Add Forge Industrial sample (10K lifecycles, 14-field schema) with README, SCHEMA, parquet, JSONL

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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - tabular-regression
+  - time-series-forecasting
+  - reinforcement-learning
+language:
+  - en
+tags:
+  - synthetic
+  - industrial-real-estate
+  - logistics
+  - warehouse
+  - supply-chain
+  - operations
+  - decision-support
+  - anomaly-detection
+  - forecasting
+  - policy-optimization
+  - capacity-planning
+  - cold-chain
+  - last-mile
+pretty_name: Forge Industrial Intelligence Pack
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: forge_industrial_sample.parquet
+---
+
+# Forge Industrial Intelligence Pack (Sample)
+
+**A synthetic industrial real estate, logistics, and operations decision-telemetry dataset for anomaly detection, forecasting, decision-support, and policy-optimization research.** Each row is a complete operational scenario lifecycle — from signal emergence through telemetry evolution, detection, forecast, impact, and recommended action — with 14 top-level context fields tying market, tenant, facility, portfolio, and decision-owner layers together.
+
+Built by [SolsticeAI](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic. No real facility data, no real tenant names, no real portfolio telemetry — market names (Dallas-Fort Worth, LA, Chicago, etc.) refer to generic submarket archetypes, not specific properties or operators.
+
+## What is included
+
+| File | Rows | Format | Purpose |
+|---|---:|---|---|
+| `forge_industrial_sample.parquet` | 10,000 | Parquet | Columnar, typed, best for analytics |
+| `forge_industrial_sample.jsonl` | 10,000 | JSON Lines | Streaming / LLM training friendly |
+
+**This sample:** 10,000 operational scenario lifecycles.  
+**Severity tiers:** `medium`, `high`, `critical` (~3,300 each)  
+**Status:** `decision_pending`, `confirmed`, `triaged` (~3,300 each)  
+**Scenario classes (7):** `port_spillover_surge`, `power_constrained_ev_yard`, `labor_gap_service_degradation`, `cold_chain_excursion_risk`, `tenant_hypergrowth_overrun`, `last_mile_cutoff_compression`, `site_selection_power_arbitrage`  
+**Markets (6):** Dallas-Fort Worth, Los Angeles, Chicago, Atlanta, New Jersey, Phoenix  
+**Tenant industries (5):** ecommerce, 3PL, retail distribution, cold chain, industrial manufacturing  
+**Action library:** 14+ playbook actions across labor, power, capex, tenant, network, and pricing levers
+
+## Record structure
+
+Each record is one operational scenario lifecycle with 14 top-level fields:
+
+| Field | Type | Contents |
+|---|---|---|
+| `schema_version` | string | Pack schema version (`1.0.0-forge-industrial-sample`) |
+| `event` | struct | `id`, `trace_id`, `timestamp`, `scenario`, `severity`, `status`, `confidence` |
+| `organization` | struct | `sector`, `market`, `submarket`, `region`, `environment`, port/airport proximity, grid headroom, vacancy, base rent, development cycle |
+| `identity_context` | struct | `principal_id`, `decision_lineage[]` (role/action/authority), `auth_method`, `stakeholder_latency_hours`, `meeting_load` |
+| `vulnerability` | struct | `class`, `risk_taxonomy[]`, `exposure`, `severity_model` (base_score + vector) |
+| `tenant_context` | struct | `tenant_id`, `industry`, `truck_profile`, growth rate, square footage, expansion openness, retention priority, churn risk |
+| `facility_context` | struct | Building characteristics, capacity, energy profile |
+| `portfolio_context` | struct | Network-level dynamics, capacity distribution |
+| `telemetry_stream` | list<struct> | Ordered telemetry readings (dock utilization, queue length, power load, service commitment risk, etc.) with per-step event labels |
+| `detection` | struct | `analytic_family`, `primary_risk_class`, `rule_logic`, `baseline_deviation`, `anomaly_score`, `confidence_band`, `signal_conflicts[]` |
+| `forecast` | struct | Forward-looking predictions (demand shift, occupancy, pricing) |
+| `impact` | struct | Economic consequences (revenue delta, NOI, capex, churn risk) |
+| `response` | struct | `recommended_actions[]`, `primary_action`, `primary_action_score`, `primary_action_reason`, `alternative_actions[]`, `decision_owner`, `execution_window_days`, `playbook_id`, `capex_gate_required`, `stakeholders[]`, `expected_operational_outcome`, `recommended_tradeoff`, `execution_risk_band` |
+| `simulation` | struct | `synthetic`, `engine`, `causal_coherence`, `friction_profile`, `ground_truth_label`, `intended_use[]` |
+
+See [SCHEMA.md](./SCHEMA.md) for the full nested field breakdown.
