Pesticides were used as a proxy measure for organic pollution. Input data are from 2007-2010 FAO statistics, the most recent available. Missing pesticide values were filled using a linear regression model of pesticides as a function of fertilizers (gaps: N=69; regression: R2 = 0.72) when fertilizer data were available or agricultural GDP (gaps: N=22; regression: R2 = 0.82) when not. These country-level average pesticides values were then dasymetrically distributed over a country’s landscape using global land cover data from 2009, derived from the Moderate Resolution imaging Spectroradiometer (MODIS) instrument at ~500m resolution. Finally, spread of the driver values into coastal waters at each pour point was modelled with a cost-path surface on the basis of a decay function that assigns a fixed amount of the driver (0.5% of the value in the previous cell) in the initial cell and then evenly distributes the remaining amount of driver in all adjacent and ‘unvisited’ cells, repeated until a minimum threshold (0.05% of global maximum) is reached. This approach to modelling river plumes is diffusive and so allows drivers to wrap around headlands and islands. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."

Since mid of 20th century, anthropogenic greenhouse gas emissions have increased, it is very possible of being driven largely by economic and population growth, and causing the global warming. Based on the global carbon emissions data of 2014 in each country from CDIAC (Carbon Dioxide Information Analysis Center) and population density data in 2015 from SEDAC (Socioeconomic Data and Applications Center), the population based global carbon emissions dataset in 0.1° resolution (2014) was developed by the model of integrating population density as an economic-population composite indicator to weighted carbon emissions. The result shows the main carbon emission areas are located in the eastern United States, eastern China, Japan, Korea, India, Southeast Asia and Europe, and there are spatial differences in each region. The result can reflect spatial distribution of the current global carbon emissions and provide basic data for global change research. The dataset was archived in .tif data format with the data size of 22.7 MB (3.92MB in compressed file). Foundation Item： Ministry of Science and Technology of P. R. China (2016YFA0602704) Data Citation： "FAN Zhixin,SU Yun*,FANG Xiuqi.2017.Population Based Global Carbon Emissions Dataset in 0.1°Resolution (2014) ( GlobalPopCarbonEmis2014 ) ,Global Change Research Data Publishing & Repository,DOI:10.3974/geodb.2017.03.12.V1"

This dataset provides estimate of the potential increase in soil organic carbon within the top 30 cm of soil in croplands after 20 years, following implementation of better land managment practices under a medium sequestration scenario. The per pixel values here take in to consideration the percent of each pixel which is classified as cropland (from the GLC-Share/GLC-02 dataset), and values have been converted to total tonnes of carbon (x 100) per pixel.<br/><br>See: <a href="https://doi.org/10.1038/s41598-017-15794-8">Zomer, R.J., Bossio, D.A., Sommer, R., Verchot, L.V., 2017. Global Sequestration Potential of Increased Organic Carbon in Cropland Soils. Scientific Reports 7, 15554</a>.<br/>For descriptions of sequrestion scenarions see: <a href="https://doi.org/10.1016/j.jenvman.2014.05.017">Sommer, R., Bossio, D., 2014. Dynamics and climate change mitigation potential of soil organic carbon sequestration. Journal of Environmental Management 144, 83–87</a>.<br/>

This dataset provides estimate of the potential increase in soil organic carbon within the top 30 cm of soil in croplands after 20 years, following implementation of better land managment practices under a high sequestration scenario. The per pixel values here take in to consideration the percent of each pixel which is classified as cropland (from the GLC-Share/GLC-02 dataset), and values have been converted to total tonnes of carbon (x 100) per pixel.<br/><br>See: <a href="https://doi.org/10.1038/s41598-017-15794-8">Zomer, R.J., Bossio, D.A., Sommer, R., Verchot, L.V., 2017. Global Sequestration Potential of Increased Organic Carbon in Cropland Soils. Scientific Reports 7, 15554</a>.<br/>For descriptions of sequrestion scenarions see: <a href="https://doi.org/10.1016/j.jenvman.2014.05.017">Sommer, R., Bossio, D., 2014. Dynamics and climate change mitigation potential of soil organic carbon sequestration. Journal of Environmental Management 144, 83–87</a>.<br/>

