Standardize Electric Sheep Africa dataset card

README.md

@@ -1,233 +1,142 @@
 ---
-annotations_creators:
-- no-annotation
-language_creators:
-- found
+license: cc-by-sa-4.0
 language:
 - en
-license: cc-by-sa-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
 task_categories:
 - tabular-classification
 - tabular-regression
-- other
-task_ids: []
+multilinguality: monolingual
+size_categories:
+- 1K<n<10K
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- education
-- health-facilities
-- transportation
-- bdi
-pretty_name: "Burundi - Accessibility Indicators"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 2184
-    - name: test
-      num_examples: 546
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "health"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "education"
+- "health-facilities"
+- "transportation"
+- "bdi"
+pretty_name: "Burundi - Accessibility Indicators | Africa (original)"
 ---
 
-# Burundi - Accessibility Indicators
-
-**Publisher:** HeiGIT (Heidelberg Institute for Geoinformation Technology) · **Source:** [HDX](https://data.humdata.org/dataset/burundi-accessibility-indicators) · **License:** `cc-by-sa` · **Updated:** 2026-02-27
-
----
-
-## Abstract
-
-This dataset provides insights into spatial accessibility to healthcare and
-education services across Burundi. It has been created using free and
-open tools such as [openrouteservice](https://openrouteservice.org/) and open
-data sources, primarily [OpenStreetMap](https://www.openstreetmap.org/) (OSM).
-
+# Burundi - Accessibility Indicators | Africa (original)
 
-To assess accessibility to education and healthcare, we use travel-time 
-isochrones—polygons representing areas reachable within a given time or distance by 
-car. We overlay these isochrones with [WorldPop](https://www.worldpop.org/) population 
-data, which provides 100m-resolution estimates. This allows us to calculate the 
-population within time intervals from 10 to 120 minutes away from hospital services and 
-distance intervals from 5 to 50 km away from schools. The unit of analysis is defined 
-by [geoboundaries](https://www.geoboundaries.org/) country borders, and where available 
-we also summarise results at finer administrative levels (ADM 1–4).
+**Size category:** `1K<n<10K` - **Formats:** `parquet` - **Sector:** health - *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-health-green)
+![downloads](https://img.shields.io/badge/HF_downloads-8-orange)
+![license](https://img.shields.io/badge/license-cc--by--sa--4.0-lightgrey)
 
-Data Structure:
+## TL;DR
 
-- **name**: Region or country name.
-- **iso**: ISO3 country code.
-- **id**: Unique identifier for the administrative unit.
-- **country**: ISO3 country code.
-- **admin_level**: Administrative level of the unit.
-- **category**: Service category — `education`, `hospitals` or
-  `primary_healthcare`.
-- **range_type**: Method used for the catchment zone — `distance` or `time`.
-- **range**: Distance (in meters) or Time away (in seconds) from schools used
-  to generate the polygon.
-- **population**: Total population within the specified range.
-- **school_age_population**: Number of school-age individuals within the range.
-- **school_age_population_share**: Cumulative percentage of school-age
-  population.
-- **school_age_population_interval**: Incremental school-age population added
-  in the current distance band.
-- **school_age_population_interval_share**: Proportion of new school-age
-  population in the current interval.
-- **population_share**: Cumulative percentage of total population.
-- **population_interval**: Incremental population added in the current distance
-  band.
-- **population_interval_share**: Share of the total population represented by
-  the current interval.
+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.
 
-This dataset is one of many [HeiGIT exports on HDX](https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology).
-See the [HeiGIT](https://heigit.org/) website for more information.
+## What This Dataset Covers
 
-We are looking forward to hearing about your use-case! Feel free to reach out
-to us and tell us about your research at
-[communications@heigit.org](mailto:communications@heigit.org) – we would be
-happy to amplify your work.
+Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.
 
-References:
-
-- [Geldsetzer, P., Reinmuth, M., Ouma, P. O., Lautenbach, S. et al. (2020)](https://www.thelancet.com/journals/lanhl/article/PIIS2666-7568(20)30010-6/fulltext)
-- [Petricola, S., Reinmuth, M., Lautenbach, S. et al. (2022)](https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-022-00315-2)
-- [Klipper, I. G., Zipf, A., and Lautenbach, S. (2021)](https://agile-giss.copernicus.org/articles/2/4/2021/)
-- [Ruiz Sánchez, R., Reinmuth, M., Albornoz, C., Lautenbach, S., and Zipf, A. (2025)](https://agile-giss.copernicus.org/articles/6/10/2025/)
-
-Further Information:
-
-- [Open Access Lens](https://giscience.github.io/open-access-lens/#/)
-
-**Limitations**:
-
-* **OSM Completeness**: This analysis relies on OpenStreetMap (OSM) data. While OSM is
-the most complete open map of the world, data quality varies significantly by region.
-In areas with unmapped roads or facilities, accessibility may be underestimated.
-
-* **Population Estimates**: Population counts are derived from WorldPop top-down
-estimates (constrained). These are statistical models based on census projections and
-satellite imagery, not direct census counts, and may contain inaccuracies at the local
-pixel level.
-
-* **Travel Time Assumptions**: Isochrones are calculated using standard vehicle speeds
-for different road types. These models do not account for real-time traffic, seasonal
-weather conditions (e.g., flooding), or road surface degradation.
-
-* **Boundary Precision**: Administrative boundaries are sourced from geoBoundaries.
-These may differ slightly from official government demarcations or other schemas.
-
-Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-02-27. Geographic scope: **BDI**.
-
-*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
-
----
+Dataset context from the existing Hugging Face card: Burundi - Accessibility Indicators Publisher: HeiGIT (Heidelberg Institute for Geoinformation Technology) · Source: HDX · License: cc-by-sa · Updated: 2026-02-27 Abstract This dataset provides insights into spatial accessibility to healthcare and education services across Burundi. It has been created using free and open tools such as openrouteservice and open data sources, primarily OpenStreetMap (OSM). To assess accessibility to education and healthcare, we use… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-education-burundi.
 
