ArcGIS and QGIS map packages, with ESRI shapefiles for the DSM2 Model Grid. These are not finalized products. Locations in these shapefiles are approximate. Monitoring Stations - shapefile with approximate locations of monitoring stations. DSM2 Grid 2025-05-28 Historical FC_2023.01 DSM2 v8.2.0, calibrated version: dsm2_8_2_grid_map_calibrated.mpkx - ArcGIS Pro map package containing all layers and symbology for the calibrated grid map. dsm2_8_2_grid_map_calibrated.mpk - ArcGIS Desktop map package containing all layers and symbology for the calibrated grid map. dsm2_8_2_0_calibrated_grid_map_qgis.zip - QGIS map package containing all layers and symbology for the calibrated grid map. dsm2_8_2_0_calibrated_gridmap_shapefiles.zip - A zip file containing all the shapefiles used in the above map packages: dsm2_8_2_0_calibrated_channels_centerlines - channel centerlines, follwing the path of CSDP centerlines dsm2_8_2_0_calibrated_network_channels - channels represented by straight line segments which are connected the upstream and downstream nodes dsm2_8_2_0_calibrated_nodes - DSM2 nodes dsm2_8_2_0_calibrated_dcd_only_nodes - Nodes that are only used by DCD dsm2_8_2_0_calibrated_and_dcd_nodes - Nodes that are shared by DSM2 and DCD dsm2_8_2_0_calibrated_and_smcd_nodes - Nodes that are shared by DSM2 and SMCD dsm2_8_2_0_calibrated_gates_actual_loc - The approximate actual locations of each gate in DSM2 dsm2_8_2_0_calibrated_gates_grid_loc - The locations of each gate in the DSM2 model grid dsm2_8_2_0_calibrated_reservoirs - The approximate locations of the reservoirs in DSM2 dsm2_8_2_0_calibrated_reservoir_connections - Lines showing connections from reservoirs to nodes in DSM2 DSM2 v8.2.1, historical version: DSM2 v8.2.1, historical version grid map release notes (PDF), updated 7/12/2022 DSM2 v8.2.1, historical version grid map, single zoom level (PDF) DSM2 v8.2.1, historical version grid map, multiple zoom levels (PDF) - PDF grid map designed to be printed on 3 foot wide plotter paper. DSM2 v8.2.1, historical version map package for ArcGIS Desktop: A map package for ArcGIS Desktop containing the grid map layers with symbology. DSM2 v8.2.1, historical version grid map shapefiles (zip): A zip file containing the shapefiles used in the grid map. Change Log 7/12/2022: The document "DSM2 v8.2.1, historical version grid map release notes (PDF)" was corrected by removing section 4.4, which incorrectly stated that the grid included channels 710-714, representing the Toe Drain, and that the Yolo Flyway restoration area was included.

Historical imagery was obtained from University of Florida’s historical Imagery site, “Aerial Photography: Florida”, the Florida Department of Transportation (FDOT) Aerial Photo Lookup System, or from the FDEP district offices. Images downloaded from UF were saved locally and georeferenced by GIS team members, whereas the imagery received from the district offices were georeferenced by District staff. It is understood that these "pre-georeferenced" tiles were georeferenced within ArcMap by various staff from the District offices. The following applies to the imagery georeferenced in-office by the Division of Water Resource Management (DWRM):The georeferencing was completed in either ArcMap 10.3.1 or ArcGIS Pro. The following standards were held for the georeferencing process: the minimum number of control points was 10 points. The RMS value was kept at or below 5.0 for all tiles georeferenced in 1st Order Polynomial, and 2.0 for those georeferenced in 2nd Order Polynomial (where 1st Order was not possible). The maximum individual residual was at or under twice the RMS. Again, these were the standards, but the accuracy is not guaranteed. To QC for human error, once all counties for the given decade were georeferenced a comparison task was completed. This QC emphasized that this data is only a visual aid in that distances can be off 50 meters or more in some areas. These are mostly areas where there were limited reference features to georectify the original images. The smallest distance found was under 10 meters. To attain more information on this QC please contact FDEP WRM GIS. As stated in the use limitation, but emphasized here, information contained herein is provided for informational purposes only. The State of Florida, Department of Environmental Protection provides geographic information systems (GIS) data and metadata with no claim as to the completeness, usefulness, or accuracy of its content, positional or otherwise. The State and its officials and employees make no warranty, express or implied, and assume no legal liability or responsibility for the ability of users to fulfill their intended purposes in accessing or using GIS data or metadata or for omissions in content regarding such data. The data could include technical inaccuracies and typographical errors. The data is presented "as is," without warranty of any kind, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. Your use of the information provided is at your own risk. In providing this data or access to it, the State assumes no obligation to assist the user in the use of such data or in the development, use, or maintenance of any applications applied to or associated with the data or metadata.Please contact GIS.Librarian@FloridaDEP.gov for more information.

