Datasets:

electricsheepafrica
/

africa-mali-operational-presence

annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - hxl
  - operational-capacity
  - operational-presence
  - who-is-doing-what-and-where-3w-4w-5w
  - mli
pretty_name: 'Mali: Operational Presence'
dataset_info:
  splits:
    - name: train
      num_examples: 3339
    - name: test
      num_examples: 834

Mali: Operational Presence

Publisher: OCHA Mali · Source: HDX · License: cc-by-igo · Updated: 2025-08-06

Abstract

The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working and what they are doing in order to identify gaps and plan for future humanitarian response. This dataset includes a list of humanitarian organizations operating in Mali at Admin 3.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-08-06. Geographic scope: MLI.

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

Dataset Characteristics


Domain	Humanitarian and development data
Unit of observation	Subnational administrative unit observations
Rows (total)	4,174
Columns	12 (0 numeric, 12 categorical, 0 datetime)
Train split	3,339 rows
Test split	834 rows
Geographic scope	MLI
Publisher	OCHA Mali
HDX last updated	2025-08-06

Variables

Geographic — organisation_accronyme (UNICEF, OMS, USAID), type_organisation (ONG int, Agence UN, ONG Nat), admin1pcod (ML05, ML04, ML03), region (Mopti, Segou, Sikasso), admin2pcod (ML0502, ML0305, ML0506) and 1 others.

Identifier / Metadata — esa_source, esa_processed.

Other — nom_organisation (Fonds des Nations Unies pour l'Enfance, Organisation mondiale de la santé, U.S. Agency for International Development), cercle (Bankass, Sikasso, Mopti), commune (Gao, Ansongo, Koro), cluster (Santé, Protection, Secal*).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mali-operational-presence")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`organisation_accronyme`	object	0.0%	UNICEF, OMS, USAID
`nom_organisation`	object	0.0%	Fonds des Nations Unies pour l'Enfance, Organisation mondiale de la santé, U.S. Agency for International Development
`type_organisation`	object	0.0%	ONG int, Agence UN, ONG Nat
`admin1pcod`	object	0.0%	ML05, ML04, ML03
`region`	object	0.0%	Mopti, Segou, Sikasso
`admin2pcod`	object	0.0%	ML0502, ML0305, ML0506
`cercle`	object	0.0%	Bankass, Sikasso, Mopti
`admin3pcod`	object	0.0%	ML070303, ML060506, ML050609
`commune`	object	0.0%	Gao, Ansongo, Koro
`cluster`	object	0.0%	Santé, Protection, Secal*
`esa_source`	object	0.0%
`esa_processed`	object	0.0%

Numeric Summary

Column	Min	Max	Mean	Median
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. 1,287 exact duplicate rows were removed. 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 Mali 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_mali_operational_presence,
  title     = {Mali: Operational Presence},
  author    = {OCHA Mali},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/mali-operational-presence},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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