Datasets:

electricsheepafrica
/

africa-south-sudan-humanitarian-needs

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-regression
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - gender-and-age-disaggregated-data-gadd
  - humanitarian-needs-overview-hno
  - hxl
  - needs-assessment
  - people-in-need-pin
  - ssd
pretty_name: 'South Sudan: Humanitarian Needs'
dataset_info:
  splits:
    - name: train
      num_examples: 8
    - name: test
      num_examples: 2

South Sudan: Humanitarian Needs

Publisher: OCHA Humanitarian Programme Cycle Tools (HPC Tools) · Source: HDX · License: cc-by · Updated: 2026-02-13

Abstract

This dataset was compiled by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. It provides the Humanitarian Country Team’s shared understanding of the crisis, including the most pressing humanitarian need and the estimated number of people who need assistance, and represents a consolidated evidence base and helps inform joint strategic response planning.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2026-02-13. Geographic scope: SSD.

Curated into ML-ready Parquet format by Electric Sheep Africa.

Dataset Characteristics


Domain	Humanitarian and development data
Unit of observation	Tabular records
Rows (total)	11
Columns	6 (2 numeric, 4 categorical, 0 datetime)
Train split	8 rows
Test split	2 rows
Geographic scope	SSD
Publisher	OCHA Humanitarian Programme Cycle Tools (HPC Tools)
HDX last updated	2026-02-13

Variables

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-05).

Other — description (Final caseload, Camp Coordination and Camp Management, Education), cluster (ALL, CCM, EDU), in_need (range 647302.0–9913863.0), targeted (range 463705.0–4343435.0).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-south-sudan-humanitarian-needs")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`description`	object	0.0%	Final caseload, Camp Coordination and Camp Management, Education
`cluster`	object	0.0%	ALL, CCM, EDU
`in_need`	float64	9.1%	647302.0 – 9913863.0 (mean 5187125.8)
`targeted`	int64	0.0%	463705.0 – 4343435.0 (mean 1776460.3636)
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-05

Numeric Summary

Column	Min	Max	Mean	Median
`in_need`	647302.0	9913863.0	5187125.8	5760312.5
`targeted`	463705.0	4343435.0	1776460.3636	1548355.0

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. 5 column(s) with >80% missing values were removed: category, population, affected, reached, info. 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 OCHA Humanitarian Programme Cycle Tools (HPC Tools) and has not been independently validated by ESA.
Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_africa_south_sudan_humanitarian_needs,
  title     = {South Sudan: Humanitarian Needs},
  author    = {OCHA Humanitarian Programme Cycle Tools (HPC Tools)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/south-sudan-humanitarian-needs},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.