Standardize Electric Sheep Africa dataset card

README.md

@@ -1,113 +1,136 @@
-# Master Datacard for Water Indicators for African Countries
+---
+license: other
+language:
+- en
+task_categories:
+- tabular-classification
+- tabular-regression
+multilinguality: monolingual
+size_categories:
+- 1K<n<10K
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "other-unclassified"
+- "csv"
+- "tabular"
+- "text"
+pretty_name: "Water Indicators For African Countries | Africa (World Health Organization)"
+---
 
-This repository contains time-series datasets for key water-related indicators for 54 African countries. The data is sourced from The World Bank and has been cleaned, processed, and organized for analysis.
+# Water Indicators For African Countries | Africa (World Health Organization)
 
-Each country has its own set of files, including a main CSV dataset and a corresponding datacard in Markdown format. The data covers the period from 1960 to 2024, where available.
+**Size category:** `1K<n<10K` - **Formats:** `csv` - **Sector:** other_unclassified - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
----
+![size](https://img.shields.io/badge/size-1K%3Cn%3C10K-blue)
+![sector](https://img.shields.io/badge/sector-other_unclassified-green)
+![downloads](https://img.shields.io/badge/HF_downloads-103-orange)
+![license](https://img.shields.io/badge/license-other-lightgrey)
 
-## Repository Structure
+## TL;DR
 
-The datasets are organized by country. Each country's folder contains:
+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.
 
-1. A CSV file with the naming convention: `{CountryName}-Water-Indicators-Dataset-{StartYear}-{EndYear}.csv`
-2. A detailed datacard named `datacard_Water.md`.
+## What This Dataset Covers
 
----
+Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.
 
-## Indicators Included
+Dataset context from the existing Hugging Face card: Master Datacard for Water Indicators for African Countries This repository contains time-series datasets for key water-related indicators for 54 African countries. The data is sourced from The World Bank and has been cleaned, processed, and organized for analysis. Each country has its own set of files, including a main CSV dataset and a corresponding datacard in Markdown format. The data covers the period from 1960 to 2024, where available. Repository Structure The… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Water-Indicators-For-African-Countries.
 
-This collection includes the following water indicators:
+## Dataset Profile
 
-- Annual freshwater withdrawals, total (% of internal resources)
-- Marine protected areas (% of territorial waters)
-- People using at least basic drinking water services (% of population)
-- People with basic handwashing facilities including soap and water (% of population)
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/Water-Indicators-For-African-Countries`](https://huggingface.co/datasets/electricsheepafrica/Water-Indicators-For-African-Countries) |
+| Sector | other_unclassified |
+| Topic tags | other_unclassified |
+| Modalities | `tabular`, `text` |
+| Formats | `csv` |
+| Size category | `1K<n<10K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2025-06-21 10:17:28+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
 
----
+## How To Read This Dataset
 
-## Countries Included
-
-This dataset covers all 54 sovereign nations of Africa as recognized by the source data:
-
-- Algeria
-- Angola
-- Benin
-- Botswana
-- Burkina Faso
-- Burundi
-- Cabo Verde
-- Cameroon
-- Central African Republic
-- Chad
-- Comoros
-- Congo, Dem. Rep.
-- Congo, Rep.
-- Cote d'Ivoire
-- Djibouti
-- Egypt, Arab Rep.
-- Equatorial Guinea
-- Eritrea
-- Eswatini
-- Ethiopia
-- Gabon
-- Gambia, The
-- Ghana
-- Guinea
-- Guinea-Bissau
-- Kenya
-- Lesotho
-- Liberia
-- Libya
-- Madagascar
-- Malawi
-- Mali
-- Mauritania
-- Mauritius
-- Morocco
-- Mozambique
-- Namibia
-- Niger
-- Nigeria
-- Rwanda
-- Sao Tome and Principe
-- Senegal
-- Seychelles
-- Sierra Leone
-- Somalia
-- South Africa
-- South Sudan
-- Sudan
-- Tanzania
-- Togo
-- Tunisia
-- Uganda
-- Zambia
-- Zimbabwe
+- 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
 
-## Data Preparation
+```python
+from datasets import load_dataset
 
-The raw data from The World Bank was processed using a Python script with the following steps:
+ds = load_dataset("electricsheepafrica/Water-Indicators-For-African-Countries")
+print(ds)
 
-1. **Filtering**: Data was filtered for each of the 54 African countries.
-2. **Reshaping**: Wide-format data (with years as columns) was melted into a long format.
-3. **Merging**: All indicator files for a country were merged into a single time-series dataset based on the 'Year' column.
-4. **Cleaning**: The 'Year' column was converted to a standard date format (`YYYY-MM-DD`).
-5. **Missing Data Handling**: Gaps in the time-series were filled using linear interpolation followed by a back-fill to ensure data continuity.
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
+```
 
----
+### Convert To Pandas When Tabular
 
-## How to Use
+```python
+from datasets import Dataset
 
-You can access the data directly through the Hugging Face Hub, either by downloading individual files or by using the `datasets` library to load the data.
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
+```
 
-```python
-from datasets import load_dataset
+## Data Quality Notes
 
-# Example: Load the dataset for Nigeria
-dataset = load_dataset('electricsheepafrica/Water-Indicators-For-African-Countries', data_files='Nigeria/Nigeria-Water-Indicators-Dataset-1960-2024.csv')
+- 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, sector, upstream_publisher, license, language.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
 
-print(dataset)
+## Source And Provenance
+
+- **Source context:** World Health Organization
+- **Publisher/source attribution:** WHO public data
+- **License:** Source-specific or other license
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/Water-Indicators-For-African-Countries](https://huggingface.co/datasets/electricsheepafrica/Water-Indicators-For-African-Countries)
+- **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_water_indicators_for_african_countries_2026,
+  title        = {Water Indicators For African Countries | Africa (World Health Organization)},
+  author       = {WHO public data},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/Water-Indicators-For-African-Countries},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/Water-Indicators-For-African-Countries}}
+}
 ```
+
+## License
+
+Released under Source-specific or other license.
+
+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`.