output relates to poverty levels?", "table_markdown": "| Country | Pop. (mil) | GDP/Person | Poverty % | Lit. % | Ag. Pop. % |\n|-------------|------------|------------|-----------|--------|------------|\n| Costa Rica | 3.9 | $8,300 | 20.6 | 96 | 20 |\n| El Salvador | 6.5 | $4,600 | 48 | 80.2 | 30 |\n| Guatemala | 13.9 | $3,900 | 75 | 70.6 | 50 |\n| Honduras | 6.7 | $2,500 | 53 | 76.1 | 34 |\n| Nicaragua | 5.1 | $2,200 | 50 | 67.5 | 42 |\n| Dom. Rep. | 8.7 | $6,300 | 25 | 84.7 | 17 |"}
{"item_id": "item_0680", "chart_task_type": "tradeoff", "query": "Can you plot the trade-off between CPU time and solution quality for the TSGW algorithm across the individual problem instances, so I can see how performance varies for each case?", "table_markdown": "| $n|m$ | TSGW CPU | TSGW PRD | TSGWnoMM CPU | TSGWnoMM PRD |\n|-------|----------|----------|--------------|--------------|\n| 20|5 | 0.0 | 0.00 | 0.0 | 0.02 |\n| 20|10 | 0.3 | 0.03 | 0.3 | 0.08 |\n| 20|20 | 0.6 | 0.07 | 0.6 | 0.11 |\n| 50|5 | 0.4 | -0.08 | 0.4 | 0.05 |\n| 50|10 | 0.9 | -0.29 | 0.8 | 0.17 |\n| 50|20 | 2.3 | 0.13 | 2.2 | 0.26 |\n| 100|5 | 0.6 | -0.03 | 0.6 | 0.14 |\n| 100|10 | 1.4 | -0.21 | 1.3 | 0.32 |\n| 100|20 | 5.0 | -0.68 | 4.5 | 0.19 |\n| 200|10 | 4.4 | -0.19 | 4.1 | 0.06 |\n| 200|20 | 8.5 | -1.12 | 7.9 | -0.04 |\n| 500|20 | 11.2 | -0.81 | 10.7 | 0.23 |\n| all | 2.96 | -0.26 | 2.62 | 0.12 |"}
{"item_id": "item_0681", "chart_task_type": "tradeoff", "query": "Can you chart how public concern for each issue trades off against trust in the EU to address it, so I can easily see which topics have high worry but low confidence?", "table_markdown": "| Issue | Concern | Trust in the EU |\n|--------------------------------------------|---------|-----------------|\n| Inflation and rise of living costs | 65% | 15% |\n| Russia/Ukraine war | 48% | 20% |\n| Energy supply and prices | 47% | 20% |\n| A new world war | 46% | 25% |\n| Climate change | 45% | 20% |\n| Israel/Palestine war | 41% | 15% |\n| Immigration | 40% | 15% |\n| Cybersecurity threats | 31% | 25% |\n| Large development of AI | 30% | 20% |\n| A new pandemic | 26% | 30% |\n| Economic competition with extra EU countries | 19% | 18% |"}
{"item_id": "item_0682", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between global satisfaction and sex equality for these methods, making it easy to spot how each option balances the two?", "table_markdown": "| INB | NBP | DST | S | E |\n|-----|-----|-----|-----|-----|\n| GS | 0 | 0 | 280 | 189 |\n| BZ | 3.9 | 11.7| 1614| 1310|\n| RGS | 87.9| 3.3 | 8276| 2080|\n| RZ$_m$ | 100 | 23.3 | 1733 | 2714 |\n| RZ$_u$ | 100 | 73.1 | 854 | 4108 |"}
{"item_id": "item_0683", "chart_task_type": "tradeoff", "query": "Can you plot the tradeoff between reconstruction accuracy and computational time for these methods, so I can easily see which ones offer the best balance?", "table_markdown": "| Single View Recon. | Chamber-L2 ($\\downarrow$) | Normal Consistency ($\\uparrow$) | NM-Vertices ($\\downarrow$) | NM-Edges ($\\downarrow$) | NM-Faces ($\\downarrow$) | Self-Intersection ($\\downarrow$) | Time ($\\downarrow$) |\n|--------------------|--------------------------|-------------------------------|---------------------------|------------------------|------------------------|-------------------------------|------------------|\n| OccNet-[1] | 8.77 | 0.814 | 1.13 | 0.85 | 0.36 | 0.00 | 871 |\n| OccNet-[2] | 8.66 | 0.814 | 2.67 | 1.79 | 0.21 | 0.03 | 1637 |\n| OccNet-[3] | 8.33 | 0.814 | 2.79 | 1.90 | 0.15 | 0.09 | 6652 |\n| NMF | 7.98 | 0.829 | 0.80 | 0.80 | 0.83 | 0.12 | 187 |\n| NMF w/ Laplace | 8.64 | 0.837 | 0.80 | 0.88 | 0.45 | 0.00 | 292 |"}
{"item_id": "item_0684", "chart_task_type": "tradeoff", "query": "Can you plot how thermal stability trades off against the glass transition temperature for these five compositions, so I can see the relationship across the full set?", "table_markdown": "| \\(x\\) | \\(T_g\\) [°C] (\\(±3^\\circ C\\)) | \\(T_x\\) [°C] (\\(±3^\\circ C\\)) | \\(\\Delta T = T_x - T_g\\) [°C] (\\(±6^\\circ C\\)) | \\(T_p\\) [°C] (\\(±3^\\circ C\\)) | Density [g/cm\\(^3\\)] (\\(±0.02\\) g/cm\\(^3\\)) |\n|------|-----------------|-----------------|-----------------|-----------------|-----------------|\n| 0 | 284 | 374 | 90 | 414 | 2.47 |\n| 2.5 | 272 | 378 | 106 | 415 | 2.47 |\n| 5 | 264 | 374 | 110 | 403 | 2.47 |\n| 7.5 | 255 | 368 | 113 | 395 | 2.46 |\n| 10 | 246 | 356 | 110 | 402 | 2.45 |"}
{"item_id": "item_0685", "chart_task_type": "tradeoff", "query": "Can you plot how net profit trades off against the percentage of 90-day overdue loans for these banks, so I can easily identify which institutions are labeled?", "table_markdown": "| Bank | Assets US$ min | Equity US$ min | Loans to customers US$ min | Total deposits US$ min | Retail deposits US$ min | Net profit US$ min | Reserves % of total loans | 90 days overdue % of total loans |\n|------------|----------------|----------------|----------------------------|------------------------|-------------------------|-------------------|--------------------------|---------------------------------|\n| KKB | 16 161 | -0.1% | 1 816 | 15 747 | 9 290 | -0.5% | 0 | 29.6% |\n| Halyk | 14 133 | -2.7% | 1 759 | 8 089 | 9 611 | -2.1% | 131 | 22.3% |\n| BTA | 13 285 | 11.4% | 717 | 13 148 | 4 684 | 1.7% | 7 383 | 65.7% |\n| BCC | 8 810 | -1.2% | 722 | 4 796 | 5 500 | 0.5% | 6 | 10.9% |\n| ATF | 7 282 | -0.4% | 547 | 5 516 | 3 625 | 1.5% | -141 | 15.5% |\n| Alliance | 3 100 | -1.2% | 231 | 3 622 | 1 327 | -4.6% | 2 081 | 61.4% |\n| Eurasian | 2 341 | -8.1% | 158 | 1 342 | 1 666 | -6.5% | -9 | 12.0% |\n| Nurbank | 2 206 | 0.9% | 307 | 1 506 | 1 457 | 3.3% | 0 | 9.4% |\n| Kaspi | 2 153 | 1.9% | 194 | 1 810 | 1 456 | 0.2% | -21 | 14.4% |\n| Сбербанк | 1 881 | 13.0% | 253 | 927 | 1 492 | 20.6% | 189 | 8.0% |\n| Top 10 | 71 352 | -0.9% | 6 703 | 56 503 | 40 108 | 2.1% | 13 053 | 34.2% |\n| Other banks| 9 877 | -3.5% | 1 609 | 5 392 | 6 379 | -9.8% | 1 184 | 21.8% |\n| **Total** | **81 229** | **1.0%** | **8 313** | **61 895** | **46 486** | **0.3%** | **14 236** | **33.2%** |"}
{"item_id": "item_0686", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between sensitivity and specificity for the individual cytokines and the combined panel, so I can easily spot how they compare?", "table_markdown": "| Cytokines | AUC (95%CI) | Cut-off | Youden value | P-value | Sensitivity | Specificity | PPV |\n|---|---|---|---|---|---|---|---|\n| IFN-a (pg/mL) | 0.734 (0.623-0.844) | 1.91 | 0.414 | <0.001 | 91.4% | 50.0% | 87.5% |\n| IL-2 (pg/mL) | 0.668 (0.546-0.790) | 1.55 | 0.367 | 0.009 | 77.1% | 59.5% | 75.8% |\n| IL-10 (pg/mL) | 0.686 (0.564-0.809) | 2.06 | 0.405 | <0.001 | 85.7% | 54.8% | 82.1% |\n| IL-8 (pg/mL) | 0.698 (0.580-0.816) | 9.46 | 0.395 | <0.001 | 80.0% | 59.5% | 90.5% |\n| IFN-a+ IL-2+IL- 10+IL-8 | 0.759 (0.652-0.865) | - | 0.443 | <0.001 | 80.0% | 64.2% | 79.4% |"}
{"item_id": "item_0687", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between bioaccumulation and remediation efficiency for Cadmium across the different treatments, so I can see how tissue concentration relates to total soil removal?", "table_markdown": "| Treatment (mg/kg) | Cd | | | Cu | | | Pb | | | Zn | | |\n|------------------|------|-------|-------|------|-------|-------|------|-------|-------|------|-------|-------|\n| | BF | TF | RF | BF | TF | RF | BF | TF | RF | BF | TF | RF |\n| CK | 2.71 | 2.43 | 0.14 | 1.08 | 1.44 | 0.01 | 0.19 | 1.58 | 0.01 | 0.12 | 0.56 | 0.01 |\n| CA-5 | 2.78 | 2.45 | 0.16 | 1.18 | 1.69 | 0.01 | 0.28 | 1.87 | 0.02 | 0.16 | 0.70 | 0.01 |\n| CA-8 | 4.47 | 3.12 | 0.29 | 2.16 | 1.18 | 0.02 | 0.32 | 2.43 | 0.02 | 0.25 | 1.0 | 0.02 |\n| EDTA-5 | 4.49 | 3.06 | 0.60 | 2.71 | 1.61 | 0.08 | 0.37 | 3.32 | 0.05 | 0.27 | 1.11 | 0.03 |\n| EDTA-8 | 7.09 | 2.12 | 0.19 | 5.20 | 2.21 | 0.04 | 1.33 | 3.23 | 0.04 | 0.55 | 3.01 | 0.06 |"}
{"item_id": "item_0688", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between sensitivity and specificity for the different ACPA test combinations, making it easy to identify which test corresponds to each point?", "table_markdown": "| ACPA test | n | Sensitivity (%) (95% CI) | Specificity (%) (95% CI) | PPV (%) (95% CI) | NPV (%) (95% CI) | LR+ | LR− | DOR |\n|----------------------------|-----|--------------------------|--------------------------|------------------|------------------|-----|-----|-----|\n| Anti-CCP2 | 28 | 29.5 (20.3 to 38.6) | 92.5 (87.1 to 97.8) | 80 (66.7 to 93.3)| 56.2 (48.3 to 64.1)| 3.93| 0.76| 5.16|\n| Anti-MCV | 29 | 30.5 (21.3 to 39.8) | 88.2 (81.6 to 94.7) | 72.5 (58.7 to 86.3)| 55.4 (47.4 to 63.4)| 2.58| 0.79| 3.28|\n| AhFibA | 30 | 31.6 (22.2 to 40.9) | 88.2 (81.6 to 94.7) | 73.2 (59.6 to 86.7)| 55.8 (47.8 to 63.8)| 2.68| 0.78| 3.45|\n| Anti-CCP2 or anti-MCV | 30 | 31.6 (22.2 to 40.9) | 87.1 (80.3 to 93.9) | 71.4 (57.8 to 85.1)| 55.5 (47.4 to 63.5)| 2.45| 0.79| 3.12|\n| Anti-CCP2 or AhFibA | 32 | 33.7 (24.2 to 43.2) | 86 (79 to 93.1) | 71.1 (57.9 to 84.4)| 55.9 (47.8 to 64.1)| 2.41| 0.77| 3.12|\n| Anti-MCV or AhFibA | 32 | 33.7 (24.2 to 43.2) | 81.7 (73.9 to 89.6) | 65.3 (52 to 78.6) | 54.7 (46.4 to 63) | 1.84| 0.81| 2.27|\n| At least one of the three ACPA | 33 | 34.7 (25.2 to 44.3) | 80.6 (72.6 to 88.7) | 64.7* (51.6 to 77.8)| 54.7 (46.4 to 63.1)| 1.79| 0.81| 2.21|\n| Anti-CCP2 and anti-MCV | 27 | 28.4 (37.5 to 19.4) | 93.5 (88.6 to 98.5) | 81.8 (68.7 to 95) | 56.1 (48.3 to 63.9)| 4.37| 0.77| 5.71|\n| Anti-CCP2 and AhFibA | 26 | 27.4 (18.4 to 36.3) | 94.6 (99 to 99.2) | 83.9 (70.9 to 96.8)| 56.1 (48.3 to 63.8)| 5.07| 0.77| 6.61|\n| Anti-MCV and AhFibA | 27 | 28.4 (19.4 to 37.5) | 94.6 (90 to 99.2) | 84.4 (71.8 to 97) | 56.4 (48.6 to 64.2)| 5.26| 0.76| 6.95|\n| Anti-CCP2 and anti-MCV and AhFibA | 26 | 27.4 (18.4 to 36.3) | 94.6 (90 to 99.2) | 83.9 (70.9 to 96.8)| 56.1 (48.3 to 63.8)| 5.07| 0.77| 6.61|"}
{"item_id": "item_0689", "chart_task_type": "tradeoff", "query": "Can you chart how molecular weight trades off against narcotic potency for the inert gases, so I can see the relationship between their mass and narcotic effect at a glance?", "table_markdown": "| | $H_2$ | $He$ | $Ne$ | $N_2$ | $Ar$ | $O_2$ |\n|--------|-------|------|------|-------|------|-------|\n| $A \\ (amu)$ | 2.02 | 4.00 | 20.18 | 28.02 | 39.44 | 32.00 |\n| $S \\ (atm^{-1})$ | | | | | | |\n| blood | 0.0149 | 0.0087 | 0.0093 | 0.0122 | 0.0260 | 0.0241 |\n| oil | 0.0502 | 0.0150 | 0.0199 | 0.0670 | 0.1480 | 0.1220 |\n| $p$ | 1.83 | 4.26 | 3.58 | 1.00 | 0.43 | |"}
{"item_id": "item_0690", "chart_task_type": "tradeoff", "query": "Can you plot the trade-off between the number of captured modes and KL divergence for these models, making it easy to spot which ones achieve high coverage with low distribution error?", "table_markdown": "| (Max $10^3$) | Modes | KL |\n|--------------|---------|--------|\n| Unrolled GAN | 48.7 | 4.32 |\n| VEEGAN | 150.0 | 2.95 |\n| WGAN-GP | 959.0 | 0.7276 |\n| PacGAN | 1000.0 ± 0.0 | 0.06 ± 1.0e−2 |\n| Our EnGAN | 1000.0 | 0.0313 |"}
{"item_id": "item_0691", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between the total gross cost and the reduction in the poverty rate for each of the nine GAI options, so I can see how the financial burden compares to the poverty impact?", "table_markdown": "| Option | Total Gross Cost ($M) | Average Benefit per Adult | % of all Adults Benefiting | % reduction in the MBM Poverty Rate | % reduction in the MBM Depth of Poverty |\n|-------------------------|-----------------------|---------------------------|----------------------------|-------------------------------------|----------------------------------------|\n| G = 50% of MBM BRR = 20%| $947.8 | $4,794 | 46.0% | -30% | -18% |\n| G = 50% of MBM BRR = 35%| $602.0 | $6,130 | 22.8% | -23% | -20% |\n| G = 50% of MBM BRR = 50%| $471.3 | $6,569 | 16.7% | -11% | -23% |\n| G = 85% of MBM BRR = 20%| $2,830.4 | $7,969 | 82.6% | -59% | -50% |\n| G = 85% of MBM BRR = 35%| $1,792.8 | $7,343 | 56.8% | -57% | -44% |\n| G = 85% of MBM BRR = 50%| $1,287.0 | $7,389 | 40.5% | -55% | -41% |\n| G = 100% of MBM BRR = 20%| $3,861.5 | $10,079 | 89.1% | -76% | -52% |\n| G = 100% of MBM BRR = 35%| $2,547.3 | $9,287 | 63.8% | -74% | -46% |\n| G = 100% of MBM BRR = 50%| $1,856.3 | $9,046 | 47.7% | -73% | -37% |"}
{"item_id": "item_0692", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between CO₂ emissions and total price for these car configurations, making it easy to identify which specific engine and trim each point represents?", "table_markdown": "| TRIM | ENGINE | CO₂ G/KM* | V.E.D. 1ST YEAR RATE | INS. GROUP (1-50) | BASIC R.R.P. | V.A.T. | TOTAL R.R.P. | ‘ON THE ROAD’ R.R.P.* | P11D VALUE | B.I.K. TAX RATE |\n|-----------------------|-------------------------------|-----------|----------------------|-------------------|--------------|--------|--------------|-----------------------|------------|----------------|\n| Feel | VTi 95 manual | 148 | £200.00 | 10E | £12,762.50 | £2,552.50 | £15,315.00 | £16,245.00 | £15,990.00 | 28% |\n| | PureTech 110 S&S manual | 119 | £160.00 | 12E | £13,550.00 | £2,710.00 | £16,260.00 | £17,150.00 | £16,935.00 | 22% |\n| | BlueHDi 100 manual | 113 | £160.00 | 14E | £14,050.00 | £2,810.00 | £16,860.00 | £17,750.00 | £17,535.00 | 24% |\n| | BlueHDi 100 manual with Family pack | 113 | £160.00 | 14E | £14,758.33 | £2,951.67 | £17,710.00 | £18,600.00 | £18,385.00 | 24% |\n| | BlueHDi 100 S&S ETG6 (Piloted manual) | 109 | £140.00 | 14E | £14,633.33 | £2,926.67 | £17,560.00 | £18,430.00 | £18,235.00 | 23% |\n| Flair | PureTech 110 S&S manual | 119 | £160.00 | 13E | £15,179.17 | £3,035.83 | £18,215.00 | £19,105.00 | £18,890.00 | 22% |\n| | BlueHDi 100 manual | 113 | £160.00 | 15E | £15,679.17 | £3,135.83 | £18,815.00 | £19,705.00 | £19,490.00 | 24% |\n| | BlueHDi 100 S&S ETG6 (Piloted manual) | 109 | £140.00 | 15E | £16,262.50 | £3,252.50 | £19,515.00 | £20,385.00 | £20,190.00 | 23% |\n| | BlueHDi 120 S&S 6-speed manual | 113 | £160.00 | 17E | £16,345.83 | £3,269.17 | £19,615.00 | £20,505.00 | £20,290.00 | 24% |"}
{"item_id": "item_0693", "chart_task_type": "tradeoff", "query": "Can you plot the relationship between the number of homes affected and the investment cost for each maintenance work type, so I can see how scale trades off against spending?", "table_markdown": "| Work Planned | Approximate number of homes or blocks | Approximate investment |\n|--------------------------------------------------|--------------------------------------|------------------------|\n| Heating upgrades | 215 | £530,000 |\n| Roofing | 66 | £600,000 |\n| Kitchen upgrades | 147 | £600,000 |\n| Replacement bathrooms | 125 | £350,000 |\n| Electrical testing (including remedial work) | 913 | £425,000 |\n| Stock Condition Survey | 738 | £33,600 |\n| Insulation programme | 230 | £116,000 |\n| Door entry systems upgrade | 9 | £50,000 |\n| Windows and doors | 11 | £70,000 |\n| Gutter cleaning and remedial work | 800 | £50,000 |\n| Close lighting improvements | 60 | £75,000 |\n| Lock-up maintenance | 17 | £10,000 |\n| **TOTAL** | | **£2,909,600** |"}
{"item_id": "item_0694", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between max RPMs and approximate weight for these chuck models so I can easily identify each model's position?", "table_markdown": "| Chuck Size | Chuck Model No. | Mount | Thru Hole | Max RPMs | Approx. Weight |\n|------------|--------------------------|-------|-----------|----------|----------------|\n| 12\" | 10-014-12-A08Z | A2-8 | 3.37\" | 1500 | 200 |\n| 18\" | 10-014-18-A11Z | A2-11 | 6.50\" | 1100 | 350 |\n| 21\" | 10-014-21-A11Z | A2-11 | 6.50\" | 1000 | 540 |\n| 21\" | 10-014-21-A15Z | A2-15 | 7.30\" | 1000 | 533 |\n| 24\" | 10-014-24-A11Z | A2-11 | 6.50\" | 850 | 720 |\n| 24\" | 10-014-24-A15Z | A2-15 | 10.50\" | 850 | 700 |\n| 24\" | 10-014-24-A20Z | A2-20 | 12.59\" | 850 | 685 |\n| 28\" | 10-014-28-A15Z | A2-15 | 10.50\" | 700 | 905 |\n| 28\" | 10-014-28-A20Z | A2-20 | 12.59\" | 700 | 920 |\n| 32\" | 10-014-32-A15Z | A2-15 | 10.50\" | 600 | 1265 |\n| 32\" | 10-014-32-A20Z | A2-20 | 12.59\" | 600 | 1255 |\n| 36\" | 10-014-36-A20Z | A2-20 | 12.59\" | 600 | 1930 |\n| 40\" | 10-014-40-A20Z | A2-20 | 12.59\" | 500 | 2350 |\n| 40\" | 10-014-40-A28Z | A2-28 | 18.50\" | 500 | 2275 |"}
{"item_id": "item_0695", "chart_task_type": "tradeoff", "query": "Can you chart how intra-ERN priority trades off against inter-ERN priority for the general items and research topics, so I can easily identify which specific topics fall where in that relationship?", "table_markdown": "| Nr | Priority (scale 1-10) | Topic | Desired support type: N=Networking C= centralised structure I = IT support | ERICA WP to cover item |\n|----|----------------------|-----------------------------------------------------------------------|--------------------------------------------------------------------------|------------------------|\n| | Intra ERN | Inter ERN | | | |\n| General | | | | | |\n| A | 8.7 | 8.6 | Strengthening the ERN Research Working Group | N | WP1 |\n| B | 8.3 | 7.9 | International collaborations | N | WP1,6 |\n| C | 7.6 | 7.5 | Raising awareness of existing research infrastructures | N | WP1,6 |\n| Research topic specific | | | | | |\n| 1 | 9.0 | 8.3 | Patient-centred outcome measures | N | WP3 |\n| 2 | 9.0 | 8.3 | Pragmatic (registry based) clinical trials | N | WP2,5 |\n| 3 | 8.2 | 8.2 | Omics / biomarker research expertise sharing | N | WP5 |\n| 4 | 8.1 | 8.2 | Legal issues (data protection, Informed Consent) | C | WP1,2,4 |\n| 5 | 8.2 | 7.9 | Harmonized data capture (incl. collection/storage and data FAIRification) | I | WP2 |\n| 6 | 7.9 | 7.6 | Genomic diagnostic expertise sharing | N | WP5 |\n| 7 | 7.9 | 7.6 | Patient involvement in clinical trials | N | WP3,4 |\n| 8 | 7.9 | 7.5 | Creation of biorepositories | N | WP2,5 |\n| 9 | 8.1 | 7.1 | Investigator initiated trial planning & execution | N | WP4 |\n| 10 | 7.4 | 7.2 | Advanced experimental therapies | N | WP4,5 |"}
{"item_id": "item_0696", "chart_task_type": "tradeoff", "query": "Can you plot the trade-off between training steps and moment fitting for the different hidden layer node configurations, making it easy to identify which setup corresponds to each point?", "table_markdown": "| Hidden layer nodes | Training steps | Moment fitting | Boost power fitting |\n|--------------------|----------------|----------------|---------------------|\n| 10—10—6 | 22 | 0.995 | 0.902 |\n| 12—12—8 | 18 | 0.993 | 0.924 |\n| 16—16—8 | 16 | 0.995 | 0.907 |\n| 16—15—12 | 11 | 0.998 | 0.983 |\n| 18—18—15 | 25 | 0.956 | 0.924 |"}
{"item_id": "item_0697", "chart_task_type": "tradeoff", "query": "Can you plot the trade-off between magazine capacity and weight for these firearm models so I can easily see which ones offer more rounds for the added heft?", "table_markdown": "| Caliber | Operating System | Magazine Capacity | Sights | Barrel Length | Overall Length | Weight (in lbs.) | Height | Grips/Stock |\n|---------------|-------------------------------------------------------|-------------------|-----------------|---------------|-----------------|------------------|---------|------------------------------|\n| USP9 | short recoil, modified Browning action | 10 | 3-dot | 4.25 in. | 7.64 in. | 1.66 | 5.35 in.| polymer frame & integral grips |\n| USP40 | short recoil, modified Browning action | 10 | 3-dot | 4.25 in. | 7.64 in. | 1.74 | 5.35 in.| polymer frame & integral grips |\n| USP45 | short recoil, modified Browning action | 10 | 3-dot | 4.41 in. | 7.87 in. | 1.90 | 5.55 in.| polymer frame & integral grips |\n| P7M8 | recoil operated, retarded inertia slide | 8 | 3-dot | 4.13 in. | 6.73 in. | 1.75 | 5.04 in.| plastic |\n| PSG1 | .308 Winchester delayed roller locked bolt | 5 | 6X Illuminated Telescopic | 25.6 in. | 47.5 in. | 17.8 | 8.26 in.| Adjustable PSG1 buttstock & adjustable contoured grip |"}
{"item_id": "item_0698", "chart_task_type": "tradeoff", "query": "Can you plot the trade-off between FindBugs and Sonar issues for the TL subjects, making it easy to identify each specific subject's position?", "table_markdown": "| Subject | Technical Debt | |\n|---|---|---|\n| | # FindBugs | #Sonar |\n| TL_Subject 1 | 5 | 34 |\n| TL_Subject 2 | 2 | 32 |\n| TL_Subject 6 | 1 | 138 |\n| TL_Subject 7 | 2 | 31 |\n| TL_Subject 8 | 2 | 32 |\n| TL_Subject 18 | 9 | 31 |\n| TL_Subject 22 | 3 | 89 |\n| TL_Subject 23 | 2 | 138 |\n| TL_Subject 24 | 2 | 124 |"}
{"item_id": "item_0699", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between recall and precision for these method combinations so I can easily see how each one balances the two metrics?", "table_markdown": "| Segment. | Text/graphic sepa. | R (%) | P (%) |\n|----------|---------------------|-------|-------|\n| | (Neumann and Matas, 2012) | 12.56 | 30.19 |\n| Colour | Proposed | 15.69 | 6.92 |\n| Proposed | (Tombre et al., 2002) | 74.18 | 61.25 |\n| Otsu | Proposed | 75.14 | 64.14 |\n| Proposed | Proposed | **75.82** | **76.15** |"}
{"item_id": "item_0700", "chart_task_type": "tradeoff", "query": "Can you chart how labor productivity trades off against the implied cost of capital for the different firm size categories, so I can easily see the relationship between these two metrics?", "table_markdown": "| | Micro | Small | Medium | Large |\n|--------------------------------|-------|-------|--------|-------|\n| **Levels Measures (Means)** | | | | |\n| Wage (US$/mo.) | 22.60 | 26.50 | 60.58 | 109.92|\n| Labor Productivity (US$/emp-yr)| 4.364 | 4.049 | 6,991 | 14,661|\n| Capital-Labor Ratio (US$/emp) | 1,733 | 2,757 | 8,188 | 16,985|\n| Implied cost of capital (% p.a.)| 1,780 | 390 | 80 | 50 |\n| Exports (% output) | 2.51 | 3.43 | 7.98 | 26.2 |\n| **Growth Measures (Medians)** | | | | |\n| Employment Growth | -1.54 | -0.357| -0.749 | 0 |\n| Output Growth | -2.48 | -3.43 | 1.32 | 0.506 |\n| Labor Productivity Growth | -2.33 | -0.831| 2.41 | -3.12 |\n| **Volatility Measures (Median)**| | | | |\n| Price Volatility | 0.735 | 0.829 | 0.681 | 0.692 |\n| Output Volatility | 0.581 | 0.52 | 0.479 | 0.359 |"}
{"item_id": "item_0701", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between effectiveness and edge bearing stress for the different dowel diameters so I can see how they compare?", "table_markdown": "| Dowel Diameter(s) (inches): | 1 | 1.25 | 1.5 | 1.75 | 2 | 1.41 | 1.66 | 1.98 |\n|-----------------------------|-----|------|-----|------|-----|------|------|------|\n| Load Transfer (%) - | | | | | | | | |\n| Deflection LTE: | 71.6| 77.2 | 80.8| 83.2 | 84.8| 75.3 | 75.9 | 79.1 |\n| Stress LTE: | 26.1| 30.1 | 33.1| 35.4 | 37.1| 28.6 | 29.0 | 31.6 |\n| Effectiveness: | 46.2| 47.0 | 47.5| 47.8 | 48.0| 46.7 | 46.8 | 47.2 |\n| Bearing Stress (psi) - | | | | | | | | |\n| Edge Loading: | 1479| 1060 | 788 | 602 | 469 | 1246 | 758 | 565 |\n| Corner Loading: | 2469| 1744 | 1284| 975 | 755 | 2060 | 1252 | 926 |"}
{"item_id": "item_0702", "chart_task_type": "tradeoff", "query": "Can you chart how the percentage reduction in LDL-C trades off against the reduction in coronary events for these studies, so I can easily identify which trial corresponds to each data point?", "table_markdown": "| Study | Statin | Mean Baseline LDL-C, mg/dL | Mean LDL-C Reduction, % | % Reduction in Coronary Events |\n|-------------|-------------------------|----------------------------|-------------------------|--------------------------------|\n| WOSCOPS | Pravastatin 40 mg | 192 | 26 | 31 (P < .001) |\n| AFCAPS/TexCAPS | Lovastatin 20 mg to 40 mg | 150 | 25 | 37 (P < .001) |\n| ASCOT | Atorvastatin 10 mg | 133 | 35 | 36 (P < .001) |\n| HOPE-3 | Rosuvastatin 10 mg | 128 | 26 | 24 (P < .002) |\n| JUPITER | Rosuvastatin 20 mg | 108 | 44 | 44 (P < .000001) |"}
{"item_id": "item_0703", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between the reported cost savings and the net present value for these municipal networks, so I can easily identify which specific projects appear in each quadrant?", "table_markdown": "| Municipal Network | Cost “Savings” according to Berkman Klein Study | Net Present Value (2010- 2014) | Adjusted Projected Cost of Network |\n|---|---|---|---|\n| Lafayette, LA | $600.00 | -$36,086,333 | $118,789,745 |\n| Morristown, TN | $324.12 | -$4,281,017 | $28,779,887 |\n| Clarksville, TN | $138.75 | -$7,442,513 | $43,253,003 |\n| Monticello, MN | $122.74 | -$25,508,327 | $27,767,517 |\n| Pulaski, TN | $237.24 | $97,948 | $9,602,904 |\n| Brookings, SD | $163.13 | $290,521 | $20,252,935 |\n| Chattanooga, TN | $107.25 | $2,062,787 | $170,101,635 |\n| Tullahoma, TN | $19.22 | $846,549 | $18,264,172 |"}
{"item_id": "item_0704", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between exhaustion and fixation percentages for the dyes at pH 7.0, making it easy to identify which dye corresponds to each point?", "table_markdown": "| Symbol of the dye | pH=7.0 | | pH=7.5 | |\n|---|---|---|---|---|\n| | Exhaustion % | Fixation % | Exhaustion % | Fixation % |\n| B1* | 88.4 | 77.1 | 89.3 | 80.4 |\n| B2* | 88.5 | 82.4 | 88.8 | 85.3 |\n| B3 | 88.4 | 29.4 | 88.0 | 24.4 |\n| B4 | 92.4 | 37.2 | 93.6 | 37.8 |\n| B5 | 89.5 | 26.6 | 89.1 | 36.9 |\n| B6 | 93.2 | 38.9 | 92.8 | 37.1 |\n| B7 | 95.4 | 36.9 | 93.1 | 35.4 |\n| B8 | 94.1 | 40.7 | 91.6 | 34.4 |"}
{"item_id": "item_0705", "chart_task_type": "tradeoff", "query": "Can you plot the trade-off between total costs and net revenue for each treatment so I can easily see which methods offer the best return relative to their expense?", "table_markdown": "| Treatment | Yield | Crop income | Fertilizer cost | Vitazyme cost | Total costs | Net revenue | Increase over control |\n|------------------------------------------------|---------|-------------|-----------------|---------------|-------------|-------------|-----------------------|\n| 1. Commercial fertilizer | 1.12 | 34,782.16 | 4,555.20 | 0 | 4,555.27 | 30,226.89 | — |\n| 2. 0.5 L/ha monthly | 0.85 | 26,474.81 | 0 | 674.94 | 674.94 | 25,799.87 | 4,427.02 |\n| 3. 1.0 L/ha monthly | 0.95 | 29,502.73 | 0 | 1,349.90 | 1,349.88 | 28,152.85 | 2,074.05 |\n| 4. 1.0 L/ha every 60 days | 0.86 | 26,785.37 | 0 | 449.96 | 449.96 | 26,335.41 | 3,891.48 |\n| 5. 1.0 L/ha every 90 days | 0.94 | 29,269.81 | 0 | 112.49 | 112.49 | 29,157.32 | 1,069.57 |\n| 6. 1.0 L/ha Sept.-Dec. | 0.98 | 30,279.11 | 0 | 224.98 | 224.98 | 30,054.13 | (-) 172.76 |\n| 7. 0.5 L/ha Apr.-June and Sept.-Dec. | 1.04 | 32,220.08 | 0 | 217.05 | 217.05 | 32,003.3 | 1,776.14 |"}
{"item_id": "item_0706", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between healthy life expectancy and per capita health spending for these countries, so I can easily see which nations deliver better outcomes relative to their costs?", "table_markdown": "| OVERALL RANKING (2013) | AUS | CAN | FRA | GER | NETH | NZ | NOR | SWE | SWIZ | UK | US |\n|------------------------|-----|-----|-----|-----|------|----|-----|-----|------|----|----|\n| Quality Care | 2 | 9 | 8 | 7 | 5 | 4 | 11 | 10 | 3 | 1 | 5 |\n| Effective Care | 4 | 7 | 9 | 6 | 5 | 2 | 11 | 10 | 8 | 1 | 3 |\n| Safe Care | 3 | 10 | 2 | 6 | 7 | 9 | 11 | 5 | 4 | 1 | 7 |\n| Coordinated Care | 4 | 8 | 9 | 10 | 5 | 2 | 7 | 11 | 3 | 1 | 6 |\n| Patient-Centered Care | 5 | 8 | 10 | 7 | 3 | 6 | 11 | 9 | 2 | 1 | 4 |\n| Access | 8 | 9 | 11 | 2 | 4 | 7 | 6 | 4 | 2 | 1 | 9 |\n| Cost-Related Problem | 9 | 5 | 10 | 4 | 8 | 6 | 3 | 1 | 7 | 1 | 11 |\n| Timeliness of Care | 6 | 11 | 10 | 4 | 2 | 7 | 8 | 9 | 1 | 3 | 5 |\n| Efficiency | 4 | 10 | 8 | 9 | 7 | 3 | 4 | 2 | 6 | 1 | 11 |\n| Equity | 5 | 9 | 7 | 4 | 8 | 10 | 6 | 1 | 2 | 2 | 11 |\n| Healthy Lives | 4 | 8 | 1 | 7 | 5 | 9 | 6 | 2 | 3 | 10 | 11 |\n| Health Expenditures/Capita, 2011** | $3,800 | $4,522 | $4,118 | $4,495 | $5,099 | $3,182 | $5,669 | $3,925 | $5,643 | $3,405 | $8,508 |"}
{"item_id": "item_0707", "chart_task_type": "tradeoff", "query": "Can you chart how blade speed trades off against thrust time for each fencer, so I can easily identify which athlete corresponds to each point?", "table_markdown": "| Fencer | Peak Blade Tip Velocity (m/s) | Blade Thrust Time (ms) | EJA Initiation (°) | EJA Termination (°) | Wrist Angle Initiation (°) | Wrist Angle Termination (°) |\n|-----------------|-------------------------------|------------------------|--------------------|---------------------|---------------------------|-----------------------------|\n| Elite Fencer A | 18.24 | 196.00 | 112.05 | 173.44 | 140.02 | 176.88 |\n| Elite Fencer B | 17.55 | 300.00 | 107.98 | 172.89 | 135.94 | 178.47 |\n| Elite Fencer C | 19.53 | 199.00 | 111.21 | 176.11 | 145.72 | 177.03 |\n| Novice Fencer D | 13.01 | 336.00 | 98.32 | 166.41 | 117.09 | 167.56 |\n| Novice Fencer E | 12.97 | 367.00 | 99.59 | 168.20 | 134.93 | 175.85 |\n| Novice Fencer F | 14.93 | 299.00 | 100.25 | 167.01 | 133.44 | 165.32 |"}
