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

Congo - 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: COG.

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)	44
Columns	14 (6 numeric, 6 categorical, 2 datetime)
Train split	35 rows
Test split	8 rows
Geographic scope	COG
Publisher	OCHA Financial Tracking System (FTS)
HDX last updated	2026-04-03

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Variables

Geographic — countrycode (COG), typeid (range 4.0–2170.0), typename (Regional response plan, Other, Consolidated appeals process), year (range 2000.0–2026.0).

Temporal — startdate, enddate.

Outcome / Measurement — percentfunded (range 17.0–138.0).

Identifier / Metadata — id (range 42.0–1213.0), name (Not specified, Democratic Republic of the Congo Regional Refugee Response Plan 2025, Republic of Congo 2001), code (RDRC_RRP25, CCOG10, CCOG01), esa_source (HDX), esa_processed (2026-04-04).

Other — requirements (range 11079843.0–59195017.0), funding (range 409836.0–36263227.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-cog-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%	COG
id	float64	61.4%	42.0 – 1213.0 (mean 580.1765)
name	object	0.0%	Not specified, Democratic Republic of the Congo Regional Refugee Response Plan 2025, Republic of Congo 2001
code	object	61.4%	RDRC_RRP25, CCOG10, CCOG01
typeid	float64	61.4%	4.0 – 2170.0 (mean 322.0)
typename	object	61.4%	Regional response plan, Other, Consolidated appeals process
startdate	datetime64[ns]	61.4%
enddate	datetime64[ns]	61.4%
year	int64	0.0%	2000.0 – 2026.0 (mean 2012.9773)
requirements	float64	63.6%	11079843.0 – 59195017.0 (mean 24432125.375)
funding	float64	2.3%	409836.0 – 36263227.0 (mean 9875319.8605)
percentfunded	float64	65.9%	17.0 – 138.0 (mean 54.6)
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	42.0	1213.0	580.1765	450.0
typeid	4.0	2170.0	322.0	110.0
year	2000.0	2026.0	2012.9773	2013.5
requirements	11079843.0	59195017.0	24432125.375	23058078.0
funding	409836.0	36263227.0	9875319.8605	8787552.0
percentfunded	17.0	138.0	54.6	46.0

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