electricsheepafrica/africa-ssd-requirements-and-funding-data

South Sudan - Requirements and Funding Data

Publisher: OCHA Financial Tracking System (FTS) · Source: HDX · License: cc-by-igo · Updated: 2026-04-03

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Abstract

FTS publishes data on humanitarian funding flows as reported by donors and recipient organizations. It presents all humanitarian funding to a country and funding that is specifically reported or that can be specifically mapped against funding requirements stated in humanitarian response plans. The data comes from OCHA's Financial Tracking Service and is encoded as utf-8.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the startdate, enddate column(s). Geographic scope: SSD.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Country-level aggregates
Rows (total)	37
Columns	14 (6 numeric, 6 categorical, 2 datetime)
Train split	29 rows
Test split	7 rows
Geographic scope	SSD
Publisher	OCHA Financial Tracking System (FTS)
HDX last updated	2026-04-03

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Variables

Geographic — countrycode (SSD), typeid (range 4.0–2071.0), typename (Humanitarian response plan, Humanitarian needs and response plan, Consolidated appeals process), year (range 2011.0–2031.0).

Temporal — startdate, enddate.

Outcome / Measurement — percentfunded (range 19.0–90.0).

Identifier / Metadata — id (range 367.0–1512.0), name (Not specified, South Sudan Humanitarian Response Plan 2018, Republic of South Sudan 2012), code (HSSD26, HSSD25, HSSD24), esa_source (HDX), esa_processed (2026-04-04).

Other — requirements (range 619673235.0–2055885216.0), funding (range 51616.0–1594424513.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ssd-requirements-and-funding-data")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
countrycode	object	0.0%	SSD
id	float64	56.8%	367.0 – 1512.0 (mean 775.75)
name	object	0.0%	Not specified, South Sudan Humanitarian Response Plan 2018, Republic of South Sudan 2012
code	object	56.8%	HSSD26, HSSD25, HSSD24
typeid	float64	56.8%	4.0 – 2071.0 (mean 669.625)
typename	object	56.8%	Humanitarian response plan, Humanitarian needs and response plan, Consolidated appeals process
startdate	datetime64[ns]	56.8%
enddate	datetime64[ns]	56.8%
year	int64	0.0%	2011.0 – 2031.0 (mean 2019.9189)
requirements	float64	56.8%	619673235.0 – 2055885216.0 (mean 1546361989.3125)
funding	int64	0.0%	51616.0 – 1594424513.0 (mean 534738488.0811)
percentfunded	float64	56.8%	19.0 – 90.0 (mean 66.6875)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

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Numeric Summary

Column	Min	Max	Mean	Median
id	367.0	1512.0	775.75	679.5
typeid	4.0	2071.0	669.625	57.0
year	2011.0	2031.0	2019.9189	2020.0
requirements	619673235.0	2055885216.0	1546361989.3125	1658733840.5
funding	51616.0	1594424513.0	534738488.0811	282928985.0
percentfunded	19.0	90.0	66.6875	70.5

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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. 2 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.

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Limitations

• Data originates from OCHA Financial Tracking System (FTS) 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: id, code, typeid, typename, startdate, enddate, requirements, percentfunded.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_ssd_requirements_and_funding_data,
  title     = {South Sudan - Requirements and Funding Data},
  author    = {OCHA Financial Tracking System (FTS)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ssd-requirements-and-funding-data},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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