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1
+ cff-version: 1.2.0
2
+ message: "If you use this dataset, please cite the associated paper."
3
+ title: "DERE Dataset"
4
+ type: dataset
5
+ authors:
6
+ - family-names: "Xu"
7
+ given-names: "Shuo"
8
+
9
+ repository-code: "https://github.com/ai-spatial/DERE"
10
+ url: "https://huggingface.co/datasets/ai-spatial/DERE"
11
+ version: "1.0.0"
12
+
13
+ preferred-citation:
14
+ type: conference-paper
15
+ title: "Knowledge-Guided Learning for Global Carbon Flux Prediction: Integrating High-Level Remote Sensing with Bottom-Up Physical Modeling"
16
+ authors:
17
+ - family-names: "Xu"
18
+ given-names: "Shuo"
19
+ - family-names: "Wang"
20
+ given-names: "Zhihao"
21
+ - family-names: "Li"
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+ given-names: "Ruohan"
23
+ - family-names: "Wang"
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+ given-names: "Ruichen"
25
+ - family-names: "Ma"
26
+ given-names: "Lei"
27
+ - family-names: "Hurtt"
28
+ given-names: "George C."
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+ - family-names: "Jia"
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+ given-names: "Xiaowei"
31
+ - family-names: "Xie"
32
+ given-names: "Yiqun"
33
+ collection-title: "Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"
34
+ year: 2026
35
+ publisher:
36
+ name: "ACM"
37
+ conference:
38
+ name: "32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining"
39
+ location:
40
+ name: "Jeju Island, Republic of Korea"
41
+ doi: "10.1145/3770855.3818927"
GlobalMask/README.md ADDED
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1
+ # GlobalMask
2
+
3
+ The released GlobalMask dataset is divided by data split. Each file contains
4
+ the same arrays and dimensions, but the number of global grid-cell samples
5
+ differs.
6
+
7
+ | Released subset | Samples |
8
+ |---|---:|
9
+ | Training split | 3373 |
10
+ | Testing split | 852 |
11
+
12
+ ## Files
13
+
14
+ - `globalmask_training_dataset.npz`
15
+ - `globalmask_testing_dataset.npz`
16
+
17
+ ## Variables
18
+
19
+ - `ed_simulation_x` — monthly ED input features
20
+ - `ed_simulation_y` — age-specific ED simulation targets
21
+ - `ed_simulation_pft_bl` — annual age-specific ED broadleaf PFT fractions
22
+ - `ed_simulation_pft_nl` — annual age-specific ED needleleaf PFT fractions
23
+ - `ed_simulation_pft_gs` — annual age-specific ED grass-and-shrub PFT fractions
24
+ - `lidar_age_weight_fraction` — LiDAR-derived forest-age fractions
25
+ - `esa_cci_bl_fraction` — annual ESA CCI broadleaf PFT fractions
26
+ - `esa_cci_nl_fraction` — annual ESA CCI needleleaf PFT fractions
27
+ - `esa_cci_gs_fraction` — annual ESA CCI grass-and-shrub PFT fractions
28
+
29
+ All released arrays use sample-first orientation. Detailed dimension
30
+ definitions and array shapes are provided in `metadata/dimension_definitions.md`.
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1
+ # InSituMatched
2
+
3
+ The released InSituMatched dataset is divided by in-situ network and data
4
+ split. Each file contains the same arrays and dimensions, but the number of
5
+ matched samples differs.
6
+
7
+ | Released subset | Training samples | Testing samples |
8
+ |---|---:|---:|
9
+ | ABoVE | 48 | 12 |
10
+ | AmeriFlux | 67 | 18 |
11
+ | FLUXNET | 68 | 16 |
12
+ | ICOS-WW | 9 | 4 |
13
+ | Multiple-network sites | 58 | 16 |
14
+
15
+ ## Files
16
+
17
+ - `InSituMatched_above_train.npz`
18
+ - `InSituMatched_above_test.npz`
19
+ - `InSituMatched_ameriflux_train.npz`
20
+ - `InSituMatched_ameriflux_test.npz`
21
+ - `InSituMatched_fluxnet_train.npz`
22
+ - `InSituMatched_fluxnet_test.npz`
23
+ - `InSituMatched_icos-ww_train.npz`
24
+ - `InSituMatched_icos-ww_test.npz`
25
+ - `InSituMatched_multiple_train.npz`
26
+ - `InSituMatched_multiple_test.npz`
27
+
28
+ ## Variables
29
+
30
+ - `ed_simulation_x` — monthly ED input features at matched in-situ locations
31
+ - `ed_simulation_y` — age-specific ED simulation targets
32
+ - `observed_y` — in-situ GPP, RECO, and NEE observations
33
+ - `lidar_age_weight_fraction` — LiDAR-derived forest-age fractions
34
+ - `esa_cci_bl_fraction` — annual ESA CCI broadleaf PFT fractions
35
+ - `esa_cci_nl_fraction` — annual ESA CCI needleleaf PFT fractions
36
+ - `esa_cci_gs_fraction` — annual ESA CCI grass-and-shrub PFT fractions
37
+
38
+ All released arrays use sample-first orientation. Detailed dimension
39
+ definitions and array shapes are provided in
40
+ `metadata/dimension_definitions.md`.
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README.md CHANGED
@@ -1,136 +1,205 @@
1
  ---
2
  language:
3
- - en
4
  pretty_name: DERE Dataset
5
  tags:
6
- - remote-sensing
7
- - carbon-flux
8
- - time-series
9
- - earth-system-science
10
- - knowledge-guided-machine-learning
11
- - process-based-modeling
12
- - gpp
13
- - reco
14
- - nee
15
  ---
16
 
17
- > **Note:** This repository is still being completed.
18
 
 
 
 
19
 
20
- # DERE Dataset
21
 
22
- Processed datasets for the accepted paper at the KDD AI4Science Track:
 
23
 
24
- **Knowledge-Guided Learning for Global Carbon Flux Prediction: Integrating High-Level Remote Sensing with Bottom-Up Physical Modeling**
25
- **Code:** https://github.com/ai-spatial/DERE
26
 
27
- This dataset repository provides the processed `.npz` files used by the DERE codebase for global carbon flux prediction. The data support experiments for the proposed DERE framework, baseline models, and KGML comparison models.
28
 
29
- DERE integrates process-based model simulations, high-level remote sensing observations, and in-situ flux measurements to predict carbon flux variables, including **GPP**, **RECO**, and **NEE**.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
30
 
31
- ## 🧩 Overview
32
 
33
- The dataset contains processed inputs, labels, normalization statistics, plant functional type information, age-weight labels, in-situ observations, and imputed in-situ labels used in the DERE pipeline.
 
34
 
35
- The data are prepared for direct use with the corresponding scripts in the DERE code repository. Each experiment typically uses three types of files:
 
 
 
36
 
37
- * `data`: model input and target data
38
- * `stat`: normalization statistics
39
- * `pft`: PFT labels, in-situ labels, age-weight information, or imputed labels
40
 
41
- ## 📁 Data Files for Each Script
 
 
 
 
 
 
 
 
42
 
43
- ```text
44
- DERE-main/Step01_DERE_Train_3PureModels_CompetitionModel.py
45
- data: res_train4_test8_extract_4types_28years_update_with_NEE_Ra_RECO.npz
46
- stat: data_stats_with_NEE_Ra_RECO.npz
47
- pft: pft_dataset_12mean_4types_28years_update.npz
48
-
49
- DERE-main/Step02_DERE_Finetune_CompetitionModel.py
50
- data: res_train4_test8_extract_28years_ageindependent_update_with_NEE_Ra_RECO.npz
51
- stat: data_stats_with_NEE_Ra_RECO.npz
52
- pft: pft_dataset_12mean_28years_ageindependent_plus_ageweight_update.npz
53
-
54
- DERE-main/Step03_DERE_Train_PFTModel.py
55
- data: 1_res_train4_test8_allx_plus_ageweight_esapft_update.npz
56
- stat: data_stats.npz
57
- pft: 1_ED_PFT_train4_test8_1992_to_2020_update.npz
58
-
59
- DERE-main/Step04_DERE_Finetune_with_InSitu.py
60
- data: res_train4_test8_extract_4types_28years_{net}.npz
61
- stat: data_stats_with_NEE_Ra_RECO.npz
62
- pft: pft_dataset_12mean_4types_28years_ESACCI_plusAW_{net}.npz
63
-
64
- DERE-main/Step05_DERE_InSitu_imputation_CSDI-main/05_DERE_InSitu_imputation.py
65
- data: res_train4_test8_extract_4types_28years_networks_with_NEE_Ra_RECO.npz
66
- stat: data_stats_with_NEE_Ra_RECO.npz
67
- pft: pft_dataset_12mean_4types_28years_ESACCI_plusAW_networks.npz
68
-
69
- DERE-main/Step06_DERE_Finetune_with_InSitu_imputation.py
70
- data: res_train4_test8_extract_4types_28years_{net}.npz
71
- stat: data_stats_with_NEE_Ra_RECO.npz
72
- pft: pft_dataset_12mean_4types_28years_ESACCI_plusAW_{net}_imputation.npz
73
-
74
- For all Baseline scripts
75
- data: res_train4_test8_extract_4types_28years_{net}.npz
76
- stat: data_stats_with_NEE_Ra_RECO.npz
77
- pft: pft_dataset_12mean_4types_28years_ESACCI_plusAW_{net}.npz
78
-
79
- For all KGML scripts
80
- Pretraining:
81
- data: res_train4_test8_extract_28years_ageindependent_update_with_NEE_Ra_RECO.npz
82
- stat: data_stats_with_NEE_Ra_RECO.npz
83
- pft: pft_dataset_12mean_28years_ageindependent_plus_ageweight_update.npz
84
- Finetuning:
85
- data: res_train4_test8_extract_4types_28years_{net}.npz
86
- stat: data_stats_with_NEE_Ra_RECO.npz
87
- pft: pft_dataset_12mean_4types_28years_ESACCI_plusAW_{net}.npz
88
- ```
89
 
90
- ## 🌐 Available Networks
 
91
 
92
- The `{net}` field in the filenames refers to one of the following networks:
 
 
 
 
 
 
93
 
94
- ```text
95
- above
96
- ameriflux
97
- fluxnet
98
- icos-ww
99
- multiple
100
- ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
101
 
102
- ## 📊 Data Format
103
 
104
- All files are stored in NumPy `.npz` format. They can be loaded with:
 
 
 
 
 
 
 
 
 
 
 
 
105
 
106
  ```python
107
  import numpy as np
108
 
109
- data = np.load("file_name.npz")
110
- print(data.files)
111
- ```
112
 
113
- The exact arrays contained in each file depend on the corresponding experiment script. Please refer to the DERE code repository for the expected keys and shapes.
 
 
 
114
 
115
- ### 🕒 Temporal Alignment
116
 
117
- The 29 `full_year` positions correspond to calendar years 1992–2020.
 
118
 
119
- For model prediction, December 1992 is used as the initial ED state (`initial_y`). The model inputs (`ed_simulation_x`) and prediction targets (`target_y`) both cover January 1993 through December 2020, corresponding to 28 years or 336 monthly time steps.
 
 
 
 
 
 
 
 
120
 
121
- The exact ordering of the 136 input features and the 10 ED simulation targets is provided in the corresponding metadata tables.
122
 
123
- ## 🔗 Code Repository
 
 
 
124
 
125
- The dataset is designed to be used with the DERE code repository:
126
 
127
- ```text
128
- https://github.com/ai-spatial/DERE
129
- ```
130
 
131
- Before running the experiments, update the data paths in the corresponding scripts according to your local dataset location.
 
 
 
 
 
 
132
 
133
- ## 📚 Citation
134
 
135
  If you use this dataset, please cite:
136
 
@@ -146,20 +215,10 @@ If you use this dataset, please cite:
146
  }
147
  ```
148
 
149
- ## 🎯 Intended Use
150
-
151
- This dataset is intended for research on:
152
-
153
- - Global carbon flux prediction
154
- - Knowledge-guided machine learning
155
- - Integration of process-based simulations, remote sensing observations, and in-situ measurements
156
- - Time-series modeling of GPP, RECO, and NEE
157
- - Reproduction and comparison of the DERE framework and baseline models
158
-
159
-
160
- ## 📬 Contact
161
 
162
- For questions or feedback, feel free to reach out:
163
 
164
- - Shuo Xu [shuoxu98@umd.edu](mailto:shuoxu98@umd.edu)
165
- - Yiqun Xie [xie@umd.edu](mailto:xie@umd.edu)
 
 
1
  ---
2
  language:
3
+ - en
4
  pretty_name: DERE Dataset
5
  tags:
6
+ - remote-sensing
7
+ - carbon-flux
8
+ - time-series
9
+ - earth-system-science
10
+ - knowledge-guided-machine-learning
11
+ - process-based-modeling
12
+ - gpp
13
+ - reco
14
+ - nee
15
  ---
16
 
17
+ # DERE Dataset
18
 
19
+ DERE is a processed multi-source ecosystem dataset for global carbon-flux
20
+ prediction. It integrates Ecosystem Demography (ED) simulations, remote-sensing
21
+ products, LiDAR-derived forest-age information, and in-situ flux observations.
22
 
23
+ The dataset supports the paper:
24
 
25
+ **Knowledge-Guided Learning for Global Carbon Flux Prediction: Integrating
26
+ High-Level Remote Sensing with Bottom-Up Physical Modeling**
27
 
28
+ Code repository: `https://github.com/ai-spatial/DERE`
 
29
 
30
+ ## Dataset organization
31
 
32
+ ```text
33
+ DERE/
34
+ ├── README.md
35
+ ├── CITATION.cff
36
+ ├── GlobalMask/
37
+ │ ├── README.md
38
+ │ ├── train/
39
+ │ │ └── globalmask_training_dataset.npz
40
+ │ └── test/
41
+ │ └── globalmask_testing_dataset.npz
42
+ ├── InSituMatched/
43
+ │ ├── README.md
44
+ │ ├── above/
45
+ │ │ ├── train/
46
+ │ │ │ └── InSituMatched_above_train.npz
47
+ │ │ └── test/
48
+ │ │ └── InSituMatched_above_test.npz
49
+ │ ├── ameriflux/
50
+ │ │ ├── train/
51
+ │ │ │ └── InSituMatched_ameriflux_train.npz
52
+ │ │ └── test/
53
+ │ │ └── InSituMatched_ameriflux_test.npz
54
+ │ ├── fluxnet/
55
+ │ │ ├── train/
56
+ │ │ │ └── InSituMatched_fluxnet_train.npz
57
+ │ │ └── test/
58
+ │ │ └── InSituMatched_fluxnet_test.npz
59
+ │ ├── icos-ww/
60
+ │ │ ├── train/
61
+ │ │ │ └── InSituMatched_icos-ww_train.npz
62
+ │ │ └── test/
63
+ │ │ └── InSituMatched_icos-ww_test.npz
64
+ │ └── mix/
65
+ │ ├── train/
66
+ │ │ └── InSituMatched_mix_train.npz
67
+ │ └── test/
68
+ │ └── InSituMatched_mix_test.npz
69
+ └── metadata/
70
+ ├── README.md
71
+ ├── dimension_definitions.md
72
+ ├── dataset_schema.json
73
+ ├── normalization_statistics.npz
74
+ ├── feature_names.csv
75
+ ├── target_names.csv
76
+ ├── pft_names.csv
77
+ ├── age_classes.csv
78
+ └── train_test_mask.npy
79
+ ```
80
 
81
+ ## GlobalMask
82
 
83
+ `GlobalMask` contains globally sampled land-grid cells selected by a fixed
84
+ train/test mask.
85
 
86
+ | Split | Samples | File |
87
+ |---|---:|---|
88
+ | Training | 3373 | `GlobalMask/train/globalmask_training_dataset.npz` |
89
+ | Testing | 852 | `GlobalMask/test/globalmask_testing_dataset.npz` |
90
 
91
+ Each file contains:
 
 
92
 
93
+ - `ed_simulation_x`
94
+ - `ed_simulation_y`
95
+ - `ed_simulation_pft_bl`
96
+ - `ed_simulation_pft_nl`
97
+ - `ed_simulation_pft_gs`
98
+ - `lidar_age_weight_fraction`
99
+ - `esa_cci_bl_fraction`
100
+ - `esa_cci_nl_fraction`
101
+ - `esa_cci_gs_fraction`
102
 
103
+ ## InSituMatched
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
104
 
105
+ `InSituMatched` contains ED simulation data and auxiliary variables aligned with
106
+ in-situ carbon-flux observations.
107
 
108
+ | Subset | Training samples | Testing samples |
109
+ |---|---:|---:|
110
+ | ABoVE | 48 | 12 |
111
+ | AmeriFlux | 67 | 18 |
112
+ | FLUXNET | 68 | 16 |
113
+ | ICOS-WW | 9 | 4 |
114
+ | Multiple-network sites | 58 | 16 |
115
 
116
+ Each file contains:
117
+
118
+ - `ed_simulation_x`
119
+ - `ed_simulation_y`
120
+ - `observed_y`
121
+ - `lidar_age_weight_fraction`
122
+ - `esa_cci_bl_fraction`
123
+ - `esa_cci_nl_fraction`
124
+ - `esa_cci_gs_fraction`
125
+
126
+ The `mix` subset contains sites occurring in more than one network.
127
+
128
+ ## Temporal alignment
129
+
130
+ The complete ED target sequence covers 29 calendar years from 1992 through
131
+ 2020.
132
+
133
+ - December 1992 is used as the initial ED state.
134
+ - Model inputs cover January 1993 through December 2020.
135
+ - Prediction targets cover January 1993 through December 2020.
136
+ - The prediction period contains 28 years, or 336 monthly time steps.
137
+
138
+ All released arrays use sample-first orientation whenever a sample dimension is
139
+ present. Detailed dimensions and released shapes are documented in
140
+ `metadata/dimension_definitions.md`.
141
 
142
+ ## Metadata
143
 
144
+ - `feature_names.csv` defines the 136 ED input features.
145
+ - `target_names.csv` defines the 10 ED simulation targets and 3 observed
146
+ carbon-flux targets.
147
+ - `pft_names.csv` defines broadleaf, needleleaf, and grass-and-shrub PFTs.
148
+ - `age_classes.csv` defines the 18 representative forest-age classes.
149
+ - `train_test_mask.npy` stores the GlobalMask sampling split.
150
+ - `normalization_statistics.npz` contains:
151
+ - `x_mean`: shape `[136]`
152
+ - `x_std`: shape `[136]`
153
+ - `y_mean`: shape `[10]`
154
+ - `y_std`: shape `[10]`
155
+
156
+ ## Loading the data
157
 
158
  ```python
159
  import numpy as np
160
 
161
+ file_path = "GlobalMask/train/globalmask_training_dataset.npz"
 
 
162
 
163
+ with np.load(file_path, allow_pickle=False) as data:
164
+ for key in data.files:
165
+ print(key, data[key].shape, data[key].dtype)
166
+ ```
167
 
