electricsheepafrica/africa-niger-operational-presence

---
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
- hxl
- operational-presence
- who-is-doing-what-and-where-3w-4w-5w
- ner
pretty_name: "Niger: Operational Presence"
dataset_info:
splits:
- name: train
num_examples: 844
- name: test
num_examples: 211
---

# Niger: Operational Presence

**Publisher:** OCHA Niger · **Source:** [HDX](https://data.humdata.org/dataset/niger-operational-presence) · **License:** `cc-by` · **Updated:** 2025-11-21

---

## 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 Niger at admin 3 level.

Each row in this dataset represents time-series observations. Temporal coverage is indicated by the `date_de_rapportage`, `date_début` column(s). Geographic scope: **NER**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Time-series observations |
| **Rows (total)** | 1,055 |
| **Columns** | 26 (1 numeric, 22 categorical, 3 datetime) |
| **Train split** | 844 rows |
| **Test split** | 211 rows |
| **Geographic scope** | NER |
| **Publisher** | OCHA Niger |
| **HDX last updated** | 2025-11-21 |

---

## Variables

**Geographic** — `acronyme` (PIN, IOM, SCI), `type_d_organisation` (ONG Internationale, ONG Nationale, Organisation des Nations Unies), `type_projet` (Urgence, Développement, Resilience), `présence_physique`.

**Temporal** — `date_de_rapportage`, `date_début`, `date_fin`.

**Identifier / Metadata** — `pcode_1`, `pcode_2`, `pcode_3`, `esa_source`, `esa_processed`.

**Other** — `nom_organisation` (Plan International Niger, Organisation des Nations Unies pour les Migrants, Save the Children International), `secteur_domaine` (Multi-Secteur, Education, Santé), `cluster` (Education, Protection Générale, Santé), `nom_projet` (MPRR, Apprendre ensemble, Éducation et Développement des Réfugiés / Refugee Education and Development (READ)), `nom_activité` (Formation en gestion entrepreneuriale (GERME) aux migrants nigériens de retour, Remise de kit d’assistance immédiate aux migrants nigériens de retour, Remise de kit réintégration aux migrants nigériens de retour) and 9 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-niger-operational-presence")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `date_de_rapportage` | datetime64[ns] | 0.0% | |
| `acronyme` | object | 0.0% | PIN, IOM, SCI |
| `nom_organisation` | object | 0.0% | Plan International Niger, Organisation des Nations Unies pour les Migrants, Save the Children International |
| `type_d_organisation` | object | 0.0% | ONG Internationale, ONG Nationale, Organisation des Nations Unies |
| `type_projet` | object | 0.4% | Urgence, Développement, Resilience |
| `secteur_domaine` | object | 1.3% | Multi-Secteur, Education, Santé |
| `cluster` | object | 0.0% | Education, Protection Générale, Santé |
| `nom_projet` | object | 0.0% | MPRR, Apprendre ensemble, Éducation et Développement des Réfugiés / Refugee Education and Development (READ) |
| `nom_activité` | object | 0.1% | Formation en gestion entrepreneuriale (GERME) aux migrants nigériens de retour, Remise de kit d’assistance immédiate aux migrants nigériens de retour, Remise de kit réintégration aux migrants nigériens de retour |
| `ce_projet_est_pris_en_compte_dans_le_plan_de_réponse_humanitaire` | object | 0.0% | Oui, Non |
| `date_début` | datetime64[ns] | 0.0% | |
| `date_fin` | datetime64[ns] | 0.0% | |
| `nombre_beneficiaires_directs` | int64 | 0.0% | 0.0 – 805967.0 (mean 33662.9839) |
| `etat_projet` | object | 0.0% | En cours, Finalisé, Planifié |
| `pcode_1` | object | 0.0% | |
| `région` | object | 0.0% | |
| `pcode_2` | object | 0.0% | |
| `département` | object | 0.0% | |
| `pcode_3` | object | 31.0% | |
| `commune` | object | 0.0% | |
| `présence_physique` | object | 0.0% | |
| `approche_aap` | object | 0.0% | |
| `institutionalisation_aap` | object | 21.8% | |
| `commentaire` | object | 64.7% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `nombre_beneficiaires_directs` | 0.0 | 805967.0 | 33662.9839 | 2750.0 |

---

## 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: `bailleur_partenaire_financier`. 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 Niger 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: `pcode_3`, `institutionalisation_aap`, `commentaire`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/niger-operational-presence) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_niger_operational_presence,
title = {Niger: Operational Presence},
author = {OCHA Niger},
year = {2025},
url = {https://data.humdata.org/dataset/niger-operational-presence},
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.*