Vessel identity and location information was obtained using two approaches. (1) Over the past 20 years, 10-20% of the vessel fleet has voluntarily participated in collecting meteorological data for the open ocean, which includes location at the time of measurement, as part of the Volunteer Observing System (VOS). (2) In order to improve maritime safety, in 2002 the International Maritime Organization SOLAS agreement required all vessels over 300 gross tonnage (GT) and vessels carrying passengers to equip Automatic Identification System (AIS) transceivers, which use the Global Positioning System (GPS) to precisely locate vessels. Eight broad classes of vessels were taken into account separately: authority, cargo, fishing, high-speed, passenger, pleasure, support, tanker and an ‘other’ class. The vessel classes which move globally (cargo, tanker, and passenger) are required to carry AIS transceivers, and in these three classes 60-70% of the total vessel fleet was observed using AIS. The resulting data layer is primarily composed of these vessel classes in both the AIS and VOS data sources, and is almost exclusively these ship types in the open ocean. We used a simple linear average of the two data sources, producing a final model resolved for the whole ocean at a resolution of 0.1 decimal degrees (~11km). Data have limited observation frequency, leading to gaps that when directly interpolated with geodesic paths, create invalid routes which cross land masses. Routing model was used to create a visibility graph of the oceans, creating valid potential movement paths. These movement paths are based on the assumption that mariners will prefer great circle distances when possible. 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."

This data layer combines estimates of pollution coming from commercial shipping and from ports. As such, it is a combination of the shipping and port volume data layers, with the port volume data plumed to estimate pollution from commercial ports (with exponential decline in intensity from the port). Ocean-based pollution is assumed to derive from commercial and recreational ship activity. No data on global recreational ship activity currently exist, and therefore we modelled this driver to oceans using a combination of the commercial shipping traffic data and port data. The shipping data provide an estimate of the occurrence of ships at a particular location, and therefore an estimate of the amount of pollution they produce (via fuel leaks, oil discharge, waste disposal, etc.) that is unique from their contribution to ship strikes, etc. described above. We recognize that ocean currents can disperse this pollution into untraveled regions, but small-scale oceanography is known for only a few select locations around the world, and pollutants are likely to be most concentrated in high traffic areas. The dispersal of port-derived pollution was modelled as a diffusive plume with a maximum distance of 100 km. These plumes were not clipped to shallow regions as was done for Invasive Species.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."

Vessel identity and location information was obtained using two approaches. (1) Over the past 20 years, 10-20% of the vessel fleet has voluntarily participated in collecting meteorological data for the open ocean, which includes location at the time of measurement, as part of the Volunteer Observing System (VOS). (2) In order to improve maritime safety, in 2002 the International Maritime Organization SOLAS agreement required all vessels over 300 gross tonnage (GT) and vessels carrying passengers to equip Automatic Identification System (AIS) transceivers, which use the Global Positioning System (GPS) to precisely locate vessels. A single year sample of the VOS data was used for analysis. These data ignores vessel type, and included observations from only 12% of the vessel fleet. The ships included are a spatially- and statistically-biased sample of the population, making the modelled results somewhat misleading. Data have limited observation frequency, leading to gaps that when directly interpolated with geodesic paths, create invalid routes which cross land masses. Routing model was used to create a visibility graph of the oceans, creating valid potential movement paths. These movement paths are based on the assumption that mariners will prefer great circle distances when possible.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."

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."

Input data are from 2007-2010 FAO statistics, the most recent available. FAO data on annual country-level fertilizer were used were available, averaged over the time periods. Missing values were filled using a linear regression model of fertilizers as a function of pesticides (gaps: N=4; regression: R2 = 0.72) when pesticide data were available or agricultural GDP (gaps: N=22; regression: R2 = 0.62) when not. These country-level average fertilizer 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."

Pesticides were used as a proxy measure for organic pollution. Input data are from FAO statistics published in the time period 2003-2006. 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 2005. 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."

This is the cumulative human impact based on raw stressors for the year 2013 (Halpern et al. 2015. Spatial and temporal changes in cumulative human impacts on the world's ocean.). The cumulative human impact for the year 2013 is the sum of all normalized stressor data adjusted for habitat/pressure vulnerability. List of stressor data: artisanal_fishing, demersal_destructive_fishing, demersal_nondest_high_bycatch, demersal_nondest_low_bycatch, inorganic, invasives, night_lights, ocean_acidification, ocean_pollution, oil_rigs, pelagic_high_bycatch, pelagic_low_bycatch, plumes_fert, plumes_pest, population, shipping, slr, sst, uv. Cumulative human impact from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: pressure and cumulative impacts data (2013, all pressures). Knowledge Network for Biocomplexity. doi:10.5063/F15718ZN."

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"