168
+ Load the normalization statistics with:
169
 
170
+ ```python
171
+ import numpy as np
172
 
173
+ with np.load(
174
+ "metadata/normalization_statistics.npz",
175
+ allow_pickle=False,
176
+ ) as stats:
177
+ x_mean = stats["x_mean"]
178
+ x_std = stats["x_std"]
179
+ y_mean = stats["y_mean"]
180
+ y_std = stats["y_std"]
181
+ ```
182
 
183
+ Standardization is performed as:
184
 
185
+ ```python
186
+ x_normalized = (x - x_mean) / x_std
187
+ y_normalized = (y - y_mean) / y_std
188
+ ```
189
 
190
+ ## Intended use
191
 
192
+ The dataset is intended for research on:
 
 
193
 
194
+ - global carbon-flux prediction
195
+ - knowledge-guided machine learning
196
+ - process-model emulation
197
+ - multi-source data fusion
198
+ - time-series modeling of GPP, RECO, and NEE
199
+ - simulation-to-observation transfer learning
200
+ - reproduction and comparison of DERE and baseline models
201
 
202
+ ## Citation
203
 
204
  If you use this dataset, please cite:
205
 
 
215
  }
216
  ```
217
 
218
+ The same citation is also provided in `CITATION.cff`.
 
 
 
 
 
 
 
 
 
 
 
219
 
220
+ ## License and source terms
221
 
222
+ The released files combine information derived from multiple upstream sources.
223
+ Users are responsible for following the applicable attribution and
224
+ redistribution terms of those sources.
gitattributes ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ *.npz filter=lfs diff=lfs merge=lfs -text
2
+ *.npy filter=lfs diff=lfs merge=lfs -text
3
+ *.parquet filter=lfs diff=lfs merge=lfs -text
4
+ *.csv text eol=lf
5
+ *.json text eol=lf
6
+ *.md text eol=lf
7
+ *.py text eol=lf
gitignore ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ __pycache__/
2
+ *.py[cod]
3
+ .DS_Store
4
+ .ipynb_checkpoints/
5
+ .pytest_cache/
6
+ .mypy_cache/
7
+ .ruff_cache/
8
+ .vscode/
9
+ .idea/
10
+ recovered_sample/
metadata/age_classes.csv ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ index,age
2
+ 0,1
3
+ 1,10
4
+ 2,20
5
+ 3,30
6
+ 4,41
7
+ 5,50
8
+ 6,60
9
+ 7,70
10
+ 8,90
11
+ 9,110
12
+ 10,140
13
+ 11,190
14
+ 12,240
15
+ 13,290
16
+ 14,340
17
+ 15,390
18
+ 16,440
19
+ 17,490
metadata/dataset_schema.json ADDED
@@ -0,0 +1,342 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "template_status": "PLACEHOLDER",
3
+ "canonical_orientation": "sample_first",
4
+ "datasets": {
5
+ "GlobalMask": {
6
+ "splits": {
7
+ "train": {
8
+ "num_samples": 3373
9
+ },
10
+ "test": {
11
+ "num_samples": 852
12
+ }
13
+ },
14
+ "variables": {
15
+ "x": {
16
+ "source": "Multiple sources; see feature_names.csv",
17
+ "dimensions": [
18
+ "sample",
19
+ "prediction_year",
20
+ "month",
21
+ "feature"
22
+ ],
23
+ "known_shapes": {
24
+ "train": [
25
+ 3373,
26
+ 28,
27
+ 12,
28
+ 136
29
+ ],
30
+ "test": [
31
+ 852,
32
+ 28,
33
+ 12,
34
+ 136
35
+ ]
36
+ }
37
+ },
38
+ "y": {
39
+ "source": "ED simulation",
40
+ "dimensions": [
41
+ "sample",
42
+ "age_class",
43
+ "full_year",
44
+ "month",
45
+ "simulation_target"
46
+ ],
47
+ "known_original_shapes": {
48
+ "train": [
49
+ 18,
50
+ 3373,
51
+ 29,
52
+ 12,
53
+ 10
54
+ ],
55
+ "test": [
56
+ 18,
57
+ 852,
58
+ 29,
59
+ 12,
60
+ 10
61
+ ]
62
+ },
63
+ "canonical_shapes": {
64
+ "train": [
65
+ 3373,
66
+ 18,
67
+ 29,
68
+ 12,
69
+ 10
70
+ ],
71
+ "test": [
72
+ 852,
73
+ 18,
74
+ 29,
75
+ 12,
76
+ 10
77
+ ]
78
+ }
79
+ },
80
+ "age_weight": {
81
+ "source": "LiDAR-derived forest-age information",
82
+ "dimensions": [
83
+ "sample",
84
+ "age_class"
85
+ ],
86
+ "known_original_shapes": {
87
+ "train": [
88
+ 18,
89
+ 3373
90
+ ],
91
+ "test": [
92
+ 18,
93
+ 852
94
+ ]
95
+ },
96
+ "canonical_shapes": {
97
+ "train": [
98
+ 3373,
99
+ 18
100
+ ],
101
+ "test": [
102
+ 852,
103
+ 18
104
+ ]
105
+ }
106
+ },
107
+ "esa_bl": {
108
+ "source": "ESA CCI PFT",
109
+ "dimensions": [
110
+ "sample",
111
+ "full_year"
112
+ ],
113
+ "status": "TODO: regenerate for all GlobalMask samples; preserve missing values"
114
+ },
115
+ "esa_nl": {
116
+ "source": "ESA CCI PFT",
117
+ "dimensions": [
118
+ "sample",
119
+ "full_year"
120
+ ],
121
+ "status": "TODO: regenerate for all GlobalMask samples; preserve missing values"
122
+ },
123
+ "esa_gs": {
124
+ "source": "ESA CCI PFT",
125
+ "dimensions": [
126
+ "sample",
127
+ "full_year"
128
+ ],
129
+ "status": "TODO: regenerate for all GlobalMask samples; preserve missing values"
130
+ },
131
+ "simulation_pft_bl": {
132
+ "source": "ED simulation",
133
+ "dimensions": [
134
+ "sample",
135
+ "age_class",
136
+ "full_year"
137
+ ],
138
+ "known_original_shapes": {
139
+ "train": [
140
+ 18,
141
+ 3373,
142
+ 29
143
+ ],
144
+ "test": [
145
+ 18,
146
+ 852,
147
+ 29
148
+ ]
149
+ },
150
+ "canonical_shapes": {
151
+ "train": [
152
+ 3373,
153
+ 18,
154
+ 29
155
+ ],
156
+ "test": [
157
+ 852,
158
+ 18,
159
+ 29
160
+ ]
161
+ }
162
+ },
163
+ "simulation_pft_nl": {
164
+ "source": "ED simulation",
165
+ "dimensions": [
166
+ "sample",
167
+ "age_class",
168
+ "full_year"
169
+ ],
170
+ "known_original_shapes": {
171
+ "train": [
172
+ 18,
173
+ 3373,
174
+ 29
175
+ ],
176
+ "test": [
177
+ 18,
178
+ 852,
179
+ 29
180
+ ]
181
+ },
182
+ "canonical_shapes": {
183
+ "train": [
184
+ 3373,
185
+ 18,
186
+ 29
187
+ ],
188
+ "test": [
189
+ 852,
190
+ 18,
191
+ 29
192
+ ]
193
+ }
194
+ },
195
+ "simulation_pft_gs": {
196
+ "source": "ED simulation",
197
+ "dimensions": [
198
+ "sample",
199
+ "age_class",
200
+ "full_year"
201
+ ],
202
+ "known_original_shapes": {
203
+ "train": [
204
+ 18,
205
+ 3373,
206
+ 29
207
+ ],
208
+ "test": [
209
+ 18,
210
+ 852,
211
+ 29
212
+ ]
213
+ },
214
+ "canonical_shapes": {
215
+ "train": [
216
+ 3373,
217
+ 18,
218
+ 29
219
+ ],
220
+ "test": [
221
+ 852,
222
+ 18,
223
+ 29
224
+ ]
225
+ }
226
+ }
227
+ }
228
+ },
229
+ "InSituMatched": {
230
+ "networks": [
231
+ "above",
232
+ "ameriflux",
233
+ "fluxnet",
234
+ "icos-ww",
235
+ "multiple"
236
+ ],
237
+ "variables": {
238
+ "simulation_x": {
239
+ "source": "Multiple sources",
240
+ "dimensions": [
241
+ "sample",
242
+ "prediction_year",
243
+ "month",
244
+ "feature"
245
+ ]
246
+ },
247
+ "simulation_y": {
248
+ "source": "ED simulation",
249
+ "dimensions": [
250
+ "sample",
251
+ "age_class",
252
+ "full_year",
253
+ "month",
254
+ "simulation_target"
255
+ ]
256
+ },
257
+ "observed_y": {
258
+ "source": "Corresponding flux-tower network",
259
+ "dimensions": [
260
+ "sample",
261
+ "full_year",
262
+ "month",
263
+ "observed_target"
264
+ ]
265
+ },
266
+ "esa_bl": {
267
+ "source": "ESA CCI PFT",
268
+ "dimensions": [
269
+ "sample",
270
+ "full_year"
271
+ ]
272
+ },
273
+ "esa_nl": {
274
+ "source": "ESA CCI PFT",
275
+ "dimensions": [
276
+ "sample",
277
+ "full_year"
278
+ ]
279
+ },
280
+ "esa_gs": {
281
+ "source": "ESA CCI PFT",
282
+ "dimensions": [
283
+ "sample",
284
+ "full_year"
285
+ ]
286
+ },
287
+ "age_weight": {
288
+ "source": "LiDAR-derived forest-age information",
289
+ "dimensions": [
290
+ "sample",
291
+ "age_class"
292
+ ]
293
+ },
294
+ "sample_id": {
295
+ "source": "Generated during packaging",
296
+ "dimensions": [
297
+ "sample"
298
+ ]
299
+ },
300
+ "simulation_pft_bl": {
301
+ "source": "ED simulation",
302
+ "dimensions": [
303
+ "sample",
304
+ "age_class",
305
+ "full_year"
306
+ ],
307
+ "known_example_shape": [
308
+ "sample",
309
+ 18,
310
+ 29
311
+ ]
312
+ },
313
+ "simulation_pft_nl": {
314
+ "source": "ED simulation",
315
+ "dimensions": [
316
+ "sample",
317
+ "age_class",
318
+ "full_year"
319
+ ],
320
+ "known_example_shape": [
321
+ "sample",
322
+ 18,
323
+ 29
324
+ ]
325
+ },
326
+ "simulation_pft_gs": {
327
+ "source": "ED simulation",
328
+ "dimensions": [
329
+ "sample",
330
+ "age_class",
331
+ "full_year"
332
+ ],
333
+ "known_example_shape": [
334
+ "sample",
335
+ 18,
336
+ 29
337
+ ]
338
+ }
339
+ }
340
+ }
341
+ }
342
+ }
metadata/dimension_definitions.md ADDED
@@ -0,0 +1,407 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Dimension definitions
2
+
3
+ This document defines the dimensions and array orientations used in the
4
+ released DERE dataset.
5
+
6
+ ## Temporal prediction setup
7
+
8
+ The complete ED target sequence spans 29 calendar years from 1992 to 2020.
9
+
10
+ The ED target values from December 1992 are used once as the initial state.
11
+ Together with the monthly input features from the following 28 years, this
12
+ initial state is used to predict the ED target variables for every month of
13
+ the following 28 years.
14
+
15
+ The model performs prediction over 336 monthly time steps:
16
+
17
+ ```text
18
+ 28 years × 12 months = 336 monthly prediction steps
19
+ ```
20
+
21
+ The temporal relationship is:
22
+
23
+ ```text
24
+ Initial state:
25
+ ed_simulation_y from the final month of the first year
26
+
27
+ Inputs:
28
+ ed_simulation_x from every month of the following 28 years
29
+
30
+ Prediction targets:
31
+ ed_simulation_y from every month of the following 28 years
32
+ ```
33
+
34
+ ## Core dimensions
35
+
36
+ | Dimension | Meaning |
37
+ |---|---|
38
+ | `sample` | A sampled global grid cell or an in-situ site matched to the corresponding simulation grid cell |
39
+ | `full_year` | Year axis of the complete ED target sequence, corresponding to 1992–2020; length 29 |
40
+ | `prediction_year` | Year axis of the prediction period, corresponding to 1993–2020; length 28 |
41
+ | `month` | Calendar month within a year; length 12 |
42
+ | `feature` | Model-input variable; length 136 |
43
+ | `simulation_target` | ED simulation output variable; length 10 |
44
+ | `observed_target` | In-situ carbon-flux variable; length 3 |
45
+ | `age_class` | Forest initial-age class used for age-specific ED simulation outputs, ED simulation PFT arrays, and LiDAR-derived age weights; length 18 |
46
+ | `network` | In-situ dataset identifier: `above` (ABoVE), `ameriflux` (AmeriFlux), `fluxnet` (FLUXNET), `icos-ww` (ICOS-WW), or `multiple` (sites occurring in more than one network, separated to keep the network-specific subsets non-overlapping) |
47
+ | `split` | Dataset partition. The released data contain training and testing splits. During model development, 10% of the training split is used as validation data. |
48
+
49
+ The 18 representative forest ages are:
50
+
51
+ ```text
52
+ [1, 10, 20, 30, 41, 50, 60, 70, 90,
53
+ 110, 140, 190, 240, 290, 340, 390, 440, 490]
54
+ ```
55
+
56
+ ## GlobalMask arrays
57
+
58
+ The GlobalMask dataset is divided into two non-overlapping subsets:
59
+
60
+ - `training split`: 3373 global grid-cell samples used for model training.
61
+ During model development, 10% of this split is selected as validation data.
62
+ - `testing split`: 852 held-out global grid-cell samples used for final model
63
+ evaluation.
64
+
65
+ The two splits contain the same variables and use the same dimension
66
+ definitions. They differ only in the number of samples.
67
+
68
+ ### `ed_simulation_x`
69
+
70
+ The array contains the monthly ED input features for the 28-year prediction
71
+ period.
72
+
73
+ Dimensions:
74
+
75
+ ```text
76
+ [sample, prediction_year, month, feature]
77
+ ```
78
+
79
+ Released shapes:
80
+
81
+ ```text
82
+ training split: [3373, 28, 12, 136]
83
+ testing split: [852, 28, 12, 136]
84
+ ```
85
+
86
+ ### `ed_simulation_y`
87
+
88
+ The array contains the complete 29-year age-specific ED simulation target
89
+ sequence.
90
+
91
+ Dimensions:
92
+
93
+ ```text
94
+ [sample, age_class, full_year, month, simulation_target]
95
+ ```
96
+
97
+ Released shapes:
98
+
99
+ ```text
100
+ training split: [3373, 18, 29, 12, 10]
101
+ testing split: [852, 18, 29, 12, 10]
102
+ ```
103
+
104
+ The initial target state is derived from the final month of the first year:
105
+
106
+ ```python
107
+ initial_y = ed_simulation_y[:, :, 0, -1, :]
108
+ ```
109
+
110
+ Derived dimensions:
111
+
112
+ ```text
113
+ [sample, age_class, simulation_target]
114
+ ```
115
+
116
+ The prediction target contains every month of the following 28 years:
117
+
118
+ ```python
119
+ target_y = ed_simulation_y[:, :, 1:, :, :]
120
+ ```
121
+
122
+ Derived dimensions:
123
+
124
+ ```text
125
+ [sample, age_class, prediction_year, month, simulation_target]
126
+ ```
127
+
128
+ The model relationship is:
129
+
130
+ ```text
131
+ initial_y from the final month of year 1
132
+ +
133
+ ed_simulation_x from all 336 months of the following 28 years
134
+
135
+ target_y for all 336 months of the following 28 years
136
+ ```
137
+
138
+ ### `ed_simulation_pft_bl`, `ed_simulation_pft_nl`, and `ed_simulation_pft_gs`
139
+
140
+ The arrays contain the annual age-specific ED simulation PFT fractions for
141
+ broadleaf, needleleaf, and grass-and-shrub vegetation.
142
+
143
+ Dimensions:
144
+
145
+ ```text
146
+ [sample, age_class, full_year]
147
+ ```
148
+
149
+ Released shapes:
150
+
151
+ ```text
152
+ training split: [3373, 18, 29]
153
+ testing split: [852, 18, 29]
154
+ ```
155
+
156
+ BL, NL, and GS are stored as separate arrays, so `pft_type` is not an explicit
157
+ dimension.
158
+
159
+ ### `lidar_age_weight_fraction`
160
+
161
+ The array contains the LiDAR-derived fraction associated with each of the
162
+ 18 forest age classes.
163
+
164
+ Dimensions:
165
+
166
+ ```text
167
+ [sample, age_class]
168
+ ```
169
+
170
+ Released shapes:
171
+
172
+ ```text
173
+ training split: [3373, 18]
174
+ testing split: [852, 18]
175
+ ```
176
+
177
+ ### `esa_cci_bl_fraction`, `esa_cci_nl_fraction`, and `esa_cci_gs_fraction`
178
+
179
+ The arrays contain the annual ESA CCI broadleaf, needleleaf, and grass-and-shrub
180
+ vegetation PFT fractions.
181
+
182
+ Dimensions:
183
+
184
+ ```text
185
+ [sample, full_year]
186
+ ```
187
+
188
+ Released shapes:
189
+
190
+ ```text
191
+ training split: [3373, 29]
192
+ testing split: [852, 29]
193
+ ```
194
+
195
+ The three PFT groups are stored as separate arrays. There is no monthly
196
+ dimension in these arrays.
197
+
198
+ ## InSituMatched arrays
199
+
200
+ The number of matched in-situ sites depends on the network subset and data
201
+ split. Therefore, the symbolic `sample` dimension is used below instead of a
202
+ fixed sample count.
203
+
204
+ ### `ed_simulation_x`
205
+
206
+ The array contains the same 28-year monthly ED input sequence used in the
207
+ GlobalMask dataset, extracted at the matched in-situ locations.
208
+
209
+ Dimensions:
210
+
211
+ ```text
212
+ [sample, prediction_year, month, feature]
213
+ ```
214
+
215
+ Shape:
216
+
217
+ ```text
218
+ [sample, 28, 12, 136]
219
+ ```
220
+
221
+ ### `ed_simulation_y`
222
+
223
+ The array contains the complete 29-year age-specific ED simulation target
224
+ sequence at the matched in-situ locations.
225
+
226
+ Dimensions:
227
+
228
+ ```text
229
+ [sample, age_class, full_year, month, simulation_target]
230
+ ```
231
+
232
+ Shape:
233
+
234
+ ```text
235
+ [sample, 18, 29, 12, 10]
236
+ ```
237
+
238
+ The initial state and prediction target are derived in the same way as for
239
+ GlobalMask:
240
+
241
+ ```python
242
+ initial_y = ed_simulation_y[:, :, 0, -1, :]
243
+ target_y = ed_simulation_y[:, :, 1:, :, :]
244
+ ```
245
+
246
+ Derived shapes:
247
+