The GIS of Mexican States, Municipalities and Islands consists of attribute and boundary data for 1990. The attribute data include population, language, education, literacy, housing Units and land cover classification from the 1990 Mexican population and housing census. The boundary data associated with the United States-Mexico border are consistent with the U.S. Census Bureau TIGER95 data. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

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.

Instructions on how to make an ArcGIS map, add georeferenced points, adjust appearances , configure pop up boxes, upload images and sharing a map. Introduces students to ArcGIS mapping. Students learn how to organize and upload designated places onto an ArcGIS map. Students learn how to configure pop-up boxes for each designated place and populate them with information they have uncovered. Students learn how to add images to their designated places on their maps. Once completed, students learn how to import into other media i.e. StoryMaps, Word documents to tell a bigger story about the places on the map.

The informed consent request and workshop survey questions given to participants after the workshop each day for 4 consecutive days.

In support of the proposed Susitna-Watana Hydroelectric Project, the Alaska Division of Geological & Geophysical Surveys (DGGS) developed a Geographic Information System (GIS)-based geologic compilation of published and unpublished maps for twelve, inch-to-mile (1:63,360-scale) quadrangles encompassing the proposed hydroelectric project footprint, including the anticipated reservoir and surrounding area. DGGS geologists reviewed and analyzed existing geologic mapping for quality and completeness, and the maps were converted for use in GIS. The conversion process included scanning and georeferencing the original hard-copy map documents, creating a geodatabase, digitizing the geologic data, assigning attributes, and producing a digital data product for public release. The best available geologic mapping was synthesized into a single compilation data layer, and is packaged along with georeferenced scans and digitized vector files of the original geologic source maps. Bedrock geology was reviewed and revised by an independent contractor to ensure consistency with current geologic interpretations of the area. This geodatabase product will be a valuable reference resource for developers, planners, and scientists working on the hydroelectric project, as well as for any other projects in the area.

This georectified digital map portrays Oregon, Washington, Idaho and British Columbia. Map date: 1863. The original paper map was scanned, georeferenced, and rectified to broaden access and to facilitate use in GIS software.Georeferenced source data: https://insideidaho.org/data/ago/uofi/library/historic-maps-spec/bancroftsMapOfORWAID.tif.zipNon-georeferenced source data: https://digital.lib.uidaho.edu/digital/collection/spec_hm/id/6/rec/1Original printed map is in Special Collections and Archives, University of Idaho Library, Moscow, ID 83844-2350; http://www.lib.uidaho.edu/special-collections/.

