Standardize Electric Sheep Africa dataset card

README.md

@@ -1,195 +1,144 @@
 ---
 license: cc-by-4.0
+language:
+- en
 task_categories:
 - tabular-classification
 - tabular-regression
-- time-series-forecasting
-language:
-- en
-tags:
-- mining
-- tailings-dam
-- dam-safety
-- risk-assessment
-- environmental
-- geotechnical
-- africa
-- synthetic-data
-- synthetic
-pretty_name: African Tailings Dam Risk Dataset
+multilinguality: monolingual
 size_categories:
 - 1K<n<10K
-data_type: synthetic
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "energy"
+- "parquet"
+- "tabular"
+- "text"
+- "mining"
+- "tailings-dam"
+- "dam-safety"
+- "risk-assessment"
+- "environmental"
+- "geotechnical"
+- "synthetic-data"
+- "synthetic"
+pretty_name: "African Tailings Dam Risk Dataset | Africa (Electric Sheep Africa metadata inventory)"
 ---
 
-> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
-
-# African Tailings Dam Risk Dataset
-
-## Dataset Description
+# African Tailings Dam Risk Dataset | Africa (Electric Sheep Africa metadata inventory)
 
-### Overview
-
-This dataset provides synthetic monitoring records and risk assessments for tailings storage facilities (TSFs) across African mining operations. Tailings dams represent one of the most significant environmental and safety risks in mining, with catastrophic failures causing loss of life and long-term environmental damage.
-
-The dataset captures dam characteristics, geotechnical monitoring data, environmental conditions, and risk indicators aligned with the Global Industry Standard on Tailings Management (GISTM).
-
-### Dataset Statistics
-
-| Attribute | Value |
-|-----------|-------|
-| Records | 5,000 |
-| Variables | 31 |
-| Temporal Coverage | 2020-2024 |
-| Geographic Scope | 8 African countries |
-| Facility Count | ~500 unique TSFs |
-| Format | CSV, Parquet |
-
-## Data Schema
+**Size category:** `1K<n<10K` - **Formats:** `parquet` - **Sector:** energy - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
-### Variable Dictionary
-
-| Variable | Type | Description | Value Range/Categories |
-|----------|------|-------------|----------------------|
-| `record_id` | string | Unique monitoring record ID | TSF-XXXXXXXX |
-| `facility_id` | string | Tailings facility identifier | FAC-XXXX |
-| `country` | categorical | Country of facility | south_africa, ghana, drc, zambia, zimbabwe, tanzania, mali, mauritania |
-| `commodity` | categorical | Primary commodity | gold, copper, platinum, iron_ore, coal, diamond, uranium, other |
-| `operational_status` | categorical | Facility status | active, inactive, closed, under_construction |
-| `dam_type` | categorical | Dam construction type | earthfill, rockfill, concrete, composite |
-| `construction_method` | categorical | Raise method (critical for stability) | upstream, downstream, centerline, dry_stack |
-| `max_height_m` | float | Maximum design height | 10-250 meters |
-| `current_height_m` | float | Current embankment height | meters |
-| `storage_capacity_mm3` | float | Total storage capacity | million m³ |
-| `current_volume_mm3` | float | Current stored volume | million m³ |
-| `catchment_area_km2` | float | Contributing catchment area | km² |
-| `year_constructed` | integer | Initial construction year | 1960-2023 |
-| `design_life_years` | integer | Designed operational life | 20-100 years |
-| `last_raise_year` | integer | Year of most recent raise | Year |
-| `consequence_classification` | categorical | Failure consequence class | extreme, very_high, high, significant, low |
-| `monitoring_date` | datetime | Date of monitoring record | 2020-01-01 to 2024-12-31 |
-| `freeboard_m` | float | Water surface to crest distance | 0.5-10 meters |
-| `piezometer_level_m` | float | Phreatic surface height | meters |
-| `seepage_rate_lpm` | float | Measured seepage rate | liters per minute |
-| `settlement_mm` | float | Cumulative crest settlement | millimeters |