{"item_id": "item_0708", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between industrial GDP growth and NO2 emissions for the provincial regions, so I can easily see how each area balances economic expansion against environmental impact?", "table_markdown": "| Region | Provincial-level regions | Urban population in 2005 (% of total) | Urban population in 2013 (% of total) | Industrial GDP annual growth rate for 2005–2013 (% yr$^{-1}$) | Thermal power generation annual growth rate for 2005–2013 (% yr$^{-1}$) | Hydropower generation annual growth rate for 2005–2013 (% yr$^{-1}$) | Capital cities* ownership between 2005 and 2012 (million vehicles) | Increase in vehicle ownership between 2005 and 2012 (%) | NO$_2$ emissions (%) |\n|--------------|--------------------------|--------------------------------------|--------------------------------------|---------------------------------------------------------------|-----------------------------------------------------------------|-----------------------------------------------------------------|---------------------------------------------------------------|---------------------------------------------------------------|---------------------|\n| Northwest | Gansu | 30.0 | 40.1 | 13.8 | 9.8 | 0.41 | Lanzhou | 2.11 | 17.5 |\n| | Inner Mongolia | 47.2 | 58.7 | 20.3 | 16.9 | 15.0 | Hohhot | 0.41 | 17.5 |\n| | Ningxia | 42.3 | 52.0 | 15.0 | 17.9 | 7.2 | Yinchuan | 0.18 | 21.3 |\n| | Qinghai | 39.3 | 48.5 | 16.0 | 12.0 | 13.2 | Xining | 0.03 | 27 |\n| | Shaanxi | 37.2 | 51.3 | 17.0 | 14.0 | 4.7 | Xi’an | 1.09 | 56.6 |\n| | Xinjiang | 37.2 | 44.5 | 12.4 | 22.8 | 18.4 | Urumqi | 0.37 | 25.7 |\n| Southwest | Chongqing | 45.2 | 58.3 | 19.4 | 11.2 | 14.5 | Chongqing | 2.79 | 40.9 |\n| | Guangxi | 33.6 | 44.8 | 17.4 | 14.9 | 11.2 | Nanning | 0.6 | 47.7 |\n| | Guizhou | 26.9 | 37.8 | 14.0 | 9.9 | 8.9 | Guiyang | 0.44 | 26.4 |\n| | Sichuan | 33.0 | 44.9 | 18.5 | 6.0 | 15.4 | Chengdu | 1.56 | 46.9 |\n| | Yunnan | 29.5 | 40.5 | 14.6 | 7.0 | 21.6 | Kunming | 1.02 | 34.1 |"}
{"item_id": "item_0709", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between training accuracy and error for the ANN models so I can easily identify which specific model corresponds to each point?", "table_markdown": "| Models | No. of Hidden Neurons Layer | Training R² | RMSE | Validation R² | RMSE |\n|--------|-----------------------------|-------------|------|---------------|------|\n| ANN 1 | 1 | 0.79 | 6.919| 0.767 | 7.231|\n| ANN 2 | 2 | 0.798 | 6.779| 0.78 | 7.024|\n| ANN 3 | 3 | 0.801 | 6.737| 0.776 | 7.089|\n| ANN 4 | 4 | 0.803 | 6.692| 0.785 | 6.948|\n| ANN 5 | 5 | 0.8 | 6.743| 0.781 | 7.012|\n| ANN 6 | 6 | 0.804 | 6.682| 0.784 | 6.967|\n| ANN 7 | 7 | 0.807 | 6.631| 0.784 | 6.954|\n| ANN 8 | 8 | 0.805 | 6.658| 0.78 | 7.017|\n| ANN 9 | 9 | **0.819** | **6.424**| **0.796** | **6.764**|\n| ANN 10 | 10 | 0.803 | 6.701| 0.779 | 7.04 |"}
{"item_id": "item_0710", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between relative humidity and moisture loss for the different cold room factor changes, making it easy to identify which specific scenario corresponds to each point?", "table_markdown": "| change from standard | caused by | r.h. % | moisture loss %/6 months |\n|----------------------|-----------|--------|--------------------------|\n| ambient temperature 20°C x 2 | ☀️ | 91.3 | 9.1 |\n| no floor insulation | 🚶 | 91.5 | 8.9 |\n| transpiration coeff. x 1/2 | 🌱 | 91.6 | 4.5 |\n| protection level x 2 | 🏙️ | 91.6 | 4.5 |\n| heat production x 2 | 🍎 | 92.6 | 7.6 |\n| half load x 1/2 | 🚶 | 93.5 | 6.5 |\n| ventilation (1 per day) | 🌬️ | 93.7 | 6.3 |\n| standard | 🚶 | 94.2 | 5.7 |\n| water on floor | 🌳 | 95.0 | 4.7 |\n| heat generation x 1/2 | 🍎 | 95.2 | 4.5 |\n| ambient temperature 5°C x 1/2 | ☀️ | 95.7 | 3.9 |\n| transpiration coeff. x 2 | 🌱 | 96.3 | 6.4 |"}
{"item_id": "item_0711", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between delivery time and average delivery rate for each interpreter, making it easy to spot how speed and duration vary across the group?", "table_markdown": "| Identification | DTM | Avg. 8.26 | TTW (Persian) | ADR (wpm) | Avg. 73.42 | FLP | FVP | RP |\n|---|---|---|---|---|---|---|---|---|\n| ST1 | 6.01 | | 530 | 89 wpm | | 7 | 5 | 21 |\n| ST2 | 7.13 | | 600 | 73.5 wpm | | 10 | 37 | 22 |\n| ST3 | 11.21 | | 680 | 68 wpm | | 19 | 63 | 40 |\n| ST4 | 7.45 | | 550 | 81.4 wpm | | 11 | 31 | 27 |\n| ST5 | 6.38 | | 524 | 86.3 wpm | | 8 | 25 | 22 |\n| ST6 | 7.01 | | 516 | 80 wpm | | 13 | 28 | 30 |\n| ST7 | 10.55 | | 594 | 57.5 wpm | | 20 | 59 | 45 |\n| ST8 | 10.3 | | 567 | 62.7 wpm | | 17 | 55 | 30 |\n| ST9 | 9.15 | | 602 | 66.5 wpm | | 17 | 49 | 25 |\n| ST10 | 7.5 | | 535 | 69.3 wpm | | 18 | 52 | 20 |\n| PRO1 | 6.14 | Avg. 5.75 | 490 | 90.4 wpm | Avg. 97.05 | 2 | 30 | 11 |\n| PRO2 | 7.34 | | 503 | 83.5 wpm | | 3 | 33 | 12 |\n| PRO3 | 5.02 | | 460 | 103.7 wpm | | 1 | 7 | 9 |\n| PRO4 | 4.5 | | 420 | 110.6 wpm | | 0 | 28 | 7 |"}
{"item_id": "item_0712", "chart_task_type": "tradeoff", "query": "Can you chart how the costs of these prevention programs trade off against their benefits, making it easy to see which specific programs offer the most value for the money spent?", "table_markdown": "| Program | Benefits | Costs | B - C |\n|-------------------------------|-----------|----------|-----------|\n| Early Childhood Education | $17,202 | $7,301 | $9,901 |\n| Nurse Family Partnership | $26,298 | $9,118 | $17,180 |\n| Soc. Dev. Project (Seattle) | $14,246 | $4,590 | $9,837 |\n| Strengthening Families | $6,656 | $851 | $5,805 |\n| Intensive Juv. Supervision | $0 | $1,482 | -$1,482 |\n| Big Brothers/Sisters | $4,058 | $4,010 | $48 |"}
{"item_id": "item_0713", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between life expectancy and infant mortality for these countries so I can easily see how each nation balances these two health outcomes?", "table_markdown": "| Country | Life Expectancy (2008) | Infant Mortality Rate per 1000 (2009) | Mortality Rate under 5 per 1000 (2009) | Population Avg. Annual (%) Growth (2009) |\n|-----------|------------------------|---------------------------------------|----------------------------------------|------------------------------------------|\n| Pakistan | 66.5 | 65.1 | 95.2 | 2.1 |\n| India | 63.7 | 30.1 | 78.6 | 1.55 |\n| Sri Lanka | 74.1 | 18.5 | 12.9 | 0.94 |\n| Bangladesh| 66.1 | 59.0 | 69.3 | 1.29 |\n| Nepal | 66.7 | 47.5 | 71.6 | 1.28 |\n| China | 73.1 | 20.2 | 29.4 | 0.66 |\n| Thailand | 68.9 | 17.6 | 15.1 | 0.62 |\n| Philippines| 71.1 | 20.5 | 27.2 | 1.96 |\n| Malaysia | 74.4 | 15.8 | 11.3 | 1.72 |\n| Indonesia | 70.8 | 29.9 | 31.8 | 1.14 |"}
{"item_id": "item_0714", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between thermal efficiency and NOx emissions for these four combustion modes so I can easily see how they compare?", "table_markdown": "| Parameter/mode | CDC | CDC | RCCI | RCCI |\n|----------------|-----|-----|------|------|\n| Fuel | DF | HVO | DF-NG-85 | HVO-NG-85 |\n| CA05 [CAD] | 353.7 | 353.4 | 355.4 | 348.8 |\n| CA50 [CAD] | 367.9 | 367.7 | 364.8 | 354.9 |\n| CoV-IMEP-% | 0.76 % | 0.82 % | 3 % | 2.92 % |\n| ITE [%] | 42.7 % | 43.2 % | 44.8 % | 41.6 % |\n| Indicated specific emissions: | | | | |\n| CO$_2$ [g/kWh] | 602 | 589 | 463 | 499 |\n| CO [g/kWh] | 0.46 | 0.33 | 2.91 | 2.23 |\n| CH$_4$ [g/kWh] | 0.0004 | 0 | 9.93 | 5.37 |\n| NMHC [g/kWh] | 0.38 | 0.29 | 0.95 | 0.79 |\n| NO$_X$ [g/kWh] | 6.25 | 6.36 | 2.25 | 7.04 |\n| PM [mg/kWh] | 72 | 41.9 | 1.14 | 4.59 |\n| PN [cm$^{-3}$] | $21 \\times 10^6$ | $17.1 \\times 10^6$ | $1.59 \\times 10^6$ | $1.52 \\times 10^6$ |"}
{"item_id": "item_0715", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between expected profit and processing time for each experiment, making it easy to identify which run corresponds to each point?", "table_markdown": "| | | | | | Goal Cell Statistics | | |\n|---|---|---|---|---|---|---|---|\n| Exp. No. | Variability % | No. of Trials | Processing Time | Expected Profit | Std. Dev. | Min. | Max. |\n| 1 | 0 | 99 | 0:01:19 | 1,482,026 | - | 1,482,026 | 1,482,026 |\n| 2 | 5 | 467 | 0:02:36 | 1,326,105 | 4,313 | 1,313,182 | 1,334,498 |\n| 3 | 10 | 165 | 0:01:24 | 1,025,981 | 8,034 | 1,005,362 | 1,056,936 |\n| 4 | 15 | 492 | 0:03:03 | 999,088 | 12,407 | 973,370 | 1,032,920 |\n| 5 | 20 | 492 | 0:02:15 | 889,361 | 14,450 | 849,281 | 921,705 |\n| 6 | 25 | 120 | 0:01:18 | 283,505 | 18,215 | 235,612 | 325,911 |\n| 7 | 30 | 495 | 0:02:17 | 760,305 | 22,212 | 695,792 | 814,226 |\n| 8 | 35 | 485 | 0:02:23 | 292,535 | 27,766 | 208,979 | 376,057 |\n| 9 | 40 | 468 | 0:02:17 | 294,095 | 32,123 | 207,724 | 370,564 |\n| 10 | 45 | 498 | 0:02:07 | 36,892 | 42,829 | (58,943) | 154,338 |\n| 11 | 50 | 488 | 0:02:10 | 147,051 | 40,343 | 51,151 | 241,366 |"}
{"item_id": "item_0716", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between Global Warming Potential and Critical Pressure for these refrigerants, making it easy to identify which specific fluid corresponds to each point?", "table_markdown": "| Refrigerant | Tcr (°C) | Pcr (kPa) | ODP | GWP | Life (y) | SG |\n|----------------------|----------|-----------|-----|-------|----------|-------|\n| R134a | 101,06 | 4059 | 0 | 1300 | 14 | A1 |\n| R744 (CO2) | 31,04 | 7380 | 0 | 1 | 29300-36100 | A1 |\n| R717 (NH3) | 132,40 | 11280 | 0 | 0 | < 0,019 | B2L |\n| R718 (H2O) | 373,95 | 22060 | 0 | 0,2±0,2 | 0,026 | A1 |\n| R245fa (ÖKO1) | 154,05 | 3640 | 0 | 858 | 7,6 | B1 |\n| R407c | 86,05 | 4634 | 0 | 1774 | 15,7 | A1 |\n| R1234ze(e) | 109,40 | 36 | 0 | 6 | N/A | A2L |\n| R410a | 70,17 | 4770 | 0 | 2088 | 16,9 | A1 |\n| R600 | 152,01 | 3796 | 0 | 4 | 12±3 | A3 |\n| R1336mzzZ (Opteon MZ)| 171,30 | 2900 | 0 | 2 | N/A | A1 |"}
{"item_id": "item_0717", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between reconstruction error and correlation for these methods, so I can easily spot which ones balance accuracy and fidelity best?", "table_markdown": "| Method | SSH only | Deep learning | Calibrated on data from | Physical model | Rmse (cm) | $\\mu_{ssh}$ () | $\\lambda_x$ (km) | $1 - \\frac{\\lambda_x}{\\lambda_{ref}}$ (% ose, osse) |\n|-----------------|----------|---------------|-------------------------|----------------|-----------|----------------|------------------|-----------------------------------------------|\n| 4DVarNet | Yes | Yes | Simulation | – | 5.9 | 0.91 | 100 | 33, 47 |\n| MUSTI | No | Yes | Satellite | – | 6.3 | 0.90 | 112 | 26, 22 |\n| ConvLstm-SST | No | Yes | Satellite | – | 6.7 | 0.90 | 108 | 28, – |\n| ConvLstm | Yes | Yes | Satellite | – | 7.2 | 0.89 | 113 | 25, – |\n| DYMOST | Yes | No | Satellite | QG | 6.7 | 0.90 | 131 | 13, 11 |\n| MIOST | Yes | No | Satellite | – | 6.8 | 0.90 | 135 | 11, 10 |\n| BFN-QG | Yes | No | Satellite | QG | 7.6 | 0.89 | 122 | 19, 21 |\n| DUACS | Yes | No | Satellite | – | 7.7 | 0.88 | 151 | 0, 0 |\n| GLORYS12 | No | No | Satellite | NEMO | 15.1 | 0.77 | 241 | –60, – |"}
{"item_id": "item_0718", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between payload capacity and energy consumption for these vehicles so I can easily see which ones offer the best efficiency for their carrying capability?", "table_markdown": "| Vehicle | Tare (kg) | Max. Speed (kph) | Payload (kg) | Range (km) | Approx. Energy consumption (wh/km) |\n|-------------|-----------|------------------|--------------|------------|-----------------------------------|\n| Starship | 18 | 6 | 18 | 3 | 25 |\n| Nuro | 680 | 56 | 110 | 16 | 140 |\n| Udelv | 1890 | 97 | 590 | 97 | 194 |\n| MD4-3000 | 10 | 72 | 5 | 36 | 22 |\n| Renault EV | 1360 | 160 | 720 | 120 | 205 |\n| Dodge RAM | 2170 | 180 | 1890 | 695 | 1016 |"}
{"item_id": "item_0719", "chart_task_type": "tradeoff", "query": "Can you plot the trade-off between precision and recall for each experimental configuration so I can see how they balance against each other?", "table_markdown": "| Train | Test | Normalization (on test) | Tokens | Attach. Errors | Label. Errors | Total Errors | Precision | Recall |\n|-------|-------|--------------------------|--------|----------------|---------------|--------------|-----------|--------|\n| WSJ | WSJ | none | 4,621 | 2,452 | 1,297 | 3,749 | 0.81 | 0.40 |\n| WSJ | EWT | none | 5,855 | 3,621 | 2,169 | 5,790 | 0.99 | 0.38 |\n| | | full | 5,617 | 3,484 | 1,959 | 5,443 | 0.97 | 0.37 |\n| EWT | EWT | none | 7,268 | 4,083 | 2,202 | 6,285 | 0.86 | 0.51 |\n| | | full | 7,131 | 3,905 | 2,147 | 6,052 | 0.85 | 0.50 |\n| WSJ+EWT | EWT | none | 5,622 | 3,338 | 1,849 | 5,187 | 0.92 | 0.40 |\n| | | full | 5,640 | 3,379 | 1,862 | 5,241 | 0.93 | 0.41 |"}
{"item_id": "item_0720", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between system unbalance and throughput for each problem, making it easy to identify which problem corresponds to each point?", "table_markdown": "| Problem number | Jobs assigned | Jobs unassigned | System unbalance | Throughput |\n|----------------|---------------|-----------------|------------------|------------|\n| 1 | 1,5,7,8 | 2,3,4,6 | 81 | 42 |\n| 2 | 1,3,4,5,6 | 2 | 202 | 63 |\n| 3 | 2,3,4,5 | 1 | 72 | 69 |\n| 4 | 1,2,3,4,5 | - | 819 | 51 |\n| 5 | 1,2,3,4,6 | 5 | 133 | 64 |\n| 6 | 2,4,5,6 | 1,3 | 178 | 46 |\n| 7 | 1,2,3,4,6 | 5 | 147 | 66 |\n| 8 | 1,5,6,7 | 2,3,4 | 111 | 46 |\n| 9 | 1,2,3,4,5,6,7 | - | 309 | 88 |\n| 10 | 1,2,4,5,6 | 3 | 127 | 58 |"}
{"item_id": "item_0721", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between max gain and 3-dB beamwidth for the different CLL fin structures, so I can easily see how each configuration balances these two metrics?", "table_markdown": "| # of CLL Fins | Max Gain (dB) | Gain Improvement (dB) | 3-dB Beamwidth (degrees) | Frequency of Max Gain (GHz) | Front-to-Back Ratio (dB) |\n|---------------|--------------|-----------------------|--------------------------|----------------------------|-------------------------|\n| 0 | 2.2 | 0 | 360° | 15.3 | 0 |\n| 2 | 5.4 | 3.2 | 165.1° | 19.3 | 9.3 |\n| 4 | 6.9 | 4.7 | 95.7° | 19.7 | 3.2 |\n| 8 | 7.8 | 5.7 | 88.9° | 20.8 | 10.7 |\n| 12 | 9.7 | 7.5 | 48.3° | 20.5 | 12.4 |"}
{"item_id": "item_0722", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between prediction error and model complexity for these methods, so I can easily see which ones achieve lower RMSE with fewer selected variables?", "table_markdown": "| Methods | MAE | MDAE | RMSE | MAPE | Variables Selected | No. of Variables Selected |\n|---|---|---|---|---|---|---|\n| LASSO | 0.564 (0.579) | 0.515 (0.510) | 0.696 (0.668) | 1.142 (1.470) | Bmi, bp, s3, s5 (Bmi, bp, s3, s5) | 4 (4) |\n| LAD LASSO | 0.561 (0.546) | 0.489 (0.526) | 0.693 (0.635) | 1.287 (1.563) | Bmi, bp, s3, s5 (Bmi, bp, s3, s5) | 4 (4) |\n| Huber LASSO | 0.558 (0.556) | 0.500 (0.509) | 0.692 (0.645) | 1.221 (1.524) | Bmi, bp, s3, s5 (Bmi, bp, s3, s5) | 4 (4) |\n| MTE LASSO | 0.564 (0.583) | 0.516 (0.503) | 0.696 (0.673) | 1.146 (1.458) | Bmi, bp, s3, s5 (Bmi, bp, s3, s5) | 4 (4) |\n| Adaptive LASSO | 0.560 (0.545) | 0.506 (0.503) | 0.694 (0.630) | 1.222 (1.664) | Bmi, bp, s1, s2, s5 (Bmi, s2, s5) | 5 (3) |\n| Adaptive LAD Lasso | 0.580 (0.532) | 0.466 (0.488) | 0.717 (0.633) | 1.259 (1.576) | Bmi, s5 (Bmi, s5) | 2 (2) |\n| Adaptive Huber LASSO | 0.575 (0.532) | 0.464 (0.502) | 0.708 (0.622) | 1.206 (1.689) | Bmi, s5 (Bmi, s5) | 2 (2) |\n| MTE Adaptive LASSO | 0.574 (0.546) | 0.502 (0.508) | 0.705 (0.632) | 1.151 (1.66) | Bmi, s5 (Bmi, s5) | 2 (2) |"}
{"item_id": "item_0723", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between energy costs and fixed costs for these countries, making it easy to spot which nations have high variable rates versus high annual fees?", "table_markdown": "| Country | Energy Costs [€/kWh] | Fixed Costs [€/consumer/y] |\n|---------|----------------------|----------------------------|\n| NL | 0.21 | -95 |\n| ES | 0.18 | 30 |\n| IT | 0.16 | 40 |\n| PT | 0.20 | 70 |\n| DE | 0.26 | 100 |\n| BE | 0.24 | 50 |\n| FR | 0.13 | 120 |\n| AT | 0.12 | 80 |"}
{"item_id": "item_0724", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between trading volume and weekly price change for these counters so I can easily spot which ones offer high liquidity versus strong short-term performance?", "table_markdown": "| Counter | Value Traded (KES m) | Foreign Investor Net Buying/(Selling) (KES m) | Foreign Investor Activity as % of total | Weekly Price Change (%) | YTD (%) | % Below 12- month high |\n|---|---|---|---|---|---|---|\n| Safaricom | 429.4 | -285.3 | 59.2% | -0.2% | -36.0% | -39.3% |\n| ABSA New Gold ETF | 251.1 | 0 | 100.0% | 0.0% | -11.2% | -12.4% |\n| BAT Kenya | 209.2 | -85.9 | 79.4% | 1.1% | -0.3% | -13.6% |\n| KCB bank | 119.7 | -42.2 | 17.7% | -2.3% | -18.8% | -21.5% |\n| StanChart bank | 109.6 | -1.2 | 0.6% | -0.2% | 13.8% | -0.2% |"}
{"item_id": "item_0725", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between cost per acre and nutritional quality for the grass species, excluding those without a DOM/CP rating, so I can easily spot which options offer the best balance?", "table_markdown": "| Species (From Millborn Seed, Brookings, SD) | Cost per lb | Full Seeding rate (lbs/A) | $ / A | DOM/CP Rating |\n|-------------------------------------------|-------------|--------------------------|--------|---------------|\n| Green Wheatgrass, Certified AC Saltlander | $10.00 | 15 | $150.00| NA |\n| Shoshone Beardless Wildrye | $3.00 | 18 | $54.00 | 6.22 |\n| Tall Wheatgrass | $4.00 | 15 | $60.00 | 8.02 |\n| Slender Wheatgrass | $5.50 | 10 | $55.00 | 4.39 |\n| Garrison Creeping Foxtail | $10.00 | 8 | $80.00 | 9.09 |\n| Thickspike Wheatgrass, Critania | $10.00 | 4.2 | $42.00 | NA |"}
{"item_id": "item_0726", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between shear strength and peel adhesion for these formulations, making it easy to identify which example corresponds to each data point?", "table_markdown": "| Example | Parts IOA | Parts MMA | Parts VOAC | Parts IOTG | (Minutes) Shear | Peel Adhesion | I.V. | Bead Storage Stability | Extrusion temperature °C. |\n|---------|-----------|-----------|------------|------------|-----------------|---------------|------|------------------------|--------------------------|\n| 1 | 470 | 20 | 10 | 0.1 | 38 | 27 | 0.71 | Storage Stable | 150° or greater |\n| 2 | 430 | 20 | 50 | 0.1 | 39 | 29 | 0.75 | Storage stable | 150° or greater |\n| 3 | 405 | 20 | 75 | 0.1 | 133 | 25 | 0.70 | Storage Stable | 150° or greater |\n| 4 | 380 | 20 | 100 | 0.1 | 278 | 27 | 0.68 | Storage Stable | 150° or greater |\n| Comparative Example 5 | 480 | 20 | 0 | 0.05 | 20 | 29 | 1.1 | Storage Stable | 170° or greater |\n| Comparative Example 6 | 480 | 20 | 0 | 0.1 | 1 | 30 | 0.70 | Agglomerated After 8 Hours In Storage jar | 150° or greater |"}
{"item_id": "item_0727", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between dry yield and oleoresin percentage for these pepper varieties so I can easily compare their performance profiles?", "table_markdown": "| Name | Pedigree | Released from | Av.yield kg/ha (dry) | Oleoresin (%) | Piperine (%) | E.Oil (%) | Remark |\n|------------|-----------------------------------------------|---------------|----------------------|---------------|--------------|-----------|------------------------------------------------------------------------|\n| Panniyur-1 | F. of Udhankota X Chettiyakanyakadan | Pepper Research Station, Panniyur in 1966 | 1242.0 | 11.8 | 5.3 | 3.5 | Pinnipure, do not tolerate heavy shade, non-pigmented growing habit, long spikes and bold berries, average yield - 2.2 kg green berries per vine, 35% dry recovery, early bearing |\n| Panniyur-6 | Clonal selection from Karimunda | Pepper Research Station, KAU, Panniyur | 2127 | 8.3 | 4.9 | 1.3 | Suitable for open cultivation as well as under shade, 33.1% dry recovery |\n| Subhakara | Clonal selection of Karimunda | IISR, Kozikode | 2352.0 | 12.4 | 3.4 | 5.0 | Suited to all pepper growing conditions, usual yield - 4.94 kg green berries vine, 35.5% dry recovery, high quality |\n| Sreekara | Clonal selection of Karimunda | IISR, Kozikode | 2677.0 | 13.0 | 5.1 | 7.0 | Adaptable to all climatic conditions in all the pepper growing tracts, 35.0% dry recovery |\n| IISR Thevam| Clonal selection of Thevannmudi | IISR, Kozikode | 2148 | 8.2 | 1.6 | 3.1 | Tolerant to Phytophthora, not affected by rust disease, suited to high altitudes and plains |\n| IISR Malabar Excel | F. of Cholamundi X Panniyur-1 | IISR, Kozikode | 1440 | 11.5 | 1.0 | 3.2 | Suited to light altitudes and rich in oleoresin |"}
{"item_id": "item_0728", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between frequency and coverage for these workloads so I can see how they balance speed against completeness?", "table_markdown": "| Workload | Description | # SDP-ICERs | Coverage (%) | Freq. (MHz) | # Slices | Slices vs. ICERs | Slice Regs. vs. ICERS | Clik. Energy vs. ICERs | DSPs |\n|-----------|--------------------------------------------------|-------------|--------------|-------------|----------|-----------------|-----------------------|------------------------|------|\n| b-tree | Search tree traversal | 1 | 86 | 231 | 1308 | -14% | -13% | -36% | 0 |\n| bzip2 | Data compression algorithm | 1 | 73 | 143 | 6172 | -23% | -33% | -42% | 0 |\n| graph5 | Sparse graph depth-first traversal | 1 | 98 | 201 | 998 | -18% | -18% | -28% | 3 |\n| mc1 | Single-depot vehicle scheduling | 2 | 42 | 193 | 3239 | -11% | -9% | -31% | 0 |\n| radix | Sorting algorithm | 1 | 94 | 130 | 3934 | -15% | -24% | -51% | 5 |\n| viterbi | Convolutional code decoder | 1 | 99 | 176 | 4583 | -10% | -31% | -52% | 0 |"}
{"item_id": "item_0729", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between publication count and citation impact for the top 10 authors, making it easy to spot who has high output versus high influence?", "table_markdown": "| Rank | Author | Publications, n | Citations, n |\n|------|----------------------|-----------------|--------------|\n| 1 | Schnall R | 15 | 217 |\n| 2 | Kuhn E | 14 | 223 |\n| 3 | Lopez-Coronado M | 14 | 130 |\n| 4 | Kim J | 14 | 43 |\n| 5 | Lee S | 13 | 365 |\n| 6 | Li J | 13 | 49 |\n| 7 | Torous J | 12 | 271 |\n| 8 | Lee JH | 10 | 107 |\n| 9 | Lee J | 10 | 46 |\n| 10 | Zhang Y | 10 | 36 |"}
{"item_id": "item_0730", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between sensitivity and specificity for these instruments so I can easily spot which ones balance both metrics best?", "table_markdown": "| Instrument | Sensitivity | Specificity | Cut-off | Youden index |\n|----------------------------------|-------------|-------------|---------|---------------|\n| Epworth Sleepiness Scale | 0.75 | 0.46 | 5.5 | 0.21 |\n| Insomnia Severity Index | 0.59 | 0.54 | 8.5 | 0.13 |\n| STOP- Bang questionnaire | 0.75 | 0.75 | 5.5 | 0.5 |\n| STOP questionnaire | 0.77 | 0.46 | 1.5 | 0.23 |\n| Pittsburgh Sleep Quality Index | 0.78 | 0.67 | 17.75 | 0.45 |"}
{"item_id": "item_0731", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between convergence frequency and average iteration steps for the six games, so I can easily identify which game corresponds to each point?", "table_markdown": "| Game no. | Average iteration steps to solutions | Convergence frequency to solutions |\n|----------|--------------------------------------|-----------------------------------|\n| Game #1 | 25.6 | 85% |\n| Game #2 | 85.2 | 27% |\n| Game #3 | 65.8 | 87% |\n| Game #4 | 128.7 | 75% |\n| Game #5 | 147.5 | 30% |\n| Game #6 | 234.1 | 10% |"}
{"item_id": "item_0732", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between recognition accuracy and execution speed for these machine learning methods so I can easily spot which ones offer the best balance?", "table_markdown": "| Method | Rate (%) | Model training (s) | Recognition execution (ms) |\n|-----------------|----------|--------------------|----------------------------|\n| SVM | 85.53 | 1.89 | 4.9 |\n| RF | 62.89 | 2.45 | 1.5 |\n| ANN | 82.42 | 4.56 | 1.78 |\n| GRBM and SVM | 79.64 | 3.87 | 7 |\n| GRBM and RF | 71.11 | 4.09 | 8 |"}
{"item_id": "item_0733", "chart_task_type": "tradeoff", "query": "Can you chart how heating capacity trades off against power draw for these heater models so I can easily spot which ones deliver the most BTUs for the least electricity?", "table_markdown": "| | EB200D/G | EB400D/G | EB600D/G | EB600TII |\n|----------------|----------|----------|----------|----------|\n| BTU’s | 175K | 399K | 588k/575k| 588k/575k|\n| Amp Draw | 6 | 8.5 | 11.5 | 17.5 |\n| CFM | 3250 | 5500 | 5500 | 6500 |\n| Static Pressure| 3 | 3 | 3 | 4 |\n| Duct Runs(max) | 150ft | 150ft | 150ft | 150ft |\n| Fork Pockets | YES | YES | YES | YES |\n| Run Time | 32 | 14 | 10 | 10 |\n| Recirculating Capable | YES | YES | YES | YES |"}
{"item_id": "item_0734", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between under-18 prevalence and female employment rates for the five Rasu entities, so I can easily see how they compare?", "table_markdown": "| Indicator | Awsi Rasu | Kilbati Rasu | Gabi Rasu | Fanti Rasu | Harri Rasu |\n|------------------------------------------------|-----------|--------------|-----------|------------|------------|\n| **Child Marriage** | | | | | |\n| Under-18 Prevalence | 66% | 66% | 63% | 71% | 63% |\n| **Population of At-Risk Girls (aged 10-14), by Profile¹** | | | | | |\n| Poverty | 7,240 | 3,791 | 6,900 | 11,700 | 9,150 |\n| Gender Inequitable Attitudes | 400 | 200 | 400 | 300 | 2,800 |\n| Limited Decision-Making | 20,200 | 19,500 | 9,600 | 14,900 | 5,900 |\n| **Community Characteristics** | | | | | |\n| Total Population | 466,000 | 403,000 | 239,000 | 332,000 | 249,000 |\n| Number of Health Centers Per 100,000 People | 7 | 4 | 10 | 4 | 4 |\n| Percent of women (aged 15-49) who are employed | 17% | 18% | 22% | 14% | 18% |\n| Percent of women (aged 18-49) who completed primary education or higher | 8% | 11% | 8% | 4% | 4% |"}
{"item_id": "item_0735", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between accuracy and wall-clock time for the integration schemes, making it easy to identify which methods offer the best balance?", "table_markdown": "| Integration scheme | $L_\\infty$-error $R_{norm} = 32$ | Time step size (s) | Wall-clock time (s) | Observed speedup $S^{obs}_S$ | Observed speedup $S^{obs}_{ML}$ |\n|--------------------|----------------------------------|---------------------|----------------------|-------------------------------|-------------------------------|\n| SDC(3,4) | $5.2 \\times 10^{-6}$ | 400 | 528 | - | - |\n| MLSDC(3,2,2,1/2) | $5.2 \\times 10^{-6}$ | 400 | 354 | 1.5 | - |\n| PFA SST(16,3,2,4,1/5) | $4.4 \\times 10^{-6}$ | 128 | 120 | 4.1 | 3.0 |\n| SDC(5,8) | $4.6 \\times 10^{-11}$ | 200 | 3,389 | - | - |\n| MLSDC(5,3,4,1/2) | $7.6 \\times 10^{-10}$ | 200 | 2,269 | 1.5 | - |\n| PFA SST(16,5,3,8,4/5) | $3.5 \\times 10^{-11}$ | 100 | 917 | 3.7 | 2.5 |"}
{"item_id": "item_0736", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between solar heat absorption and visible light transmission for the different water thicknesses, so I can see how they balance against each other?", "table_markdown": "| Water Thickness | Absorbed Energy | Solar Heat Absorption | $T_{\\text{vis}}$ |\n|-----------------|-----------------|-----------------------|-----------------|\n| cm | W/m² | 250-2500 nm | 380–700 nm |\n| 1 | 407.956 | 30.33 % | 99.03 % |\n| 5 | 543.046 | 40.38 % | 95.49 % |\n| 10 | 607.308 | 45.16 % | 91.74 % |\n| 15 | 647.966 | 48.18 % | 88.59 % |\n| Total Solar Energy | 1344.873 | | |"}
{"item_id": "item_0737", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between performance and cost efficiency for these server models so I can easily identify which configurations offer the best balance?", "table_markdown": "| Model | Processor/ # CPUs | # Nodes | MHz | L2 Cache (KB) | tpmC | $/tmpC | Database | AIX | Availability Date |\n|---|---|---|---|---|---|---|---|---|---|\n| #F50 | 604e/4 | 1 | 166 | 0.2 | 8,142.40 | 62.71 | Sybase 11.5 | 4.2.1 | 02/09/98 |\n| #F50 | 604e/4 | 1 | 332 | 0.2 | 9,853.13 | 64.22 | Sybase 11.5 | 4.2.1 | 02/20/98 |\n| #R50 | 604e/8 | 1 | 200 | 2 | 9,165.13 | 98.83 | Sybase 11.5 | 4.2.1 | 09/30/97 |\n| #S70 | RS64/12 | 1 | 125 | 4 | 18,666.73 | 108.62 | Oracle V8. | 4.3.0 | 09/02/98 |\n| #S70 | RS64 II/12 | 1 | 262 | 8 | 34,139.63 | 88.09 | Oracle V8. | 4.3.1 | 01/21/99 |\n| #S7A | RS64 II/12 | 5 | 262 | 8 | 110,434.10 | 122.44 | Oracle OPS | 4.3.2 | 06/28/99 |\n| #H70 | RS64 II/4 | 1 | 340 | 4 | 17,133.73 | 78.5 | Oracle V815 | 4.3.2 | 11/19/99 |\n| #S80 | RS64 III/24 | 1 | 450 | 8 | 135,815.70 | 52.7 | Oracle V816 | 4.3.3 | 03/01/00 |\n| #F80 | RS64 III/6 | 1 | 500 | 4 | 33,571.39 | 58.94 | Oracle V816 | 4.3.3 | 06/09/00 |\n| M80 | RS64 III/8 | 1 | 500 | 4 | 66,750.27 | 45.46 | Oracle V817 | 4.3.3 | 09/30/00 |\n| p680 | RS64 IV/24 | 1 | 600 | 16 | 220,807.27 | 43.3 | Oracle V817 | 4.3.3 | 04/13/01 |"}
