| --- |
| 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: |
| splits: |
| - 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.* |
