Standardize Electric Sheep Africa dataset card

README.md

@@ -1,149 +1,146 @@
 ---
-annotations_creators:
-- no-annotation
-language_creators:
-- found
+license: cc-by-4.0
 language:
 - en
-license: cc-by-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
 task_categories:
 - tabular-classification
 - tabular-regression
-task_ids: []
+multilinguality: monolingual
+size_categories:
+- 1K<n<10K
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- demographics
-- education
-- indicators
-- socioeconomics
-- sustainable-development
-- sustainable-development-goals-sdg
-- mus
-pretty_name: "Mauritius - Education Indicators"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 4690
-    - name: test
-      num_examples: 1172
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "economics-finance"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "demographics"
+- "education"
+- "indicators"
+- "socioeconomics"
+- "sustainable-development"
+- "sustainable-development-goals-sdg"
+- "mus"
+- "economic"
+pretty_name: "Mauritius - Education Indicators | Africa (original)"
 ---
 
-# Mauritius - Education Indicators
-
-**Publisher:** UNESCO · **Source:** [HDX](https://data.humdata.org/dataset/unesco-data-for-mauritius) · **License:** `cc-by-igo` · **Updated:** 2026-03-02
+# Mauritius - Education Indicators | Africa (original)
 
----
+**Size category:** `1K<n<10K` - **Formats:** `parquet` - **Sector:** economics_finance - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
-## Abstract
+![size](https://img.shields.io/badge/size-1K%3Cn%3C10K-blue)
+![sector](https://img.shields.io/badge/sector-economics_finance-green)
+![downloads](https://img.shields.io/badge/HF_downloads-22-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
 
-Education indicators for Mauritius.
+## TL;DR
 
-Contains data from the UNESCO Institute for Statistics [bulk data service](http://data.uis.unesco.org) covering the following categories: SDG 4 Global and Thematic (made 2026 February), Other Policy Relevant Indicators (made 2026 February), Demographic and Socio-economic (made 2026 February)
+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.
 
-Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-02. Geographic scope: **MUS**.
+## What This Dataset Covers
 
-*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.
 
----
+Dataset context from the existing Hugging Face card: Mauritius - Education Indicators Publisher: UNESCO · Source: HDX · License: cc-by-igo · Updated: 2026-03-02 Abstract Education indicators for Mauritius. Contains data from the UNESCO Institute for Statistics bulk data service covering the following categories: SDG 4 Global and Thematic (made 2026 February), Other Policy Relevant Indicators (made 2026 February), Demographic and Socio-economic (made 2026 February) Each row in this dataset represents country-level… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-unesco-data-for-mauritius.
 
-## Dataset Characteristics
+## Dataset Profile
 
-| | |
+| Field | Value |
 |---|---|
-| **Domain** | Education |
-| **Unit of observation** | Country-level aggregates |
-| **Rows (total)** | 5,863 |
-| **Columns** | 6 (2 numeric, 4 categorical, 0 datetime) |
-| **Train split** | 4,690 rows |
-| **Test split** | 1,172 rows |
-| **Geographic scope** | MUS |
-| **Publisher** | UNESCO |
-| **HDX last updated** | 2026-03-02 |
-
----
-
-## Variables
-
-**Geographic** — `country_id` (MUS), `year` (range 1970.0–2025.0).
-
-**Outcome / Measurement** — `value` (range 0.0–5118765.0).
-
-**Identifier / Metadata** — `indicator_id` (GER.5T8, GER.5T8.GPIA, GER.5T8.F), `esa_source` (HDX), `esa_processed` (2026-04-04).
-
----
-
-## Quick Start
+| Hugging Face repo | [`electricsheepafrica/africa-unesco-data-for-mauritius`](https://huggingface.co/datasets/electricsheepafrica/africa-unesco-data-for-mauritius) |
+| Sector | economics_finance |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, demographics, education, indicators, socioeconomics, sustainable-development, sustainable-development-goals-sdg, mus |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `1K<n<10K` |
+| Countries | Mauritius |
+| ISO3 coverage | `MUS` |
+| Last modified on HF | `2026-04-04 14:25:54+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-unesco-data-for-mauritius")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
+ds = load_dataset("electricsheepafrica/africa-unesco-data-for-mauritius")
+print(ds)
 
-print(train.shape)
-train.head()
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
 ```
 
----
-
-## Schema
+### Convert To Pandas When Tabular
 
-| Column | Type | Null % | Range / Sample Values |
-|---|---|---|---|
-| `indicator_id` | object | 0.0% | GER.5T8, GER.5T8.GPIA, GER.5T8.F |
-| `country_id` | object | 0.0% | MUS |
-| `year` | int64 | 0.0% | 1970.0 – 2025.0 (mean 2009.6988) |
-| `value` | float64 | 0.0% | 0.0 – 5118765.0 (mean 5969.3706) |
-| `esa_source` | object | 0.0% | HDX |
-| `esa_processed` | object | 0.0% | 2026-04-04 |
-
----
-
-## Numeric Summary
-
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-| `year` | 1970.0 | 2025.0 | 2009.6988 | 2012.0 |
-| `value` | 0.0 | 5118765.0 | 5969.3706 | 10.206 |
+```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`. 2 column(s) with >80% missing values were removed: `magnitude`, `qualifier`. 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 4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-unesco-data-for-mauritius](https://huggingface.co/datasets/electricsheepafrica/africa-unesco-data-for-mauritius)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
 
-- Data originates from UNESCO 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/unesco-data-for-mauritius) 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_unesco_data_for_mauritius,
-  title     = {Mauritius - Education Indicators},
-  author    = {UNESCO},
-  year      = {2026},
-  url       = {https://data.humdata.org/dataset/unesco-data-for-mauritius},
-  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
+@misc{electric_sheep_africa_africa_unesco_data_for_mauritius_2026,
+  title        = {Mauritius - Education Indicators | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-unesco-data-for-mauritius},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-unesco-data-for-mauritius}}
 }
 ```
 
+## 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.
+
 ---
 
-*[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`.