{"item_id": "item_0738", "chart_task_type": "tradeoff", "query": "Can you plot the relationship between the number of embryos authorised and the number actually used for each research licence, so I can see how usage scales with authorization limits?", "table_markdown": "| Licence number | Licence holder | Licence title | Embryos authorised to be used under licence | Embryos used in licensed activity up to 31 August 2019 |\n|---|---|---|---|---|\n| 309702B | Genea Limited | Development of methods for pre-implantation genetic and metabolic evaluation of human embryos | 220 | 58 |\n| 309703 | Genea Limited | Development of human embryonic stem (ES) cells | 300 (plus up to 20 inner cell masses which may be transferred from 309702A or 309702B) | 249 (plus 12 embryos first used in 309702A and then transferred to 309703) |\n| 309710 | Genea Limited | Derivation of human embryonic stem cells from embryos identified through preimplantation genetic diagnosis to be affected by known genetic conditions | 500 | 304 |\n| 309718 | Genea Limited | Use of excess ART embryos and clinically unusable eggs for validation of an IVF device | 345 | 259 |\n| 309719 | Genea Limited | Use of excess ART embryos for the development of improved IVF culture media | 640 | 58 |\n| Total for current licences | | | 2005 | 928 |"}
{"item_id": "item_0739", "chart_task_type": "tradeoff", "query": "Can you plot how vehicle utilization trades off against travel time for the different strategies, so I can easily see which approach balances resource usage and speed?", "table_markdown": "| Strategy | Performance Parameters | | Average Storage Cycle Time (sec) | | | | |\n|---|---|---|---|---|---|---|---|\n| Arrival Rate λ = 112 pallets per hour | Average Number Waiting | Vehicle Utilization | Waiting to be paired | Waiting for Vehicle | Travel Time | Waiting to be paired | Waiting for Vehicle |\n| Single Cycle | 0.6 | 72% | 0.0 | 20.8 | 95.9 | 0.0 | 20.8 |\n| Opportunistic Interleaving | 0.1 | 60% | 0.0 | 4.2 | 91.5 | 0.0 | 4.2 |\n| Same-aisle Pairing | 492.3 | 48% | 13946.2 | 40.2 | 73.8 | 14427.6 | 40.2 |\n| Same-side Pairing | 139.9 | 58% | 3697.8 | 58.6 | 73.8 | 5130.4 | 58.6 |\n| Same-side Pairing (Closest) | 128.0 | 50% | 2005.0 | 42.7 | 73.8 | 2166.0 | 42.7 |"}
{"item_id": "item_0740", "chart_task_type": "tradeoff", "query": "Can you chart the tradeoff between 2020 stud fees and the number of horses with 100+ Beyer figures for these sires, so I can easily identify which stallions are represented?", "table_markdown": "| Sire | 2020 Fee | 100+ Beyer Horses | 100+ Beyer Performances |\n|--------------------|----------|-------------------|-------------------------|\n| More Than Ready | $80,000 | 6 | 9 |\n| Speightstown | $70,000 | 5 | 9 |\n| Tapit | $200,000 | 5 | 8 |\n| Curlin | $175,000 | 4 | 7 |\n| Candy Ride (ARG) | $100,000 | 4 | 4 |\n| Uncle Mo | $125,000 | 4 | 4 |\n| Quality Road | $200,000 | 3 | 3 |\n| Into Mischief | $175,000 | 3 | 5 |\n| Medaglia d’Oro | $200,000 | 2 | 4 |\n| War Front | $250,000 | 1 | 4 |"}
{"item_id": "item_0741", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual headcount numbers for each faculty rank from 2019 to 2023 along with how widely they scattered relative to their own usual level, so it's obvious right away which rank was the most stable.", "table_markdown": "| Rank | 2019 | 2020 | 2021 | 2022 | 2023 |\n|-----------------|------|------|------|------|------|\n| Professor | 30 | 32 | 31 | 27 | 27 |\n| Associate Professor | 32 | 33 | 32 | 31 | 29 |\n| Assistant Professor | 52 | 57 | 57 | 55 | 55 |\n| Instructor | 8 | 5 | 8 | 9 | 8 |\n| Lecturer | 27 | 25 | 23 | 23 | 23 |\n| **Total** | 149 | 152 | 151 | 145 | 142 |"}
{"item_id": "item_0742", "chart_task_type": "tradeoff", "query": "Can you chart the trade-off between impact strength and water absorption for the experiments, making it easy to spot which specific sample corresponds to each point?", "table_markdown": "| Experiment number | Ratio of resin and hardener | Resin and hardener in composite [wt. %] | Length of coir fiber [mm] | Coir fiber in composite w [wt. %] | Betel Nut fiber in composite [wt. %] | Impact strength [J/cm$^2$] | Tensile strength [MPa] | Flexural strength [MPa] | Hardness [Rockwell C scale] | Water absorption [%] |\n|-------------------|-----------------------------|----------------------------------------|---------------------------|----------------------------------|--------------------------------------|--------------------------|-----------------------|-------------------------|--------------------------|---------------------|\n| 1 | 1.25:1 | 80 | 50 | 5 | 15 | 7.9 | 1.1 | 0.95 | 65.00 | 0.07 |\n| 2 | | | 10 | 10 | 10 | 8.2 | 4.2 | 0.85 | 62.33 | 0.11 |\n| 3 | | | 15 | 5 | 5 | 8.4 | 4.4 | 0.55 | 53.66 | 0.11 |\n| 4 | | | 30 | 5 | 15 | 8.0 | 2.6 | 1.00 | 68.00 | 0.14 |\n| 5 | | | 10 | 10 | 10 | 8.1 | 3.9 | 0.80 | 61.33 | 0.14 |\n| 6 | | | 15 | 5 | 5 | 9.5 | 4.2 | 0.75 | 53.33 | 0.08 |\n| 7 | | | 15 | 5 | 15 | 7.5 | 2.5 | 0.95 | 62.33 | 0.08 |\n| 8 | | | 10 | 10 | 10 | 7.7 | 3.2 | 0.70 | 57.33 | 0.11 |\n| 9 | | | 15 | 5 | 5 | 8.1 | 3.3 | 0.65 | 53.00 | 0.20 |"}
{"item_id": "item_0743", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual memory utilization values for the three DDoS scenarios along with how much they scattered, so it's easy to spot which one was the most stable.", "table_markdown": "| ID | DDOS1_dmz | DDOS1_inside | DDOS2_dmz |\n|---|---|---|---|\n| 1 | 10 | 13 | 7.8 |\n| 2 | 9.8 | 14.2 | 7.5 |\n| 3 | 10 | 12.8 | 6 |\n| 4 | 9.8 | 13 | 5.9 |\n| 5 | 9.8 | 13.4 | 6 |\n| 6 | 9.2 | 14.3 | 5.9 |\n| 7 | 10 | 14.2 | 5.9 |\n| 8 | 10.1 | 13 | 5.7 |\n| 9 | 10.1 | 14.2 | 5.8 |\n| 10 | 9.9 | 14.3 | 5.8 |\n| AVG | 9.87 | 13.64 | 6.23 |"}
{"item_id": "item_0744", "chart_task_type": "stability_volatility", "query": "I'd like to see the yearly inputs for N, P2O5, and K2O, with the actual figures and how much they fluctuated, so it's easy to spot which nutrient was the most stable.", "table_markdown": "| Nutrient | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | Average |\n|----------|------|------|------|------|------|------|------|------|------|------|---------|\n| N | 64.0 | 63.0 | 45.0 | 68.0 | 79.1 | 51.4 | 46.0 | 42.0 | 57.0 | 40.2 | 55.6 |\n| P\\(_2\\)O\\(_5\\) | 71.0 | 59.1 | 33.0 | 26.1 | 33.4 | 50.2 | 63.2 | 23.5 | 42.4 | 34.1 | 43.6 |\n| K\\(_2\\)O | 82.1 | 59.1 | 67.0 | 48.0 | 32.4 | 42.2 | 54.2 | 28.3 | 52.5 | 37.7 | 50.4 |"}
{"item_id": "item_0745", "chart_task_type": "stability_volatility", "query": "Could you show me both the actual daily traffic volume for each road segment from 2015 to 2039 and how widely those values fluctuated, so I can easily spot which one stands out as the most stable?", "table_markdown": "| Year | Begin Project to 1.6 miles south of I-20 (FM 1450) | 1.6 miles south of I-20 (FM 1450) to FM 2007 | FM 2007 to FM 1776 | FM 1776 to I-10 in Fort Stockton | I-10 in Fort Stockton to End Project |\n|------|--------------------------------------------------|---------------------------------------------|-------------------|---------------------------------|-------------------------------------|\n| 2015 | 10,000 | 5,000 | 2,000 | 3,000 | 10,000 |\n| 2016 | 9,000 | 4,000 | 2,000 | 3,000 | 10,000 |\n| 2017 | 10,000 | 5,000 | 2,000 | 3,000 | 10,000 |\n| 2018 | 12,000 | 6,000 | 2,000 | 3,000 | 10,000 |\n| 2019 | 13,000 | 7,000 | 2,000 | 3,000 | 10,000 |\n| Future (2039) | 15,000 | 8,000 | 2,000 | 3,000 | 10,000 |"}
{"item_id": "item_0746", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual monthly withdrawal transactions for each ATM location and how widely they fluctuated, so it's obvious which one was the most volatile.", "table_markdown": "| ATM Location | SEP '15 | OCT '15 | NOV '15 | DEC '15 | JAN '16 | FEB '16 | MAR '16 | APR '16 | MAY '16 | JUN '16 | JUL '16 | AUG '16 | TOTAL |\n|-----------------------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|-------|\n| John Peace Library Food Court | 986 | 1,042 | 861 | 502 | 518 | 709 | 1,028 | 1,120 | 460 | 379 | 316 | 398 | 8,319 |\n| Downtown Campus | 470 | 497 | 444 | 307 | 360 | 356 | 436 | 501 | 278 | 263 | 220 | 271 | 4,403 |\n| Flawn Sciences Bldg | 540 | 652 | 413 | 221 | 360 | 470 | 521 | 523 | 214 | 199 | 170 | 291 | 4,574 |\n| University Center A | 344 | 91 | Removed | | | | | | | | | | 435 |\n| University Center B | N/A | 174 | 265 | 117 | 164 | N/A | 268 | 207 | 99 | 104 | 92 | 130 | 1,620 |"}
{"item_id": "item_0747", "chart_task_type": "stability_volatility", "query": "I want to see both the actual construction values for each sector from 2008 to 2013 and how widely they fluctuated, so it's easy to spot which one was the most volatile.", "table_markdown": "| Year | SINGLE FAMILY RESIDENTIAL | MULTI-FAMILY RESIDENTIAL | COMMERCIAL / INSTITUTIONAL | INDUSTRIAL | TOTAL |\n|------|---------------------------|--------------------------|----------------------------|------------|-------|\n| 2008 | $12,662,150 | $2,125,000 | $24,594,968 | $25,056,204| $64,438,322 |\n| 2009 | $11,544,460 | $1,607,000 | $29,726,550 | $3,538,620 | $46,416,630 |\n| 2010 | $7,211,075 | $6,458,915 | $6,673,500 | $1,800,100 | $22,143,590 |\n| 2011 | $8,991,103 | $8,972,999 | $11,318,715 | $2,600,000 | $31,882,817 |\n| 2012 | $12,508,415 | $10,567,000 | $49,415,535 | $2,214,000 | $74,704,950 |\n| 2013 | $12,337,795 | $7,040,000 | $15,225,731 | $2,513,650 | $40,220,011 |\n| TOTAL| $65,254,998 | $36,770,914 | $139,565,149 | $37,058,924| $278,649,985|"}
{"item_id": "item_0748", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual prices for each service size from 2017 to 2021 alongside how much they wobbled relative to their typical level, so it's easy to spot which size was the most volatile.", "table_markdown": "| Service Size | 12/1/2017 | 10/1/2018 | 10/1/2019 | 10/1/2020 | 10/1/2021 |\n|--------------|-----------|-----------|-----------|-----------|-----------|\n| 3/4-inch | $28.59 | $33.02 | $38.16 | $42.95 | $48.58 |\n| 1-inch | $28.59 | $33.02 | $38.16 | $42.95 | $48.58 |\n| 1 1/2-inch | $54.13 | $63.00 | $72.89 | $82.40 | $93.43 |\n| 2-inch | $84.78 | $98.99 | $114.58 | $129.74 | $147.24 |\n| 3-inch | $166.51 | $194.96 | $225.73 | $255.97 | $290.75 |\n| 4-inch | $258.45 | $302.92 | $350.78 | $397.98 | $452.19 |\n| 6-inch | $513.84 | $602.81 | $698.14 | $792.46 | $900.65 |\n| 8-inch | $820.32 | $962.68 | $1,114.97 | $1,265.83 | $1,438.79 |"}
{"item_id": "item_0749", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual annual inflation rates for all six countries alongside how widely they fluctuated, so the most volatile one stands out immediately.", "table_markdown": "| Description | 2018 Q1 | 2018 Q2 | 2018 Q3 | 2018 Q4 | 2019 Q1 | 2019 Q2 | 2019 Q3 |\n|----------------------|---------|---------|---------|---------|---------|---------|---------|\n| Bosnia and Herzegovina| 0.0 | 1.4 | 1.6 | 1.7 | 1.0 | 0.5 | 0.4 |\n| Kosovo | 0.0 | 0.7 | 1.4 | 0.2 | 2.2 | 3.3 | 2.0 |\n| Montenegro | 3.7 | 3.7 | 2.4 | 1.7 | 0.4 | 0.5 | -0.1 |\n| North Macedonia | 1.5 | 1.5 | 1.6 | 1.2 | 1.2 | 1.2 | 0.6 |\n| Serbia | 1.6 | 1.8 | 3.4 | 2.0 | 2.4 | 2.3 | 1.3 |\n| Albania | 1.9 | 2.2 | 2.2 | 1.8 | 1.6 | 1.4 | 1.4 |"}
{"item_id": "item_0750", "chart_task_type": "stability_volatility", "query": "I'd like to see the Median Paid Hours Per Resident Day for Aide, LPN, and RN from 2018 to 2022, along with how spread out each role's figures were, so I can quickly tell which was the most stable.", "table_markdown": "| Year | Aide | LPN | RN |\n|------|-------|-------|-------|\n| 2018 | 2.48 | 0.92 | 0.50 |\n| 2019 | 2.47 | 0.91 | 0.51 |\n| 2020 | 2.53 | 0.95 | 0.55 |\n| 2021 | 2.47 | 0.95 | 0.54 |\n| 2022 | 2.42 | 0.93 | 0.50 |"}
{"item_id": "item_0751", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual cream measurements for each of the six experimental conditions along with how much they varied, so it's easy to spot which one was the most consistent.", "table_markdown": "| T °C (ext) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 5 (2) | 3.8 | 3.8 | 3.8 | 3.8 | 3.7 | 3.8 | 3.8 | 3.8 | 3.7 | 3.8 | 3.9 | 3.8 | 3.8 |\n| 25 (2) | 3.9 | 3.9 | 3.8 | 3.9 | 3.8 | 3.7 | 4 | 3.9 | 3.8 | 3.9 | 3.8 | 3.8 | 3.9 |\n| 45 (2) | 3.4 | 3.3 | 3.4 | 3.3 | 3.2 | 3.5 | 3.4 | 3.4 | 3.6 | 3.5 | 3.3 | 3.5 | 3.8 |\n| 5 (3) | 3.6 | 3.5 | 3.9 | 3.9 | 4.5 | 4.1 | 3.6 | 4.1 | 3.9 | 3.9 | 3.9 | 3.9 | 4 |\n| 25 (3) | 3.3 | 3.5 | 3.8 | 4.2 | 4.4 | 3.6 | 3.3 | 3.8 | 3.9 | 4.3 | 3.5 | 3.8 | 4.1 |\n| 45 (3) | 2.9 | 2.3 | 2.8 | 2.5 | 2.7 | 2.8 | 2.9 | 2.6 | 2.9 | 2.7 | 2.8 | 2.9 | 3 |"}
{"item_id": "item_0752", "chart_task_type": "stability_volatility", "query": "Could you show me the individual stretch zone width values for each of the four specimens along with how widely they scattered, so it's obvious right away which one was the most stable?", "table_markdown": "| Positions | SHB1 | SHB2 | SHB4 | SHB6 |\n|-----------|------|------|------|------|\n| 1 | 108 | 100 | 105 | 36 |\n| 2 | 109 | 64 | 103 | 35 |\n| 3 | 99 | 104 | 102 | 48 |\n| 4 | 103 | 91 | 101 | 59 |\n| 5 | 95 | 79 | 105 | 60 |\n| 6 | 97 | 96 | 104 | 66 |\n| 7 | 96 | 93 | 111 | 90 |\n| 8 | 82 | 78 | 120 | 95 |\n| 9 | 98 | 95 | 104 | 106 |\n| 9-point average | 98 | 88 | 106 | 66 |\n| 3-point average | 98 | 89 | 103 | 62 |"}
{"item_id": "item_0753", "chart_task_type": "stability_volatility", "query": "Show me the actual yearly figures for certified specialists in each specialty area alongside how much each wobbled relative to its own baseline, so it's obvious which one stayed the most stable.", "table_markdown": "| | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 |\n|--------------------------|------|------|------|------|------|------|------|------|\n| NBLSC Family Law Trial Advocacy | 3 | 4 | 4 | 4 | 6 | 6 | 5 | 4 |\n| NBLSC Civil Trial Practice | 157 | 157 | 160 | 156 | 159 | 154 | 145 | 139 |\n| NBLSC Criminal Law | 18 | 18 | 18 | 15 | 13 | 14 | 13 | 13 |\n| MSBA Civil Trial Practice | 333 | 336 | 339 | 336 | 326 | 326 | 320 | 323 |\n| MSBA Real Property | 334 | 343 | 339 | 348 | 344 | 356 | 349 | 362 |\n| ABC Consumer Bankruptcy | 3 | 4 | 5 | 6 | 6 | 4 | 5 | 5 |\n| ABC Business Bankruptcy | 7 | 7 | 7 | 7 | 8 | 7 | 7 | 7 |\n| ABC Creditors' Rights | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |\n| NELF Elder Law | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 4 |\n| **TOTAL** | 859 | 873 | 876 | 877 | 867 | 872 | 849 | 860 |"}
{"item_id": "item_0754", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual marijuana treatment admission percentages for each age group from 2006 to 2018, along with how widely they swung, so it's obvious right away which group was the most stable.", "table_markdown": "| | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 |\n|--------|------|------|------|------|------|------|------|------|------|------|------|------|------|\n| 12-17 | 31.2 | 28.2 | 28.3 | 28.7 | 29.0 | 27.7 | 24.1 | 22.4 | 20.1 | 20.0 | 22.4 | 21.6 | 20.0 |\n| 18-20 | 13.0 | 13.3 | 13.0 | 14.0 | 12.9 | 11.9 | 12.1 | 11.2 | 9.2 | 9.7 | 9.5 | 10.5 | 9.6 |\n| 21-25 | 20.0 | 20.2 | 19.6 | 20.2 | 20.5 | 19.9 | 20.5 | 20.9 | 22.3 | 20.4 | 19.3 | 18.1 | 18.0 |\n| 26+ | 35.8 | 38.3 | 39.1 | 37.1 | 37.6 | 40.5 | 43.3 | 45.5 | 48.3 | 49.3 | 48.8 | 49.9 | 52.5 |"}
{"item_id": "item_0755", "chart_task_type": "stability_volatility", "query": "Could you show me each Pro Am Sportsman pilot's individual flight scores alongside how much they fluctuated, so it's obvious right away who was the most consistent?", "table_markdown": "| | Name | Plane | Static | Flight 1 | Flight 2 | Fight 3 | Flight 4 | Flight 5 | Flight Avg |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | Randy Hansen | Turbo Beaver | 5 | 92.25 | 96.25 | 98.75 | 93.5 | 97.25 | 97.42 |\n| 2 | Rick Stucky | P 47 | 5 | 94 | 97.75 | 98.75 | 95.5 | 95 | 97.33 |\n| 3 | Gale Vasquez | Extra 300 | 5 | 93.5 | 92.5 | 98.5 | 95 | 96.5 | 96.67 |\n| 4 | Phil Tallman | Clipped Wing Cub | 5 | 91.5 | 96.5 | 97.5 | 94.5 | 93.25 | 96.17 |\n| 5 | Ron Clark | Taylorcraft | 5 | 90.75 | 92.25 | 96 | 92.75 | 94 | 94.25 |\n| 6 | Paul Fleming | Waco YMF 5 | 5 | 25.5 | 62 | 87.5 | 79.5 | 0 | 76.33 |"}
{"item_id": "item_0756", "chart_task_type": "stability_volatility", "query": "Could you show me the actual monthly business activity values for each of the four sectors from July to December 2020, along with how widely they swung, so it stands out which sector was the most stable?", "table_markdown": "| | All-Sector Business Activity | Service Sector Business Activity | Manufacturing Business Activity | Construction Business Activity |\n|----------------|------------------------------|----------------------------------|---------------------------------|--------------------------------|\n| Jul '20 | 53.3 | 50.8 | 61.9 | 63.4 |\n| Aug '20 | 50.6 | 50.0 | 53.5 | 51.4 |\n| Sep '20 | 51.8 | 51.3 | 52.7 | 54.2 |\n| Oct '20 | 47.0 | 46.8 | 47.1 | 48.7 |\n| Nov '20 | 47.4 | 46.0 | 53.1 | 50.5 |\n| Dec '20 | 48.8 | 46.4 | 56.4 | 57.0 |"}
{"item_id": "item_0757", "chart_task_type": "stability_volatility", "query": "Could you show the monthly slaughter percentages for Phoenix, Tucson, and Other areas, displaying the actual numbers and how widely each swung relative to its typical level, so I can quickly tell which region was the most stable?", "table_markdown": "| Month | Phoenix | Tucson | Other |\n|-----------|---------|--------|-------|\n| January | 78 | 10 | 12 |\n| February | 79 | 9 | 12 |\n| March | 80 | 9 | 11 |\n| April | 80 | 9 | 11 |\n| May | 78 | 9 | 13 |\n| June | 81 | 8 | 11 |\n| July | 79 | 9 | 12 |\n| August | 81 | 9 | 10 |\n| September | 79 | 11 | 10 |\n| October | 78 | 10 | 12 |\n| November | 77 | 11 | 12 |\n| December | 78 | 11 | 11 |"}
{"item_id": "item_0758", "chart_task_type": "stability_volatility", "query": "Show me the actual height measurements for Points 1, 2, and 3 across the sessions, along with how widely they fluctuated relative to their own typical levels, so it's obvious right away which point was the most stable.", "table_markdown": "| Number of session | Time [h] | X Easting [m] | Y Northing [m] | H Height [m] |\n|-------------------|----------|---------------|----------------|--------------|\n| **Point 1** | | | | |\n| Session 1 | 1:09 | 5889993.397 | 6508631.163 | 80.394 |\n| Session 2 | 2:03 | 5889993.399 | 6508631.158 | 80.411 |\n| Session 3 | 5:06 | 5889993.407 | 6508631.169 | 80.402 |\n| Session 4 | 6:13 | 5889993.400 | 6508631.159 | 80.413 |\n| Session 5 | 7:02 | 5889993.409 | 6508631.149 | 80.405 |\n| **Point 2** | | | | |\n| Session 1 | 1:09 | 5890003.106 | 6508633.936 | 80.941 |\n| Session 2 | 2:03 | 5890003.123 | 6508633.940 | 80.948 |\n| Session 3 | 5:06 | 5890003.122 | 6508633.952 | 80.943 |\n| Session 4 | 6:13 | 5890003.130 | 6508633.927 | 80.952 |\n| Session 5 | 7:02 | 5890003.120 | 6508633.940 | 80.946 |\n| **Point 3** | | | | |\n| Session 1 | 1:09 | 5889998.357 | 6508641.253 | 80.528 |\n| Session 2 | 2:03 | 5889998.353 | 6508641.240 | 80.531 |\n| Session 3 | 5:06 | 5889998.350 | 6508641.229 | 80.526 |\n| Session 4 | 6:13 | 5889998.353 | 6508641.242 | 80.546 |\n| Session 5 | 7:02 | 5889998.348 | 6508641.240 | 80.537 |"}
{"item_id": "item_0759", "chart_task_type": "stability_volatility", "query": "Help me see the actual capital expenditure figures for CIL, SCCL, NLC, and S&T/RE/EMSC D.D/VRS category along with how much each one wobbled, so it's easy to spot which entity was the most stable.", "table_markdown": "| Year | Capital Expenditure of CIL | Capital Expenditure of SCCL | Capital Expenditure of NLC | S&T/RE/EMSC D.D/VRS# |\n|-----------------------|----------------------------|-----------------------------|----------------------------|----------------------|\n| 2007 – 08 (BE) | 2472.14 | 570.58 | 2006.97 | 250.00 |\n| 2007 – 08 (RE) | 2066.97 | 520.00 | 1930.00 | 377.00 |\n| 2007 – 08 (Actual) | 2033.51 | 573.97 | 1766.71 | 279.80 |\n| 2008-09 (BE) | 3214.70 | 665.30 | 2717.00 | 300.00 |\n| 2008-09 (RE) | 2755.00 | 665.30 | 1895.34 | 286.45 |\n| 2008-09 (Actual) | 2507.17 | 650.44 | 1559.41 | 197.49 |\n| 2009-10 (BE) | 2900.00 | 580.57 | 1893.84 | 135.54 |\n| 2009-10 (RE) | 3100.00 | 633.94 | 1231.34 | 622.72 |\n| 2009-10 (Actual) | 2809.99 | 888.67 | 1363.10 | 237.29 |\n| 2010-11 (BE) | 3800.00 | 1334.93 | 1983.46 | 400.00 |\n| 2010-11 (RE) | 3615.00 | 1124.57 | 1444.65 | 470.52 |\n| 2010-11 (Actual) | 2539.72 | 643.81 | 1444.65 | 218.00 |\n| 2011-12 (BE) | 4220.00 | 2804.30 | 1858.55 | 222.59 |\n| 2011-12 (RE) | 4195.00 | 1389.61 | 1417.85 | 240.52 |\n| 2011-12 (Actual) | 3727.17 | 1070.56 | 1684.38 | 327.57 |\n| 2012-13 (BE) | 4275.00 | 3220.33 | 1687.45 | 544.00 |\n| 2012-13 (RE) | 4100.00 | 3220.33 | 1782.26 | 416.00 |\n| 2012-13 (Actual) | 1592.82 | 1068.57 | 1261.31 | 147.70 |"}
{"item_id": "item_0760", "chart_task_type": "stability_volatility", "query": "Could you show the monthly mileage for routes Ea and Eb, including the actual numbers and how widely they swung, so it's easy to spot which route was the most consistent?", "table_markdown": "| | Ea | Eb |\n|-------|------|------|\n| 1961 | | |\n| July | 361 | 451 |\n| Aug. | 449 | 358 |\n| Sept. | 0 | 356 |\n| Oct. | 440 | 344 |\n| Nov. | 331 | 347 |\n| Dec. | 260 | 339 |\n| 1962 | | |\n| Jan. | 0 | 0 | (Ship did not visit U.S.A. in this month)\n| Feb. | 463 | 349 | (Not yet analysed)"}
{"item_id": "item_0761", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual yearly security force fatalities for each state, along with how widely they swung relative to their own typical levels, so it's easy to spot which one was the most volatile.", "table_markdown": "| States | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021* | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| Andhra Pradesh | 1 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |\n| Bihar | 10 | 25 | 7 | 3 | 15 | 0 | 3 | 1 | 0 | 0 | 64 |\n| Chhattisgarh | 30 | 36 | 64 | 41 | 35 | 59 | 57 | 19 | 37 | 37 | 415 |\n| Jharkhand | 25 | 26 | 14 | 5 | 8 | 4 | 10 | 13 | 2 | 4 | 111 |\n| Maharashtra | 13 | 7 | 11 | 4 | 1 | 3 | 2 | 15 | 3 | 0 | 59 |\n| Odisha | 15 | 7 | 1 | 3 | 3 | 9 | 1 | 1 | 2 | 0 | 42 |\n| Telangana | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |"}
{"item_id": "item_0762", "chart_task_type": "stability_volatility", "query": "Help me see the actual monthly PM10 levels for the four monitoring locations, along with how widely each one swung relative to its own usual level, so I can easily spot which location was the most stable.", "table_markdown": "| Area | Aug-19 | Sep-19 | Oct-19 | Nov-19 | Dec-19 | Jan-20 | Feb-20 | Mar-20 | | |\n|---|---|---|---|---|---|---|---|---|---|---|\n| Ambient Air (PM10) (µg/m3) | | | | | | | | | | 76.52 |\n| Near weigh bridge | 60 | 73.3 | 79.7 | 86.2 | 79.4 | 76.2 | 77.2 | 74.9 | 75.81 | |\n| Near CCR building | 76.2 | 80.5 | 73.8 | 68.4 | 71.3 | 79.1 | 81.1 | 75.8 | 75.30 | |\n| Near raw material yard | 80.1 | 75.9 | 84.8 | 75.8 | 79.9 | 77.2 | 71.5 | 83.2 | 78.74 | |\n| Near cement mill | 78.2 | 87.6 | 78.4 | 67.2 | 63.1 | 73.5 | 78.4 | 80.7 | 76.23 | |\n| Ambient Air (PM2.5)(µg/m3) | | | | | | | | | | 41.07 |\n| Near weigh bridge | 37.2 | 41.7 | 44.2 | 48.5 | 40.2 | 39.6 | 36.5 | 34.2 | 40.36 | |\n| Near CCR building | 40.7 | 38.1 | 35.5 | 39.9 | 34.4 | 33.9 | 39.6 | 43.4 | 38.80 | |\n| Near raw material yard | 48.2 | 43.6 | 48.8 | 40.3 | 44.7 | 41.1 | 30.9 | 44.5 | 43.00 | |\n| Near cement mill | 53.9 | 50.5 | 46 | 34.8 | 38.5 | 36.8 | 33.9 | 37.3 | 42.13 | |\n| Ambient Air SO2(µg/m3) | | | | | | | | | | 11.36 |\n| Near weigh bridge | 11.7 | 9.5 | 8.9 | 9.3 | 8.8 | 8.6 | 8.9 | 9.5 | 9.51 | |\n| Near CCR building | 14.4 | 17.6 | 14.1 | 10.7 | 8.2 | 8.9 | 9.2 | 11.5 | 12.24 | |\n| Near raw material yard | 9.8 | 12.7 | 16 | 13.9 | 11.2 | 12.0 | 7.6 | 9 | 11.46 | |\n| Near cement mill | 12.5 | 16.1 | 13.3 | 14.8 | 9.4 | 10.6 | 10.8 | 8.7 | 12.23 | |\n| Ambient Air NO2(µg/m3) | | | | | | | | | | 25.85 |\n| Near weigh bridge | 29.6 | 20.4 | 17.6 | 20.2 | 23.9 | 20.9 | 21.6 | 18.3 | 21.66 | |\n| Near CCR building | 32.5 | 29.3 | 33.6 | 28.1 | 19.4 | 24.7 | 23.3 | 28.2 | 27.77 | |\n| Near raw material yard | 21.7 | 26.8 | 29.4 | 32.3 | 26.8 | 29.6 | 19.1 | 24.3 | 25.77 | |\n| Near cement mill | 28.2 | 33.7 | 28.1 | 32.2 | 24 | 23.5 | 28.4 | 22.8 | 28.20 | |\n| CO(mg/m3) | | | | | | | | | | 0.64 |\n| Near weigh bridge | 0.57 | 0.66 | 0.73 | 0.68 | 0.72 | 0.52 | 0.78 | 0.61 | 0.68 | |\n| Near CCR building | 0.73 | 0.69 | 0.82 | 0.58 | 0.66 | 0.54 | 0.54 | 0.45 | 0.64 | |\n| Near raw material yard | 0.65 | 0.77 | 0.52 | 0.6 | 0.77 | 0.63 | 0.39 | 0.58 | 0.61 | |"}
{"item_id": "item_0763", "chart_task_type": "stability_volatility", "query": "Could you show me each boat's individual race results alongside how widely they scattered relative to their own typical level, so I can quickly tell which one stayed the most consistent?", "table_markdown": "| Rank | Boat | Class | SailNo | HelmName | CrewName | R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1st | Jack O'Lantern | Sprite | 33 | Martin Scarth | Angela Docherty | (2.0) | (3.0) | (2.0) | (2.0) | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 16 |\n| 2nd | Cygnet | Sprite | 204 | David Nichols | Chris Mills | (3.0) | 1.0 | 1.0 | 1.0 | (4.0) | (2.0) | 2.0 | 2.0 | 1.0 | (4.0) | 21 |\n| 3rd | Ceres | Sprite | 27 | Chris Briggs | David Banfield | (5.0) | 2.0 | (4.0) | (4.0) | 3.0 | 4.0 | (5.0) | 3.0 | 3.0 | 3.0 | 36 |\n| 4th | Djinn | Sprite | 15 | Marcus Gallo | Matt Ogburn | 1.0 | 6.0 | (6.5) | (6.5) | 2.0 | 6.0 | 3.0 | 4.0 | (7.5) | (7.5) | 50 |\n| 5th | Nida | Sprite | 23 | Chris Nichols | Kathryn Nichols | 4.0 | 6.0 | 3.0 | 3.0 | 5.0 | 3.0 | (8.0) | (8.0) | (7.5) | (7.5) | 55 |\n| 6th | Mustard Seed | Sprite | 29 | Lulla Waterfield | Anne Slack | (7.0) | 6.0 | (6.5) | (6.5) | (7.0) | 5.0 | 4.0 | 6.0 | 4.0 | 5.0 | 57 |\n| 7th | Aurora | Sprite | 26 | Peter Jenkin | Martin Jordan | (7.0) | 6.0 | (6.5) | 6.5 | (7.0) | (8.0) | 6.0 | 5.0 | 5.0 | 2.0 | 59 |\n| 8th | Pegasus | Sprite | 210 | Sheena Berney | Lesley Lansley | (7.0) | 6.0 | 6.5 | 6.5 | (7.0) | (7.0) | (7.0) | 7.0 | 6.0 | 6.0 | 66 |"}
{"item_id": "item_0764", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual monthly inspection counts for each category from January to May alongside how widely they fluctuated relative to their usual level, so the most stable one stands out.", "table_markdown": "| INSPECTIONS, PHONE CALLS, & PLAN REVIEW | JANUARY | FEBRUARY | MARCH | APRIL | MAY | JUNE | JULY | AUGUST | SEPTEMBER | OCTOBER | NOVEMBER | DECEMBER | TOTAL |\n|-----------------------------------------|---------|----------|-------|-------|------|------|------|--------|-----------|---------|----------|----------|-------|\n| Building - William Kraft | 53 | 67 | 0 | 12 | 11 | | | | | | | | 143 |\n| Plumbing - William Kraft | 12 | 15 | 1 | 14 | 18 | | | | | | | | 60 |\n| Heating - William Kraft | 7 | 8 | 0 | 1 | 2 | | | | | | | | 18 |\n| Zoning Calls - W. Kraft | 15 | 27 | 24 | 4 | 10 | | | | | | | | 80 |\n| Plan Review - W. Kraft | 16 | 19 | 8 | 26 | 45 | | | | | | | | 114 |\n| Administrative Calls - W. Kraft | 52 | 44 | 56 | 88 | 81 | | | | | | | | 321 |\n| Code Enf. - W. Kraft | 6 | 3 | 15 | 18 | 20 | | | | | | | | 62 |\n| Building - Frank Silla | 67 | 44 | 84 | 112 | 90 | | | | | | | | 397 |\n| Heating - Frank Silla | 35 | 15 | 36 | 35 | 30 | | | | | | | | 151 |\n| Electrical - Frank Silla | 55 | 31 | 50 | 54 | 55 | | | | | | | | 245 |\n| Code Enforcement | 0 | 58 | 104 | 79 | 277 | | | | | | | | 518 |\n| **TOTAL INSPECTIONS:** | **318** | **238** | **378**| **443**| **639**| **0**| **0**| **0**| **0**| **0**| **0**| **0**| **2109**|"}