This raster layer represents the number of threatened species of mammals, amphibians and birds potentially occurring in each ~300 m grid cell. <br> Species range data were rasterised at 10 arc-seconds (approximately 300m at the equator) from polygon maps developed for the IUCN Red List (IUCN, 2017; BirdLife international and Handbook of the Birds of the World 2017). Each range was then refined by removing areas of unsuitable land cover (Bontemps et al., 2011) using information on species’ habitat preferences (IUCN, 2017). Areas outside the species’ known altitudinal limits (IUCN, 2017) were also removed using elevation data (Danielson and Gesch, 2011). If species had no habitat preference data available, ranges were refined only by altitude. If altitude limits were missing, then extreme values (for either min or max, or both) outside the global min/max of the elevation dataset were applied. This effectively meant there was no altitude refinement in such cases. This refinement process produced extent of suitable habitat (ESH) maps for each species. These maps were then summed together with equal weighting into a single richness layer. <br> The three taxonomic groups included are those that have been comprehensively assessed for the IUCN Red List, and are used as a proxy to represent threatened terrestrial biodiversity. Thus, users should be aware of the taxonomic bias and that marine areas have been removed from analysis. Similarly, areas of permanent snow and ice have values of zero due to a lack of corresponding IUCN habitat types. <br> <br><b>References:</b> <br> Bontemps, S., Defourny, P., Radoux, J., Van Bogaert, E., Lamarche, C., Achard, F., Mayaux, P., Boettcher, M., Brockmann, C., Kirches, G. and Zülkhe, M. (2013). Consistent global land cover maps for climate modelling communities: current achievements of the ESA’s land cover CCI. In: Proceedings of the ESA Living Planet Symposium, September 2013 (pp. 9-13). <br> Danielson, J.J., and Gesch, D.B. (2011). Global multi-resolution terrain elevation data 2010 (GMTED2010): U.S. Geological Survey Open-File Report 2011–1073, 26 p. <a href="https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf" target="_blank">https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf</a> IUCN (2017). <a href="http://www.iucnredlist.org" target="_blank">The IUCN Red List of Threatened Species. Version 2017-3.</a> <br><br> <b>Attribution:</b><br>IUCN 2017. <a href="http://www.iucnredlist.org" target="_blank">The IUCN Red List of Threatened Species. Version 2017-1</a>.<br> <a href="http://www.iucnredlist.org/info/terms-of-use" target="_blank">Terms and Conditions</a> <br>Note that commercial use is not permitted.

The problem of maternal mortality is a recurring problem in Sub-Saharan Africa and the poorest population and residing away from health centers is the most affected. AccessMod is a computer program designed to help these countries examine the geographical aspect of their health system. And so to map the physical accessibility in terms of travel time to the health center. The accessibility map helps to guide decision-makers by showing them areas of low access, ie places where the population must walk to reach health facilities. Increasing accessibility in these places will at the same time improve maternal health.

This raster layer represents the number of species of mammals, amphibians and birds whose distributions overlap each ~300m grid cell. <br> Species range data were rasterised at 10 arc-seconds (approximately 300m at the equator) from polygon maps developed for the IUCN Red List (IUCN, 2017; BirdLife international and Handbook of the Birds of the World 2017). Each range was then refined by removing areas of unsuitable land cover (Bontemps et al., 2011) using information on species’ habitat preferences (IUCN, 2017). Areas outside the species’ known altitudinal limits (IUCN, 2017) were also removed using elevation data (Danielson and Gesch, 2011). If species had no habitat preference data available, ranges were refined only by altitude. If altitude limits were missing, then extreme values (for either min or max, or both) outside the global min/max of the elevation dataset were applied. This effectively meant there was no altitude refinement in such cases. This refinement process produced extent of suitable habitat (ESH) maps for each species. These maps were then summed together into a single layer with equal weighting. <br> The three taxonomic groups included are those that have been comprehensively assessed for the IUCN Red List, and are used as a proxy to represent terrestrial biodiversity. Thus, users should be aware of the taxonomic bias and that marine areas have been removed from analysis. Similarly, areas of permanent snow and ice have values of zero due to a lack of corresponding IUCN habitat types. <br> <br><b>References:</b> <br> Bontemps, S., Defourny, P., Radoux, J., Van Bogaert, E., Lamarche, C., Achard, F., Mayaux, P., Boettcher, M., Brockmann, C., Kirches, G. and Zülkhe, M. (2013). Consistent global land cover maps for climate modelling communities: current achievements of the ESA’s land cover CCI. In: Proceedings of the ESA Living Planet Symposium, September 2013 (pp. 9-13). <br> Danielson, J.J., and Gesch, D.B. (2011). Global multi-resolution terrain elevation data 2010 (GMTED2010): U.S. Geological Survey Open-File Report 2011–1073, 26 p. <a href="https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf" target="_blank">https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf</a> IUCN (2017). <a href="http://www.iucnredlist.org" target="_blank">The IUCN Red List of Threatened Species. Version 2017-3.</a> <br><br> <b>Attribution:</b><br>IUCN 2017. <a href="http://www.iucnredlist.org" target="_blank">The IUCN Red List of Threatened Species. Version 2017-1</a>.<br> <a href="http://www.iucnredlist.org/info/terms-of-use" target="_blank">Terms and Conditions</a> <br>Note that commercial use is not permitted.

