Datasets:

electricsheepafrica
/

africa-mali-violence-against-civilians-and-vital-civilian-facilities

annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - tabular-classification
  - tabular-regression
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - aid-worker-security
  - aid-workers
  - complex-emergency-conflict-security
  - conflict-violence
  - damage-assessment
  - disease
  - education
  - education-facilities-schools
  - mli
pretty_name: >-
  Mali (MLI): Attacks on Aid Operations, Education, Food and Water Systems and
  Health Care
dataset_info:
  splits:
    - name: train
      num_examples: 109
    - name: test
      num_examples: 27

Mali (MLI): Attacks on Aid Operations, Education, Food and Water Systems and Health Care

Publisher: Insecurity Insight · Source: HDX · License: cc-by-sa · Updated: 2026-04-13

Abstract

This page contains information on reported incidents of violence and threats affecting aid operations and workers, education, food systems and health care services in Mali. Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

Each row in this dataset represents discrete events or incidents. Temporal coverage is indicated by the date, date_event_entered column(s). Geographic scope: MLI.

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

Dataset Characteristics


Domain	Food security and nutrition
Unit of observation	Discrete events or incidents
Rows (total)	137
Columns	42 (26 numeric, 13 categorical, 3 datetime)
Train split	109 rows
Test split	27 rows
Geographic scope	MLI
Publisher	Insecurity Insight
HDX last updated	2026-04-13

Variables

Geographic — country (Mali), country_iso (MLI), admin_1 (Mopti, Gao, Timbuktu), location_of_incident (Road, No information, Open Space), aid_workers_killed_in_captivity (range 0.0–1.0) and 4 others.

Temporal — date, date_event_entered, date_event_modified.

Demographic — female_aid_workers_killed (range 0.0–1.0), male_aid_workers_killed (range 0.0–3.0), female_aid_workers_injured (range 0.0–1.0), male_aid_workers_injured (range 0.0–3.0), female_aid_workers_kidnapped (range 0.0–3.0) and 3 others.

Outcome / Measurement — organisation_affected (INGO, LNGO, NGO).

Identifier / Metadata — reported_perpetrator_name (Unidentified armed actor, Jama'at Nasr al-Islam wal Muslimin, Criminal), aid_workers_killed (range 0.0–4.0), aid_workers_injured (range 0.0–6.0), aid_workers_kidnapped (range 0.0–8.0), aid_workers_arrested (range 0.0–5.0) and 12 others.

