README.md

---
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](https://data.humdata.org/dataset/mali-violence-against-civilians-and-vital-civilian-facilities) · **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](https://insecurityinsight.org/country-pages/mali).   Also included are datasets cited in the [Safeguarding Health in Conflict Coalition (SHCC)'s](https://www.safeguardinghealth.org/) 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](https://huggingface.co/electricsheepafrica).*

---

## 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

```python
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 | Min | Max | Mean | Median |
|---|---|---|---|---|
| `aid_workers_killed` | 0.0 | 4.0 | 0.2409 | 0.0 |
| `aid_workers_injured` | 0.0 | 6.0 | 0.3431 | 0.0 |
| `aid_workers_kidnapped` | 0.0 | 8.0 | 1.6204 | 1.0 |
| `aid_workers_arrested` | 0.0 | 5.0 | 0.0949 | 0.0 |
| `aid_workers_killed_in_captivity` | 0.0 | 1.0 | 0.0073 | 0.0 |
| `international_aid_workers_killed` | 0.0 | 1.0 | 0.0219 | 0.0 |
| `international_aid_workers_killed_in_captivity` | 0.0 | 1.0 | 0.0073 | 0.0 |
| `national_aid_workers_killed` | 0.0 | 3.0 | 0.1971 | 0.0 |
| `national_aid_workers_killed_in_captivity` | 0.0 | 0.0 | 0.0 | 0.0 |
| `female_aid_workers_killed` | 0.0 | 1.0 | 0.0073 | 0.0 |
| `female_aid_workers_killed_in_captivity` | 0.0 | 1.0 | 0.0073 | 0.0 |
| `male_aid_workers_killed` | 0.0 | 3.0 | 0.1168 | 0.0 |
| `male_aid_workers_killed_in_captivity` | 0.0 | 0.0 | 0.0 | 0.0 |
| `international_aid_workers_injured` | 0.0 | 3.0 | 0.0365 | 0.0 |
| `national_aid_workers_injured` | 0.0 | 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](https://data.humdata.org/dataset/mali-violence-against-civilians-and-vital-civilian-facilities) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@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](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*