--- - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'This is a subset of Populated Alaska Places that is used primarily for large scale mapping. Locations of statewide cities were entered by latitude-longitude, digitized from USGS 1:250,000 quadrangle maps, and heads-up digitizing from USGS 1:250,000 maps based on relative positioning of digital hydrography and digital township lines. Many locations were later updated by snapping them to points in the USGSNAMES coverage obtained from USGS. List of populated places were acquired from Alaska Department of Community and Regional Affairs (DCRA). The classifications of cities is determined by the Department of Community and Regional Affairs. An excellent description of city classifications was prepared by the Local Boundary Commission Staff of the Alaska Department of Community & Regional Affairs.' description_attribution: http://dnr.alaska.gov/mdfiles/cities.txt doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/adnr-alaska-towns-1998.yaml identifier: adnr-alaska-towns-1998 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: ADNR Alaska Towns 1998 native_id: ~ processing_level: ~ publication_year: 1998 release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/adnr-alaska-towns-1998 url: http://www.asgdc.state.ak.us/#7 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: 'Cite datasets in publications such as journal papers, articles, presentations, posters, and websites. Include the DOI when available (each RTC ALOS PALSAR dataset processed by ASF has an assigned DOI). Please send copies of, or links to, published works citing data, imagery, or tools accessed through ASF to uso@asf.alaska.edu with "New Publication" on subject line.' data_qualifier: ~ description: "The Phased Array type L-band Synthetic Aperture Radar (PALSAR) is an active microwave sensor using L-band frequency to achieve cloud-free and day-and-night land observation. It provides higher performance than theJERS-1's synthetic aperture radar (SAR). Fine resolution in a conventional mode, but PALSAR will have another advantageous observation mode. ScanSAR, which will enable us to acquire a 250 to 350km width of SAR images (depending on the number of scans) at the expense of spatial resolution. This swath is three to five times wider than conventional SAR images. The development of the PALSAR is a joint project between JAXA and the Japan Resources Observation System Organization (JAROS)." description_attribution: https://www.eorc.jaxa.jp/ALOS/en/about/palsar.htm doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/asf-alos-palsar.yaml identifier: asf-alos-palsar lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: ASF ALOS PALSAR native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/asf-alos-palsar url: https://www.asf.alaska.edu/sar-data/palsar/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "The Consumer Price Index for All Urban Consumers (CPI-U) is a monthly measure of the average change over time in the prices paid by consumers for a market basket of consumer goods and services. The CPI-U is based on the spending patterns of urban consumers. Index data are available for the U.S. City Average (or national average), for various geographic areas (regions and metropolitan areas), for national population size classes of urban areas, and for cross-classifications of regions and size classes. Individual indexes are available for more than 200 items (e.g., apples, men's shirts, airline fares), and over 120 different combinations of items (e.g., fruits and vegetables, food at home, food and beverages, and All items)." description_attribution: https://www.bls.gov/help/one_screen/cu.htm doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/bls-all-urban-consumers-current-series-consumer-price-index-cpi.yaml identifier: bls-all-urban-consumers-current-series-consumer-price-index-cpi lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'BLS All Urban Consumers (Current Series) - (Consumer Price Index - CPI)' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/bls-all-urban-consumers-current-series-consumer-price-index-cpi url: https://www.bls.gov/data/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: 'Peters et al, 2007, "An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker", PNAS, November 27, 2007 , vol. 104, no. 48, 18925-18930.' data_qualifier: ~ description: 'CarbonTracker is a CO2 measurement and modeling system developed by NOAA to keep track of sources (emissions to the atmosphere) and sinks (removal from the atmosphere) of carbon dioxide around the world. CarbonTracker uses atmospheric CO2 observations from a host of collaborators and simulated atmospheric transport to estimate these surface fluxes of CO2. The current release of CarbonTracker, CT2017, provides global estimates of surface-atmosphere fluxes of CO2 from January 2000 through December 2016.' description_attribution: https://www.esrl.noaa.gov/gmd/ccgg/carbontracker/ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/carbontracker-ct2007b-release.yaml identifier: carbontracker-ct2007b-release lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: CarbonTracker CT2007B release native_id: CT2007B processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/carbontracker-ct2007b-release url: https://www.esrl.noaa.gov/gmd/ccgg/carbontracker/CT2007B/ variables: ~ version: CT2007B vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The observations of atmospheric CO2 mole fraction by over 40 different laboratories are at the heart of CarbonTracker. They inform us on changes in the carbon cycle, whether they are regular (such as the seasonal growth and decay of leaves and trees), or irregular (such as the release of tons of carbon by a wildfire). The results in CarbonTracker depend directly on the quality, amount and location of observations available, and the degree of detail at which we can monitor the carbon cycle reliably increases strongly with the density of our observing network' description_attribution: https://www.carbontracker.eu/version.shtml doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/carbontracker-europe-2015-cte2015.yaml identifier: carbontracker-europe-2015-cte2015 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: CarbonTracker Europe 2015 (CTE2015) native_id: ~ processing_level: ~ publication_year: ~ release_dt: 1970-01-01T00:33:36 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/carbontracker-europe-2015-cte2015 url: https://www.carbontracker.eu/version.shtml variables: ~ version: CTE2015 vertical_extent: ~ - access_dt: 2013-05-06T00:00:00 attributes: 'Monthly maximum 1-day precipitation, Maximum number of consecutive days with RR < 1mm, and other temperature and precipitation indices (see http://etccdi.pacificclimate.org/list_27_indices.shtml).' cite_metadata: "Sillmann, J., V. V. Kharin, F. W. Zwiers, X. Zhang, and D. Bronaugh, 2013a: Climate extremes indices in the CMIP5 multi-model ensemble. Part 1: Model evaluation in the present climate. J. Geophys. Res., doi:10.1002/jgrd.50203.\r\n\r\nSillmann, J., V. V. Kharin, F. W. Zwiers, X. Zhang, and D. Bronaugh, 2013b: Climate extremes indices in the CMIP5 multi-model ensemble. Part 2: Future projections. J. Geophys. Res., doi:10.1002/jgrd.50188." data_qualifier: ~ description: 'The climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) are computed for a number of global climate models participating in the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5), and reanalyses. The definitions of the indices are given here. The validation of climate extremes indices and analysis of their projected future changes simulated by the CMIP5 models is presented in Sillmann et al. (2013a, 2013b)' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/cccma-etccdi-extreme-indices-archive.yaml identifier: cccma-etccdi-extreme-indices-archive lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: ETCCDI Extremes Indices Archive native_id: Unknown processing_level: ~ publication_year: 2013 release_dt: 2012-09-12T00:00:00 scale: ~ scope: ~ spatial_extent: 'maximum_latitude: 90; minimum_latitude: -90; maximum_longitude: 180; minimum_longitude: -180;' spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: 1850-01-01T00:00:00 2300-12-31T23:59:59 temporal_resolution: ~ type: ~ uri: /dataset/cccma-etccdi-extreme-indices-archive url: http://www.cccma.ec.gc.ca/data/climdex/climdex.shtml variables: ~ version: N/A vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ArboNET is a national arboviral surveillance system managed by CDC and state health departments. In addition to human disease, ArboNET maintains data on arboviral infections among presumptive viremic blood donors, veterinary disease cases, mosquitoes, dead birds, and sentinel animals. As with other national surveillance data, ArboNET data have several limitations that should be considered in analysis, interpretation, and reporting: ArboNET is a passive surveillance system. It is dependent on clinicians considering the diagnosis of an arboviral disease and obtaining the appropriate diagnostic test, and reporting of laboratory-confirmed cases to public health authorities. Diagnosis and reporting are incomplete, and the incidence of arboviral diseases is underestimated. Reported neuroinvasive disease cases are considered the most accurate indicator of arboviral activity in humans because of the substantial associated morbidity. In contrast, reported cases of nonneuroinvasive arboviral disease are more likely to be affected by disease awareness and healthcare-seek­ing behavior in different communities and by the availability and specificity of laboratory tests performed. Surveillance data for nonneuroinvasive disease should be interpreted with cau­tion and generally should not be used to make comparisons between geographic areas or over time. Provisional ArboNET data are provided to help track recent arboviral disease activity. However, these data may change substantially before they are finalized. Provisional data from the current year should not be combined with or compared to final data from previous years.' description_attribution: https://www.cdc.gov/westnile/resourcepages/survResources.html doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/cdc-arbonet.yaml identifier: cdc-arbonet lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: CDC Arbonet native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-arbonet url: https://wwwn.cdc.gov/arbonet/Maps/ADB_Diseases_Map/index.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "COVIS data\r\n\r\nData are collected at local level and clinically confirmed cases of any Vibrio infection (any species) are reported to the COVIS system (Cholera and Other Vibrio Surveillance). \r\n\r\nCOVIS surveillance reports allow for the reporting of metadata including: patient information, geographic location of isolation, type of infection (wound, blood, gastrointestinal), source of exposure, species confirmed, foreign travel, food consumption, etc. Completeness in reporting of clinical information, epidemiological information, clinical information, and seafood trace-back currently ranges from 21% - 71%." description_attribution: http://www.cdc.gov/nationalsurveillance/cholera-vibrio-surveillance.html doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/cdc-cholera-other-vibrio-illness-surveillance.yaml identifier: cdc-cholera-other-vibrio-illness-surveillance lat_max: 71.32 lat_min: 18.91 lon_max: -66.57 lon_min: -160.16 name: Cholera and Other Vibrio Illness Surveillance COVIS native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1996-01-01T00:00:01 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-cholera-other-vibrio-illness-surveillance url: http://www.cdc.gov/nationalsurveillance/cholera-vibrio-surveillance.html variables: 'confirmed vibrio infections per 1,000,000 people' version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: The FluView National Flu Activity Map is a complementary widget to the state-by-state flu map widget introduced in the 2007-2008 flu season. This interactive map allows users to see the most recent seasonal influenza activity map for the entire country as well as the activity levels from previous weeks in the current flu season. description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/cdc-fluview-national-flu-activity-map.yaml identifier: cdc-fluview-national-flu-activity-map lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: FluView National Flu Activity Map native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-fluview-national-flu-activity-map url: http://www.cdc.gov/Widgets/#fluview variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "This is an RSS Feed of Food Safety information that’s produced in real-time by the CDC. This RSS feed is the integration of two other XML feeds, one from the USDA's Food Safety Inspection Service (FSIS) - http://www.fsis.usda.gov/RSS/usdarss.xml - and one from the FDA's Food Safety Recalls - http://www.fda.gov/AboutFDA/ContactFDA/StayInformed/RSSFeeds/FoodSafety/.... Most users will prefer the CDC feed linked above, but developers may prefer the individual XML feeds." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/cdc-food-safety-information-rss-feed.yaml identifier: cdc-food-safety-information-rss-feed lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Food Safety Information RSS feed native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-food-safety-information-rss-feed url: http://www2c.cdc.gov/podcasts/createrss.asp?c=146 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Health Data Interactive (HDI) presents a broad range of important public health indicators through an interactive web-based application that provides access to pre-tabulated national and state data for the US. The primary objective is to provide national estimates of public health measures cross tabulated by a common set of variables. HDI tables contain national health statistics for infants, children, adolescents, adults, and older adults. The tables can be customized by age, gender, race/ethnicity, and geographic location to explore different trends and patterns.n' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/cdc-health-data-interactive-hdi.yaml identifier: cdc-health-data-interactive-hdi lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Health Data Interactive (HDI) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-health-data-interactive-hdi url: http://www.cdc.gov/nchs/hdi.htm variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The National Ambulatory Medical Care Survey (NAMCS) is a national survey designed to meet the need for objective, reliable information about the provision and use of ambulatory medical care services in the United States. Findings are based on a sample of visits to non-federal employed office-based physicians who are primarily engaged in direct patient care.' description_attribution: http://www.cdc.gov/nchs/ahcd/ahcd_questionnaires.htm doi: ~ end_time: 1972-12-31T19:00:00 href: https://data.globalchange.gov/dataset/cdc-national-ambulatory-medical-care-survey-namcs.yaml identifier: cdc-national-ambulatory-medical-care-survey-namcs lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: National Ambulatory Medical Care Survey (NAMCS) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1972-12-31T19:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-national-ambulatory-medical-care-survey-namcs url: https://www.cdc.gov/nchs/ahcd/index.htm variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The National Death Index (NDI) is a centralized database of death record information on file in state vital statistics offices. Working with these state offices, the National Center for Health Statistics (NCHS) established the NDI as a resource to aid epidemiologists and other health and medical investigators with their mortality ascertainment activities.

