electricsheepafrica/africa-3w-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
- education
- who-is-doing-what-and-where-3w-4w-5w
- eth
pretty_name: "3W Operational Presence"
dataset_info:
splits:
- name: train
num_examples: 4059
- name: test
num_examples: 1014
---

# 3W Operational Presence

**Publisher:** OCHA Ethiopia · **Source:** [HDX](https://data.humdata.org/dataset/3w-operational-presence) · **License:** `cc-by` · **Updated:** 2024-09-13

---

## Abstract

The Who Does What Where is a core humanitarian dataset for coordination. This data contains operational presence of humanitarian partners in Ethiopia at admin3 level by cluster.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2024-09-13. Geographic scope: **ETH**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Education |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 5,074 |
| **Columns** | 14 (1 numeric, 13 categorical, 0 datetime) |
| **Train split** | 4,059 rows |
| **Test split** | 1,014 rows |
| **Geographic scope** | ETH |
| **Publisher** | OCHA Ethiopia |
| **HDX last updated** | 2024-09-13 |

---

## Variables

**Geographic** — `acronyms` (RWB, SCI, NDRMC), `organizationtype` (International NGO, Government, United Nations), `region` (Somali, Oromia, SNNP), `zone` (Borena, Jarar, Doolo), `woreda` (Moyale, Babile, Warder) and 1 others.

**Identifier / Metadata** — `esa_source`, `esa_processed`.

**Other** — `organization` (Save the Children, UNICEF, National Disaster Risk Management Commission), `hotspot` (range 0.0–3.0), `sector` (WASH, Agriculture, Food), `activities` (Distribution of HH water treatment products (PUR, aquatab, filters), Water trucking/tankering, Hygiene kit distribution (all kits including, bathing and laundry soap, sanitary pads, buckets or jerrycans, nappies and potties) ), `status` (Completed, Ongoing, Planned) and 1 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-3w-operational-presence")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `organization` | object | 0.0% | Save the Children, UNICEF, National Disaster Risk Management Commission |
| `acronyms` | object | 0.0% | RWB, SCI, NDRMC |
| `organizationtype` | object | 0.0% | International NGO, Government, United Nations |
| `region` | object | 0.0% | Somali, Oromia, SNNP |
| `zone` | object | 0.0% | Borena, Jarar, Doolo |
| `woreda` | object | 0.0% | Moyale, Babile, Warder |
| `woreda_code` | object | 0.0% | ET050704, ET041220, ET050586 |
| `hotspot` | float64 | 0.0% | 0.0 – 3.0 (mean 1.1918) |
| `sector` | object | 0.0% | WASH, Agriculture, Food |
| `activities` | object | 23.6% | Distribution of HH water treatment products (PUR, aquatab, filters), Water trucking/tankering, Hygiene kit distribution (all kits including, bathing and laundry soap, sanitary pads, buckets or jerrycans, nappies and potties) |
| `status` | object | 0.0% | Completed, Ongoing, Planned |
| `implementing_partner_s` | object | 0.0% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `hotspot` | 0.0 | 3.0 | 1.1918 | 1.0 |

---

## 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`. 1,234 exact duplicate rows were removed. 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: `activities`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/3w-operational-presence) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_3w_operational_presence,
title = {3W Operational Presence},
author = {OCHA Ethiopia},
year = {2024},
url = {https://data.humdata.org/dataset/3w-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.*