{"item_id": "item_0765", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual figures for each electricity balance component from 2008 to 2014, plus how widely they wobbled relative to their normal levels, so I can quickly tell which one was the most volatile.", "table_markdown": "| | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 |\n|----------------------|--------|--------|--------|--------|--------|--------|--------|\n| Generation | 8 450.5| 8 407.7| 10 057.7| 10 104.5| 9 694.7| 10 058.7| 10 369.6|\n| Hydropower plants | 7 169.0| 7 417.0| 9 374.9| 7 892.5| 7 222.6| 8 271.0| 8 333.7|\n| Thermal power plants | 1 281.5| 990.7 | 682.8 | 2 212.1| 2 472.1| 1 787.7| 2 035.9|\n| Import | 649.2 | 258.2 | 222.1 | 471.0 | 614.6 | 484.1 | 851.9 |\n| Export | 680.0 | 749.4 | 1 524.3| 930.6 | 528.2 | 450.4 | 603.6 |\n| Consumption | 8 419.7| 7 916.5| 8 755.4| 9 644.9| 9 781.2| 10 092.5| 10 618.0|\n| Domestic consumption | 8 075.0| 7 642.1| 8 441.1| 9 256.6| 9 379.4| 9 690.2| 10 170.1|\n| Losses | 344.7 | 274.4 | 314.3 | 388.3 | 401.8 | 402.3 | 448.0 |"}
{"item_id": "item_0766", "chart_task_type": "stability_volatility", "query": "Could you show me the shrimp catch numbers by age group from 1981 to 1992, along with how widely they scattered compared to where each normally sits, so I can quickly tell which age group was the most volatile.", "table_markdown": "| Year | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 |\n|------|------|------|------|------|------|------|------|------|------|------|------|------|\n| Age | | | | | | | | | | | | |\n| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14842| 0 | 0 | 0 |\n| 4 | 10185| 5727 | 5227 | 29642| 7042 | 12095| 29070| 68271| 54333| 37565| 27551| 29309|\n| 5 | 25193| 31393| 85626| 67170| 47888| 87594| 107865| 117991| 153831| 280921| 83542| 177805|\n| 6 | 67540| 31605| 137640| 48678| 87607| 87227| 219554| 164742| 187355| 149443| 366162| 296017|\n| 7 | 433111| 143390| 372267| 128453| 229581| 179586| 408509| 378235| 541457| 348701| 411488| 473822|\n| Total| 536029| 212115| 580760| 271943| 352118| 366502| 764998| 742081| 936776| 816630| 888743| 976953|"}
{"item_id": "item_0767", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual figures for Quincy Public Schools Appropriation and City of Quincy Budget from FY09 to FY18, along with how widely they swung, so it's obvious right away which stream was the most stable.", "table_markdown": "| Fiscal Year | Quincy Public Schools Appropriation | City of Quincy Budget |\n|-------------|------------------------------------|-----------------------|\n| FY09 | 81,233,562 | 20,502,522 |\n| FY10 | 81,083,562 | 27,020,890 |\n| FY11 | 82,413,157 | 25,029,132 |\n| FY12 | 86,238,446 | 31,808,512 |\n| FY13 | 87,558,165 | 29,999,668 |\n| FY14 | 91,158,165 | 35,913,222 |\n| FY15 | 94,106,723 | 35,955,141 |\n| FY16 | 95,587,723 | 39,525,687 |\n| FY17 | 98,748,022 | 41,303,332 |\n| FY18* | 101,656,951 | 41,021,887 |"}
{"item_id": "item_0768", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual corn yield numbers for Jalisco, Veracruz, and Mexico as a whole from 1940 to 1967, plus how widely they swung relative to their usual levels, so it's obvious which region was the most stable.", "table_markdown": "| Year | Jalisco | Veracruz | Mexico as a whole |\n|------|---------|----------|-------------------|\n| 1940 | 549 | 1,050 | 626 |\n| 1950 | 731 | 1,069 | 791 |\n| 1957 | 1,052 | 1,085 | 835 |\n| 1958 | 916 | 958 | 828 |\n| 1959 | 1,250 | 1,158 | 880 |\n| 1960 | 1,378 | 1,214 | 975 |\n| 1961 | 1,464 | 1,085 | 993 |\n| 1962 | 1,450 | 1,086 | 997 |\n| 1963 | 1,340 | 1,590 | 946 |\n| 1964 | 1,683 | 1,432 | 1,113 |\n| 1965 | 1,679 | 1,600b | 1,124 |\n| 1966 | 1,828 | 1,650b | 1,090 |\n| 1967 | 2,046 | 1,650b | 1,204 |"}
{"item_id": "item_0769", "chart_task_type": "stability_volatility", "query": "I'd like to see each water year's actual monthly Supply Canal diversions and how widely they swung relative to their own typical level, so it's obvious right away which year was the most volatile.", "table_markdown": "| Water Year | October | November | December | January | February | March | September |\n|------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|\n| 1991 | 42163 | 39757 | 41190 | 53640 | 72320 | 63478 | 65362 |\n| 1992 | 52345 | 55492 | 58890 | 67370 | 85830 | 125167 | 74407 |\n| 1993 | 54445 | 54967 | 69510 | 79380 | 81760 | 129999 | 77575 |\n| 1994 | 104828 | 88982 | 102450 | 96970 | 91010 | 90741 | 53499 |\n| 1995 | 61861 | 52103 | 60980 | 63060 | 56970 | 61772 | 129503 |\n| 1996 | 131308 | 120617 | 92480 | 91140 | 99300 | 117356 | 130078 |\n| 1997 | 116471 | 123731 | 100620 | 94460 | 109520 | 133351 | 131328 |\n| 1998 | 135057 | 126349 | 131060 | 122370 | 122260 | 132180 | 129999 |\n| 1999 | 134541 | 126944 | 108240 | 107130 | 115580 | 126646 | 131109 |\n| 2000 | 134957 | 129899 | 131260 | 133811 | 125845 | 130832 | 70135 |\n| 2001 | 61479 | 63325 | 56212 | 75908 | 76594 | 72552 | 71356 |\n| 2002 | 42768 | 45160 | 64780 | 61810 | 53970 | 61389 | 42318 |\n| 2003 | 40926 | 39267 | 38330 | 39320 | 35550 | 40473 | 31968 |\n| 2004 | 19895 | 19418 | 21166 | 24234 | 28398 | 30984 | 31930 |\n| 2005 | 21033 | 25268 | 27648 | 29094 | 26175 | 29725 | 25446 |\n| 2006 | 27590 | 25052 | 30626 | 32806 | 27000 | 36790 | 23836 |\n| 2007 | 28503 | 26807 | 28934 | 28779 | 35585 | 42923 | 24413 |\n| 2008 | 27882 | 26317 | 28853 | 34557 | 33283 | 32367 | 29570 |\n| 2009 | 42471 | 32034 | 27532 | 36086 | 32490 | 32158 | 48867 |\n| 2010 | 69282 | 79872 | 57991 | 79266 | 55678 | 91164 | 130820 |\n| 2011 | 134805 | 128795 | 118111 | 91826 | 93161 | 132970 | 131367 |\n| 2012 | 134785 | 127460 | 120617 | 112996 | 120004 | 105596 | 57089 |"}
{"item_id": "item_0770", "chart_task_type": "stability_volatility", "query": "I'd like to see the yearly appeal counts for allowed and refused cases, along with how widely each fluctuated, so it's obvious right away which category was the most stable.", "table_markdown": "| Year | Allowed | Refused |\n|------------|---------|---------|\n| 2003/4 | 17955 | 5700 |\n| 2004/5 | 20615 | 19514 |\n| 2005/6 | 9643 | 13306 |\n| 2006/7 | 4260 | 6684 |\n| 2007/8 | 6618 | 6390 |\n| 2008/9 | 5532 | 5268 |\n| 2009/10 | 4539 | 5805 |\n| 2010/11 | 2773 | 2680 |\n| 2011/12 | 1901 | 5416 |\n| 2012/13 | 1938 | 5485 |\n| 2013/14 | 1850 | 5320 |\n| 2014/15 | 1580 | 4242 |\n| 2015/16 | 2396 | 4499 |"}
{"item_id": "item_0771", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual production figures for Sundance, Brazeau, and Other along with how much each wobbled relative to its typical level, so I can quickly tell which stayed the most stable.", "table_markdown": "| | 2021 Q1 22 | 2021 Q2 22 | 2021 Q3 22 | Oct 22 | Nov 22 | Dec 22 | Q4 22 | 2022 Jan 23 | 2022 Feb 23 | 2022 Mar 23 | 2022 Apr 23 |\n|----------------|------------|------------|------------|--------|--------|--------|-------|-------------|-------------|-------------|-------------|\n| Sundance | 70 | 78 | 76 | 75 | 75 | 75 | 75 | 76 | 73 | 71 | 70 | 69 |\n| Brazeau | 17 | 19 | 23 | 24 | 25 | 26 | 26 | 23 | 27 | 27 | 28 | 28 |\n| Other | 4 | 5 | 5 | 5 | 4 | 4 | 5 | 5 | 5 | 5 | 4 | 4 |\n| Total | 91 | 101 | 104 | 105 | 105 | 106 | 105 | 104 | 105 | 103 | 102 | 101 |\n| Liquids % | 13% | 11% | 13% | 13% | 13% | 12% | 12% | 12% | 12% | 12% | 12% | 12% |"}
{"item_id": "item_0772", "chart_task_type": "stability_volatility", "query": "Could you show me each proposal entity's individual rater ratings across all raters alongside how much they scattered relative to their own typical level, so I can easily spot which one stayed the most stable?", "table_markdown": "| | Justice Point Milwaukee WI | Wisconsin Community Services Milwaukee WI | Rock Valley Janesville WI | Attic Madison WI |\n|----------------|----------------------------|------------------------------------------|---------------------------|------------------|\n| Rater 1 | 87 | 70 | 65 | 27 |\n| Rater 2 | 80 | 65 | 60 | 35 |\n| Rater 3 | 86 | 81 | 56 | 17 |\n| Rater 4 | 79 | 77 | 63 | 45 |\n| Rater 5 | 81 | 80 | 71 | 40 |\n| Rater 6 | 85 | 73 | 45 | 33 |\n| **Total** | 498 | 446 | 360 | 197 |"}
{"item_id": "item_0773", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual sample sizes for each location across the six waves, along with how widely they swung relative to their own usual level, so it's easy to spot which location stayed the most consistent.", "table_markdown": "| | Wave 1 | Wave 2 | Wave 3 | Wave 4 | Wave 5 | Wave 6 | Total |\n|-------|--------|--------|--------|--------|--------|--------|-------|\n| Sydney| 269 | 266 | 323 | 289 | 291 | 305 | 1743 |\n| Melbourne| 360 | 382 | 364 | 396 | 317 | 369 | 2188 |\n| Brisbane| 191 | 188 | 170 | 170 | 191 | 197 | 1107 |\n| Adelaide| 290 | 285 | 264 | 253 | 260 | 247 | 1599 |\n| Perth | 211 | 211 | 195 | 156 | 160 | 166 | 1099 |\n| Hobart | 95 | 85 | 95 | 69 | 66 | 79 | 489 |\n| ACT | 213 | 212 | 206 | 205 | 204 | 205 | 1245 |\n| Rest NSW| 183 | 185 | 160 | 182 | 200 | 187 | 1097 |\n| Rest VIC| 109 | 91 | 109 | 121 | 147 | 117 | 694 |\n| Rest QLD| 153 | 164 | 176 | 167 | 170 | 161 | 991 |\n| Rest SA | 49 | 57 | 60 | 74 | 66 | 73 | 379 |\n| Rest WA | 33 | 35 | 37 | 67 | 70 | 58 | 300 |\n| Rest TAS| 120 | 124 | 120 | 136 | 142 | 133 | 775 |\n| NT | 32 | 27 | 21 | 20 | 22 | 24 | 146 |\n| **Total** | **2308** | **2312** | **2300** | **2305** | **2306** | **2321** | **13852** |"}
{"item_id": "item_0774", "chart_task_type": "stability_volatility", "query": "I'd like to see the monthly insolation numbers for these six cities along with how much they fluctuate, so the most stable one stands out.", "table_markdown": "| Month | Dhaka | Rajshahi | Sylhet | Bogra | Barisal | Jessore |\n|---------|-------|----------|--------|-------|---------|--------|\n| January | 4.03 | 3.96 | 4.00 | 4.01 | 4.17 | 4.25 |\n| February| 4.78 | 4.47 | 4.63 | 4.69 | 4.81 | 4.85 |\n| March | 5.33 | 5.88 | 5.20 | 5.68 | 5.30 | 4.50 |\n| April | 5.71 | 6.24 | 5.24 | 5.87 | 5.94 | 6.23 |\n| May | 5.71 | 6.17 | 5.37 | 6.02 | 5.75 | 6.09 |\n| June | 4.8 | 5.25 | 4.53 | 5.26 | 4.39 | 5.12 |\n| July | 4.41 | 4.79 | 4.14 | 4.34 | 4.20 | 4.81 |\n| August | 4.82 | 5.16 | 4.56 | 4.84 | 4.42 | 4.93 |\n| September| 4.41 | 4.96 | 4.07 | 4.67 | 4.48 | 4.57 |\n| October | 4.61 | 4.88 | 4.61 | 4.65 | 4.71 | 4.68 |\n| November| 4.27 | 4.42 | 4.32 | 4.35 | 4.35 | 4.24 |\n| December| 3.92 | 3.82 | 3.85 | 3.87 | 3.95 | 3.97 |\n| Average | 4.73 | 5.00 | 4.54 | 4.85 | 4.71 | 4.85 |"}
{"item_id": "item_0775", "chart_task_type": "stability_volatility", "query": "I'd like to see the yearly values for CGE, CPI, POP, and GNPD from 1970 to 1979 alongside how widely each one fluctuated, so I can easily spot which indicator was the most volatile.", "table_markdown": "| Year | 1970 | 1971 | 1972 | 1973 | 1974 | 1975 | 1976 | 1977 | 1978 | 1979 |\n|------|------|------|------|------|------|------|------|------|------|------|\n| CGE | 100.0| 105.0| 113.0| 139.1| 163.0| 175.3| 167.7| 185.2| 209.4| 214.0|\n| CPI | 100.0| 104.3| 107.7| 114.4| 127.0| 138.6| 146.6| 156.1| 168.0| 187.0|\n| POP | 100.0| 100.3| 100.6| 101.0| 101.3| 101.6| 101.9| 102.2| 102.6| 102.9|\n| GNPD | 100.0| 107.0| 113.3| 121.5| 134.1| 146.9| 157.2| 169.9| 183.6| 200.7|"}
{"item_id": "item_0776", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual unemployment figures for each Portuguese region from 1998 to 2006, along with how much they fluctuated relative to their normal level, so it's easy to spot which one was the most volatile.", "table_markdown": "| | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 |\n|----------------|------|------|------|------|------|------|------|------|------|\n| **Unemployment rate (%)** | | | | | | | | | |\n| Norte | 5.0 | 4.4 | 4.1 | 3.7 | 4.9 | 6.8 | 7.7 | 8.8 | 8.9 |\n| Centro | 2.8 | 2.3 | 2.2 | 2.8 | 3.1 | 3.6 | 4.3 | 5.2 | 5.5 |\n| Lisboa | 6.1 | 5.9 | 5.3 | 5.2 | 6.8 | 8.2 | 7.7 | 8.6 | 8.5 |\n| Alentejo | 7.9 | 6.6 | 5.3 | 6.9 | 7.5 | 8.2 | 8.9 | 9.2 | 9.2 |\n| Algarve | 6.0 | 4.7 | 3.5 | 3.8 | 5.2 | 6.1 | 5.5 | 6.2 | 5.5 |\n| Açores | 4.4 | 3.2 | 2.9 | 2.3 | 2.6 | 2.9 | 3.4 | 4.1 | 3.8 |\n| Madeira | 3.6 | 2.7 | 2.5 | 2.6 | 2.5 | 3.4 | 3.0 | 4.6 | 5.4 |\n| **Long-term unemployment (%)** | | | | | | | | | |\n| Portugal | 45.4 | 41.2 | 43.8 | 40.0 | 37.3 | 37.7 | 46.2 | 49.9 | 51.7 |"}
{"item_id": "item_0777", "chart_task_type": "stability_volatility", "query": "Could you show me the actual rate of return figures for the three accounting methods from 1976 to 1990, plus how widely they swung, so I can easily spot which was the most volatile.", "table_markdown": "| Year | Statutory | GAAP No UCGs | GAAP with UCGs |\n|--------|-----------|--------------|----------------|\n| 1976 | 11.4 | 11.4 | 19.3 |\n| 1977 | 23.0 | 21.3 | 18.6 |\n| 1978 | 21.9 | 20.2 | 21.0 |\n| 1979 | 18.2 | 16.7 | 20.9 |\n| 1980 | 15.5 | 14.3 | 20.1 |\n| 1981 | 12.9 | 12.0 | 8.8 |\n| 1982 | 9.5 | 9.1 | 12.4 |\n| 1983 | 8.8 | 8.5 | 10.0 |\n| 1984 | 1.3 | 1.9 | -1.0 |\n| 1985 | 2.6 | 4.3 | 9.2 |\n| 1986 | 15.0 | 15.1 | 16.7 |\n| 1987 | 13.8 | 16.7 | 14.8 |\n| 1988 | 13.4 | 14.5 | 16.0 |\n| 1989 | 9.7 | 10.2 | 14.0 |\n| 1990 | 7.8 | 8.4 | 4.7 |"}
{"item_id": "item_0778", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual quarterly deficiency counts for 'Sufficient Staff' and 'RN 8 hrs per day' alongside how much each varied, so it's obvious right away which standard stayed the most consistent.", "table_markdown": "| Federal Minimum Nursing Standards - Number of Deficiencies by CY Quarter | Qtr 3 2010 | Qtr 4 2010 | Qtr 1 2011 | Qtr 2 2011 | Qtr 3 2011 | Qtr 4 2011 | Qtr 1 2012 | Qtr 2 2012 | Qtr 3 2012 | Qtr 4 2012 | Qtr 1 2013 |\n|------------------------------------------------------------------------|------------|------------|------------|------------|------------|------------|------------|------------|------------|------------|------------|\n| Sufficient Staff (F353) | 8 | 5 | 9 | 4 | 5 | 4 | 10 | 5 | 5 | 4 | 8 |\n| RN 8 hrs per day 7 days a week (F354) | 7 | 1 | 2 | 3 | 2 | 2 | 6 | 4 | 3 | 1 | 2 |"}
{"item_id": "item_0779", "chart_task_type": "stability_volatility", "query": "Could you show the actual Prior Year Carryforward balances for each area from 2017-18 to 2021-22, along with how widely they scattered relative to their own typical level, so I can easily spot which one was the most volatile?", "table_markdown": "| Prior Year Carryforward | 2017-18 | 2018-19 | 2019-20 | 2020-21 | 2021-22 |\n|-------------------------|---------|---------|---------|---------|---------|\n| **Athletics** | $337,100 | $543,224 | $732,328 | $814,798 | $1,153,496 |\n| **Campus Recreation** | $293,302 | $468,839 | $656,336 | $703,152 | $870,501 |\n| **Cross Cultural Center** | $0 | $404 | $4,272 | $8,539 | $14,059 |\n| **Women’s Resource Center** | $0 | $2,541 | $23,974 | $46,334 | $70,488 |\n| **Student Affairs (Unallocated)** | $4,892 | $6,060 | $6,531 | $7,017 | $7,524 |\n| **One-Time Adjustments** | $9,330 | | | | |\n| **Capital Projects Returned Funds** | $9,330 | | | | |\n| **Planned Uses of Carryforward** | | | | | |\n| **Minor Capital Projects – Campus Rec** | | | | | |"}
{"item_id": "item_0780", "chart_task_type": "stability_volatility", "query": "I'd like to see the yearly figures for recreation visits and the three camping overnight categories, along with how widely each fluctuated, so it's easy to spot which was the most stable.", "table_markdown": "| Year | Recreation Visits | Tent Camper Overnights | RV Camper Overnights | Backcountry Camper Overnights |\n|------|-------------------|------------------------|----------------------|-------------------------------|\n| 2001 | 279,873,926 | 3,326,852 | 2,404,840 | 2,032,886 |\n| 2002 | 277,299,880 | 3,357,513 | 2,404,824 | 1,906,473 |\n| 2003 | 266,099,641 | 3,302,637 | 2,400,232 | 1,816,088 |\n| 2004 | 276,908,337 | 3,128,014 | 2,321,669 | 1,725,309 |\n| 2005 | 273,488,751 | 2,974,269 | 2,168,287 | 1,668,558 |\n| 2006 | 272,623,980 | 2,882,297 | 2,109,404 | 1,659,484 |\n| 2007 | 275,581,547 | 3,003,270 | 2,107,541 | 1,704,059 |\n| 2008 | 274,852,949 | 2,959,761 | 2,012,532 | 1,797,912 |\n| 2009 | 285,579,941 | 3,184,255 | 2,150,170 | 1,860,162 |\n| 2010 | 281,303,769 | 3,277,151 | 2,256,692 | 1,763,541 |\n| 2011 | 278,939,216 | 3,229,241 | 2,155,330 | 1,715,611 |\n| 2012 | 282,765,682 | 3,203,413 | 2,121,646 | 1,816,904 |\n| 2013 | 273,630,895 | 2,768,708 | 1,791,921 | 1,573,734 |\n| 2014 | 292,800,082 | 3,246,320 | 2,053,965 | 1,888,095 |"}
{"item_id": "item_0781", "chart_task_type": "stability_volatility", "query": "I'd like to see the individual head movement figures for each disk scheduling algorithm across the five runs, plus how widely they swung, so the most stable one stands out.", "table_markdown": "| S.No. | FCFS | SSTF | Scan | Look | C-Scan |\n|---|---|---|---|---|---|\n| 1 | 540 | 240 | 290 | 240 | 363 |\n| 2 | 631 | 276 | 280 | 276 | 348 |\n| 3 | 264 | 217 | 270 | 224 | 393 |\n| 4 | 322 | 189 | 235 | 189 | 363 |\n| 5 | 640 | 235 | 275 | 245 | 378 |\n| Average | 479 | 231 | 270 | 235 | 369 |"}
{"item_id": "item_0782", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual daily usage figures for each transport mode across the week alongside how much they wobbled relative to their own typical level, so the most stable mode stands out.", "table_markdown": "| Day | Mon | Tue | Wed | Thu | Fri | Average |\n|-----|-----|-----|-----|-----|-----|---------|\n| Walk| 20 | 24 | 22 | 20 | 24 | 22 |\n| Scooter| 3 | 3 | 4 | 3 | 3 | 3 |\n| Bike | 7 | 7 | 7 | 7 | 7 | 7 |\n| Bus | 1 | 2 | 2 | 1 | 2 | 2 |\n| Car | 60 | 55 | 59 | 57 | 54 | 59 |"}
{"item_id": "item_0783", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual shipment numbers for Single Engine, Multi-Engine, Total Piston, Turbo-Prop, and Jet aircraft from 1962 to 1981, along with how widely each swung, so the most volatile type stands out.", "table_markdown": "| Year | Total | Single Engine | Multi-Engine | Total Piston | Turbo-Prop | Jet |\n|------|---------|---------------|--------------|--------------|------------|-----|\n| 1962 | 6,697 | 5,690 | 1,007 | 6,697 | - | - |\n| 1963 | 7,569 | 6,248 | 1,321 | 7,569 | - | - |\n| 1964 | 9,336 | 7,718 | 1,606 | 9,324 | 9 | 3 |\n| 1965 | 11,852 | 9,873 | 1,780 | 11,653 | 87 | 112 |\n| 1966 | 15,768 | 13,250 | 2,192 | 15,442 | 165 | 161 |\n| 1967 | 13,577 | 11,557 | 1,773 | 13,330 | 149 | 98 |\n| 1968 | 13,698 | 11,398 | 1,959 | 13,357 | 248 | 93 |\n| 1969 | 12,457 | 10,054 | 2,078 | 12,132 | 214 | 111 |\n| 1970 | 7,292 | 5,942 | 1,159 | 7,101 | 135 | 56 |\n| 1971 | 7,466 | 6,287 | 1,043 | 7,330 | 89 | 47 |\n| 1972 | 9,774 | 7,898 | 1,548 | 9,446 | 179 | 149 |\n| 1973 | 13,646 | 10,780 | 2,413 | 13,193 | 247 | 206 |\n| 1974 | 14,166 | 11,562 | 2,135 | 13,697 | 250 | 219 |\n| 1975 | 14,056 | 11,439 | 2,116 | 13,555 | 305 | 196 |\n| 1976 | 15,449 | 12,783 | 2,120 | 14,903 | 359 | 187 |\n| 1977 | 16,907 | 14,057 | 2,195 | 16,252 | 428 | 227 |\n| 1978 | 17,811 | 14,398 | 2,634 | 17,032 | 548 | 231 |\n| 1979 | 17,048 | 13,286 | 2,843 | 16,129 | 637 | 282 |\n| 1980 | 11,877 | 8,640 | 2,116 | 10,756 | 795 | 326 |\n| 1981 | 9,457 | 6,608 | 1,542 | 8,150 | 918 | 389 |"}
{"item_id": "item_0784", "chart_task_type": "stability_volatility", "query": "Could you show me the actual monthly submission counts for the three fiscal years along with how widely they swung, so it's obvious right away which year was the most volatile?", "table_markdown": "| | APR | MAY | JUN | JUL | AUG | SEP | OCT | NOV | DEC | JAN | FEB | MAR |\n|-------|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|\n| CSAP 2008 -2009 | | | | 7 | 5 | 9 | 12 | 10 | 20 | 3 | 9 | 10 |\n| CSAP 2009 - 2010 | 20 | 8 | 13 | 3 | 9 | 10 | 7 | 9 | 11 | 2 | 10 | 17 |\n| CSAP 2010 - 2011 | 7 | 8 | 7 | 2 | 7 | 4 | 9 | 10 | 15 | 69 | 0 | 5 |"}
{"item_id": "item_0785", "chart_task_type": "stability_volatility", "query": "I'd like to see the yearly amounts for the three revenue streams from 2004 to 2014 alongside how much they wobbled relative to their own baselines, so I can easily spot which stayed the most stable.", "table_markdown": "| | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 (Year to Date) |\n|--------------------------|------------|------------|------------|------------|------------|------------|------------|------------|------------|------------|---------------------|\n| Accounts Receivable to Jail | $14,515.00 | $21,202.12 | $28,377.12 | $27,409.62 | $27,437.12 | $28,907.12 | $30,753.80 | $1,125.74 | $1,375.74 | $1,065.74 | $1,065.74 |\n| Jail Restitution Received | $2,820.00 | $4,325.00 | $3,545.00 | $4,805.00 | $1,980.00 | $1,413.62 | $1,820.00 | $350.00 | $1,885.00 | $0.00 | |\n| Revenue from Sheriff's / Other fees | - | - | $4,153.18 | $4,730.76 | $4,166.51 | $6,662.54 | $5,055.89 | $4,553.75 | $3,305.01 | $3,901.23 | $25.00 |"}
{"item_id": "item_0786", "chart_task_type": "stability_volatility", "query": "I want to see the actual yearly percentages for each demographic group from 2003 to 2011 alongside how widely they swung relative to their own baseline, so I can quickly tell which demographic was the most volatile.", "table_markdown": "| | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 |\n|----------------------|------|------|------|------|------|------|------|------|------|\n| African American | 1.7% | 2.6% | 3.1% | 4.0% | 5.2% | 6.0% | 6.4% | 6.8% | 6.3% |\n| Hispanic | 11.6%| 12.3%| 13.0%| 15.1%| 16.4%| 16.8%| 17.1%| 16.8%| 18.7%|\n| White | 84.6%| 82.7%| 81.3%| 78.0%| 75.0%| 73.8%| 72.9%| 72.8%| 69.5%|\n| Economically Disadvantaged | 19.4%| 19.1%| 19.4%| 21.0%| 20.2%| 20.6%| 21.2%| 22.9%| 23.2%|"}
{"item_id": "item_0787", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual attendance numbers for each of the six outdoor ice skating rinks from 2016/17 to 2021/22, plus how widely they scattered, so I can quickly tell which one was the most stable.", "table_markdown": "| Rink Attendance | 2016/17 | 2017/18 | 2018/19 | 2019/20 | 2020/21 | 2021/22 |\n|-----------------|---------|---------|---------|---------|---------|---------|\n| Campus | 870 | 1,070 | 849 | 957 | 424 | 471 |\n| Frank Olson | 883 | 1,047 | 632 | 926 | 452 | 743 |\n| McKennan | 1,346 | 1,507 | 1,238 | 1,755 | 1,225 | 1,451 |\n| Memorial* | 3,473 | 4,520 | 2,010 | 3,654 | 2,375 | 2,819 |\n| Sherman* | 1,279 | 1,629 | 1,269 | 1,802 | 550 | 1,063 |\n| Tuthill | 3,000 | 3,100 | 2,299 | 2,756 | 2,962 | 2,875 |\n| Total | 10,851 | 12,873 | 8,297 | 10,893 | 7,988 | 9,422 |"}
{"item_id": "item_0788", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual yearly incident numbers for narcotics and cocaine from 1995 to 2009, plus how widely each swung relative to its own baseline, so it's obvious right away which drug type was the most volatile.", "table_markdown": "| Year | Narcotics possession | Cocaine possession |\n|------|----------------------|--------------------|\n| 1995 | 1301 | 138 |\n| 1996 | 1547 | 119 |\n| 1997 | 1900 | 156 |\n| 1998 | 3024 | 281 |\n| 1999 | 3024 | 203 |\n| 2000 | 2490 | 209 |\n| 2001 | 1102 | 472 |\n| 2002 | 952 | 212 |\n| 2003 | 901 | 117 |\n| 2004 | 898 | 185 |\n| 2005 | 814 | 223 |\n| 2006 | 551 | 284 |\n| 2007 | 698 | 275 |\n| 2008 | 694 | 411 |\n| 2009 | 902 | 599 |"}
{"item_id": "item_0789", "chart_task_type": "stability_volatility", "query": "Show me the actual physiological weight loss values for each packaging material across the observation days, along with how much they wobbled, so I can easily spot which one was the most stable.", "table_markdown": "| Days of observation | 0 | 3 | 6 | 9 | 12 | 15 | 18 |\n|---|---|---|---|---|---|---|---|\n| RR1 | 0 | 2.4 | 2.96 | 3.9 | 4.16 | 5.83 | 6.26 |\n| RR2 | 0 | 1.61 | 1.73 | 2.3 | 3.03 | 3.96 | 4.26 |\n| RR3 | 0 | 2 | 2.93 | 3.86 | 3.83 | 5.13 | 5.7 |"}
{"item_id": "item_0790", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual exchange rate figures for the BDC and Interbank segments, plus how widely each fluctuated relative to its own usual level, so it's obvious right away which one stayed the most stable.", "table_markdown": "| Average Exchange Rate (₦/US$) | Q4-15 | Q1-16 | Q2-16 | Q3-16 | Q4-16 | Q1-17 | Q2-17 | Q3-17 | Q4-17 |\n|-------------------------------|-------|-------|-------|-------|-------|-------|-------|-------|-------|\n| Investors and Exporters Window | N/A | N/A | N/A | N/A | N/A | N/A | 376.81 | 362.15 | 360.47 |\n| BDC | 238.69 | 313.49 | 336.67 | 397.24 | 445.03 | 472.49 | 379.05 | 365.56 | 362.49 |\n| Interbank | 196.99 | 197.00 | 206.88 | 303.17 | 305.21 | 305.64 | 305.76 | 305.81 | 305.96 |\n| Premium (%) | | | | | | | | | |\n| rBAS/Interbank | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |\n| BDC/Interbank | 21.2 | 59.1 | 62.7 | 31.0 | 45.8 | 54.6 | 19.3 | 19.5 | 18.5 |"}
{"item_id": "item_0791", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual barge rig counts for Parker, Competitor A, Competitor B, and Others from 2010 to 2017, along with how widely each varied, so I can easily spot which fleet was the most stable.", "table_markdown": "| Year | Others | Competitor B | Competitor A | Parker |\n|------|--------|-------------|-------------|-------|\n| 2010 | 9 | 3 | 17 | 13 |\n| 2011 | 9 | 3 | 17 | 13 |\n| 2012 | 9 | 3 | 17 | 13 |\n| 2013 | 9 | 3 | 8 | 12 |\n| 2014 | 5 | 3 | 8 | 13 |\n| 2015 | 2 | 3 | 8 | 13 |\n| 2016 | 3 | 10 | 8 | 13 |\n| 2017 | 3 | 8 | | 13 |"}
{"item_id": "item_0792", "chart_task_type": "stability_volatility", "query": "Could you show the actual revenue numbers for each auxiliary system from 2015 to 2024, plus how widely they scattered relative to their own usual levels, so I can quickly tell which was the most volatile?", "table_markdown": "| General Revenues: | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |\n|-------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|\n| **Auxiliary Systems:** | | | | | | | | | | |\n| Bookstores | $3,806,182 | $3,653,609 | $3,650,966 | $3,710,582 | $3,549,179 | $3,130,000 | $3,400,000 | $3,600,000 | $3,710,000 | $3,820,000 |\n| Food services | 1,920,944 | 1,952,452 | 2,148,581 | 2,566,006 | 2,322,979 | 2,030,000 | 2,200,000 | 2,500,000 | 2,580,000 | 2,660,000 |\n| Gardner Student Center | 304,050 | 313,482 | 329,947 | 428,912 | 401,516 | 420,000 | 460,000 | 475,000 | 490,000 | 505,000 |\n| Road Scholar | 2,870,350 | 3,334,557 | 3,753,453 | 4,584,819 | 4,683,189 | 2,780,000 | 3,000,000 | 3,500,000 | 4,000,000 | 4,250,000 |\n| Student Housing Facilities | 834,094 | 910,085 | 2,091,778 | 2,229,578 | 2,345,817 | 2,350,000 | 2,400,000 | 4,700,000 | 4,830,000 | 4,975,000 |\n| Student Building Fees | 536,781 | 850,363 | 1,583,277 | 1,685,701 | 1,650,769 | 1,880,000 | 1,935,000 | 1,990,000 | 2,050,000 | 2,110,000 |\n| Investment Income/Amount of Unrestricted Gifts | 142,221 | 208,658 | 341,319 | 519,942 | 1,213,719 | 700,000 | 400,000 | 425,000 | 450,000 | 500,000 |\n| Parking services | 159,684 | 152,240 | 214,041 | 269,218 | 284,924 | 310,000 | 450,000 | 600,000 | 750,000 | 900,000 |\n| Greater Zion sponsorship | 0 | 0 | 0 | 0 | 0 | 500,000 | 500,000 | 500,000 | 500,000 | 500,000 |\n| **Total General Revenues available for debt service** | $10,574,306 | $11,375,446 | $14,113,362 | $15,994,758 | $16,452,092 | $14,100,000 | $14,745,000 | $18,290,000 | $19,360,000 | $20,220,000 |"}
{"item_id": "item_0793", "chart_task_type": "stability_volatility", "query": "Can you show me the actual annual inflation rates for Zones 1, 2, and 3 from July 2021 to July 2022, along with how widely each zone's figures swung, so it's obvious right away which was the most volatile?", "table_markdown": "| | Zone 1 | Zone 2 | Zone 3 |\n|-------|--------|--------|--------|\n| Jul-21| 3.8 | 3.6 | 5.0 |\n| Aug -21| 3.1 | 3.1 | 4.5 |\n| Sep -21| 3.1 | 3.1 | 4.6 |\n| Oct -21| 3.2 | 3.3 | 4.7 |\n| Nov -21| 4.0 | 3.7 | 4.9 |\n| Dec-21| 4.2 | 4.2 | 5.5 |\n| Jan-22| 4.1 | 5.3 | 4.4 |\n| Feb -22| 3.9 | 5.3 | 4.1 |\n| Mar -22| 3.8 | 5.3 | 4.4 |\n| Apr-22| 5.1 | 6.3 | 5.3 |\n| May-22| 4.8 | 6.3 | 5.1 |\n| Jun-22| 5.6 | 6.7 | 5.6 |\n| Jul-22| 6.2 | 7.7 | 6.6 |"}
{"item_id": "item_0794", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual figures for each circulation service metric over the five fiscal years, along with how widely they swung relative to their own typical levels, so it's obvious which one was the most volatile.", "table_markdown": "| Circulation Services | FY 2006-2007 | FY 2007-2008 | FY 2008-2009 | FY 2009-2010 | FY 2010-2011 |\n|-----------------------------------------------------------|--------------|--------------|--------------|--------------|--------------|\n| Van Houten Library hours open in a typical week | 107.50 | 107.50 | 107.50 | 94.50 | 94.50 |\n| Littman Architecture Library hours open in a typical week| 72.00 | 72.00 | 72.00 | 72.00 | 72.00 |\n| User visits to the Van Houten Library | 425,209 | 405,231 | 402,375 | 384,985 | 380,479 |\n| User visits to the Littman Architecture Library | 77,450 | 75,147 | 80,082 | 90,265 | 102,032 |\n| Circulation (Van Houten and Littman Architecture Library)| 40,553 | 37,777 | 33,440 | 41,665 | 33,752 |\n| Items borrowed from other libraries | 1,691 | 1,688 | 1,832 | 2,096 | 1,589 |\n| Items on loan to other libraries | 981 | 918 | 1,463 | 2,459 | 3,459 |\n| Print volumes held | 215,939 | 220,618 | 223,998 | 227,813 | * 170,186 |"}
