Datasets:

electricsheepafrica
/

africa-eri-requirements-and-funding-data

annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - tabular-classification
  - tabular-regression
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - covid-19
  - funding
  - humanitarian-financial-tracking-service-fts
  - eri
pretty_name: Eritrea - Requirements and Funding Data
dataset_info:
  splits:
    - name: train
      num_examples: 26
    - name: test
      num_examples: 6

Eritrea - Requirements and Funding Data

Publisher: OCHA Financial Tracking System (FTS) · Source: HDX · License: cc-by-igo · Updated: 2026-04-06

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. Data was last updated on HDX on 2026-04-06. Geographic scope: ERI.

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

Dataset Characteristics


Domain	Humanitarian and development data
Unit of observation	Country-level aggregates
Rows (total)	33
Columns	6 (2 numeric, 4 categorical, 0 datetime)
Train split	26 rows
Test split	6 rows
Geographic scope	ERI
Publisher	OCHA Financial Tracking System (FTS)
HDX last updated	2026-04-06

Variables

Geographic — countrycode (ERI), year (range 2000.0–2026.0).

Identifier / Metadata — name (Not specified, Horn of Africa 2006 , Eritrea 2005), esa_source (HDX), esa_processed (2026-04-06).

Other — funding (range 2611218.0–123728759.0).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-eri-requirements-and-funding-data")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`countrycode`	object	0.0%	ERI
`name`	object	0.0%	Not specified, Horn of Africa 2006 , Eritrea 2005
`year`	int64	0.0%	2000.0 – 2026.0 (mean 2011.2727)
`funding`	int64	0.0%	2611218.0 – 123728759.0 (mean 23788291.7576)
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-06

Numeric Summary

Column	Min	Max	Mean	Median
`year`	2000.0	2026.0	2011.2727	2010.0
`funding`	2611218.0	123728759.0	23788291.7576	13802903.0

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. 8 column(s) with >80% missing values were removed: id, code, typeid, typename, startdate, enddate.... The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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.
Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_africa_eri_requirements_and_funding_data,
  title     = {Eritrea - Requirements and Funding Data},
  author    = {OCHA Financial Tracking System (FTS)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/eri-requirements-and-funding-data},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.