electricsheepasia/asia-operational-presence-afghanistan-who-does-what-where-october

Afghanistan - Who does What Where (October to December 2022)

Publisher: OCHA Afghanistan · Source: HDX · License: cc-by · Updated: 2025-04-15

Abstract

The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working, what they are doing and their capability in order to identify gaps, avoid duplication of efforts, and plan for future humanitarian response (if needed). The data includes a list of humanitarian organizations by district and cluster, as well as a unique count of organizations. An interactive map of the 3W data can be accessed here.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-04-15. Geographic scope: AFG.

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

Dataset Characteristics

Variables

Geographic — region (Southern, Western, Capital), province (Hirat, Kandahar, Kabul), district (Arghandab, Kohsan, Adraskan), org_acronym (UNICEF, IOM, ACTED), org_type (International NGO, National NGO, United Nations).

Identifier / Metadata — prov_code (AF32, AF27, AF01), dist_code (AF3213, AF3209, AF3203), org_name (United Nations Children's Fund, International Organization for Migration, Agency For Technical Cooperation & Development), cluster_sector_code (ESNFIOC, PROTECTIONOC, FSACOC), `clustersector_name` (Operational Capacity: Emergency Shelter & Non-Food Items, Operational Capacity: Protection, Operational Capacity: Food Security & Agriculture) and 2 others.

Other — question.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-operational-presence-afghanistan-who-does-what-where-october")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

No numeric columns.

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. 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 Afghanistan 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_operational_presence_afghanistan_who_does_what_where_october,
  title     = {Afghanistan - Who does What Where (October to December 2022)},
  author    = {OCHA Afghanistan},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/afghanistan-who-does-what-where-october-to-december-2022},
  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.