{"item_id": "item_0795", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual values for each lab marker from 2010 to 2015, along with how widely they fluctuated relative to their own typical level, so the most volatile one stands out.", "table_markdown": "| | 2010 | 2012 | 2013 | 2014 | 2015 |\n|----------------|--------|--------|--------|--------|--------|\n| BNP [pg/mL] | 592* | 1146* | 1182* | 1705* | 1846* |\n| LDH [U/L] | 278 | 256 | 256 | 250 | 303 |\n| CK [U/L] | 77.7 | 297.2* | 358* | 301* | 460* |\n| CK-MB [U/L] | n/a | n/a | 35 | 45 | 48 |\n| ALT [U/L] | 18 | 12 | 12 | 7 | 8 |\n| AST [U/L] | 24 | 20 | 27 | 22 | 25 |\n| PLT [mm$^{-3}$]| 225 000 | 161 000 | 167 000 | 135 000* | 133 000* |"}
{"item_id": "item_0796", "chart_task_type": "stability_volatility", "query": "Show me the actual spending figures for each transfer category from 2010 to 2016 alongside how widely they varied relative to their own levels, so I can quickly tell which one was the most volatile.", "table_markdown": "| | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 |\n|---|---|---|---|---|---|---|---|\n| Domestic | 375.8 | 485.42 | 632.9 | 772.39 | 880.4 | 996.6 | 1,522.8 |\n| Foreign | 39.86 | 41.77 | 48.4 | 55.71 | 61.3 | 63.59 | 61.24 |\n| Pen. & Grat. | 183.4 8 | 131.52 | 147.1 | 139.73 | 182.8 1 | 208.11 | 168 |\n| FCT& Others | 147.5 | 260.07 | 245.6 | 474.1 | 268.4 2 | 221.53 | 198.27 |\n| Cont. & Subv. | 131.7 | 37.4 | 73.6 | - | - | 29.99 | 97.04 |"}
{"item_id": "item_0797", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual yearly membership numbers for Joey Scouts, Cub Scouts, Scouts, Venturer Scouts, and Rovers from 2009 to 2014, plus how widely each swung relative to its usual level, so it's easy to spot which section was the most stable.", "table_markdown": "| Section | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 |\n|--------------------------------|------|------|------|------|------|------|\n| Joey Scouts | 1203 | 1093 | 1314 | 1329 | 1205 | 1299 |\n| Cub Scouts | 5851 | 5779 | 5524 | 6372 | 6479 | 6251 |\n| Scouts | 4971 | 5270 | 5746 | 5402 | 5539 | 5807 |\n| Venturer Scouts | 1498 | 1459 | 1545 | 1600 | 1410 | 1271 |\n| Rovers¹ | 792 | 792 | 707 | 698 | 680 | 725 |\n| All Training Sections | 14315| 14393| 14836| 15401| 15313| 15353|\n| Leaders³/⁶ | 2880 | 2913 | 2994 | 3223 | 3353 | 2928 |\n| Trainee Leaders⁵ | 566 | 709 | 690 | 646 | 668 | 756 |\n| Advisers² | 88 | 96 | 94 | 100 | 96 | 110 |\n| Scout Fellowship Members² | 180 | 194 | 162 | 181 | 211 | 200 |\n| Joey Scout Helpers¹ | 13 | 12 | 16 | 8 | 9 | 5 |\n| Cub Scout Instructors¹ | 11 | 19 | 26 | 18 | 23 | 19 |\n| All Members | 18053| 18336| 18818| 19577| 19673| 19371|\n| Joey Scout Mobs | 129 | 132 | 134 | 133 | 136 | 128 |\n| Cub Scout Packs | 419 | 419 | 415 | 427 | 437 | 426 |\n| Scout Troops | 371 | 376 | 381 | 388 | 395 | 398 |\n| Venturer Scout Units | 185 | 183 | 198 | 196 | 183 | 171 |\n| Rover Crews | 62 | 66 | 66 | 67 | 64 | 69 |\n| Groups³ | 436 | 435 | 440 | 456 | 464 | 460 |\n| Scout Fellowships | 23 | 23 | 21 | 21 | 21 | 19 |\n| Districts | 57 | 62 | 62 | 63 | 66 | 66 |\n| Regions | 10 | 10 | 10 | 10 | 10 | 10 |"}
{"item_id": "item_0798", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual monthly fall rates for each fiscal year along with how widely they swung, so I can quickly tell which year was the most stable.", "table_markdown": "| OCT | NOV | DEC | JAN | FEB | MAR | APR | MAY | JUN | JUL | AUG | SEP |\n|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|\n| FY 2008 | 3.51 | 4.09 | 3.55 | 3.29 | 3.93 | 3.24 | 2.71 | 3 | 3.13 | 3.37 | 3.49 | 4.44 |\n| FY 2009 | 3.4 | 3.5 | 4 | 5.12 | 2.82 | 3.24 | 3.22 | 3.61| 3.28 | 3.5 | 3.2 | 3.2 |\n| FY 2010 | 3.49 | 2.74 | 3.24 | 2.99 | 3.5 | 2.74 | 3.07 | 2.45| 2.1 | 2.77 | 2.66 | 2.78 |\n| FY 2011 | 2.99 | 2.51 | 1.68 | 2.52 | 2.45 | 2.76 | 3.11 | 1.92| 2.57 | 2.18 | 1.65 | 2.15 |\n| FY 2012 | 2.23 | 2.22 | 2.47 | 2.06 | 2.17 | 2.20 | 1.64 | 1.77| 2.16 | 2.07 | 2.58 | 1.89 |\n| FY 2013 | 2.08 | 1.94 | 2.16 | 2.44 | 1.84 | 1.85 | 1.89 | 1.89| 1.41 | 1.79 | 2.14 | 1.68 |"}
{"item_id": "item_0799", "chart_task_type": "stability_volatility", "query": "I want to see the actual monthly figures for each of the 15 commodity group types in 2011 alongside how widely they swung, so the most volatile one stands out clearly.", "table_markdown": "| Year and month 1/ | | Type A | Type B | Type C | Type D | Type E | Type F | Type G | Type H | Type I | Type J | Type K | Type L | Type M | Type N | Type O |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 2011 | January | 419 | 1 899 | 774 | 40 | 491 | 376 | 203 | 105 | 146 | 99 | 172 | 70 | 281 | 83 | 445 |\n| | February | 460 | 2 442 | 924 | 51 | 486 | 445 | 242 | 132 | 163 | 104 | 183 | 70 | 298 | 95 | 506 |\n| | March | 481 | 2 305 | 945 | 46 | 562 | 457 | 270 | 135 | 177 | 112 | 219 | 69 | 288 | 108 | 597 |\n| | April | 469 | 2 386 | 1 078 | 42 | 544 | 447 | 253 | 141 | 169 | 114 | 229 | 71 | 291 | 93 | 535 |\n| | May | 524 | 2 434 | 929 | 47 | 562 | 466 | 265 | 140 | 169 | 117 | 214 | 64 | 349 | 101 | 602 |\n| | June | 535 | 2 124 | 1 001 | 41 | 565 | 452 | 279 | 157 | 175 | 116 | 226 | 62 | 354 | 105 | 606 |\n| | July | 527 | 2 472 | 1 101 | 49 | 570 | 348 | 255 | 136 | 148 | 118 | 203 | 65 | 368 | 101 | 573 |\n| | August | 577 | 2 555 | 1 014 | 52 | 568 | 472 | 283 | 157 | 154 | 123 | 233 | 71 | 385 | 109 | 662 |\n| | September | 555 | 2 718 | 1 089 | 53 | 568 | 449 | 296 | 164 | 161 | 124 | 234 | 66 | 397 | 110 | 702 |\n| | October | 543 | 2 665 | 999 | 57 | 576 | 473 | 299 | 187 | 164 | 125 | 252 | 80 | 389 | 103 | 692 |\n| | November | 561 | 2 662 | 1 085 | 61 | 625 | 471 | 328 | 175 | 166 | 129 | 266 | 83 | 385 | 121 | 739 |\n| | December | 463 | 2 375 | 1 247 | 51 | 554 | 402 | 260 | 141 | 146 | 111 | 206 | 93 | 310 | 101 | 595 |\n| | Total | 6 114 | 29 037 | 12 186 | 590 | 6 671 | 5 258 | 3 233 | 1 770 | 1 938 | 1 392 | 2 637 | 864 | 4 095 | 1 230 | 7 254 |\n| 2012 | January | 422 | 2 389 | 955 | 45 | 575 | 458 | 233 | 121 | 142 | 115 | 192 | 74 | 325 | 89 | 572 |\n| | February | 445 | 2 587 | 1 030 | 46 | 586 | 505 | 268 | 206 | 156 | 117 | 224 | 74 | 370 | 113 | 668 |\n| | March | 459 | 2 549 | 1 029 | 48 | 573 | 463 | 270 | 157 | 178 | 117 | 214 | 71 | 332 | 113 | 603 |\n| | April | 442 | 2 422 | 1 040 | 47 | 558 | 493 | 265 | 163 | 168 | 113 | 212 | 68 | 389 | 107 | 596 |\n| | May | 483 | 2 264 | 1 022 | 49 | 588 | 500 | 293 | 164 | 153 | 122 | 229 | 76 | 399 | 115 | 683 |\n| | June | 544 | 2 498 | 1 193 | 46 | 569 | 481 | 299 | 167 | 166 | 115 | 212 | 75 | 395 | 117 | 682 |"}
{"item_id": "item_0800", "chart_task_type": "stability_volatility", "query": "Help me see the actual monthly tax payments for Best Western, Foster Inn, and Zybell House in 2019, plus how widely each swung, so I can quickly tell which hotel was the most stable.", "table_markdown": "| Monthly Tax Payment | Best Western | Foster Inn | Zybell House | Tax Expended | Balance |\n|---------------------|-------------|------------|--------------|--------------|---------|\n| Balance brought forward from 12-31-2018 | | | | | 9,943.72 |\n| Jan-19 | 1081.98 | 244.41 | 142.07 | | 11,412.18 |\n| Feb-19 | 504.59 | 291.38 | 83.35 | | 12,291.50 |\n| Mar-19 | 1,607.71 | 116.96 | 45.54 | | 14,061.71 |\n| Apr-19 | 2,004.18 | 149.77 | 67.12 | | 16,282.78 |\n| May-19 | 2,335.83 | 139.00 | 74.26 | 9,750.00 | 9,081.87 |\n| Jun-19 | 2,654.97 | 426.32 | 145.15 | | 12,308.31 |\n| Jul-19 | 2,772.61 | 395.53 | 53.46 | | 15,529.91 |\n| Aug-19 | 972.72 | 299.40 | 105.55 | 1,956.84 | 14,950.74 |\n| Sep-19 | 696.23 | 371.39 | 165.33 | | 16,183.69 |\n| Oct-19 | 2,097.75 | 456.85 | | 9,750.00 | 8,988.29 |\n| Nov-19 | 452.00 | | | | 9,440.29 |\n| Dec-19 | 401.89 | | | | 9,842.18 |"}
{"item_id": "item_0801", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual percentage figures for USA, Taiwan, Hong Kong, and Mainland China from 2006 to 2015, along with how widely each one swung, so I can quickly tell which region was the most stable.", "table_markdown": "| Year | USA | Taiwan | Hong Kong | Mainland China |\n|------|-----|--------|-----------|----------------|\n| 06 | 70% | 14% | 7% | 8% |\n| 07 | 73% | 13% | 7% | 9% |\n| 08 | 75% | 11% | 7% | 9% |\n| 09 | 77% | 7% | 7% | 9% |\n| 10 | 78% | 7% | 7% | 9% |\n| 11 | 79% | 6% | 6% | 8% |\n| 12 | 79% | 6% | 6% | 8% |\n| 13 | 80% | 6% | 6% | 8% |\n| 14 | 78% | 5% | 5% | 10% |\n| 15 | 77% | 5% | 6% | 12% |"}
{"item_id": "item_0802", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual annual bushmeat arrest counts for each district from 2005 to 2010 alongside how widely they swung relative to their own typical level, so it's obvious which was the most volatile.", "table_markdown": "| DISTRICT | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 |\n|---|---|---|---|---|---|---|\n| MAKUENI | 2544 | 1121 | 2075 | 899 | 3185 | 520 |\n| KAJIADO | 1065 | 1160 | 3958 | 2411 | 6055 | 712 |\n| NAKURU | 1016 | 416 | 1451 | 4426 | 3698 | 639 |\n| NYERI | 1000 | 95 | 18 | 65 | 720 | 30 |\n| TAITA TAVETA | 694 | 888 | 2279 | 1602 | 1961 | 1948 |\n| NAROK | 615 | 18 | 57 | 51 | 308 | 10 |\n| MACHAKOS | 390 | 237 | 175 | 153 | 92 | 66 |\n| LAMU | 340 | 450 | 10605 | 76 | 100 | 20 |\n| SAMBURU | 300 | 0 | 0 | 650 | 30 | 0 |\n| TANA RIVER | 230 | 240 | 0 | 0 | 1493 | 688 |"}
{"item_id": "item_0803", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual injury frequency rates for Meridian employees and on-site contractors from FY15 to FY19, plus how widely each group's figures swung, so it's easy to spot which was the most stable.", "table_markdown": "| Year | Meridian employees | Meridian on-site contractors |\n|------|--------------------|-----------------------------|\n| FY15 | 1.2 | 2.7 |\n| FY16 | 1.7 | 3.1 |\n| FY17 | 0.2 | 3.6 |\n| FY18 | 0.7 | 1.8 |\n| FY19 | 1.3 | 4.0 |"}
{"item_id": "item_0804", "chart_task_type": "stability_volatility", "query": "Could you show me the figures for Accidents, Fatalities, Injuries, and Drunk drivers over the years alongside how widely each swung relative to its own usual level, so it's obvious right away which was the most volatile?", "table_markdown": "| Year | 1940 | 1950 | 1960 | 1970 | 1971 | 1972 | 1973 |\n|------|------|------|------|------|------|------|------|\n| Accidents | 226 | 235 | 439 | 732 | 645 | 646 | 712 |\n| Fatalities | 2 | 0 | 2 | 4 | 1 | 2 | 7 |\n| Injuries | 133 | 120 | 249 | 515 | 481 | 484 | 548 |\n| Drunk drivers | NA | NA | NA | 44 | 46 | 45 | 73 |"}
{"item_id": "item_0805", "chart_task_type": "stability_volatility", "query": "I'd like to see the Mathematics pass numbers for Level 4 Total, Level 5 Total, and Higher (Grade A-C) from 2007 to 2017, plus how much each wobbled relative to its own typical level, so it's easy to spot which stayed most stable.", "table_markdown": "| | 2,007 | 2,008 | 2,009 | 2,010 | 2,011 | 2,012 | 2,013 | 2,014 | 2,015 | 2,016 | 2,017 |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| Intermediate 1 | 6,935 | 7,612 | 7,260 | 7,166 | 8,361 | 8,190 | 7,295 | 3,091 | 2 17 | | |\n| National 4 | | | | | | | | 20,945 | 26,151 | 26,595 | 26,546 |\n| Standard Grade (Grade 3 or 4) | 20,401 | 21,537 | 19,960 | 17,844 | 17,958 | 17,450 | 16,618 | | | | |\n| Level 4 Total | 27,336 | 29,149 | 27,220 | 25,010 | 26,319 | 25,640 | 23,913 | 24,036 | 26,368 | 26,595 | 26,546 |\n| Intermediate 2 | 13,185 | 14,191 | 15,477 | 15,513 | 16,331 | 16,143 | 17,263 | 12,801 | 1,249 | | |\n| Standard Grade (Grade 1 or 2) | 16,151 | 15,952 | 15,340 | 13,977 | 14,099 | 13,852 | 14,157 | | | | |\n| National 5 (Grade A-C) | | | | | | | | 15,928 | 22,571 | 26,497 | 26,953 |\n| Level 5 Total | 29,336 | 30,143 | 30,817 | 29,490 | 30,430 | 29,995 | 31,420 | 28,729 | 23,820 | 26,497 | 26,953 |\n| Higher (Grade A-C) | 13,263 | 14,176 | 13,806 | 14,955 | 14,890 | 15,140 | 15,058 | 15,779 | 15,200 | 13,906 | 13,978 |\n| Lifeskills Mathematics | | | | | | | | | | | |\n| National 4 | | | | | | | | 1,479 | 3,656 | 3,567 | 2,945 |\n| National 5 | | | | | | | | 1 20 | 8 56 | 1,018 | 1,213 |"}
{"item_id": "item_0806", "chart_task_type": "stability_volatility", "query": "Could you show me the actual yearly VC funding amounts for each industry from 2015 to 2021, plus how widely they scattered relative to their own typical level, so I can quickly tell which sector was the most volatile?", "table_markdown": "| Industry | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |\n|-------------------|--------|--------|--------|--------|--------|--------|--------|\n| Health | $514m | $602m | $1.8b | $588m | $835m | $1.1b | $1.2b |\n| Fintech | $27m | $18.5m | $532m | $297m | $271m | $231m | $530m |\n| Enterprise Software| $35.7m | $64.8m | $154m | $203m | $733m | $372m | $477m |\n| Security | $56.8m | $190m | $10.4m | $23.4m | $695m | $37.3m | $293m |\n| Energy | $32.4m | $21.2m | $12.8m | $55.9m | $154m | $246m | $164m |\n| Food | $4.3m | $16.4m | $36.7m | $34.4 | $26m | $36.4m | $91.7m |\n| Marketing | $23.6m | $14.4m | $22m | $65.5m | $6.6m | $127m | $89.5m |\n| Real Estate | $2.6m | $12.2m | $3.7m | $18.6m | $3.4m | $18.6m | $84.7m |\n| Transportation | $24.3m | $13.9m | $43.3m | $108m | $118m | $137m | $76m |\n| Robotics | $8.4m | $24.2m | $14.2m | $84.4m | $46.4m | $63.1m | $34.1m |"}
{"item_id": "item_0807", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual freeboard levels for Sumps 1, 2, and 3, along with how widely each swung relative to its own typical level, so it's easy to spot which one remained the most stable.", "table_markdown": "| Measured Date | Sump 1 (B099/B100) Freeboard (in) | Sump 2 (B103/B104) Freeboard (in) | Sump 3 (B107/B108) Freeboard (in) | Depth to DNAPL (in) | Comments |\n|---------------|----------------------------------|----------------------------------|----------------------------------|---------------------|----------|\n| 8/14/2019 | 2.5 | 28 | 29 | Not measureable | |\n| 8/21/2019 | 0 | 27.5 | 26.5 | Not measureable | |\n| 8/28/2019 | 44.5 | 47.9 | 45 | Not measureable | Water from sumps pumped out |\n| 9/4/2019 | 19 | 42 | 41.5 | Not measureable | |\n| 9/13/2019 | 0 | 39.5 | 38 | Not measureable | |\n| 9/20/2019 | 0 | 3 | 2.5 | Not measureable | |\n| 9/25/2019 | 0 | 42 | 42.5 | Not measureable | Water from sumps pumped out |\n| 10/2/2019 | 2.5 | 42.5 | 42 | Not measureable | Sheen visible in B107/B108 sump, less than 0.1 gal od DNAPL recovered |\n| 10/9/2019 | 3 | 42 | 41.5 | Not measureable | Sheen visible in B107/B108 sump, less than 0.1 gal od DNAPL recovered |\n| 10/16/2019 | 0 | 39.5 | 39 | Not measureable | Less than 0.1 gal of DNAPL recovered from B107/B108 Sump |\n| 10/24/2019 | 3 | 35 | 25 | Not measureable | Less than 0.1 gal of DNAPL recovered from B107/B108 Sump |\n| 10/29/2019 | 0 | 24 | 23 | Not measureable | Water from sumps pumped out |\n| 10/30/2019 | 0 | 40 | 39 | Not measureable | Slight sheen visible in B107/B108 sump |\n| 11/6/2019 | 9 | 39 | 38.5 | Not measureable | |\n| 11/13/2019 | 7 | 30 | 29 | Not measureable | Less than 0.1 gal of DNAPL recovered from B107/B108 Sump |\n| 11/19/2019 | 4 | 26 | 25.5 | Not measureable | |\n| 11/27/2019 | 0 | 25 | 23 | Not measureable | |\n| 12/3/2019 | 2 | 25.5 | 25 | Not measureable | Less than 0.1 gal of DNAPL recovered from B107/B108 Sump |\n| 12/11/2019 | 1.5 | 17 | 16.54 | Not measureable | Less than 0.1 gal of DNAPL recovered from B107/B108 Sump |\n| 12/17/2019 | 5 | 19.5 | 17.5 | Not measureable | |\n| 12/23/2019 | 10 | 21 | 20.5 | Not measureable | |\n| 1/7/2020 | 9 | 13 | 12.5 | Not measureable | |\n| 1/8/2020 | 9 | 13 | 12.5 | Not measureable | Water from sumps pumped out |\n| 1/17/2020 | 0 | 32 | 31.5 | Not measureable | |\n| 1/21/2020 | 2.5 | 26.5 | 26 | Not measureable | |\n| 1/28/2020 | 0 | 0 | 0 | Not measureable | |\n| 2/4/2020 | 2 | 11 | 10.5 | Not measureable | |\n| 2/12/2020 | 0 | 0 | 0 | Not measureable | |\n| 2/18/2020 | 1.5 | 11.5 | 10.25 | Not measureable | Water from sumps pumped out on 2/20/2020 |\n| 2/27/2020 | 2 | 42 | 36 | Not measureable | |"}
{"item_id": "item_0808", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual yearly weights for each industry category in Multi-Sector Equity Funds from 2001 to 2009, along with how widely they fluctuated relative to their own typical level, so the most consistent one stands out right away.", "table_markdown": "| Sector | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 |\n|---|---|---|---|---|---|---|---|---|---|\n| % Software | 6.19 | 5.24 | 5.46 | 5.06 | 5 | 5.49 | 5.61 | 5.4 | 6.12 |\n| % Hardware | 11.07 | 8.72 | 10.21 | 9.86 | 9.83 | 9.75 | 11.73 | 9.03 | 11.08 |\n| % Media | 2.31 | 1.96 | 2.11 | 2.76 | 3.04 | 3.51 | 4.06 | 3.81 | 3.62 |\n| % Telecommunications | 4.86 | 3.83 | 4.73 | 2.87 | 2.32 | 2.38 | 2.34 | 2.3 | 3.08 |\n| % Healthcare | 12.27 | 15.06 | 12.75 | 12.87 | 14.17 | 13.55 | 14.27 | 15.12 | 15.24 |\n| % Consumer Services | 10.24 | 9.9 | 8.41 | 9.81 | 10.1 | 11.11 | 10.88 | 11.09 | 10.41 |\n| % Business Services | 6.98 | 8.43 | 6.89 | 8.29 | 8.34 | 7.89 | 7.33 | 7.55 | 7.23 |\n| % Financial Services | 13.75 | 13.74 | 14.27 | 17.92 | 17.08 | 17.09 | 17.2 | 18.03 | 16.71 |\n| % Consumer Goods | 9.11 | 8.9 | 8.14 | 7.53 | 6.84 | 7.16 | 7.23 | 8.14 | 7.16 |\n| % Industrial Materials | 11.93 | 12.4 | 14.72 | 12.98 | 12.39 | 12.82 | 11.1 | 11.06 | 10.51 |\n| % Energy | 8.99 | 8.98 | 9.91 | 7.92 | 8.92 | 7.2 | 6.08 | 6.41 | 5.88 |\n| % Utility | 2.29 | 2.71 | 2.4 | 2.13 | 1.97 | 1.93 | 1.79 | 1.93 | 2.32 |"}
{"item_id": "item_0809", "chart_task_type": "stability_volatility", "query": "Could you show me the quarterly FX rates for all seven currencies in 2018, with the actual values and how widely they swung relative to their typical levels, so it's easy to spot which was the most volatile?", "table_markdown": "| FX Assumptions | Q1 2018 | Q2 2018 | Q3 2018 | Q4 2018 | FY 2018 | Average |\n|----------------------|---------|---------|---------|---------|---------|---------|\n| Euro (EUR) | 0.81 | 0.84 | 0.86 | 0.88 | 0.85 | |\n| Brazilian Real (BRL)| 3.25 | 3.60 | 3.96 | 3.81 | 3.65 | |\n| Mexican Peso (MXN) | 18.71 | 19.42 | 18.98 | 19.85 | 19.24 | |\n| Colombian Peso (COP)| 2,858.33| 2,838.34| 2,961.69| 3,162.98| 2,955.34| |\n| Chilean Peso (CLP) | 601.97 | 620.73 | 663.19 | 679.62 | 641.38 | |\n| Peruvian Soles (PEN) | 3.24 | 3.26 | 3.29 | 3.36 | 3.29 | |\n| Argentinean Peso (ARS)| 19.71 | 23.55 | 32.09 | 37.12 | 28.12 | |"}
{"item_id": "item_0810", "chart_task_type": "stability_volatility", "query": "I want to see both the actual accident call volumes for each of the six towns from 2004 to 2008 and how much they fluctuated, making it obvious right away which one was the most volatile.", "table_markdown": "| | Closter | Alpine | Harrington Park | Haworth | Northvale | Norwood |\n|-------|---------|--------|-----------------|---------|-----------|---------|\n| 2004 | 484 | 75 | 71 | 55 | 112 | 133 |\n| 2005 | 527 | 74 | 84 | 53 | 127 | 108 |\n| 2006 | 460 | 72 | 67 | 63 | 106 | 98 |\n| 2007 | 434 | 82 | 91 | 77 | 146 | 117 |\n| 2008 | 374 | 75 | 68 | 63 | 175 | 122 |"}
{"item_id": "item_0811", "chart_task_type": "stability_volatility", "query": "I'd like to see the yearly waiver counts for MCO and HCAO alongside how much they fluctuated over the last decade so it's obvious right away which program has been the most stable.", "table_markdown": "| Fiscal Year | MCO Waivers Granted | HCAO Waivers Granted |\n|-------------|---------------------|----------------------|\n| FY08-09 | 3 | 3 |\n| FY09-10 | 2 | 2 |\n| FY10-11 | 0 | 0 |\n| FY11-12 | 3 | 3 |\n| FY12-13 | 2 | 3 |\n| FY13-14 | 6 | 8 |\n| FY14-15 | 5 | 3 |\n| FY15-16 | 2 | 3 |\n| FY16-17 | 3 | 4 |\n| FY17-18 | 3 | 4 |\n| FY18-19 | 3 | 3 |\n| FY19-20 | 3 | 5 |\n| **TOTAL** | **35** | **41** |"}
{"item_id": "item_0812", "chart_task_type": "baseline_index", "query": "I'm curious how Northside and Southside median property values changed from 2021 to 2023 when measured against their own 2021 starting points. Show them so it's easy to spot which side grew the most relative to where it began.", "table_markdown": "| Median Property Values in Dublin | 2021 | 2022 | 2023 |\n|---------------------------------|--------|--------|--------|\n| Northside | €400,000 | €447,500 | €450,000 |\n| Southside | €475,000 | €625,000 | €650,000 |"}
{"item_id": "item_0813", "chart_task_type": "stability_volatility", "query": "Help me see the actual monthly meal counts for the EAST and WEST regions alongside how spread out each was relative to its usual level, so I can quickly tell which was the most stable.", "table_markdown": "| # MEALS | EAST | WEST | TOTAL |\n|---------|------|------|-------|\n| Oct-17 | 57612| 9888 | 67500 |\n| Nov-17 | 59004| 10692| 69696 |\n| Dec-17 | 50844| 8640 | 59484 |\n| Jan-18 | 55092| 10656| 65748 |\n| Feb-18 | 42252| 8388 | 50640 |\n| Mar-18 | 48780| 8148 | 56928 |\n| Apr-18 | 52104| 9672 | 61776 |\n| May-18 | 51300| 9552 | 60852 |\n| Jun-18 | 52404| 8340 | 60744 |\n| Jul-18 | 52236| 8160 | 60396 |\n| Aug-18 | 56196| 0 | 56196 |\n| Sep-18 | 50376| 0 | 50376 |\n| **Annual Total** | **628200** | **92136** | **720336** |"}
{"item_id": "item_0814", "chart_task_type": "stability_volatility", "query": "Could you show me the actual yearly figures for the five output indicators alongside how much they wobbled relative to their typical levels, so I can easily spot which one remained the most stable.", "table_markdown": "| Output indicators | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 |\n|----------------------------------------------------------------------------------|------|------|------|------|------|------|------|------|------|------|------|\n| 1. Number of Permit to Transport Fibers (PTFs) issued | 1,334| 1,292| 1,358| 1,191| 5,175| 1,893| 1,984| 2,007| 1,336| 7,220| -2,045|\n| 2. Number of Primary Certificate of Fiber Inspection (PCFI) issued | 767 | 822 | 821 | 770 | 3,180| 883 | 875 | 1,386| 592 | 3,736| -556 |\n| 3. Number of licenses issued | 392 | 394 | 349 | 293 | 1,428| 396 | 397 | 391 | 278 | 1,462| -34 |\n| 4. Number of enforcement actions undertaken | 2,115| 2,119| 2,086| 1,940| 8,260| 2,869| 3,087| 2,075| 2,698| 10,729| -2,469|\n| 5. Number of sites and facilities monitored | 422 | 427 | 377 | 317 | 1,543| 331 | 422 | 412 | 278 | 1,443| 100 |"}
{"item_id": "item_0815", "chart_task_type": "baseline_index", "query": "I'd like to see each SEN category shown relative to its 2012/13 starting point, so I can easily spot which one gained the most ground from its starting level by 2017/18.", "table_markdown": "| SEN Categories | 2017/18 | 2016/17 | 2012/13 |\n|--------------------------------|---------|---------|---------|\n| General Cognitive & Learning Difficulties | 49,998 | 48,689 | 45,868 |\n| Social, Emotional and Behavioural Difficulties | 11,660 | 10,619 | 7,607 |\n| Autism/Aspergers | 9,247 | 7,972 | 5,505 |\n| Severe learning difficulties | 2,642 | 2,422 | 2,092 |"}
{"item_id": "item_0816", "chart_task_type": "stability_volatility", "query": "I'd like to see the yearly revenue figures for each of these business-type activities, alongside how widely they swung relative to their own usual level, so it's obvious right away which stream was the most volatile over the years.", "table_markdown": "| Charges for services: | 2002/2003 | 2003/2004 | 2004/2005 | 2005/2006 | 2006/2007 | 2007/2008 | 2008/2009 | 2009/2010 | 2010/2011 |\n|-----------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|\n| Public safety | $293,522 | $310,762 | $356,703 | $306,954 | $383,518 | $421,978 | $438,932 | $339,786 | $530,879 |\n| Public works | 157,213 | 143,089 | 179,104 | 141,803 | 140,649 | 151,785 | 319,112 | 231,564 | 262,393 |\n| Culture and recreation| 421,507 | 537,726 | 599,761 | 577,257 | 616,982 | 598,707 | 494,131 | 448,271 | 478,394 |\n| Community and economic development | 244,164 | 349,250 | 372,703 | 407,977 | 398,479 | 405,832 | 358,812 | 480,137 | 430,574 |\n| General government | 324,125 | 307,054 | 324,733 | 432,880 | 382,812 | 410,042 | 473,567 | 506,364 | 545,322 |\n| Operating grants and contributions | 3,940,767 | 4,271,667 | 4,617,475 | 4,805,002 | 4,263,038 | 4,229,961 | 4,736,388 | 5,211,201 | 4,861,170 |\n| Capital grants and contributions | 644,403 | 2,544,051 | 894,158 | 1,733,592 | 2,018,077 | 963,114 | 108,213 | 1,726,091 | 3,398,652 |"}
{"item_id": "item_0817", "chart_task_type": "baseline_index", "query": "Show me the life expectancy trends for Coloured males and females from 1936 to 1985, scaled so they all begin at the same level, making it easy to spot which group climbed the furthest in proportion to its 1936 starting value.", "table_markdown": "| Year | Coloured Male | | | | | Coloured Female | | | |\n|------|---------------|----------|----------|----------|----------|----------------|----------|----------|----------|\n| | Q5 | H | U | e(0) | Q5 | H | U | e(0) |\n| 1921 | | | | | | | | | |\n| 1936 | 0.278 | 0.604 | 0.867 | 40.18 | 0.262 | 0.599 | 0.991 | 40.86 |\n| 1946 | 0.230 | 0.546 | 0.814 | 41.70 | 0.217 | 0.526 | 0.970 | 44.00 |\n| 1951 | 0.220 | 0.497 | 0.816 | 44.82 | 0.203 | 0.469 | 0.952 | 47.77 |\n| 1970 | 0.177 | 0.408 | 0.745 | 49.62 | 0.160 | 0.331 | 0.823 | 54.28 |\n| 1980 | 0.094 | 0.308 | 0.756 | 54.34 | 0.083 | 0.230 | 0.831 | 62.55 |\n| 1985 | 0.068 | 0.263 | 0.788 | 57.92 | 0.060 | 0.195 | 0.838 | 65.52 |"}
{"item_id": "item_0818", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual performance rates for Commercial HMO, Medicaid PPO, and Medicare HMO from 2004 to 2015, along with how widely each one fluctuated, so it's obvious right away which insurance type was the most stable.", "table_markdown": "| Year | Commercial HMO | Medicaid PPO | Medicare HMO |\n|------|----------------|--------------|--------------|\n| 2015 | 82.8 | 81.1 | 71.1 |\n| 2014 | 82.4 | 80.4 | 69.5 |\n| 2013 | 80.7 | 78.4 | 66.5 |\n| 2012 | 80.2 | 78.9 | 68.0 |\n| 2011 | 80.2 | 79.3 | 66.7 |\n| 2010 | 77.6 | 76.6 | 64.9 |\n| 2009 | 77.4 | 75.5 | 62.3 |\n| 2008 | 75.6 | 74.1 | 61.4 |\n| 2007 | 74.7 | 73.5 | 58.7 |\n| 2006 | 72.7 | 69.4 | 56.0 |\n| 2005 | 69.7 | 64.5 | 52.0 |\n| 2004 | 72.6 | - | 54.4 |"}
{"item_id": "item_0819", "chart_task_type": "stability_volatility", "query": "Show me the actual percentage shares of troll salmon vessels by state from 1977 to 1984, along with how widely each figure scattered, so it's obvious right away which state had the most stable presence.", "table_markdown": "| Year | Oregon | California | Washington | Other/Unknown |\n|------|--------|------------|------------|---------------|\n| 1977 | 83.8 | 6.9 | 8.7 | 0.6 |\n| 1978 | 83.6 | 5.9 | 10.0 | 0.5 |\n| 1979 | 82.5 | 6.5 | 10.3 | 0.7 |\n| 1980 | 80.4 | 8.5 | 9.6 | 1.5 |\n| 1981 | 81.2 | 7.4 | 9.9 | 1.6 |\n| 1982 | 82.1 | 6.3 | 10.2 | 1.4 |\n| 1983 | 85.0 | 3.9 | 10.1 | 1.0 |\n| 1984a/ | 85.2 | 2.9 | 11.0 | 0.9 |"}
{"item_id": "item_0820", "chart_task_type": "baseline_index", "query": "Can you put together a view of private research funding for Epilepsy, Parkinson’s, Autism, and Alzheimer’s, resetting each to its 2008 starting point so I can quickly see which disease pulled ahead the most compared with where it began by 2013?", "table_markdown": "| Disease | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 Estimated |\n|-----------------------|------|------|------|------|------|----------------|\n| Epilepsy | $145 | $149 | $161 | $152 | $153 | $152 |\n| Parkinson’s Disease | $152 | $186 | $172 | $151 | $151 | $151 |\n| Autism | $118 | $106 | $218 | $169 | $169 | $170 |\n| Alzheimer’s Disease | $412 | $534 | $529 | $448 | $498 | $529 |"}
