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The Cryosphere, 13(2), pp.665-674.","url":"https://doi.org/10.5194/tc-13-665-2019"}],"geographicCoverage":[{"country":"Antarctica"}],"dataSources":["REMA is constructed from hundreds of thousands of individual stereoscopic Digital Elevation Models (DEM) extracted from pairs of submeter (0.32 to 0.5 m) resolution DigitalGlobe satellite imagery, including data from WorldView-1, WorldView-2, and WorldView-3, and a small number from GeoEye-1, acquired between 2009 and 2017, with most collected in 2015 and 2016, over the austral summer seasons (mostly December to March)."],"authors":["Howat, Ian","Morin, Paul","Porter, Claire","Noh, Myong-Jong"]},{"name":"EarthDEM - Mosaics, Version 1.1","type":"dataset","url":"https://doi.org/10.7910/DVN/L2BEKY","global_id":"doi:10.7910/DVN/L2BEKY","description":"The EarthDEM Project provides high-resolution digital surface models (DSMs) for mid-latitude (non-polar) regions using optical stereo imagery, high-performance computing, and open-source photogrammetry software. This record represents the DSM mosaic dataset, version 1.1, built from over 20 years of photogrammetrically-derived elevation data with coregistration, blunder-masking, and void-filling applied.","published_at":"2024-11-08T18:23:28Z","publisher":"Polar Geospatial Center","citationHtml":"Porter, Claire; Howat, Ian; Husby, Erik; Noh, Myoung-Jon; Power, Devin; Khuvis, Samuel; Danish, Evan; Tomko, Karen; Gardiner, Judith; Negrete, Adelaide; Yadav, Bidhyananda; Klassen, James; Kelleher, Cole; Cloutier, Michael; Bakker, Jesse; Enos, Jeremy; Arnold, Galen; Bauer, Greg; Morin, Paul, 2024, \"EarthDEM - Mosaics, Version 1.1\", <a href=\"https://doi.org/10.7910/DVN/L2BEKY\" target=\"_blank\">https://doi.org/10.7910/DVN/L2BEKY</a>, Harvard Dataverse, V1","identifier_of_dataverse":"pgc","name_of_dataverse":"Polar Geospatial Center","citation":"Porter, Claire; Howat, Ian; Husby, Erik; Noh, Myoung-Jon; Power, Devin; Khuvis, Samuel; Danish, Evan; Tomko, Karen; Gardiner, Judith; Negrete, Adelaide; Yadav, Bidhyananda; Klassen, James; Kelleher, Cole; Cloutier, Michael; Bakker, Jesse; Enos, Jeremy; Arnold, Galen; Bauer, Greg; Morin, Paul, 2024, \"EarthDEM - Mosaics, Version 1.1\", https://doi.org/10.7910/DVN/L2BEKY, Harvard Dataverse, V1","publicationStatuses":["Published"],"storageIdentifier":"s3://10.7910/DVN/L2BEKY","keywords":["Remote Sensing","Digital Elevation Model","Digital Surface Model","Photogrammetry"],"subjects":["Earth and Environmental Sciences"],"fileCount":0,"versionId":431199,"versionState":"RELEASED","majorVersion":1,"minorVersion":1,"createdAt":"2024-11-08T17:20:39Z","updatedAt":"2024-11-08T20:22:21Z","contacts":[{"name":"Polar Geospatial Center","affiliation":"University of Minnesota"}],"publications":[{}],"producers":["Polar Geospatial Center"],"geographicCoverage":[{"country":"United States"},{"country":"Nepal"},{"country":"New Zealand"},{"country":"Indonesia"},{"country":"Christmas Island"},{"country":"American Samoa"},{"country":"Ecuador"},{"country":"Samoa"}],"dataSources":["EarthDEM is constructed from hundreds of thousands of individual Digital Elevation Models (DEM) extracted from pairs of submeter (0.32 to 0.5 m) resolution Maxar satellite imagery, including data from WorldView-1, WorldView-2, and WorldView-3, and a small number from GeoEye-1."],"authors":["Porter, Claire","Howat, Ian","Husby, Erik","Noh, Myoung-Jon","Power, Devin","Khuvis, Samuel","Danish, Evan","Tomko, Karen","Gardiner, Judith","Negrete, Adelaide","Yadav, Bidhyananda","Klassen, James","Kelleher, Cole","Cloutier, Michael","Bakker, Jesse","Enos, Jeremy","Arnold, Galen","Bauer, Greg","Morin, Paul"]},{"name":"EarthDEM - Strips, Version 4.1","type":"dataset","url":"https://doi.org/10.7910/DVN/YJAH5X","global_id":"doi:10.7910/DVN/YJAH5X","description":"The