electricsheepafrica/africa-interagency-response-plans

Interagency Response Plans

Publisher: HDX · Source: HDX · License: cc-by · Updated: 2025-08-26

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Abstract

The 2024 GHO launched in December 2023 required $46.6 billion to assist 181 million of the 301 million people in need of aid in 73 countries.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-08-26. Geographic scope: AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 25 others.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Tabular records
Rows (total)	43
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	34 rows
Test split	8 rows
Geographic scope	AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 25 others
Publisher	HDX
HDX last updated	2025-08-26

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Variables

Geographic — interagency_response_plans (#country+name, Afghanistan (RRP), Ukraine), iso_alpha_3_codes (#country+code, MLI, VEN), plan_type (Humanitarian response plan, Regional response plan, Flash appeal), data_explorer_pin (TRUE, FALSE, #meta+included).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-08).

Other — `` (range 1.0–42.0), in_need (range 0.0–28600000.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-interagency-response-plans")
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
``	float64	2.3%	1.0 – 42.0 (mean 21.5)
interagency_response_plans	object	0.0%	#country+name, Afghanistan (RRP), Ukraine
iso_alpha_3_codes	object	37.2%	#country+code, MLI, VEN
plan_type	object	0.0%	Humanitarian response plan, Regional response plan, Flash appeal
in_need	float64	2.3%	0.0 – 28600000.0 (mean 8283801.1667)
data_explorer_pin	object	0.0%	TRUE, FALSE, #meta+included
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-08

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Numeric Summary

Column	Min	Max	Mean	Median
``	1.0	42.0	21.5	21.5
in_need	0.0	28600000.0	8283801.1667	5100000.0

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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. 1 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 HDX 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: iso_alpha_3_codes.
• This dataset spans 33 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_interagency_response_plans,
  title     = {Interagency Response Plans},
  author    = {HDX},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/interagency-response-plans},
  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.