Other — geo_precision (censored), reported_perpetrator (NSA, No Information, Criminal), weapon_carried_used (Firearms, No Information on the Weapon Used, Unspecified IED), programme_focus (No information, Health, Multiple).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mali-violence-against-civilians-and-vital-civilian-facilities")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`date`	datetime64[ns]	0.0%
`country`	object	0.0%	Mali
`country_iso`	object	0.0%	MLI
`admin_1`	object	0.0%	Mopti, Gao, Timbuktu
`geo_precision`	object	0.0%	censored
`location_of_incident`	object	0.0%	Road, No information, Open Space
`reported_perpetrator`	object	0.0%	NSA, No Information, Criminal
`reported_perpetrator_name`	object	0.0%	Unidentified armed actor, Jama'at Nasr al-Islam wal Muslimin, Criminal
`weapon_carried_used`	object	0.0%	Firearms, No Information on the Weapon Used, Unspecified IED
`organisation_affected`	object	0.0%	INGO, LNGO, NGO
`programme_focus`	object	0.0%	No information, Health, Multiple
`aid_workers_killed`	int64	0.0%	0.0 – 4.0 (mean 0.2409)
`aid_workers_injured`	int64	0.0%	0.0 – 6.0 (mean 0.3431)
`aid_workers_kidnapped`	int64	0.0%	0.0 – 8.0 (mean 1.6204)
`aid_workers_arrested`	int64	0.0%	0.0 – 5.0 (mean 0.0949)
`known_kidnapping_or_arrest_outcome`	object	32.8%
`aid_workers_killed_in_captivity`	int64	0.0%	0.0 – 1.0 (mean 0.0073)
`international_aid_workers_killed`	int64	0.0%	0.0 – 1.0 (mean 0.0219)
`international_aid_workers_killed_in_captivity`	int64	0.0%	0.0 – 1.0 (mean 0.0073)
`national_aid_workers_killed`	int64	0.0%	0.0 – 3.0 (mean 0.1971)
`national_aid_workers_killed_in_captivity`	int64	0.0%	0.0 – 0.0 (mean 0.0)
`female_aid_workers_killed`	int64	0.0%	0.0 – 1.0 (mean 0.0073)
`female_aid_workers_killed_in_captivity`	int64	0.0%	0.0 – 1.0 (mean 0.0073)
`male_aid_workers_killed`	int64	0.0%	0.0 – 3.0 (mean 0.1168)
`male_aid_workers_killed_in_captivity`	int64	0.0%	0.0 – 0.0 (mean 0.0)
`international_aid_workers_injured`	int64	0.0%	0.0 – 3.0 (mean 0.0365)
`national_aid_workers_injured`	int64	0.0%	0.0 – 5.0 (mean 0.2117)
`female_aid_workers_injured`	int64	0.0%	0.0 – 1.0 (mean 0.0073)
`male_aid_workers_injured`	int64	0.0%	0.0 – 3.0 (mean 0.0803)
`international_aid_workers_kidnapped`	int64	0.0%	0.0 – 4.0 (mean 0.1095)
`national_aid_workers_kidnapped`	int64	0.0%	0.0 – 7.0 (mean 0.9854)
`female_aid_workers_kidnapped`	int64	0.0%	0.0 – 3.0 (mean 0.0949)
`male_aid_workers_kidnapped`	int64	0.0%
`international_aid_workers_arrested`	int64	0.0%
`national_aid_workers_arrested`	int64	0.0%
`female_aid_workers_arrested`	int64	0.0%
`male_aid_workers_arrested`	int64	0.0%
`sind_event_id`	int64	0.0%
`date_event_entered`	datetime64[ns]	0.0%
`date_event_modified`	datetime64[ns]	0.0%
`esa_source`	object	0.0%
`esa_processed`	object	0.0%

Numeric Summary

Column	Max	Mean	Median
`aid_workers_killed`	4.0	0.2409	0.0
`aid_workers_injured`	6.0	0.3431	0.0
`aid_workers_kidnapped`	8.0	1.6204	1.0
`aid_workers_arrested`	5.0	0.0949	0.0
`aid_workers_killed_in_captivity`	1.0	0.0073	0.0
`international_aid_workers_killed`	1.0	0.0219	0.0
`international_aid_workers_killed_in_captivity`	1.0	0.0073	0.0
`national_aid_workers_killed`	3.0	0.1971	0.0
`national_aid_workers_killed_in_captivity`	0.0	0.0	0.0
`female_aid_workers_killed`	1.0	0.0073	0.0
`female_aid_workers_killed_in_captivity`	1.0	0.0073	0.0
`male_aid_workers_killed`	3.0	0.1168	0.0
`male_aid_workers_killed_in_captivity`	0.0	0.0	0.0
`international_aid_workers_injured`	3.0	0.0365	0.0
`national_aid_workers_injured`	5.0	0.2117	0.0

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. 3 column(s) with >80% missing values were removed: event_description, latitude, longitude. 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 Insecurity Insight 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: known_kidnapping_or_arrest_outcome.
Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_africa_mali_violence_against_civilians_and_vital_civilian_facilities,
  title     = {Mali (MLI): Attacks on Aid Operations, Education, Food and Water Systems and Health Care},
  author    = {Insecurity Insight},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/mali-violence-against-civilians-and-vital-civilian-facilities},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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