EarthDEM Project provides high-resolution digital surface models (DSMs) for mid-latitude (non-polar) regions using optical stereo imagery, high-performance computing, and open-source photogrammetry software. EarthDEM version 4.1 DSM strips are a 20+ year time series of elevation data derived from satellite imagery using photogrammetric methods and strip assembly method version 4.1.","published_at":"2024-11-21T18:51:39Z","publisher":"Polar Geospatial Center","citationHtml":"Porter, Claire; Howat, Ian; Husby, Erik; Noh, Myoung-Jon; Khuvis, Samuel; Danish, Evan; Tomko, Karen; Gardiner, Judith; Negrete, Adelaide; Yadav, Bidhyananda; Klassen, James; Kelleher, Cole; Cloutier, Michael; Bakker, Jesse; Power, Devin; Enos, Jeremy; Arnold, Galen; Bauer, Greg; Morin, Paul, 2024, \"EarthDEM - Strips, Version 4.1\", <a href=\"https://doi.org/10.7910/DVN/YJAH5X\" target=\"_blank\">https://doi.org/10.7910/DVN/YJAH5X</a>, Harvard Dataverse, V1","identifier_of_dataverse":"pgc","name_of_dataverse":"Polar Geospatial Center","citation":"Porter, Claire; Howat, Ian; Husby, Erik; Noh, Myoung-Jon; Khuvis, Samuel; Danish, Evan; Tomko, Karen; Gardiner, Judith; Negrete, Adelaide; Yadav, Bidhyananda; Klassen, James; Kelleher, Cole; Cloutier, Michael; Bakker, Jesse; Power, Devin; Enos, Jeremy; Arnold, Galen; Bauer, Greg; Morin, Paul, 2024, \"EarthDEM - Strips, Version 4.1\", https://doi.org/10.7910/DVN/YJAH5X, Harvard Dataverse, V1","publicationStatuses":["Published"],"storageIdentifier":"s3://10.7910/DVN/YJAH5X","keywords":["Remote Sensing","Digital Elevation Model","Digital Surface Model","Photogrammetry"],"subjects":["Earth and Environmental Sciences"],"fileCount":0,"versionId":431194,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2024-11-08T19:21:58Z","updatedAt":"2024-11-21T18:51:39Z","contacts":[{"name":"Polar Geospatial Center","affiliation":"University of Minnesota"}],"publications":[{}],"producers":["Polar Geospatial Center"],"geographicCoverage":[{"country":"United States"}],"dataSources":["EarthDEM is constructed from hundreds of thousands of individual Digital Elevation Models (DEM) extracted from pairs of submeter (0.32 to 0.5 m) resolution Maxar satellite imagery, including data from WorldView-1, WorldView-2, and WorldView-3, and a small number from GeoEye-1."],"authors":["Porter, Claire","Howat, Ian","Husby, Erik","Noh, Myoung-Jon","Khuvis, Samuel","Danish, Evan","Tomko, Karen","Gardiner, Judith","Negrete, Adelaide","Yadav, Bidhyananda","Klassen, James","Kelleher, Cole","Cloutier, Michael","Bakker, Jesse","Power, Devin","Enos, Jeremy","Arnold, Galen","Bauer, Greg","Morin, Paul"]},{"name":"Replication Data for: MangPrim-21: Establishing the Relationship between Non-Human Primates and Mangroves Forests at the Global, National, and Local Scales.","type":"dataset","url":"https://doi.org/10.7910/DVN/QRYBYR","global_id":"doi:10.7910/DVN/QRYBYR","description":"Replication SQL code as per the supplemental methods. 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Con = Continuous raster data (lesser used), not presence or absence. Units are m2 unless otherwise specified. These Raster data are scaled. You must multiply them by .0001 to get actual values in square meters.","published_at":"2024-03-21T23:28:04Z","publisher":"GMC-21","citationHtml":"Hamilton, Stuart; Presotto, 2024, \"GMC-21 2000-2020 Raster Con ESRI geodatabase\", <a href=\"https://doi.org/10.7910/DVN/GSOQNZ\" target=\"_blank\">https://doi.org/10.7910/DVN/GSOQNZ</a>, Harvard Dataverse, V1","identifier_of_dataverse":"GMC-21","name_of_dataverse":"GMC-21","citation":"Hamilton, Stuart; Presotto, 2024, \"GMC-21 2000-2020 Raster Con ESRI geodatabase\", https://doi.org/10.7910/DVN/GSOQNZ, Harvard Dataverse, V1","publicationStatuses":["Published"],"storageIdentifier":"s3://10.7910/DVN/GSOQNZ","keywords":["Mangrove Ecology"],"subjects":["Earth