electricsheepafrica/africa-nigeria-who-is-doing-what-where-3ws

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- hxl
- who-is-doing-what-and-where-3w-4w-5w
- nga
pretty_name: "Nigeria: Who is Doing What Where (3Ws)"
dataset_info:
splits:
- name: train
num_examples: 8113
- name: test
num_examples: 2028
---

# Nigeria: Who is Doing What Where (3Ws)

**Publisher:** OCHA Nigeria · **Source:** [HDX](https://data.humdata.org/dataset/nigeria-who-is-doing-what-where-3ws) · **License:** `other-pd-nr` · **Updated:** 2025-05-05

---

## Abstract

Northeast Nigeria Who is Doing What Where (3Ws)

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-05-05. Geographic scope: **NGA**.

*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)** | 10,142 |
| **Columns** | 18 (1 numeric, 17 categorical, 0 datetime) |
| **Train split** | 8,113 rows |
| **Test split** | 2,028 rows |
| **Geographic scope** | NGA |
| **Publisher** | OCHA Nigeria |
| **HDX last updated** | 2025-05-05 |

---

## Variables

**Geographic** — `org_acronym` (UNICEF, IRC, UNHCR), `type_of_organization` (INGO, UN Agency, NNGO), `operation_type` (Reporting, Implementing, #operation+type), `states` (Borno, Adamawa, Yobe), `state_pcode` (NGA008, NGA002, NGA036) and 4 others.

**Temporal** — `month`.

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

**Other** — `organization` (United Nations Children's Emergency Fund, International Rescue Committee, United Nations High Commissioner for Refugees), `sector` (Protection, Nutrition, Water, Sanitation & Hygiene), `activities` (Other (specify in remarks column), Community sensitisation outreach and dialogue, House to House Visits), `status` (Ongoing, Completed, completed), `ishrp` and 1 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-who-is-doing-what-where-3ws")
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% | United Nations Children's Emergency Fund, International Rescue Committee, United Nations High Commissioner for Refugees |
| `org_acronym` | object | 0.0% | UNICEF, IRC, UNHCR |
| `type_of_organization` | object | 0.0% | INGO, UN Agency, NNGO |
| `operation_type` | object | 0.0% | Reporting, Implementing, #operation+type |
| `sector` | object | 0.0% | Protection, Nutrition, Water, Sanitation & Hygiene |
| `activities` | object | 4.6% | Other (specify in remarks column), Community sensitisation outreach and dialogue, House to House Visits |
| `status` | object | 0.0% | Ongoing, Completed, completed |
| `states` | object | 0.0% | Borno, Adamawa, Yobe |
| `state_pcode` | object | 0.0% | NGA008, NGA002, NGA036 |
| `lga` | object | 0.0% | Jere, Maiduguri, Bama |
| `lga_pcode` | object | 0.0% | |
| `ishrp` | object | 0.0% | |
| `response_type` | object | 0.0% | |
| `isrp` | object | 0.0% | |
| `month` | object | 0.0% | |
| `year` | float64 | 0.0% | 2022.0 – 2022.0 (mean 2022.0) |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 2022.0 | 2022.0 | 2022.0 | 2022.0 |

---

## 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 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 OCHA Nigeria 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/nigeria-who-is-doing-what-where-3ws) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_nigeria_who_is_doing_what_where_3ws,
title = {Nigeria: Who is Doing What Where (3Ws)},
author = {OCHA Nigeria},
year = {2025},
url = {https://data.humdata.org/dataset/nigeria-who-is-doing-what-where-3ws},
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.*