{"item_id": "item_0821", "chart_task_type": "baseline_index", "query": "Could you show the agricultural area for each county re-expressed relative to its 2007 starting point, so I can easily spot which one grew the most from its baseline?", "table_markdown": "| Indicators | Total agricultural holdings | | | Agricultural holdings without legal status | | | Agricultural holdings with legal status | | |\n|---|---|---|---|---|---|---|---|---|---|\n| | 2007 | 2010 | 2016 | 2007 | 2010 | 2016 | 2007 | 2010 | 2016 |\n| South West Oltenia | 1,629,490 | 1,607,752.66 | 1,479,930.68 | 1,292,262 | 2,772,192.7 | 968,198.77 | 337,229 | 502,953.02 | 511,731.91 |\n| Dolj | 553,349 | 534,392.05 | 477,324 | 445,885 | 923,209 | 274,721.58 | 107,464 | 186,344.64 | 202,602.81 |\n| Gorj | 225,498 | 219,050.52 | 188,1 | 186,588 | 374,688 | 156,820.46 | 38,91 | 55,075.61 | 312,80.43 |\n| Mehedinţi | 271,218 | 259,169.99 | 221,122 | 238,639 | 459,761 | 176,699.33 | 32,579 | 52,698.73 | 44,423.07 |\n| Olt | 360,794 | 405,831.23 | 406,304 | 234,091 | 640,395 | 209,011.84 | 126,703 | 171,242.57 | 197,292.91 |\n| Vâlcea | 218,631 | 189,308.87 | 187,078 | 187,059 | 374,137 | 150,945.56 | 31,572 | 37,591.47 | 36,132.69 |"}
{"item_id": "item_0822", "chart_task_type": "baseline_index", "query": "Could you show the maintenance spending for Boards A, B, and C from 2000/01 to 2004/05 relative to where each one began, so I can easily spot which board pulled ahead the most compared with where it started?", "table_markdown": "| School Year | Board A | Board B | Board C |\n|---|---|---|---|\n| 2004/05 | 41.05 | 51.32 | 60.75 |\n| 2003/04 | 38.53 | 49.57 | 62.86 |\n| 2002/03 | 38.15 | 47.69 | 67.96 |\n| 2001/02 | 34.84 | 44.22 | 67.93 |\n| 2000/01 | 35.34 | 43.18 | 72.09 |"}
{"item_id": "item_0823", "chart_task_type": "baseline_index", "query": "Can you put together a view of chemsex levels for both groups relative to their Week 0 starting points, so I can easily spot which group fell furthest below its baseline by Week 12?", "table_markdown": "| Measure | Group 1 M | (n = 15) SD | Group 2 M | (n = 14) SD |\n|-------------|-----------|-------------|-----------|-------------|\n| Chemsex | | | | |\n| Week 0 | 41.53 | 4.90 | 51.13 | 7.78 |\n| Week 8 | 32.40 | 7.44 | 42.07 | 5.56 |\n| Week 12 | 31.20 | 7.74 | 38.08 | 4.54 |\n| CAMS-R | | | | |\n| Week 0 | 24.47 | 6.19 | 20.14 | 3.92 |\n| Week 8 | 29.87 | 3.48 | 30.29 | 3.02 |\n| Week 12 | 29.53 | 3.34 | 29.86 | 2.93 |\n| SSE | | | | |\n| Week 0 | 8.51 | 2.65 | 8.90 | 0.46 |\n| Week 8 | 14.87 | 4.46 | 15.43 | 1.10 |\n| Week 12 | 18.47 | 3.58 | 17.50 | 1.01 |\n| SWEMWBS | | | | |\n| Week 0 | 12.73 | 3.45 | 11.29 | 2.20 |\n| Week 8 | 21.13 | 3.83 | 20.88 | 3.88 |\n| Week 12 | 21.27 | 4.83 | 22.14 | 3.13 |"}
{"item_id": "item_0824", "chart_task_type": "baseline_index", "query": "Show me how deferred taxes, total non-oil revenue, and adjusted non-oil revenues moved from 2008 to 2018 relative to where each one began, so it's easy to spot which metric grew the most from its starting point.", "table_markdown": "| Year | Aggregate deferred taxes of sampled firms (N’billion) | Total non -oil revenue, (N’billion) | Adjusted non -oil revenues (N’billion) |\n|---|---|---|---|\n| 2008 | 12.424 | 4185.64 | 4198.064 |\n| 2009 | 24.626 | 2847.32 | 2871.946 |\n| 2010 | 25.035 | 3184.72 | 3209.755 |\n| 2011 | 26.611 | 3431.03 | 3457.641 |\n| 2012 | 34.053 | 3751.68 | 3785.733 |\n| 2013 | 33.244 | 4031.83 | 4065.074 |\n| 2014 | 21.341 | 3629.61 | 3650.951 |\n| 2015 | 65.19 | 3553.54 | 3618.730 |\n| 2016 | 43.572 | 3089.18 | 3132.752 |\n| 2017 | 145.874 | 2642.98 | 2788.854 |\n| 2018 | 111.053 | 3193.44 | 3304.493 |\n| Total | 543.025 | 37,540.97 | 38,083.993 |"}
{"item_id": "item_0825", "chart_task_type": "baseline_index", "query": "I'd like to see the profitability of the six vegetable categories shown relative to their July 1st starting points, so it's easy to spot which one dropped the most compared to where it began.", "table_markdown": "| Date | Floral leaves | Cauliflower | Aquatic | Eggplant | Pepper | Edible fungi |\n|--------|---------------|-------------|---------|----------|-----------|--------------|\n| 1-Jul | 97458.118 | 16247.417 | 14999.915 | 3005.180 | 56494.129 | 31935.165 |\n| 2-Jul | 87990.708 | 15879.543 | 15426.196 | 2319.565 | 51978.419 | 30785.089 |\n| 3-Jul | 78614.925 | 15412.114 | 15835.740 | 1694.794 | 47419.129 | 29606.387 |\n| 4-Jul | 69415.228 | 14882.153 | 16268.354 | 1143.931 | 42844.710 | 28399.600 |\n| 5-Jul | 60532.685 | 14259.605 | 16710.984 | 680.564 | 38284.967 | 27165.287 |\n| 6-Jul | 51904.265 | 13530.393 | 17163.796 | 318.762 | 33771.055 | 27108.630 |\n| 7-Jul | 43648.670 | 12679.805 | 17626.955 | 73.009 | 29292.225 | 24616.411 |"}
{"item_id": "item_0826", "chart_task_type": "baseline_index", "query": "Could you show the loan volumes for each college tier from 2012-13 to 2015-16 as if they all started at the same level? I want it to stand out which tier gained the most ground relative to where it began.", "table_markdown": "| | Research | Regional | Community |\n|---|---|---|---|\n| 2012-13 | $196,134,058 | $175,045,362 | $129,344,497 |\n| 2013-14 | $194,177,996 | $165,224,885 | $113,578,231 |\n| 2014-15 | $198,070,495 | $158,431,482 | $104,353,677 |\n| 2015-16 | $203,310,584 | $157,809,000 | $98,691,342 |"}
{"item_id": "item_0827", "chart_task_type": "baseline_index", "query": "I'm curious how corn yields for all regions changed from 1940 to 1967 when viewed relative to their 1940 starting points, so it stands out immediately which region gained the most ground from its starting level.", "table_markdown": "| Region | 1940 | 1950 | 1960 | 1959 | 1960 | 1961 | 1962 | 1963 | 1964 | 1965 | 1966 | 1967 |\n|-----------------|------|------|------|------|------|------|------|------|------|------|------|------|\n| North | 518 | 683 | 733 | 655 | 745 | 734 | 750 | 621 | 914 | 794 | 755 | 631 |\n| Gulf of Mexico | 983 | 1,008| 944 | 1,097| 1,161| 1,059| 1,052| 1,443| 1,357| 1,471| 1,650| 1,650|\n| Pacific North | 870 | 1,044| 1,257| 1,386| 1,282| 1,523| 1,522| 1,324| 1,372| 1,799| 1,217| 1,634|\n| Pacific South | 664 | 846 | 832 | 939 | 972 | 960 | 964 | 1,018| 1,043| 989 | 917 | 898 |\n| Center-High Valleys | 644 | 763 | 869 | 781 | 764 | 751 | 763 | 629 | 879 | 979 | 934 | 949 |\n| Center-Bajio | 534 | 757 | 788 | 884 | 1,067| 1,144| 1,140| 969 | 1,308| 1,223| 1,377| 1,664|\n| Mexico as a whole | 626 | 836 | 839 | 880 | 975 | 993 | 995 | 946 | 1,133| 1,124| 1,090| 1,204|"}
{"item_id": "item_0828", "chart_task_type": "stability_volatility", "query": "Show me the annual Tautog recreational harvest by state from 1981 to 2008, with the actual figures and how widely they swung relative to their usual level, so I can quickly tell which state was the most stable.", "table_markdown": "| | MA | RI | CT | NY | NJ | DE | MD | VA |\n|---|---|---|---|---|---|---|---|---|\n| 1981 | 790,611 | 664,568 | 242,336 | 1,496,039 | 161,423 | 6,585 | 10,295 | 742,653 |\n| 1982 | 3,226,869 | 777,931 | 610,608 | 1,674,949 | 1,241,155 | 428,036 | 90,644 | 271,920 |\n| 1983 | 1,837,263 | 615,595 | 458,581 | 1,124,844 | 414,956 | 4,438 | 6,550 | 1,267,164 |\n| 1984 | 733,876 | 1,809,822 | 733,711 | 541,805 | 717,260 | 95,739 | 79,110 | 669,870 |\n| 1985 | 328,042 | 277,385 | 471,185 | 2,034,903 | 741,656 | 144,858 | 1,107 | 298,796 |\n| 1986 | 7,862,585 | 2,042,584 | 838,345 | 2,833,206 | 2,132,571 | 264,744 | 10,049 | 918,139 |\n| 1987 | 1,751,372 | 507,424 | 1,106,606 | 2,288,075 | 2,130,955 | 387,075 | 266,093 | 442,750 |\n| 1988 | 2,255,930 | 612,123 | 610,172 | 2,380,285 | 1,331,832 | 249,803 | 446,947 | 1,410,003 |\n| 1989 | 1,076,365 | 296,889 | 1,038,217 | 1,018,016 | 1,289,186 | 743,338 | 78,391 | 806,337 |\n| 1990 | 895,326 | 389,579 | 199,999 | 1,980,289 | 1,256,488 | 142,627 | 59,720 | 229,442 |\n| 1991 | 798,890 | 1,007,548 | 648,633 | 2,352,646 | 2,189,144 | 354,497 | 106,222 | 619,215 |\n| 1992 | 1,668,485 | 656,713 | 1,048,638 | 1,199,558 | 2,485,693 | 183,855 | 159,730 | 255,996 |\n| 1993 | 752,598 | 389,734 | 531,024 | 1,800,794 | 1,361,612 | 217,881 | 105,232 | 758,409 |\n| 1994 | 373,188 | 328,668 | 417,439 | 585,037 | 330,551 | 152,034 | 177,358 | 1,101,129 |\n| 1995 | 309,224 | 237,094 | 402,617 | 369,643 | 1,722,714 | 793,339 | 115,993 | 613,348 |\n| 1996 | 397,284 | 248,840 | 245,817 | 193,046 | 1,123,173 | 158,751 | 26,484 | 778,314 |\n| 1997 | 166,042 | 301,109 | 84,297 | 331,530 | 483,639 | 204,419 | 182,995 | 391,257 |\n| 1998 | 96,694 | 316,338 | 231,622 | 208,743 | 41,431 | 257,347 | 27,648 | 273,516 |\n| 1999 | 363,472 | 223,762 | 61,142 | 761,447 | 511,672 | 358,329 | 37,677 | 203,249 |\n| 2000 | 442,816 | 203,601 | 58,475 | 258,099 | 1,812,959 | 373,580 | 56,127 | 188,187 |\n| 2001 | 502,248 | 165,380 | 63,157 | 171,928 | 1,482,613 | 159,961 | 72,357 | 127,556 |\n| 2002 | 521,611 | 265,116 | 447,139 | 2,135,221 | 1,184,560 | 652,008 | 104,247 | 116,798 |\n| 2003 | 221,842 | 479,344 | 603,862 | 315,383 | 164,326 | 200,619 | 43,212 | 308,838 |\n| 2004 | 123,394 | 546,289 | 449,293 | 1,235,936 | 215,039 | 459,403 | 39,592 | 631,680 |\n| 2005 | 249,146 | 494,811 | 306,536 | 390,516 | 122,593 | 243,928 | 125,184 | 416,663 |\n| 2006 | 251,975 | 402,234 | 702,189 | 945,348 | 699,378 | 434,339 | 44,343 | 535,700 |\n| 2007 | 337,974 | 951,287 | 960,086 | 776,008 | 1,151,046 | 277,941 | 273,586 | 211,860 |\n| 2008 | 96,584 | 458,127 | 784,443 | 961,102 | 557,788 | 423,929 | 82,194 | 189,232 |"}
{"item_id": "item_0829", "chart_task_type": "baseline_index", "query": "Help me compare unemployment for Belgium, Germany, Great Britain, and the U.S.A. from 1929 to 1931, measured against where each country began. I want to easily spot which nation's joblessness climbed furthest from its starting level.", "table_markdown": "| In March | Belgium (trade unions) | Germany | Great Britain (trade unions) | U. S. A. (trade unions) |\n|----------|------------------------|---------|-----------------------------|------------------------|\n| 1929 | 0.9 | 16.9 | 10.0 | 6.6 |\n| 1930 | 2.2 | 21.7 | 13.7 | 13.6 |\n| 1931 | 11.3 | 33.6 | 21.5 | 18.1 |"}
{"item_id": "item_0830", "chart_task_type": "baseline_index", "query": "Help me compare disability rates for men aged 45–64 across education levels from 1969 to 1978, relative to where each started. I want it to stand out which group grew the most in proportion to its baseline.", "table_markdown": "| Year | Less than high school | High school graduate | More than high school |\n|------|-----------------------|----------------------|-----------------------|\n| 1969 | 10.6 | 4.0 | 2.8 |\n| 1974 | 15.1 | 5.4 | 3.5 |\n| 1978 | 17.1 | 7.4 | 3.9 |"}
{"item_id": "item_0831", "chart_task_type": "baseline_index", "query": "Show me the temperature readings for all sensors as if they all began from the same point, so it's obvious right away which component heated up the most relative to its starting level.", "table_markdown": "| Time | Drain Contact | Ambient | Cable | Source Contact | Insulator |\n|---|---|---|---|---|---|\n| 0 | 16.6 | 17.7 | 15.8 | 16.6 | 17.1 |\n| 1 | 78.8 | 22.3 | 43.7 | 67.8 | 42.3 |\n| 2 | 90.1 | 17.5 | 45.8 | 75.6 | 48.8 |\n| 3 | 90.3 | 16.8 | 51.4 | 75.7 | 48.6 |\n| 4 | 90 | 17.5 | 51.6 | 75.6 | 48.5 |\n| 5 | 90.2 | 17 | 51.7 | 75.5 | 47.9 |\n| 6 | 90.2 | 17.1 | 51.8 | 75.5 | 48 |"}
{"item_id": "item_0832", "chart_task_type": "baseline_index", "query": "Help me compare men's and women's enrollment growth from 2011-12 to 2017-18 with each series starting from its own 2011-12 level, so it's easy to spot which gender climbed furthest from its baseline.", "table_markdown": "| Year | 2011-12 | 2013-14 | 2015-16 | 2017-18 |\n|----------|---------|---------|---------|---------|\n| All Categories | 20.8 | 23 | 24.5 | 25.8 |\n| Male | 22.1 | 23.9 | 25.4 | 26.3 |\n| Female | 19.4 | 22 | 23.5 | 25.4 |"}
{"item_id": "item_0833", "chart_task_type": "baseline_index", "query": "I'm curious how the number of tourists in Constanta and Tulcea changed from 2014 to 2016 relative to their own starting levels, so it's obvious which county pulled ahead the most compared with where it began.", "table_markdown": "| | 2014 | | | 2015 | | | 2016 | | |\n|---|---|---|---|---|---|---|---|---|---|\n| | Total | Romanian | Foreign | Total | Romanian | Foreign | Total | Romanian | Foreign |\n| CT | 883.947 | 832.988 | 50.959 | 1.021.475 | 961.723 | 59.752 | 1.163.023 | 1.102.123 | 60.900 |\n| TL | 66.242 | 52.760 | 13.482 | 69.076 | 53.384 | 15.692 | 69.412 | 52.242 | 17.170 |\n| Total | 950.189 | 885.748 | 64.441 | 1.090.551 | 1.015.107 | 75.444 | 1.232.435 | 1.154.365 | 78.070 |"}
{"item_id": "item_0834", "chart_task_type": "stability_volatility", "query": "I want to see the degree day accumulations for each city from 1998 to 2005 and how widely they swung, so I can easily spot which location was the most stable.", "table_markdown": "| City | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 |\n|--------------|------|------|------|------|------|------|------|------|\n| Akron - Canton | 771 | 488 | 932 | 614 | 1132 | 778 | 1294 | 905 |\n| Cincinnati | 1156 | 808 | 1336 | 953 | 1541 | 1123 | 1726 | 1273 |\n| Cleveland | 786 | 513 | 945 | 636 | 1148 | 804 | 1309 | 930 |\n| Columbus | 1076 | 746 | 1258 | 894 | 1478 | 1078 | 1655 | 1221 |\n| Dayton | 1033 | 720 | 1213 | 865 | 1423 | 1040 | 1604 | 1186 |\n| Mansfield | 792 | 512 | 953 | 638 | 1151 | 802 | 1320 | 935 |\n| Norwalk | 793 | 519 | 960 | 651 | 1164 | 820 | 1326 | 946 |\n| Piketon | 1137 | 783 | 1312 | 923 | 1522 | 1098 | 1711 | 1252 |\n| Toledo | 807 | 529 | 983 | 670 | 1196 | 847 | 1359 | 976 |\n| Wooster | 827 | 543 | 989 | 670 | 1185 | 831 | 1353 | 964 |\n| Youngstown | 750 | 473 | 900 | 587 | 1083 | 736 | 1234 | 852 |"}
{"item_id": "item_0835", "chart_task_type": "baseline_index", "query": "Can you put together a view of electrical conductivity for each blood group as if they all began at the same level? I want to quickly see which group increased the most in proportion to its starting value over the two hours.", "table_markdown": "| Exposure Time (min) | | | | | | | |\n|---|---|---|---|---|---|---|---|\n| | Blood Group A | | Blood Group B | | Blood Group AB | | |\n| | Norm al | Irradiate d | Norma l | Irradiate d | Norma l | Irradiate d | Norma l |\n| 0 | 8.24 | 8.24 | 6.86 | 6.86 | 5.27 | 5.27 | 10.51 |\n| 15 | 8.24 | 10.85 | 6.90 | 10.86 | 5.53 | 6.17 | 10.51 |\n| 30 | 8.24 | 11.65 | 6.79 | 13.37 | 5.59 | 6.45 | 10.60 |\n| 45 | 8.24 | 11.52 | 6.90 | 13.78 | 5.59 | 7.66 | 10.55 |\n| 60 | 8.24 | 11.83 | 6.70 | 14.14 | 5.70 | 8.71 | 10.65 |\n| 75 | 8.24 | 12.25 | 6.58 | 14.29 | 5.80 | 9.02 | 10.70 |\n| 90 | 8.24 | 12.58 | 6.90 | 15.10 | 5.79 | 9.61 | 10.71 |\n| 105 | 8.24 | 13.12 | 6.65 | 15.52 | 5.58 | 9.86 | 10.81 |\n| 120 | 8.24 | 13.59 | 6.51 | 15.29 | 5.60 | 9.76 | 10.61 |"}
{"item_id": "item_0836", "chart_task_type": "baseline_index", "query": "I'm curious how credit unions, members, shares, and loans grew from 1935 to 1948 when measured against their 1935 starting levels. I want it to be obvious which metric climbed furthest from its baseline.", "table_markdown": "| Year | Number of reporting credit unions | Number of members | Amount of shares | Amount of loans |\n|------|----------------------------------|-------------------|-----------------|----------------|\n| 1935 | 762 | 118,665 | $2,224,610 | $1,830,489 |\n| 1936 | 1,125 | 307,634 | 8,572,776 | 7,399,124 |\n| 1937 | 2,292 | 432,441 | 17,780,140 | 14,000,740 |\n| 1938 | 2,753 | 631,436 | 26,809,367 | 23,824,703 |\n| 1939 | 3,172 | 849,806 | 43,314,433 | 37,663,782 |\n| 1940 | 3,739 | 1,126,222 | 65,780,063 | 55,801,026 |\n| 1941 | 4,144 | 1,396,696 | 96,816,948 | 69,249,487 |\n| 1942 | 4,070 | 1,347,519 | 109,498,801 | 42,886,750 |\n| 1943 | 3,839 | 1,302,363 | 116,938,974 | 35,228,153 |\n| 1944 | 3,795 | 1,303,801 | 133,588,147 | 34,405,467 |\n| 1945 | 3,757 | 1,216,625 | 140,613,962 | 35,155,414 |\n| 1946 | 3,747 | 1,191,315 | 140,613,962 | 56,800,363 |\n| 1947 | 3,845 | 1,445,915 | 192,410,043 | 91,170,179 |\n| 1948 | 4,058 | 1,628,339 | 235,008,368 | 137,642,327 |"}
{"item_id": "item_0837", "chart_task_type": "baseline_index", "query": "Could you show the pH levels for each tomato puree sample over the 45 days as if they all began at the same point, so it's easy to spot which treatment drifted furthest from its starting value?", "table_markdown": "| Days | C | T 1 | T 2 | T3 | T4 |\n|---|---|---|---|---|---|\n| 0 | 5 | 4.7 | 4.71 | 4.6 | 4.61 |\n| 15 | 5.5 | 4.9 | 4.88 | 4.68 | 4.66 |\n| 30 | 5.7 | 4.7 | 4.99 | 4.68 | 4.71 |\n| 45 | 6 | 5 | 5 | 4.74 | 4.75 |"}
{"item_id": "item_0838", "chart_task_type": "baseline_index", "query": "Help me compare the sales profit for Refractories, Unshaped refractory materials, and Proppants from 2015 to 2017, with each measured against its 2015 level, so I can quickly see which one pulled ahead the most compared with where it began.", "table_markdown": "| Product | 2015 | 2016 | 2017 | | Growth rate, % | |\n|---|---|---|---|---|---|---|\n| | | | | | 2016 to 2015 | 2017 to 2016 |\n| Refractories (molded refractory materials) | 747009 | 771416 | 1084793 | 103,27 | | |\n| Unshaped refractory materials | 73662 | 87765 | 92170 | 119,15 | | |\n| Proppants | 528452 | 670496 | 866731 | 126,88 | | |\n| Total | 1349123 | 1529677 | 2043694 | 113,38 | | |"}
{"item_id": "item_0839", "chart_task_type": "baseline_index", "query": "I'd like to see the population of all nine states from 2006 to 2015 displayed relative to their 2006 starting points, making it easy to spot which state climbed furthest from its baseline.", "table_markdown": "| States | Land Area | Male | Female | 2006 | 2010 | 2015 |\n|---|---|---|---|---|---|---|\n| Abia | 4,877 | 1,434,193 | 1,399,806 | 2,838,999 | 2,839,262 | 2,842,475 |\n| Akwa Ibom | 6,806 | 2,044,510 | 1,875,698 | 3,920,208 | 3,920,417 | 3,925,684 |\n| Bayelsa | 11,007 | 902,648 | 800,710 | 1,703,358 | 1,703,621 | 1,706,834 |\n| Cross River | 21,930 | 1,492,465 | 1,396,501 | 2,888,966 | 2,889,229 | 2,892,442 |\n| Delta | 17,163 | 2,074, 306 | 2,024,085 | 4,098,397 | 4,098,660 | 4,101,873 |\n| Edo | 19,698 | 1,640,461 | 1,577,871 | 3,218,595 | 3,218,595 | 3,221,808 |\n| Imo | 5,165 | 2,032,286 | 1,902,613 | 3,934,899 | 3,935,162 | 3,935,162 |\n| Rivers | 10,378 | 2,710,665 | 2,474,735 | 5,185,400 | 5,185,663 | 5,188,876 |\n| Ondo | 15,086 | 1,761,263 | 1,679,761 | 3,441,024 | 3,441,287 | 3,441,287 |"}
{"item_id": "item_0840", "chart_task_type": "baseline_index", "query": "I'd like to see the growth of hospital, long-term care, and therapeutic meal services from FY 2009 to FY 2014 shown as if they all started at the same level, so it's easy to spot which segment grew the most relative to where it began.", "table_markdown": "| Year | Hospital meal services | Meal services for long-term care facilities | Therapeutic meal delivery services |\n|------|------------------------|--------------------------------------------|-----------------------------------|\n| FY 2009 | 12,540 | 6,946 | 669 |\n| FY 2010 | 12,438 | 7,153 | 620 |\n| FY 2011 | 12,186 | 7,339 | 730 |\n| FY 2012 | 12,116 | 7,788 | 850 |\n| FY 2013 | 12,043 | 8,072 | 970 |\n| FY 2014 | 11,971 | 8,316 | 1,050 |"}
{"item_id": "item_0841", "chart_task_type": "baseline_index", "query": "Can you put together a view of CSPO and CSPK production from 2015 to 2019, showing each series relative to its 2015 starting point, so I can quickly tell which product reached the highest point compared with where it began?", "table_markdown": "| Year | CSPO | CSPK |\n|------|-------|-------|\n| 2015 | 12,623,139 | 2,883,200 |\n| 2016 | 10,850,196 | 2,444,103 |\n| 2017 | 11,710,919 | 2,683,835 |\n| 2018 | 13,619,600 | 3,315,973 |\n| 2019 | 14,290,537 | 3,208,976 |"}
{"item_id": "item_0842", "chart_task_type": "baseline_index", "query": "Help me compare the sales of companies A, B, and C from 2013 to 2016 as if they all started from the same level, so it's obvious which one gained the most ground from its 2013 starting point.", "table_markdown": "| Company | 2013 | 2014 | 2015 | 2016 |\n|---------|------|------|------|------|\n| A | 260 | 300 | 345 | 375 |\n| B | 1050 | 1270 | 1350 | 1465 |\n| C | 680 | 725 | 815 | 880 |"}
{"item_id": "item_0843", "chart_task_type": "baseline_index", "query": "Could you show the defence spending for all seven countries from 1988 to 2011 relative to where each one began, so I can easily spot which one gained the most ground from its starting level?", "table_markdown": "| Country | 1988 | 1990 | 1995 | 2000 | 2005 | 2010 | 2011 |\n|---------|--------|--------|--------|--------|--------|--------|--------|\n| USA | 540.42 | 511.00 | 399.04 | 382.06 | 562.04 | 698.28 | 689.59 |\n| France | 65.27 | 65.77 | 60.58 | 57.62 | 60.73 | 59.10 | 58.24 |\n| UK | 53.75 | 54.30 | 44.66 | 44.31 | 53.68 | 58.10 | 57.88 |\n| India | 16.71 | 17.58 | 18.33 | 25.84 | 33.69 | 46.09 | 44.28 |\n| Brazil | 19.90 | 46.54 | 20.38 | 22.46 | 23.68 | 34.38 | 31.58 |\n| Canada | 19.34 | 19.22 | 16.27 | 14.62 | 16.64 | 23.11 | 23.08 |\n| Australia | 13.23 | 13.18 | 14.03 | 15.47 | 18.41 | 23.22 | 22.96 |"}
{"item_id": "item_0844", "chart_task_type": "baseline_index", "query": "I'm curious how M1 and M3 money supply have grown since 1999-00 when viewed relative to where each began, making it obvious which measure expanded the most in proportion to its starting value.", "table_markdown": "| Year | M1 (Narrow Money) | M3 (Broad Money) |\n|--------|-------------------|------------------|\n| 1999-00| 3417.96 | 11241.74 |\n| 2000-01| 3794.33 | 13132.04 |\n| 2001-02| 4228.24 | 14983.36 |\n| 2002-03| 4735.58 | 17179.36 |\n| 2003-04| 5786.94 | 20056.54 |\n| 2004-05| 6497.66 | 22456.53 |\n| 2005-06| 8263.89 | 27194.93 |\n| 2006-07| 9679.25 | 33100.38 |\n| 2007-08| 11558.10 | 40178.55 |\n| 2008-09| 12596.71 | 47947.75 |\n| 2009-10| 14892.68 | 56026.98 |\n| 2010-11| 16383.45 | 65041.16 |\n| 2011-12| 17373.94 | 73848.31 |\n| 2012-13| 18975.26 | 83898.19 |\n| 2013-14| 20597.62 | 95173.86 |\n| 2014-15| 22924.04 | 105501.68 |\n| 2015-16| 26025.38 | 116176.15 |\n| 2016-17| 26819.57 | 127919.40 |\n| 2017-18| 32673.31 | 139625.87 |"}
{"item_id": "item_0845", "chart_task_type": "baseline_index", "query": "I'm curious how the Older People funding scenarios compare when viewed relative to their 2016/17 starting points, so I can easily spot which one moves furthest from where it began.", "table_markdown": "| Older People (excludes A&CM) £000’S | | | | | | | |\n|---|---|---|---|---|---|---|---|\n| Older People | 2016/17 | 2017/18 | 2018/19 | 2019/20 | 2020/21 | 2021/22 | 2022/2 |\n| OP POPPI | £1,287,166 | £1,231,415 | £1,327,123 | £1,389,913 | £1,423,484 | £1,457,055 | £1,490,6 |\n| SCENARIO 1 | £1,287,166 | £1,300,908 | £1,354,298 | £1,419,409 | £1,455,846 | £1,508,590 | £1,562,9 |\n| SCENARIO 2 | £1,287,166 | £1,300,911 | £1,405,076 | £1,507,210 | £1,613,850 | £1,721,778 | £1,832,8 |\n| SCENARIO 3 | £1,287,166 | £1,300,505 | £1,308,440 | £1,289,253 | £1,293,261 | £1,272,165 | £1,250,4 |"}
{"item_id": "item_0846", "chart_task_type": "baseline_index", "query": "Show me the active forward flexion recovery trajectories for both treatment groups relative to their starting points, so it's obvious right away which pulled ahead the most compared with where each began through 12 weeks post-injection.", "table_markdown": "| Range of Motion | Study Group | Baseline Mean (SD) Degrees [p-value] | 3 Weeks Mean (SD) Degrees [p-value] | 6 Weeks Mean (SD) Degrees [p-value] | 12 Weeks Mean (SD) Degrees [p-value] |\n|---|---|---|---|---|---|\n| FF Active | local | 108(24) | 126(17) | 139(28) | 135(18) |\n| - | local + steroid | 96(23) | 110(20) | 114(30) | 117(23) |\n| - | - | [.39] | [.34] | [.16] | [.18] |\n| FF Passive | local | 113(23) | 131(20) | 143(26) | 138(21) |\n| - | local + steroid | 103(23) | 113(21) | 120(27) | 124(23) |\n| - | - | [.49] | [.33] | [.52] | [.27] |\n| LE Active | local | 91(31) | 105(34) | 117(36) | 109(26) |\n| - | local + steroid | 83(21) | 108(24) | 110(32) | 100(22) |\n| - | - | [.33] | [.82] | [.56] | [.36] |\n| LE Passive | local | 95(31) | 111(36) | 122(37) | 111(26) |\n| - | local + steroid | 87(21) | 111(25) | 117(28) | 104(23) |\n| - | - | [.41] | [.96] | [.69] | [.46] |\n| IR Abduction Active | local | 33(28) | 21(14) | 50(30) | 24(29) |\n| - | local + steroid | 33(27) | 14(18) | 31(20) | 26(18) |\n| - | - | [.95] | [.37] | [.07] | [.86] |\n| IR Abduction Passive | local | 37(31) | 22(14) | 55(33) | 29(31) |\n| - | local + steroid | 38(30) | 17(20) | 35(21) | 29(21) |\n| - | - | [.95] | [.48] | [.08] | [.98] |\n| ER Abduction Active ActiveAcActive | local | 41(33) | 58(31) | 68(25) | 63(27) |\n| - | local + steroid | 43(29) | 60(25) | 63(23) | 62(22) |\n| - | - | [.87] | [.82] | [.58] | [.88] |\n| ER Abduction Passive | local | 44(35) | 62(35) | 74(27) | 65(28) |\n| - | local + steroid | 49(31) | 64(28) | 71(21) | 64(23) |\n| - | - | [.71] | [.83] | [.74] | [.93] |"}
{"item_id": "item_0847", "chart_task_type": "baseline_index", "query": "I'd like to see the elongation of the four film embodiments relative to their dry-state baseline, so it's easy to spot which grew the most in proportion to its starting value after 30 minutes of water immersion.", "table_markdown": "| Elongation % | No Water Immersion | 15 Min. Water Immersion | 30 Min. Water Immersion |\n|--------------|--------------------|------------------------|------------------------|\n| **Inventive Coated Film Embodiments** | | | |\n| 12 (BIS Adipic on Starch and PVOH Film) | 270 | 319 | 312 |\n| 13 (HQ-DDCA on Starch and PVOH Film) | 130 | 223 | 232 |\n| 14 (BIS CHD on Starch and PVOH Film) | 218 | 311 | 351 |\n| **Control** | | | |\n| 15 (No Coating on Starch and PVOH Film) | 205 | 126 | 154 |"}
{"item_id": "item_0848", "chart_task_type": "baseline_index", "query": "Can you put together a view of visits to Moab Field Office and Arches National Park from 2007 to 2011, adjusted so both begin at the same point in 2007, so it's easy to spot which pulled ahead the most compared with where it began?", "table_markdown": "| Year | Visits to Moab Field Office | Visits to Arches National Park |\n|------|----------------------------|-------------------------------|\n| 2007 | 1,493,700 | 860,181 |\n| 2008 | 1,706,389 | 928,795 |\n| 2009 | 1,674,504 | 996,312 |\n| 2010 | 1,834,724 | 1,014,405 |\n| 2011 | 1,809,702 | 1,023,669 (YTD NOV 2011) |"}
{"item_id": "item_0849", "chart_task_type": "baseline_index", "query": "Help me compare Indigenous Year 12 attainment for Tasmania and Australia from 2006 to 2020, showing each region relative to its own 2006 starting level so it's easy to spot which region climbed furthest from its baseline.", "table_markdown": "| | 2006 | 2009 | 2012 | 2015 | 2018 | 2020 |\n|----------------|------|------|------|------|------|------|\n| Tasmania | 55.2 | 58.7 | 61.1 | 64.85| 68 | 69.7 |\n| Australia | 41.4 | 43.9 | 49.8 | 56.9 | 63.3 | 66.1 |"}
{"item_id": "item_0850", "chart_task_type": "baseline_index", "query": "I'd like to see the production trends for all six cash crops showing how each changed relative to where it began in 1998/99, so it's easy to spot which one gained the most ground from its starting level by 2002/03.", "table_markdown": "| Cash crops | 1998/99 | 1999/00 | 2000/01 | 2001/02 | 2002/03\\(^a\\) |\n|------------|---------|---------|---------|---------|---------------|\n| Cashew nuts| 103 | 121 | 122 | 67 | 88 |\n| Coffee | 47 | 48 | 58 | 38 | 50 |\n| Cotton seed| 106 | 101 | 123 | 149 | 189 |\n| Tea | 22 | 25 | 26 | 25 | 28 |\n| Sisal | 23 | 21 | 21 | 24 | 24 |\n| Tobacco | 38 | 32 | 25 | 28 | 32 |"}
{"item_id": "item_0851", "chart_task_type": "baseline_index", "query": "Could you show the price paths for Gold and Silver from 2008 to 2016, measured against their 2008 starting points? I want it to be obvious right away which metal moved furthest from that baseline.", "table_markdown": "| Year | Gold | Silver |\n|------|------|--------|\n| 2008 | $750 | $5 |\n| 2009 | $800 | $5 |\n| 2010 | $1,000 | $5 |\n| 2011 | $1,250 | $15 |\n| 2012 | $1,500 | $25 |\n| 2013 | $1,250 | $25 |\n| 2014 | $1,000 | $15 |\n| 2015 | $1,000 | $15 |\n| Half 2016 | $1,100 | $15 |"}
{"item_id": "item_0852", "chart_task_type": "baseline_index", "query": "Show me the four forestry industries from 2006 to 2009 as if they all started from the same baseline, making it easy to spot which one fell the hardest relative to its own 2006 level.", "table_markdown": "| Industry | 2006 | 2008 | 2009 | 2006-09 % change |\n|---|---|---|---|---|\n| Forestry and Logging | 273 | 187.2 | 156.2 | -42.8 |\n| Support Activities | 56.1 | 39.4 | 38.8 | -30.8 |\n| Wood Products Manufacturing | 446.3 | 282.5 | 268.6 | -39.8 |\n| Paper Manufacturing | 639.4 | 484.3 | 449.6 | -29.7 |\n| Total | 1414.8 | 993.4 | 913.2 | -35.5 |"}
