Standardize Electric Sheep Africa dataset card

README.md

@@ -1,95 +1,144 @@
 ---
 license: cc-by-4.0
-task_categories:
-- tabular-classification
 language:
 - en
-tags:
-- environmental-health
-- asbestos
-- mesothelioma
-- occupational-health
-- lung-cancer
-- synthetic
-- sub-saharan-africa
-pretty_name: Asbestos Exposure & Mesothelioma (SSA)
+task_categories:
+- tabular-classification
+- tabular-regression
+multilinguality: monolingual
 size_categories:
 - 10K<n<100K
-configs:
-- config_name: former_mining_community
-  data_files: data/asbestos_mining_community.csv
-  default: true
-- config_name: urban_construction
-  data_files: data/asbestos_urban_construction.csv
-- config_name: rural_asbestos_roofing
-  data_files: data/asbestos_rural_roofing.csv
-data_type: synthetic
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "health"
+- "csv"
+- "tabular"
+- "text"
+- "environmental-health"
+- "asbestos"
+- "mesothelioma"
+- "occupational-health"
+- "lung-cancer"
+- "synthetic"
+- "sub-saharan-africa"
+- "cancer"
+pretty_name: "Asbestos Exposure & Mesothelioma (SSA) | Africa (Electric Sheep Africa metadata inventory)"
 ---
 
-> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
+# Asbestos Exposure & Mesothelioma (SSA) | Africa (Electric Sheep Africa metadata inventory)
 
-# Asbestos Exposure & Mesothelioma in Sub-Saharan Africa
+**Size category:** `10K<n<100K` - **Formats:** `csv` - **Sector:** health - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
-## Abstract
+![size](https://img.shields.io/badge/size-10K%3Cn%3C100K-blue)
+![sector](https://img.shields.io/badge/sector-health-green)
+![downloads](https://img.shields.io/badge/HF_downloads-103-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
 
-Synthetic dataset modelling asbestos exposure pathways, fibre types, and health outcomes (mesothelioma, asbestosis, lung cancer) across three settings in SSA. South Africa was a global leader in asbestos production; Wagner (1960) discovered the mesothelioma link there. WHO Africa reports asbestos use continues despite warnings, particularly in roofing, construction, and brake linings. Latency period is 20-50 years.
+## TL;DR
 
-### Scenarios
+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.
 
-- **Former Mining Community**: South Africa-type communities near closed asbestos mines with high crocidolite/amosite exposure.
-- **Urban Construction**: Cities with ongoing chrysotile use in building materials and demolition.
-- **Rural Asbestos Roofing**: Widespread asbestos-cement roofing in rural areas with chronic low-level exposure.
+## What This Dataset Covers
 
-## Parameterization Evidence
+Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.
 
-| Parameter | Value | Source | Year |
-| --- | --- | --- | --- |
-| Asbestos causes mesothelioma, asbestosis, lung cancer | Health effects | WHO Fact Sheet; IARC Group 1 | 2023 |
-| SA global leader in asbestos production; ban in 2002 | History | ScienceDirect; asbestos.com | 2004 |
-| Wagner (1960) discovered mesothelioma-asbestos link in SA | Discovery | PMC1522094 | 2005 |
-| Asbestos use continues in Africa despite warnings | Ongoing use | WHO Africa | 2023 |
-| Eastern SSA: substantial increases in asbestos lung cancer | GBD trend | PMC12573932 (GBD 2021) | 2024 |
-| Mesothelioma mortality lower than expected in SA due to HIV | Co-morbidity | PubMed 21422006 | 2011 |
-| Latency period 20-50 years | Disease natural history | WHO | 2023 |
+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. Asbestos Exposure & Mesothelioma in Sub-Saharan Africa Abstract Synthetic dataset modelling asbestos exposure pathways, fibre types, and health outcomes (mesothelioma, asbestosis, lung cancer) across three settings in SSA. South Africa was a global leader in asbestos production; Wagner (1960) discovered the mesothelioma link… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-mental-health-asbestos-mesothelioma-all.
 
-## Validation
+## Dataset Profile
 
-![Validation Report](validation_report.png)
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-synth-mental-health-asbestos-mesothelioma-all`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-mental-health-asbestos-mesothelioma-all) |
+| Sector | health |
+| Topic tags | environmental-health, asbestos, mesothelioma, occupational-health, lung-cancer, synthetic, sub-saharan-africa |
+| Modalities | `tabular`, `text` |
+| Formats | `csv` |
+| Size category | `10K<n<100K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2026-04-14 22:46:00+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/asbestos-mesothelioma", "former_mining_community")
+
+ds = load_dataset("electricsheepafrica/africa-synth-mental-health-asbestos-mesothelioma-all")
+print(ds)
+
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
 ```
 
-## Limitations
+### Convert To Pandas When Tabular
 
-- Synthetic data; not for clinical decision-making.
-- Latency modelling simplified; real exposure-disease relationships are complex.
-- Does not capture legacy contamination mapping or remediation status.
+```python
+from datasets import Dataset
 
-## References
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
+```
 
-1. WHO. Asbestos Fact Sheet. 2023.
-2. WHO Africa. Asbestos use continues in Africa. 2023.
-3. PMC1522094. Asbestos-related disease in South Africa. 2005.
-4. Wagner JC. Diffuse pleural mesothelioma and asbestos exposure in SA. *Br J Ind Med*, 1960.
-5. PMC12573932. Global burden of lung cancer from occupational asbestos (GBD 2021). 2024.
-6. PubMed 21422006. Mesothelioma mortality trends in South Africa 1995-2007. 2011.
+## Data Quality Notes
+
+- 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.
+
+## Source And Provenance
+
+- **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-mental-health-asbestos-mesothelioma-all](https://huggingface.co/datasets/electricsheepafrica/africa-synth-mental-health-asbestos-mesothelioma-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
-@dataset{electricsheepafrica_asbestos_mesothelioma_2025,
-  title={Asbestos Exposure and Mesothelioma in Sub-Saharan Africa},
-  author={Electric Sheep Africa},
-  year={2025},
-  publisher={HuggingFace},
-  url={https://huggingface.co/datasets/electricsheepafrica/asbestos-mesothelioma}
+@misc{electric_sheep_africa_africa_synth_mental_health_asbestos_mesothelioma_all_2026,
+  title        = {Asbestos Exposure & Mesothelioma (SSA) | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-mental-health-asbestos-mesothelioma-all},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-mental-health-asbestos-mesothelioma-all}}
 }
 ```
 
 ## License
 
-CC-BY-4.0
+Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+
+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.
+
+---
+
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.