+
+## Why this dataset is useful
+
+Most public operations / logistics datasets are either flat sensor-stream snapshots or narrow single-scenario studies. Industrial real estate telemetry (dock utilization, grid headroom, tenant churn signals, decision lineage) is effectively never published. This pack is shaped around what operational-intelligence and decision-policy teams actually need:
+
+- Full scenario lifecycles rather than isolated sensor frames
+- Balanced severity and status distributions across decision-pending, triaged, and confirmed cases
+- Coupled detection → forecast → impact → response layers per record (not just telemetry)
+- Economic impact fields (revenue, NOI, capex, churn) for decision-aware modeling
+- Decision-lineage metadata for policy-interpretability research
+- Cross-scenario coverage (port, power, labor, cold chain, tenant, last-mile, site selection)
+- Compact parquet (~3.8 MB) that fits inside CI and notebooks, with a fuller JSONL for LLM training
+
+## Typical use cases
+
+- Operational-anomaly detection models
+- Capacity-planning and forecasting pipelines
+- Decision-support and recommendation engines
+- RL / policy optimization for industrial operations
+- Tenant-churn and retention modeling
+- Site-selection and network-optimization research
+- LLM fine-tuning on operational narratives and decision rationale
+- Scenario-mining for logistics debugging
+- Executive-dashboard and BI template development
+
+## Quick start
+
+```python
+import pandas as pd
+import pyarrow.parquet as pq
+
+df = pq.read_table("forge_industrial_sample.parquet").to_pandas()
+
+# Severity distribution
+print(df["event"].apply(lambda e: e["severity"]).value_counts())
+
+# Scenario × primary action cross-tab
+df["scenario"] = df["event"].apply(lambda e: e["scenario"])
+df["primary_action"] = df["response"].apply(lambda r: r["primary_action"])
+print(pd.crosstab(df["scenario"], df["primary_action"]))
+
+# Average impact.revenue_delta_usd by market
+df["market"] = df["organization"].apply(lambda o: o["market"])
+df["rev_delta"] = df["impact"].apply(lambda i: i.get("revenue_delta_usd"))
+print(df.groupby("market")["rev_delta"].mean().round(0))
+```
+
+Streaming form:
+
+```python
+import json
+
+with open("forge_industrial_sample.jsonl") as f:
+    for line in f:
+        lifecycle = json.loads(line)
+        # one operational scenario lifecycle per line
+```
+
+## Responsible use
+
+This dataset is intended for **research, model development, and decision-policy simulation** around industrial real estate, logistics, and operations. It contains synthesized telemetry, decision lineages, and economic-impact rollups — it does **not** contain real facility telemetry, real tenant identities, real portfolio NOI, or identifiable stakeholder data. Models trained on this data should be validated against real operational data under appropriate organizational review before being used for live recommendations or capex decisions.
+
+## License
+
+Released under **CC BY 4.0**. Use freely for research, operations-tool prototyping, education, and commercial development with attribution.
+
+## Get the full pack
+
+This Hugging Face repo is a **10K-lifecycle sample**. The production pack scales to 5M+ lifecycles with expanded market and submarket coverage, richer per-step telemetry (ms-level sensor traces where applicable), additional scenario classes (ESG signals, geopolitical supply disruption, labor automation events, bonded warehouse flows), multi-year longitudinal traces per tenant, parquet + JSONL + BI-tool-aligned delivery, and buyer-specific variants.
+
+**Self-serve (Stripe checkout):**
+- [**Sample Scale tier — $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) — ~25K records, one subject, 72-hour delivery.
+
+**Full pack + enterprise scope:**
+- [www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets) — per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.
+
+**Procurement catalog:**
+- [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) — available via Datarade / Monda.
+
+## Citation
+
+```bibtex
+@dataset{solstice_forge_industrial_pack_2026,
+  title        = {Forge Industrial Intelligence Pack (Sample)},
+  author       = {SolsticeAI},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/solsticestudioai/forge-industrial-pack}
+}
+```

SCHEMA.md

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+# Forge Industrial Intelligence Pack — Schema
+
+One row = one complete operational scenario lifecycle. All records share the same fourteen top-level fields.
+
+Schema version: `1.0.0-forge-industrial-sample`
+
+## Top-level fields
+
+### `schema_version` — string
+Schema identifier. Constant within a sample release.
+
+### `event` — struct
+Identifier fields and the overall status/severity for the lifecycle.