1H NMR profiling is nowadays a consolidated technique for the identification of geographical origin of food samples. The common approach consists in correlating NMR spectra of food samples to their territorial origin by multivariate classification statistical algorithms. In the present work we illustrate an alternative perspective to exploit territorial information, contained in the NMR spectra, which is based on the implementation of a Geographic Information System (GIS). NMR spectra are used to build a GIS map permitting the identification of territorial regions having strong similarities in the chemical content of the produced food (terroir units). These terroir units can, in turn, be used as input for labeling samples to be analyzed by traditional classification methods. In this work we describe the methods and the algorithms which permits to produce GIS maps from NMR profiles and apply the described method to the analysis of the geographical distribution of olive oils in an Italian region. In particular, we analyzed by 1H NMR up to 98 georeferenced olive oil samples produced in the Abruzzo Italian region. By using the first principal component of the NMR variables selected according to the Moran test, we produced a GIS map, in which we identified two regions incidentally corresponding to the provinces of Teramo and Pescara. We then labeled the samples according to the province of provenience and built a LDA model which provides a classification ability up to 99 % . A comparison between the variables selected in the geostatistics and classification steps is finally performed.

The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. 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).

An ArcGIS shapefile layer showing the extent of all extant Adelie penguin (Pygoscelis adeliae) colonies at Nelly Island, Windmill Islands, December, 1990.

The colony boundaries were digitised from Linhof aerial photographs (ANTC1219, run 53, frames 5-7) that were georeferenced to the Windmill Islands Topopoly GIS dataset.

Data quality cannot be accurately assessed. Errors in the georeferencing process could not be quantified, and there are positional discrepancies between overlapping aerial photographs are up to 2.4 m. Thus, absolute errors in the position of the colonies cannot be quantified, but the colony boundaries should be within ~0.5m of their location within the photographs.

The shapefile and the georeferenced aerial photographs are available for download from a Related URL.

This work was completed as part of ASAC project 1219 (ASAC_1219).

Georeferenced (to WGS1984) and cropped set of about 820 historic maps of Burma at a scale of 1 inch per mile (63,360) covering about 75% of the country. Those topographic maps, originally produced and published by the Great Trigonometrical Survey of India between 1899 and 1946, have been scanned and shared with the public as part of the "Old Survey Of India Maps” Community under a CC BY 4.0 International Licence. Many of these maps are reprints of earlier maps produced before the war. Most mapsheets are early editions (edition 1 or edition 2).

Each of the 820 map sheet scans was georeferenced using the Latitude-Longitude corner coordinates in Everest 1830 projection. Those map sheets were cropped, keeping only the map area - to allow a seamless mosaic without the mapframe overlapping adjacent map sheets when several map sheets are put together in a GIS. Those cropped map sheets were projected from Everest 1830 to WGS1984 (EPSG4326) - standard GPS - projection to make them easier to use and combine with other GIS data.

Those map sheets can be loaded directly in any GIS such as QGIS or ESRI ArcGIS as well as Google Earth.

• The mm_OI_JBv2024 folder contains the cropped end georeferenced map sheets in jpg-format as well as accompagning georeference and metadata incl.
  
  • The mm_OI_JBv2024_kmlLinks contains kml files to easily load the mapsheets into Google Earth
  • The mm_historicOI_EPSG4326.gdb contains an ESRI mosaic dataset to easily load all mapsheets into ArcGIS
• The mm_OI_JBv2024_scanMaps folder contains the uncropped original map scans (renamed though) in jpg-format.
• The mm_topoOI_JBv7_masterlist.xlsx is a masterlist cataloguing all map sheets for easier use and matching them with the original source files as shared as part of the "Old Survey Of India Maps" (e.g. to identify new mapsheets should new maps be released)
• The indexMaps folder contains small scale index maps to locate the map sheets using their map sheet Grid-Letter-nomenclature

All georeferenced map scans are based on maps shared by John Brown via Zenodo

• https://zenodo.org/records/8040798 (63k Maps of Burma, version 7, Published June 14, 2023)
• https://zenodo.org/records/10463372 (63k Maps of Burma--additional 1--20240105, version 1, Published January 5, 2024)

The file naming convention is to first give the number of the 4 degree x 4 degree block followed by the letter (A to P) of the sixteen 1 degree x 1 degree blocks in each 4 degree block eg. 38 D, and this is followed by a number from 1 to 16 to indicate the number of the map in the 1 degree block.