248
+ ```text
249
+ initial_y:
250
+ [sample, 18, 10]
251
+
252
+ target_y:
253
+ [sample, 18, 28, 12, 10]
254
+ ```
255
+
256
+ ### `observed_y`
257
+
258
+ The array contains the in-situ carbon-flux observations.
259
+
260
+ Dimensions:
261
+
262
+ ```text
263
+ [sample, full_year, month, observed_target]
264
+ ```
265
+
266
+ Shape:
267
+
268
+ ```text
269
+ [sample, 29, 12, 3]
270
+ ```
271
+
272
+ The three observed target variables are:
273
+
274
+ ```text
275
+ GPP
276
+ RECO
277
+ NEE
278
+ ```
279
+
280
+ For model evaluation, predictions are compared with the available in-situ
281
+ observations over the 28-year prediction period. Missing observation time
282
+ steps are excluded from evaluation.
283
+
284
+ ### `lidar_age_weight_fraction`
285
+
286
+ The array contains one LiDAR-derived fraction for each of the 18 age classes.
287
+
288
+ Dimensions:
289
+
290
+ ```text
291
+ [sample, age_class]
292
+ ```
293
+
294
+ Shape:
295
+
296
+ ```text
297
+ [sample, 18]
298
+ ```
299
+
300
+ ### `esa_cci_bl_fraction`, `esa_cci_nl_fraction`, and `esa_cci_gs_fraction`
301
+
302
+ The arrays contain the annual ESA CCI broadleaf, needleleaf, and grass-and-shrub
303
+ vegetation PFT fractions at the matched in-situ locations.
304
+
305
+ Dimensions:
306
+
307
+ ```text
308
+ [sample, full_year]
309
+ ```
310
+
311
+ Shape:
312
+
313
+ ```text
314
+ [sample, 29]
315
+ ```
316
+
317
+ The three PFT groups are stored as separate arrays. There is no monthly
318
+ dimension in these arrays.
319
+
320
+ ## Conceptual relationship to the paper
321
+
322
+ In the paper:
323
+
324
+ - `x_(s,t)` denotes physical and environmental conditions at location `s` and
325
+ time `t`.
326
+ - `c_k` denotes an initial forest-age state.
327
+ - `(y^P_(s,t))_k` denotes the ED simulation output corresponding to initial age
328
+ state `c_k`.
329
+ - `y_(s,t)` denotes the in-situ carbon-flux observation.
330
+ - `z_(s,t)` denotes the aggregated satellite PFT observation.
331
+ - `alpha_k` denotes the weight associated with an initial forest-age state.
332
+
333
+ In the released prediction setup, the first-year final-month ED target values
334
+ provide the one-time initial target state. The monthly input features from the
335
+ following 28 years are then used to predict the monthly ED target values over
336
+ the same 28-year period.
337
+
338
+ ## Released-array summary
339
+
340
+ All released arrays use sample-first orientation whenever a `sample`
341
+ dimension is present.
342
+
343
+ ### GlobalMask
344
+
345
+ ```text
346
+ ed_simulation_x:
347
+ [sample, prediction_year, month, feature]
348
+
349
+ ed_simulation_y:
350
+ [sample, age_class, full_year, month, simulation_target]
351
+
352
+ ed_simulation_pft_bl:
353
+ [sample, age_class, full_year]
354
+
355
+ ed_simulation_pft_nl:
356
+ [sample, age_class, full_year]
357
+
358
+ ed_simulation_pft_gs:
359
+ [sample, age_class, full_year]
360
+
361
+ lidar_age_weight_fraction:
362
+ [sample, age_class]
363
+
364
+ esa_cci_bl_fraction:
365
+ [sample, full_year]
366
+
367
+ esa_cci_nl_fraction:
368
+ [sample, full_year]
369
+
370
+ esa_cci_gs_fraction:
371
+ [sample, full_year]
372
+ ```
373
+
374
+ ### InSituMatched
375
+
376
+ ```text
377
+ ed_simulation_x:
378
+ [sample, prediction_year, month, feature]
379
+
380
+ ed_simulation_y:
381
+ [sample, age_class, full_year, month, simulation_target]
382
+
383
+ lidar_age_weight_fraction:
384
+ [sample, age_class]
385
+
386
+ esa_cci_bl_fraction:
387
+ [sample, full_year]
388
+
389
+ esa_cci_nl_fraction:
390
+ [sample, full_year]
391
+
392
+ esa_cci_gs_fraction:
393
+ [sample, full_year]
394
+
395
+ observed_y:
396
+ [sample, full_year, month, observed_target]
397
+ ```
398
+
399
+ The following arrays are derived from `ed_simulation_y`:
400
+
401
+ ```text
402
+ initial_y:
403
+ [sample, age_class, simulation_target]
404
+
405
+ target_y:
406
+ [sample, age_class, prediction_year, month, simulation_target]
407
+ ```
metadata/dimension_definitions.md.bak ADDED
@@ -0,0 +1,380 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Dimension definitions
2
+
3
+ This document defines the dimensions and array orientations used in the
4
+ released DERE dataset.
5
+
6
+ ## Temporal prediction setup
7
+
8
+ The complete ED target sequence spans 29 calendar years from 1992 to 2020.
9
+
10
+ The ED target values from December 1992 are used once as the initial state.
11
+ Together with the monthly input features from the following 28 years, this
12
+ initial state is used to predict the ED target variables for every month of
13
+ the following 28 years.
14
+
15
+ The model performs prediction over 336 monthly time steps:
16
+
17
+ ```text
18
+ 28 years × 12 months = 336 monthly prediction steps
19
+ ```
20
+
21
+ The temporal relationship is:
22
+
23
+ ```text
24
+ Initial state:
25
+ ed_simulation_y from the final month of the first year
26
+
27
+ Inputs:
28
+ ed_simulation_x from every month of the following 28 years
29
+
30
+ Prediction targets:
31
+ ed_simulation_y from every month of the following 28 years
32
+ ```
33
+
34
+ ## Core dimensions
35
+
36
+ | Dimension | Meaning |
37
+ |---|---|
38
+ | `sample` | A sampled global grid cell or an in-situ site matched to the corresponding simulation grid cell |
39
+ | `full_year` | Year axis of the complete ED target sequence, corresponding to 1992–2020; length 29 |
40
+ | `prediction_year` | Year axis of the prediction period, corresponding to 1993–2020; length 28 |
41
+ | `month` | Calendar month within a year; length 12 |
42
+ | `feature` | Model-input variable; length 136 |
43
+ | `simulation_target` | ED simulation output variable; length 10 |
44
+ | `observed_target` | In-situ carbon-flux variable; length 3 |
45
+ | `age_class` | Forest initial-age class used for age-specific ED simulation outputs, ED simulation PFT arrays, and LiDAR-derived age weights; length 18 |
46
+ | `network` | In-situ dataset identifier: `above` (ABoVE), `ameriflux` (AmeriFlux), `fluxnet` (FLUXNET), `icos-ww` (ICOS-WW), or `multiple` (sites occurring in more than one network, separated to keep the network-specific subsets non-overlapping) |
47
+ | `split` | Dataset partition. The released data contain training and testing splits. During model development, 10% of the training split is used as validation data. |
48
+
49
+ The 18 representative forest ages are:
50
+
51
+ ```text
52
+ [1, 10, 20, 30, 41, 50, 60, 70, 90,
53
+ 110, 140, 190, 240, 290, 340, 390, 440, 490]
54
+ ```
55
+
56
+ ## GlobalMask arrays
57
+
58
+ The GlobalMask dataset is divided into two non-overlapping subsets:
59
+
60
+ - `training split`: 3373 global grid-cell samples used for model training.
61
+ During model development, 10% of this split is selected as validation data.
62
+ - `testing split`: 852 held-out global grid-cell samples used for final model
63
+ evaluation.
64
+
65
+ The two splits contain the same variables and use the same dimension
66
+ definitions. They differ only in the number of samples.
67
+
68
+ ### `ed_simulation_x`
69
+
70
+ The array contains the monthly ED input features for the 28-year prediction
71
+ period.
72
+
73
+ Dimensions:
74
+
75
+ ```text
76
+ [sample, prediction_year, month, feature]
77
+ ```
78
+
79
+ Released shapes:
80
+
81
+ ```text
82
+ training split: [3373, 28, 12, 136]
83
+ testing split: [852, 28, 12, 136]
84
+ ```
85
+
86
+ ### `ed_simulation_y`
87
+
88
+ The array contains the complete 29-year age-specific ED simulation target
89
+ sequence.
90
+
91
+ Dimensions:
92
+
93
+ ```text
94
+ [sample, age_class, full_year, month, simulation_target]
95
+ ```
96
+
97
+ Released shapes:
98
+
99
+ ```text
100
+ training split: [3373, 18, 29, 12, 10]
101
+ testing split: [852, 18, 29, 12, 10]
102
+ ```
103
+
104
+ The initial target state is derived from the final month of the first year:
105
+
106
+ ```python
107
+ initial_y = ed_simulation_y[:, :, 0, -1, :]
108
+ ```
109
+
110
+ Derived dimensions:
111
+
112
+ ```text
113
+ [sample, age_class, simulation_target]
114
+ ```
115
+
116
+ The prediction target contains every month of the following 28 years:
117
+
118
+ ```python
119
+ target_y = ed_simulation_y[:, :, 1:, :, :]
120
+ ```
121
+
122
+ Derived dimensions:
123
+
124
+ ```text
125
+ [sample, age_class, prediction_year, month, simulation_target]
126
+ ```
127
+
128
+ The model relationship is:
129
+
130
+ ```text
131
+ initial_y from the final month of year 1
132
+ +
133
+ ed_simulation_x from all 336 months of the following 28 years
134
+
135
+ target_y for all 336 months of the following 28 years
136
+ ```
137
+
138
+ ### `ed_simulation_pft_bl`, `ed_simulation_pft_nl`, and `ed_simulation_pft_gs`
139
+
140
+ The arrays contain the annual age-specific ED simulation PFT fractions for
141
+ broadleaf, needleleaf, and grass-and-shrub vegetation.
142
+
143
+ Dimensions:
144
+
145
+ ```text
146
+ [sample, age_class, full_year]
147
+ ```
148
+
149
+ Released shapes:
150
+
151
+ ```text
152
+ training split: [3373, 18, 29]
153
+ testing split: [852, 18, 29]
154
+ ```
155
+
156
+ BL, NL, and GS are stored as separate arrays, so `pft_type` is not an explicit
157
+ dimension.
158
+
159
+ ### `lidar_age_weight_fraction`
160
+
161
+ The array contains the LiDAR-derived fraction associated with each of the
162
+ 18 forest age classes.
163
+
164
+ Dimensions:
165
+
166
+ ```text
167
+ [sample, age_class]
168
+ ```
169
+
170
+ Released shapes:
171
+
172
+ ```text
173
+ training split: [3373, 18]
174
+ testing split: [852, 18]
175
+ ```
176
+
177
+ ### `esa_cci_bl_fraction`, `esa_cci_nl_fraction`, and `esa_cci_gs_fraction`
178
+
179
+ The arrays contain the annual ESA CCI broadleaf, needleleaf, and grass-and-shrub
180
+ vegetation PFT fractions.
181
+
182
+ Dimensions:
183
+
184
+ ```text
185
+ [sample, full_year]
186
+ ```
187
+
188
+ Released shapes:
189
+
190
+ ```text
191
+ training split: [3373, 29]
192
+ testing split: [852, 29]
193
+ ```
194
+
195
+ The three PFT groups are stored as separate arrays. There is no monthly
196
+ dimension in these arrays.
197
+
198
+ ## InSituMatched arrays
199
+
200
+ The number of matched in-situ sites depends on the network subset and data
201
+ split. Therefore, the symbolic `sample` dimension is used below instead of a
202
+ fixed sample count.
203
+
204
+ ### `ed_simulation_x`
205
+
206
+ The array contains the same 28-year monthly ED input sequence used in the
207
+ GlobalMask dataset, extracted at the matched in-situ locations.
208
+
209
+ Dimensions:
210
+
211
+ ```text
212
+ [sample, prediction_year, month, feature]
213
+ ```
214
+
215
+ Shape:
216
+
217
+ ```text
218
+ [sample, 28, 12, 136]
219
+ ```
220
+
221
+ ### `ed_simulation_y`
222
+
223
+ The array contains the complete 29-year age-specific ED simulation target
224
+ sequence at the matched in-situ locations.
225
+
226
+ Dimensions:
227
+
228
+ ```text
229
+ [sample, age_class, full_year, month, simulation_target]
230
+ ```
231
+
232
+ Shape:
233
+
234
+ ```text
235
+ [sample, 18, 29, 12, 10]
236
+ ```
237
+
238
+ The initial state and prediction target are derived in the same way as for
239
+ GlobalMask:
240
+
241
+ ```python
242
+ initial_y = ed_simulation_y[:, :, 0, -1, :]
243
+ target_y = ed_simulation_y[:, :, 1:, :, :]
244
+ ```
245
+
246
+ Derived shapes:
247
+
248
+ ```text
249
+ initial_y:
250
+ [sample, 18, 10]
251
+
252
+ target_y:
253
+ [sample, 18, 28, 12, 10]
254
+ ```
255
+
256
+ ### `observed_y`
257
+
258
+ The array contains the in-situ carbon-flux observations.
259
+
260
+ Dimensions:
261
+
262
+ ```text
263
+ [sample, full_year, month, observed_target]
264
+ ```
265
+
266
+ Shape:
267
+
268
+ ```text
269
+ [sample, 29, 12, 3]
270
+ ```
271
+
272
+ The three observed target variables are:
273
+
274
+ ```text
275
+ GPP
276
+ RECO
277
+ NEE
278
+ ```
279
+
280
+ For model evaluation, predictions are compared with the available in-situ
281
+ observations over the 28-year prediction period. Missing observation time
282
+ steps are excluded from evaluation.
283
+
284
+ ### `lidar_age_weight_fraction`
285
+
286
+ The array contains one LiDAR-derived fraction for each of the 18 age classes.
287
+
288
+ Dimensions:
289
+
290
+ ```text
291
+ [sample, age_class]
292
+ ```
293
+
294
+ Shape:
295
+
296
+ ```text
297
+ [sample, 18]
298
+ ```
299
+
300
+ ### `esa_cci_bl_fraction`, `esa_cci_nl_fraction`, and `esa_cci_gs_fraction`
301
+
302
+ The arrays contain the annual ESA CCI broadleaf, needleleaf, and grass-and-shrub
303
+ vegetation PFT fractions at the matched in-situ locations.
304
+
305
+ Dimensions:
306
+
307
+ ```text
308
+ [sample, full_year]
309
+ ```
310
+
311
+ Shape:
312
+
313
+ ```text
314
+ [sample, 29]
315
+ ```
316
+
317
+ The three PFT groups are stored as separate arrays. There is no monthly
318
+ dimension in these arrays.
319
+
320
+ ## Conceptual relationship to the paper
321
+
322
+ In the paper:
323
+
324
+ - `x_(s,t)` denotes physical and environmental conditions at location `s` and
325
+ time `t`.
326
+ - `c_k` denotes an initial forest-age state.
327
+ - `(y^P_(s,t))_k` denotes the ED simulation output corresponding to initial age
328
+ state `c_k`.
329
+ - `y_(s,t)` denotes the in-situ carbon-flux observation.
330
+ - `z_(s,t)` denotes the aggregated satellite PFT observation.
331
+ - `alpha_k` denotes the weight associated with an initial forest-age state.
332
+
333
+ In the released prediction setup, the first-year final-month ED target values
334
+ provide the one-time initial target state. The monthly input features from the
335
+ following 28 years are then used to predict the monthly ED target values over
336
+ the same 28-year period.
337
+
338
+ ## Released-array summary
339
+
340
+ ```text
341
+ ed_simulation_x:
342
+ [sample, prediction_year, month, feature]
343
+
344
+ ed_simulation_y:
345
+ [sample, age_class, full_year, month, simulation_target]
346
+
347
+ ed_simulation_pft_bl:
348
+ [sample, age_class, full_year]
349
+
350
+ ed_simulation_pft_nl:
351
+ [sample, age_class, full_year]
352
+
353
+ ed_simulation_pft_gs:
354
+ [sample, age_class, full_year]
355
+
356
+ lidar_age_weight_fraction:
357
+ [sample, age_class]
358
+
359
+ esa_cci_bl_fraction:
360
+ [sample, full_year]
361
+
362
+ esa_cci_nl_fraction:
363
+ [sample, full_year]
364
+
365
+ esa_cci_gs_fraction:
366
+ [sample, full_year]
367
+
368
+ observed_y:
369
+ [sample, full_year, month, observed_target]
370
+ ```
371
+
372
+ The following arrays are derived from `ed_simulation_y`:
373
+
374
+ ```text
375
+ initial_y:
376
+ [sample, age_class, simulation_target]
377
+
378
+ target_y:
379
+ [sample, age_class, prediction_year, month, simulation_target]
380
+ ```
metadata/feature_names.csv ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ index_start,index_end,name,unit,description
2
+ 0,0,ta_m,K,Monthly averaged air temperature
3
+ 1,1,pr,mm,Total monthly precipitation
4
+ 2,2,tsl1,K,Monthly averaged soil temp. at 0.0988 m
5
+ 3,3,tsl2,K,Monthly averaged soil temp. at 0.1952 m
6
+ 4,4,tsl3,K,Monthly averaged soil temp. at 0.3859 m
7
+ 5,5,tsl4,K,Monthly averaged soil temp. at 0.7626 m
8
+ 6,6,tsl5,K,Monthly averaged soil temp. at 1.5071 m