+
+| Field | Type | Notes |
+|---|---|---|
+| `id` | string | Stable event identifier, e.g., `FORGE-100000`. |
+| `trace_id` | string (UUID) | Cross-links telemetry within the lifecycle. |
+| `timestamp` | string (ISO-8601) | Scenario anchor time. |
+| `scenario` | string | One of: `port_spillover_surge`, `power_constrained_ev_yard`, `labor_gap_service_degradation`, `cold_chain_excursion_risk`, `tenant_hypergrowth_overrun`, `last_mile_cutoff_compression`, `site_selection_power_arbitrage`. |
+| `severity` | string | `medium`, `high`, `critical`. |
+| `status` | string | `decision_pending`, `triaged`, `confirmed`. |
+| `confidence` | double | 0–1 confidence of the event label. |
+
+### `organization` — struct
+Market-level context for the facility/portfolio.
+
+| Field | Type | Notes |
+|---|---|---|
+| `sector` | string | Always `industrial_real_estate`. |
+| `market` | string | Generic submarket archetype (e.g., `Dallas-Fort Worth`, `Los Angeles`, `Chicago`, `Atlanta`, `New Jersey`, `Phoenix`). Used as a type-label, not a specific-property reference. |
+| `submarket` | string | Submarket handle (e.g., `Inland Port`, `Inland Empire`, `Joliet`, `West Valley`). |
+| `region` | string | Regional bucket (`south_central_us`, `west_coast_us`, etc.). |
+| `environment` | string | Deployment bucket (e.g., `production_portfolio`). |
+| `port_proximity_km` | int | Proximity to major port, km. |
+| `airport_proximity_km` | int | Proximity to major airport, km. |
+| `highway_access_score` | double | 0–1. |
+| `labor_tightness` | double | 0–1. |
+| `grid_headroom_mw` | double | Available grid capacity (MW). |
+| `industrial_vacancy_pct` | double | Submarket vacancy rate (percent). |
+| `base_rent_psf_yr` | double | Submarket base rent ($/sqft/yr). |
+| `population_density` | int | People per sq km. |
+| `development_cycle_days` | int | Typical time-to-delivery for spec construction. |
+
+### `identity_context` — struct
+Decision owner and authorization lineage.
+
+| Field | Type | Notes |
+|---|---|---|
+| `principal_id` | string | Synthetic identity handle (e.g., `ops://tnt-XXX/node/fac-YYY`). |
+| `decision_lineage` | list<struct> | Ordered `role` / `action` / `authority_level` entries showing the approval chain. |
+| `auth_method` | string | How the decision was authenticated (e.g., `tower_dashboard`). |
+| `is_automated_recommendation` | bool | Whether the decision started as an automated rec. |
+| `stakeholder_latency_hours` | int | Hours of stakeholder lag. |
+| `meeting_load` | string | `low` / `moderate` / `high`. |
+
+### `vulnerability` — struct
+Risk taxonomy and severity vector.
+
+| Field | Type | Notes |
+|---|---|---|
+| `class` | string | Scenario-class label (same as `simulation.ground_truth_label`). |
+| `risk_taxonomy` | list<string> | Ordered taxonomy codes (e.g., `LOG-LOC-001`, `LOG-DEM-004`). |
+| `exposure` | string | Primary exposure dimension (e.g., `demand_pressure`, `service_risk`). |
+| `severity_model.base_score` | double | 0–10 severity base. |
+| `severity_model.vector` | string | Vector string encoding severity dimensions (e.g., `LOG:3.1/EX:O/FR:C/...`). |
+
+### `tenant_context` — struct
+Synthetic tenant profile tied to this scenario.
+
+| Field | Type | Notes |
+|---|---|---|
+| `tenant_id` | string | Synthetic tenant identifier (e.g., `TNT-189FCA4461`). |
+| `industry` | string | `ecommerce`, `3pl`, `retail_distribution`, `cold_chain`, `industrial_manufacturing`. |
+| `truck_profile` | string | `parcel_heavy`, `mixed_freight`, `palletized`, `reefer`, `inbound_component`. |
+| `order_growth_rate` | double | Growth rate delta. |
+| `sqft_footprint` | int | Facility sqft. |
+| `expansion_open` | bool | Whether the tenant is open to expanding. |
+| `retention_priority` | string | `standard`, `high`, `strategic`. |
+| `risk_of_churn` | double | 0–1 churn risk. |
+
+### `facility_context` — struct
+Building-level attributes: capacity, energy, clear heights, dock counts, refrigeration, automation level.
+
+### `portfolio_context` — struct
+Network-level dynamics: submarket share, adjacent-node capacity, cross-site elasticity.
+
+### `telemetry_stream` — list<struct>
+Variable-length ordered telemetry readings representing the evolving system state across the scenario. Each element includes an event-name label matching one item in the scenario's event sequence, plus relevant signal readings (dock utilization, queue length, power load, service commitment risk, truck turn time, temperature delta, etc.).
