electricsheepafrica/africa-hrp-projects-cod

Democratic Republic of the Congo: Response Plan projects

Publisher: OCHA Humanitarian Programme Cycle Tools (HPC Tools) · Source: HDX · License: cc-by-igo · Updated: 2025-12-29

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Abstract

Projects proposed, in progress, or completed as part of the annual Democratic Republic of the Congo Humanitarian Response Plans (HRPs) or other Humanitarian Programme Cycle plans. The original data is available on https://hpc.tools

Important: some projects in Democratic Republic of the Congo might be missing, and others might not apply specifically to Democratic Republic of the Congo. See Caveats under the Additional information tab.

Each row in this dataset represents time-series observations. Temporal coverage is indicated by the startdate, enddate column(s). Geographic scope: COD.

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)	11
Columns	12 (1 numeric, 9 categorical, 2 datetime)
Train split	8 rows
Test split	2 rows
Geographic scope	COD
Publisher	OCHA Humanitarian Programme Cycle Tools (HPC Tools)
HDX last updated	2025-12-29

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Variables

Temporal — startdate, enddate.

Identifier / Metadata — name (#activity+name, FAO response, IOM), versioncode (#activity+code+v_hpc, HCOVD20-COVD-165886-1, HCOVD20-COVD-165887-1), response_plan_code (OCOVD20, #response+plan+code), esa_source (HDX), esa_processed (2026-04-05).

Other — currentrequestedfunds (range 2000000.0–450000000.0), objective (COVID-19 GHRP , #description+objective), globalclusters (COVID-19, #sector+cluster+global+name), organizations (#org+prog+name, Food and Agriculture Organization of the United Nations, International Organization for Migration), plans (GHRP (COVID) Global Humanitarian Response Plan 2020, #response+plan+name).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-hrp-projects-cod")
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
name	object	0.0%	#activity+name, FAO response, IOM
versioncode	object	0.0%	#activity+code+v_hpc, HCOVD20-COVD-165886-1, HCOVD20-COVD-165887-1
currentrequestedfunds	float64	9.1%	2000000.0 – 450000000.0 (mean 201200000.0)
objective	object	0.0%	COVID-19 GHRP , #description+objective
startdate	datetime64[ns]	9.1%
enddate	datetime64[ns]	9.1%
globalclusters	object	0.0%	COVID-19, #sector+cluster+global+name
organizations	object	0.0%	#org+prog+name, Food and Agriculture Organization of the United Nations, International Organization for Migration
plans	object	0.0%	GHRP (COVID) Global Humanitarian Response Plan 2020, #response+plan+name
response_plan_code	object	0.0%	OCOVD20, #response+plan+code
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
currentrequestedfunds	2000000.0	450000000.0	201200000.0	120000000.0

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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) with >80% missing values were removed: partners, locations. 3 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 Humanitarian Programme Cycle Tools (HPC Tools) and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_hrp_projects_cod,
  title     = {Democratic Republic of the Congo: Response Plan projects},
  author    = {OCHA Humanitarian Programme Cycle Tools (HPC Tools)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/hrp-projects-cod},
  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.