-## Dataset Characteristics
+## Dataset Profile
 
-| | |
+| Field | Value |
 |---|---|
-| **Domain** | Public health |
-| **Unit of observation** | Country-level aggregates |
-| **Rows (total)** | 2,730 |
-| **Columns** | 14 (5 numeric, 9 categorical, 0 datetime) |
-| **Train split** | 2,184 rows |
-| **Test split** | 546 rows |
-| **Geographic scope** | BDI |
-| **Publisher** | HeiGIT (Heidelberg Institute for Geoinformation Technology) |
-| **HDX last updated** | 2026-02-27 |
-
----
-
-## Variables
-
-**Geographic** — `country` (BDI), `admin_level` (ADM2, ADM1, ADM0), `category` (education), `range_type` (DISTANCE), `population_type` (school_age, total) and 4 others.
-
-**Identifier / Metadata** — `name` (Kirundo, Kayanza, Bururi), `id` (87207978B16018837471159, 21766830B421358417112, 21766830B26172674635426), `esa_source` (HDX), `esa_processed` (2026-04-27).
-
-**Other** — `range` (range 5000.0–50000.0).
-
----
-
-## Quick Start
+| Hugging Face repo | [`electricsheepafrica/africa-education-burundi`](https://huggingface.co/datasets/electricsheepafrica/africa-education-burundi) |
+| Sector | health |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, education, health-facilities, transportation, bdi |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `1K<n<10K` |
+| Countries | Burundi |
+| ISO3 coverage | `BDI` |
+| Last modified on HF | `2026-04-27 00:54:17+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-education-burundi")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
+ds = load_dataset("electricsheepafrica/africa-education-burundi")
+print(ds)
 
-print(train.shape)
-train.head()
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
 ```
 
----
-
-## Schema
-
-| Column | Type | Null % | Range / Sample Values |
-|---|---|---|---|
-| `name` | object | 0.0% | Kirundo, Kayanza, Bururi |
-| `id` | object | 0.0% | 87207978B16018837471159, 21766830B421358417112, 21766830B26172674635426 |
-| `country` | object | 0.0% | BDI |
-| `admin_level` | object | 0.0% | ADM2, ADM1, ADM0 |
-| `category` | object | 0.0% | education |
-| `range_type` | object | 0.0% | DISTANCE |
-| `range` | int64 | 0.0% | 5000.0 – 50000.0 (mean 27736.2637) |
-| `population_type` | object | 0.0% | school_age, total |
-| `population` | int64 | 0.0% | 0.0 – 11133971.0 (mean 131005.37) |
-| `population_share` | float64 | 0.0% | 0.0 – 100.0 (mean 73.1507) |
-| `population_interval` | int64 | 0.0% | 0.0 – 2438116.0 (mean 16677.0319) |
-| `population_interval_share` | float64 | 0.0% | 0.0 – 99.98 (mean 9.5526) |
-| `esa_source` | object | 0.0% | HDX |
-| `esa_processed` | object | 0.0% | 2026-04-27 |
-
----
-
-## Numeric Summary
+### Convert To Pandas When Tabular
 
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-| `range` | 5000.0 | 50000.0 | 27736.2637 | 30000.0 |
-| `population` | 0.0 | 11133971.0 | 131005.37 | 41943.5 |
-| `population_share` | 0.0 | 100.0 | 73.1507 | 88.37 |
-| `population_interval` | 0.0 | 2438116.0 | 16677.0319 | 2153.5 |
-| `population_interval_share` | 0.0 | 99.98 | 9.5526 | 3.8 |
+```python
+from datasets import Dataset
 
----
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
+```
 
-## Curation
+## Data Quality Notes
 
-Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 1 column(s) with >80% missing values were removed: `iso`. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
+- 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
 
-## Limitations
+- **Source context:** original
+- **Publisher/source attribution:** original
+- **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-education-burundi](https://huggingface.co/datasets/electricsheepafrica/africa-education-burundi)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
 
-- Data originates from HeiGIT (Heidelberg Institute for Geoinformation Technology) and has not been independently validated by ESA.
-- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
-- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/burundi-accessibility-indicators) for the publisher's own methodology notes and caveats.
+## 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{hdx_africa_education_burundi,
-  title     = {Burundi - Accessibility Indicators},
-  author    = {HeiGIT (Heidelberg Institute for Geoinformation Technology)},
-  year      = {2026},
-  url       = {https://data.humdata.org/dataset/burundi-accessibility-indicators},
-  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
+@misc{electric_sheep_africa_africa_education_burundi_2026,
+  title        = {Burundi - Accessibility Indicators | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-education-burundi},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-education-burundi}}
 }
 ```
 
+## License
+
+Released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/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.
+
 ---
 
-*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.