electricsheepafrica/africa-3w-dataset-on-the-organizations-involved-in-the-response-to-the-ebola-crisis

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- disease
- epidemics-outbreaks
- geodata
- health
- who-is-doing-what-and-where-3w-4w-5w
- gin
- lbr
- nga
- sle
pretty_name: "3W Dataset on the Organizations Involved in the Response to the Ebola Crisis"
dataset_info:
splits:
- name: train
num_examples: 978
- name: test
num_examples: 244
---

# 3W Dataset on the Organizations Involved in the Response to the Ebola Crisis

**Publisher:** OCHA West and Central Africa (ROWCA) · **Source:** [HDX](https://data.humdata.org/dataset/3w-dataset-on-the-organizations-involved-in-the-response-to-the-ebola-crisis) · **License:** `other-pd-nr` · **Updated:** 2023-09-28

---

## Abstract

Who, What, Where (3W) dataset on the Ebola response effort. Some entries have a maximum level of desegregation up to administrative level 3. The dataset contains data from Guinea, Liberia, Sierra Leone, and Nigeria.

**This dataset is updated weekly.** Last Update 17 Nov. 2014

**Note:** If your humanitarian organization would like to make a correction or update the dataset, please contact the OCHA focal point for the respective country. Contacts can be found at https://wca.humanitarianresponse.info/fr/emergencies/virus-ebola

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2023-09-28. Geographic scope: **GIN, LBR, NGA, SLE**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 1,223 |
| **Columns** | 12 (0 numeric, 12 categorical, 0 datetime) |
| **Train split** | 978 rows |
| **Test split** | 244 rows |
| **Geographic scope** | GIN, LBR, NGA, SLE |
| **Publisher** | OCHA West and Central Africa (ROWCA) |
| **HDX last updated** | 2023-09-28 |

---

## Variables

**Geographic** — `country` (Liberia, Guinea, Sierra Leone), `admin1_name` (N'Zerekore, Montserrado, Lofa), `admin1_pcode` (GIN008, LBR11, LBR08), `admin2_name` (Macenta, Conakry, Gueckedou), `admin2_pcode` (GIN008003, GIN002005, GIN008006) and 3 others.

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

**Other** — `organisation` (United Nations Children's Fund, World Health Organization, Ministry of Health), `sectors` (Social Mobilisation/Health Promotion, Communication/Mobilisation sociale, Coordination).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-3w-dataset-on-the-organizations-involved-in-the-response-to-the-ebola-crisis")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country` | object | 0.0% | Liberia, Guinea, Sierra Leone |
| `admin1_name` | object | 0.0% | N'Zerekore, Montserrado, Lofa |
| `admin1_pcode` | object | 0.0% | GIN008, LBR11, LBR08 |
| `admin2_name` | object | 49.6% | Macenta, Conakry, Gueckedou |
| `admin2_pcode` | object | 50.1% | GIN008003, GIN002005, GIN008006 |
| `acronym` | object | 0.0% | UNICEF, WHO, MoH |
| `organisation` | object | 0.1% | United Nations Children's Fund, World Health Organization, Ministry of Health |
| `type` | object | 0.0% | International NGO, UN Agency, International Organization |
| `sectors` | object | 0.0% | Social Mobilisation/Health Promotion, Communication/Mobilisation sociale, Coordination |
| `activity_project` | object | 73.9% | Communication, Works closely with partners, including
Ministries of Health and the Red Cross, to carry out
prevention and emergency preparedness activities
across the subregion.
Provision of essential medicines, disinfectants, family
hygiene kits, supplies and equipment, and supports
Communication for Development (C4D) media and
door-to-door activities for prevention and response
efforts.
Communication, Coordination |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
_No numeric columns._

---

## 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: `city_locations`, `status_activities`, `comments`. 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 West and Central Africa (ROWCA) 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: `admin2_name`, `admin2_pcode`, `activity_project`.
- This dataset spans 4 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/3w-dataset-on-the-organizations-involved-in-the-response-to-the-ebola-crisis) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_3w_dataset_on_the_organizations_involved_in_the_response_to_the_ebola_crisis,
title = {3W Dataset on the Organizations Involved in the Response to the Ebola Crisis},
author = {OCHA West and Central Africa (ROWCA)},
year = {2023},
url = {https://data.humdata.org/dataset/3w-dataset-on-the-organizations-involved-in-the-response-to-the-ebola-crisis},
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.*