electricsheepafrica/africa-sierra-leone-who-does-what-where

---
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
- who-is-doing-what-and-where-3w-4w-5w
- sle
pretty_name: "SIERRA LEONE - Who Does What Where"
dataset_info:
splits:
- name: train
num_examples: 2624
- name: test
num_examples: 656
---

# SIERRA LEONE - Who Does What Where

**Publisher:** OCHA West and Central Africa (ROWCA) · **Source:** [HDX](https://data.humdata.org/dataset/sierra-leone-who-does-what-where) · **License:** `cc-by-igo` · **Updated:** 2023-03-03

---

## Abstract

Who does what where data (3W) in Sierra Leone as of October 2015. This data includes list of organization, area, activity and date of activity.

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the `date_updated`, `activity_start_date` column(s). Geographic scope: **SLE**.

*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)** | 3,280 |
| **Columns** | 13 (0 numeric, 10 categorical, 3 datetime) |
| **Train split** | 2,624 rows |
| **Test split** | 656 rows |
| **Geographic scope** | SLE |
| **Publisher** | OCHA West and Central Africa (ROWCA) |
| **HDX last updated** | 2023-03-03 |

---

## Variables

**Geographic** — `agency_type` (International Organization/NGO, United Nations, National Organization/NGO), `activity` (Capacity Building, EDU_Distribution of radios, Cash transfers), `province` (Northern, Eastern, Southern), `district` (Bombali, Port Loko, Kenema), `activity_start_date` and 1 others.

**Temporal** — `date_updated`.

**Identifier / Metadata** — `pcode` (SLE020407, SLE030311, SLE010206), `esa_source` (HDX), `esa_processed` (2026-04-08).

**Other** — `sector` (Education, Food Security, WASH), `organisation` (United Nations Children's Fund, Plan International, Save the Children), `chiefdom` (Maforki, Lower Banta, Koya).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-sierra-leone-who-does-what-where")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `date_updated` | datetime64[ns] | 1.8% | |
| `sector` | object | 0.0% | Education, Food Security, WASH |
| `organisation` | object | 0.0% | United Nations Children's Fund, Plan International, Save the Children |
| `agency_type` | object | 0.0% | International Organization/NGO, United Nations, National Organization/NGO |
| `activity` | object | 0.3% | Capacity Building, EDU_Distribution of radios, Cash transfers |
| `province` | object | 0.4% | Northern, Eastern, Southern |
| `district` | object | 0.5% | Bombali, Port Loko, Kenema |
| `chiefdom` | object | 6.4% | Maforki, Lower Banta, Koya |
| `pcode` | object | 7.8% | SLE020407, SLE030311, SLE010206 |
| `activity_start_date` | datetime64[ns] | 5.6% | |
| `activity_end_date` | datetime64[ns] | 8.9% | |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-08 |

---

## 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`. 1,013 exact duplicate rows were removed. 3 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 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.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/sierra-leone-who-does-what-where) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_sierra_leone_who_does_what_where,
title = {SIERRA LEONE - Who Does What Where},
author = {OCHA West and Central Africa (ROWCA)},
year = {2023},
url = {https://data.humdata.org/dataset/sierra-leone-who-does-what-where},
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.*