{"item_id": "item_0853", "chart_task_type": "baseline_index", "query": "Help me compare the CFQ-R Respiratory Symptom Score trajectories for the three participants from month 0 to 12, shown relative to where each one began, so it's easy to spot which improved the most in proportion to its starting value.", "table_markdown": "| Month | Participant 1 | Participant 2 | Participant 3 |\n|-------|---------------|---------------|---------------|\n| 0 | 70 | 60 | 80 |\n| 3 | 75 | 75 | 75 |\n| 6 | 80 | 65 | 85 |\n| 9 | 85 | 80 | 85 |\n| 12 | 80 | 75 | 90 |"}
{"item_id": "item_0854", "chart_task_type": "baseline_index", "query": "Could you show the serum 25(OH)D concentration for all four treatment groups relative to where each one began? I want it easy to spot which regimen climbed the furthest from its own baseline by 12 months.", "table_markdown": "| Time Point | D₃ (1600 IU daily) | D₂ (1600 IU daily) | Change from baseline; ratio D₃/D₂ | P | D₃ (50,000 IU monthly) | D₂ (50,000 IU monthly) |\n|------------|-------------------|-------------------|---------------------------------|------|------------------------|------------------------|\n| Base | 29.9 (2.5) | 32.0 (2.1) | | | 36.3 (2.1) | 31.1 (2.2) |\n| 1 month | 34.4 (1.8) | 32.9 (2.0) | 1.13 (1.02–1.24) | 0.01 | 38.5 (2.2) | 32.5 (1.8) |\n| 2 months | 35.9 (1.9) | 34.5 (1.7) | 1.11 (0.98–1.26) | 0.10 | 40.2 (2.4) | 32.8 (2.2) |\n| 3 months | 37.5 (1.9) | 33.8 (1.8) | 1.19 (1.03–1.37) | 0.02 | 41.7 (2.4) | 32.8 (2.1) |\n| 6 months | 40.3 (2.4) | 36.8 (2.0) | 1.17 (1.00–1.37) | 0.05 | 42.3 (2.5) | 34.1 (2.1) |\n| 9 months | 39.5 (2.4) | 36.9 (2.1) | 1.14 (0.97–1.34) | 0.11 | 44.0 (2.8) | 35.1 (2.4) |\n| 12 months | 39.0 (2.4) | 38.1 (2.0) | 1.09 (1.00–1.29) | 0.32 | 45.2 (3.3) | 34.7 (2.3) |\n| Pooled | | | 1.14 (1.00–1.29) | 0.05 | | |"}
{"item_id": "item_0855", "chart_task_type": "baseline_index", "query": "Can you put together a view of the production trends for all crane types from 2000 to 2008, with every type starting from its 2000 level, so it's easy to spot which one gained the most ground from its starting level?", "table_markdown": "| Products name | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 |\n|---|---|---|---|---|---|---|---|---|---|\n| Elecric gantry cranes, sets | 61 | 77 | 64 | 97 | 110 | 92 | 87 | 97 | 168 |\n| Electric hoists, sets | 7494 | 8102 | 6287 | 6988 | 7212 | 6209 | 7146 | 8934 | 8515 |\n| Single girder electric cranes overhead and travelling, sets | 1305 | 1596 | 1638 | 1598 | 1546 | 1497 | 1768 | 2212 | 2740 |\n| Elecric overhead cranes of special purpose with capacity 20 t and more, sets | 9 | 11 | 6 | 17 | 13 | 9 | 20 | 24 | 24 |\n| Grab electric overhead cranes, sets | 15 | 25 | 14 | 10 | 16 | 22 | 26 | 21 | 46 |\n| Elecric overhead cranes of special purpose, sets | 134 | 105 | 74 | 83 | 94 | 117 | 107 | 189 | 96 |\n| Elecric overhead cranes with capacity 20 t and more, sets | 24 | 13 | 9 | 11 | 6 | 8 | 10 | 9 | 9 |\n| Elecric overhead cranes of general purpose, sets | 387 | 578 | 581 | 560 | 374 | 568 | 447 | 750 | 636 |"}
{"item_id": "item_0856", "chart_task_type": "baseline_index", "query": "Show me the private and public sector debt from 1976 to 1982, displayed as if they both started at the same level, so it's easy to spot which sector grew the most relative to where it began.", "table_markdown": "| Year | Private Sector Debt | Public Sector Debt |\n|------|---------------------|--------------------|\n| 1976 | 32.6 | 20 |\n| 1977 | 35 | 24 |\n| 1978 | 38 | 27 |\n| 1979 | 42 | 35 |\n| 1980 | 46 | 34 |\n| 1981 | 64 | 48 |\n| 1982*| 80 | 46 |"}
{"item_id": "item_0857", "chart_task_type": "baseline_index", "query": "Help me compare hydrogen demand for the EU, China, US, Japan, and South Korea from 2030 to 2050, shown relative to where each began in 2030. I want to easily spot which region gained the most ground from its starting level.", "table_markdown": "| Region/Country | 2030 | 2040 | 2050 |\n|----------------|------|------|------|\n| EU | 7 | 21 | 48 |\n| China | 4 | 15 | 39 |\n| US | 2 | 8 | 21 |\n| Japan | 5 | 13 | 33 |\n| South Korea | 1 | 4 | 9 |"}
{"item_id": "item_0858", "chart_task_type": "baseline_index", "query": "Help me compare the overall beer market and BR member sales from 2004 to 2011, with each measured against its 2004 baseline so I can easily spot which segment bounced back the most relative to where it started after the 2009 crisis.", "table_markdown": "| Year | Beer market | The sales of the BR members |\n|---|---|---|\n| 2004 | 14,5 | 9,73 |\n| 2005 | 15,2 | 10,45 |\n| 2006 | 17,7 | 12,6 |\n| 2007 | 19,4 | 15,15 |\n| 2008 | 20,2 | 16,46 |\n| 2009 | 17,6 | 16 |\n| 2010 | 17 | 15,3 |\n| 2011 | 17 | 15,6 |"}
{"item_id": "item_0859", "chart_task_type": "baseline_index", "query": "Show me how registered vehicles and population grew since 1998-1999, as if they both started from the same level. I want it to stand out which one gained the most ground from its starting level.", "table_markdown": "| Year | No. of vehicles registered in LGA | Population |\n|------------|----------------------------------|------------|\n| 1998-1999 | 45,000 | 60,000 |\n| 1999-2000 | 47,000 | 62,000 |\n| 2000-2001 | 49,000 | 64,000 |\n| 2001-2002 | 51,000 | 66,000 |\n| 2002-2003 | 53,000 | 68,000 |\n| 2003-2004 | 55,000 | 70,000 |\n| 2004-2005 | 57,000 | 72,000 |\n| 2005-2006 | 59,000 | 74,000 |\n| 2006-2007 | 61,000 | 76,000 |"}
{"item_id": "item_0860", "chart_task_type": "baseline_index", "query": "Can you put together a view of the cost trajectories for all coverage types from 2002 to 2005, with each plan starting from its own 2002 level, so I can quickly tell which one climbed furthest from its baseline?", "table_markdown": "| Type Coverage | 2002 | 2003* | 2004* | 2005* | % & $ Increase 2002–2005* |\n|---------------|--------|--------|--------|--------|---------------------------|\n| PPO | | | | | |\n| (Family) | $8,173 | $9,399 | $10,997| $12,866| 57% |\n| (Single) | $3,175 | $3,651 | $ 4,272| $ 4,998| 57% |\n| HMO | | | | | |\n| (Family) | $7,541 | $8,657 | $10,146| $11,871| 57% |\n| (Single) | $2,764 | $3,179 | $ 3,683| $ 4,309| 57% |\n| Indemnity | | | | | |\n| (Family) | $8,479 | $9,750 | $11,407| $13,347| 57% |\n| (Single) | $3,582 | $4,119 | $ 4,819| $ 5,638| 57% |\n| % Increase | 14.7% | 15%* | 17%* | 17%* | |"}
{"item_id": "item_0861", "chart_task_type": "baseline_index", "query": "Can you put together a view of male and female correct condom use knowledge across visits, scaled to start from each group's first visit level? I want it easy to spot which gender improved most relative to where it began.", "table_markdown": "| | | Number of visits | | |\n|---|---|---|---|---|\n| | | 1 | 2 | 3 |\n| Heard about condom | | | | |\n| Male | Percentage | 86.5 | 97.9 | 98.8 |\n| | Number | 111 | 47 | 81 |\n| Female | Percentage | 63.8 | 91.3 | 95.7 |\n| | Number | 163 | 69 | 93 |\n| Know correct | use of condom (Tho | se who have heard of | condom) | |\n| Male | Percentage | 43.8 | 58.7 | 93.8 |\n| | Number | 96 | 46 | 80 |\n| Female | Percentage | 59.6 | 92.1 | 98.9 |\n| | Number | 104 | 63 | 89 |\n| Condom can | prevent pregnancy | | | |\n| Male | Percentage | 50.5 | 74.5 | 87.7 |\n| | Number | 111 | 47 | 81 |\n| Female | Percentage | 38.0 | 76.8 | 91.4 |\n| | Number | 163 | 69 | 93 |\n| Consistent an | d correct use of con | dom can prevent HIV | and STI | |\n| Male | Percentage | 54.1 | 76.6 | 85.2 |\n| | Number | 111 | 47 | 81 |\n| Female | Percentage | 19.6 | 47.8 | 71.0 |"}
{"item_id": "item_0862", "chart_task_type": "baseline_index", "query": "I'm curious how downstream bandwidth for all application categories evolved from 2015 to 2025 when viewed relative to their 2015 levels, so I can spot which category moved furthest from its baseline.", "table_markdown": "| Application category | Downstream bandwidth in 2015 | Assumed CAGR (%) | Downstream bandwidth in 2020* | Downstream bandwidth in 2025 |\n|---------------------------------------|------------------------------|------------------|-------------------------------|-------------------------------|\n| Basic internet | 2 | 25 | ~6 | ~20 |\n| Home office/VPN | 16 | 30 | ~60 | ~250 |\n| Cloud computing | 16 | 30 | ~60 | ~250 |\n| State of the art media and entertainment (4k, 3D, UHD) | 14 | 20 | ~40 | ~90 |\n| Progressive media (8k, VR) | 25 | 30 | ~100 | ~300 |\n| Communication | 1.5 | 20 | ~5 | ~8 |\n| Video communication (HD) | 8 | 15 | ~10 | ~25 |\n| Gaming | 25 | 30 | ~100 | ~300 |\n| E-Health | 2.5 | 30 | ~10 | ~50 |\n| E-Home/E-facility | 2.5 | 30 | ~10 | ~50 |\n| Mobile Offloading | 2 | 30 | ~10 | ~15 |"}
{"item_id": "item_0863", "chart_task_type": "baseline_index", "query": "Can you put together a view of Manitoba, Saskatchewan, and Alberta's population from 1901 to 1921, showing each as if they all started from the same level? I want to easily spot which province grew the most relative to where it began.", "table_markdown": "| Provinces | 1901 Total | 1906 Total | 1911 Males | 1911 Females | 1911 Total | 1916 Males | 1916 Females | 1916 Total | 1921 Total |\n|-----------|------------|------------|------------|--------------|------------|------------|-------------|------------|------------|\n| Manitoba | 255,211 | 365,688 | 253,056 | 208,574 | 461,630 | 294,609 | 259,251 | 553,860 | 610,118 |\n| Saskatchewan | 91,279 | 257,763 | 291,730 | 200,702 | 492,432 | 363,787 | 284,048 | 647,835 | 757,510 |\n| Alberta | 73,022 | 185,412 | 223,989 | 150,674 | 374,663 | 277,256 | 219,269 | 496,525 | 588,454 |\n| **Total** | **419,512** | **808,863** | **768,775** | **559,950** | **1,328,725** | **935,952** | **762,568** | **1,698,220** | **1,956,082** |"}
{"item_id": "item_0864", "chart_task_type": "baseline_index", "query": "I'd like to see the price trajectories of these 8 stocks shown relative to where each began on 28/09/2004, so I can easily spot which one pulled ahead the most compared with its own starting level by the end of the period.", "table_markdown": "| date | AlfaA | AmTelA1 | AmxI | BImboA | Cemex CPO | Elektra | Femsaubd | gcarsoa1 |\n|------------|---------|---------|---------|---------|-----------|---------|----------|----------|\n| 28/09/2004 | 42.090 | 23.800 | 22.027 | 24.752 | 63.800 | 76.200 | 50.200 | 51.843 |\n| 29/09/2004 | 42.880 | 24.420 | 22.226 | 24.655 | 64.720 | 76.790 | 50.620 | 52.787 |\n| 30/09/2004 | 43.060 | 24.600 | 22.206 | 24.439 | 64.090 | 76.480 | 50.300 | 51.992 |\n| 01/10/2004 | 43.480 | 24.890 | 22.756 | 25.203 | 64.800 | 76.750 | 50.830 | 52.250 |\n| 04/10/2004 | 43.280 | 25.250 | 23.185 | 25.350 | 65.760 | 76.400 | 50.870 | 52.558 |\n| 05/10/2004 | 43.100 | 24.600 | 22.956 | 25.340 | 65.470 | 76.610 | 51.040 | 52.648 |\n| 06/10/2004 | 42.860 | 24.300 | 22.526 | 25.144 | 67.140 | 76.690 | 51.140 | 52.518 |\n| 07/10/2004 | 42.990 | 24.310 | 22.506 | 25.291 | 66.580 | 78.000 | 51.010 | 52.379 |\n| 08/10/2004 | 42.150 | 23.810 | 22.007 | 24.214 | 65.120 | 79.500 | 50.920 | 52.131 |\n| $\\vdots$ | $\\vdots$ | $\\vdots$ | $\\vdots$ | $\\vdots$ | $\\vdots$ | $\\vdots$ | $\\vdots$ | $\\vdots$ |"}
{"item_id": "item_0865", "chart_task_type": "baseline_index", "query": "I'm curious how single-family home sales in Sullivan County's 14 towns changed from 2005 to 2011 relative to their 2005 starting points, so it stands out which town dropped the most compared to where it began.", "table_markdown": "| Town/City | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 |\n|-----------|------|------|------|------|------|------|------|\n| Acworth | 14 | 7 | 8 | 11 | 6 | 7 | 4 |\n| Charlestown| 22 | 37 | 29 | 18 | 19 | 9 | 11 |\n| Claremont | 148 | 126 | 105 | 65 | 54 | 47 | 28 |\n| Cornish | 12 | 15 | 6 | 7 | 6 | 8 | 2 |\n| Croydon | 15 | 7 | 3 | 4 | 5 | 4 | 2 |\n| Goshen | 11 | 10 | 10 | 9 | 5 | 6 | |\n| Grantham | 91 | 86 | 66 | 45 | 34 | 47 | 30 |\n| Langdon | 6 | 6 | 5 | | 7 | 5 | |\n| Lempster | 19 | 11 | 16 | 8 | 7 | 4 | 3 |\n| Newport | 74 | 48 | 48 | 20 | 30 | 16 | 26 |\n| Plainfield| 14 | 8 | 14 | 7 | 5 | 5 | 17 |\n| Springfield| 20 | 20 | 13 | 15 | 7 | 18 | 11 |\n| Unity | 15 | 15 | 11 | 11 | 9 | 3 | 5 |\n| Washington| 36 | 34 | 26 | 15 | 10 | 5 | 7 |"}
{"item_id": "item_0866", "chart_task_type": "baseline_index", "query": "I'd like to see the Core, Buffer, and Transition mangrove areas from 2000 to 2020 displayed relative to where each began in 2000, so it's easy to spot which zone moved furthest from its baseline.", "table_markdown": "| Year | Core Area (km$^2$) | Buffer Area (km$^2$) | Transition Area (km$^2$) | Total Area (km$^2$) |\n|------|-------------------|---------------------|------------------------|---------------------|\n| 2000 | 903.2 | 1084.7 | 86.2 | 2074.1 |\n| 2005 | 880.9 (-4.5) | 1067.6 (-3.4) | 100.3 (+2.8) | 2048.8 |\n| 2010 | 869.9 (-2.2) | 1053.2 (-2.9) | 109.5 (+1.8) | 2032.6 |\n| 2015 | 855.9 (-2.8) | 1046.6 (-1.3) | 128.7 (+3.8) | 2031.2 |\n| 2020 | 845.2 (-2.2) | 1032.9 (-2.7) | 167.4 (+7.7) | 2045.4 |\n| 2000–2020 (% change) | −6.42% | −4.78% | +94.20% | −1.38% |"}
{"item_id": "item_0867", "chart_task_type": "baseline_index", "query": "I'm curious how pork imports and exports from 1986 to 2005 look when viewed relative to their 1986 starting points, so I can easily spot which one grew the most in proportion to its own baseline.", "table_markdown": "| Year | Pork Imports | Pork Exports |\n|---|---|---|\n| 1986 | 1122 | 86 |\n| 1987 | 1195 | 109 |\n| 1988 | 1137 | 195 |\n| 1989 | 896 | 268 |\n| 1990 | 898 | 243 |\n| 1991 | 775 | 290 |\n| 1992 | 646 | 420 |\n| 1993 | 740 | 446 |\n| 1994 | 744 | 549 |\n| 1995 | 664 | 787 |\n| 1996 | 619 | 970 |\n| 1997 | 634 | 1044 |\n| 1998 | 705 | 1230 |\n| 1999 | 827 | 1277 |\n| 2000 | 967 | 1287 |\n| 2001 | 951 | 1559 |\n| 2002 | 1071 | 1612 |\n| 2003 | 1185 | 1717 |\n| 2004 | 1099 | 2181 |\n| 2005 | 1024 | 2660 |"}
{"item_id": "item_0868", "chart_task_type": "baseline_index", "query": "I'd like to see the shadow economy trends for Estonia, Latvia, and Lithuania from 2009 to 2020 shown relative to their 2009 starting values, so it's obvious right away which country's trajectory pulled away from the others the most.", "table_markdown": "| Year | Estonia | Latvia | Lithuania |\n|------|---------|--------|-----------|\n| 2009 | 20.2% | 36.6% | 17.7% |\n| 2010 | 19.4% | 38.1% | 18.8% |\n| 2011 | 18.9% | 30.2% | 17.1% |\n| 2012 | 19.2% | 21.1% | 18.2% |\n| 2013 | 15.7% | 23.8% | 15.3% |\n| 2014 | 13.2% | 23.5% | 12.5% |\n| 2015 | 14.9% | 21.3% | 15.0% |\n| 2016 | 15.4% | 20.7% | 16.5% |\n| 2017 | 18.2% | 22.0% | 18.2% |\n| 2018 | 16.7% | 24.2% | 18.7% |\n| 2019 | 14.3% | 23.9% | 18.2% |\n| 2020 | 16.5% | 25.5% | 20.4% |\n| Average 2009–2020 | 16.9% | 25.9% | 17.2% |"}
{"item_id": "item_0869", "chart_task_type": "baseline_index", "query": "Show me each state's enrollment trend relative to its Fall 2016 baseline, so it's easy to spot which one climbed furthest from its starting level.", "table_markdown": "| State | FALL 2018 Enrollment | % Change from Prior Year | FALL 2017 Enrollment | % Change from Prior Year | FALL 2016 Enrollment | % Change from Prior Year |\n|-------------|----------------------|--------------------------|----------------------|--------------------------|----------------------|--------------------------|\n| Iowa | 196,511 | -2.5% | 201,485 | -0.1% | 201,644 | -1.4% |\n| Kansas | 184,721 | -1.1% | 186,768 | -2.0% | 190,521 | -1.2% |\n| Kentucky | 239,774 | 0.4% | 238,922 | -0.6% | 240,259 | -1.1% |\n| Louisiana | 224,534 | 0.9% | 222,640 | -0.5% | 223,719 | -3.4% |\n| Maine | 64,383 | -1.5% | 65,361 | -1.9% | 66,599 | -2.5% |\n| Maryland | 337,683 | -2.5% | 346,501 | -0.2% | 347,279 | -2.9% |\n| Massachusetts| 433,745 | -2.5% | 444,670 | 0.0% | 444,702 | -2.9% |\n| Michigan | 496,668 | -3.8% | 516,291 | -3.7% | 536,389 | -4.6% |\n| Minnesota | 354,820 | -2.1% | 362,416 | -3.4% | 375,066 | -2.5% |\n| Mississippi | 163,428 | -1.6% | 166,005 | -1.1% | 167,922 | 0.0% |\n| Missouri | 338,230 | -2.6% | 347,315 | -3.9% | 361,320 | -2.4% |\n| Montana | 46,610 | -2.5% | 47,811 | -1.4% | 48,497 | -0.9% |\n| Nebraska | 126,561 | -0.8% | 127,526 | -0.9% | 128,656 | -3.5% |\n| Nevada | 109,995 | -0.1% | 110,085 | 1.4% | 108,515 | -3.2% |\n| New Hampshire| 152,065 | 2.9% | 147,773 | 1.2% | 145,966 | 17.2% |\n| New Jersey | 379,812 | -1.0% | 383,465 | -0.6% | 385,842 | -1.5% |"}
{"item_id": "item_0870", "chart_task_type": "baseline_index", "query": "Help me compare National and Business Debtline's energy arrears from 2018 to 2022, showing each trajectory relative to its own 2018 starting point so it's easy to spot which service pulled ahead the most compared with where it began.", "table_markdown": "| % of clients with energy | | | | | |\n|---|---|---|---|---|---|\n| | 2018 | 2019 | 2020 | 2021 | 2022 |\n| arrears | | | | | |\n| National Debtline | 18% | 24% | 30% | 34% | 38% |\n| Business Debtline | 16% | 17% | 19% | 21% | 22% |"}
{"item_id": "item_0871", "chart_task_type": "baseline_index", "query": "I'd like to see a view of Normal BP counts for the Atenolol and Combination therapy groups, with each starting from its Day 1 level, so it's easy to spot which gained the most ground relative to where it began.", "table_markdown": "| Atenolol treatment | | | | | Combination therapy | | |\n|---|---|---|---|---|---|---|---|\n| Visits | Normal BP | Elevated BP | Stage 1 Hypertension | Stage 2 Hypertension | Normal BP | Elevated BP | Stage 1 Hypertension |\n| Day 1 | 4 | 8 | 27 | 11 | 4 | 7 | 30 |\n| Day 5 | 6 | 24 | 16 | 4 | 30 | 14 | 6 |\n| Day 10 | 10 | 18 | 20 | 2 | 36 | 10 | 4 |\n| Day 15 | 10 | 20 | 19 | 1 | 42 | 8 | 0 |\n| Day 20 | 16 | 19 | 15 | 0 | 46 | 4 | 0 |\n| Day 25 | 24 | 18 | 8 | 0 | 48 | 2 | 0 |\n| Day 30 | 40 | 10 | 0 | 0 | 48 | 2 | 0 |"}
{"item_id": "item_0872", "chart_task_type": "baseline_index", "query": "I'd like to see cement production trends for France, Italy, Germany, and the UK from 2001 to 2016, shown with every series starting from its own 2001 level so it's easy to spot which country fell the furthest relative to where it began.", "table_markdown": "| Country | 2001 | 2008 | 2010 | 2015 | 2016 |\n|---------------|------|------|------|------|------|\n| France | 19.1 | 21.2 | 18.0 | 15.6 | 15.9 |\n| Italy | 39.8 | 43.0 | 34.4 | 20.8 | 19.3 |\n| Germany | 32.1 | 33.6 | 29.9 | 31.1 | 32.7 |\n| United Kingdom| 11.9 | 10.5 | 7.9 | 9.6 | 9.4 |"}
{"item_id": "item_0873", "chart_task_type": "baseline_index", "query": "Show me the five health and wellness segments from 2008 to 2018 relative to their 2008 starting points, so I can easily spot which category pulled ahead the most compared with where it began.", "table_markdown": "| Year | Fortified/Functional (FF) | Naturally Healthy (NH) | Better For You (BFY) | Organic | Food Intolerance |\n|------|--------------------------|------------------------|---------------------|---------|------------------|\n| 2008 | 16,812.8 | 20,508.4 | 660.1 | 84.7 | 48.4 |\n| 2009 | 20,674.9 | 22,850.7 | 757.1 | 144.2 | 57.6 |\n| 2010 | 24,933.6 | 26,430.5 | 861.2 | 284.0 | 67.1 |\n| 2011 | 30,369.0 | 30,747.5 | 1,017.8 | 477.6 | 79.5 |\n| 2012 | 36,043.8 | 35,438.4 | 1,177.3 | 671.2 | 94.2 |\n| 2013 | 42,184.2 | 41,310.4 | 1,402.5 | 977.6 | 112.1 |\n| 2014F| 49,706.5 | 47,832.5 | 1,639.9 | 1,364.4 | 134.1 |\n| 2015F| 58,034.2 | 54,925.6 | 1,892.1 | 1,839.1 | 160.4 |\n| 2016F| 67,315.0 | 62,502.0 | 2,159.3 | 2,338.1 | 192.0 |\n| 2017F| 77,569.9 | 70,772.7 | 2,438.0 | 2,883.1 | 229.3 |\n| 2018F| 88,794.8 | 79,762.4 | 2,721.7 | 3,443.2 | 272.8 |"}
{"item_id": "item_0874", "chart_task_type": "baseline_index", "query": "Could you show how employment for each sector changes from 2005 to 2030 as if they all started from the same level, so I can quickly tell which climbed furthest from its baseline?", "table_markdown": "| | Construction | Manufacturing | TCU | Wholesale | Retail | FIRE | Service | Private | Government | Total Empl |\n|----------------|--------------|---------------|-------|-----------|--------|------|---------|---------|------------|------------|\n| **2005** | | | | | | | | | | |\n| Number of Jobs | 729 | 2979 | 1068 | 3459 | 1804 | 539 | 4646 | 15224 | 394 | 30842 |\n| **2015** | | | | | | | | | | |\n| Number of Jobs | 548 | 2566 | 1241 | 2965 | 2017 | 692 | 6029 | 16055 | 485 | 32598 |\n| Growth Percentage | -24.8% | -13.9% | 16.2% | -14.3% | 11.8% | 28.4%| 29.8% | 5.5% | 23.1% | 5.7% |\n| **2030** | | | | | | | | | | |\n| Number of Jobs | 742 | 3469 | 1480 | 4065 | 2061 | 602 | 5746 | 18165 | 511 | 36841 |\n| Growth Percentage | 1.8% | 16.4% | 38.6% | 17.5% | 14.2% | 11.7%| 23.7% | 19.3% | 29.7% | 19.5% |"}
{"item_id": "item_0875", "chart_task_type": "baseline_index", "query": "Show me the transit ridership share at airport stops from 2012 to 2016 for all six agencies, relative to each city's 2012 starting point, so I can easily spot which one dropped the most in proportion to where it began.", "table_markdown": "| | Portland | Atlanta | Chicago O'Hare | Chicago Midway | Minneapolis | Washington D.C. |\n|----------------|----------|---------|----------------|----------------|-------------|-----------------|\n| **2012** | 7.92% | 6.42% | 5.28% | 14.52% | 5.60% | 10.23% |\n| **2013** | 7.73% | 6.37% | 5.21% | 13.60% | 4.85% | 9.84% |\n| **2014** | 7.97% | 6.19% | 5.02% | 13.24% | 4.60% | 9.97% |\n| **2015** | 7.56% | 6.05% | 5.27% | 12.81% | 5.51% | 9.37% |\n| **2016** | 6.90% | 5.66% | 5.13% | 12.27% | 4.98% | 8.31% |"}
{"item_id": "item_0876", "chart_task_type": "baseline_index", "query": "Could you show per capita health spending for all divisions from 1997 to 2012 as if they all started from the same level in 1997, so I can easily spot which one gained the most ground from its starting level?", "table_markdown": "| Year | Dhaka | Chittagong | Rajshahi | Khulna | Barisal | Sylhet |\n|---|---|---|---|---|---|---|\n| | Taka | Taka | Taka | Taka | Taka | Taka |\n| 1997 | 349 | 527 | 382 | 357 | 277 | 381 |\n| 1998 | 384 | 566 | 409 | 382 | 301 | 410 |\n| 1999 | 423 | 610 | 439 | 411 | 330 | 440 |\n| 2000 | 479 | 606 | 459 | 434 | 461 | 559 |\n| 2001 | 558 | 644 | 518 | 490 | 535 | 635 |\n| 2002 | 632 | 705 | 580 | 554 | 604 | 692 |\n| 2003 | 673 | 760 | 608 | 571 | 626 | 680 |\n| 2004 | 759 | 850 | 686 | 676 | 722 | 760 |\n| 2005 | 884 | 945 | 746 | 732 | 799 | 827 |\n| 2006 | 1,019 | 1,064 | 850 | 868 | 936 | 954 |\n| 2007 | 1,187 | 1,183 | 994 | 1,036 | 998 | 1,004 |\n| 2008 | 1,484 | 1,294 | 1,059 | 1,207 | 1,024 | 1,085 |\n| 2009 | 1,658 | 1,411 | 1,230 | 1,445 | 1,163 | 1,135 |\n| 2010 | 2,002 | 1,622 | 1,466 | 1,721 | 1,378 | 1,164 |\n| 2011 | 2,390 | 1,857 | 1,729 | 2,065 | 1,631 | 1,265 |\n| 2012 | 2,722 | 1,930 | 1,886 | 2,371 | 1,827 | 1,379 |"}
{"item_id": "item_0877", "chart_task_type": "baseline_index", "query": "I'd like to see the re-offending rates for each district tracked against their 2005 starting points, making it easy to spot which district fell the furthest from its own baseline by 2011.", "table_markdown": "| District | Jan to Dec 2005 | Jan to Dec 2006 | Jan to Dec 2007 | Jan to Dec 2008 | Jan to Dec 2009 | Jan to Dec 2010 | July 2010 to June 2011 |\n|---|---|---|---|---|---|---|---|\n| North Warwickshire | 23.1 | 20.3 | 22.8 | 21.5 | 19.5 | 18.5 | 19.7 |\n| Nuneaton & Bedworth | 31.4 | 28.3 | 25.7 | 25.9 | 26.4 | 22.1 | 21.8 |\n| Rugby | 24.3 | 27.6 | 23.7 | 21.8 | 24.6 | 18.7 | 21.9 |\n| Stratof rd-on-Avon | 22.7 | 20.1 | 19.7 | 19.7 | 19.4 | 14.9 | 16.1 |\n| Warwick | 27.4 | 26.4 | 24.2 | 26.8 | 27.0 | 23.1 | 24.8 |"}
{"item_id": "item_0878", "chart_task_type": "baseline_index", "query": "Can you put together a view of Revenue, EBITDA, and PAT since FY2001, scaled so they all start from the same level? I want it to stand out right away which one pulled ahead the most compared with where it began by FY2009.", "table_markdown": "| RM ‘mil | FY2001 | FY2002 | FY2003 | FY2004 | FY2005 | FY2006 | FY2007 | FY2008 | FY2009 |\n|---|---|---|---|---|---|---|---|---|---|\n| Revenue | 138.9 | 180.2 | 265.1 | 418.1 | 641.8 | 992.6 | 1,228.8 | 1,377.9 | 1,529.1 |\n| EBITDA | 23.9 | 27.1 | 39.5 | 60.6 | 89.2 | 130.3 | 175.7 | 197.8 | 287.5 |\n| EBITDA margin | 17.2% | 15.0% | 14.9% | 14.5% | 13.9% | 13.1% | 14.3% | 14.4% | 18.8% |\n| PBT | 17.2 | 20.2 | 29.3 | 45.2 | 65.7 | 91.8 | 118.6 | 134.6 | 222.0 |\n| PBT margin | 12.4% | 11.2% | 11.1% | 10.8% | 10.2% | 9.2% | 9.7% | 9.8% | 14.5% |\n| PAT | 15.9 | 17.8 | 25.7 | 39.9 | 58.2 | 84.8 | 88.7 | 108.1 | 168.1 |"}
{"item_id": "item_0879", "chart_task_type": "baseline_index", "query": "Help me compare paved and agricultural roads in Saudi Arabia from 1970 to 1990 by showing each relative to its 1970 level, so it's obvious which grew the most in proportion to its starting value.", "table_markdown": "| Year | Paved Roads | Agricultural Roads |\n|------|-------------|--------------------|\n| 1970 | 8440 | 3487 |\n| 1975 | 12167 | 8510 |\n| 1980 | 21581 | 24186 |\n| 1985 | 29655 | 50655 |\n| 1990 | 38000 | 78000 |"}
{"item_id": "item_0880", "chart_task_type": "baseline_index", "query": "Help me compare the total production trends for each burn severity relative to their 1974 starting points. I want to easily spot which treatment dipped furthest from its baseline at first but bounced back closest to where it started.", "table_markdown": "| Community component | Burn severity | 1974 | 1975 | 1976 | 1977 | 1980 | 1986 |\n|---------------------|--------------|--------|--------|--------|--------|--------|--------|\n| | | (kg/ha)| | | | | |\n| Forbs | Control | 1146 | 1604 | 1435 | 1220 | 1846 | 1430 |\n| | | 69 | 131 | 37 | 67 | 137 | 50 |\n| | Low | 1088 | 1255 | 2435* | 1528 | 2354 | 1546 |\n| | | 107 | 120 | 203 | 201 | 252 | 61 |\n| | Moderate | 977 | 972* | 2389* | 1668 | 2454 | 1699* |\n| | | 120 | 176 | 272 | 164 | 89 | 37 |\n| | High | 1061 | 610* | 2793* | 2101* | 3014* | 1593* |\n| | | 124 | 135 | 163 | 204 | 193 | 37 |\n| Grasses | Control | 451 | 376 | 468 | 189 | 270 | 291 |\n| | | 17 | 87 | 110 | 28 | 24 | 23 |\n| | Low | 447 | 198* | 526 | 273 | 399 | 486* |\n| | | 86 | 36 | 94 | 56 | 106 | 42 |\n| | Moderate | 313 | 132* | 394 | 227 | 310 | 332 |\n| | | 48 | 25 | 69 | 31 | 65 | 23 |\n| | High | 226 | 46* | 148* | 82 | 151 | 482* |\n| | | 36 | 21 | 56 | 18 | 24 | 26 |\n| Shrubs | Control | 199 | 266 | 256 | 360 | 388 | 215 |\n| | | 42 | 18 | 76 | 113 | 101 | 34 |\n| | Low | 197 | 71* | 165 | 108* | 130* | 99* |\n| | | 45 | 29 | 46 | 21 | 37 | 16 |\n| | Moderate | 180 | 81* | 310 | 158* | 143* | 111* |\n| | | 34 | 44 | 81 | 43 | 35 | 13 |\n| | High | 249 | 41* | 197 | 110* | 115* | 111* |\n| | | 59 | 15 | 40 | 32 | 22 | 18 |\n| Total | Control | 1797 | 2246 | 2160 | 1769 | 2504 | 1936 |\n| | | 64 | 210 | 72 | 159 | 63 | 68 |\n| | Low | 1731 | 1524 | 3126 | 1909 | 2883 | 2130 |\n| | | 145 | 138 | 191 | 225 | 182 | 74 |\n| | Moderate | 1470 | 1185 | 3094 | 2053 | 2907 | 2141 |\n| | | 140 | 223 | 314 | 194 | 82 | 42 |\n| | High | 1536 | 697 | 3139 | 2294 | 3281 | 2186 |\n| | | 107 | 161 | 139 | 215 | 170 | 45 |"}
{"item_id": "item_0881", "chart_task_type": "baseline_index", "query": "Show me how Household, Commercial/Industrial, and Total Municipal & Industrial waste changed from 2003 to 2020, measured against their 2003 starting points, so it's obvious which sector gained the most ground from its starting level.", "table_markdown": "| Year | Household | Commercial/Industrial | Total Municipal & Industrial |\n|------|-----------|-----------------------|------------------------------|\n| 2003 | 459,282 | 659,755 | 1,119,037 |\n| 2004 | 475,696 | 676,958 | 1,152,653 |\n| 2005 | 492,187 | 701,328 | 1,193,515 |\n| 2006 | 508,832 | 726,576 | 1,235,408 |\n| 2007 | 523,016 | 744,014 | 1,267,029 |\n| 2008 | 537,199 | 758,150 | 1,295,349 |\n| 2009 | 551,383 | 771,797 | 1,323,180 |\n| 2010 | 565,567 | 787,232 | 1,352,799 |\n| 2011 | 574,932 | 797,467 | 1,372,399 |\n| 2012 | 584,298 | 807,834 | 1,392,131 |\n| 2013 | 593,663 | 818,335 | 1,411,998 |\n| 2014 | 603,028 | 828,974 | 1,432,002 |\n| 2015 | 612,394 | 839,750 | 1,452,144 |\n| 2016 | 621,723 | 855,706 | 1,477,429 |\n| 2017 | 629,171 | 871,964 | 1,501,135 |\n| 2018 | 636,618 | 888,531 | 1,525,150 |\n| 2019 | 644,066 | 905,414 | 1,549,479 |\n| 2020 | 651,513 | 922,616 | 1,574,130 |"}
{"item_id": "item_0882", "chart_task_type": "baseline_index", "query": "Show me the import trends for Rice, Wheat, Maize, Soybean oil, and Palm oil from 2018-19 to 2021-22, set up so each begins at the same 2018-19 level. I want to easily spot which grew the most relative to where it started.", "table_markdown": "| Commodity | (‘000’ BDT) | | | |\n|---|---|---|---|---|\n| | 2018-19 | 2019-20 | 2020-21 | 2021-22 |\n| Rice | 10113281 | 1990757 | 75552440 | 37970653 |\n| Wheat | 123625351 | 142901568 | 162928635 | 193775326 |\n| Maize | 26565458 | 28590301 | 48232374 | 61411497 |\n| Soybean oil | 114490388 | 81219127 | 111819513 | 169855596 |\n| Palm oil | 132339151 | 152879778 | 186823760 | 283677394 |"}
