README.md

---
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.*