-| `inclinometer_displacement_mm` | float | Lateral displacement | millimeters |
-| `rainfall_24h_mm` | float | 24-hour precipitation | millimeters |
-| `earthquake_pga` | float | Peak ground acceleration | g (gravitational) |
-| `water_balance_status` | categorical | Water management status | surplus, balanced, deficit |
-| `factor_of_safety` | float | Calculated stability FoS | 0.8-2.5 |
-| `risk_score` | float | Composite risk score | 0-100 |
-| `risk_category` | categorical | Risk classification | low, medium, high, critical |
-| `last_inspection_days_ago` | integer | Days since last inspection | 1-730 days |
-| `governance_score` | integer | Management system score | 20-100 |
-| `has_eap` | boolean | Emergency Action Plan exists | True/False |
-
-## Methodology
-
-### Tailings Dam Risk Framework
-
-The dataset models risk factors identified in major tailings dam failures and GISTM requirements:
-
-**Construction Method Risk Ranking**:
-| Method | Risk Level | Prevalence | Notes |
-|--------|------------|------------|-------|
-| Upstream | Highest | 35% | Sequential raises on tailings beach; liquefaction vulnerable |
-| Centerline | Medium | 25% | Raises vertically; moderate stability |
-| Downstream | Lower | 30% | Raises downstream; best stability |
-| Dry Stack | Lowest | 10% | Filtered tailings; no impoundment |
-
-**Key Risk Indicators Modeled**:
-
-1. **Factor of Safety (FoS)**: Ratio of resisting to driving forces
-   - Critical threshold: FoS < 1.3
-   - Upstream dams: Mean FoS reduced by 0.2
-   - Dry stack: Mean FoS increased by 0.3
-
-2. **Freeboard**: Buffer against overtopping
-   - Critical threshold: < 1.5 meters
-
-3. **Seepage**: Indicator of internal erosion
-   - Warning threshold: > 100 LPM
-
-4. **Displacement**: Slope movement indicator
-   - Warning threshold: > 30 mm cumulative
-
-### Historical Context
-
-Major African tailings incidents informing risk modeling:
-- Merriespruit (South Africa, 1994): 17 fatalities, upstream dam
-- Samarco analogs: Liquefaction failure modes
-- Regional seismicity considerations
-
-### Sensor Correlation Structure
-
-Monitoring parameters are correlated to reflect realistic failure precursors:
-- Rainfall → Piezometer rise → Seepage increase → Settlement/displacement
-- Seismic events → Pore pressure spike → Stability reduction
-
-## Limitations
-
-1. **Simplified Geotechnics**: Complex soil mechanics reduced to statistical distributions
-2. **Sensor Density**: Real TSFs have hundreds of instruments; dataset simplified
-3. **Failure Events**: Actual failures not explicitly modeled (rare events)
-4. **Site Specificity**: Generic African context; site investigations required
-5. **Climate Projections**: Historical patterns; climate change effects not modeled
-6. **Governance Proxies**: Management quality simplified to single score
-
-## Ethical Considerations
-
-- **Public Safety**: Tailings failures can kill hundreds and devastate communities
-- **Environmental Justice**: Downstream communities often marginalized
-- **Corporate Accountability**: Dataset should not be used to obscure real risks
-- **Regulatory Implications**: Not a substitute for proper engineering assessment
-- **Transparency**: Supports calls for public disclosure of TSF risks
-
-## Intended Uses
-
-### Appropriate Uses
-
-- Development of early warning algorithms
-- Research on risk indicator correlations
-- Educational demonstrations of dam safety monitoring
-- Benchmarking ML approaches for anomaly detection
-- Policy research on tailings governance
-
-### Inappropriate Uses
-
-- Actual dam safety assessments
-- Regulatory compliance certification
-- Insurance or liability determinations
-- Investment decisions on specific facilities
-- Replacing qualified geotechnical engineering
+![size](https://img.shields.io/badge/size-1K%3Cn%3C10K-blue)
+![sector](https://img.shields.io/badge/sector-energy-green)
+![downloads](https://img.shields.io/badge/HF_downloads-50-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
 