+
+### `detection` — struct
+Anomaly-signature metadata.
+
+| Field | Type | Notes |
+|---|---|---|
+| `analytic_family` | string | Analytic family label (e.g., `industrial_real_estate_behavioral_intelligence`). |
+| `primary_risk_class` | string | Primary risk dimension. |
+| `rule_logic` | string | SQL-like rule expression for the detector. |
+| `baseline_deviation` | string | Short English description of the deviation pattern. |
+| `anomaly_score` | double | 0–1. |
+| `confidence_band` | string | `low` / `medium` / `high` / `very_high`. |
+| `forecast_pressure_delta` | double | Delta vs baseline forecast. |
+| `signal_conflicts` | list<string> | Conflicting signal labels, if any. |
+
+### `forecast` — struct
+Forward-looking predictions tied to this scenario: demand shift, occupancy trajectory, pricing outlook.
+
+### `impact` — struct
+Economic consequences: revenue delta, NOI impact, capex required, churn risk delta.
+
+### `response` — struct
+Recommended actions and execution context.
+
+| Field | Type | Notes |
+|---|---|---|
+| `recommended_actions` | list<string> | Top actions ranked by score. |
+| `primary_action` | string | Selected action (e.g., `shift_overflow_to_satellite`). |
+| `primary_action_score` | double | Scoring metric for the primary action. |
+| `primary_action_reason` | string | Short reason code. |
+| `alternative_actions` | list<struct> | `action` / `score` / `reason` triples. |
+| `decision_owner` | string | Role who owns the decision (e.g., `operations_lead`, `market_officer`, `development_committee`). |
+| `execution_window_days` | int | Execution SLA. |
+| `playbook_id` | string | Synthetic playbook identifier, prefixed `FORGE-PB-`. |
+| `capex_gate_required` | bool | Whether capex approval is required. |
+| `stakeholders` | list<string> | Additional stakeholder roles. |
+| `expected_operational_outcome` | string | `partial_success`, `success`, `mitigation`, etc. |
+| `recommended_tradeoff` | string | Short tradeoff description (e.g., `preserve_capital_with_higher_latency_risk`). |
+| `execution_risk_band` | string | `low`, `moderate`, `elevated`, `high`. |
+
+### `simulation` — struct
+Simulation engine provenance and ground-truth labels.
+
+| Field | Type | Notes |
+|---|---|---|
+| `synthetic` | bool | Always `true`. |
+| `engine` | string | Simulation engine label (`forge_industrial_sim_v1`). |
+| `causal_coherence` | string | Coherence mode descriptor (e.g., `rich_schema_with_balanced_policy`). |
+| `friction_profile` | struct | Numeric friction dimensions: `budget_pressure`, `labor_shortage`, `power_stress`, `demand_pressure`, `service_risk`, `portfolio_tightness`, `capital_availability`, `entitlement_friction`, `weather_disruption`, `upside_mode` (bool). |
+| `ground_truth_label` | string | Scenario class (e.g., `Network_Optimization`, `Labor_Constraint`, `Temperature_Control_Risk`). |
+| `intended_use` | list<string> | ML use-case tags (e.g., `forecasting`, `site_selection`, `tenant_expansion_decisions`). |
+
+## Distribution of this sample
+
+- 10,000 lifecycles total.
+- Severity: balanced across `medium` / `high` / `critical` (~3,300 each).
+- Status: balanced across `decision_pending` / `triaged` / `confirmed` (~3,300 each).
+- Scenario: balanced across 7 scenario classes (~1,400 each).
+- Market: balanced across 6 submarket archetypes (~1,600 each).
+- Industry: balanced across 5 tenant archetypes (~2,000 each).
+
+## Sanitization notes
+
+- Internal identifier prefix (`PLG-REAL-*`) has been normalized to `FORGE-*`.
+- Internal playbook prefix (`PLG-PB-*`) has been normalized to `FORGE-PB-*`.
+- Internal engine label (`SIMA-inspired industrial reality generator`) has been normalized to `forge_industrial_sim_v1`.
+- Market names (Dallas-Fort Worth, LA, Chicago, Atlanta, NJ, Phoenix) are used as submarket archetypes for industrial-real-estate type labels, not specific property or operator references.
+- No real facility telemetry, real tenant identities, real portfolio NOI, or identifiable stakeholder data are present.
+
+## Relationship to the full pack
+
+The production pack scales to 5M+ lifecycles with expanded market and submarket coverage, richer per-step telemetry, additional scenario classes (ESG signals, geopolitical supply disruption, labor automation events, bonded warehouse flows), and multi-year longitudinal traces per tenant. See the pack card for commercial access.

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