{"item_id": "item_0883", "chart_task_type": "baseline_index", "query": "I'm curious how Ghana and Nigeria's GDP per capita grew from 2001 to 2012 when viewed relative to their 2001 starting levels, so it's easy to see which country grew the most relative to where it started.", "table_markdown": "| Year | Ghana | Nigeria |\n|---|---|---|\n| | GDP per capita | GDP per capita |\n| 2001 | 275.48 | 350.29 |\n| 2002 | 311.64 | 457.47 |\n| 2003 | 375.96 | 510.42 |\n| 2004 | 426.26 | 645.93 |\n| 2005 | 501.86 | 804.15 |\n| 2006 | 929.95 | 1014.76 |\n| 2007 | 1099.09 | 1130.88 |\n| 2008 | 1234.44 | 1376.02 |\n| 2009 | 1096.53 | 1090.75 |\n| 2010 | 1326.07 | 1437.05 |\n| 2011 | 1594.03 | 1496.30 |\n| 2012 | 1604.91 | 1555.36 |"}
{"item_id": "item_0884", "chart_task_type": "baseline_index", "query": "Could you show the production paths for these nine industrial products under the Reference scenario from 2010 to 2050, relative to where each one began, so I can quickly tell which grew the most in proportion to its starting value?", "table_markdown": "| Scenario | Subsector | Product | 2010 | 2030 | 2050 |\n|---|---|---|---|---|---|\n| Reference | Chemical industry | Ammonia | 2.68 | 2.98 | 3.16 |\n| Reference | Chemical industry | Ethylene | 5.06 | 5.9 | 6.64 |\n| Reference | Iron and steel | Steel | 43.83 | 46.22 | 44.36 |\n| Reference | Iron and steel | Steel products | 38.1 | 39.58 | 37.99 |\n| Reference | Non-ferrous metals | Aluminium | 1.01 | 1.16 | 1.16 |\n| Reference | Non-metallic mineral products | Cement | 29.89 | 35.28 | 33.77 |\n| Reference | Non-metallic mineral products | Container glass | 4.69 | 4.43 | 3.44 |\n| Reference | Non-metallic mineral products | Flat glass | 2.26 | 2.17 | 2 |\n| Reference | Paper and printing | Paper | 23.06 | 20.34 | 18.96 |\n| Regulatory | Chemical industry | Ammonia | 2.68 | 2.66 | 2.37 |\n| Regulatory | Chemical industry | Ethylene | 5.06 | 5.4 | 5.31 |\n| Regulatory | Iron and steel | Steel | 43.83 | 43.1 | 35.29 |\n| Regulatory | Iron and steel | Steel products | 38.1 | 37.31 | 32.68 |\n| Regulatory | Non-ferrous metals | Aluminium | 1.01 | 1.15 | 1.13 |\n| Regulatory | Non-metallic mineral products | Cement | 29.89 | 32.26 | 27.02 |\n| Regulatory | Non-metallic mineral products | Container glass | 4.69 | 4.24 | 3.1 |\n| Regulatory | Non-metallic mineral products | Flat glass | 2.26 | 2.17 | 2 |\n| Regulatory | Paper and printing | Paper | 23.06 | 20.34 | 18.96 |\n| Incentives | Chemical industry | Ammonia | 2.68 | 2.66 | 2.37 |\n| Incentives | Chemical industry | Ethylene | 5.06 | 5.4 | 5.31 |\n| Incentives | Iron and steel | Steel | 43.83 | 43.1 | 35.29 |\n| Incentives | Iron and steel | Steel products | 38.1 | 37.31 | 32.68 |\n| Incentives | Non-ferrous metals | Aluminium | 1.01 | 1.15 | 1.13 |\n| Incentives | Non-metallic mineral products | Cement | 29.89 | 32.26 | 27.02 |\n| Incentives | Non-metallic mineral products | Container glass | 4.69 | 4.24 | 3.1 |\n| Incentives | Non-metallic mineral products | Flat glass | 2.26 | 2.17 | 2 |"}
{"item_id": "item_0885", "chart_task_type": "baseline_index", "query": "I'm curious how the study abroad destinations changed relative to where each one began in 2006-2007, so it's easy to spot which one gained the most ground from its starting level by 2014-2015.", "table_markdown": "| Destination | 2006- 2007 | 2007- 2008 | 2008- 2009 | 2009- 2010 | 2010- 2011 | 2011- 2012 | 2012- 2013 | 2013- 2014 | 2014- 2015 |\n|---|---|---|---|---|---|---|---|---|---|\n| Africa | 8 | 11 | 1 | 14 | 27 | 45 | 43 | 29 | 19 |\n| Asia | 6 | 10 | 4 | 15 | 23 | 16 | 18 | 20 | 20 |\n| Oceania | 20 | 21 | 9 | 12 | 9 | 9 | 8 | 7 | 2 |\n| North America | 10 | 1 | 10 | 0 | 10 | 21 | 15 | 17 | 0 |\n| Latin America/ Caribbean | 20 | 40 | 49 | 62 | 94 | 85 | 127 | 96 | 133 |\n| Europe | 202 | 239 | 242 | 236 | 309 | 234 | 278 | 217 | 231 |\n| Various/Multiple | 13 | 3 | 5 | 7 | 17 | 72 | 53 | 50 | 83 |"}
{"item_id": "item_0886", "chart_task_type": "baseline_index", "query": "I'm curious how subscription and perpetual licensing revenues from FY 2018 to FY 2022 look when measured against where each began, so I can easily spot which one gained the most ground from its starting level.", "table_markdown": "| Year | Revenues from subscription | Revenues from perpetual licensing |\n|------|----------------------------|----------------------------------|\n| FY 2018 | 1,790 | 2,340 |\n| FY 2019 | 1,940 | 2,530 |\n| FY 2020 | 2,160 | 2,860 |\n| FY 2021 | 2,500 | 2,910 |\n| FY 2022 | 3,100 | 3,160 |"}
{"item_id": "item_0887", "chart_task_type": "baseline_index", "query": "I'd like to see the production of CFC-22, CFC-12, and CFC-11 from 1985 to 1994 measured against their 1985 starting levels, so it's obvious which compound fell the furthest relative to where it began.", "table_markdown": "| Year | CFC-22 | CFC-12 | CFC-11 |\n|------|--------|--------|--------|\n| 1985 | 235 | 302 | 176 |\n| 1986 | 271 | 322 | 202 |\n| 1987 | 275 | 335 | 198 |\n| 1988 | 333 | 414 | 249 |\n| 1989 | 343 | 392 | 192 |\n| 1990 | 306 | 209 | 135 |\n| 1991 | 314 | 157 | 99 |\n| 1992 | 331 | 163 | 100 |\n| 1993 | 291 | 185 | 72 |\n| 1994 | 306 | 127 | 19 |\n| 1995 | 262 | 55 | NA |\n| Average | 297 | 242 | 144 |"}
{"item_id": "item_0888", "chart_task_type": "baseline_index", "query": "Can you put together a view of the heart rate trajectories for both groups relative to where each began, so it's obvious right away which one fell furthest below its starting level at the 1-minute mark?", "table_markdown": "| Time | Group I (n-30) | Group II (n-30) | F |\n|---|---|---|---|\n| Baseline | 81.53±12.29 | 84.53±9.81 | 2.79 |\n| At 0 minute | 73.03±11.37 | 76.73±8.33 | 4.32 |\n| At 1 minute | 69.03±10.65 | 74.83±8.65 | 2.68 |\n| At 2 minute | 79±8.53 | 80.76±10.22 | 0 |\n| At 5 minute | 75.90±8.26 | 79.13±9.19 | 0.185 |\n| At 10 minute | 74.86±9.11 | 78.60±8.70 | 0.572 |\n| At 30 minute | 73.43±9.79 | 78.53±10.14 | 0.266 |\n| At 60 minute | 74.23±9.52 | 79.06±10.05 | 0 |"}
{"item_id": "item_0889", "chart_task_type": "baseline_index", "query": "I'm curious how the Vitamin C levels for each treatment change relative to where they began, so it's easy to spot which one fell furthest below its starting point by Day 45.", "table_markdown": "| Days | C | T 1 | T 2 | T3 | T4 |\n|---|---|---|---|---|---|\n| 0 | 21.79 | 24.6 | 25.64 | 23.7 | 25.63 |\n| 15 | 21.66 | 24.4 | 25.44 | 23.62 | 25.51 |\n| 30 | 21.64 | 24.2 | 25.42 | 23.59 | 25.42 |\n| 45 | 21.59 | 24.2 | 25.32 | 23.59 | 25.36 |"}
{"item_id": "item_0890", "chart_task_type": "baseline_index", "query": "Can you put together a view of Disabled & ESRD and Elderly Medicare beneficiaries since 1970, shown relative to where each began, so it's easy to spot which group climbed furthest from its starting level?", "table_markdown": "| Calendar Year | Disabled & ESRD | Elderly |\n|---------------|-----------------|---------|\n| 1970 | 20.4 | 20.4 |\n| 1980 | 3.0 | 25.5 |\n| 1990 | 3.3 | 31.0 |\n| 2000 | 5.4 | 34.1 |\n| 2010 | 7.3 | 38.6 |\n| 2020 | 8.7 | 52.2 |\n| 2030 | 8.6 | 68.2 |"}
{"item_id": "item_0891", "chart_task_type": "baseline_index", "query": "Show me how pesticide usage for Wheat, Maize, and Pulses changed from 2014 to 2018 when each crop is viewed relative to its own starting point, so I can quickly tell which one pulled ahead the most compared with where it began.", "table_markdown": "| Yea r | Cro p | Total Pesticid e Used (kg) | Total Fertilize r Used (kg) | Pesticid e Intensit y (kg/ha) |\n|---|---|---|---|---|\n| 2014 | Wheat | 1,200 | 5,000 | 12 |\n| | Maize | 800 | 3,500 | 10 |\n| | Pulses | 400 | 2,000 | 6.7 |\n| 2015 | Wheat | 1,100 | 4,500 | 10 |\n| | Maize | 750 | 3,200 | 8.8 |\n| | Pulses | 350 | 1,800 | 6.4 |\n| 2016 | Wheat | 1,000 | 4,000 | 10.5 |\n| | Maize | 700 | 2,800 | 9.3 |\n| | Pulses | 300 | 1,500 | 6 |\n| 2017 | Wheat | 1,300 | 5,500 | 12.4 |\n| | Maize | 900 | 3,800 | 10 |\n| | Pulses | 400 | 2,200 | 6.2 |\n| 2018 | Wheat | 1,200 | 4,800 | 12 |\n| | Maize | 950 | 4,000 | 10 |\n| | Pulses | 350 | 1,800 | 5.8 |"}
{"item_id": "item_0892", "chart_task_type": "baseline_index", "query": "Could you show me the daily sales for the six vegetable categories from July 1 to July 7, scaled so they all begin at the same level, so it's easy to spot which one grew the most relative to where it started?", "table_markdown": "| Date | Floral leaves | Cauliflower | Aquatic | Eggplant | Pepper | Edible fungi |\n|--------|---------------|-------------|---------|----------|--------|--------------|\n| 1-Jul | 114.773 | 19.435 | 19.519 | 13.833 | 69.345 | 41.831 |\n| 2-Jul | 109.896 | 20.145 | 19.692 | 11.983 | 65.874 | 40.416 |\n| 3-Jul | 104.807 | 20.911 | 19.866 | 10.032 | 62.205 | 38.961 |\n| 4-Jul | 99.506 | 21.733 | 20.041 | 7.978 | 58.338 | 37.468 |\n| 5-Jul | 93.993 | 22.612 | 20.217 | 5.822 | 54.274 | 35.936 |\n| 6-Jul | 88.268 | 23.548 | 20.394 | 3.564 | 50.013 | 35.936 |\n| 7-Jul | 82.332 | 24.540 | 20.573 | 1.205 | 45.554 | 32.753 |"}
{"item_id": "item_0893", "chart_task_type": "baseline_index", "query": "Could you show me the population trajectories for each county from 2020 to 2060 as if they all started from the same level, so it's obvious right away which one climbed furthest from its 2020 baseline?", "table_markdown": "| County | 2020 | 2025 | 2030 | 2035 | 2040 | 2045 | 2050 | 2055 | 2060 |\n|----------|---------|---------|---------|---------|---------|---------|---------|---------|---------|\n| Baldwin | 44,428 | 44,033 | 43,637 | 42,429 | 41,221 | 39,673 | 38,125 | 36,966 | 35,806 |\n| Barrow | 86,383 | 101,650 | 116,916 | 133,311 | 149,706 | 169,546 | 189,385 | 214,663 | 239,941 |\n| Clarke | 129,779 | 137,942 | 146,104 | 152,472 | 158,840 | 163,856 | 168,872 | 174,972 | 181,071 |\n| Greene | 18,717 | 20,632 | 22,546 | 23,526 | 24,505 | 25,760 | 27,014 | 28,998 | 30,982 |\n| Hancock | 8,193 | 7,915 | 7,637 | 7,321 | 7,004 | 6,781 | 6,557 | 6,520 | 6,482 |\n| Jackson | 74,700 | 85,097 | 95,493 | 105,291 | 115,088 | 125,858 | 136,627 | 148,718 | 160,808 |\n| Laurens | 47,296 | 47,351 | 47,405 | 47,185 | 46,964 | 46,477 | 45,989 | 45,591 | 45,193 |\n| Morgan | 19,138 | 19,948 | 20,757 | 21,598 | 22,438 | 23,322 | 24,206 | 25,267 | 26,328 |\n| Oconee | 41,737 | 47,332 | 52,926 | 58,246 | 63,566 | 69,313 | 75,060 | 81,260 | 87,460 |\n| Putnam | 21,885 | 22,097 | 22,308 | 22,325 | 22,341 | 22,410 | 22,478 | 22,844 | 23,209 |\n| Walton | 95,814 | 102,497 | 109,179 | 116,900 | 124,621 | 133,307 | 141,993 | 152,323 | 162,652 |\n| Washington | 20,302 | 20,156 | 20,009 | 19,731 | 19,452 | 19,024 | 18,595 | 18,331 | 18,066 |\n| Wilkinson | 8,919 | 8,640 | 8,361 | 8,076 | 7,791 | 7,443 | 7,095 | 6,880 | 6,665 |\n| **Total** | **617,291** | **665,285** | **713,278** | **758,408** | **803,537** | **852,767** | **901,996** | **963,330** | **1,024,663** |"}
{"item_id": "item_0894", "chart_task_type": "baseline_index", "query": "Help me compare the U.S. export categories to Korea from 2011 to 2013 by showing how each shifted relative to its 2011 starting point, so it's easy to spot which one fell the furthest from its starting level.", "table_markdown": "| Selected U.S. Exports to Korea 2011-2013 (in millions) | | | |\n|---|---|---|---|\n| | 2011 | 2012 | 2013 |\n| Total Exports | $43,399 | $42,283 | $41,555 |\n| Corn | 1,831.6 | 626.2 | 120.9 |\n| Meat, Poultry | 1,336.8 | 1,122.6 | 995.4 |\n| Metallurgical Coal | 873.2 | 662.8 | 430.3 |\n| Steelmaking Materials | 1,431.3 | 1,224.3 | 1,011.0 |\n| Electric Apparatus | 689.4 | 740.1 | 830.4 |\n| Excavating Machinery | 68.7 | 67.9 | 94.3 |\n| Semiconductors | 3,514.2 | 4,059.2 | 3,785.2 |\n| Passenger Cars | 413.1 | 615.9 | 746.8 |\n| Pharmaceuticals | 688.7 | 889.3 | 1,056.1 |\n| Military Aircraft | 208.4 | 256.0 | 48.7 |"}
{"item_id": "item_0895", "chart_task_type": "baseline_index", "query": "I'd like to see the population trends for the three elderly age groups from 1980 to 2011 shown relative to where each one began, so it's easy to spot which group grew the most in proportion to its starting value.", "table_markdown": "| Age | 1980 | 1991 | 1995 | 2001 | 2011 |\n|-------|------|------|------|------|------|\n| 65-74 | 465.0| 436.5| 445.2| 433.9| 447.0|\n| 75-84 | 226.0| 259.2| 252.2| 268.0| 275.0|\n| 85+ | 46.7 | 69.1 | 80.0 | 90.0 | 102.0|\n| | 737.7| 764.8| 777.4| 792.1| 824.0|"}
{"item_id": "item_0896", "chart_task_type": "baseline_index", "query": "Can you put together a view of attendance for each event type from 2006 to 2011, measured against each one's 2006 baseline, so it's easy to spot which grew or fell the most relative to where it started?", "table_markdown": "| Event Types | 2006 | 2007 | 2008 | 2009 | 2010 | 2011* | Percent Change** | Average** |\n|----------------|--------|--------|--------|--------|--------|--------|------------------|-----------|\n| Association | 6,905 | 6,885 | 5,505 | 5,945 | 5,000 | 3,889 | -27.6% | 6,048 |\n| Christmas Party| 1,560 | 1,340 | 1,355 | 1,495 | 1,580 | 1,372 | 1.3% | 1,466 |\n| Corporate | 9,090 | 9,660 | 8,700 | 6,165 | 7,996 | 4,181 | -12.0% | 8,322 |\n| Educational | 773 | 255 | 560 | 215 | 50 | 465 | -93.5% | 371 |\n| Fraternal | 1,920 | 2,160 | 1,920 | 2,020 | 2,030 | 1,900 | 5.7% | 2,010 |\n| Government | 2,950 | 1,550 | 2,545 | 1,175 | 2,015 | 1,907 | -31.7% | 2,047 |\n| Religious | 1,405 | 1,445 | 1,915 | 2,470 | 9,255 | 1,587 | 558.7% | 3,298 |\n| Social | 11,571 | 11,555 | 9,950 | 10,620 | 10,535 | 9,785 | -9.0% | 10,846 |\n| Wedding | 3,490 | 2,440 | 2,370 | 2,940 | 3,495 | 2,415 | 0.1% | 2,947 |\n| **Total Attendance** | 39,664 | 37,290 | 34,820 | 33,045 | 41,956 | 27,501 | 5.8% | -- |"}
{"item_id": "item_0897", "chart_task_type": "baseline_index", "query": "Help me compare the trajectories of all six outcome measures over the year, showing each one relative to its own starting point, so I can easily spot which improved the most compared to where it began.", "table_markdown": "| Outcome | Time | N | Mean | SD | P Value* |\n|--------------------------|----------|----|---------|------|----------|\n| FRI | Baseline | 29 | 51.47 | 15.62| Ref |\n| | 1 wk | 29 | 49.83 | 15.72| 1.000 |\n| | 4 wk | 27 | 43.25 | 16.68| .001 |\n| | 8 wk | 29 | 37.99 | 19.60| <.001 |\n| | 6 mo | 28 | 38.55 | 21.80| .001 |\n| | 1 y | 21 | 33.98 | 20.35| .001 |\n| | P value over time† | | | | <.001 |\n| SF-36 Pain | Baseline | 29 | 43.28 | 21.11| Ref |\n| | 1 wk | 29 | 40.20 | 21.76| >.999 |\n| | 4 wk | 29 | 55.17 | 19.98| .015 |\n| | 8 wk | 29 | 61.29 | 22.19| .001 |\n| | 6 mo | 28 | 57.95 | 25.45| .030 |\n| | 1 y | 21 | 67.79 | 23.51| .001 |\n| | P value over time† | | | | <.001 |\n| SF-36 Physical Function | Baseline | 29 | 56.40 | 18.52| Ref |\n| | 1 wk | 29 | 51.63 | 20.46| .353 |\n| | 4 wk | 28 | 58.43 | 21.17| >.999 |\n| | 8 wk | 29 | 61.70 | 22.89| .923 |\n| | 6 mo | 28 | 67.14 | 24.18| .195 |\n| | 1 y | 21 | 73.20 | 19.38| <.001 |\n| | P value over time† | | | | <.001 |\n| Current Pain | Baseline | 29 | 4.74 | 2.21 | Ref |\n| | 1 wk | 29 | 4.21 | 1.99 | .436 |\n| | 4 wk | 29 | 4.00 | 2.21 | .215 |\n| | 8 wk | 29 | 3.09 | 2.59 | .001 |\n| | 6 mo | 28 | 3.60 | 2.49 | .091 |\n| | 1 y | 21 | 3.15 | 2.38 | .063 |\n| | P value over time† | | | | .007 |\n| Best Pain | Baseline | 29 | 2.81 | 1.78 | Ref |\n| | 1 wk | 29 | 2.88 | 1.83 | >.999 |\n| | 4 wk | 29 | 2.53 | 1.83 | >.999 |\n| | 8 wk | 28 | 2.00 | 2.06 | .036 |\n| | 6 mo | 28 | 2.00 | 2.33 | .308 |\n| | 1 y | 21 | 2.10 | 2.20 | >.999 |\n| | P value over time† | | | | .040 |\n| Worst Pain | Baseline | 29 | 7.98 | 1.56 | Ref |\n| | 1 wk | 29 | 6.86 | 1.94 | .001 |\n| | 4 wk | 28 | 6.41 | 1.85 | <.001 |\n| | 8 wk | 29 | 5.82 | 2.33 | <.001 |\n| | 6 mo | 28 | 6.32 | 2.12 | <.001 |\n| | 1 y | 21 | 5.86 | 2.20 | .002 |\n| | P value over time† | | | | <.001 |"}
{"item_id": "item_0898", "chart_task_type": "baseline_index", "query": "Show me the property value trends for all five areas from 2021 to 2023, as if they all started from the same level, so it stands out which region climbed furthest from its baseline.", "table_markdown": "| Area | 2021 | 2022 | 2023 |\n|------------|--------|--------|--------|\n| Dublin | 440,000| 525,000| 530,000|\n| Rest of Leinster | 300,000| 285,000| 390,000|\n| Munster | 300,000| 365,000| 340,000|\n| Connacht | 275,000| 290,000| 300,000|\n| Ulster | 300,000| 245,000| 315,000|"}
{"item_id": "item_0899", "chart_task_type": "baseline_index", "query": "Help me compare the discharge for all six washes from 2008 to 2010, expressed relative to where each one began. I want it to stand out clearly which wash fell the furthest below its starting level.", "table_markdown": "| Wash # | Pre-Fence Overflow (cfs), 2008 | 2009 Overflow (cfs) | 2010 Overflow (cfs) | Discharge Percent Change |\n|--------|-------------------------------|---------------------|---------------------|--------------------------|\n| 1 | 544 | 434 | 361 | -34% |\n| 2 | 1698 | 821 | 698 | -59% |\n| 3 | 2487 | 2239 | 2224 | -11% |\n| 4 | 949 | 426 | 300 | -68% |\n| 5 | 459 | 544 | 533 | +16% |\n| 6 | 398 | 280 | 177 | -56% |"}
{"item_id": "item_0900", "chart_task_type": "baseline_index", "query": "I'm curious how grip strength recovered for both treatment groups across the 3-, 6-, and 12-week visits when measured against each group's own starting level. Make it easy to spot which one bounced back the furthest toward its baseline.", "table_markdown": "| - | Baseline | 3 Weeks | 6 Weeks | 12 Weeks |\n|---|---|---|---|---|\n| Local Anesthetic Mean (SD) | 10.8 (6.8) | 13.9 (5.1) | 12.6 (5.1) | 13.4 (5.3) |\n| Local Anesthetic plus Corticosteroid Mean (SD) | 15.8 (5.9) | 18.6 (22.5) | 13.8 (5.2) | 14.5 (6.7) |\n| p-value | .023 | .31 | .70 | .72 |"}
{"item_id": "item_0901", "chart_task_type": "baseline_index", "query": "Could you show the production of maize, sorghum, wheat, beans, and peas from 1977/78 to 1991/92 with each crop starting from its own 1977/78 level, so it's easy to spot which one fell the furthest from where it began?", "table_markdown": "| YEAR | MAIZE | SORGHUM | WHEAT | BEANS | PEAS |\n|--------|----------|----------|----------|---------|----------|\n| 1977/78| 143,168 | 85,775 | 57,906 | 10,783 | 4,427 |\n| 1978/79| 124,856 | 68,952 | 33,629 | 8,350 | 6,856 |\n| 1979/80| 105,619 | 59,286 | 28,194 | 3,585 | 4,562 |\n| 1980/81| 105,674 | 47,729 | 16,993 | 3,517 | 3,198 |\n| 1981/82| 83,028 | 26,158 | 14,462 | 4,898 | 4,525 |\n| 1982/83| 76,180 | 30,687 | 14,810 | 1,624 | 3,367 |\n| 1983/84| 79,384 | 33,768 | 17,127 | 1,338 | 3,639 |\n| 1984/85| 92,350 | 54,823 | 18,434 | 2,478 | 3,277 |\n| 1985/86| 86,488 | 33,440 | 11,009 | 2,779 | 1,502 |\n| 1986/87| 94,912 | 31,232 | 18,520 | 3,344 | 1,467 |\n| 1987/88| 159,726 | 53,135 | 19,237 | 7,383 | 2,564 |\n| 1988/89| 137,227 | 31,140 | 29,653 | 9,706 | 1,473 |\n| 1989/90| 171,579 | 36,062 | 33,162 | 13,071 | 1,950 |\n| 1990/91| 48,918 | 10,043 | 7,026 | 2,465 | 745 |\n| 1991/92| 61,074 | 19,468 | 11,854 | 1,303 | 1,375 |"}
{"item_id": "item_0902", "chart_task_type": "baseline_index", "query": "I'd like to see the cost trajectories of all healthcare categories from 1988 to 2000 as if they all started from the same 1988 level, so it's easy to spot which grew the most relative to where it began.", "table_markdown": "| | 1988 | 1992 | 1996 | 2000 |\n|----------------------|--------|------------|------------|------------|\n| Hospitals | 14,643 | 18,607 (+27%) | 20,694 (+11%) | 23,554 (+14%) |\n| Mental health care | 2,289 | 3,855 (+68%) | 4,506 (+24%) | 6,127 (+36%) |\n| Handicapped, provisions | 3,647 | 4,524 (+24%) | 5,595 (+24%) | 6,748 (+21%) |\n| Elderly, provisions | 8,364 | 9,955 (+19%) | 11,182 (+12%) | 13,170 (+18%) |\n| Extramural care | 7,759 | 9,450 (+28%) | 8,023 (-15%) | 9,846 (+23%) |\n| Pharmateucal supplies| 3,970 | 5,524 (+39%) | 6,746 (+22%) | 8,240 (+22%) |\n| Preventive care | 589 | 760 (+29%) | 1,334 (+75%) | 1,603 (+20%) |\n| Overhead, misc. | 2,215 | 2,895 (+31%) | 3,162 (+9%) | 5,289 (+74%) |\n| Total | 44,178 | 55,570 (+26%) | 61,242 (+10%) | 74,902 (+22%) |"}
{"item_id": "item_0903", "chart_task_type": "baseline_index", "query": "I'm curious how the salaries for Lieutenants and Sergeants from 2012 to 2016 look when measured against their 2012 starting points. I want it to be easy to spot which rank grew the most relative to where it started.", "table_markdown": "| | 01/01/2012 | 01/01/2013 | 01/01/2014 | 01/01/2015 | 01/01/2016 |\n|----------------|------------|------------|------------|------------|------------|\n| **Lieutenant** | 104,372 | 106,459 | 108,589 | 110,760 | 112,976 |\n| **Sergeant** | 94,885 | 96,783 | 98,718 | 100,693 | 102,707 |"}
{"item_id": "item_0904", "chart_task_type": "baseline_index", "query": "Can you put together a view of the HDRS, HARS, and BDI scores across the treatment sessions, showing how each moved from its own baseline? I want it to be obvious which scale improved the most relative to where it began.", "table_markdown": "| | Baseline | After 5 | After 10 | After 15 | After 20 |\n|----------------------|-----------|-----------|-----------|-----------|-----------|\n| **HDRS** | | | | | |\n| Mean ± SD | 27.4 ± 4.3| 21.7 ± 6.2| 17.9 ± 8.7| 18.3 ± 6.8| 12.6 ± 3.36|\n| ANOVA P-value | .041 | .026 | .0002 | | |\n| F-value | 8.9 | 7.5 | 10.5 | | |\n| λ | 17.9 | 22.7 | 42.2 | | |\n| **HARS** | | | | | |\n| Mean ± SD | 21.9 ± 3.5| 16.7 ± 4.9| 13.1 ± 4.46| 12.5 ± 4.9| 9 ± 2.9|\n| ANOVA P-value | .007 | .0007 | <.0001 | | |\n| F-value | 7.7 | 10 | 17.9 | | |\n| λ | 15.4 | 30.15 | 71.6 | | |\n| **BDI** | | | | | |\n| Mean ± SD | Mean = 32 ± 9| 27.3 ± 11.9| 27 ± 14.3| 23.66 ± 13.2| 17.2 ± 7.6|\n| ANOVA P-value | .18 | .05 | .02 | | |\n| F-value | 2.3 | 3.2 | 3.9 | | |\n| λ | 4.7 | 9.8 | 15.8 | | |"}
{"item_id": "item_0905", "chart_task_type": "baseline_index", "query": "Show me how each share class and the total net assets changed from 2015 to 2017 relative to where each one began, so I can easily spot which grew the most in proportion to its starting value.", "table_markdown": "| Date | Total | Share class A | Share class B | Share class C | Share class I | Share class R |\n|------------|----------------|----------------|----------------|----------------|----------------|----------------|\n| 31/12/2015 | 52,023,077.37 | 7,233,934.76 | 12,441,499.14 | 30,177,945.69 | 1,886,469.48 | 283,228.30 |\n| 31/12/2016 | 59,548,274.11 | 6,502,331.30 | 8,619,036.37 | 37,286,749.36 | 2,766,066.34 | 4,374,090.74 |\n| 31/12/2017 | 167,336,495.08 | 7,083,802.43 | 17,013,376.34 | 108,783,593.85 | 4,915,465.31 | 29,540,257.15 |"}
{"item_id": "item_0906", "chart_task_type": "growth_speed", "query": "Can you chart the growth of the NSE and BSE share price highs and lows from April 2014 to March 2015? I want it to be obvious which series expanded the fastest, rather than just showing the final prices.", "table_markdown": "| | | NSE | | | BSE | |\n|---|---|---|---|---|---|---|\n| Share Price by Month High | | | Low | High | | Low |\n| April 2014 | 32.35 | | 28.60 | 32.40 | | 28.50 |\n| May | 47.00 | | 26.50 | 46.50 | | 25.50 |\n| June 41,50 35.05 | | | | 40.70 | | 35.00 |\n| July | 38.90 | | 33.00 | 38.50 | | 33.40 |\n| August 37.00 30.50 | | | | 36.65 | | 31.40 |\n| September | 43.00 | | 33.05 | 41.70 | | 33.00 |\n| October | 44.95 | | 34.30 | 42.50 | | 35.00 |\n| November 44.95 36.40 | | | | 44.80 | | 36.15 |\n| December | 43.50 | | 35.80 | 43.95 | | 35.50 |\n| January 2015 42.00 35.00 | | | | 42.55 | | 36.05 |\n| February | 39.90 | | 34.15 | 39.00 | | 33.65 |\n| March | 36.00 | | 26.20 | 37.00 | | 30.30 |"}
{"item_id": "item_0907", "chart_task_type": "paired_gap", "query": "Can you chart the attendance gap between overall and male students for each year level, making it easy to spot at a glance which form has the biggest disparity?", "table_markdown": "| FORM | Overall | Male |\n|---|---|---|\n| Kinder | 94.80% | 95.00% |\n| Year 1 | 94.50% | 94.50% |\n| Year 2 | 94.30% | 93.70% |\n| Year 3 | 95.30% | 95.60% |\n| Year 4 | 93.60% | 96.20% |\n| Year 5 | 94.70% | 95.30% |\n| Year 6 | 94.70% | 94.10% |\n| Year 7 | 95.70% | 95.50% |\n| Year 8 | 95.10% | 95.60% |\n| Year 9 | 93.50% | 96.00% |\n| Year 10 | 92.60% | 92.90% |\n| Year 11 | 94.50% | 94.30% |\n| Year 12 | 94.20% | 94.90% |"}
{"item_id": "item_0908", "chart_task_type": "growth_speed", "query": "Can you chart the population growth for Mineral, Archuleta, and Rio Grande counties from 2000 to 2015 so I can see at a glance which one grew the fastest?", "table_markdown": "| Community profile, by county | Population 2000 | Population 2010 | Population 2015 | Median household income, 2015 $ | Median age, 2015 | Population below poverty level, 2015 |\n|------------------------------|-----------------|-----------------|-----------------|---------------------------------|-----------------|-------------------------------------|\n| Mineral | 831 | 712 | 733 | 48,125 | 60.9 | 7.6% |\n| Archuleta | 9,898 | 12,084 | 12,174 | 46,646 | 49.6 | 11.7% |\n| Rio Grande | 12,413 | 11,982 | 11,745 | 39,672 | 40.9 | 19.2% |\n| Colorado | 4,301,261 | 5,029,196 | 5,278,906 | 60,629 | 36.3 | 12.7% |"}
{"item_id": "item_0909", "chart_task_type": "stability_volatility", "query": "I'd like to see the actual operating income figures for each business segment from 2011 to 2015, plus how widely they scattered relative to their own typical levels, so it's easy to spot which one was the most volatile.", "table_markdown": "| 営業利益 / セグメント利益 Operating income / Segment earnings | 2015 | 2014 | 2013 | 2012 | 2011 |\n|----------------------------------------------------------|------|------|------|------|------|\n| 冷蔵倉庫事業 Refrigerated Warehousing Business | 4,748 | 4,792 | 4,756 | 4,351 | 3,846 |\n| 食品販売事業 Food Sales Business | 1,189 | 1,285 | 721 | (1,346) | 2,023 |\n| その他 Other | 36 | 32 | 6 | 9 | 11 |\n| 消去又は全社 (調整額) Elimination and Corporate (Adjusted) | (2,099) | (2,004) | (1,754) | (1,930) | (1,687) |\n| 合計 Total | 3,874 | 4,105 | 3,729 | 1,083 | 4,193 |"}
{"item_id": "item_0910", "chart_task_type": "composition_compare", "query": "Can you chart the marital status mix for women versus men so I can easily spot any differences in their relationship structures?", "table_markdown": "| | Total (N=101) | Women (n=41) | Men (n=60) | Subjects With Current Major Depression (n=38) | Subjects Without Current Major Depression (n=63) |\n|--------------------------|---------------|--------------|------------|-----------------------------------------------|-------------------------------------------------|\n| Mean±SD age, y | 43.3±11.1 | 42.0±11.1 | 44.1±11.1 | 42.2±10.9 | 44.0±11.2 |\n| Marital status, % of subjects | | | | | |\n| Married | 41.0 | 43.9 | 39.0 | 34.2 | 45.2 |\n| Separated or divorced | 24.0 | 19.5 | 27.1 | 39.5 | 14.5 |\n| Other | 35.0 | 36.6 | 33.9 | 26.3 | 40.3 |\n| White subjects, % | 94.0 | 95.1 | 93.3 | 92.1 | 95.2 |\n| Education, % of subjects† | | | | | |\n| Some college or more | 79.2 | 90.2 | 71.7 | 76.3 | 81.0 |\n| High school diploma or less | 20.8 | 9.8 | 28.3 | 23.7 | 19.0 |\n| Employment, % of subjects‡ | | | | | |\n| Employed full-time | 41.4 | 24.4 | 53.4 | 31.6 | 47.5 |\n| Disabled or retired | 25.3 | 14.6 | 32.8 | 29.0 | 23.0 |\n| Unemployed | 15.1 | 24.4 | 8.6 | 21.1 | 11.5 |\n| Other | 18.2 | 36.5 | 5.2 | 18.4 | 18.1 |"}
{"item_id": "item_0911", "chart_task_type": "baseline_index", "query": "Could you show the growth of nursing homes, beds, and homes taking terminally ill from 1987 to 1993, measured against each category's 1987 starting point? I want it clear which one climbed furthest from its baseline.", "table_markdown": "| Year | Nursing Homes | Beds | Homes taking Terminally Ill |\n|------|---------------|--------|-----------------------------|\n| 1987 | 157 | 4729 | 29 |\n| 1991 | 347 | 11936 | 116 |\n| 1993 | 427 | 16477 | 139 |"}
{"item_id": "item_0912", "chart_task_type": "baseline_index", "query": "I'd like to see OTT Video Service Users and AVOD Viewers from 2019 to 2023 displayed relative to where each one began, so it's obvious right away which segment climbed furthest from its baseline.", "table_markdown": "| Year | OTT Video Service Users (millions) | AVOD Viewers (millions) |\n|------|-----------------------------------|-------------------------|\n| 2019 | 218.5M | 83.9M |\n| 2021 | 236.5M | 129.0M |\n| 2023 | 244.0M | 150.6M |"}