electricsheepafrica/africa-niger-operational-presence

Niger: Operational Presence

Publisher: OCHA Niger · Source: HDX · 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.

---

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

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 for the publisher's own methodology notes and caveats.

---

Citation

@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 — Africa's ML dataset infrastructure. Lagos, Nigeria.