electricsheepafrica/africa-global-operational-presence

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- hxl
- operational-presence
- afg
- bfa
- bdi
- cmr
- caf
pretty_name: "Global Operational Presence"
dataset_info:
splits:
- name: train
num_examples: 30893
- name: test
num_examples: 7723
---

# Global Operational Presence

**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/global-operational-presence) · **License:** `cc-by-igo` · **Updated:** 2025-03-10

---

## Abstract

This dataset contains standardised Operational Presence data

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `start_date`, `end_date` column(s). Geographic scope: **AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 17 others**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 38,617 |
| **Columns** | 15 (1 numeric, 12 categorical, 2 datetime) |
| **Train split** | 30,893 rows |
| **Test split** | 7,723 rows |
| **Geographic scope** | AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 17 others |
| **Publisher** | HDX |
| **HDX last updated** | 2025-03-10 |

---

## Variables

**Geographic** — `country_iso3` (AFG, YEM, SOM), `admin_1_pcode` (AF06, YE15, MZ01), `admin_1_name` (Nangarhar, Ta'iz, Cabo Delgado), `admin_2_pcode` (SO2201, SO2401, CM007005), `admin_2_name` (Banadir, Baydhaba, Mezam) and 2 others.

**Temporal** — `start_date`, `end_date`.

**Identifier / Metadata** — `org_name` (United Nations Children's Fund, UN Refugee Agency, World Food Programme), `dataset_id` (d3575877-d3b6-4eec-a116-d9b8a633a399, 3a4d0513-9aeb-4177-9f6d-448c7e740463, be09b1a5-e457-448f-8d2d-63b0debc1742), `resource_id` (6b43d67e-ef25-46a4-b6ba-b0605324043e, a001ae78-3f6d-4d09-9e3e-05090e1c8e45, 1176ba40-f695-4edd-ab07-90e08226e52e), `esa_source`, `esa_processed`.

**Other** — `sector` (PRO, HEA, FSC).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_iso3` | object | 0.0% | AFG, YEM, SOM |
| `admin_1_pcode` | object | 1.4% | AF06, YE15, MZ01 |
| `admin_1_name` | object | 1.8% | Nangarhar, Ta'iz, Cabo Delgado |
| `admin_2_pcode` | object | 5.0% | SO2201, SO2401, CM007005 |
| `admin_2_name` | object | 4.4% | Banadir, Baydhaba, Mezam |
| `org_name` | object | 0.0% | United Nations Children's Fund, UN Refugee Agency, World Food Programme |
| `org_acronym` | object | 0.0% | UNICEF, UNHCR, WFP |
| `org_type` | float64 | 3.3% | 433.0 – 504.0 (mean 440.9388) |
| `sector` | object | 0.0% | PRO, HEA, FSC |
| `start_date` | datetime64[ns] | 0.0% | |
| `end_date` | datetime64[ns] | 0.0% | |
| `dataset_id` | object | 0.0% | d3575877-d3b6-4eec-a116-d9b8a633a399, 3a4d0513-9aeb-4177-9f6d-448c7e740463, be09b1a5-e457-448f-8d2d-63b0debc1742 |
| `resource_id` | object | 0.0% | 6b43d67e-ef25-46a4-b6ba-b0605324043e, a001ae78-3f6d-4d09-9e3e-05090e1c8e45, 1176ba40-f695-4edd-ab07-90e08226e52e |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `org_type` | 433.0 | 504.0 | 440.9388 | 441.0 |

---

## 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) with >80% missing values were removed: `admin_3_pcode`, `admin_3_name`. 1,832 exact duplicate rows were removed. 3 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 HDX and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- This dataset spans 25 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/global-operational-presence) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_global_operational_presence,
title = {Global Operational Presence},
author = {HDX},
year = {2025},
url = {https://data.humdata.org/dataset/global-operational-presence},
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.*