9
+ 7,7,tsl6,K,Monthly averaged soil temp. at 10 m
10
+ 8,31,co2,ppm,Monthly average of hourly CO? ambient
11
+ 32,32,dst,1,Disturbance rate
12
+ 33,56,hus,1,Monthly average of hourly air specific humidity
13
+ 57,80,ta_h,K,Monthly average of hourly air temperature
14
+ 81,104,rsds,W m?�,Monthly average of hourly DSR
15
+ 105,128,sfcWind,m s?�,Monthly average of hourly wind speed
16
+ 129,129,k_sat,mm yr?�,Saturated hydraulic conductivity
17
+ 130,130,s_theta,m� m?�,Saturated water content in MVG
18
+ 131,131,r_theta,m� m?�,Residual water content in MVG
19
+ 132,132,L,1,Parameter L in MVG
20
+ 133,133,n,1,Parameter n in MVG
21
+ 134,134,m,1,Parameter m in MVG
22
+ 135,135,sd,mm,Soil depth to bedrock
metadata/insitu_site_metadata.csv ADDED
@@ -0,0 +1,317 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ network,split,site_id,latitude,longitude
2
+ above,train,0,68.3541488,19.050333
3
+ above,train,1,68.74617,-133.50171
4
+ above,train,2,55.87962,-98.48081
5
+ above,train,3,53.91634,-104.69203
6
+ above,train,4,53.15,-104.1
7
+ above,train,5,64.182029,19.556539
8
+ above,train,6,55.91667,-98.96444
9
+ above,train,7,67.05,62.933333
10
+ above,train,8,61.3079,-121.2992
11
+ above,train,9,67.997239,24.209178
12
+ above,train,10,70.4696,-157.4089
13
+ above,train,11,64.8689,-111.5748
14
+ above,train,12,65.12367,-147.48756
15
+ above,train,13,65.11983333,-147.5123528
16
+ above,train,14,65.396775,-149.1214944
17
+ above,train,15,53.62889,-106.19779
18
+ above,train,16,64.11277777,19.45694444
19
+ above,train,17,68.61304,161.34143
20
+ above,train,18,62.7833,30.9333
21
+ above,train,19,54.47,-113.32
22
+ above,train,20,60.125,17.91805556
23
+ above,train,21,60.016,133.824
24
+ above,train,22,54.25392,-105.8775
25
+ above,train,23,60.9,68.7
26
+ above,train,24,60.8008,89.3507
27
+ above,train,25,52.29027778,-75.25416667
28
+ above,train,26,54.95384,-112.46698
29
+ above,train,27,56.4615278,32.9220833
30
+ above,train,28,53.98717,-105.11779
31
+ above,train,29,60.08649722,17.47950278
32
+ above,train,30,56.63583,-99.94833
33
+ above,train,31,69.5056,-148.225
34
+ above,train,32,63.153,-123.252
35
+ above,train,33,67.7549,29.690137
36
+ above,train,34,67.36238641,26.63859066
37
+ above,train,35,60.64183,23.95952
38
+ above,train,36,49.6925,-74.34206
39
+ above,train,37,70.82913889,147.4942778
40
+ above,train,38,64.1382,-51.3784
41
+ above,train,39,63.8784,-149.2536
42
+ above,train,40,72.3738231,126.4957919
43
+ above,train,41,48.27333333,106.8508333
44
+ above,train,42,60.99825,16.21727778
45
+ above,train,43,68.4865,-155.7503
46
+ above,train,44,48.2167,-82.1556
47
+ above,train,45,61.84741,24.29477
48
+ above,train,46,63.79025,-68.56005
49
+ above,train,47,70.35,-148.55
50
+ ameriflux,train,0,38.0992,-121.4993
51
+ ameriflux,train,1,38.2006,-122.0264
52
+ ameriflux,train,2,35.089,-111.762
53
+ ameriflux,train,3,42.5378,-72.1715
54
+ ameriflux,train,4,27.3836,-81.9509
55
+ ameriflux,train,5,44.1467,-89.5002
56
+ ameriflux,train,6,43.0645,-116.7486
57
+ ameriflux,train,7,33.3482,-79.2322
58
+ ameriflux,train,8,45.7624,-122.3303
59
+ ameriflux,train,9,39.0603,-78.0716
60
+ ameriflux,train,10,46.7697,-100.9154
61
+ ameriflux,train,11,40.1776,-112.4524
62
+ ameriflux,train,12,43.6405,-80.4123
63
+ ameriflux,train,13,64.8618,-163.7002
64
+ ameriflux,train,14,27.8446,-109.2977
65
+ ameriflux,train,15,40.4619,-103.0293
66
+ ameriflux,train,16,65.1198,-147.429
67
+ ameriflux,train,17,-16.498,-56.412
68
+ ameriflux,train,18,63.8811,-145.7514
69
+ ameriflux,train,19,65.3968,-148.9348
70
+ ameriflux,train,20,39.2298,-92.1167
71
+ ameriflux,train,21,34.4385,-106.2377
72
+ ameriflux,train,22,31.8173,-110.8508
73
+ ameriflux,train,23,46.7815,-117.0821
74
+ ameriflux,train,24,33.4012,-97.57
75
+ ameriflux,train,25,35.799,-76.656
76
+ ameriflux,train,26,49.8673,-125.3336
77
+ ameriflux,train,27,38.8929,-78.1395
78
+ ameriflux,train,28,-3.8344,-73.319
79
+ ameriflux,train,29,47.1617,-99.1066
80
+ ameriflux,train,30,32.5849,-106.6032
81
+ ameriflux,train,31,38.09,-109.39
82
+ ameriflux,train,32,39.0561,-95.1907
83
+ ameriflux,train,33,44.9535,-110.5391
84
+ ameriflux,train,34,28.125,-81.4362
85
+ ameriflux,train,35,41.3795,-82.5125
86
+ ameriflux,train,36,-7.9682,-38.3842
87
+ ameriflux,train,37,31.5659,-110.1344
88
+ ameriflux,train,38,28.7084,-80.7427
89
+ ameriflux,train,39,68.6058,-149.311
90
+ ameriflux,train,40,43.0896,-89.4158
91
+ ameriflux,train,41,52.7008,-83.955
92
+ ameriflux,train,42,38.7441,-92.2
93
+ ameriflux,train,43,38.8901,-76.56
94
+ ameriflux,train,44,39.0824,-96.5603
95
+ ameriflux,train,45,34.3349,-106.7442
96
+ ameriflux,train,46,44.7143,-93.0898
97
+ ameriflux,train,47,40.8608,-77.8488
98
+ ameriflux,train,48,49.1293,-122.9849
99
+ ameriflux,train,49,39.0882,-75.4372
100
+ ameriflux,train,50,65.154,-147.5026
101
+ ameriflux,train,51,34.4255,-105.8615
102
+ ameriflux,train,52,38.9488,-91.9945
103
+ ameriflux,train,53,45.2091,-68.747
104
+ ameriflux,train,54,35.8884,-106.5321
105
+ ameriflux,train,55,43.3448,-89.7117
106
+ ameriflux,train,56,32.5417,-87.8039
107
+ ameriflux,train,57,40.2759,-105.5459
108
+ ameriflux,train,58,55.1119,-122.8414
109
+ ameriflux,train,59,29.6893,-81.9934
110
+ ameriflux,train,60,36.8193,-97.8198
111
+ ameriflux,train,61,58.6658,-93.83
112
+ ameriflux,train,62,64.6963,-148.3235
113
+ ameriflux,train,63,46.2339,-89.5373
114
+ ameriflux,train,64,44.3167,-79.9333
115
+ ameriflux,train,65,50.1774,-97.8686
116
+ ameriflux,train,66,39.2167,-86.5406
117
+ fluxnet,train,0,55.6905,12.1918
118
+ fluxnet,train,1,-30.1913,120.6541
119
+ fluxnet,train,2,-28.2395,-56.1886
120
+ fluxnet,train,3,41.3966,-106.8024
121
+ fluxnet,train,4,46.0827,-89.9792
122
+ fluxnet,train,5,38.1087,-121.6531
123
+ fluxnet,train,6,47.1167,11.3175
124
+ fluxnet,train,7,-15.2588,132.3706
125
+ fluxnet,train,8,44.7171,-0.7693
126
+ fluxnet,train,9,41.8494,13.5881
127
+ fluxnet,train,10,-34.0021,140.5891
128
+ fluxnet,train,11,36.8336,-2.2523
129
+ fluxnet,train,12,-13.0769,131.1178
130
+ fluxnet,train,13,37.0979,-2.9658
131
+ fluxnet,train,14,47.8064,11.3275
132
+ fluxnet,train,15,30.4978,91.0664
133
+ fluxnet,train,16,50.8706,6.4497
134
+ fluxnet,train,17,54.7252,90.0022
135
+ fluxnet,train,18,44.3869,142.3186
136
+ fluxnet,train,19,26.7414,115.0581
137
+ fluxnet,train,20,52.1666,5.7436
138
+ fluxnet,train,21,35.2617,137.0788
139
+ fluxnet,train,22,60.8986,23.5134
140
+ fluxnet,train,23,70.8291,147.4943
141
+ fluxnet,train,24,70.4696,-157.4089
142
+ fluxnet,train,25,53.9872,-105.1178
143
+ fluxnet,train,26,45.2009,9.061
144
+ fluxnet,train,27,41.7902,111.8971
145
+ fluxnet,train,28,-36.6732,145.0294
146
+ fluxnet,train,29,36.9695,-3.4758
147
+ fluxnet,train,30,42.3804,12.0266
148
+ fluxnet,train,31,2.973,102.3062
149
+ fluxnet,train,32,56.4842,9.5872
150
+ fluxnet,train,33,-22.283,133.249
151
+ fluxnet,train,34,53.8759,12.889
152
+ fluxnet,train,35,-14.5636,132.4776
153
+ fluxnet,train,36,-34.9893,146.2907
154
+ fluxnet,train,37,46.6869,-91.1528
155
+ fluxnet,train,38,13.2829,30.4783
156
+ fluxnet,train,39,45.8126,8.6336
157
+ fluxnet,train,40,-33.4648,-66.4598
158
+ fluxnet,train,41,49.0996,13.3047
159
+ fluxnet,train,42,34.2547,-89.8735
160
+ fluxnet,train,43,44.4992,-121.6224
161
+ fluxnet,train,44,42.7102,-80.3574
162
+ fluxnet,train,45,-3.018,-54.9714
163
+ fluxnet,train,46,67.9972,24.2092
164
+ fluxnet,train,47,23.1733,112.5361
165
+ fluxnet,train,48,-23.8587,148.4746
166
+ fluxnet,train,49,42.4025,128.0958
167
+ fluxnet,train,50,68.4865,-155.7503
168
+ fluxnet,train,51,47.2858,7.7319
169
+ fluxnet,train,52,42.0467,116.2836
170
+ fluxnet,train,53,-12.5452,131.3072
171
+ fluxnet,train,54,68.613,161.3414
172
+ fluxnet,train,55,55.8796,-98.4808
173
+ fluxnet,train,56,15.4028,-15.4322
174
+ fluxnet,train,57,67.3624,26.6386
175
+ fluxnet,train,58,-15.4391,23.2525
176
+ fluxnet,train,59,37.6086,101.3269
177
+ fluxnet,train,60,51.8922,14.0337
178
+ fluxnet,train,61,5.2685,-2.6942
179
+ fluxnet,train,62,31.8214,-110.8661
180
+ fluxnet,train,63,37.37,101.18
181
+ fluxnet,train,64,44.5934,123.5092
182
+ fluxnet,train,65,-17.1507,133.3502
183
+ fluxnet,train,66,49.4944,18.5429
184
+ fluxnet,train,67,45.8059,-90.0799
185
+ icos-ww,train,0,49.035975,17.9699
186
+ icos-ww,train,1,56.4476,32.9019
187
+ icos-ww,train,2,37.914998,-3.227659
188
+ icos-ww,train,3,36.940046,-2.033208
189
+ icos-ww,train,4,64.1725,19.738
190
+ icos-ww,train,5,39.934592,-5.775881
191
+ icos-ww,train,6,53.32309,-7.641774
192
+ icos-ww,train,7,50.96381,13.48978
193
+ icos-ww,train,8,31.34504459,35.05198851
194
+ multiple,train,0,47.2864,7.7337
195
+ multiple,train,1,36.4267,-99.42
196
+ multiple,train,2,67.7549,29.61
197
+ multiple,train,3,41.3665,-106.2399
198
+ multiple,train,4,48.4764,2.7801
199
+ multiple,train,5,48.2167,-82.1556
200
+ multiple,train,6,44.493653,-0.956092
201
+ multiple,train,7,50.979868,5.631851
202
+ multiple,train,8,55.9117,-98.3822
203
+ multiple,train,9,64.182,19.5565
204
+ multiple,train,10,49.6925,-74.3421
205
+ multiple,train,11,64.1308,-51.3861
206
+ multiple,train,12,28.6086,-80.6715
207
+ multiple,train,13,55.9058,-98.5247
208
+ multiple,train,14,45.9562,11.2813
209
+ multiple,train,15,43.496438,1.237878
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211
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212
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+ multiple,train,19,61.8474,24.2948
214
+ multiple,train,20,41.6285,-83.3471
215
+ multiple,train,21,44.4526,-121.5589
216
+ multiple,train,22,54.2539,-105.8775
217
+ multiple,train,23,41.4646,-82.9962
218
+ multiple,train,24,36.9266,-2.7521
219
+ multiple,train,25,56.0737,9.3341
220
+ multiple,train,26,36.6358,-99.5975
221
+ multiple,train,27,40.0329,-105.5464
222
+ multiple,train,28,52.08656,11.22235
223
+ multiple,train,29,48.6741,7.06465
224
+ multiple,train,30,45.197755,10.741966
225
+ multiple,train,31,50.5516,4.7462
226
+ multiple,train,32,36.3566,-119.0922
227
+ multiple,train,33,67.98721472,24.24301028
228
+ multiple,train,34,46.0147,11.0458
229
+ multiple,train,35,51.0997,10.9146
230
+ multiple,train,36,43.7413,3.5957
231
+ multiple,train,37,44.5794,-121.5
232
+ multiple,train,38,36.6058,-97.4888
233
+ multiple,train,39,46.242,-89.3477
234
+ multiple,train,40,31.7438,-110.0522
235
+ multiple,train,41,31.7894,-110.8277
236
+ multiple,train,42,53.8662,13.6834
237
+ multiple,train,43,43.549649,1.106103
238
+ multiple,train,44,28.4583,-80.6709
239
+ multiple,train,45,47.1158,8.5378
240
+ multiple,train,46,38.0499,-121.765
241
+ multiple,train,47,47.3229,2.2841
242
+ multiple,train,48,49.5021,18.5369
243
+ multiple,train,49,35.5497,-98.0402
244
+ multiple,train,50,45.740481,12.750297
245
+ multiple,train,51,56.09763,13.41897
246
+ multiple,train,52,47.4783,8.3644
247
+ multiple,train,53,46.5869,11.4337
248
+ multiple,train,54,60.08649722,17.47950278
249
+ multiple,train,55,41.5545,-83.8438
250
+ multiple,train,56,5.2788,-52.9249
251
+ multiple,train,57,48.6815483,16.9463317
252
+ above,test,0,64.208888,100.463555
253
+ above,test,1,62.255,129.168
254
+ above,test,2,68.6068,-149.2958
255
+ above,test,3,60.64683333,24.356167
256
+ above,test,4,68.633333,-149.575556
257
+ above,test,5,69.14057,27.26985
258
+ above,test,6,55.5375,-112.3343
259
+ above,test,7,64.86627,-147.85553
260
+ above,test,8,71.59427,128.88782
261
+ above,test,9,68.35,18.816667
262
+ above,test,10,54.09156,-106.00526
263
+ above,test,11,69.1423,-148.8412
264
+ ameriflux,test,0,63.8784,-149.2536
265
+ ameriflux,test,1,46.6889,-119.4641
266
+ ameriflux,test,2,39.3232,-86.4131
267
+ ameriflux,test,3,38.7745,-97.5684
268
+ ameriflux,test,4,37.1088,-119.7323
269
+ ameriflux,test,5,38.0369,-121.7547
270
+ ameriflux,test,6,40.8155,-104.7456
271
+ ameriflux,test,7,45.4937,-89.5857
272
+ ameriflux,test,8,41.1651,-96.4766
273
+ ameriflux,test,9,44.0646,-71.2881
274
+ ameriflux,test,10,32.9505,-87.3933
275
+ ameriflux,test,11,35.7879,-75.9038
276
+ ameriflux,test,12,31.1948,-84.4686
277
+ ameriflux,test,13,35.689,-83.5019
278
+ ameriflux,test,14,35.4106,-99.0588
279
+ ameriflux,test,15,-54.9733,-66.7335
280
+ ameriflux,test,16,45.5089,-89.5864
281
+ ameriflux,test,17,37.3783,-80.5248
282
+ fluxnet,test,0,41.8406,-88.241
283
+ fluxnet,test,1,-37.4222,144.0944
284
+ fluxnet,test,2,51.3282,10.3678
285
+ fluxnet,test,3,-22.287,133.64
286
+ fluxnet,test,4,54.0916,-106.0053
287
+ fluxnet,test,5,-36.6499,145.5759
288
+ fluxnet,test,6,-14.1593,131.3881
289
+ fluxnet,test,7,52.2403,5.0713
290
+ fluxnet,test,8,-33.6152,150.7236
291
+ fluxnet,test,9,38.8953,-120.6328
292
+ fluxnet,test,10,53.6289,-106.1978
293
+ fluxnet,test,11,65.1237,-147.4876
294
+ fluxnet,test,12,-37.4259,145.1878
295
+ fluxnet,test,13,45.9542,11.2853
296
+ fluxnet,test,14,41.3658,-106.2397
297
+ fluxnet,test,15,-17.1175,145.6301
298
+ icos-ww,test,0,49.4437236,16.6965125
299
+ icos-ww,test,1,50.311874,4.968113
300
+ icos-ww,test,2,49.573257,15.078773
301
+ icos-ww,test,3,38.701839,-6.785881
302
+ multiple,test,0,56.4615,32.9221
303
+ multiple,test,1,31.7365,-109.9419
304
+ multiple,test,2,40.5237,14.9574
305
+ multiple,test,3,38.4133,-120.9508
306
+ multiple,test,4,40.0201,-83.0183
307
+ multiple,test,5,46.7393,-91.1663
308
+ multiple,test,6,49.0247,14.7704
309
+ multiple,test,7,50.9626,13.5651
310
+ multiple,test,8,48.8442,1.9519
311
+ multiple,test,9,45.8444,7.5781
312
+ multiple,test,10,51.11218,3.85043
313
+ multiple,test,11,50.3049,5.9981
314
+ multiple,test,12,51.0792,10.4522
315
+ multiple,test,13,56.6358,-99.9483
316
+ multiple,test,14,51.3076,4.5198
317
+ multiple,test,15,42.7068,-80.3483
metadata/normalization_statistics.npz ADDED
Binary file (3.33 kB). View file
 
metadata/pft_names.csv ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ index,abbreviation,full_name,unit,description
2
+ 0,BL,Broadleaf,1,Proportion of broadleaf vegetation
3
+ 1,NL,Needleleaf,1,Proportion of needleleaf vegetation
4
+ 2,GS,Grass and Shrub,1,Proportion of grass and shrub vegetation
metadata/repository_manifest.csv ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ path,category,status,source,required_action
2
+ README.md,Documentation,Prepared,DERE design + GeoSR-Bench organizational reference,Review and replace TODOs
3
+ LICENSE_PLACEHOLDER.md,License,Placeholder,Unknown,Verify all source licenses
4
+ CITATION.cff,Citation,Placeholder,User/project metadata,"Add DOI, date, final citation"
5
+ metadata/feature_names.csv,Metadata,Template,Current inventory / user definitions,Complete TODO fields
6
+ metadata/target_names.csv,Metadata,Template,Current inventory / user definitions,Complete TODO fields
7
+ metadata/pft_names.csv,Metadata,Template,Current inventory / user definitions,Complete TODO fields
8
+ metadata/age_classes.csv,Metadata,Template,Current inventory / user definitions,Complete TODO fields
9
+ metadata/insitu_site_metadata.csv,Metadata,Template,Current inventory / user definitions,Complete TODO fields
10
+ metadata/source_licenses.csv,Metadata,Template,Current inventory / user definitions,Complete TODO fields
11
+ metadata/dataset_schema.json,Metadata,Template,Current inventory / user definitions,Complete TODO fields
12