Assists investigators in determining whether persons in their studies have died and, if so, provide the names of the states in which those deaths occurred, the dates of death, and the corresponding death certificate numbers. Investigators can then make arrangements with the appropriate state offices to obtain copies of death certificates or specific statistical information such as manner of death or educational level. Cause of death codes may also be obtained using the NDI Plus service.

Records from 1979 through 2011 are currently available and contain a standard set of identifying information on each death. Death records are added to the NDI file annually, approximately 12 months after the end of a particular calendar year. 2012 should be available summer 2014. Early Release Program for 2013 is now available.

The NDI service is available to investigators solely for statistical purposes in medical and health research. The service is not accessible to organizations or the general public for legal, administrative, or genealogy purposes.' description_attribution: http://www.cdc.gov/nchs/ndi.htm doi: ~ end_time: 2010-12-31T19:00:00 href: https://data.globalchange.gov/dataset/cdc-national-death-index.yaml identifier: cdc-national-death-index lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: National Death Index native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1978-12-31T19:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-national-death-index url: http://www.cdc.gov/nchs/ndi/index.htm variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The National Environmental Public Health Tracking Network is a system of integrated health, exposure, and hazard information and data from a variety of national, state, and city sources. On the Tracking Network, you can explore information and view maps, tables, and charts about health and environment across the country.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/cdc-national-environmental-public-health-tracking-network-tracking-network.yaml identifier: cdc-national-environmental-public-health-tracking-network-tracking-network lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: CDC National Environmental Public Health Tracking Network (Tracking Network) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-national-environmental-public-health-tracking-network-tracking-network url: http://www.cdc.gov/ephtracking/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: Interactive online tool for creating and manipulating tables based on birth and perinatal (fetal and infant death) data files. Tabulated data can be graphed or mapped within VitalStats or exported to Excel for further analysis. description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/cdc-vitalstats.yaml identifier: cdc-vitalstats lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: VitalStats native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-vitalstats url: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The United States Cancer Statistics (USCS) online databases in WONDER provide cancer incidence and mortality data for the United States for the years since 1999, by year, state and metropolitan areas (MSA), age group, race, ethnicity, gender, childhood cancer classifications and cancer site. Report case counts, deaths, crude and age-adjusted incidence and death rates, and 95% confidence intervals for rates. The USCS data are the official federal statistics on cancer incidence from registries having high-quality data and cancer mortality statistics for 50 states and the District of Columbia. USCS are produced by the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI), in collaboration with the North American Association of Central Cancer Registries (NAACCR). Mortality data are provided by the Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Vital Statistics System (NVSS).' description_attribution: http://wonder.cdc.gov/cancer.html doi: ~ end_time: 2006-12-30T19:00:00 href: https://data.globalchange.gov/dataset/cdc-wonder-cancer-statistics.yaml identifier: cdc-wonder-cancer-statistics lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'CDC WONDER: Cancer Statistics' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1998-12-31T19:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-wonder-cancer-statistics url: http://wonder.cdc.gov/cancer.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The CDC WONDER Mortality - Underlying Cause of Death online database is a county-level national mortality and population database spanning the years since 1979 -2008. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., Census region, Census division, state, and county), age group (including infant age groups), race (years 1979-1998: White, Black, and Other; years 1999-2008: American Indian or Alaska Native, Asian or Pacific Islander, Black or African American, and White), Hispanic origin (years 1979-1998: not available; years 1999-present: Hispanic or Latino, not Hispanic or Latino, Not Stated), gender, year of death, and underlying cause of death (years 1979-1998: 4-digit ICD-9 code and 72 cause-of-death recode; years 1999-present: 4-digit ICD-10 codes and 113 cause-of-death recode, as well as the Injury Mortality matrix classification for Intent and Mechanism), and urbanization level of residence (2006 NCHS urban-rural classification scheme for counties). The Compressed Mortality data are produced by the National Center for Health Statistics.' description_attribution: http://wonder.cdc.gov/wonder/help/cmf.html doi: ~ end_time: 2009-12-30T19:00:00 href: https://data.globalchange.gov/dataset/cdc-wonder-compressed-mortality-underlying-cause-death.yaml identifier: cdc-wonder-compressed-mortality-underlying-cause-death lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'CDC WONDER: Compressed Mortality - Underlying Cause of Death' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1967-12-31T19:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-wonder-compressed-mortality-underlying-cause-death url: http://wonder.cdc.gov/mortSQL.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The Daily Air Temperature and Heat Index data available on CDC WONDER are county-level daily average air temperatures and heat index measures spanning the years 1979-2010. Temperature data are available in Fahrenheit or Celsius scales. Reported measures are the average temperature, number of observations, and range for the daily maximum and minimum air temperatures, and also percent coverage for the daily maximum heat index. Data are available by place (combined 48 contiguous states, region, division, state, county), time (year, month, day) and specified maximum and minimum air temperature, and heat index value. The data are derived from the North America Land Data Assimilation System (NLDAS) through NLDAS Phase 2, a collaboration project among several groups: the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC), the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Princeton University, the National Weather Service (NWS) Office of Hydrological Development (OHD), the University of Washington, and the NCEP Climate Prediction Center (CPC). In a study funded by the NASA Applied Sciences Program/Public Health Program, scientists at NASA Marshall Space Flight Center/ Universities Space Research Association developed the analysis to produce the data available on CDC WONDER.' description_attribution: http://wonder.cdc.gov/wonder/help/nldas.html doi: ~ end_time: 2010-12-30T19:00:00 href: https://data.globalchange.gov/dataset/cdc-wonder-daily-air-temperatures-and-heat-index.yaml identifier: cdc-wonder-daily-air-temperatures-and-heat-index lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'CDC WONDER: Daily Air Temperatures and Heat Index' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1978-12-31T19:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-wonder-daily-air-temperatures-and-heat-index url: http://wonder.cdc.gov/nasa-nldas.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The Detailed Mortality - Underlying Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the years 1999-2009. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., region, state, and county), age group (including infants and single-year-of-age cohorts), race (4 groups), Hispanic ethnicity, gender, year of death, and cause-of-death (4-digit ICD-10 code or group of codes, injury intent and mechanism categories, or drug and alcohol related causes), year, month and week day of death, place of death and whether an autopsy was performed. The data are produced by the National Center for Health Statistics.' description_attribution: http://wonder.cdc.gov/wonder/help/ucd.html doi: ~ end_time: 2009-12-30T19:00:00 href: https://data.globalchange.gov/dataset/cdc-wonder-detailed-mortality-underlying-cause-death.yaml identifier: cdc-wonder-detailed-mortality-underlying-cause-death lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'CDC WONDER: Detailed Mortality - Underlying Cause of Death' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1998-12-31T19:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-wonder-detailed-mortality-underlying-cause-death url: http://wonder.cdc.gov/ucd-icd10.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "The Mortality - Infant Deaths (from Linked Birth / Infant Death Records) online databases on CDC WONDER provide counts and rates for deaths of children under 1 year of age, occuring within the United States to U.S. residents. Information from death certificates has been linked to corresponding birth certificates. Data are available by county of mother's residence, child's age, underlying cause of death, gender, birth weight, birth plurality, birth order, gestational age at birth, period of prenatal care, maternal race and ethnicity, maternal age, maternal education and marital status. Data are available since 1995. The data are produced by the National Center for Health Statistics." description_attribution: http://wonder.cdc.gov/wonder/help/lbd.html doi: ~ end_time: 2005-12-30T19:00:00 href: https://data.globalchange.gov/dataset/cdc-wonder-mortality-infant-deaths.yaml identifier: cdc-wonder-mortality-infant-deaths lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'CDC WONDER: Mortality - Infant Deaths' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1994-12-31T19:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-wonder-mortality-infant-deaths url: http://wonder.cdc.gov/lbd.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The Mortality - Multiple Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the years 1999-2009. