This one is particularly nice for GeoHubNG because it gives us Bayelsa-specific biodiversity data. The GBIF record identifies the University of Calabar as publisher and confirms CC BY 4.0.

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.

An official geospatial dataset from Nigeria’s National Oil Spill Detection and Response Agency (NOSDRA) containing mapped oil-spill records from 1994 through March 2019. The dataset is published through an ArcGIS Feature Service and can be queried and converted into other formats. It is useful for oil-spill mapping, environmental monitoring, petroleum-sector analysis, pollution studies, spatial risk assessment and environmental impact analysis. Publisher: National Oil Spill Detection and Response Agency (NOSDRA), Nigeria.

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 historical ecological and remote-sensing research dataset from the Olokemeji grassland site in Nigeria covering 1956–1964. The dataset contains ASCII time-series files with measurements of above-ground biomass, dead matter and net primary productivity, providing a rare long-term ecological record for Nigerian grassland research. Publisher: NASA.

A national flood and drought monitoring resource combining ground observations, satellite information and modelled climate and hydrological data for near-real-time monitoring and forecasting of hydrological variability in Nigeria. The system provides access to maps, time series, summary statistics and warning information for current and potential future flood and drought conditions. Publisher: UNESCO Intergovernmental Hydrological Programme Water Information Network System (IHP-WINS).

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 repository of ground-based solar and meteorological measurements collected under the West African Power Pool solar resource measurement campaign. The Nigerian observations cover Bauchi from September 2021 to September 2023 and Kano from September 2021 to September 2023, with downloadable CSV, XLS and supporting PDF reports. Publisher: World Bank Group / CSP Services GmbH.

A high-resolution WorldPop raster dataset estimating Nigeria’s population by sex and age group for 2025. The data are provided at approximately 100 m resolution in GeoTIFF format and include age groups from infants through older age categories. Publisher: WorldPop, University of Southampton.