+ metadata/dimension_definitions.md,Metadata,Template,Current inventory / user definitions,Complete TODO fields
13
+ metadata/repository_manifest.xlsx,Metadata,Template,Current inventory / user definitions,Complete TODO fields
14
+ metadata/train_test_mask.npy,Metadata,Missing,Original global mask,Copy original file
15
+ GlobalMask/train/data.npz,Data,Missing,Simulation + ESA CCI + LiDAR age weight,Replace placeholder
16
+ GlobalMask/test/data.npz,Data,Missing,Simulation + ESA CCI + LiDAR age weight,Replace placeholder
17
+ InSituMatched/above/train/data.npz,Data,Missing,Simulation + flux tower + ESA CCI + LiDAR age weight,Replace placeholder; expected 48 samples
18
+ InSituMatched/above/test/data.npz,Data,Missing,Simulation + flux tower + ESA CCI + LiDAR age weight,Replace placeholder; expected 12 samples
19
+ InSituMatched/ameriflux/train/data.npz,Data,Missing,Simulation + flux tower + ESA CCI + LiDAR age weight,Replace placeholder; expected 67 samples
20
+ InSituMatched/ameriflux/test/data.npz,Data,Missing,Simulation + flux tower + ESA CCI + LiDAR age weight,Replace placeholder; expected 18 samples
21
+ InSituMatched/fluxnet/train/data.npz,Data,Missing,Simulation + flux tower + ESA CCI + LiDAR age weight,Replace placeholder; expected 68 samples
22
+ InSituMatched/fluxnet/test/data.npz,Data,Missing,Simulation + flux tower + ESA CCI + LiDAR age weight,Replace placeholder; expected 16 samples
23
+ InSituMatched/icos-ww/train/data.npz,Data,Missing,Simulation + flux tower + ESA CCI + LiDAR age weight,Replace placeholder; expected 9 samples
24
+ InSituMatched/icos-ww/test/data.npz,Data,Missing,Simulation + flux tower + ESA CCI + LiDAR age weight,Replace placeholder; expected 4 samples
25
+ InSituMatched/multiple/train/data.npz,Data,Missing,Simulation + flux tower + ESA CCI + LiDAR age weight,Replace placeholder; expected 58 samples
26
+ InSituMatched/multiple/test/data.npz,Data,Missing,Simulation + flux tower + ESA CCI + LiDAR age weight,Replace placeholder; expected 16 samples
27
+ viewer/global_mask/train.csv,Viewer,Header template,Generated manifest,Run create_viewer_tables.py
28
+ viewer/global_mask/test.csv,Viewer,Header template,Generated manifest,Run create_viewer_tables.py
29
+ viewer/insitu_matched/above/train.csv,Viewer,Header template,Generated manifest,Run create_viewer_tables.py
30
+ viewer/insitu_matched/above/test.csv,Viewer,Header template,Generated manifest,Run create_viewer_tables.py
31
+ viewer/insitu_matched/ameriflux/train.csv,Viewer,Header template,Generated manifest,Run create_viewer_tables.py
32
+ viewer/insitu_matched/ameriflux/test.csv,Viewer,Header template,Generated manifest,Run create_viewer_tables.py
33
+ viewer/insitu_matched/fluxnet/train.csv,Viewer,Header template,Generated manifest,Run create_viewer_tables.py
34
+ viewer/insitu_matched/fluxnet/test.csv,Viewer,Header template,Generated manifest,Run create_viewer_tables.py
35
+ viewer/insitu_matched/icos-ww/train.csv,Viewer,Header template,Generated manifest,Run create_viewer_tables.py
36
+ viewer/insitu_matched/icos-ww/test.csv,Viewer,Header template,Generated manifest,Run create_viewer_tables.py
37
+ viewer/insitu_matched/multiple/train.csv,Viewer,Header template,Generated manifest,Run create_viewer_tables.py
38
+ viewer/insitu_matched/multiple/test.csv,Viewer,Header template,Generated manifest,Run create_viewer_tables.py
39
+ scripts/load_dataset_example.py,Script,Ready,Repository preparation,Review and run as applicable
40
+ scripts/create_viewer_tables.py,Script,Ready,Repository preparation,Review and run as applicable
41
+ scripts/validate_dataset.py,Script,Ready,Repository preparation,Review and run as applicable
42
+ scripts/create_pure_mixed_subsets.py,Script,Guarded placeholder,Repository preparation,Review and run as applicable
43
+ scripts/extract_esa_cci_for_global_mask.py,Script,Guarded placeholder,Repository preparation,Review and run as applicable
44
+ scripts/match_insitu_sites.py,Script,Guarded placeholder,Repository preparation,Review and run as applicable
metadata/repository_manifest.xlsx ADDED
Binary file (6.67 kB). View file
 
metadata/source_licenses.csv ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ data_component,source_name,source_url,license_or_terms,redistribution_allowed,citation,status_notes
2
+ simulation_y,ED simulation,TODO,TODO,TODO,TODO,Verify before release
3
+ simulation_pft,ED simulation,TODO,TODO,TODO,TODO,Verify before release
4
+ input_features,Multiple sources,See feature_names.csv,TODO per feature,TODO,TODO,Verify each feature
5
+ observed_y,Flux-tower networks,TODO per network,TODO per network,TODO,TODO,Verify each network
6
+ esa_cci_pft,ESA CCI PFT,TODO,TODO,TODO,TODO,Verify product terms
7
+ age_weight,LiDAR-derived forest age,TODO,TODO,TODO,TODO,Verify source and derivation
metadata/target_names.csv ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ index,name,unit,description
2
+ 0,Height,m,Vegetation canopy height
3
+ 1,AGB,kgC m-2 yr-1,Aboveground biomass
4
+ 2,SC,kgC m-2 yr-1, Soil carbon
5
+ 3, LAI,1,Leaf area index
6
+ 4, GPP,kgC m-2 yr-1,Gross primary production
7
+ 5, NPP,kgC m-2 yr-1,Net primary production
8
+ 6, Rh,kgC m-2 yr-1,Heterotrophic respiration
9
+ 7, NEE,kgC m-2 yr-1,Net ecosystem exchange
10
+ 8, Ra,kgC m-2 yr-1,Autotrophic respiration
11
+ 9, RECO,kgC m-2 yr-1,Ecosystem respiration
metadata/train_test_mask.md ADDED
@@ -0,0 +1,65 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # `train_test_mask.npy`
2
+
3
+ `train_test_mask.npy` stores the spatial sample-selection mask used to define
4
+ the GlobalMask training and testing subsets.
5
+
6
+ ## Array definition
7
+
8
+ ```text
9
+ shape: (360, 720)
10
+ dtype: int64
11
+ dimensions: [row, column]
12
+ spatial resolution: 0.5°
13
+ ```
14
+
15
+ The mask contains 259,200 grid cells in total.
16
+
17
+ ## Mask values
18
+
19
+ | Value | Meaning | Number of grid cells |
20
+ |---|---|---:|
21
+ | `0` | Grid cell not selected | 254,975 |
22
+ | `1` | Training sample | 3,373 |
23
+ | `2` | Testing sample | 852 |
24
+
25
+ ## Selected samples
26
+
27
+ ```text
28
+ training samples: 3,373
29
+ testing samples: 852
30
+ total selected: 4,225
31
+ ```
32
+
33
+ The selected-cell counts match the sample dimensions of the released
34
+ GlobalMask arrays:
35
+
36
+ ```text
37
+ GlobalMask/train: 3,373 samples
38
+ GlobalMask/test: 852 samples
39
+ ```
40
+
41
+ ## Approximate selection principle
42
+
43
+ The mask represents a sparse global sample rather than all 0.5° grid cells.
44
+ Candidate land grid cells were spatially subsampled at approximately every
45
+ four grid cells to reduce redundancy while retaining broad geographic
46
+ coverage. The selected cells were then divided into training and testing
47
+ subsets at approximately an 80:20 ratio.
48
+
49
+ Grid cells not included in the sampled set are assigned `0`, training cells
50
+ are assigned `1`, and testing cells are assigned `2`.
51
+
52
+ ## Loading example
53
+
54
+ ```python
55
+ import numpy as np
56
+
57
+ mask = np.load("metadata/train_test_mask.npy")
58
+
59
+ train_rows, train_cols = np.where(mask == 1)
60
+ test_rows, test_cols = np.where(mask == 2)
61
+
62
+ print(mask.shape)
63
+ print(len(train_rows))
64
+ print(len(test_rows))
65
+ ```
metadata/train_test_mask.npy ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f97b7f2b5065459b519f1d9be0b846156cae446c3262962ef8d66eeffeeb536d
3
+ size 2073728
viewer/README.md ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Viewer tables
2
+
3
+ This directory contains lightweight sample-index tables for the Hugging Face
4
+ Dataset Viewer. The multidimensional scientific arrays remain in the NPZ files.
5
+
6
+ Each row identifies one sample and records:
7
+
8
+ - `sample_index` — zero-based row index in the corresponding NPZ file
9
+ - `split` — training or testing split
10
+ - `network` — in-situ network or subset; blank for GlobalMask
11
+ - `data_file` — repository path to the corresponding NPZ file
12
+
13
+ The CSV files do not duplicate or flatten the full multidimensional arrays.
viewer/global_mask/test.csv ADDED
@@ -0,0 +1,853 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ sample_index,split,network,data_file
2
+ 0,test,,GlobalMask/test/globalmask_testing_dataset.npz
3
+ 1,test,,GlobalMask/test/globalmask_testing_dataset.npz
4
+ 2,test,,GlobalMask/test/globalmask_testing_dataset.npz
5
+ 3,test,,GlobalMask/test/globalmask_testing_dataset.npz
6
+ 4,test,,GlobalMask/test/globalmask_testing_dataset.npz
7
+ 5,test,,GlobalMask/test/globalmask_testing_dataset.npz
8
+ 6,test,,GlobalMask/test/globalmask_testing_dataset.npz
9
+ 7,test,,GlobalMask/test/globalmask_testing_dataset.npz
10
+ 8,test,,GlobalMask/test/globalmask_testing_dataset.npz
11
+ 9,test,,GlobalMask/test/globalmask_testing_dataset.npz
12
+ 10,test,,GlobalMask/test/globalmask_testing_dataset.npz
13
+ 11,test,,GlobalMask/test/globalmask_testing_dataset.npz
14
+ 12,test,,GlobalMask/test/globalmask_testing_dataset.npz
15
+ 13,test,,GlobalMask/test/globalmask_testing_dataset.npz
16
+ 14,test,,GlobalMask/test/globalmask_testing_dataset.npz
17
+ 15,test,,GlobalMask/test/globalmask_testing_dataset.npz
18
+ 16,test,,GlobalMask/test/globalmask_testing_dataset.npz
19
+ 17,test,,GlobalMask/test/globalmask_testing_dataset.npz
20
+ 18,test,,GlobalMask/test/globalmask_testing_dataset.npz
21
+ 19,test,,GlobalMask/test/globalmask_testing_dataset.npz
22
+ 20,test,,GlobalMask/test/globalmask_testing_dataset.npz
23
+ 21,test,,GlobalMask/test/globalmask_testing_dataset.npz
24
+ 22,test,,GlobalMask/test/globalmask_testing_dataset.npz
25
+ 23,test,,GlobalMask/test/globalmask_testing_dataset.npz
26
+ 24,test,,GlobalMask/test/globalmask_testing_dataset.npz
27
+ 25,test,,GlobalMask/test/globalmask_testing_dataset.npz
28
+ 26,test,,GlobalMask/test/globalmask_testing_dataset.npz
29
+ 27,test,,GlobalMask/test/globalmask_testing_dataset.npz
30
+ 28,test,,GlobalMask/test/globalmask_testing_dataset.npz
31
+ 29,test,,GlobalMask/test/globalmask_testing_dataset.npz
32
+ 30,test,,GlobalMask/test/globalmask_testing_dataset.npz
33
+ 31,test,,GlobalMask/test/globalmask_testing_dataset.npz
34
+ 32,test,,GlobalMask/test/globalmask_testing_dataset.npz
35
+ 33,test,,GlobalMask/test/globalmask_testing_dataset.npz
36
+ 34,test,,GlobalMask/test/globalmask_testing_dataset.npz
37
+ 35,test,,GlobalMask/test/globalmask_testing_dataset.npz
38
+ 36,test,,GlobalMask/test/globalmask_testing_dataset.npz
39
+ 37,test,,GlobalMask/test/globalmask_testing_dataset.npz
40
+ 38,test,,GlobalMask/test/globalmask_testing_dataset.npz
41
+ 39,test,,GlobalMask/test/globalmask_testing_dataset.npz
42
+ 40,test,,GlobalMask/test/globalmask_testing_dataset.npz
43
+ 41,test,,GlobalMask/test/globalmask_testing_dataset.npz
44
+ 42,test,,GlobalMask/test/globalmask_testing_dataset.npz
45
+ 43,test,,GlobalMask/test/globalmask_testing_dataset.npz
46
+ 44,test,,GlobalMask/test/globalmask_testing_dataset.npz
47
+ 45,test,,GlobalMask/test/globalmask_testing_dataset.npz
48
+ 46,test,,GlobalMask/test/globalmask_testing_dataset.npz
49
+ 47,test,,GlobalMask/test/globalmask_testing_dataset.npz
50
+ 48,test,,GlobalMask/test/globalmask_testing_dataset.npz
51
+ 49,test,,GlobalMask/test/globalmask_testing_dataset.npz
52
+ 50,test,,GlobalMask/test/globalmask_testing_dataset.npz
53
+ 51,test,,GlobalMask/test/globalmask_testing_dataset.npz
54
+ 52,test,,GlobalMask/test/globalmask_testing_dataset.npz
55
+ 53,test,,GlobalMask/test/globalmask_testing_dataset.npz
56
+ 54,test,,GlobalMask/test/globalmask_testing_dataset.npz
57
+ 55,test,,GlobalMask/test/globalmask_testing_dataset.npz
58
+ 56,test,,GlobalMask/test/globalmask_testing_dataset.npz
59
+ 57,test,,GlobalMask/test/globalmask_testing_dataset.npz
60
+ 58,test,,GlobalMask/test/globalmask_testing_dataset.npz
61
+ 59,test,,GlobalMask/test/globalmask_testing_dataset.npz
62
+ 60,test,,GlobalMask/test/globalmask_testing_dataset.npz
63
+ 61,test,,GlobalMask/test/globalmask_testing_dataset.npz
64
+ 62,test,,GlobalMask/test/globalmask_testing_dataset.npz
65
+ 63,test,,GlobalMask/test/globalmask_testing_dataset.npz
66
+ 64,test,,GlobalMask/test/globalmask_testing_dataset.npz
67
+ 65,test,,GlobalMask/test/globalmask_testing_dataset.npz
68
+ 66,test,,GlobalMask/test/globalmask_testing_dataset.npz
69
+ 67,test,,GlobalMask/test/globalmask_testing_dataset.npz
70
+ 68,test,,GlobalMask/test/globalmask_testing_dataset.npz
71
+ 69,test,,GlobalMask/test/globalmask_testing_dataset.npz
72
+ 70,test,,GlobalMask/test/globalmask_testing_dataset.npz
73
+ 71,test,,GlobalMask/test/globalmask_testing_dataset.npz
74
+ 72,test,,GlobalMask/test/globalmask_testing_dataset.npz
75
+ 73,test,,GlobalMask/test/globalmask_testing_dataset.npz
76
+ 74,test,,GlobalMask/test/globalmask_testing_dataset.npz
77
+ 75,test,,GlobalMask/test/globalmask_testing_dataset.npz
78
+ 76,test,,GlobalMask/test/globalmask_testing_dataset.npz
79
+ 77,test,,GlobalMask/test/globalmask_testing_dataset.npz
80
+ 78,test,,GlobalMask/test/globalmask_testing_dataset.npz
81
+ 79,test,,GlobalMask/test/globalmask_testing_dataset.npz
82
+ 80,test,,GlobalMask/test/globalmask_testing_dataset.npz
83
+ 81,test,,GlobalMask/test/globalmask_testing_dataset.npz
84
+ 82,test,,GlobalMask/test/globalmask_testing_dataset.npz
85
+ 83,test,,GlobalMask/test/globalmask_testing_dataset.npz
86
+ 84,test,,GlobalMask/test/globalmask_testing_dataset.npz
87
+ 85,test,,GlobalMask/test/globalmask_testing_dataset.npz
88
+ 86,test,,GlobalMask/test/globalmask_testing_dataset.npz
89
+ 87,test,,GlobalMask/test/globalmask_testing_dataset.npz
90
+ 88,test,,GlobalMask/test/globalmask_testing_dataset.npz
91
+ 89,test,,GlobalMask/test/globalmask_testing_dataset.npz
92
+ 90,test,,GlobalMask/test/globalmask_testing_dataset.npz
93
+ 91,test,,GlobalMask/test/globalmask_testing_dataset.npz
94
+ 92,test,,GlobalMask/test/globalmask_testing_dataset.npz
95
+ 93,test,,GlobalMask/test/globalmask_testing_dataset.npz
96
+ 94,test,,GlobalMask/test/globalmask_testing_dataset.npz
97
+ 95,test,,GlobalMask/test/globalmask_testing_dataset.npz
98
+ 96,test,,GlobalMask/test/globalmask_testing_dataset.npz
99
+ 97,test,,GlobalMask/test/globalmask_testing_dataset.npz
100
+ 98,test,,GlobalMask/test/globalmask_testing_dataset.npz
101
+ 99,test,,GlobalMask/test/globalmask_testing_dataset.npz
102
+ 100,test,,GlobalMask/test/globalmask_testing_dataset.npz
103
+ 101,test,,GlobalMask/test/globalmask_testing_dataset.npz
104
+ 102,test,,GlobalMask/test/globalmask_testing_dataset.npz
105
+ 103,test,,GlobalMask/test/globalmask_testing_dataset.npz
106
+ 104,test,,GlobalMask/test/globalmask_testing_dataset.npz
107
+ 105,test,,GlobalMask/test/globalmask_testing_dataset.npz
108
+ 106,test,,GlobalMask/test/globalmask_testing_dataset.npz
109
+ 107,test,,GlobalMask/test/globalmask_testing_dataset.npz
110
+ 108,test,,GlobalMask/test/globalmask_testing_dataset.npz
111
+ 109,test,,GlobalMask/test/globalmask_testing_dataset.npz
112
+ 110,test,,GlobalMask/test/globalmask_testing_dataset.npz
113
+ 111,test,,GlobalMask/test/globalmask_testing_dataset.npz
114
+ 112,test,,GlobalMask/test/globalmask_testing_dataset.npz
115