-## Citation
+## TL;DR
 
-```bibtex
-@dataset{electric_sheep_africa_tailings_dam_2024,
-  title = {African Tailings Dam Risk Dataset},
-  author = {Electric Sheep Africa},
-  year = {2024},
-  publisher = {Hugging Face},
-  url = {https://huggingface.co/datasets/electricsheepafrica/african-mining-tailings-dam},
-  note = {Synthetic dataset for tailings dam risk research aligned with GISTM}
-}
+This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
+
+## What This Dataset Covers
+
+Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.
+
+Dataset context from the existing Hugging Face card: ⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference. African Tailings Dam Risk Dataset Dataset Description Overview This dataset provides synthetic monitoring records and risk assessments for tailings storage facilities (TSFs) across African mining operations. Tailings dams represent one of the most significant environmental and safety risks in mining, with… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-mining-tailings-dam-all.
+
+## Dataset Profile
+
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-synth-mining-tailings-dam-all`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-mining-tailings-dam-all) |
+| Sector | energy |
+| Topic tags | mining, tailings-dam, dam-safety, risk-assessment, environmental, geotechnical, synthetic-data, synthetic |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `1K<n<10K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2026-04-14 22:39:20+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
+
+## How To Read This Dataset
+
+- Start from the repository files and the dataset viewer when available.
+- Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
+- Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
+- Preserve missing values until you have a defensible imputation rule.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("electricsheepafrica/africa-synth-mining-tailings-dam-all")
+print(ds)
+
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
 ```
 
-## References
+### Convert To Pandas When Tabular
 
-1. ICOLD (International Commission on Large Dams). (2001). *Tailings Dams: Risk of Dangerous Occurrences*. Bulletin 121.
+```python
+from datasets import Dataset
+
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
+```
 
-2. Global Tailings Review. (2020). *Global Industry Standard on Tailings Management (GISTM)*. ICMM, UNEP, PRI.
+## Data Quality Notes
 
-3. Santamarina, J.C., Torres-Cruz, L.A., & Bachus, R.C. (2019). Why coal ash and tailings dam disasters occur. *Science*, 364(6440), 526-528.
+- This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
+- Exact schema, row counts, and source files should be inspected in the repository data files.
+- Metadata gaps from the inventory: country, upstream_publisher.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
 
-4. Bowker, L.N., & Chambers, D.M. (2015). The risk, public liability, & economics of tailings storage facility failures. *Earthwork Act*.
+## Source And Provenance
 
-5. Franks, D.M., et al. (2021). Tailings facility disclosures reveal stability risks. *Scientific Reports*, 11, 5353.
+- **Source context:** Electric Sheep Africa metadata inventory
+- **Publisher/source attribution:** Public dataset metadata
+- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-synth-mining-tailings-dam-all](https://huggingface.co/datasets/electricsheepafrica/africa-synth-mining-tailings-dam-all)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
+
+## Suggested Analyses
+
+- Inspect schema and missingness before modeling.
+- Profile variables by geography, time, and subgroup columns where present.
+- Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
+- Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.
+
+## Citation
+
+```bibtex
+@misc{electric_sheep_africa_africa_synth_mining_tailings_dam_all_2026,
+  title        = {African Tailings Dam Risk Dataset | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-mining-tailings-dam-all},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-mining-tailings-dam-all}}
+}
+```
 
 ## License
 
-This dataset is released under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/) (CC-BY-4.0).
+Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
 
-## Contact
+Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.
+
+## About Electric Sheep Africa
+
+Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
+
+---
 
-For questions or feedback, please open an issue on the dataset repository or contact Electric Sheep Africa.
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.