Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,709
datasets available to search
ShareScore release 0.9.0
Dataset results
3,709 results for “urbanization.”
Annual PM2.5 Concentrations for Countries and Urban Areas, 1998-2016
The Annual PM2.5 Concentrations for Countries and Urban Areas, 1998-2016, consists of mean concentrations of particulate matter (PM2.5) for countries and urban areas. The PM2.5 data are from the Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. The urban areas are from the Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extent Polygons, Revision 02, and its time series runs from 1998 to 2016. The country averages are population-weighted such that concentrations in populated areas count more toward the country average than concentrations in less populated areas, and its time series runs from 2008 to 2015.
Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Land and Geographic Unit Area Grids
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Land and Geographic Unit Area Grids measure land areas in square kilometers and the mean Unit size (population-weighted) in square kilometers. The land area grid permits the summation of areas (net of permanent ice and water) at the same resolution as the population density, count, and urban-rural grids. The mean Unit size grids provide a quantitative surface that indicates the size of the input Unit(s) from which population count and density grids are derived. Additional global grids are created from the 30 arc-second grid at 1/4, 1/2, and 1 degree resolutions. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).
Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100
The Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of one-eighth degree (7.5 arc-minutes). Spatial urban land projections are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts and adaptation. This data set presents a set of global, spatially explicit urban land scenarios that are consistent with the Shared Socioeconomic Pathways (SSPs) to produce an empirically-grounded set of urban land spatial distributions over the 21st century. A data-science approach is used exploiting 15 diverse data sets, including a newly available 40-year global time series of fine-spatial-resolution remote sensing observations from the Landsat satellite series. The SSPs are developed to support future climate and global change research, the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), along with Special Reports.
SDG Indicator 11.2.1: Urban Access to Public Transport, 2023 Release
The SDG Indicator 11.2.1: Urban Access to Public Transport, 2023 Release, part of the SDGI collection, measures the proportion of the population in a city that has convenient access to public transport. UN SDG 11 is "make cities and human settlements inclusive, safe, resilient and sustainable". Improving access to public transport services is integral to achieving the objectives of SDG 11. According to the UN Sustainable Transport, Sustainable Development 2021 Interagency Report, "only about half the world's urban population have convenient access to public transport". The report highlights that access to sustainable transport can help reduce food insecurity, boost economies, empower women, and connect people to key health, education, and financial services. As one measure of progress towards SDG 11, the UN has established SDG indicator 11.2.1. The indicator was computed as the proportion of WorldPop gridded population within either 0.5 kilometer walking distance to a low-capacity OpenStreetMap (OSM) public transport point or 1 kilometer walking distance to a high-capacity OSM public transport point. Cities were delineated using the European Commission Joint Research Centre (JRC) Urban Center Database (UCDB). The SDG indicator 11.2.1 data set provides estimates for the proportion of population with convenient access to public transport for 5,749 urban centers across 178 countries.
Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extent Polygons, Revision 02
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extent Polygons, Revision 02 is an update to Revision 01, which included new settlements and represented the first time that SEDAC released polygons (in Esri shapefile format) with the settlement name (or name of the largest city in the case of multi-city agglomerations). The shapefile consists of polygons defined by the extent of the nighttime lights and approximated urban extents (circles) based on buffered settlement points. Revision 01 also included new urban extents identified from multiple sources and corrected georeferencing for some settlements (see separate documentation for Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points, Revision 01 for the data and methods). Revision 01 was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with CUNY Institute for Demographic Research (CIDR). Revision 02 was produced by CIESIN.
2015 Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods
The 2015 Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods data set models urban settlements in the Continental United States (CONUS) as of 2015. When applied to the combination of daytime spectral and nighttime lights satellite data, the machine learning methods achieved high accuracy at an intermediate-resolution of 500 meters at large spatial scales. The input data for these models were two types of satellite imagery: Visible Infrared Imaging Radiometer Suite (VIIRS) Nighttime Light (NTL) data from the Day/Night Band (DNB), and Moderate Resolution Imaging Spectroradiometer (MODIS) corrected daytime Normalized Difference Vegetation Index (NDVI). Although several machine learning methods were evaluated, including Random Forest (RF), Gradient Boosting Machine (GBM), Neural Network (NN), and the Ensemble of RF, GBM, and NN (ESB), the highest accuracy results were achieved with NN, and those results were used to delineate the urban extents in this data set.
Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extents Grid
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extents Grid distinguishes urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).
Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019 v1
The Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1 data set contains annual predictions of the chemical concentrations at a hyper resolution (50m x 50m grid cells) in urban areas and at a high resolution (1km x 1km grid cells) in non-urban areas for the years 2000 to 2019. Particulate matter with an aerodynamic diameter less than 2.5 �m (PM2.5) increases mortality and morbidity. PM2.5 is composed of a mixture of chemical components that vary across space and time. Due to limited hyperlocal data availability, less is known about health risks of PM2.5 components, their U.S.-wide exposure disparities, or which species are driving the biggest intra-urban changes in PM2.5 mass. The national super-learned models were developed across the U.S. for hyperlocal estimation of annual mean elemental carbon, ammonium, nitrate, organic carbon, and sulfate concentrations across 3,535 urban areas at a 50m spatial resolution, and at a 1km resolution for non-urban areas from 2000 to 2019. Using Machine-Learning models (ML), combined with either a Generalized Additive Model (GAM) Ensemble Geographically-Weighted-Averaging (GAM-ENWA) or Super-Learning (SL) and approximately 82 billion predictions across 20 years, hyperlocal super-learned PM2.5 components are now available for further research. The overall R-squared values of 10-fold cross validated models ranged from 0.910 to 0.970 on the training sets for these components, while on the test sets the R-squared values ranged from 0.860 to 0.960. Remarkable spatiotemporal intra-urban and inter-urban variabilities were found in PM2.5 components. The Coordinate Reference System (CRS) for predictions is the World Geodetic System 1984 (WGS84) and the Units for the PM2.5 Components are �g/m^3. The data are provided in RDS tabular format, a file format native to the R programming language, but can also be opened by other languages such as Python.