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, up to twenty additional multiple causes (Boolean set analysis), and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., region, state, and county), age group (including infants and single-year-of-age cohorts), race (4 groups), Hispanic ethnicity, gender, year of death, and cause-of-death (4-digit ICD-10 code or group of codes, injury intent and mechanism categories, or drug and alcohol related causes), year, month and week day of death, place of death and whether an autopsy was performed. The data are produced by the National Center for Health Statistics.' description_attribution: http://wonder.cdc.gov/wonder/help/mcd.html doi: ~ end_time: 2009-12-30T19:00:00 href: https://data.globalchange.gov/dataset/cdc-wonder-mortality-multiple-cause-death.yaml identifier: cdc-wonder-mortality-multiple-cause-death lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'CDC WONDER: Mortality - Multiple Cause of Death' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1998-12-31T19:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-wonder-mortality-multiple-cause-death url: http://wonder.cdc.gov/mcd.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The CDC WONDER Mortality - Underlying Cause of Death online database is a county-level national mortality and population database spanning the years since 1979. Data are updated annually. The number of deaths, crude death rates, age-adjusted death rates, standard errors and confidence intervals for death rates can be obtained by place of residence (total U.S., Census region, Census division, state, and county), age group (including infant age groups), race (years 1979-1998: White, Black, and Other; years 1999-present: American Indian or Alaska Native, Asian or Pacific Islander, Black or African American, and White), Hispanic origin (years 1979-1998: not available; years 1999-present: Hispanic or Latino, not Hispanic or Latino, Not Stated), gender, year of death, and underlying cause ofdeath (years 1979-1998: 4-digit ICD-9 code and 72 cause-of-death recode; years 1999-present: 4-digit ICD-10 codes and 113 cause-of-death recode, as well as the Injury Mortality matrix classification for Intent and Mechanism), and urbanization level of residence (2006 NCHS urban-rural classification scheme for counties). The data are produced by the National Center for Health Statistics.' description_attribution: http://wonder.cdc.gov/wonder/help/cmf.html doi: ~ end_time: 2007-12-30T19:00:00 href: https://data.globalchange.gov/dataset/cdc-wonder-mortality-underlying-cause-death.yaml identifier: cdc-wonder-mortality-underlying-cause-death lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'CDC WONDER: Mortality - Underlying Cause of Death' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1998-12-31T19:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-wonder-mortality-underlying-cause-death url: http://wonder.cdc.gov/mortSQL.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The Population - Bridged-Race July 1st Estimates online databases report bridged-race population estimates of the July 1st resident population of the United States, based on Census 2000 counts, for use in calculating vital rates. These estimates result from "bridging" the 31 race categories used in Census 2000, as specified in the 1997 Office of Management and Budget (OMB) standards for the collection of data on race and ethnicity, to the four race categories specified under the 1977 standards (Asian or Pacific Islander, Black or African American, American Indian or Alaska Native, White). Many data systems, such as vital statistics, are continuing to use the 1977 OMB standards during the transition to full implementation of the 1997 OMB standards. Postcensal estimates are available for year 2000 - 2009; intercensal estimates are available for the years 1990-1999. Obtain population counts by Year, State, County, Race (4-categories), Ethnicity, Sex and Age (1-year or 5-year groups). The data are released by the National Center for Health Statistics.' description_attribution: http://wonder.cdc.gov/wonder/help/bridged-race.html doi: ~ end_time: 2009-12-30T19:00:00 href: https://data.globalchange.gov/dataset/cdc-wonder-population-bridged-race-july-1st-estimates.yaml identifier: cdc-wonder-population-bridged-race-july-1st-estimates lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'CDC WONDER: Population - Bridged-Race July 1st Estimates' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1989-12-31T19:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdc-wonder-population-bridged-race-july-1st-estimates url: http://wonder.cdc.gov/bridged-race-population.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: ~ description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/cdec-water-storage-levels-shasta-dam-reservoir.yaml identifier: cdec-water-storage-levels-shasta-dam-reservoir lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Storage Levels in the Shasta Dam Reservoir native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdec-water-storage-levels-shasta-dam-reservoir url: http://cdec.water.ca.gov/resapp/ResDetail?resid=SHA variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: 'Houghton, R.A. 2008. Carbon Flux to the Atmosphere from Land-Use Changes: 1850-2005. In TRENDS: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A.' data_qualifier: ~ description: 'The methods and data sources used to derive this time series of flux estimates are described in Houghton (1999, 2003), Houghton and Hackler (1995), and Houghton et al. (1983). In summary, this database provides estimates of regional and global net carbon fluxes, on a year-by-year basis from 1850 through 2005, resulting from changes in land use (such as harvesting of forest products and clearing for agriculture), taking into account not only the initial removal and oxidation of the carbon in the vegetation, but also subsequent regrowth and changes in soil carbon. The net flux of carbon to the atmosphere from changes in land use from 1850 to 2005 was modeled as a function of documented land-use change and changes in aboveground and belowground carbon following changes in land use. Annual rates of land-use change (for example, conversion of forest to cropland) and per hectare changes in carbon stocks (vegetation, slash, wood products, and soils) as a result of changes in land use were used in a carbon accounting model to calculate the annual net flux of carbon between land and the atmosphere that results from land management. The net flux includes both emissions of carbon from deforestation and sinks of carbon in forests recovering from harvests or agricultural abandonment. Changes in land use included the expansion and contraction of croplands and pastures, plantation establishment, and harvest of wood. Carbon budgeting included only those ecosystems converted to other uses or harvested; unmanaged ecosystems were not considered. Further, rates of growth and decomposition were ecosystem specific and did not vary in response to variations in climatic factors, CO2 concentrations, or other elements of environmental change. The analyses were spatially aggregated. Two to six types of ecosystems, with average carbon stocks, were considered for each of ten world regions.' description_attribution: https://cdiac.ess-dive.lbl.gov/trends/landuse/houghton/houghton.html doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/cdiac-carbon-flux-atmosphere-land-use-changes-1850-2005.yaml identifier: cdiac-carbon-flux-atmosphere-land-use-changes-1850-2005 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Carbon flux to the atmosphere from land-use changes 1850-2005 native_id: ~ processing_level: ~ publication_year: 2008 release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/cdiac-carbon-flux-atmosphere-land-use-changes-1850-2005 url: https://cdiac.ess-dive.lbl.gov/trends/landuse/houghton/1850-2005.txt variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: 'Marland, G., T.A. Boden, and R.J. Andres. 2008. Global, Regional, and National Fossil Fuel CO2 Emissions. In Trends: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A.' data_qualifier: ~ description: "Publications containing historical energy statistics make it possible to estimate fossil fuel CO2 emissions back to 1751. Etemad et al. (1991) published a summary compilation that tabulates coal, brown coal, peat, and crude oil production by nation and year. Footnotes in the Etemad et al.(1991) publication extend the energy statistics time series back to 1751. Summary compilations of fossil fuel trade were published by Mitchell (1983, 1992, 1993, 1995). Mitchell's work tabulates solid and liquid fuel imports and exports by nation and year. These pre-1950 production and trade data were digitized and CO2 emission calculations were made following the procedures discussed in Marland and Rotty (1984) and Boden et al. (1995). Further details on the contents and processing of the historical energy statistics are provided in Andres et al. (1999). The 1950 to present CO2 emission estimates are derived primarily from energy statistics published by the United Nations (2006), using the methods of Marland and Rotty (1984). The energy statistics were compiled primarily from annual questionnaires distributed by the U.N. Statistical Office and supplemented by official national statistical publications. As stated in the introduction of the Statistical Yearbook, \"in a few cases, official sources are supplemented by other sources and estimates, where these have been subjected to professional scrutiny and debate and are consistent with other independent sources.\" Data from the U.S. Department of Interior's Geological Survey (USGS 2007) were used to estimate CO2 emitted during cement production. Values for emissions from gas flaring were derived primarily from U.N. data but were supplemented with data from the U.S. Department of Energy's Energy Information Administration (1994), Rotty (1974), and data provided by G. Marland. Greater details about these methods are provided in Marland and Rotty (1984), Boden et al. (1995), and Andres et al. (1999)." description_attribution: https://cdiac.ess-dive.lbl.gov/trends/emis/overview.html doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/cdiac-global-regional-national-fossil-fuel-co2-emissions.yaml identifier: cdiac-global-regional-national-fossil-fuel-co2-emissions lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'CDIAC Global, Regional, and National Fossil-Fuel CO2 Emissions' native_id: ~ processing_level: ~ publication_year: 2008 release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: 1751-01-01/2005-12-31 temporal_resolution: ~ type: ~ uri: /dataset/cdiac-global-regional-national-fossil-fuel-co2-emissions url: https://cdiac.ess-dive.lbl.gov/trends/emis/overview.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: '2010 Census Summary File 1 [name of state1 or United States]/prepared by the U.S. Census Bureau, 2011. 