electricsheepafrica/africa-unsom-sgrunpresence

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- complex-emergency-conflict-security
- peacekeeping
- som
pretty_name: "Peace and Security Pillar: UN Presence in Somalia, Data From Secretary General Reports"
dataset_info:
splits:
- name: train
num_examples: 22
- name: test
num_examples: 5
---

# Peace and Security Pillar: UN Presence in Somalia, Data From Secretary General Reports

**Publisher:** United Nations Peace and Security Data Hub · **Source:** [HDX](https://data.humdata.org/dataset/unsom-sgrunpresence) · **License:** `cc-by-igo` · **Updated:** 2026-04-09

---

## Abstract

This dataset was last updated in April 2023 and will no longer receive updates. Historical data remains available for reference.

This dataset is an extraction of information from the Secretary General's Report on the situation in Somalia (SG reports) since 2013. It provides the number of UN personnel, including international and national, in the country.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `date`, `last_update` column(s). Geographic scope: **SOM**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Conflict and security |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 28 |
| **Columns** | 11 (3 numeric, 6 categorical, 2 datetime) |
| **Train split** | 22 rows |
| **Test split** | 5 rows |
| **Geographic scope** | SOM |
| **Publisher** | United Nations Peace and Security Data Hub |
| **HDX last updated** | 2026-04-09 |

---

## Variables

**Geographic** — `country_or_area` (range 706.0–706.0).

**Temporal** — `date`, `last_update`.

**Outcome / Measurement** — `number_of_un_international_staff` (range 152.0–783.0), `number_of_un_national_staff` (range 75.0–1556.0).

**Identifier / Metadata** — `id` (Somalia), `m49_code` (SOM), `source` (S/2023/109, S/2022/665, S/2013/709), `esa_source` (HDX), `esa_processed` (2026-04-09).

**Other** — `narratives` (United Nations entities remained present in Baidoa, Beledweyne, Berbera, Boosaaso, Dhooble, Dhuusamarreeb, Doolow, Gaalkacyo, Garoowe, Hargeysa, Jawhar, Kismaayo and Mogadishu. As at 7 February, 783 international staff and 1,556 national staff were deployed throughout Somalia., United Nations entities remained present in Baidoa, Beledweyne, Berbera, Boosaaso, Dhooble, Dhuusamarreeb, Doolow, Gaalkacyo, Garoowe, Hargeysa, Jawhar, Kismaayo and Mogadishu. As at 23 August, 630 international staff and 1,361 national staff were deployed throughout Somalia., As at 7 November, a total of 329 international staff members from UNSOM and United Nations agencies, funds and programmes were deployed in Somalia.).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-unsom-sgrunpresence")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `id` | object | 0.0% | Somalia |
| `country_or_area` | float64 | 10.7% | 706.0 – 706.0 (mean 706.0) |
| `m49_code` | object | 0.0% | SOM |
| `date` | datetime64[ns] | 0.0% | |
| `number_of_un_international_staff` | float64 | 0.0% | 152.0 – 783.0 (mean 459.0714) |
| `number_of_un_national_staff` | float64 | 7.1% | 75.0 – 1556.0 (mean 1183.1923) |
| `narratives` | object | 0.0% | United Nations entities remained present in Baidoa, Beledweyne, Berbera, Boosaaso, Dhooble, Dhuusamarreeb, Doolow, Gaalkacyo, Garoowe, Hargeysa, Jawhar, Kismaayo and Mogadishu. As at 7 February, 783 international staff and 1,556 national staff were deployed throughout Somalia., United Nations entities remained present in Baidoa, Beledweyne, Berbera, Boosaaso, Dhooble, Dhuusamarreeb, Doolow, Gaalkacyo, Garoowe, Hargeysa, Jawhar, Kismaayo and Mogadishu. As at 23 August, 630 international staff and 1,361 national staff were deployed throughout Somalia., As at 7 November, a total of 329 international staff members from UNSOM and United Nations agencies, funds and programmes were deployed in Somalia. |
| `source` | object | 0.0% | S/2023/109, S/2022/665, S/2013/709 |
| `last_update` | datetime64[ns] | 0.0% | |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-09 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `country_or_area` | 706.0 | 706.0 | 706.0 | 706.0 |
| `number_of_un_international_staff` | 152.0 | 783.0 | 459.0714 | 419.0 |
| `number_of_un_national_staff` | 75.0 | 1556.0 | 1183.1923 | 1264.0 |

---

## 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`. 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 United Nations Peace and Security Data Hub 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](https://data.humdata.org/dataset/unsom-sgrunpresence) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_unsom_sgrunpresence,
title = {Peace and Security Pillar: UN Presence in Somalia, Data From Secretary General Reports},
author = {United Nations Peace and Security Data Hub},
year = {2026},
url = {https://data.humdata.org/dataset/unsom-sgrunpresence},
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.*