Field data associated with blackfly/vector-control monitoring along communities and the Ose River system around the Edo–Ondo border. The underlying study says the collected data are openly available through Figshare.

This is an occurrence dataset covering fauna in Nigeria’s south-southern coastal environments. The GBIF record identifies the University of Lagos as publisher and explicitly gives CC BY 4.0.

Geophysical electromagnetic survey data made available through the Nigeria Geological Survey Agency’s GeoData Centre. The NGSA describes its GeoData Centre as the authoritative repository for Nigerian geoscientific data and provides an online EM Data Download service covering survey areas including Birnin Gwari, Ilesha and Benue. The data are valuable for geological mapping, mineral exploration, groundwater investigation, structural interpretation and geophysical GIS analysis. Publisher: Nigeria Geological Survey Agency (NGSA).

A multi-year remote-sensing land-use and urban-land-cover dataset covering Nigeria from 2016 to 2020. The product contains 30 m annual land-use maps and 10 m urban land-cover classes derived from Landsat and Sentinel-2 time series using Random Forest machine-learning models. The maps were independently validated, with reported overall accuracies of approximately 65–80% for Tier-1 land-use and 60–80% for Tier-2 urban land-cover products, depending on year and country. Data are provided in Cloud Optimized GeoTIFF (COG) format. Publisher: ORNL DAAC / NASA.

A detailed 2024 land-cover classification for four Nigerian states: Kebbi, Niger, Ondo and Cross River. The classification was derived from 30 m Sentinel-2 surface-reflectance imagery using supervised machine-learning methods and the Random Forest algorithm. A total of 2,333 training samples were collected and the resulting classification achieved an overall accuracy of 83 percent. The dataset uses a national land-cover legend adapted from the West African Land Cover Reference System and was developed under GEF Food Systems Integrated Program and FOLUR activities. Publisher: FAO Agro-Informatics Platform.

A multi-source Earth Observation and participatory-mapping dataset for assessing flooding, crop damage and crop recovery during the 2020 and 2022 flood events in Hadejia, Nigeria. The package contains flood-extent and progression layers derived from Sentinel-1 and PlanetScope imagery using machine learning, flood-frequency layers, flooded-cropland layers, land-cover layers, crop-recovery layers, permanent-water data, training and validation samples, survey data and QGIS layer styles. The downloadable ZIP is approximately 142.8 MB. Creators: Lukumon Olaitan Lateef, Hugo Costa and Pedro Cabral and collaborators

A gridded geospatial dataset representing the distance from each approximately 100 m grid-cell centre to the nearest inland waterbody in Nigeria. The dataset was produced using global inland-water data from the ESA Climate Change Initiative and geodesic distance calculations. It can support hydrological analysis, environmental modelling, agricultural suitability studies, settlement planning, water-access analysis and spatial risk assessment. Publisher: WorldPop, University of Southampton and CIESIN; source water data from the ESA Climate Change Initiative.

A collection of approximately 100 m GeoTIFF layers representing the distance from each grid cell to the nearest edge of selected ESA CCI land-cover classes in Nigeria. The layers include cultivated areas, woody-tree areas, shrub areas, herbaceous areas, sparse vegetation, aquatic vegetation, artificial surfaces and bare areas. The dataset can support landscape analysis, land-use modelling, ecological studies, agricultural analysis, fragmentation assessment and remote sensing research. Publisher: WorldPop, University of Southampton and CIESIN; source land-cover data from the ESA Climate Change Initiative.

A geospatial classification of Nigeria’s degree of urbanisation for 2025, produced using WorldPop Global2 population data and the GHSL GHS-DUG tool. The release includes 1 km GeoTIFF classification grids, spatial entities and population statistics representing different settlement and urbanisation classes across Nigeria. Publisher: WorldPop, University of Southampton, as part of the DEGURBA project

High-resolution modelled geospatial estimates of DTP1, DTP3 and MCV1 vaccination coverage and the number of zero-dose children across Nigeria from 2000 to 2024. The release includes approximately 1 km GeoTIFF raster layers and administrative-level GIS/CSV data for Nigeria at national, geopolitical-zone, state and Local Government Area levels. The estimates were produced using Demographic and Health Surveys, Multiple Indicator Cluster Surveys and geospatial covariates within a Bayesian geostatistical modelling framework. Publisher: WorldPop, University of Southampton; VaxPop team.