2010 Census Summary File 1 Urban/Rural Update [name of state1 or United States]/prepared by the U.S. Census Bureau, 2012' data_qualifier: ~ description: 'Summary File 1 (SF 1) contains the data compiled from the questions asked of all people and about every housing unit. Population items include sex, age, race, Hispanic or Latino origin, household relationship, household type, household size, family type, family size, and group quarters. Housing items include occupancy status, vacancy status, and tenure (whether a housing unit is owner-occupied or renter-occupied). There are 177 population tables (identified with a ‘‘P’’) and 58 housing tables (identified with an ‘‘H’’) shown down to the block level; 82 population tables (identified with a ‘‘PCT’’) and 4 housing tables (identified with an “HCT”) shown down to the census tract level; and 10 population tables (identified with a “PCO”) shown down to the county level, for a total of 331 tables. The SF 1 Urban/Rural Update added 2 PCT tables, increasing the total number to 333 tables. There are 14 population tables and 4 housing tables shown down to the block level and 5 population tables shown down to the census tract level that are repeated by the major race and Hispanic or Latino groups. SF 1 includes population and housing characteristics for the total population, population totals for an extensive list of race (American Indian and Alaska Native tribes, Asian, and Native Hawaiian and Other Pacific Islander) and Hispanic or Latino groups, and population and housing characteristics for a limited list of race and Hispanic or Latino groups. Population and housing items may be cross-tabulated. Selected aggregates and medians also are provided. A complete listing of subjects in this file is found in the “Subject Locator” chapter' description_attribution: https://www.census.gov/data/datasets/2010/dec/summary-file-1.html doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/census-2010-summary-file-1-2011.yaml identifier: census-2010-summary-file-1-2011 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'U.S. Census 2010 Census Summary File 1 - 2011' native_id: ~ processing_level: ~ publication_year: 2011 release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-2010-summary-file-1-2011 url: https://www.census.gov/prod/cen2010/doc/sf1.pdf variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "The 2014 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Alaska Native Regional Corporations (ANRCs) were created pursuant to the Alaska Native Claims Settlement Act (ANCSA), which is federal legislation (Pub. L. 92-203, 85 Stat. 688 (1971); 43 U.S.C. 1602 et seq. (2000)) enacted in 1971, as a \"Regional Corporation\" and organized under the laws of the State of Alaska to conduct both the for-profit and non-profit affairs of Alaska Natives within a defined region of Alaska. For the Census Bureau, ANRCs are considered legal geographic entities. Twelve ANRCs cover the entire state of Alaska except for the area within the Annette Island Reserve (a federally recognized American Indian reservation under the governmental authority of the Metlakatla Indian Community). A thirteenth ANRC represents Alaska Natives who do not live in Alaska and do not identify with any of the twelve corporations. The Census Bureau does not provide data for this thirteenth ANRC because it has no defined geographic extent and thus it does not appear in the Cartographic Boundary Files. The Census Bureau offers representatives of the twelve non-profit ANRCs in Alaska the opportunity to review and update the ANRC boundaries before each decennial census." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/census-2014-cartographic-boundary-file-state-alaska-native-regional-corporation-alaska-1-500000.yaml identifier: census-2014-cartographic-boundary-file-state-alaska-native-regional-corporation-alaska-1-500000 lat_max: 71.365162 lat_min: 51.214183 lon_max: 179.77847 lon_min: -179.148909 name: '2014 Cartographic Boundary File, State-Alaska Native Regional Corporation for Alaska, 1:500,000' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-2014-cartographic-boundary-file-state-alaska-native-regional-corporation-alaska-1-500000 url: http://www2.census.gov/geo/tiger/GENZ2014/shp/cb_2014_02_anrc_500k.zip variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The cartographic boundary files are simplified representations of selected geographic areas from the Census Bureau’s MAF/TIGER geographic database. These boundary files are specifically designed for small scale thematic mapping. Generalized boundary files are clipped to a simplified version of the U.S. outline. As a result, some off-shore areas may be excluded from the generalized files.' description_attribution: https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/census-cartographic-boundary-shapefiles-counties.yaml identifier: census-cartographic-boundary-shapefiles-counties lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'U.S. Census 2010 Cartographic Boundary Shapefiles - Counties' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-cartographic-boundary-shapefiles-counties url: https://www.census.gov/cgi-bin/geo/shapefiles/index.php variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The cartographic boundary files are simplified representations of selected geographic areas from the Census Bureau’s MAF/TIGER geographic database. These boundary files are specifically designed for small scale thematic mapping. Generalized boundary files are clipped to a simplified version of the U.S. outline. As a result, some off-shore areas may be excluded from the generalized files.' description_attribution: https://www.census.gov/geo/maps-data/data/cbf/cbf_state.html doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/census-cartographic-boundary-shapefiles-states.yaml identifier: census-cartographic-boundary-shapefiles-states lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'U.S. Census Bureau Cartographic Boundary Shapefiles - States' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-cartographic-boundary-shapefiles-states url: https://www.census.gov/cgi-bin/geo/shapefiles/index.php variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The Commodity Flow Survey provides information on commodities shipped, their value, weight, and mode of transportation, as well as the origin and destination of shipments of commodities from manufacturing, mining, wholesale, and selected retail and services establishments. It is undertaken through a partnership between the Bureau of the Census, U.S. Department of Commerce, and the Bureau of Transportation Statistics, Research and Innovative Technology Administration.' description_attribution: http://www.census.gov/econ/cfs/definitions.html doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/census-commodity-flow-survey.yaml identifier: census-commodity-flow-survey lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Commodity Flow Survey native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-commodity-flow-survey url: https://www.census.gov/programs-surveys/cfs.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: This layer represents locations of American Indian reservations in Alaska. description_attribution: https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BE37B0B2C-EB0B-436C-B993-C18D8895E522%7D doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/census-epa-tribal-areas-3-of-4-alaska-reservations.yaml identifier: census-epa-tribal-areas-3-of-4-alaska-reservations lat_max: 55.300967 lat_min: 54.985892 lon_max: -131.293888 lon_min: -131.667861 name: 'EPA Tribal Areas (3 of 4): Alaska Reservations' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-epa-tribal-areas-3-of-4-alaska-reservations url: https://edg.epa.gov/data/Public/OEI/OIAA/Tribes/EPAtribes.zip variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Each year, the United States Census Bureau produces and publishes estimates of the population for the nation, states, counties, state/county equivalents, and Puerto Rico. 1 We estimate the resident population for each year since the most recent decennial census by using measures of population change. The resident population includes all people currently residing in the United States. With each annual release of population estimates, the Population Estimates program revises and updates the entire time series of estimates from April 1, 2010 to July 1 of the current year, which we refer to as the vintage year. We use the term “vintage” to denote an entire time series created with a consistent population starting point and methodology. The release of a new vintage of estimates supersedes any previous series and incorporates the most up-to-date input data and methodological improvements.' description_attribution: https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2010-2017/2017-natstcopr-meth.pdf?# doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/census-metropolitan-micropolitan-statistical-areas-population-totals-2010-2017.yaml identifier: census-metropolitan-micropolitan-statistical-areas-population-totals-2010-2017 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'U.S. Census Metropolitan and Micropolitan Statistical Areas Population Totals: 2010-2017' native_id: ~ processing_level: ~ publication_year: 2017 release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-metropolitan-micropolitan-statistical-areas-population-totals-2010-2017 url: https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "Census population estimates by year\r\n\r\nEach year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census and produce a time series of estimates of population, demographic components of change, and housing units." description_attribution: http://www.census.gov/popest/data/datasets.html doi: ~ end_time: 2014-07-01T23:59:59 href: https://data.globalchange.gov/dataset/census-population-estimates.yaml identifier: census-population-estimates lat_max: 71.32 lat_min: 18.91 lon_max: -66.57 lon_min: -160.16 name: Annual Estimates of the Resident Population for the United States native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1900-07-01T00:00:01 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-population-estimates url: https://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'First published in 1878, the Statistical Abstract serves as the official federal summary of statistics and provides over 1,400 tables of benchmark measures on the demographic, housing, social, political, and economic condition of the United States.' description_attribution: http://www.census.gov/library/publications/time-series/statistical_abstracts.html doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/census-statistical-abstract.yaml identifier: census-statistical-abstract lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Statistical Abstract of the United States native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-statistical-abstract url: http://www.census.gov/library/publications/time-series/statistical_abstracts.