electricsheepafrica/africa-3w-ocha-guinea-as-of-16-june-2015

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- disease
- who-is-doing-what-and-where-3w-4w-5w
- gin
pretty_name: "3W OCHA Guinea as of 16 June 2015"
dataset_info:
splits:
- name: train
num_examples: 648
- name: test
num_examples: 162
---

# 3W OCHA Guinea as of 16 June 2015

**Publisher:** OCHA Guinea (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/3w-ocha-guinea-as-of-16-june-2015) · **License:** `cc-by-igo` · **Updated:** 2023-03-02

---

## Abstract

illustration of who does what and where in Guinea

Each row in this dataset represents tabular records. Data was last updated on HDX on 2023-03-02. Geographic scope: **GIN**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Epidemiology and disease surveillance |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 810 |
| **Columns** | 9 (0 numeric, 9 categorical, 0 datetime) |
| **Train split** | 648 rows |
| **Test split** | 162 rows |
| **Geographic scope** | GIN |
| **Publisher** | OCHA Guinea (inactive) |
| **HDX last updated** | 2023-03-02 |

---

## Variables

**Geographic** — `acronym` (OMS, UNDP, UNFPA), `org_type` (UN Agency, International NGO, International Organization), `activity_type` (Ongoing), `admin_unit_name` (Nzerekore, Macenta, Beyla).

**Identifier / Metadata** — `pcode` (GIN008005, GIN008004, GIN008001), `esa_source` (HDX), `esa_processed` (2026-04-07).

**Other** — `organisation` (World Health Organization, United Nations Development Programme, United Nations Population Fund), `sector` (Coordination, Communication/Mobilisation sociale, Surveillance & suivi).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-3w-ocha-guinea-as-of-16-june-2015")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `acronym` | object | 0.0% | OMS, UNDP, UNFPA |
| `organisation` | object | 0.0% | World Health Organization, United Nations Development Programme, United Nations Population Fund |
| `org_type` | object | 0.0% | UN Agency, International NGO, International Organization |
| `sector` | object | 0.0% | Coordination, Communication/Mobilisation sociale, Surveillance & suivi |
| `activity_type` | object | 0.0% | Ongoing |
| `admin_unit_name` | object | 0.0% | Nzerekore, Macenta, Beyla |
| `pcode` | object | 0.0% | GIN008005, GIN008004, GIN008001 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-07 |

---

## 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`. 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 Guinea (inactive) 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/3w-ocha-guinea-as-of-16-june-2015) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_3w_ocha_guinea_as_of_16_june_2015,
title = {3W OCHA Guinea as of 16 June 2015},
author = {OCHA Guinea (inactive)},
year = {2023},
url = {https://data.humdata.org/dataset/3w-ocha-guinea-as-of-16-june-2015},
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.*