This analysis of 35 years’ worth of satellite data (at approximately 25 square kilometer resolution at the equator) provides a comprehensive record of global land-change dynamics during the period 1982–2016. Contrary to the prevailing view that forest area has declined globally — tree cover has increased by 2.24 million km2 (+7.1% relative to the 1982 level), largely the result of a net loss in the tropics being outweighed by a net gain in the extratropics. Global bare ground cover has decreased by 1.16 million km2 (−3.1%), most notably in agricultural regions in Asia. Of all land changes, 60% are associated with direct human activities and 40% with indirect drivers such as climate change. Land-use change exhibits regional dominance, including tropical deforestation and agricultural expansion, temperate reforestation or afforestation, cropland intensification and urbanization. Consistently across all climate domains, montane systems have gained tree cover and many arid and semi-arid ecosystems have lost vegetation cover.<br><br>For full details see: <a href="https://doi.org/10.1038/s41586-018-0411-9">Song, X.-P., Hansen, M.C., Stehman, S.V., Potapov, P.V., Tyukavina, A., Vermote, E.F., Townshend, J.R., 2018. Global land change from 1982 to 2016. Nature 1</a><br/>.

This digitial elevation model (DEM) is derived from CGIAR-CSI SRTM v4.1 and ASTER GDEM v2 data products, and has been processed and mergedm inculding void-filling and smoothing of irregularities, to provide continuous coverage of ~91% of the globe.<br><br>For full processing and merging methodology see: <a href="https://www.sciencedirect.com/science/article/abs/pii/S0924271613002360"target="_blank">Robinson et al. (2014)</a>.<br/><br>Data can also be downloaded from <a href="http://www.earthenv.org/DEM"target="_blank">EarthEnv</a>.<br/><br>This data available under a <a href="https://creativecommons.org/licenses/by/4.0/"target="_blank">Creative Commons Attribution 4.0 International License</a> and is provided without any warranty of any kind whatsoever, either express or implied, including warranties of merchantability and fitness for a particular purpose. The creators of the product shall not be liable for incidental, consequential, or special damages arising out of the use of any data.<br/>

The Human Footprint (HFP) provides a measure of the direct and indirect human pressures on the environment globally in years 1993 and 2009. It is derived from remotely-sensed and bottom-up survey information compiled on eight measured variables. This represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps. Data on human pressures were acquired or developed for: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. Pressures were then overlaid to create the standardized Human Footprint maps for all non-Antarctic land areas. The Human Footprint maps find a range of uses as proxies for human disturbance of natural systems and can provide an increased understanding of the human pressures that drive macro-ecological patterns, as well as for tracking environmental change and informing conservation science and application. HFP values range from 0 (no human impact) to 50 (heavily human impacted).<br><br>See: <a href=""https://www.nature.com/articles/ncomms12558"">Venter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11</a>.<br/><br>Data can also be downloaded from <a href=""https://datadryad.org/resource/doi:10.5061/dryad.052q5"">Dryad<a/>.<br/>