This Number Letter Number designation is followed by the map series type either OI (contains a LCC grid) or OILatLon (only has a Lat-Lon grid), followed by the edition and year of the edition, followed by the date of publication/print. If the information is not available an "X" (for edition) or "0000" (for an unknown year) is used. A best-guess approach was used if the edition and print year and version information was ambiguous.

The files as shared via the "Old Survey Of India Maps" have been renamed to standardize the file naming, sometimes correcting them and to make them unique in the case several editions of the same map sheet were available.

A topographical index produced by the Survey of India is provided to assist the viewer in selecting a particular map of interest.

Geo-referenced datasets.

This is a GIS file set of the Gede ruins. The data was generated from laser scans, photogrammetric techniques and GPS data. The data maps the site of the Gede ruins in Kilifi County in Kenya. All data is in either the unprojected Geographic (GCS WGS84) or the projected Universal Transverse Mercator 37 South (UTM37S WGS84) coordinate system.The data is packaged as an ESRI Map Package (.mpk). If you are not an ESRI user and wish to unpack the package please rename the file extension to .zip and use a programme, such as 7-Zip, to unpack the package. The package contains shapefiles and images which are compatible with most GIS software. The ruins of Gede (also Gedi), a traditional Arab-African Swahili town, are located just off Kenya’s coastline, some 90km north of Mombasa. Gede was a small town built entirely from stones and rocks, and most of the original foundations are still visible today. Remaining structures at the site include coral stone buildings, mosques, houses and a palace. The town was abandoned in the early 17th century, and Gede’s buildings date back to the 15th century, although it is believed that the site could have been inhabited as early as the 11th or 12th century. The Zamani Project spatially documented the Gede ruins in 2010. In addition to the three principal structures of the Great Mosque, the Small Mosque and the Palace, remains of other structures in the immediate vicinity were also documented.The Zamani Project seeks to increase awareness and knowledge of tangible cultural heritage in Africa and internationally by creating metrically accurate digital representations of historical sites. Digital spatial data of cultural heritage sites can be used for research and education, for restoration and conservation and as a record for future generations. The Zamani Project operates as a non-profit organisation within the University of Cape Town.This text has been adapted from the UNESCO website (https://whc.unesco.org/en/tentativelists/5501/).The Zamani Project received funding from the Andrew W Mellon Foundation at the time of the project.

Map source providing the foundation for Phase 2 GLRI Data Processing for the Georeferencing Dashboards.

Historical imagery was obtained from University of Florida’s historical Imagery site, “Aerial Photography :Florida”located through searches within the link, http://ufdc.ufl.edu/aerials.Images were downloaded and saved as targets locally and georeferenced by GIS team members. The georeferencing was completed in either ArcGIS 10.3.1 or within ArcPro. The following were held as the standard for the georeferencing process, but there is no gauruntee these were upheld. The minimum number of control points was 10 points. The RMS value was kept at or below half the cell size of the image being georeferenced. The maximum individual residual was at or under twice the RMS. Again, these were the standards, but the accuracy is not gaurunteed. To QC for human error, once all counties for the given decade were georeferenced a comparison task was completed. This QC emphazied that this data is only a visual aid in that distances can be off 50 meters or more in some areas. These are mostly areas where there were limited reference features to georectify the original images. The smallest distance found was under 10 meters. To attain more information on this QC please contact FDEP WRM GIS.As stated in the use limitation, but emphasized here, information contained herein is provided for informational purposes only. The State of Florida, Department of Environmental Protection provides geographic information systems (GIS) data and metadata with no claim as to the completeness, usefulness, or accuracy of its content, positional or otherwise. The State and its officials and employees make no warranty, express or implied, and assume no legal liability or responsibility for the ability of users to fulfill their intended purposes in accessing or using GIS data or metadata or for omissions in content regarding such data. The data could include technical inaccuracies and typographical errors. The data is presented "as is," without warranty of any kind, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. Your use of the information provided is at your own risk. In providing this data or access to it, the State assumes no obligation to assist the user in the use of such data or in the development, use, or maintenance of any applications applied to or associated with the data or metadata.The decades for each are given below:Manatee -1940, Sarasota-1948, Lee-1944, Broward-1947, Indian River-1943, Brevard-1943, St.Lucie-1944, Duval-1943, Nassau-1943, St.Johns-1943, Volusia-1943, Flagler-1943, Esacmbia-1941, Santa Rosa-1946, Okaloosa-1941, Walton-1941, Martin-1940

