The Global Land Surface Water Dataset in 30m Resolution in 2010 (GlobeLand30-WTR2010 for short) was developed based on data mining methodology by integrating and analyzing the 9907 scenes of the USA Landsat TM5, ETM+ data and 2640 scenes of the China environment disaster mitigation satellite (HJ-1) data in 2010（±1）. The total area of the land surface water is 3,675,400 km2, which is 2.73% of the global land surface area. More than 40% of land surface water is located in North America. The global data were organized into 853 tiles, according to the 5° (latitude) x 6° (longitude) within the region from 60°S to 60 N, and 5° (latitude) x 12° (longitude) within the region from 60° N to 80°N (the Antarctic continent is not included). The data tiles are combined into 5 compressed data groups (Asia, Europe, North America, South America, and Africa, and Oceanic Countries), Four different data files are comprised in each of these data groups. They are: (1) land surface water data (raster data with GeoTIFF format); (2) coordinate information data (TIFF WORD format); (3) areas of selected remote sensing data (.shp format); and (4) a metadata file (XML format). In addition, the 853 data file list, including the file names, corresponding geographic coordinates and zoning codes, are listed at the file. The dataset is one of the layers of the Global Land Cover Dataset in 30m Resolution in 2010 (GlobeLand30_2010), which were donated to the United Nations by China in September 2014. Data citation: CHEN Jun et al. : Global Land Surface Water Dataset in 30m Resolution (2010) ( GlobeLand30-WTR2010 ) ,Global Change Research Data Publishing & Repository,DOI:10.3974/geodb.2014.02.01.V1, http://www.geodoi.ac.cn/WebEn/doi.aspx?DOI=10.3974/geodb.2014.02.01.V1 Available at: http://www.geodoi.ac.cn/WebEn/doi.aspx?Id=159

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."

The Global Reservoir and Dam Database, Version 1 (Revision 01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.

Countries distinguish between metropolitan (homeland) and independent and semi-independent portions of sovereign states. If you want to see the dependent overseas regions broken out (like in ISO codes, see France for example), use map units instead. Each country is coded with a world region that roughly follows the United Nations setup. Countries are coded with standard ISO and FIPS codes. French INSEE codes are also included. Includes some thematic data from the United Nations (1), U.S. Central Intelligence Agency, and elsewhere.

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."

World Countries is a detailed dataset of country level boundaries which can be used at both large and small scales. It has been designed to be used as a basemap and includes an additional Disputed Boundaries layer that can be used to edit boundaries to fit a users needs and view of the political world. Included are attributes for local and official names and country codes, along with continent and display fields. Particularly useful are the Land_Type and Land_Rank fields which separate polygons based on their size. These attributes can be used for rendering at different scales by providing the ability to turn off small islands which may clutter small scale views.

The Global Artificial Land Surface in 30 meters resolution (GlobeLand30-ATS2010 for short) was developed based on the data mining methodology by integrating and analyzing the 9907 scenes of the USA Landsat TM5, ETM+ data and 2640 scenes of the China environment disaster mitigation satellite (HJ-1) data in 2010 （±1）. Since the Artificial Land Surface is mostly a mosaic of, for example, buildings, trees, roads, small-water bodies, and grasslands that are frequently combined, it makes data mining for identifying the artificial land surface more difficult. The Pixel-Object-Knowledge (POK）methodology was applied in this study and data development. The 30m dataset shows where and how many residents there are in cities and villages, as well as industrial lands, airports, and roads worldwide. Data citation: CHEN Jun et al. : 2014.Global Artificial Land Surface Dataset in 30m Resolution (2010) ( GlobeLand30_ATS2010 ) ,Global Change Research Data Publishing & repository, DOI:10.3974/geodb.2014.02.02.V1 Available at: http://www.geodoi.ac.cn/WebEn/doi.aspx?Id=163

International boundary resources are prepared by United Nations Cartographic Section. The 1:1million dataset derived initially from VMAP0 has been corrected to better reflect the cartographic practise of UN Cartographic Section. These datasets are intended to provide the United Nations community with worldwide coverage of international boundaries consistent with the boundary representations that are used by the U.N. Cartographic Section at scales of 1:1 million and 1:15 million. Under no circumstances should this dataset, and/or any map derived from it, be construed as an official representation or endorsement of these international boundaries by the United Nations.