and Environmental Sciences"],"fileCount":4,"versionId":498516,"versionState":"RELEASED","majorVersion":1,"minorVersion":2,"createdAt":"2023-09-14T00:20:50Z","updatedAt":"2025-07-22T04:46:35Z","contacts":[{"name":"Hamilton, Stuart","affiliation":"East Carolina University"}],"publications":[{"citation":"https://www.igi-global.com/article/a-global-database-to-monitor-annual-mangrove-forest-change-2000-2020/361727","url":"https://www.igi-global.com/article/a-global-database-to-monitor-annual-mangrove-forest-change-2000-2020/361727"}],"geographicCoverage":[{"country":""}],"authors":["Hamilton, Stuart","Presotto"]},{"name":"Extracted Data From: Smart Location Database","type":"dataset","url":"https://doi.org/10.7910/DVN/WY9T73","image_url":"https://dataverse.harvard.edu/api/datasets/10863355/logo","global_id":"doi:10.7910/DVN/WY9T73","description":"This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information: https://catalog.data.gov/dataset/smart-location-database7 If you have questions about the underlying data stored here, please contact Thomas John (thomas.john@epa.gov). If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu. \"The Smart Location Database is a nationwide geographic data resource for measuring location efficiency. It includes more than 90 attributes summarizing characteristics, such as housing density, diversity of land use, neighborhood design, destination accessibility, transit service, employment and demographics. Most attributes are available for every census block group in the United States. A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. EPA first released a beta version of the Smart Location Database in 2011. The initial full version was released in 2013, and the database was updated to its current version in 2021.\" Quote from https://www.epa.gov/smartgrowth/smart-location-mapping and https://catalog.data.gov/dataset/smart-location-database7","published_at":"2025-02-19T20:54:22Z","publisher":"Extracted Data Contributions","citationHtml":"Office of Sustainable Communities; Office of Policy, 2025, \"Extracted Data From: Smart Location Database\", <a href=\"https://doi.org/10.7910/DVN/WY9T73\" target=\"_blank\">https://doi.org/10.7910/DVN/WY9T73</a>, Harvard Dataverse, V1","identifier_of_dataverse":"cafe-extracted-data","name_of_dataverse":"Extracted Data Contributions","citation":"Office of Sustainable Communities; Office of Policy, 2025, \"Extracted Data From: Smart Location Database\", https://doi.org/10.7910/DVN/WY9T73, Harvard Dataverse, V1","publicationStatuses":["Published"],"storageIdentifier":"s3xxxxl://10.7910/DVN/WY9T73","keywords":["Land Use","Urban"],"subjects":["Earth and Environmental Sciences","Social Sciences"],"fileCount":8,"versionId":436070,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2025-02-04T00:46:56Z","updatedAt":"2025-02-19T20:54:22Z","contacts":[{"name":"CAFE","affiliation":""}],"geographicCoverage":[{"country":"United States"}],"authors":["Office of Sustainable Communities","Office of Policy"]},{"name":"Extracted Data From: Access to Jobs and Workers via Transit","type":"dataset","url":"https://doi.org/10.7910/DVN/HQBF1L","image_url":"https://dataverse.harvard.edu/api/datasets/10865402/logo","global_id":"doi:10.7910/DVN/HQBF1L","description":"This submission includes publicly available data extracted in its original form. If you have questions about the underlying data stored here, please contact Thomas John (thomas.john@epa.gov)/EPA Office of Sustainable Communities. If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu. \"The Access to Jobs and Workers Via Transit tool is a free geospatial data resource and web mapping tool for comparing the accessibility of neighborhoods via public transit service. Its indicators summarize accessibility to jobs as well as accessibility by workers, households, and