electricsheepafrica/africa-nigeria-3w

---
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:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- hxl
- operational-presence
- who-is-doing-what-and-where-3w-4w-5w
- nga
pretty_name: "Nigeria: Operational Presence"
dataset_info:
splits:
- name: train
num_examples: 3842
- name: test
num_examples: 960
---

# Nigeria: Operational Presence

**Publisher:** OCHA Nigeria · **Source:** [HDX](https://data.humdata.org/dataset/nigeria-3w) · **License:** `cc-by-igo` · **Updated:** 2026-01-14

---

## 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 Nigeria at LGA level.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2026-01-14. 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)** | 4,803 |
| **Columns** | 16 (1 numeric, 15 categorical, 0 datetime) |
| **Train split** | 3,842 rows |
| **Test split** | 960 rows |
| **Geographic scope** | NGA |
| **Publisher** | OCHA Nigeria |
| **HDX last updated** | 2026-01-14 |

---

## Variables

**Geographic** — `org_acronym` (AAH/ACF, WHO, IOM), `org_type` (NNGO, INGO, UN), `state` (Borno, Adamawa, Yobe), `state_pcode` (NG008, NG002, NG036), `lga` (Gwoza, Bama, Jere) and 3 others.

**Temporal** — `month`.

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

**Other** — `organisation` (Action Against Hunger, World Health Organization, International Organization for Migration), `project_sector` (PRO, HEA, NUT), `activities` (Nutrition, Provide access to Primary Health Care Services, Provide Mental Health and Psycho-Social Services), `status` (Ongoing, Completed, active), `ishrp`.

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `organisation` | object | 0.0% | Action Against Hunger, World Health Organization, International Organization for Migration |
| `org_acronym` | object | 0.0% | AAH/ACF, WHO, IOM |
| `org_type` | object | 0.0% | NNGO, INGO, UN |
| `project_sector` | object | 0.0% | PRO, HEA, NUT |
| `activities` | object | 4.1% | Nutrition, Provide access to Primary Health Care Services, Provide Mental Health and Psycho-Social Services |
| `status` | object | 0.2% | Ongoing, Completed, active |
| `state` | object | 0.0% | Borno, Adamawa, Yobe |
| `state_pcode` | object | 0.0% | NG008, NG002, NG036 |
| `lga` | object | 0.0% | Gwoza, Bama, Jere |
| `lga_pcode` | object | 0.0% | NG008011, NG008003, NG008013 |
| `ishrp` | object | 0.0% | |
| `response_type` | object | 0.0% | |
| `month` | object | 0.0% | |
| `year` | float64 | 0.0% | 2024.0 – 2024.0 (mean 2024.0) |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 2024.0 | 2024.0 | 2024.0 | 2024.0 |

---

## 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) with >80% missing values were removed: `isrp`. 2,669 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 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-3w) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_nigeria_3w,
title = {Nigeria: Operational Presence},
author = {OCHA Nigeria},
year = {2026},
url = {https://data.humdata.org/dataset/nigeria-3w},
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.*