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "The American Indian/Alaska Native/Native Hawaiian (AIANNH) Areas Shapefile includes the following legal entities: federally recognized American Indian reservations and off-reservation trust land areas, state-recognized American Indian reservations, and Hawaiian home lands (HHLs). The statistical entities included are Alaska Native village statistical areas (ANVSAs), Oklahoma tribal statistical areas (OTSAs), tribal designated statistical areas (TDSAs), and state designated tribal statistical areas (SDTSAs). Joint use areas are also included in this shapefile refer to areas that are administered jointly and/or claimed by two or more American Indian tribes. The Census Bureau designates both legal and statistical joint use areas as unique geographic entities for the purpose of presenting statistical data. Note that tribal subdivisions and Alaska Native Regional Corporations (ANRCs) are additional types of American Indian/Alaska Native areas stored by the Census Bureau, but are displayed in separate shapefiles because of how they fall within the Census Bureau's geographic hierarchy. The State of Hawaii's Office of Hawaiian Home Lands provides the legal boundaries for the HHLs. The boundaries for ANVSAs, OTSAs, and TDSAs were delineated for the 2010 Census through the Tribal Statistical Areas Program (TSAP) by participants from the federally recognized tribal governments. The Bureau of Indian Affairs (BIA) within the U.S. Department of the Interior (DOI) provides the list of federally recognized tribes and only provides legal boundary information when the tribes need supporting records, if a boundary is based on treaty or another document that is historical or open to legal interpretation, or when another tribal, state, or local government challenges the depiction of a reservation or off-reservation trust land. The boundaries for federally recognized American Indian reservations and off-reservation trust lands are as of January 1, 2013, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries for state-recognized American Indian reservations and for SDTSAs were delineated by a state governor-appointed liaisons for the 2010 Census through the State American Indian Reservation Program and TSAP respectively." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/census-tiger-line-shapefile-2014-series-information-file-for-the-current-american-indian-ala.yaml identifier: census-tiger-line-shapefile-2014-series-information-file-for-the-current-american-indian-ala lat_max: 71.441059 lat_min: -14.601813 lon_max: 179.859681 lon_min: -179.231086 name: 'TIGER/Line Shapefile, 2014, Series Information File for the Current American Indian/Alaska Native/Native Hawaiian Areas (AIANNH) National Shapefile' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-tiger-line-shapefile-2014-series-information-file-for-the-current-american-indian-ala url: http://www2.census.gov/geo/tiger/TIGER2014/AIANNH variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "Alaska Native Regional Corporations (ANRCs) were created pursuant to the Alaska Native Claims Settlement Act (ANCSA), which is federal legislation Pub. L. 92-203, 85 Stat. 688 (1971); 43 U.S.C. 1602 et seq. (2000)) enacted in 1971, as a 'Regional Corporation' and organized under the laws of the State of Alaska as \"Regional Corporations\" to conduct both the for profit and non profit affairs of Alaska Natives within defined regions of Alaska. Twelve ANRCs cover the entire State of Alaska except for the area within the Annette Island Reserve (an American Indian Reservation under the governmental authority of the Metlakatla Indian Community). There is a thirteenth ANRC that represents the eligible Alaska Natives living outside of Alaska that are not members of any of the twelve ANRCs within the State of Alaska. Because it has no defined geographic extent, this thirteenth ANRC does not appear in the TIGER/Line Shapefiles and the Census Bureau does not provide data for it. The Census Bureau offers representatives of the twelve ANRCs the opportunity to review and update the ANRC boundaries. ANRCs are represented by a 5 character FIPS code unique within Alaska and a nationally unique 8 character National Standard (GNIS) code." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/census-tiger-line-shapefile-2014-series-information-for-the-current-alaska-native-regional-corporation844f0.yaml identifier: census-tiger-line-shapefile-2014-series-information-for-the-current-alaska-native-regional-corporation844f0 lat_max: 71.441059 lat_min: -14.601813 lon_max: 179.859681 lon_min: -179.231086 name: 'TIGER/Line Shapefile, 2014, Series Information for the Current Alaska Native Regional Corporation (ANRC) State-based Shapefiles' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-tiger-line-shapefile-2014-series-information-for-the-current-alaska-native-regional-corporation844f0 url: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2014.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "American Indian tribal subdivisions are administrative subdivisions of federally recognized American Indian reservations/off-reservation trust lands or Oklahoma tribal statistical areas (OTSAs). These entities are internal units of self-government and/or administration that serve social, cultural, and/or economic purposes for the American Indian tribe or tribes on the reservations/off-reservation trust lands or OTSAs. The Census Bureau obtains the boundary and attribute information for tribal subdivisions on federally recognized American Indian reservations and off-reservation trust lands from federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey (BAS) For the 2010 Census, the boundaries for tribal subdivisions on OTSAs were also obtained from federally recognized tribal governments through the Tribal Statistical Areas Program (TSAP). Note that tribal subdivisions do not exist on all reservations/off-reservation trust lands or OTSAs, rather only where they were submitted to the Census Bureau by the federally recognized tribal government for that area. The boundaries for American Indian tribal subdivisions are as of January 1, 2013, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries for tribal subdivisions on OTSAs are those reported as of January 1, 2010 through TSAP." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/census-tiger-line-shapefile-2014series-information-for-the-current-american-indian-tribal-subdivision-.yaml identifier: census-tiger-line-shapefile-2014series-information-for-the-current-american-indian-tribal-subdivision- lat_max: 71.441059 lat_min: -14.601813 lon_max: 179.859681 lon_min: -179.231086 name: 'TIGER/Line Shapefile, 2014,Series Information for the Current American Indian Tribal Subdivision (AITS) National Shapefile' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-tiger-line-shapefile-2014series-information-for-the-current-american-indian-tribal-subdivision- url: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2014.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are mostly as of January 1, 2013, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS). However, some changes made after January 2013, including the addition and deletion of counties, are included." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/census-tiger-line-shapefile-2014series-information-for-the-current-county-and-equivalent-national-shap.yaml identifier: census-tiger-line-shapefile-2014series-information-for-the-current-county-and-equivalent-national-shap lat_max: 71.441059 lat_min: -14.601813 lon_max: 179.859681 lon_min: -179.231086 name: 'TIGER/Line Shapefile, 2014,Series Information for the Current County and Equivalent National Shapefile' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-tiger-line-shapefile-2014series-information-for-the-current-county-and-equivalent-national-shap url: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2014.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The TIGERweb application allows users to access and view geospatial features, geographic area information, and associated attributes from the U.S. Census Bureau Topologically Integrated Geographic Encoding and Referencing System (TIGER) geodatabase. The TIGERweb is intended to meet the needs of users inside and outside the Census Bureau for access to geospatial data contained within the TIGER geodatabase without requiring the use of a GIS. The TIGERweb map layers are grouped by the following geographies: Transportation (Roads and Railroads), Tribal Census Tracts and Block Groups, Census Tracts and Blocks, Military Installations, School Districts, Places and County Subdivisions, American Indian, Alaska Native, and Native Hawaiian Areas, Legislative Areas, Census Regions and Divisions, Urban Areas - Census 2000, Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas, Hydrography, States and Counties. Labels are included for the map layers.' description_attribution: http://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_main.html doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/census-tigerweb-2010.yaml identifier: census-tigerweb-2010 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: TIGERweb 2010 native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/census-tigerweb-2010 url: http://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_main.html variables: ~ version: ~ vertical_extent: ~ - access_dt: 2013-04-15T00:00:00 attributes: 'Daily precipitation, daily maximum temperature, daily minimum temperature' cite_metadata: 'Hayhoe, K., Stoner, et al., (2013), Development and Dissemination of a High-Resolution National Climate Change Dataset. http://cida.usgs.gov/thredds/fileServer/dcp/files/Hayhoe_USGS_downscaled_database_final_report.pdf' data_qualifier: ~ description: 'In this project, we used an advanced statistical downscaling method that combines high-resolution observations with outputs from 16 different global climate models based on 4 future emission scenarios to generate the most comprehensive dataset of daily temperature and precipitation projections available for climate change impacts in the U.S. The gridded dataset covers the continental United States, southern Canada and northern Mexico at one-eighth degree resolution and Alaska at one-half degree resolution. The high-resolution projections produced by this work have been rigorously quality-controlled for both errors and biases in the global climate and statistical downscaling models. We also calculated projected future changes in a broad range