population. Coverage is limited to metropolitan regions served by transit agencies that share their service data in a standard format called GTFS . A collection of performance indicators and regional benchmarks for consistently comparing neighborhoods (census block groups) across the US in regards to their accessibility to jobs or workers via public transit service. Accessibility was modeled by calculating total travel time between block group centroids inclusive of walking to/from transit stops, wait times, and transfers. Block groups that can be accessed in 45 minutes or less from the origin block group are considered accessible. Indicators reflect public transit service in December 2012 and employment/worker counts in 2010. Coverage is limited to census block groups within metropolitan regions served by transit agencies who share their service data in a standardized format called GTFS.\" Quote from https://catalog.data.gov/dataset/access-to-jobs-and-workers-via-transit-download7 and https://www.epa.gov/smartgrowth/smart-location-mapping","published_at":"2025-02-19T20:01:46Z","publisher":"Extracted Data Contributions","citationHtml":"Office of Sustainable Communities, 2025, \"Extracted Data From: Access to Jobs and Workers via Transit\", <a href=\"https://doi.org/10.7910/DVN/HQBF1L\" target=\"_blank\">https://doi.org/10.7910/DVN/HQBF1L</a>, Harvard Dataverse, V1","identifier_of_dataverse":"cafe-extracted-data","name_of_dataverse":"Extracted Data Contributions","citation":"Office of Sustainable Communities, 2025, \"Extracted Data From: Access to Jobs and Workers via Transit\", https://doi.org/10.7910/DVN/HQBF1L, Harvard Dataverse, V1","publicationStatuses":["Published"],"storageIdentifier":"s3xxxxl://10.7910/DVN/HQBF1L","keywords":["Urban","Suburban"],"subjects":["Earth and Environmental Sciences","Social Sciences"],"fileCount":8,"versionId":436129,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2025-02-04T15:43:45Z","updatedAt":"2025-02-19T20:01:46Z","contacts":[{"name":"CAFE","affiliation":""}],"geographicCoverage":[{"country":"United States"}],"authors":["Office of Sustainable Communities"]},{"name":"Global chickens distribution in 2010 (5 minutes of arc)","type":"dataset","url":"https://doi.org/10.7910/DVN/SUFASB","image_url":"https://dataverse.harvard.edu/api/datasets/3085175/logo","global_id":"doi:10.7910/DVN/SUFASB","description":"This dataset contains the global distribution of chickens in 2010 expressed in total number of chickens per pixel (5 min of arc) according to the Gridded Livestock of the World database (GLW 3). Please go through the 1_Ch_2010_Metadata.html file for more information about this dataset and the set of included files.","published_at":"2018-10-24T13:48:41Z","publisher":"Gridded Livestock of the World –  2010 (GLW 3)","citationHtml":"Gilbert, Marius; Nicolas, Ga&euml;lle; Cinardi, Giusepina; Van Boeckel, Thomas P.; Vanwambeke, Sophie; Wint, G. R. William; Robinson, Timothy P., 2018, \"Global chickens distribution in 2010 (5 minutes of arc)\", <a href=\"https://doi.org/10.7910/DVN/SUFASB\" target=\"_blank\">https://doi.org/10.7910/DVN/SUFASB</a>, Harvard Dataverse, V3","identifier_of_dataverse":"glw_3","name_of_dataverse":"Gridded Livestock of the World –  2010 (GLW 3)","citation":"Gilbert, Marius; Nicolas, Gaëlle; Cinardi, Giusepina; Van Boeckel, Thomas P.; Vanwambeke, Sophie; Wint, G. R. William; Robinson, Timothy P., 2018, \"Global chickens distribution in 2010 (5 minutes of arc)\", https://doi.org/10.7910/DVN/SUFASB, Harvard Dataverse, V3","publicationStatuses":["Published"],"storageIdentifier":"s3://10.7910/DVN/SUFASB","subjects":["Agricultural Sciences"],"fileCount":8,"versionId":145676,"versionState":"RELEASED","majorVersion":3,"minorVersion":0,"createdAt":"2017-12-05T12:53:16Z","updatedAt":"2018-10-24T13:48:41Z","contacts":[{"name":"Robinson, Timothy