+ 113,test,,GlobalMask/test/globalmask_testing_dataset.npz
116
+ 114,test,,GlobalMask/test/globalmask_testing_dataset.npz
117
+ 115,test,,GlobalMask/test/globalmask_testing_dataset.npz
118
+ 116,test,,GlobalMask/test/globalmask_testing_dataset.npz
119
+ 117,test,,GlobalMask/test/globalmask_testing_dataset.npz
120
+ 118,test,,GlobalMask/test/globalmask_testing_dataset.npz
121
+ 119,test,,GlobalMask/test/globalmask_testing_dataset.npz
122
+ 120,test,,GlobalMask/test/globalmask_testing_dataset.npz
123
+ 121,test,,GlobalMask/test/globalmask_testing_dataset.npz
124
+ 122,test,,GlobalMask/test/globalmask_testing_dataset.npz
125
+ 123,test,,GlobalMask/test/globalmask_testing_dataset.npz
126
+ 124,test,,GlobalMask/test/globalmask_testing_dataset.npz
127
+ 125,test,,GlobalMask/test/globalmask_testing_dataset.npz
128
+ 126,test,,GlobalMask/test/globalmask_testing_dataset.npz
129
+ 127,test,,GlobalMask/test/globalmask_testing_dataset.npz
130
+ 128,test,,GlobalMask/test/globalmask_testing_dataset.npz
131
+ 129,test,,GlobalMask/test/globalmask_testing_dataset.npz
132
+ 130,test,,GlobalMask/test/globalmask_testing_dataset.npz
133
+ 131,test,,GlobalMask/test/globalmask_testing_dataset.npz
134
+ 132,test,,GlobalMask/test/globalmask_testing_dataset.npz
135
+ 133,test,,GlobalMask/test/globalmask_testing_dataset.npz
136
+ 134,test,,GlobalMask/test/globalmask_testing_dataset.npz
137
+ 135,test,,GlobalMask/test/globalmask_testing_dataset.npz
138
+ 136,test,,GlobalMask/test/globalmask_testing_dataset.npz
139
+ 137,test,,GlobalMask/test/globalmask_testing_dataset.npz
140
+ 138,test,,GlobalMask/test/globalmask_testing_dataset.npz
141
+ 139,test,,GlobalMask/test/globalmask_testing_dataset.npz
142
+ 140,test,,GlobalMask/test/globalmask_testing_dataset.npz
143
+ 141,test,,GlobalMask/test/globalmask_testing_dataset.npz
144
+ 142,test,,GlobalMask/test/globalmask_testing_dataset.npz
145
+ 143,test,,GlobalMask/test/globalmask_testing_dataset.npz
146
+ 144,test,,GlobalMask/test/globalmask_testing_dataset.npz
147
+ 145,test,,GlobalMask/test/globalmask_testing_dataset.npz
148
+ 146,test,,GlobalMask/test/globalmask_testing_dataset.npz
149
+ 147,test,,GlobalMask/test/globalmask_testing_dataset.npz
150
+ 148,test,,GlobalMask/test/globalmask_testing_dataset.npz
151
+ 149,test,,GlobalMask/test/globalmask_testing_dataset.npz
152
+ 150,test,,GlobalMask/test/globalmask_testing_dataset.npz
153
+ 151,test,,GlobalMask/test/globalmask_testing_dataset.npz
154
+ 152,test,,GlobalMask/test/globalmask_testing_dataset.npz
155
+ 153,test,,GlobalMask/test/globalmask_testing_dataset.npz
156
+ 154,test,,GlobalMask/test/globalmask_testing_dataset.npz
157
+ 155,test,,GlobalMask/test/globalmask_testing_dataset.npz
158
+ 156,test,,GlobalMask/test/globalmask_testing_dataset.npz
159
+ 157,test,,GlobalMask/test/globalmask_testing_dataset.npz
160
+ 158,test,,GlobalMask/test/globalmask_testing_dataset.npz
161
+ 159,test,,GlobalMask/test/globalmask_testing_dataset.npz
162
+ 160,test,,GlobalMask/test/globalmask_testing_dataset.npz
163
+ 161,test,,GlobalMask/test/globalmask_testing_dataset.npz
164
+ 162,test,,GlobalMask/test/globalmask_testing_dataset.npz
165
+ 163,test,,GlobalMask/test/globalmask_testing_dataset.npz
166
+ 164,test,,GlobalMask/test/globalmask_testing_dataset.npz
167
+ 165,test,,GlobalMask/test/globalmask_testing_dataset.npz
168
+ 166,test,,GlobalMask/test/globalmask_testing_dataset.npz
169
+ 167,test,,GlobalMask/test/globalmask_testing_dataset.npz
170
+ 168,test,,GlobalMask/test/globalmask_testing_dataset.npz
171
+ 169,test,,GlobalMask/test/globalmask_testing_dataset.npz
172
+ 170,test,,GlobalMask/test/globalmask_testing_dataset.npz
173
+ 171,test,,GlobalMask/test/globalmask_testing_dataset.npz
174
+ 172,test,,GlobalMask/test/globalmask_testing_dataset.npz
175
+ 173,test,,GlobalMask/test/globalmask_testing_dataset.npz
176
+ 174,test,,GlobalMask/test/globalmask_testing_dataset.npz
177
+ 175,test,,GlobalMask/test/globalmask_testing_dataset.npz
178
+ 176,test,,GlobalMask/test/globalmask_testing_dataset.npz
179
+ 177,test,,GlobalMask/test/globalmask_testing_dataset.npz
180
+ 178,test,,GlobalMask/test/globalmask_testing_dataset.npz
181
+ 179,test,,GlobalMask/test/globalmask_testing_dataset.npz
182
+ 180,test,,GlobalMask/test/globalmask_testing_dataset.npz
183
+ 181,test,,GlobalMask/test/globalmask_testing_dataset.npz
184
+ 182,test,,GlobalMask/test/globalmask_testing_dataset.npz
185
+ 183,test,,GlobalMask/test/globalmask_testing_dataset.npz
186
+ 184,test,,GlobalMask/test/globalmask_testing_dataset.npz
187
+ 185,test,,GlobalMask/test/globalmask_testing_dataset.npz
188
+ 186,test,,GlobalMask/test/globalmask_testing_dataset.npz
189
+ 187,test,,GlobalMask/test/globalmask_testing_dataset.npz
190
+ 188,test,,GlobalMask/test/globalmask_testing_dataset.npz
191
+ 189,test,,GlobalMask/test/globalmask_testing_dataset.npz
192
+ 190,test,,GlobalMask/test/globalmask_testing_dataset.npz
193
+ 191,test,,GlobalMask/test/globalmask_testing_dataset.npz
194
+ 192,test,,GlobalMask/test/globalmask_testing_dataset.npz
195
+ 193,test,,GlobalMask/test/globalmask_testing_dataset.npz
196
+ 194,test,,GlobalMask/test/globalmask_testing_dataset.npz
197
+ 195,test,,GlobalMask/test/globalmask_testing_dataset.npz
198
+ 196,test,,GlobalMask/test/globalmask_testing_dataset.npz
199
+ 197,test,,GlobalMask/test/globalmask_testing_dataset.npz
200
+ 198,test,,GlobalMask/test/globalmask_testing_dataset.npz
201
+ 199,test,,GlobalMask/test/globalmask_testing_dataset.npz
202
+ 200,test,,GlobalMask/test/globalmask_testing_dataset.npz
203
+ 201,test,,GlobalMask/test/globalmask_testing_dataset.npz
204
+ 202,test,,GlobalMask/test/globalmask_testing_dataset.npz
205
+ 203,test,,GlobalMask/test/globalmask_testing_dataset.npz
206
+ 204,test,,GlobalMask/test/globalmask_testing_dataset.npz
207
+ 205,test,,GlobalMask/test/globalmask_testing_dataset.npz
208
+ 206,test,,GlobalMask/test/globalmask_testing_dataset.npz
209
+ 207,test,,GlobalMask/test/globalmask_testing_dataset.npz
210
+ 208,test,,GlobalMask/test/globalmask_testing_dataset.npz
211
+ 209,test,,GlobalMask/test/globalmask_testing_dataset.npz
212
+ 210,test,,GlobalMask/test/globalmask_testing_dataset.npz
213
+ 211,test,,GlobalMask/test/globalmask_testing_dataset.npz
214
+ 212,test,,GlobalMask/test/globalmask_testing_dataset.npz
215
+ 213,test,,GlobalMask/test/globalmask_testing_dataset.npz
216
+ 214,test,,GlobalMask/test/globalmask_testing_dataset.npz
217
+ 215,test,,GlobalMask/test/globalmask_testing_dataset.npz
218
+ 216,test,,GlobalMask/test/globalmask_testing_dataset.npz
219
+ 217,test,,GlobalMask/test/globalmask_testing_dataset.npz
220
+ 218,test,,GlobalMask/test/globalmask_testing_dataset.npz
221
+ 219,test,,GlobalMask/test/globalmask_testing_dataset.npz
222
+ 220,test,,GlobalMask/test/globalmask_testing_dataset.npz
223
+ 221,test,,GlobalMask/test/globalmask_testing_dataset.npz
224
+ 222,test,,GlobalMask/test/globalmask_testing_dataset.npz
225
+ 223,test,,GlobalMask/test/globalmask_testing_dataset.npz
226
+ 224,test,,GlobalMask/test/globalmask_testing_dataset.npz
227
+ 225,test,,GlobalMask/test/globalmask_testing_dataset.npz
228
+ 226,test,,GlobalMask/test/globalmask_testing_dataset.npz
229
+ 227,test,,GlobalMask/test/globalmask_testing_dataset.npz
230
+ 228,test,,GlobalMask/test/globalmask_testing_dataset.npz
231
+ 229,test,,GlobalMask/test/globalmask_testing_dataset.npz
232
+ 230,test,,GlobalMask/test/globalmask_testing_dataset.npz
233
+ 231,test,,GlobalMask/test/globalmask_testing_dataset.npz
234
+ 232,test,,GlobalMask/test/globalmask_testing_dataset.npz
235
+ 233,test,,GlobalMask/test/globalmask_testing_dataset.npz
236
+ 234,test,,GlobalMask/test/globalmask_testing_dataset.npz
237
+ 235,test,,GlobalMask/test/globalmask_testing_dataset.npz
238
+ 236,test,,GlobalMask/test/globalmask_testing_dataset.npz
239
+ 237,test,,GlobalMask/test/globalmask_testing_dataset.npz
240
+ 238,test,,GlobalMask/test/globalmask_testing_dataset.npz
241
+ 239,test,,GlobalMask/test/globalmask_testing_dataset.npz
242
+ 240,test,,GlobalMask/test/globalmask_testing_dataset.npz
243
+ 241,test,,GlobalMask/test/globalmask_testing_dataset.npz
244
+ 242,test,,GlobalMask/test/globalmask_testing_dataset.npz
245
+ 243,test,,GlobalMask/test/globalmask_testing_dataset.npz
246
+ 244,test,,GlobalMask/test/globalmask_testing_dataset.npz
247
+ 245,test,,GlobalMask/test/globalmask_testing_dataset.npz
248
+ 246,test,,GlobalMask/test/globalmask_testing_dataset.npz
249
+ 247,test,,GlobalMask/test/globalmask_testing_dataset.npz
250
+ 248,test,,GlobalMask/test/globalmask_testing_dataset.npz
251
+ 249,test,,GlobalMask/test/globalmask_testing_dataset.npz
252
+ 250,test,,GlobalMask/test/globalmask_testing_dataset.npz
253
+ 251,test,,GlobalMask/test/globalmask_testing_dataset.npz
254
+ 252,test,,GlobalMask/test/globalmask_testing_dataset.npz
255
+ 253,test,,GlobalMask/test/globalmask_testing_dataset.npz
256
+ 254,test,,GlobalMask/test/globalmask_testing_dataset.npz
257
+ 255,test,,GlobalMask/test/globalmask_testing_dataset.npz
258
+ 256,test,,GlobalMask/test/globalmask_testing_dataset.npz
259
+ 257,test,,GlobalMask/test/globalmask_testing_dataset.npz
260
+ 258,test,,GlobalMask/test/globalmask_testing_dataset.npz
261
+ 259,test,,GlobalMask/test/globalmask_testing_dataset.npz
262
+ 260,test,,GlobalMask/test/globalmask_testing_dataset.npz
263
+ 261,test,,GlobalMask/test/globalmask_testing_dataset.npz
264
+ 262,test,,GlobalMask/test/globalmask_testing_dataset.npz
265
+ 263,test,,GlobalMask/test/globalmask_testing_dataset.npz
266
+ 264,test,,GlobalMask/test/globalmask_testing_dataset.npz
267
+ 265,test,,GlobalMask/test/globalmask_testing_dataset.npz
268
+ 266,test,,GlobalMask/test/globalmask_testing_dataset.npz
269
+ 267,test,,GlobalMask/test/globalmask_testing_dataset.npz
270
+ 268,test,,GlobalMask/test/globalmask_testing_dataset.npz
271
+ 269,test,,GlobalMask/test/globalmask_testing_dataset.npz
272
+ 270,test,,GlobalMask/test/globalmask_testing_dataset.npz
273
+ 271,test,,GlobalMask/test/globalmask_testing_dataset.npz
274
+ 272,test,,GlobalMask/test/globalmask_testing_dataset.npz
275
+ 273,test,,GlobalMask/test/globalmask_testing_dataset.npz
276
+ 274,test,,GlobalMask/test/globalmask_testing_dataset.npz
277
+ 275,test,,GlobalMask/test/globalmask_testing_dataset.npz
278
+ 276,test,,GlobalMask/test/globalmask_testing_dataset.npz
279
+ 277,test,,GlobalMask/test/globalmask_testing_dataset.npz
280
+ 278,test,,GlobalMask/test/globalmask_testing_dataset.npz
281
+ 279,test,,GlobalMask/test/globalmask_testing_dataset.npz
282
+ 280,test,,GlobalMask/test/globalmask_testing_dataset.npz
283
+ 281,test,,GlobalMask/test/globalmask_testing_dataset.npz
284
+ 282,test,,GlobalMask/test/globalmask_testing_dataset.npz
285
+ 283,test,,GlobalMask/test/globalmask_testing_dataset.npz
286
+ 284,test,,GlobalMask/test/globalmask_testing_dataset.npz
287
+ 285,test,,GlobalMask/test/globalmask_testing_dataset.npz
288
+ 286,test,,GlobalMask/test/globalmask_testing_dataset.npz
289
+ 287,test,,GlobalMask/test/globalmask_testing_dataset.npz
290
+ 288,test,,GlobalMask/test/globalmask_testing_dataset.npz
291
+ 289,test,,GlobalMask/test/globalmask_testing_dataset.npz
292
+ 290,test,,GlobalMask/test/globalmask_testing_dataset.npz
293
+ 291,test,,GlobalMask/test/globalmask_testing_dataset.npz
294
+ 292,test,,GlobalMask/test/globalmask_testing_dataset.npz
295
+ 293,test,,GlobalMask/test/globalmask_testing_dataset.npz
296
+ 294,test,,GlobalMask/test/globalmask_testing_dataset.npz
297
+ 295,test,,GlobalMask/test/globalmask_testing_dataset.npz
298
+ 296,test,,GlobalMask/test/globalmask_testing_dataset.npz
299
+ 297,test,,GlobalMask/test/globalmask_testing_dataset.npz
300
+ 298,test,,GlobalMask/test/globalmask_testing_dataset.npz
301
+ 299,test,,GlobalMask/test/globalmask_testing_dataset.npz
302
+ 300,test,,GlobalMask/test/globalmask_testing_dataset.npz
303
+ 301,test,,GlobalMask/test/globalmask_testing_dataset.npz
304
+ 302,test,,GlobalMask/test/globalmask_testing_dataset.npz
305
+ 303,test,,GlobalMask/test/globalmask_testing_dataset.npz
306
+ 304,test,,GlobalMask/test/globalmask_testing_dataset.npz
307
+ 305,test,,GlobalMask/test/globalmask_testing_dataset.npz
308
+ 306,test,,GlobalMask/test/globalmask_testing_dataset.npz
309
+ 307,test,,GlobalMask/test/globalmask_testing_dataset.npz
310
+ 308,test,,GlobalMask/test/globalmask_testing_dataset.npz
311
+ 309,test,,GlobalMask/test/globalmask_testing_dataset.npz
312
+ 310,test,,GlobalMask/test/globalmask_testing_dataset.npz
313
+ 311,test,,GlobalMask/test/globalmask_testing_dataset.npz
314
+ 312,test,,GlobalMask/test/globalmask_testing_dataset.npz
315
+ 313,test,,GlobalMask/test/globalmask_testing_dataset.npz
316
+ 314,test,,GlobalMask/test/globalmask_testing_dataset.npz
317
+ 315,test,,GlobalMask/test/globalmask_testing_dataset.npz
318
+ 316,test,,GlobalMask/test/globalmask_testing_dataset.npz
319
+ 317,test,,GlobalMask/test/globalmask_testing_dataset.npz
320
+ 318,test,,GlobalMask/test/globalmask_testing_dataset.npz
321
+ 319,test,,GlobalMask/test/globalmask_testing_dataset.npz
322
+ 320,test,,GlobalMask/test/globalmask_testing_dataset.npz
323
+ 321,test,,GlobalMask/test/globalmask_testing_dataset.npz
324
+ 322,test,,GlobalMask/test/globalmask_testing_dataset.npz
325
+ 323,test,,GlobalMask/test/globalmask_testing_dataset.npz
326
+ 324,test,,GlobalMask/test/globalmask_testing_dataset.npz
327
+ 325,test,,GlobalMask/test/globalmask_testing_dataset.npz
328
+ 326,test,,GlobalMask/test/globalmask_testing_dataset.npz
329
+ 327,test,,GlobalMask/test/globalmask_testing_dataset.npz
330
+ 328,test,,GlobalMask/test/globalmask_testing_dataset.npz
331
+ 329,test,,GlobalMask/test/globalmask_testing_dataset.npz
332
+ 330,test,,GlobalMask/test/globalmask_testing_dataset.npz
333
+ 331,test,,GlobalMask/test/globalmask_testing_dataset.npz
334
+ 332,test,,GlobalMask/test/globalmask_testing_dataset.npz
335
+ 333,test,,GlobalMask/test/globalmask_testing_dataset.npz
336
+ 334,test,,GlobalMask/test/globalmask_testing_dataset.npz
337
+ 335,test,,GlobalMask/test/globalmask_testing_dataset.npz
338
+ 336,test,,GlobalMask/test/globalmask_testing_dataset.npz
339
+ 337,test,,GlobalMask/test/globalmask_testing_dataset.npz
340
+ 338,test,,GlobalMask/test/globalmask_testing_dataset.npz
341
+ 339,test,,GlobalMask/test/globalmask_testing_dataset.npz
342
+ 340,test,,GlobalMask/test/globalmask_testing_dataset.npz
343
+ 341,test,,GlobalMask/test/globalmask_testing_dataset.npz
344
+ 342,test,,GlobalMask/test/globalmask_testing_dataset.npz
345
+ 343,test,,GlobalMask/test/globalmask_testing_dataset.npz
346
+ 344,test,,GlobalMask/test/globalmask_testing_dataset.npz
347
+ 345,test,,GlobalMask/test/globalmask_testing_dataset.npz
348
+ 346,test,,GlobalMask/test/globalmask_testing_dataset.npz
349
+ 347,test,,GlobalMask/test/globalmask_testing_dataset.npz
350
+ 348,test,,GlobalMask/test/globalmask_testing_dataset.npz
351
+ 349,test,,GlobalMask/test/globalmask_testing_dataset.npz
352
+ 350,test,,GlobalMask/test/globalmask_testing_dataset.npz
353
+ 351,test,,GlobalMask/test/globalmask_testing_dataset.npz
354
+ 352,test,,GlobalMask/test/globalmask_testing_dataset.npz
355
