Datasets:

electricsheepafrica
/

africa-solidarity-with-south-sudan

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 · 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.

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

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 for the publisher's own methodology notes and caveats.

Citation

@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 — Africa's ML dataset infrastructure. Lagos, Nigeria.