P.","affiliation":"Food and Agriculture Organization of the United Nations"},{"name":"Gilbert, Marius","affiliation":"Université Libre de Bruxelles"}],"publications":[{"citation":"Gilbert M, G Nicolas, G Cinardi, S Vanwambeke, TP Van Boeckel, GRW Wint, TP Robinson (2018) Global Distribution Data for Cattle, Buffaloes, Horses, Sheep, Goats, Pigs, Chickens and Ducks in 2010. Nature Scientific data, 5:180227.","url":"https://doi.org/10.1038/sdata.2018.227"}],"geographicCoverage":[{"country":""}],"authors":["Gilbert, Marius","Nicolas, Gaëlle","Cinardi, Giusepina","Van Boeckel, Thomas P.","Vanwambeke, Sophie","Wint, G. R. William","Robinson, Timothy P."]},{"name":"Extracted Data From: Inflation Reduction Act Disadvantaged Communities Map Data","type":"dataset","url":"https://doi.org/10.7910/DVN/FMKBXS","global_id":"doi:10.7910/DVN/FMKBXS","description":"This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information \"The Environmental and Climate Justice Program (ECJ Program), created by the Inflation Reduction Act (IRA), provides funding for financial and technical assistance to carry out environmental and climate justice activities to benefit disadvantaged communities. EPA has created the EPA Disadvantaged Community Environmental and Climate Justice Program map to assist potential applicants seeking to identify whether a community is disadvantaged for the purposes of implementing the ECJ Program. The EPA Disadvantaged Communities Environmental and Climate Justice program map includes the following components: EPA IRA Disadvantaged Communities 1.0 map EPA IRA Disadvantaged Communities 2.0 map Any area of American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, or the U.S. Virgin Islands The EPA IRA Disadvantaged Communities maps combines multiple datasets that individually can be used to determine whether a community is disadvantaged for the purposes of implementing programs under the IRA. All data sets are assigned values at the Census block group level. The criteria and associated datasets used in the maps are: Any census tract that is included as disadvantaged in the Climate and Economic Justice Screening Tool (CEJST) Any census block group at or above the 90th percentile for any of EJScreen’s Supplemental Indexes when compared to the nation or state, and/or any of the following geographic areas within the Tribal lands category in EJScreen: Alaska Native Allotments Alaska Native Villages American Indian Reservations American Indian Off-reservation Trust Lands Oklahoma Tribal Statistical Areas The EPA IRA Disadvantaged Communities 1.0 map uses data from EJScreen version 2.2. The EPA IRA Disadvantaged Communities 2.0 map uses data from EJScreen version 2.3. To further assist applicants, EPA has provided the underlying data for the map\" [Quote from https://www.epa.gov/environmentaljustice/inflation-reduction-act-disadvantaged-communities-map] Note: If you have questions about the underlying data, please contact the Environmental Protection Agency (environmental-justice@epa.gov). If you have questions or recommendations related to this metadata entry, please contact the CAFE Data Management team at: climatecafe@bu.edu","published_at":"2025-02-14T13:05:53Z","publisher":"Extracted Data Contributions","citationHtml":"Environmental Protection Agency, 2025, \"Extracted Data From: Inflation Reduction Act Disadvantaged Communities Map Data\", <a href=\"https://doi.org/10.7910/DVN/FMKBXS\" target=\"_blank\">https://doi.org/10.7910/DVN/FMKBXS</a>, Harvard Dataverse, V1, UNF:6:CJE7UdDkKerx5Y9kV5nRgA== [fileUNF]","identifier_of_dataverse":"cafe-extracted-data","name_of_dataverse":"Extracted Data Contributions","citation":"Environmental Protection Agency, 2025, \"Extracted Data From: Inflation Reduction Act Disadvantaged Communities Map Data\", https://doi.org/10.7910/DVN/FMKBXS, Harvard