of impact-relevant indicators, from seasonal temperature to extreme precipitation days. The results of the error and bias tests and the indicator calculations are made available as part of this database.' description_attribution: https://cida.usgs.gov/thredds/catalog.html?dataset=cida.usgs.gov/thredds/dcp/conus_pr doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/cida_usgs_gov_thredds_dcp_conus_pr.yaml identifier: cida_usgs_gov_thredds_dcp_conus_pr lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Eighth degree-CONUS Daily Downscaled Climate Projections native_id: 0bdee6a2-f677-4714-8d36-0ba570972aba processing_level: ~ publication_year: 2010 release_dt: 2013-12-05T20:42:39 scale: ~ scope: ~ spatial_extent: 'maximum_latitude: 51.68; minimum_latitude: 21.44; maximum_longitude: -65.74; minimum_longitude: -127.97;' spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: 1960-01-01T00:00:00 2099-12-31T23:59:59 temporal_resolution: ~ type: ~ uri: /dataset/cida_usgs_gov_thredds_dcp_conus_pr url: https://cida.usgs.gov/thredds/catalog.html?dataset=cida.usgs.gov/thredds/dcp/conus_pr variables: ~ version: N/A vertical_extent: ~ - access_dt: 2013-04-15T00:00:00 attributes: 'daily precipitation, maximum temperature, minimum temperature, wind speed' cite_metadata: "Daily gridded meteorological data obtained from the Surface Water Modeling group at the University of Washington from their web site at http://www.hydro.washington.edu/Lettenmaier/Data/gridded/, the development of which is described by Maurer et al. (2002):\r\n\r\nMaurer, E.P., A.W. Wood, J.C. Adam, D.P. Lettenmaier, and B. Nijssen, 2002, A Long-Term Hydrologically-Based Data Set of Land Surface Fluxes and States for the Conterminous United States, J. Climate 15, 3237-3251" data_qualifier: ~ description: 'A model-derived dataset of land surface states and fluxes is presented for the conterminous United States and portions of Canada and Mexico. The dataset spans the period 1950–2000, and is at a 3-h time step with a spatial resolution of ⅛ degree. The data are distinct from reanalysis products in that precipitation is a gridded product derived directly from observations, and both the land surface water and energy budgets balance at every time step. The surface forcings include precipitation and air temperature (both gridded from observations), and derived downward solar and longwave radiation, vapor pressure deficit, and wind. Simulated runoff is shown to match observations quite well over large river basins' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/daily-1-8-degree-gridded-meteorological-data-1-jan-1949-31-december-2010.yaml identifier: daily-1-8-degree-gridded-meteorological-data-1-jan-1949-31-december-2010 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'Daily 1/8-degree gridded meteorological data [1 Jan 1949 - 31 Dec 2010]' native_id: 10.1175/1520-0442(2002)015<3237:ALTHBD>2.0.CO;2 processing_level: ~ publication_year: 2011 release_dt: 2011-03-11T00:00:00 scale: ~ scope: ~ spatial_extent: 'maximum_latitude: 51.68; minimum_latitude: 21.44; maximum_longitude: -65.74; minimum_longitude: -127.97;' spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: 1949-01-01T00:00:00 2000-07-31T23:59:59 temporal_resolution: ~ type: ~ uri: /dataset/daily-1-8-degree-gridded-meteorological-data-1-jan-1949-31-december-2010 url: http://www.engr.scu.edu/~emaurer/gridded_obs/index_gridded_obs.html variables: ~ version: TBD vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'This dataset, released by DoD, contains geographic information for major installations, ranges, and training areas in the United States and its territories. This release integrates site information about DoD installations, training ranges, and land assets in a format which can be immediately put to work in commercial geospatial information systems. Homeland Security/Homeland Defense, law enforcement, and readiness planners will benefit from immediate access to DoD site location data during emergencies. Land use planning and renewable energy planning will also benefit from use of this data. Users are advised that the point and boundary location datasets are intended for planning purposes only, and do not represent the legal or surveyed land parcel boundaries.' description_attribution: http://www.acq.osd.mil/ie/bei/opengov/release_notes.pdf doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/dod-2639.yaml identifier: dod-2639 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'Military Installations, Ranges, and Training Areas' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/dod-2639 url: https://www.acq.osd.mil/eie/BSI/BEI_DISDI.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Comprehensive monthly and annual time series on all energy sources. Data on production, consumption, reserves, stocks, prices, imports, and exports. Monthly time series extend back to 1973 and annual time series extend back to 1949. National-level data on major end-use sectors ,i.e., residential, commercial, industrial, and transportation.' description_attribution: ~ doi: ~ end_time: 1970-01-01T00:33:33 href: https://data.globalchange.gov/dataset/doe-019-1295783927.yaml identifier: doe-019-1295783927 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Total Energy Data and Statistics native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1970-01-01T00:32:29 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-019-1295783927 url: https://www.eia.gov/totalenergy/data/annual/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Monthly, quarterly, and annual data on electricity generation, consumption, retail sales, price, revenue from retail sales, useful thermal output, fossil fuel stocks, fossil fuel receipts, and quality of fossil fuel. Data organized by fuel type, i.e., coal petroleum, natural gas, nuclear, hydroelectric, wind, solar, geothermal, and wood. Also, data organized by sector, i.e., electric power, electric utility, independent power producers, commercial, and industrial. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm' description_attribution: ~ doi: ~ end_time: 1970-01-01T00:33:34 href: https://data.globalchange.gov/dataset/doe-019-3299699499.yaml identifier: doe-019-3299699499 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Electricity Data and Statistics Application Programming Interface (API) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1970-01-01T00:32:20 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-019-3299699499 url: https://www.eia.gov/electricity/data.php variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'State-level data on all energy sources. Data include production, consumption, reserves, stocks, prices, imports, and exports. Data are collated from state-specific data reported elsewhere on the EIA website and are the most recent values available. The system provides data back from 1960. While some SEDS data series come directly from surveys conducted by EIA, many are estimated using other available information. These estimations are necessary for the compilation of "total energy" estimates. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-019-3988165895.yaml identifier: doe-019-3988165895 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: State Energy Data System (SEDS) Application Programming Interface (API) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-019-3988165895 url: https://www.eia.gov/state/seds/seds-data-fuel.php?sid=US variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Data on uranium and nuclear fuel, nuclear power plants and reactors, radioactive waste, and nuclear power generation. International data on nuclear generation also available. Monthly, quarterly, and annual data available.' description_attribution: ~ doi: ~ end_time: 1970-01-01T00:34:00 href: https://data.globalchange.gov/dataset/doe-019-5291681930.yaml identifier: doe-019-5291681930 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Nuclear & Uranium Data and Statistics native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1970-01-01T00:33:33 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-019-5291681930 url: https://www.eia.gov/nuclear/data.php variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Annual data on proved reserves of crude oil, natural gas, and natural gas liquids in the U.S. Based on EIA Form-23L data. Proved reserves are estimated volumes of hydrocarbon resources that analysis of geologic and engineering data demonstrates with reasonable certainty are recoverable under existing economic and operating conditions. Reserves estimates change from year to year as new discoveries are made, existing fields are more thoroughly appraised, existing reserves are produced, and prices and technologies change.' description_attribution: http://www.eia.gov/survey/form/eia_23l/instructions.pdf doi: ~ end_time: 1970-01-01T00:33:32 href: https://data.globalchange.gov/dataset/doe-019-5404456523.yaml identifier: doe-019-5404456523 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'U.S. Crude Oil, Natural Gas, and Natural Gas Liquids Reserves 2012' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1970-01-01T00:33:31 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-019-5404456523 url: https://www.eia.gov/naturalgas/crudeoilreserves/archive/2012/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: Annual data on the number and production volumes of oil and natural gas wells by state. Annual time series extend back to 1919. description_attribution: ~ doi: ~ end_time: 2009-12-31T00:00:00 href: https://data.globalchange.gov/dataset/doe-019-5418154829.yaml identifier: doe-019-5418154829 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Annual Distribution and Production of Oil and Gas Wells by State From 1919-Latest Year Available native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 2009-01-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-019-5418154829 url: https://www.eia.gov/naturalgas/archive/petrosystem/petrosysog.