This georectified digital map portrays mines in the Coeur d'Alene Mining District of Idaho. Map date: 1920. The original paper map was scanned, georeferenced, and rectified to broaden access and to facilitate use in GIS software.Georeferenced source data: https://res1insideidahod-o-torg.vcapture.xyz/data/ago/uofi/library/historic-maps-spec/cdaMiningDistrict.tif.zipNon-georeferenced source data: https://res1digitald-o-tlibd-o-tuidahod-o-tedu.vcapture.xyz/digital/collection/spec_hm/id/2/rec/2Original printed map is in Special Collections and Archives, University of Idaho Library, Moscow, ID 83844-2350; http://www.lib.uidaho.edu/special-collections/.

Line layer containing 10 foot contour lines in Blacksburg November 2017. This data was created by the GIS team from the Town of Blacksburg and has been curated by Virginia Tech University Libraries in order to provide access to the data. This data is meant for general use only. Virginia Tech’s University Library is acting as a steward for this data and any questions about its use should refer to our Terms of Use Page.

Introduction

Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the Pan-Tibetan Highlands (new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the Tibetan Plateau and three adjacent mountain regions: the Himalaya, Hengduan Mountains and Mountains of Central Asia, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.

The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:

(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.

(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.

(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.

Note: High Mountain Asia: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. Third Pole: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ sensu lato defined by some previous studies, but is more specific.

More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.

Data processing

We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the Layer to KML tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the shapefile function of the raster package, and wrote it as a geojson file using the geojson_write function in the geojsonio package.

Version

Version 2022.1.

Acknowledgements

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.

Citation

Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision

Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940

Contacts

Dr. Jie LIU: E-mail: liujie@mail.kib.ac.cn;

Mr. Guangfu ZHU: zhuguangfu@mail.kib.ac.cn

Institution: Kunming Institute of Botany, Chinese Academy of Sciences

Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China

Copyright

This dataset is available under the Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

