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

Algeria - Requirements and Funding Data

Publisher: OCHA Financial Tracking System (FTS) · Source: HDX · License: cc-by-igo · Updated: 2026-04-13

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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 time-series observations. Temporal coverage is indicated by the date, firstreporteddate column(s). Geographic scope: DZA.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Time-series observations
Rows (total)	20
Columns	33 (9 numeric, 19 categorical, 5 datetime)
Train split	16 rows
Test split	4 rows
Geographic scope	DZA
Publisher	OCHA Financial Tracking System (FTS)
HDX last updated	2026-04-13

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Variables

Geographic — budgetyear (range 2024.0–2026.0), srcorganizationtypes (Governments, Multilateral Organizations), srclocations (ESP, DEU, HRV), srcusageyearstart (range 2023.0–2026.0), srcusageyearend (range 2023.0–2026.0) and 11 others.

Temporal — date, firstreporteddate, decisiondate, updatedat.

Outcome / Measurement — amountusd (range 0.0–6000000.0), originalamount (range 0.0–4000000.0).

Identifier / Metadata — id (range 295408.0–383347.0), refcode, esa_source, esa_processed.

Other — description (Enhancing Livelihoods and Promoting Self-reliance among Sahrawi Refugees, Amélioration de l’accès à l’eau potable en qualité et en quantité dans les camps des réfugiés sahraouis de Tindouf - Algérie 2025, Algeria Interim Country Strategic Plan July 2019-December 2024 and Libya Country Strategic Plan 2023-2025), srcorganization (Spain, Government of, European Commission's Humanitarian Aid and Civil Protection Department, Germany, Government of), destorganization (Danish Refugee Council, World Food Programme, Solidaridad Internacional), destglobalclusters (Food Security, Health, Water Sanitation Hygiene), method and 2 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-dza-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
date	datetime64[ns]	0.0%
budgetyear	int64	0.0%	2024.0 – 2026.0 (mean 2025.25)
description	object	0.0%	Enhancing Livelihoods and Promoting Self-reliance among Sahrawi Refugees, Amélioration de l’accès à l’eau potable en qualité et en quantité dans les camps des réfugiés sahraouis de Tindouf - Algérie 2025, Algeria Interim Country Strategic Plan July 2019-December 2024 and Libya Country Strategic Plan 2023-2025
amountusd	int64	0.0%	0.0 – 6000000.0 (mean 924512.05)
srcorganization	object	0.0%	Spain, Government of, European Commission's Humanitarian Aid and Civil Protection Department, Germany, Government of
srcorganizationtypes	object	0.0%	Governments, Multilateral Organizations
srclocations	object	35.0%	ESP, DEU, HRV
srcusageyearstart	int64	0.0%	2023.0 – 2026.0 (mean 2024.95)
srcusageyearend	int64	0.0%	2023.0 – 2026.0 (mean 2025.1)
destorganization	object	5.0%	Danish Refugee Council, World Food Programme, Solidaridad Internacional
destorganizationtypes	object	5.0%	NGOs, Multilateral Organizations, Red Cross/Red Crescent Organizations
destglobalclusters	object	15.0%	Food Security, Health, Water Sanitation Hygiene
destlocations	object	0.0%	DZA, DZA,LBY, BFA,CAF,CMR,COD,DZA,ETH,LBY,MLI,NER,NGA,SDN,SSD,TCD
destusageyearstart	int64	0.0%	2023.0 – 2026.0 (mean 2025.75)
destusageyearend	int64	0.0%	2026.0 – 2026.0 (mean 2026.0)
contributiontype	object	0.0%	financial
flowtype	object	0.0%	Standard, Parked
method	object	0.0%
boundary	object	0.0%
onboundary	object	0.0%
status	object	0.0%
firstreporteddate	datetime64[ns]	0.0%
decisiondate	datetime64[ns]	10.0%
keywords	object	80.0%
originalamount	float64	5.0%	0.0 – 4000000.0 (mean 586689.6842)
originalcurrency	object	5.0%
exchangerate	float64	5.0%	0.851 – 0.948 (mean 0.8849)
id	int64	0.0%	295408.0 – 383347.0 (mean 353873.65)
refcode	object	5.0%
createdat	datetime64[ns]	0.0%
updatedat	datetime64[ns]	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
budgetyear	2024.0	2026.0	2025.25	2025.0
amountusd	0.0	6000000.0	924512.05	304907.0
srcusageyearstart	2023.0	2026.0	2024.95	2025.0
srcusageyearend	2023.0	2026.0	2025.1	2025.0
destusageyearstart	2023.0	2026.0	2025.75	2026.0
destusageyearend	2026.0	2026.0	2026.0	2026.0
originalamount	0.0	4000000.0	586689.6842	246575.0
exchangerate	0.851	0.948	0.8849	0.865
id	295408.0	383347.0	353873.65	363643.5

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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. 6 column(s) with >80% missing values were removed: destplan, destplancode, destplanid, destproject, destprojectcode, destemergency. 5 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: srclocations, keywords.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

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