electricsheepasia/asia-aid-flows-iati-israel

Israel - Current IATI Aid Activities

Publisher: International Aid Transparency Initiative · Source: HDX · License: hdx-other · Updated: 2026-05-06

Abstract

List of active aid activities shared via the International Aid Transparency Initiative (IATI). Includes both humanitarian and development activities. More information on each activity (including financial data) is available from http://www.d-portal.org

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the day_start, day_end column(s). Geographic scope: ISR.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).

Dataset Characteristics

Variables

Geographic — day_start, day_end, day_length (range 0.0–9496.0), country_code, country_percent (range 0.0–100.0).

Outcome / Measurement — sector_percent (range 0.0–100.0).

Identifier / Metadata — aid (http://d-portal.org/q.html?aid=US-GOV-1-720201851689, http://d-portal.org/q.html?aid=US-GOV-1-720201951689, http://d-portal.org/q.html?aid=US-GOV-1-720202250574), reporting_ref (US-GOV-7, US-GOV-11, US-GOV-1), funder_ref (US, US-GOV-1, XM-DAC-928), title (DoD Excess Defense Articles, Grant Authority, Grants, Subsidies and Contributions, USAID Travel and Transportation), status_code (Finalisation, Closed, Implementation) and 3 others.

Other — reporting (Department of Defense, Department of State, U.S. Agency for International Development), slug (dod-israel, state-israel, dod-multiplecountries), description (Grants, Subsidies and Contributions, Administrative costs and operating expenses of USAID contributing to Travel and Transportation., Administrative costs and operating expenses of USAID contributing to Advisory and Assistance Services.), commitment (range -630000.0–3064050000.0), spend (range -53483.0–515027400.0) and 8 others.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-aid-flows-iati-israel")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

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 International Aid Transparency Initiative 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_asia_aid_flows_iati_israel,
  title     = {Israel - Current IATI Aid Activities},
  author    = {International Aid Transparency Initiative},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/iati-isr},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.