Dataverse, V1, UNF:6:CJE7UdDkKerx5Y9kV5nRgA== [fileUNF]","publicationStatuses":["Published"],"storageIdentifier":"s3xxxxl://10.7910/DVN/FMKBXS","keywords":["Environmental Justice","Infrastructure"],"subjects":["Earth and Environmental Sciences","Medicine, Health and Life Sciences","Social Sciences"],"fileCount":12,"versionId":435624,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2025-01-27T19:02:16Z","updatedAt":"2025-02-14T13:05:53Z","contacts":[{"name":"CAFE","affiliation":""}],"geographicCoverage":[{"country":"United States"}],"authors":["Environmental Protection Agency"]},{"name":"Extracted Data From:  Historical Map & Chart Collection of NOAA's Nautical Charts, Hydrographic Surveys, Topographic Surveys, Geodetic Surveys, City Plans, and Civil War Battle Maps Starting from the mid 1700's","type":"dataset","url":"https://doi.org/10.7910/DVN/AWDKXC","image_url":"https://dataverse.harvard.edu/api/datasets/11037766/logo","global_id":"doi:10.7910/DVN/AWDKXC","description":"This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information If you have questions about the underlying data stored here, please contact Ben Galdi (ben.galdi@noaa.gov). If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu. The Historical Map and Chart Collection of the Office of Coast Survey contains over 35000 historical maps and charts from the mid 1700s up through the 2020s, including the final cancelled editions of NOAA's raster charts. These images are available for viewing or download through the image catalog at https://historicalcharts.noaa.gov/. The Collection includes some of the nation's earliest nautical charts, hydrographic surveys, topographic surveys, bathymetric maps, annual reports, coast pilots, geodetic surveys, city plans, and Civil War battle maps. The Collection is a rich primary historical archive and a testament to the artistry of copper plate engraving technology of the nineteenth and twentieth centuries. Notable offerings include maps of Vancouver's explorations, the \"Wilkes Atlas\" of the U.S. Whistler's Anacapa Island chart, an extensive Civil War collection, a large scale topographic series of Washington, D.C., city plans, the reengraving of the famous 1792 L'Enfant and Ellicott plan for Washington D.C., and many artistic perspective sketches that were once an integral part of hydrographic surveys and published charts. [Quote from https://www.fisheries.noaa.gov/inport/item/39977]","published_at":"2025-07-08T19:49:28Z","publisher":"Extracted Data Contributions","citationHtml":"Galdi, Ben, 2025, \"Extracted Data From: Historical Map &amp; Chart Collection of NOAA's Nautical Charts, Hydrographic Surveys, Topographic Surveys, Geodetic Surveys, City Plans, and Civil War Battle Maps Starting from the mid 1700's\", <a href=\"https://doi.org/10.7910/DVN/AWDKXC\" target=\"_blank\">https://doi.org/10.7910/DVN/AWDKXC</a>, Harvard Dataverse, V1","identifier_of_dataverse":"cafe-extracted-data","name_of_dataverse":"Extracted Data Contributions","citation":"Galdi, Ben, 2025, \"Extracted Data From: Historical Map & Chart Collection of NOAA's Nautical Charts, Hydrographic Surveys, Topographic Surveys, Geodetic Surveys, City Plans, and Civil War Battle Maps Starting from the mid 1700's\", https://doi.org/10.7910/DVN/AWDKXC, Harvard Dataverse, V1","publicationStatuses":["Published"],"storageIdentifier":"s3xxxxl://10.7910/DVN/AWDKXC","keywords":["TOPOGRAPHICAL RELIEF MAPS","BATHYMETRY/SEAFLOOR TOPOGRAPHY"],"subjects":["Arts and Humanities","Earth and Environmental Sciences"],"fileCount":1309,"versionId":441288,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2025-03-31T21:59:22Z","updatedAt":"2025-07-08T19:49:28Z","contacts":[{"name":"CAFE","affiliation":""}],"publications":[{}],"geographicCoverage":[{"country":""}],"authors":["Galdi, Ben"]}],"count_in_response":10}}