To provide a global gridded data set of commonly-used agricultural pesticides crucial to assess human and ecosystem exposure to potential toxicants for environmental modeling, assessment of agricultural chemical contamination and risk analysis, and other related research at global, regional, national, and local levels.

To provide global urban, rural, and total population base year and projection grids based on the Shared Socioeconomic Pathways (SSPs) data at a resolution of 1-km (about 30 arc-seconds) for climate, socioeconomic, environmental, and other related research.

To visualize changes in brightness and extent of global nighttime lights networks over two decades with improved radiometric accuracy and finer spatial resolution.

To provide global urban, rural, and total population base year and projection grids based on the Shared Socioeconomic Pathways (SSPs) data at a resolution of one-eighth degree (7.5 arc-minutes) for climate, socioeconomic, environmental, and other related research.

To provide a cumulative measure of human modification of terrestrial lands based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially-explicit global data sets.

To provide spatially-explicit data that relatively rank global land suitability referred to as Development Potential Indices (DPIs) for 13 sectors related to renewable energy, fossil fuels, mining and agriculture as an aid to conservation priority setting.

To provide high-resolution population and urban classification data for India for 2011.

To provide gridded data that identify the level of intensity and frequency of food insecurity over the 10 years between 2009 and 2019, as well as hotspot areas that have experienced consecutive food insecurity events, based on FEWS NET Food Security Data.

To provide quantitative metrics for evaluating a country's environmental performance in different policy categories relative to clearly defined targets.

To provide a future development threat map based on combining these resources: agricultural expansion, urban expansion, conventional oil and gas, unconventional oil and gas, coal, mining, biofuels, solar, and wind.

To provide satellite altimeter corrected digital elevation data for environmental, socioeconomic, climate and other related research, such as flood risk assessment, land deformations, landslide modelling, urban planning, and sea level rise impacts.

To provide data on changes in freshwater availability in order to better understand impacts on regional food supplies, human and ecosystem health, energy generation, and social unrest.

To provide representations of urban areas in the Continental U.S. in the year 2015 to support sustainable urban development planning in accordance with the growth of urban areas.

To provide data that can be used to calculate trace gas and aerosol emissions, due to fires; monitor and assess national efforts to manage emissions.

To provide spatially explicit, historic, city-level population data from 3700 BC to AD 2000 for improved understanding of contemporary and historical urbanization trends.

To provide estimates of population density for the years 2000, 2005, 2010, 2015, and 2020, based on counts consistent with national censuses and population registers with respect to relative spatial distribution, but adjusted to match United Nations country totals.

To provide estimates of population density for the years 2000, 2005, 2010, 2015, and 2020, based on counts consistent with national censuses and population registers, as raster data to facilitate data integration.

To provide estimates of population counts by age and sex for the year 2010, consistent with national censuses and population registers, as raster data to facilitate data integration.

To provide estimates of population count for the years 2000, 2005, 2010, 2015, and 2020, consistent with national censuses and population registers, as raster data to facilitate data integration.

To provide context for the population count and density rasters, and explicit information on the spatial precision of the input boundary data.