+ 353,test,,GlobalMask/test/globalmask_testing_dataset.npz
356
+ 354,test,,GlobalMask/test/globalmask_testing_dataset.npz
357
+ 355,test,,GlobalMask/test/globalmask_testing_dataset.npz
358
+ 356,test,,GlobalMask/test/globalmask_testing_dataset.npz
359
+ 357,test,,GlobalMask/test/globalmask_testing_dataset.npz
360
+ 358,test,,GlobalMask/test/globalmask_testing_dataset.npz
361
+ 359,test,,GlobalMask/test/globalmask_testing_dataset.npz
362
+ 360,test,,GlobalMask/test/globalmask_testing_dataset.npz
363
+ 361,test,,GlobalMask/test/globalmask_testing_dataset.npz
364
+ 362,test,,GlobalMask/test/globalmask_testing_dataset.npz
365
+ 363,test,,GlobalMask/test/globalmask_testing_dataset.npz
366
+ 364,test,,GlobalMask/test/globalmask_testing_dataset.npz
367
+ 365,test,,GlobalMask/test/globalmask_testing_dataset.npz
368
+ 366,test,,GlobalMask/test/globalmask_testing_dataset.npz
369
+ 367,test,,GlobalMask/test/globalmask_testing_dataset.npz
370
+ 368,test,,GlobalMask/test/globalmask_testing_dataset.npz
371
+ 369,test,,GlobalMask/test/globalmask_testing_dataset.npz
372
+ 370,test,,GlobalMask/test/globalmask_testing_dataset.npz
373
+ 371,test,,GlobalMask/test/globalmask_testing_dataset.npz
374
+ 372,test,,GlobalMask/test/globalmask_testing_dataset.npz
375
+ 373,test,,GlobalMask/test/globalmask_testing_dataset.npz
376
+ 374,test,,GlobalMask/test/globalmask_testing_dataset.npz
377
+ 375,test,,GlobalMask/test/globalmask_testing_dataset.npz
378
+ 376,test,,GlobalMask/test/globalmask_testing_dataset.npz
379
+ 377,test,,GlobalMask/test/globalmask_testing_dataset.npz
380
+ 378,test,,GlobalMask/test/globalmask_testing_dataset.npz
381
+ 379,test,,GlobalMask/test/globalmask_testing_dataset.npz
382
+ 380,test,,GlobalMask/test/globalmask_testing_dataset.npz
383
+ 381,test,,GlobalMask/test/globalmask_testing_dataset.npz
384
+ 382,test,,GlobalMask/test/globalmask_testing_dataset.npz
385
+ 383,test,,GlobalMask/test/globalmask_testing_dataset.npz
386
+ 384,test,,GlobalMask/test/globalmask_testing_dataset.npz
387
+ 385,test,,GlobalMask/test/globalmask_testing_dataset.npz
388
+ 386,test,,GlobalMask/test/globalmask_testing_dataset.npz
389
+ 387,test,,GlobalMask/test/globalmask_testing_dataset.npz
390
+ 388,test,,GlobalMask/test/globalmask_testing_dataset.npz
391
+ 389,test,,GlobalMask/test/globalmask_testing_dataset.npz
392
+ 390,test,,GlobalMask/test/globalmask_testing_dataset.npz
393
+ 391,test,,GlobalMask/test/globalmask_testing_dataset.npz
394
+ 392,test,,GlobalMask/test/globalmask_testing_dataset.npz
395
+ 393,test,,GlobalMask/test/globalmask_testing_dataset.npz
396
+ 394,test,,GlobalMask/test/globalmask_testing_dataset.npz
397
+ 395,test,,GlobalMask/test/globalmask_testing_dataset.npz
398
+ 396,test,,GlobalMask/test/globalmask_testing_dataset.npz
399
+ 397,test,,GlobalMask/test/globalmask_testing_dataset.npz
400
+ 398,test,,GlobalMask/test/globalmask_testing_dataset.npz
401
+ 399,test,,GlobalMask/test/globalmask_testing_dataset.npz
402
+ 400,test,,GlobalMask/test/globalmask_testing_dataset.npz
403
+ 401,test,,GlobalMask/test/globalmask_testing_dataset.npz
404
+ 402,test,,GlobalMask/test/globalmask_testing_dataset.npz
405
+ 403,test,,GlobalMask/test/globalmask_testing_dataset.npz
406
+ 404,test,,GlobalMask/test/globalmask_testing_dataset.npz
407
+ 405,test,,GlobalMask/test/globalmask_testing_dataset.npz
408
+ 406,test,,GlobalMask/test/globalmask_testing_dataset.npz
409
+ 407,test,,GlobalMask/test/globalmask_testing_dataset.npz
410
+ 408,test,,GlobalMask/test/globalmask_testing_dataset.npz
411
+ 409,test,,GlobalMask/test/globalmask_testing_dataset.npz
412
+ 410,test,,GlobalMask/test/globalmask_testing_dataset.npz
413
+ 411,test,,GlobalMask/test/globalmask_testing_dataset.npz
414
+ 412,test,,GlobalMask/test/globalmask_testing_dataset.npz
415
+ 413,test,,GlobalMask/test/globalmask_testing_dataset.npz
416
+ 414,test,,GlobalMask/test/globalmask_testing_dataset.npz
417
+ 415,test,,GlobalMask/test/globalmask_testing_dataset.npz
418
+ 416,test,,GlobalMask/test/globalmask_testing_dataset.npz
419
+ 417,test,,GlobalMask/test/globalmask_testing_dataset.npz
420
+ 418,test,,GlobalMask/test/globalmask_testing_dataset.npz
421
+ 419,test,,GlobalMask/test/globalmask_testing_dataset.npz
422
+ 420,test,,GlobalMask/test/globalmask_testing_dataset.npz
423
+ 421,test,,GlobalMask/test/globalmask_testing_dataset.npz
424
+ 422,test,,GlobalMask/test/globalmask_testing_dataset.npz
425
+ 423,test,,GlobalMask/test/globalmask_testing_dataset.npz
426
+ 424,test,,GlobalMask/test/globalmask_testing_dataset.npz
427
+ 425,test,,GlobalMask/test/globalmask_testing_dataset.npz
428
+ 426,test,,GlobalMask/test/globalmask_testing_dataset.npz
429
+ 427,test,,GlobalMask/test/globalmask_testing_dataset.npz
430
+ 428,test,,GlobalMask/test/globalmask_testing_dataset.npz
431
+ 429,test,,GlobalMask/test/globalmask_testing_dataset.npz
432
+ 430,test,,GlobalMask/test/globalmask_testing_dataset.npz
433
+ 431,test,,GlobalMask/test/globalmask_testing_dataset.npz
434
+ 432,test,,GlobalMask/test/globalmask_testing_dataset.npz
435
+ 433,test,,GlobalMask/test/globalmask_testing_dataset.npz
436
+ 434,test,,GlobalMask/test/globalmask_testing_dataset.npz
437
+ 435,test,,GlobalMask/test/globalmask_testing_dataset.npz
438
+ 436,test,,GlobalMask/test/globalmask_testing_dataset.npz
439
+ 437,test,,GlobalMask/test/globalmask_testing_dataset.npz
440
+ 438,test,,GlobalMask/test/globalmask_testing_dataset.npz
441
+ 439,test,,GlobalMask/test/globalmask_testing_dataset.npz
442
+ 440,test,,GlobalMask/test/globalmask_testing_dataset.npz
443
+ 441,test,,GlobalMask/test/globalmask_testing_dataset.npz
444
+ 442,test,,GlobalMask/test/globalmask_testing_dataset.npz
445
+ 443,test,,GlobalMask/test/globalmask_testing_dataset.npz
446
+ 444,test,,GlobalMask/test/globalmask_testing_dataset.npz
447
+ 445,test,,GlobalMask/test/globalmask_testing_dataset.npz
448
+ 446,test,,GlobalMask/test/globalmask_testing_dataset.npz
449
+ 447,test,,GlobalMask/test/globalmask_testing_dataset.npz
450
+ 448,test,,GlobalMask/test/globalmask_testing_dataset.npz
451
+ 449,test,,GlobalMask/test/globalmask_testing_dataset.npz
452
+ 450,test,,GlobalMask/test/globalmask_testing_dataset.npz
453
+ 451,test,,GlobalMask/test/globalmask_testing_dataset.npz
454
+ 452,test,,GlobalMask/test/globalmask_testing_dataset.npz
455
+ 453,test,,GlobalMask/test/globalmask_testing_dataset.npz
456
+ 454,test,,GlobalMask/test/globalmask_testing_dataset.npz
457
+ 455,test,,GlobalMask/test/globalmask_testing_dataset.npz
458
+ 456,test,,GlobalMask/test/globalmask_testing_dataset.npz
459
+ 457,test,,GlobalMask/test/globalmask_testing_dataset.npz
460
+ 458,test,,GlobalMask/test/globalmask_testing_dataset.npz
461
+ 459,test,,GlobalMask/test/globalmask_testing_dataset.npz
462
+ 460,test,,GlobalMask/test/globalmask_testing_dataset.npz
463
+ 461,test,,GlobalMask/test/globalmask_testing_dataset.npz
464
+ 462,test,,GlobalMask/test/globalmask_testing_dataset.npz
465
+ 463,test,,GlobalMask/test/globalmask_testing_dataset.npz
466
+ 464,test,,GlobalMask/test/globalmask_testing_dataset.npz
467
+ 465,test,,GlobalMask/test/globalmask_testing_dataset.npz
468
+ 466,test,,GlobalMask/test/globalmask_testing_dataset.npz
469
+ 467,test,,GlobalMask/test/globalmask_testing_dataset.npz
470
+ 468,test,,GlobalMask/test/globalmask_testing_dataset.npz
471
+ 469,test,,GlobalMask/test/globalmask_testing_dataset.npz
472
+ 470,test,,GlobalMask/test/globalmask_testing_dataset.npz
473
+ 471,test,,GlobalMask/test/globalmask_testing_dataset.npz
474
+ 472,test,,GlobalMask/test/globalmask_testing_dataset.npz
475
+ 473,test,,GlobalMask/test/globalmask_testing_dataset.npz
476
+ 474,test,,GlobalMask/test/globalmask_testing_dataset.npz
477
+ 475,test,,GlobalMask/test/globalmask_testing_dataset.npz
478
+ 476,test,,GlobalMask/test/globalmask_testing_dataset.npz
479
+ 477,test,,GlobalMask/test/globalmask_testing_dataset.npz
480
+ 478,test,,GlobalMask/test/globalmask_testing_dataset.npz
481
+ 479,test,,GlobalMask/test/globalmask_testing_dataset.npz
482
+ 480,test,,GlobalMask/test/globalmask_testing_dataset.npz
483
+ 481,test,,GlobalMask/test/globalmask_testing_dataset.npz
484
+ 482,test,,GlobalMask/test/globalmask_testing_dataset.npz
485
+ 483,test,,GlobalMask/test/globalmask_testing_dataset.npz
486
+ 484,test,,GlobalMask/test/globalmask_testing_dataset.npz
487
+ 485,test,,GlobalMask/test/globalmask_testing_dataset.npz
488
+ 486,test,,GlobalMask/test/globalmask_testing_dataset.npz
489
+ 487,test,,GlobalMask/test/globalmask_testing_dataset.npz
490
+ 488,test,,GlobalMask/test/globalmask_testing_dataset.npz
491
+ 489,test,,GlobalMask/test/globalmask_testing_dataset.npz
492
+ 490,test,,GlobalMask/test/globalmask_testing_dataset.npz
493
+ 491,test,,GlobalMask/test/globalmask_testing_dataset.npz
494
+ 492,test,,GlobalMask/test/globalmask_testing_dataset.npz
495
+ 493,test,,GlobalMask/test/globalmask_testing_dataset.npz
496
+ 494,test,,GlobalMask/test/globalmask_testing_dataset.npz
497
+ 495,test,,GlobalMask/test/globalmask_testing_dataset.npz
498
+ 496,test,,GlobalMask/test/globalmask_testing_dataset.npz
499
+ 497,test,,GlobalMask/test/globalmask_testing_dataset.npz
500
+ 498,test,,GlobalMask/test/globalmask_testing_dataset.npz
501
+ 499,test,,GlobalMask/test/globalmask_testing_dataset.npz
502
+ 500,test,,GlobalMask/test/globalmask_testing_dataset.npz
503
+ 501,test,,GlobalMask/test/globalmask_testing_dataset.npz
504
+ 502,test,,GlobalMask/test/globalmask_testing_dataset.npz
505
+ 503,test,,GlobalMask/test/globalmask_testing_dataset.npz
506
+ 504,test,,GlobalMask/test/globalmask_testing_dataset.npz
507
+ 505,test,,GlobalMask/test/globalmask_testing_dataset.npz
508
+ 506,test,,GlobalMask/test/globalmask_testing_dataset.npz
509
+ 507,test,,GlobalMask/test/globalmask_testing_dataset.npz
510
+ 508,test,,GlobalMask/test/globalmask_testing_dataset.npz
511
+ 509,test,,GlobalMask/test/globalmask_testing_dataset.npz
512
+ 510,test,,GlobalMask/test/globalmask_testing_dataset.npz
513
+ 511,test,,GlobalMask/test/globalmask_testing_dataset.npz
514
+ 512,test,,GlobalMask/test/globalmask_testing_dataset.npz
515
+ 513,test,,GlobalMask/test/globalmask_testing_dataset.npz
516
+ 514,test,,GlobalMask/test/globalmask_testing_dataset.npz
517
+ 515,test,,GlobalMask/test/globalmask_testing_dataset.npz
518
+ 516,test,,GlobalMask/test/globalmask_testing_dataset.npz
519
+ 517,test,,GlobalMask/test/globalmask_testing_dataset.npz
520
+ 518,test,,GlobalMask/test/globalmask_testing_dataset.npz
521
+ 519,test,,GlobalMask/test/globalmask_testing_dataset.npz
522
+ 520,test,,GlobalMask/test/globalmask_testing_dataset.npz
523
+ 521,test,,GlobalMask/test/globalmask_testing_dataset.npz
524
+ 522,test,,GlobalMask/test/globalmask_testing_dataset.npz
525
+ 523,test,,GlobalMask/test/globalmask_testing_dataset.npz
526
+ 524,test,,GlobalMask/test/globalmask_testing_dataset.npz
527
+ 525,test,,GlobalMask/test/globalmask_testing_dataset.npz
528
+ 526,test,,GlobalMask/test/globalmask_testing_dataset.npz
529
+ 527,test,,GlobalMask/test/globalmask_testing_dataset.npz
530
+ 528,test,,GlobalMask/test/globalmask_testing_dataset.npz
531
+ 529,test,,GlobalMask/test/globalmask_testing_dataset.npz
532
+ 530,test,,GlobalMask/test/globalmask_testing_dataset.npz
533
+ 531,test,,GlobalMask/test/globalmask_testing_dataset.npz
534
+ 532,test,,GlobalMask/test/globalmask_testing_dataset.npz
535
+ 533,test,,GlobalMask/test/globalmask_testing_dataset.npz
536
+ 534,test,,GlobalMask/test/globalmask_testing_dataset.npz
537
+ 535,test,,GlobalMask/test/globalmask_testing_dataset.npz
538
+ 536,test,,GlobalMask/test/globalmask_testing_dataset.npz
539
+ 537,test,,GlobalMask/test/globalmask_testing_dataset.npz
540
+ 538,test,,GlobalMask/test/globalmask_testing_dataset.npz
541
+ 539,test,,GlobalMask/test/globalmask_testing_dataset.npz
542
+ 540,test,,GlobalMask/test/globalmask_testing_dataset.npz
543
+ 541,test,,GlobalMask/test/globalmask_testing_dataset.npz
544
+ 542,test,,GlobalMask/test/globalmask_testing_dataset.npz
545
+ 543,test,,GlobalMask/test/globalmask_testing_dataset.npz
546
+ 544,test,,GlobalMask/test/globalmask_testing_dataset.npz
547
+ 545,test,,GlobalMask/test/globalmask_testing_dataset.npz
548
+ 546,test,,GlobalMask/test/globalmask_testing_dataset.npz
549
+ 547,test,,GlobalMask/test/globalmask_testing_dataset.npz
550
+ 548,test,,GlobalMask/test/globalmask_testing_dataset.npz
551
+ 549,test,,GlobalMask/test/globalmask_testing_dataset.npz
552
+ 550,test,,GlobalMask/test/globalmask_testing_dataset.npz
553
+ 551,test,,GlobalMask/test/globalmask_testing_dataset.npz
554
+ 552,test,,GlobalMask/test/globalmask_testing_dataset.npz
555
+ 553,test,,GlobalMask/test/globalmask_testing_dataset.npz
556
+ 554,test,,GlobalMask/test/globalmask_testing_dataset.npz
557
+ 555,test,,GlobalMask/test/globalmask_testing_dataset.npz
558
+ 556,test,,GlobalMask/test/globalmask_testing_dataset.npz
559
+ 557,test,,GlobalMask/test/globalmask_testing_dataset.npz
560
+ 558,test,,GlobalMask/test/globalmask_testing_dataset.npz
561
+ 559,test,,GlobalMask/test/globalmask_testing_dataset.npz
562
+ 560,test,,GlobalMask/test/globalmask_testing_dataset.npz
563
+ 561,test,,GlobalMask/test/globalmask_testing_dataset.npz
564
+ 562,test,,GlobalMask/test/globalmask_testing_dataset.npz
565
+ 563,test,,GlobalMask/test/globalmask_testing_dataset.npz
566
+ 564,test,,GlobalMask/test/globalmask_testing_dataset.npz
567
+ 565,test,,GlobalMask/test/globalmask_testing_dataset.npz
568
+ 566,test,,GlobalMask/test/globalmask_testing_dataset.npz
569
+ 567,test,,GlobalMask/test/globalmask_testing_dataset.npz
570
+ 568,test,,GlobalMask/test/globalmask_testing_dataset.npz
571
+ 569,test,,GlobalMask/test/globalmask_testing_dataset.npz
572
+ 570,test,,GlobalMask/test/globalmask_testing_dataset.npz
573
+ 571,test,,GlobalMask/test/globalmask_testing_dataset.npz
574
+ 572,test,,GlobalMask/test/globalmask_testing_dataset.npz
575
+ 573,test,,GlobalMask/test/globalmask_testing_dataset.npz
576
+ 574,test,,GlobalMask/test/globalmask_testing_dataset.npz
577
+ 575,test,,GlobalMask/test/globalmask_testing_dataset.npz
578
+ 576,test,,GlobalMask/test/globalmask_testing_dataset.npz
579
+ 577,test,,GlobalMask/test/globalmask_testing_dataset.npz
580
+ 578,test,,GlobalMask/test/globalmask_testing_dataset.npz
581
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582
+ 580,test,,GlobalMask/test/globalmask_testing_dataset.npz
583
+ 581,test,,GlobalMask/test/globalmask_testing_dataset.npz
584
+ 582,test,,GlobalMask/test/globalmask_testing_dataset.npz
585
+ 583,test,,GlobalMask/test/globalmask_testing_dataset.npz
586
+ 584,test,,GlobalMask/test/globalmask_testing_dataset.npz
587
+ 585,test,,GlobalMask/test/globalmask_testing_dataset.npz
588
+ 586,test,,GlobalMask/test/globalmask_testing_dataset.npz
589
+ 587,test,,GlobalMask/test/globalmask_testing_dataset.npz
590
+ 588,test,,GlobalMask/test/globalmask_testing_dataset.npz
591
+ 589,test,,GlobalMask/test/globalmask_testing_dataset.npz
592
+ 590,test,,GlobalMask/test/globalmask_testing_dataset.npz
593
+ 591,test,,GlobalMask/test/globalmask_testing_dataset.npz
594
+ 592,test,,GlobalMask/test/globalmask_testing_dataset.npz
595
