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

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: SDN.

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)	59
Columns	14 (6 numeric, 6 categorical, 2 datetime)
Train split	47 rows
Test split	11 rows
Geographic scope	SDN
Publisher	OCHA Financial Tracking System (FTS)
HDX last updated	2026-04-03

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Variables

Geographic — countrycode (SDN), typeid (range 4.0–2170.0), typename (Consolidated appeals process, Humanitarian response plan, Consolidated inter-agency appeal), year (range 2000.0–2029.0).

Temporal — startdate, enddate.

Outcome / Measurement — percentfunded (range 16.0–482.0).

Identifier / Metadata — id (range 46.0–1514.0), name (Not specified, Sudan Humanitarian Needs and Response Plan 2026, Sudan 2001), code (HSDN26, CSDN12, CSDN01), esa_source (HDX), esa_processed (2026-04-05).

Other — requirements (range 6097545.0–4162518656.0), funding (range 377188.0–1906866410.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-sdn-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%	SDN
id	float64	50.8%	46.0 – 1514.0 (mean 519.6207)
name	object	0.0%	Not specified, Sudan Humanitarian Needs and Response Plan 2026, Sudan 2001
code	object	50.8%	HSDN26, CSDN12, CSDN01
typeid	float64	50.8%	4.0 – 2170.0 (mean 695.7241)
typename	object	50.8%	Consolidated appeals process, Humanitarian response plan, Consolidated inter-agency appeal
startdate	datetime64[ns]	50.8%
enddate	datetime64[ns]	50.8%
year	int64	0.0%	2000.0 – 2029.0 (mean 2013.678)
requirements	float64	50.8%	6097545.0 – 4162518656.0 (mean 1358931260.8966)
funding	int64	0.0%	377188.0 – 1906866410.0 (mean 447016217.661)
percentfunded	float64	50.8%	16.0 – 482.0 (mean 73.7241)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-05

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Numeric Summary

Column	Min	Max	Mean	Median
id	46.0	1514.0	519.6207	413.0
typeid	4.0	2170.0	695.7241	110.0
year	2000.0	2029.0	2013.678	2014.0
requirements	6097545.0	4162518656.0	1358931260.8966	1132952016.0
funding	377188.0	1906866410.0	447016217.661	184926048.0
percentfunded	16.0	482.0	73.7241	59.0

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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_sdn_requirements_and_funding_data,
  title     = {Sudan - Requirements and Funding Data},
  author    = {OCHA Financial Tracking System (FTS)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/sdn-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.