Standardize Electric Sheep Africa dataset card

README.md

@@ -1,74 +1,144 @@
 ---
 license: cc-by-4.0
-task_categories:
-- tabular-classification
 language:
 - en
-tags:
-- climate-health
-- maternal-health
-- neonatal
-- preterm-birth
-- heat-exposure
-- synthetic
-- sub-saharan-africa
-pretty_name: Climate & Maternal-Neonatal Outcomes (SSA)
+task_categories:
+- tabular-classification
+- tabular-regression
+multilinguality: monolingual
 size_categories:
 - 10K<n<100K
-configs:
-- config_name: heat_exposed_pregnancy
-  data_files: data/maternal_heat_exposed.csv
-  default: true
-- config_name: drought_food_insecure_pregnancy
-  data_files: data/maternal_drought_food_insecure.csv
-- config_name: flood_disrupted_services
-  data_files: data/maternal_flood_disrupted.csv
-data_type: synthetic
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "health"
+- "csv"
+- "tabular"
+- "text"
+- "climate-health"
+- "maternal-health"
+- "neonatal"
+- "preterm-birth"
+- "heat-exposure"
+- "synthetic"
+- "sub-saharan-africa"
+- "maternal"
+pretty_name: "Climate & Maternal-Neonatal Outcomes (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.
+# Climate & Maternal-Neonatal Outcomes (SSA) | Africa (Electric Sheep Africa metadata inventory)
+
+**Size category:** `10K<n<100K` - **Formats:** `csv` - **Sector:** health - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+
+![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-51-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
+
+## TL;DR
+
+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
+
+Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.
+
+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. Climate & Maternal-Neonatal Outcomes in Sub-Saharan Africa Abstract Synthetic dataset modelling climate impacts on maternal and neonatal health across three scenarios in SSA. Heat exposure increases preterm birth risk by 16% per 1°C rise (Chersich et al., 2020). Food insecurity from drought drives maternal anaemia and LBW.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-maternal-health-climate-maternal-neonatal-all.
+
+## Dataset Profile
 
-# Climate & Maternal-Neonatal Outcomes in Sub-Saharan Africa
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-synth-maternal-health-climate-maternal-neonatal-all`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-maternal-health-climate-maternal-neonatal-all) |
+| Sector | health |
+| Topic tags | climate-health, maternal-health, neonatal, preterm-birth, heat-exposure, 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 22:45:13+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
 
-## Abstract
+## How To Read This Dataset
 
-Synthetic dataset modelling climate impacts on maternal and neonatal health across three scenarios in SSA. Heat exposure increases preterm birth risk by 16% per 1°C rise (Chersich et al., 2020). Food insecurity from drought drives maternal anaemia and LBW. Flooding disrupts facility access, increasing unattended deliveries.
+- 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.
 
-### Scenarios
+## Usage
 
-- **Heat Exposed Pregnancy**: High ambient temperature and heatwave exposure.
-- **Drought Food Insecure Pregnancy**: Chronic food insecurity with maternal undernutrition.
-- **Flood Disrupted Services**: Flood-related disruption of maternal health services.
+```python
+from datasets import load_dataset
 
-## Parameterization Evidence
+ds = load_dataset("electricsheepafrica/africa-synth-maternal-health-climate-maternal-neonatal-all")
+print(ds)
 
-| Parameter | Value | Source | Year |
-| --- | --- | --- | --- |
-| Heat exposure +16% preterm risk per 1°C | Dose-response | Chersich et al., BMJ | 2020 |
-| NMR 24-35/1000 in SSA | Neonatal mortality | UN IGME | 2023 |
-| MMR 380-550/100K in SSA | Maternal mortality | WHO MMEIG | 2023 |
-| Malaria in pregnancy 20-30% in endemic areas | Co-morbidity | WHO | 2022 |
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
+```
+
+### Convert To Pandas When Tabular
+
+```python
+from datasets import Dataset
+
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
+```
+
+## 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.
 
-![Validation Report](validation_report.png)
+## Source And Provenance
 
-## References
+- **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-maternal-health-climate-maternal-neonatal-all](https://huggingface.co/datasets/electricsheepafrica/africa-synth-maternal-health-climate-maternal-neonatal-all)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
 
-1. Chersich MF, et al. Associations between high temperatures and adverse birth outcomes. *BMJ*, 2020.
-2. UN IGME. Levels and trends in child mortality. 2023.
-3. WHO MMEIG. Maternal mortality estimates. 2023.
+## 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_climate_maternal_neonatal_2025,
-  title={Climate and Maternal-Neonatal Outcomes in Sub-Saharan Africa},
-  author={Electric Sheep Africa},
-  year={2025},
-  publisher={HuggingFace},
-  url={https://huggingface.co/datasets/electricsheepafrica/climate-maternal-neonatal}
+@misc{electric_sheep_africa_africa_synth_maternal_health_climate_maternal_neonatal_all_2026,
+  title        = {Climate & Maternal-Neonatal Outcomes (SSA) | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-maternal-health-climate-maternal-neonatal-all},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-maternal-health-climate-maternal-neonatal-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`.