+ 593,test,,GlobalMask/test/globalmask_testing_dataset.npz
596
+ 594,test,,GlobalMask/test/globalmask_testing_dataset.npz
597
+ 595,test,,GlobalMask/test/globalmask_testing_dataset.npz
598
+ 596,test,,GlobalMask/test/globalmask_testing_dataset.npz
599
+ 597,test,,GlobalMask/test/globalmask_testing_dataset.npz
600
+ 598,test,,GlobalMask/test/globalmask_testing_dataset.npz
601
+ 599,test,,GlobalMask/test/globalmask_testing_dataset.npz
602
+ 600,test,,GlobalMask/test/globalmask_testing_dataset.npz
603
+ 601,test,,GlobalMask/test/globalmask_testing_dataset.npz
604
+ 602,test,,GlobalMask/test/globalmask_testing_dataset.npz
605
+ 603,test,,GlobalMask/test/globalmask_testing_dataset.npz
606
+ 604,test,,GlobalMask/test/globalmask_testing_dataset.npz
607
+ 605,test,,GlobalMask/test/globalmask_testing_dataset.npz
608
+ 606,test,,GlobalMask/test/globalmask_testing_dataset.npz
609
+ 607,test,,GlobalMask/test/globalmask_testing_dataset.npz
610
+ 608,test,,GlobalMask/test/globalmask_testing_dataset.npz
611
+ 609,test,,GlobalMask/test/globalmask_testing_dataset.npz
612
+ 610,test,,GlobalMask/test/globalmask_testing_dataset.npz
613
+ 611,test,,GlobalMask/test/globalmask_testing_dataset.npz
614
+ 612,test,,GlobalMask/test/globalmask_testing_dataset.npz
615
+ 613,test,,GlobalMask/test/globalmask_testing_dataset.npz
616
+ 614,test,,GlobalMask/test/globalmask_testing_dataset.npz
617
+ 615,test,,GlobalMask/test/globalmask_testing_dataset.npz
618
+ 616,test,,GlobalMask/test/globalmask_testing_dataset.npz
619
+ 617,test,,GlobalMask/test/globalmask_testing_dataset.npz
620
+ 618,test,,GlobalMask/test/globalmask_testing_dataset.npz
621
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622
+ 620,test,,GlobalMask/test/globalmask_testing_dataset.npz
623
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624
+ 622,test,,GlobalMask/test/globalmask_testing_dataset.npz
625
+ 623,test,,GlobalMask/test/globalmask_testing_dataset.npz
626
+ 624,test,,GlobalMask/test/globalmask_testing_dataset.npz
627
+ 625,test,,GlobalMask/test/globalmask_testing_dataset.npz
628
+ 626,test,,GlobalMask/test/globalmask_testing_dataset.npz
629
+ 627,test,,GlobalMask/test/globalmask_testing_dataset.npz
630
+ 628,test,,GlobalMask/test/globalmask_testing_dataset.npz
631
+ 629,test,,GlobalMask/test/globalmask_testing_dataset.npz
632
+ 630,test,,GlobalMask/test/globalmask_testing_dataset.npz
633
+ 631,test,,GlobalMask/test/globalmask_testing_dataset.npz
634
+ 632,test,,GlobalMask/test/globalmask_testing_dataset.npz
635
+ 633,test,,GlobalMask/test/globalmask_testing_dataset.npz
636
+ 634,test,,GlobalMask/test/globalmask_testing_dataset.npz
637
+ 635,test,,GlobalMask/test/globalmask_testing_dataset.npz
638
+ 636,test,,GlobalMask/test/globalmask_testing_dataset.npz
639
+ 637,test,,GlobalMask/test/globalmask_testing_dataset.npz
640
+ 638,test,,GlobalMask/test/globalmask_testing_dataset.npz
641
+ 639,test,,GlobalMask/test/globalmask_testing_dataset.npz
642
+ 640,test,,GlobalMask/test/globalmask_testing_dataset.npz
643
+ 641,test,,GlobalMask/test/globalmask_testing_dataset.npz
644
+ 642,test,,GlobalMask/test/globalmask_testing_dataset.npz
645
+ 643,test,,GlobalMask/test/globalmask_testing_dataset.npz
646
+ 644,test,,GlobalMask/test/globalmask_testing_dataset.npz
647
+ 645,test,,GlobalMask/test/globalmask_testing_dataset.npz
648
+ 646,test,,GlobalMask/test/globalmask_testing_dataset.npz
649
+ 647,test,,GlobalMask/test/globalmask_testing_dataset.npz
650
+ 648,test,,GlobalMask/test/globalmask_testing_dataset.npz
651
+ 649,test,,GlobalMask/test/globalmask_testing_dataset.npz
652
+ 650,test,,GlobalMask/test/globalmask_testing_dataset.npz
653
+ 651,test,,GlobalMask/test/globalmask_testing_dataset.npz
654
+ 652,test,,GlobalMask/test/globalmask_testing_dataset.npz
655
+ 653,test,,GlobalMask/test/globalmask_testing_dataset.npz
656
+ 654,test,,GlobalMask/test/globalmask_testing_dataset.npz
657
+ 655,test,,GlobalMask/test/globalmask_testing_dataset.npz
658
+ 656,test,,GlobalMask/test/globalmask_testing_dataset.npz
659
+ 657,test,,GlobalMask/test/globalmask_testing_dataset.npz
660
+ 658,test,,GlobalMask/test/globalmask_testing_dataset.npz
661
+ 659,test,,GlobalMask/test/globalmask_testing_dataset.npz
662
+ 660,test,,GlobalMask/test/globalmask_testing_dataset.npz
663
+ 661,test,,GlobalMask/test/globalmask_testing_dataset.npz
664
+ 662,test,,GlobalMask/test/globalmask_testing_dataset.npz
665
+ 663,test,,GlobalMask/test/globalmask_testing_dataset.npz
666
+ 664,test,,GlobalMask/test/globalmask_testing_dataset.npz
667
+ 665,test,,GlobalMask/test/globalmask_testing_dataset.npz
668
+ 666,test,,GlobalMask/test/globalmask_testing_dataset.npz
669
+ 667,test,,GlobalMask/test/globalmask_testing_dataset.npz
670
+ 668,test,,GlobalMask/test/globalmask_testing_dataset.npz
671
+ 669,test,,GlobalMask/test/globalmask_testing_dataset.npz
672
+ 670,test,,GlobalMask/test/globalmask_testing_dataset.npz
673
+ 671,test,,GlobalMask/test/globalmask_testing_dataset.npz
674
+ 672,test,,GlobalMask/test/globalmask_testing_dataset.npz
675
+ 673,test,,GlobalMask/test/globalmask_testing_dataset.npz
676
+ 674,test,,GlobalMask/test/globalmask_testing_dataset.npz
677
+ 675,test,,GlobalMask/test/globalmask_testing_dataset.npz
678
+ 676,test,,GlobalMask/test/globalmask_testing_dataset.npz
679
+ 677,test,,GlobalMask/test/globalmask_testing_dataset.npz
680
+ 678,test,,GlobalMask/test/globalmask_testing_dataset.npz
681
+ 679,test,,GlobalMask/test/globalmask_testing_dataset.npz
682
+ 680,test,,GlobalMask/test/globalmask_testing_dataset.npz
683
+ 681,test,,GlobalMask/test/globalmask_testing_dataset.npz
684
+ 682,test,,GlobalMask/test/globalmask_testing_dataset.npz
685
+ 683,test,,GlobalMask/test/globalmask_testing_dataset.npz
686
+ 684,test,,GlobalMask/test/globalmask_testing_dataset.npz
687
+ 685,test,,GlobalMask/test/globalmask_testing_dataset.npz
688
+ 686,test,,GlobalMask/test/globalmask_testing_dataset.npz
689
+ 687,test,,GlobalMask/test/globalmask_testing_dataset.npz
690
+ 688,test,,GlobalMask/test/globalmask_testing_dataset.npz
691
+ 689,test,,GlobalMask/test/globalmask_testing_dataset.npz
692
+ 690,test,,GlobalMask/test/globalmask_testing_dataset.npz
693
+ 691,test,,GlobalMask/test/globalmask_testing_dataset.npz
694
+ 692,test,,GlobalMask/test/globalmask_testing_dataset.npz
695
+ 693,test,,GlobalMask/test/globalmask_testing_dataset.npz
696
+ 694,test,,GlobalMask/test/globalmask_testing_dataset.npz
697
+ 695,test,,GlobalMask/test/globalmask_testing_dataset.npz
698
+ 696,test,,GlobalMask/test/globalmask_testing_dataset.npz
699
+ 697,test,,GlobalMask/test/globalmask_testing_dataset.npz
700
+ 698,test,,GlobalMask/test/globalmask_testing_dataset.npz
701
+ 699,test,,GlobalMask/test/globalmask_testing_dataset.npz
702
+ 700,test,,GlobalMask/test/globalmask_testing_dataset.npz
703
+ 701,test,,GlobalMask/test/globalmask_testing_dataset.npz
704
+ 702,test,,GlobalMask/test/globalmask_testing_dataset.npz
705
+ 703,test,,GlobalMask/test/globalmask_testing_dataset.npz
706
+ 704,test,,GlobalMask/test/globalmask_testing_dataset.npz
707
+ 705,test,,GlobalMask/test/globalmask_testing_dataset.npz
708
+ 706,test,,GlobalMask/test/globalmask_testing_dataset.npz
709
+ 707,test,,GlobalMask/test/globalmask_testing_dataset.npz
710
+ 708,test,,GlobalMask/test/globalmask_testing_dataset.npz
711
+ 709,test,,GlobalMask/test/globalmask_testing_dataset.npz
712
+ 710,test,,GlobalMask/test/globalmask_testing_dataset.npz
713
+ 711,test,,GlobalMask/test/globalmask_testing_dataset.npz
714
+ 712,test,,GlobalMask/test/globalmask_testing_dataset.npz
715
+ 713,test,,GlobalMask/test/globalmask_testing_dataset.npz
716
+ 714,test,,GlobalMask/test/globalmask_testing_dataset.npz
717
+ 715,test,,GlobalMask/test/globalmask_testing_dataset.npz
718
+ 716,test,,GlobalMask/test/globalmask_testing_dataset.npz
719
+ 717,test,,GlobalMask/test/globalmask_testing_dataset.npz
720
+ 718,test,,GlobalMask/test/globalmask_testing_dataset.npz
721
+ 719,test,,GlobalMask/test/globalmask_testing_dataset.npz
722
+ 720,test,,GlobalMask/test/globalmask_testing_dataset.npz
723
+ 721,test,,GlobalMask/test/globalmask_testing_dataset.npz
724
+ 722,test,,GlobalMask/test/globalmask_testing_dataset.npz
725
+ 723,test,,GlobalMask/test/globalmask_testing_dataset.npz
726
+ 724,test,,GlobalMask/test/globalmask_testing_dataset.npz
727
+ 725,test,,GlobalMask/test/globalmask_testing_dataset.npz
728
+ 726,test,,GlobalMask/test/globalmask_testing_dataset.npz
729
+ 727,test,,GlobalMask/test/globalmask_testing_dataset.npz
730
+ 728,test,,GlobalMask/test/globalmask_testing_dataset.npz
731
+ 729,test,,GlobalMask/test/globalmask_testing_dataset.npz
732
+ 730,test,,GlobalMask/test/globalmask_testing_dataset.npz
733
+ 731,test,,GlobalMask/test/globalmask_testing_dataset.npz
734
+ 732,test,,GlobalMask/test/globalmask_testing_dataset.npz
735
+ 733,test,,GlobalMask/test/globalmask_testing_dataset.npz
736
+ 734,test,,GlobalMask/test/globalmask_testing_dataset.npz
737
+ 735,test,,GlobalMask/test/globalmask_testing_dataset.npz
738
+ 736,test,,GlobalMask/test/globalmask_testing_dataset.npz
739
+ 737,test,,GlobalMask/test/globalmask_testing_dataset.npz
740
+ 738,test,,GlobalMask/test/globalmask_testing_dataset.npz
741
+ 739,test,,GlobalMask/test/globalmask_testing_dataset.npz
742
+ 740,test,,GlobalMask/test/globalmask_testing_dataset.npz
743
+ 741,test,,GlobalMask/test/globalmask_testing_dataset.npz
744
+ 742,test,,GlobalMask/test/globalmask_testing_dataset.npz
745
+ 743,test,,GlobalMask/test/globalmask_testing_dataset.npz
746
+ 744,test,,GlobalMask/test/globalmask_testing_dataset.npz
747
+ 745,test,,GlobalMask/test/globalmask_testing_dataset.npz
748
+ 746,test,,GlobalMask/test/globalmask_testing_dataset.npz
749
+ 747,test,,GlobalMask/test/globalmask_testing_dataset.npz
750
+ 748,test,,GlobalMask/test/globalmask_testing_dataset.npz
751
+ 749,test,,GlobalMask/test/globalmask_testing_dataset.npz
752
+ 750,test,,GlobalMask/test/globalmask_testing_dataset.npz
753
+ 751,test,,GlobalMask/test/globalmask_testing_dataset.npz
754
+ 752,test,,GlobalMask/test/globalmask_testing_dataset.npz
755
+ 753,test,,GlobalMask/test/globalmask_testing_dataset.npz
756
+ 754,test,,GlobalMask/test/globalmask_testing_dataset.npz
757
+ 755,test,,GlobalMask/test/globalmask_testing_dataset.npz
758
+ 756,test,,GlobalMask/test/globalmask_testing_dataset.npz
759
+ 757,test,,GlobalMask/test/globalmask_testing_dataset.npz
760
+ 758,test,,GlobalMask/test/globalmask_testing_dataset.npz
761
+ 759,test,,GlobalMask/test/globalmask_testing_dataset.npz
762
+ 760,test,,GlobalMask/test/globalmask_testing_dataset.npz
763
+ 761,test,,GlobalMask/test/globalmask_testing_dataset.npz
764
+ 762,test,,GlobalMask/test/globalmask_testing_dataset.npz
765
+ 763,test,,GlobalMask/test/globalmask_testing_dataset.npz
766
+ 764,test,,GlobalMask/test/globalmask_testing_dataset.npz
767
+ 765,test,,GlobalMask/test/globalmask_testing_dataset.npz
768
+ 766,test,,GlobalMask/test/globalmask_testing_dataset.npz
769
+ 767,test,,GlobalMask/test/globalmask_testing_dataset.npz
770
+ 768,test,,GlobalMask/test/globalmask_testing_dataset.npz
771
+ 769,test,,GlobalMask/test/globalmask_testing_dataset.npz
772
+ 770,test,,GlobalMask/test/globalmask_testing_dataset.npz
773
+ 771,test,,GlobalMask/test/globalmask_testing_dataset.npz
774
+ 772,test,,GlobalMask/test/globalmask_testing_dataset.npz
775
+ 773,test,,GlobalMask/test/globalmask_testing_dataset.npz
776
+ 774,test,,GlobalMask/test/globalmask_testing_dataset.npz
777
+ 775,test,,GlobalMask/test/globalmask_testing_dataset.npz
778
+ 776,test,,GlobalMask/test/globalmask_testing_dataset.npz
779
+ 777,test,,GlobalMask/test/globalmask_testing_dataset.npz
780
+ 778,test,,GlobalMask/test/globalmask_testing_dataset.npz
781
+ 779,test,,GlobalMask/test/globalmask_testing_dataset.npz
782
+ 780,test,,GlobalMask/test/globalmask_testing_dataset.npz
783
+ 781,test,,GlobalMask/test/globalmask_testing_dataset.npz
784
+ 782,test,,GlobalMask/test/globalmask_testing_dataset.npz
785
+ 783,test,,GlobalMask/test/globalmask_testing_dataset.npz
786
+ 784,test,,GlobalMask/test/globalmask_testing_dataset.npz
787
+ 785,test,,GlobalMask/test/globalmask_testing_dataset.npz
788
+ 786,test,,GlobalMask/test/globalmask_testing_dataset.npz
789
+ 787,test,,GlobalMask/test/globalmask_testing_dataset.npz
790
+ 788,test,,GlobalMask/test/globalmask_testing_dataset.npz
791
+ 789,test,,GlobalMask/test/globalmask_testing_dataset.npz
792
+ 790,test,,GlobalMask/test/globalmask_testing_dataset.npz
793
+ 791,test,,GlobalMask/test/globalmask_testing_dataset.npz
794
+ 792,test,,GlobalMask/test/globalmask_testing_dataset.npz
795
+ 793,test,,GlobalMask/test/globalmask_testing_dataset.npz
796
+ 794,test,,GlobalMask/test/globalmask_testing_dataset.npz
797
+ 795,test,,GlobalMask/test/globalmask_testing_dataset.npz
798
+ 796,test,,GlobalMask/test/globalmask_testing_dataset.npz
799
+ 797,test,,GlobalMask/test/globalmask_testing_dataset.npz
800
+ 798,test,,GlobalMask/test/globalmask_testing_dataset.npz
801
+ 799,test,,GlobalMask/test/globalmask_testing_dataset.npz
802
+ 800,test,,GlobalMask/test/globalmask_testing_dataset.npz
803
+ 801,test,,GlobalMask/test/globalmask_testing_dataset.npz
804
+ 802,test,,GlobalMask/test/globalmask_testing_dataset.npz
805
+ 803,test,,GlobalMask/test/globalmask_testing_dataset.npz
806
+ 804,test,,GlobalMask/test/globalmask_testing_dataset.npz
807
+ 805,test,,GlobalMask/test/globalmask_testing_dataset.npz
808
+ 806,test,,GlobalMask/test/globalmask_testing_dataset.npz
809
+ 807,test,,GlobalMask/test/globalmask_testing_dataset.npz
810
+ 808,test,,GlobalMask/test/globalmask_testing_dataset.npz
811
+ 809,test,,GlobalMask/test/globalmask_testing_dataset.npz
812
+ 810,test,,GlobalMask/test/globalmask_testing_dataset.npz
813
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814
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818
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820
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821
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823
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824
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825
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833
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840
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850
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853
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viewer/global_mask/train.csv ADDED
The diff for this file is too large to render. See raw diff
 
viewer/insitu_matched/above/test.csv ADDED
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viewer/insitu_matched/above/train.csv ADDED
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1
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viewer/insitu_matched/ameriflux/test.csv ADDED
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viewer/insitu_matched/ameriflux/train.csv ADDED
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viewer/insitu_matched/fluxnet/test.csv ADDED
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viewer/insitu_matched/fluxnet/train.csv ADDED
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