Datasets:

electricsheepafrica
/

africa-who-historical-data-for-tun

@@ -1,162 +1,141 @@
 ---
-annotations_creators:
-- no-annotation
-language_creators:
-- found
 language:
 - en
-license: other
-multilinguality:
-- monolingual
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
 task_categories:
 - tabular-classification
 - tabular-regression
-task_ids: []
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- hxl
-- indicators
-- tun
-pretty_name: "Tunisia - Historical Health Indicators"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 7397
-    - name: test
-      num_examples: 1849
 ---
-# Tunisia - Historical Health Indicators
-**Publisher:** World Health Organization · **Source:** [HDX](https://data.humdata.org/dataset/who-historical-data-for-tun) · **License:** `hdx-other` · **Updated:** 2025-02-07
----
-## Abstract
-This dataset contains historical data from WHO's [data portal](https://www.who.int/gho/en/).
-Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-02-07. Geographic scope: **TUN**.
-*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
----
-## Dataset Characteristics
-| | |
 |---|---|
-| **Domain** | Humanitarian and development data |
-| **Unit of observation** | First-level administrative unit observations |
-| **Rows (total)** | 9,247 |
-| **Columns** | 18 (6 numeric, 12 categorical, 0 datetime) |
-| **Train split** | 7,397 rows |
-| **Test split** | 1,849 rows |
-| **Geographic scope** | TUN |
-| **Publisher** | World Health Organization |
-| **HDX last updated** | 2025-02-07 |
----
-## Variables
-**Geographic** — `gho_display` (Mean BMI (kg/m&#xb2;) (crude estimate), Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol), Adolescent mortality rate (per 1 000 age specific cohort)), `year_display` (range 1961.0–2025.0), `startyear` (range 1961.0–2025.0), `endyear` (range 1961.0–2025.0), `region_code` (EMR, #region+code) and 4 others.
-**Outcome / Measurement** — `value` (No data, No, Yes).
-**Identifier / Metadata** — `gho_code` (NCD_BMI_MEANC, SA_0000001400_ARCHIVED, CHILDMORT10TO19), `dimension_code` (SEX_FMLE, SEX_MLE, SEX_BTSX), `dimension_name` (Female, Male, Both sexes), `esa_source`, `esa_processed`.
-**Other** — `numeric` (range 0.0–2071202991.0), `low` (range 0.0–97577.0), `high` (range 0.0–145576.0).
----
-## Quick Start
 ```python
 from datasets import load_dataset
-ds    = load_dataset("electricsheepafrica/africa-who-historical-data-for-tun")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
-print(train.shape)
-train.head()
 ```
----
-## Schema
-| Column | Type | Null % | Range / Sample Values |
-|---|---|---|---|
-| `gho_code` | object | 0.0% | NCD_BMI_MEANC, SA_0000001400_ARCHIVED, CHILDMORT10TO19 |
-| `gho_display` | object | 0.0% | Mean BMI (kg/m&#xb2;) (crude estimate), Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol), Adolescent mortality rate (per 1 000 age specific cohort) |
-| `year_display` | float64 | 0.0% | 1961.0 – 2025.0 (mean 2009.3673) |
-| `startyear` | float64 | 0.0% | 1961.0 – 2025.0 (mean 2009.3526) |
-| `endyear` | float64 | 0.0% | 1961.0 – 2025.0 (mean 2009.3673) |
-| `region_code` | object | 0.0% | EMR, #region+code |
-| `region_display` | object | 0.0% | Eastern Mediterranean, #region+name |
-| `country_code` | object | 0.0% | TUN, #country+code |
-| `country_display` | object | 0.0% | Tunisia, #country+name |
-| `dimension_type` | object | 21.3% | SEX, WEALTHDECILE, DHSMICSGEOREGION |
-| `dimension_code` | object | 21.3% | SEX_FMLE, SEX_MLE, SEX_BTSX |
-| `dimension_name` | object | 22.3% | Female, Male, Both sexes |
-| `numeric` | float64 | 25.8% | 0.0 – 2071202991.0 (mean 1226387.6764) |
-| `value` | object | 1.2% | No data, No, Yes |
-| `low` | float64 | 46.8% | 0.0 – 97577.0 (mean 108.1606) |
-| `high` | float64 | 46.9% | 0.0 – 145576.0 (mean 185.4945) |
-| `esa_source` | object | 0.0% |  |
-| `esa_processed` | object | 0.0% |  |
----
-## Numeric Summary
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-| `year_display` | 1961.0 | 2025.0 | 2009.3673 | 2011.0 |
-| `startyear` | 1961.0 | 2025.0 | 2009.3526 | 2011.0 |
-| `endyear` | 1961.0 | 2025.0 | 2009.3673 | 2011.0 |
-| `numeric` | 0.0 | 2071202991.0 | 1226387.6764 | 28.2149 |
-| `low` | 0.0 | 97577.0 | 108.1606 | 24.699 |
-| `high` | 0.0 | 145576.0 | 185.4945 | 36.4645 |
----
-## Curation
-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: `gho_url`. 66 exact duplicate rows were removed. 6 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
----
-## Limitations
-- Data originates from World Health Organization and has not been independently validated by ESA.
-- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
-- The following columns have >20% missing values and should be treated with caution in modelling: `dimension_type`, `dimension_code`, `dimension_name`, `numeric`, `low`, `high`.
-- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/who-historical-data-for-tun) for the publisher's own methodology notes and caveats.
----
 ## Citation
 ```bibtex
-@dataset{hdx_africa_who_historical_data_for_tun,
-  title     = {Tunisia - Historical Health Indicators},
-  author    = {World Health Organization},
-  year      = {2025},
-  url       = {https://data.humdata.org/dataset/who-historical-data-for-tun},
-  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
 }
 ```
 ---
-*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*

 ---
+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"
+- "health"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "hxl"
+- "indicators"
+- "tun"
+pretty_name: "Tunisia - Historical Health Indicators | Africa (original)"
 ---
+# Tunisia - Historical Health Indicators | Africa (original)
+**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-9-orange)
+![license](https://img.shields.io/badge/license-other-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: Tunisia - Historical Health Indicators Publisher: World Health Organization · Source: HDX · License: hdx-other · Updated: 2025-02-07 Abstract This dataset contains historical data from WHO's data portal. Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-02-07. Geographic scope: TUN. Curated into ML-ready Parquet format by Electric Sheep Africa. Dataset Characteristics Domain… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-who-historical-data-for-tun.
+## Dataset Profile
+| Field | Value |
 |---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-who-historical-data-for-tun`](https://huggingface.co/datasets/electricsheepafrica/africa-who-historical-data-for-tun) |
+| Sector | health |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, hxl, indicators, tun |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `1K<n<10K` |
+| Countries | Tunisia |
+| ISO3 coverage | `TUN` |
+| Last modified on HF | `2026-04-24 16:09:49+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-who-historical-data-for-tun")
+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:** original
+- **Publisher/source attribution:** original
+- **License:** Source-specific or other license
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-who-historical-data-for-tun](https://huggingface.co/datasets/electricsheepafrica/africa-who-historical-data-for-tun)
+- **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_who_historical_data_for_tun_2026,
+  title        = {Tunisia - Historical Health Indicators | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-who-historical-data-for-tun},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-who-historical-data-for-tun}}
 }
 ```
+## 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`.