Sixty-seven maps from Indian Land Cessions in the United States, compiled by Charles C. Royce and published as the second part of the two-part Eighteenth Annual Report of the Bureau of American Ethnology to the Secretary of the Smithsonian Institution, 1896-1897 have been scanned, georeferenced in JPEG2000 format, and digitized to create this feature class of cession maps. The mapped cessions and reservations included in the 67 maps correspond to entries in the Schedule of Indian Land Cessions, indicating the number and location of each cession by or reservation for the Indian tribes from the organization of the Federal Government to and including 1894, together with descriptions of the tracts so ceded or reserved, the date of the treaty, law or executive order governing the same, the name of the tribe or tribes affected thereby, and historical data and references bearing thereon, as set forth in the subtitle of the Schedule. Go to this URL for full metadata: https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.TRIBALCEDEDLANDS.xml Each Royce map was georeferenced against one or more of the following USGS 1:2,000,000 National Atlas Feature Classes contained in \NatlAtlas_USGS.gdb: cities_2mm, hydro_ln_2mm, hydro_pl_2mm, plss_2mm, states_2mm. Cessions were digitized as a file geodatabase (GDB) polygon feature class, projected as NAD83 USA_Contiguous_Lambert_Conformal_Conic, which is the same projection used to georeference the maps. The feature class was later reprojected to WGS 1984 Web Mercator (auxiliary sphere) to optimize it for the Tribal Connections Map Viewer. Polygon boundaries were digitized as to not deviate from the drawn polygon edge to the extent that space could be seen between the digitized polygon and the mapped polygon at a viewable scale. Topology was maintained between coincident edges of adjacent polygons. The cession map number assigned by Royce was entered into the feature class as a field attribute. The Map Cession ID serves as the link referencing relationship classes and joining additional attribute information to 752 polygon features, to include the following: 1. Data transcribed from Royce's Schedule of Indian Land Cessions: a. Date(s), in the case of treaties, the date the treaty was signed, not the date of the proclamation; b. Tribe(s), the tribal name(s) used in the treaty and/or the Schedule; and c. Map Name(s), the name of the map(s) on which a cession number appears; 2. URLs for the corresponding entry in the Schedule of Indian Land Cessions (Internet Archive) for each unique combination of a Date and reference to a Map Cession ID (historical references in the Schedule are included); 3. URLs for the corresponding treaty text, including the treaties catalogued by Charles J. Kappler in Indian Affairs: Laws and Treaties (HathiTrust Digital Library), executive order or other federal statute (Library of Congress and University of Georgia) identified in each entry with a reference to a Map Cession ID or IDs; 4. URLs for the image of the Royce map(s) (Library of Congress) on which a given cession number appears; 5. The name(s) of the Indian tribe or tribes related to each mapped cession, including the name as it appeared in the Schedule or the corresponding primary text, as well as the name of the present-day Indian tribe or tribes; and 6. The present-day states and counties included wholly or partially within a Map Cession boundary. During the 2017-2018 revision of the attribute data, it was noted that 7 of the Cession Map IDs are missing spatial representation in the Feature Class. The missing data is associated with the following Cession Map IDs: 47 (Illinois 1), 65 (Tennessee and Bordering States), 128 (Georgia), 129 (Georgia), 130 (Georgia), 543 (Indian Territory 3), and 690 (Iowa 2), which will be updated in the future. This dataset revises and expands the dataset published in 2015 by the U.S. Forest Service and made available through the Tribal Connections viewer, the Forest Service Geodata Clearinghouse, and Data.gov. The 2018 dataset is a result of collaboration between the Department of Agriculture, U.S. Forest Service, Office of Tribal Relations (OTR); the Department of the Interior, National Park Service, National NAGPRA Program; the U.S. Environmental Protection Agency, Office of International and Tribal Affairs, American Indian Environmental Office; and Dr. Claudio Saunt of the University of Georgia. The Forest Service and Dr. Saunt independently digitized and georeferenced the Royce cession maps and developed online map viewers to display Native American land cessions and reservations. Dr. Saunt subsequently undertook additional research to link Schedule entries, treaty texts, federal statutes and executive orders to cession and reservation polygons, which he agreed to share with the U.S. Forest Service. OTR revised the data, linking the Schedule entries, treaty texts, federal statues and executive orders to all 1,172 entries in the attribute table. The 2018 dataset has incorporated data made available by the National NAGPRA Program, specifically the Indian tribe or tribes related to each mapped cession, including the name as it appeared in the Schedule or the corresponding primary text and the name of the present-day Indian tribe or tribes, as well as the present-day states and counties included wholly or partially within a Map Cession boundary. This data replaces in its entirety the National NAGPRA data included in the dataset published in 2015. The 2015 dataset incorporated data presented in state tables compiled from the Schedule of Indian Land Cessions by the National NAGPRA Program. In recent years the National NAGPRA Program has been working to ensure the accuracy of this data, including the reevaluation of the present-day Indian tribes and the provision of references for their determinations. Changes made by the OTR have not been reviewed or approved by the National NAGPRA Program. The Forest Service will continue to collaborate with other federal agencies and work to improve the accuracy of the data included in this dataset. Errors identified since the dataset was published in 2015 have been corrected, and we request that you notify us of any additional errors we may have missed or that have been introduced. Please contact Rebecca Hill, Policy Analyst, U.S. Forest Service, Office of Tribal Relations, at rebeccahill@fs.usda.gov with any questions or concerns with regard to the data included in this dataset.