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africa-lby-requirements-and-funding-data

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metadata 
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
  - lby
pretty_name: Libya - Requirements and Funding Data
dataset_info:
  splits:
    - name: train
      num_examples: 28
    - name: test
      num_examples: 7

Libya - Requirements and Funding Data

Publisher: OCHA Financial Tracking System (FTS) · Source: HDX · License: cc-by-igo · Updated: 2026-04-03

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: LBY.

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) 35
Columns 14 (6 numeric, 6 categorical, 2 datetime)
Train split 28 rows
Test split 7 rows
Geographic scope LBY
Publisher OCHA Financial Tracking System (FTS)
HDX last updated 2026-04-03

Variables

Geographic — countrycode (LBY), typeid (range 4.0–111.0), typename (Humanitarian response plan, Regional response plan, Flash appeal), year (range 2005.0–2027.0).

Temporal — startdate, enddate.

Outcome / Measurement — percentfunded (range 5.0–128.0).

Identifier / Metadata — id (range 365.0–1523.0), name (Not specified, Sudan Emergency: Regional Refugee Response Plan 2026, Sudan Emergency: Regional Refugee Response Plan 2025), code (RREG26a, RRSDN25, FLBY24), esa_source (HDX), esa_processed (2026-04-04).

Other — requirements (range 10676371.0–312740102.0), funding (range 60976.0–150268390.0).

Quick Start 

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-lby-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% LBY
id float64 57.1% 365.0 – 1523.0 (mean 867.8667)
name object 0.0% Not specified, Sudan Emergency: Regional Refugee Response Plan 2026, Sudan Emergency: Regional Refugee Response Plan 2025
code object 57.1% RREG26a, RRSDN25, FLBY24
typeid float64 57.1% 4.0 – 111.0 (mean 32.8)
typename object 57.1% Humanitarian response plan, Regional response plan, Flash appeal
startdate datetime64[ns] 57.1%
enddate datetime64[ns] 57.1%
year int64 0.0% 2005.0 – 2027.0 (mean 2018.3429)
requirements float64 60.0% 10676371.0 – 312740102.0 (mean 114052953.4286)
funding int64 0.0% 60976.0 – 150268390.0 (mean 44350035.7714)
percentfunded float64 60.0% 5.0 – 128.0 (mean 62.6429)
esa_source object 0.0% HDX
esa_processed object 0.0% 2026-04-04

Numeric Summary

Column Min Max Mean Median
id 365.0 1523.0 867.8667 931.0
typeid 4.0 111.0 32.8 5.0
year 2005.0 2027.0 2018.3429 2019.0
requirements 10676371.0 312740102.0 114052953.4286 110215636.5
funding 60976.0 150268390.0 44350035.7714 39729359.0
percentfunded 5.0 128.0 62.6429 61.5

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.

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.

Citation 

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