---
license: cc-by-4.0
language:
- en
task_categories:
- tabular-classification
- tabular-regression
multilinguality: monolingual
size_categories:
- 10K<n<100K
tags:
- "africa"
- "electric-sheep-africa"
- "open-data"
- "metadata-backed"
- "health"
- "csv"
- "tabular"
- "text"
- "environmental-health"
- "arsenic"
- "groundwater"
- "skin-lesions"
- "cancer"
- "water-safety"
- "synthetic"
- "sub-saharan-africa"
pretty_name: "Arsenic in Groundwater & Health (SSA) | Africa (World Health Organization)"
---

# Arsenic in Groundwater & Health (SSA) | Africa (World Health Organization)

**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-88-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. Arsenic in Groundwater & Health in Sub-Saharan Africa Abstract Synthetic dataset modelling arsenic exposure from contaminated groundwater, biomarker levels, and health outcomes across three hydrogeological scenarios in SSA. WHO estimates 140 million people in 70+ countries drink water with arsenic >10 µg/L. In SSA, geogenic… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-arsenic-groundwater-health-senegal.

## Dataset Profile

| Field | Value |
|---|---|
| Hugging Face repo | [`electricsheepafrica/africa-synth-cancer-arsenic-groundwater-health-senegal`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-arsenic-groundwater-health-senegal) |
| Sector | health |
| Topic tags | environmental-health, arsenic, groundwater, skin-lesions, cancer, water-safety, synthetic, sub-saharan-africa |
| Modalities | `tabular`, `text` |
| Formats | `csv` |
| Size category | `10K<n<100K` |
| Countries | Senegal |
| ISO3 coverage | `SEN` |
| Last modified on HF | `2026-04-14 22:45:43+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-cancer-arsenic-groundwater-health-senegal")
print(ds)

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: upstream_publisher.
- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

## Source And Provenance

- **Source context:** World Health Organization
- **Publisher/source attribution:** WHO public data
- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-arsenic-groundwater-health-senegal](https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-arsenic-groundwater-health-senegal)
- **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_cancer_arsenic_groundwater_health_senegal_2026,
  title        = {Arsenic in Groundwater & Health (SSA) | Africa (World Health Organization)},
  author       = {WHO public data},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-arsenic-groundwater-health-senegal},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-arsenic-groundwater-health-senegal}}
}
```

## License

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`.