Add README.md

README.md

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 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- tabular-classification
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- eastern-africa
+- education
+- employment
+- gender
+- health
+- indicators
+- ssd
+pretty_name: "South Sudan: Trained Nurses and Midwives Employment"
 dataset_info:
-  features:
-  - name: unnamed_0
-    dtype: float64
-  - name: midwives_students_enrolled_from_2019_to_2024
-    dtype: string
-  - name: unnamed_2
-    dtype: string
-  - name: unnamed_3
-    dtype: float64
-  - name: unnamed_5
-    dtype: float64
-  - name: unnamed_6
-    dtype: string
-  - name: unnamed_7
-    dtype: float64
-  - name: unnamed_8
-    dtype: float64
-  - name: unnamed_9
-    dtype: float64
-  - name: unnamed_11
-    dtype: float64
-  - name: unnamed_14
-    dtype: string
-  - name: unnamed_19
-    dtype: string
-  - name: unnamed_20
-    dtype: timestamp[ns]
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 12748
-    num_examples: 88
-  - name: test
-    num_bytes: 3284
-    num_examples: 22
-  download_size: 15743
-  dataset_size: 16032
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 88
+    - name: test
+      num_examples: 22
 ---
+
+# South Sudan: Trained Nurses and Midwives Employment
+
+**Publisher:** Solidarity with South Sudan · **Source:** [HDX](https://data.humdata.org/dataset/solidarity-with-south-sudan) · **License:** `cc-by-igo` · **Updated:** 2025-05-05
+
+---
+
+## Abstract
+
+The data has been assembled by the Solidarity with South Sudan Catholic Health Training Institute (CHTI) based in Wau, Western Bahr al-Ghazal, South Sudan. It provides insights into the employment of qualified nurses and midwives in the National Health System of South Sudan.
+
+Each row in this dataset represents tabular records. Temporal coverage is indicated by the `unnamed_20` column(s). Geographic scope: **SSD**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Public health |
+| **Unit of observation** | Tabular records |
+| **Rows (total)** | 110 |
+| **Columns** | 15 (7 numeric, 7 categorical, 1 datetime) |
+| **Train split** | 88 rows |
+| **Test split** | 22 rows |
+| **Geographic scope** | SSD |
+| **Publisher** | Solidarity with South Sudan |
+| **HDX last updated** | 2025-05-05 |
+
+---
+
+## Variables
+
+**Identifier / Metadata** — `unnamed_0` (range 1.0–106.0), `midwives_students_enrolled_from_2019_to_2024` (Western Equatoria, Western Bhar El Ghazal, Warrap), `unnamed_2` (Wau, Tambura Yambio, Wau ), `unnamed_3` (range 1.0–50.0), `unnamed_5` (range 1.0–53.0) and 10 others.
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-solidarity-with-south-sudan")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `unnamed_0` | float64 | 3.6% | 1.0 – 106.0 (mean 53.5) |
+| `midwives_students_enrolled_from_2019_to_2024` | object | 2.7% | Western Equatoria, Western Bhar El Ghazal, Warrap |
+| `unnamed_2` | object | 2.7% | Wau, Tambura Yambio, Wau  |
+| `unnamed_3` | float64 | 53.6% | 1.0 – 50.0 (mean 1.9608) |
+| `unnamed_5` | float64 | 50.9% | 1.0 – 53.0 (mean 1.963) |
+| `unnamed_6` | object | 50.9% | ABCAB (PASS), ABBAC (PASS), ABBAA (PASS) |
+| `unnamed_7` | float64 | 49.1% | 1.0 – 55.0 (mean 1.9643) |
+| `unnamed_8` | float64 | 52.7% | 1.0 – 51.0 (mean 1.9615) |
+| `unnamed_9` | float64 | 68.2% | 1.0 – 34.0 (mean 1.9429) |
+| `unnamed_11` | float64 | 53.6% | 1.0 – 50.0 (mean 1.9608) |
+| `unnamed_14` | object | 68.2% | Registered Midwife, Registered Midwife , Position  |
+| `unnamed_19` | object | 68.2% | Apuk- Warrap State , Comboni Hospital-Wau, Comboni hospital- Wau |
+| `unnamed_20` | datetime64[ns] | 65.5% |  |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-19 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `unnamed_0` | 1.0 | 106.0 | 53.5 | 53.5 |
+| `unnamed_3` | 1.0 | 50.0 | 1.9608 | 1.0 |
+| `unnamed_5` | 1.0 | 53.0 | 1.963 | 1.0 |
+| `unnamed_7` | 1.0 | 55.0 | 1.9643 | 1.0 |
+| `unnamed_8` | 1.0 | 51.0 | 1.9615 | 1.0 |
+| `unnamed_9` | 1.0 | 34.0 | 1.9429 | 1.0 |
+| `unnamed_11` | 1.0 | 50.0 | 1.9608 | 1.0 |
+
+---
+
+## 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`. 10 column(s) with >80% missing values were removed: `unnamed_4`, `unnamed_10`, `unnamed_12`, `unnamed_13`, `unnamed_15`, `unnamed_16`.... 1 exact duplicate rows were removed. 8 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 Solidarity with South Sudan 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: `unnamed_3`, `unnamed_5`, `unnamed_6`, `unnamed_7`, `unnamed_8`, `unnamed_9`, `unnamed_11`, `unnamed_14`....
+- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/solidarity-with-south-sudan) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_solidarity_with_south_sudan,
+  title     = {South Sudan: Trained Nurses and Midwives Employment},
+  author    = {Solidarity with South Sudan},
+  year      = {2025},
+  url       = {https://data.humdata.org/dataset/solidarity-with-south-sudan},
+  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.*