Low Elevation Coastal Zone (LECZ) Urban-Rural Population Estimates, Global Rural-Urban Mapping Project (GRUMP), Alpha Version
The Low Elevation Coastal Zone (LECZ) Urban-Rural Population Estimates consists of country-level estimates of urban, rural and total population and land area country-wide and in the LECZ, if applicable. Additionally, the data set provides the number of urban extents, their population and land area that intersect the LECZ, by city-size population classifications of less than 100,000, 100,000 to 500,000, 500,000 to 1,000,000, 1,000,000 to 5,000,000, and more than 5,000,000. All estimates are based on GRUMP Alpha data products. The LECZ was generated using SRTM Digital Elevation Model data and includes all land area that is contiguous with the coast and 10 meters or less in elevation. All grids used for population, land area, urban mask, and LECZ were of 30 arc-second (~1 km ) resolution. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Institute for Environment and Development (IIED).
Georeferenced Population Datasets of Mexico (GEO-MEX): Urban Place GIS Coverage of Mexico
The Urban Place GIS Coverage of Mexico is a vector based point Geographic Information System (GIS) coverage of 696 urban places in Mexico. Each Urban Place is geographically referenced down to one tenth of a minute. The attribute data include time-series population and selected census/geographic data items for Mexican urban places from from 1921 to 1990. The cartographic data include urban place point locations on a state boundary file of Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI) and the Environmental Research Institute (ERI) of Michigan.
Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): National Identifier Grid
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): National Identifier Grid is derived from the land area grid to create a raster surface where pixels (cells) that cover the same nation or territory have the same value. The countries and territories are not official representations of country boundaries; rather they represent the area covered by the statistical data as provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).
Systematic surveillance of SARS-CoV-2 reveals dynamics of variant mutagenesis and transmission in a large urban population
GEO Series GSE231416. Homo sapiens; synthetic construct; Severe acute respiratory syndrome coronavirus 2. 4153 samples. Type: Expression profiling by high throughput sequencing; Other.
Exposure to urban particulate matter (EHC-93)
GEO Series GSE1796. Rattus norvegicus. 5 samples. Type: Expression profiling by array.
mRNA expression data from A549 exposed to fresh or aged urban air mixtures
GEO Series GSE23735. Homo sapiens. 8 samples. Type: Expression profiling by array.
Effect of Pre- and Postnatal Exposure to urban PM2.5 on the Transcriptome of the Developing and Early-Life Mouse Lung
GEO Series GSE104656. Mus musculus. 26 samples. Type: Expression profiling by array.
FIGURE 1 in Description of larva and pupa of Phylloicus cressae Prather 2003 (Trichoptera Calamoceratidae) from a montane forest stream in the peri-urban area of Caracas Venezuela
FIGURE 1: Phylloicus cressae, larva; head capsule. 1A, dorsal; 1B, ventral. Head chaetotaxy in dorsal view, 1A: FC1–FC6 = frontoclypeal tactile setae, A1–A2 = anterior tactile seta, L1 = lateral tactile seta, posterolateral to stemmata, S2 and S3 = stemmatal tactile seta, P1–P4 = posterior tactile seta.
FIG. 1 in Parasitism by bat flies on an urban population of Cynopterus brachyotis in Singapore
FIG. 1. Map of Singapore with the study area enlarged, showing the approximate areas covered by secondary forest in Kent Ridge Road and Kent Ridge Park
FIG. 3 in Parasitism by bat flies on an urban population of Cynopterus brachyotis in Singapore
FIG. 3. Scatter-plot of number of bat flies versus an index of bat body condition (quotient of body mass over forearm length). Circles indicate adult individuals and triangles indicate juveniles
FIG. 2 in Parasitism by bat flies on an urban population of Cynopterus brachyotis in Singapore
FIG. 2. Histograms showing frequency of L. f. ferrarii counts on parasitized C. brachyotis individuals by host (A) sex, (B) age, and (C) reproductive status
FIGURE 6 in Frenopyxis stierlitzi gen. nov., sp. nov.-new testate amoeba (Amoebozoa Arcellinida) from the urban parks with notes on the systematics of the family Centropyxidae Jung, 1942
FIGURE 6. Representatives of the genera from the family Centropyxidae. a–b—Centropyxis aculeata (after Leidy 1879), c–d—Armipyxis discoides (after Penard 1890), e–f—Proplagiopyxis nuda (after Schönborn 1964), g–h—Centropyxiella lucida (after Golemansky 1971), i–j—Collaropyxidia stankovići (after Živković 1975), k–m—Conicocassis pontigulasiformis (after Nasser & Patterson 2015), n–o—Oopyxis danubialis (after Godeanu 1972).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.