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Monthly and yearly forecasts of energy production, consumption, and price at the national level and by energy type. Monthly forecasts extend 18 months and yearly forecasts extend to 2040. International yearly projections by region extend to 2040.' description_attribution: http://www.eia.gov/analysis/ doi: ~ end_time: 1970-01-01T00:33:34 href: https://data.globalchange.gov/dataset/doe-019-5492098884.yaml identifier: doe-019-5492098884 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Energy Analysis & Projections native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1970-01-01T00:32:20 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-019-5492098884 url: http://www.eia.gov/analysis/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "This 2009 version represents the 13th iteration of the RECS program. First conducted in 1978, the Residential Energy Consumption Survey is a national sample survey that collects energy-related data for housing units occupied as a primary residence and the households that live in them. Data were collected from 12,083 households selected at random using a complex multistage, area-probability sample design. The sample represents 113.6 million U.S. households, the Census Bureau's statistical estimate for all occupied housing units in 2009 derived from their American Community Survey (ACS)." description_attribution: http://www.eia.gov/consumption/residential/data/2009/xls/recs2009_public_codebook.xlsx doi: ~ end_time: 1970-01-01T00:33:29 href: https://data.globalchange.gov/dataset/doe-019-5945672389.yaml identifier: doe-019-5945672389 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'Residential Energy Consumption Survey (RECS) Files, Energy Consumption, 2009' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1970-01-01T00:32:58 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-019-5945672389 url: http://www.eia.gov/consumption/residential/data/2009/index.cfm?view=microdata variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The Office of Electricity Delivery and Energy Reliability (OE) provides national leadership to ensure that the Nationa?Ts energy delivery system is secure, resilient and reliable. OE works to develop new technologies to improve the infrastructure that brings electricity into our homes, offices, and factories, and the federal and state electricity policies and programs that shape electricity system planning and market operations. OE also works to bolster the resiliency of the electric grid and assists with restoration when major energy supply interruptions occur.' description_attribution: ~ doi: ~ end_time: 1970-01-01T00:33:34 href: https://data.globalchange.gov/dataset/doe-019-6668545421.yaml identifier: doe-019-6668545421 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Electricity Delivery and Energy Reliability website native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1970-01-01T00:33:23 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-019-6668545421 url: http://energy.gov/oe/office-electricity-delivery-and-energy-reliability variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Bioenergy Knowledge Discovery Framework (BioKDF) supports the bioenergy infrastructure effort to move the nation towards sustainable, renewable energy solution. The BioKDF facilitates informed decision making by providing a means to synthesize, analyze, and visualize vast amounts of information in a relevant and succinct manner. It is designed to comprehensively analyze the economic and environmental impacts of various development options for biomass feedstocks, biorefineries, and related infrastructure.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-bioenergy-knowledge-discovery-framework-kdf-3c7ba.yaml identifier: doe-bioenergy-knowledge-discovery-framework-kdf-3c7ba lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Bioenergy Knowledge Discovery Framework (KDF) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-bioenergy-knowledge-discovery-framework-kdf-3c7ba url: https://bioenergykdf.net/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: Total annual biofuels consumption and production data by country was compiled by the Energy Information Administration (EIA). Data is presented as thousand barrels per day. description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-biofuels-consumption-and-production-by-country-2000-2010-11ff9.yaml identifier: doe-biofuels-consumption-and-production-by-country-2000-2010-11ff9 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'Biofuels Consumption and Production by Country (2000 - 2010)' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-biofuels-consumption-and-production-by-country-2000-2010-11ff9 url: http://www.eia.gov/countries/data.cfm#undefined variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'CREST is an economic cash flow model designed to allow policymakers, regulators, and the renewable energy community to assess project economics, design cost-based incentives (e.g., feed-in tariffs), and evaluate the impact of various state and federal support structures. CREST is a suite of four analytic tools, for solar (photovoltaic and solar thermal), wind, geothermal, and anaerobic digestion technologies. It is also used by project managers to do preliminary back-of-the-envelope calculations of the year one cost of energy for renewable energy projects.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-cost-renewable-energy-spreadsheet-tool-crest-fbab0.yaml identifier: doe-cost-renewable-energy-spreadsheet-tool-crest-fbab0 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Cost of Renewable Energy Spreadsheet Tool (CREST) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-cost-renewable-energy-spreadsheet-tool-crest-fbab0 url: https://www.nrel.gov/analysis/crest.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Detailed inventory of available renewable energy (RE) resource assessment data. Although the type, amount, and regional distribution of resource information vary by resource, assessments are available for each RE category (conducted by DOE and various private and public organizations). Solar, wind and geothermal resources have assessment products available at numerous scales (national, regional, and site specific). Assessments are available for biomass and hydropower resources at a national level, with only limited information available at the regional and site-specific levels. Ocean energy has the least resource assessment information available. This information was compiled by NREL and initially published in the 2006 Report to Congress on Renewable Energy Resource Assessment Information for the United States (Original document courtesy of archive.org). This datasets was last updated in January, 2011. For each assessment, the inventory includes: data name, data type, source, period of record, spatial coverage, spatial resolution, temporal scale, units, stated accuracy, availability, URL, update frequency, and additional notes.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-detailed-renewable-energy-resource-assessment-data-inventory-us-9636d.yaml identifier: doe-detailed-renewable-energy-resource-assessment-data-inventory-us-9636d lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Detailed Renewable Energy Resource Assessment Data Inventory (US) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-detailed-renewable-energy-resource-assessment-data-inventory-us-9636d url: http://www.nrel.gov/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The DOE Geothermal Data Repository (GDR) is the submission point for all data collected using funds from the Geothermal Technology Office of the U.S. Department of Energy. It was established to receive, manage and make available all geothermal-relevant data generated from projects funded by the DOE Geothermal Technologies Office. This includes data from GTO-funded projects associated with any portion of the geothermal project life-cycle (exploration, development, operation), as well as data produced by GTO-funded research.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-doe-geothermal-data-repository-5a93c.yaml identifier: doe-doe-geothermal-data-repository-5a93c lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: DOE Geothermal Data Repository native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-doe-geothermal-data-repository-5a93c url: http://gdr.openei.org/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: This dataset contains information about hundreds of designated user-facilities and R&D equipment funded by the U.S. Department of Energy and accessible to the private sector. These facilities reside at DOE's description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-doe-user-facilities-and-rd-equipment-edfae.yaml identifier: doe-doe-user-facilities-and-rd-equipment-edfae lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: DOE User Facilities and RD Equipment native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-doe-user-facilities-and-rd-equipment-edfae url: http://energy.gov/sites/prod/files/DOE_Facilities_Inventory_2014_16_06.csv variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "This dataset is the United States Oil and Gas Supply, part of the U.S. Energy Information Administration's (EIA's) Annual Energy Outlook (AEO) that highlights changes in the AEO Reference case projections for key energy topics. The Annual Energy Outlook presents a projection and analysis of US energy supply, demand, and prices through 2035. The projections are based on results from EIA's National Energy Modeling System." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-eia-data-2009-united-states-oil-and-gas-supply-0734c.yaml identifier: doe-eia-data-2009-united-states-oil-and-gas-supply-0734c lat_max: 49.3845 lat_min: 24.50600591138299 lon_max: -66.92161367187504 lon_min: -124.76249999999999 name: 'AEO: United States Oil and Gas Supply' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-eia-data-2009-united-states-oil-and-gas-supply-0734c url: http://www.eia.doe.gov/oiaf/archive/aeo09/aeoref_tab.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'This dataset is the 2011 United States Oil and Gas Supply, part of the Annual Energy Outlook that highlights changes in the AEO Reference case projections for key energy topics.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-eia-data-2011-united-states-oil-and-gas-supply-37fad.yaml identifier: doe-eia-data-2011-united-states-oil-and-gas-supply-37fad lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'EIA Data: 2011 United States Oil and Gas Supply' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-eia-data-2011-united-states-oil-and-gas-supply-37fad url: http://www.eia.doe.gov/forecasts/aeo/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Provides annual energy generation for all states by fuel source (e.g. coal, gas, solar, wind) in 2009, reported in MWh. Also includes facility-level data (directly from EIA Form 923).' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-energy-generation-by-state-and-technology-2009-69f4f.yaml identifier: doe-energy-generation-by-state-and-technology-2009-69f4f lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Energy Generation by State and Technology (2009) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-energy-generation-by-state-and-technology-2009-69f4f url: http://en.openei.org/doe-opendata/dataset/9ef7c547-17fc-4fe8-87e8-ffb27fcaafd9/resource/0c6f15cc-61c7-4340-80de-947fb5d8d1ef/download/2009state.re.gen.by.technology.xls variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "

