electricsheepafrica/africa-ethiopia-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-presence
- who-is-doing-what-and-where-3w-4w-5w
- eth
pretty_name: "Ethiopia: Operational Presence"
dataset_info:
splits:
- name: train
num_examples: 2325
- name: test
num_examples: 581
---

# Ethiopia: Operational Presence

**Publisher:** OCHA Ethiopia · **Source:** [HDX](https://data.humdata.org/dataset/ethiopia-operational-presence) · **License:** `cc-by` · **Updated:** 2026-03-26

---

## 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 Ethiopia at Admin 3.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2026-03-26. Geographic scope: **ETH**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 2,907 |
| **Columns** | 19 (2 numeric, 17 categorical, 0 datetime) |
| **Train split** | 2,325 rows |
| **Test split** | 581 rows |
| **Geographic scope** | ETH |
| **Publisher** | OCHA Ethiopia |
| **HDX last updated** | 2026-03-26 |

---

## Variables

**Geographic** — `activity_status` (On-going, Completed, Planned), `donor_acronym` (BHA, OCHA - EHF, ECHO), `program_partner_acronym` (ACF, MT, IOM), `implementing_partner_acronym` (ACF, MTI, IOM), `implementing_partner_type` (International NGO, National NGO, UN Agency) and 7 others.

**Temporal** — `month` (December).

**Identifier / Metadata** — `donor_name` (Bureau of Humanitarian Assistance, OCHA - EHF, European Commission's Humanitarian aid and Civil Protection department), `program_partner_name` (Action Against Hunger, Medical Teams International, International Organization for Migration), `implementing_partner_name` (Action Against Hunger, Medical Teams International, International Organization for Migration), `esa_source`, `esa_processed`.

**Other** — `cluster`.

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `month` | object | 0.0% | December |
| `activity_status` | object | 0.0% | On-going, Completed, Planned |
| `donor_acronym` | object | 24.3% | BHA, OCHA - EHF, ECHO |
| `donor_name` | object | 24.3% | Bureau of Humanitarian Assistance, OCHA - EHF, European Commission's Humanitarian aid and Civil Protection department |
| `program_partner_acronym` | object | 1.3% | ACF, MT, IOM |
| `program_partner_name` | object | 3.5% | Action Against Hunger, Medical Teams International, International Organization for Migration |
| `implementing_partner_acronym` | object | 0.0% | ACF, MTI, IOM |
| `implementing_partner_name` | object | 1.5% | Action Against Hunger, Medical Teams International, International Organization for Migration |
| `implementing_partner_type` | object | 0.0% | International NGO, National NGO, UN Agency |
| `region` | object | 0.0% | Tigray, Amhara, Oromia |
| `zone` | object | 0.0% | |
| `woreda` | object | 0.0% | |
| `woredapcod` | object | 0.0% | |
| `cluster` | object | 0.0% | |
| `activity` | object | 0.0% | |
| `lat` | float64 | 0.0% | 3.6849 – 14.5314 (mean 11.3271) |
| `long` | float64 | 0.0% | 33.3194 – 46.6509 (mean 38.831) |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `lat` | 3.6849 | 14.5314 | 11.3271 | 12.6366 |
| `long` | 33.3194 | 46.6509 | 38.831 | 39.0478 |

---

## 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`. 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 Ethiopia 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: `donor_acronym`, `donor_name`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/ethiopia-operational-presence) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_ethiopia_operational_presence,
title = {Ethiopia: Operational Presence},
author = {OCHA Ethiopia},
year = {2026},
url = {https://data.humdata.org/dataset/ethiopia-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.*