The Form EIA-411, “Coordinated Bulk Power Supply \r Program Report,” collects information from the Nation’s power system \r planners about the electricity supply, both capacity and energy, that is\r needed to serve current demand and for future growth.

\r \r

The reported data can be used to examine such issues\r as: the reliability of the U.S. electricity system; projections which \r assess future demand growth and plans for constructing new generating \r and transmission facilities; and consequences of unavailable or \r constrained capacity on usage of the existing generation base.

\r \r

Reliability of the electric power system covers \r three areas: the security of the electrical systems; the usage of \r proper operational practices that adhere to mandatory standards; and the\r ability to plan for adequate supply to meet future demand. Data \r collected on the Form EIA-411 focuses on planning for adequacy of \r supply. Separately, the Department of Energy collects information \r covering security and selected operational practices under the Form OE-417, \"Electric Emergency Incident and Disturbance Report.\"

\r \r

The information on this page includes historical \r (with and without projections) and current data (with projections) for \r the reported year:

\r \r

Only projections, but no historical data, are reported for:

\r \r

The data provided here are aggregated by the North \r American Reliability Corporation (NERC) using data provided by the \r regional entities within NERC that oversee the development and \r implementation of the mandatory national and regional reliability \r standards. There are currently eight regions covering all of Canada and\r the contiguous United States plus a small part of Mexico (Baja \r California Norte) in North America. The data presented here is for the \r United States. For data years prior to 2005, 10 NERC regions are \r indicated, however, from 2005 on, there are 8 regions. (See maps at the\r top right). Users should expect some differences in geographic \r reporting coverage from these regional realignments." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-form-eia-411-coordinated-bulk-power-supply-program-report-045b6.yaml identifier: doe-form-eia-411-coordinated-bulk-power-supply-program-report-045b6 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'Form EIA-411, “Coordinated Bulk Power Supply Program Report,”' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-form-eia-411-coordinated-bulk-power-supply-program-report-045b6 url: http://www.eia.doe.gov/cneaf/electricity/page/eia411/eia411.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The National Geothermal Data System (NGDS) is a catalog of documents and datasets that provide information about geothermal resources located primarily within the United States (although information from other parts of the world is also included. The catalog, which is funded by the DOE Geothermal Technology Office, is designed to accelerate the development of U.S. geothermal resources, and can be used to determine geothermal potential, guide exploration and development, make data-driven policy decisions, minimize development risks, understand how geothermal activities affect your community and the environment, and guide investments.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-national-geothermal-data-system-d6d40.yaml identifier: doe-national-geothermal-data-system-d6d40 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: National Geothermal Data System native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-national-geothermal-data-system-d6d40 url: http://geothermaldata.org variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The Oak Ridge National Laboratory (ORNL) National Hydropower Asset Assessment Program (NHAAP) is an integrated energy, water, and ecosystem research and geospatial data integration effort for efficient,' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-national-hydropower-asset-assessment-program-fc5d5.yaml identifier: doe-national-hydropower-asset-assessment-program-fc5d5 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: National Hydropower Asset Assessment Program native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-national-hydropower-asset-assessment-program-fc5d5 url: http://nhaap.ornl.gov variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "

\r\n The National Solar Radiation Data Base (NSRDB) is the most comprehensive collection of solar data freely available. The 1991 - 2005 NSRDB contains hourly solar radiation (including global, direct, and diffuse) and meteorological data for 1,454 stations. NCDC's Integrated Surface Data (ISD) were the key data source for this effort, with much of the solar data modeled/estimated based on the surface observations. This dataset builds on the 1961-1990 NSRDB, which contains data for 239 stations. These data are extremely useful in estimating solar energy potential across the U.S., and in estimating heating/cooling requirements for buildings based on heat-gain from solar radiation. More information available at http://www.ncdc.noaa.gov/oa/reds/\r\n
\r\n \r\n
\r\n \r\n
\r\n Files available from either:\r\n \r\n
\r\n \r\n
" description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-national-solar-radiation-data-base-c18c3.yaml identifier: doe-national-solar-radiation-data-base-c18c3 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: National Solar Radiation Data Base native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-national-solar-radiation-data-base-c18c3 url: https://rredc.nrel.gov/solar/old_data/nsrdb/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "This dataset contains information about the biomass resources generated by county in the United States. It includes the following feedstock categories: crop residues, forest residues, primary mill residues, secondary mill residues, and urban wood waste.\r \r The estimates are based on county-level statistics and/or point-source data gathered from the U.S. Department of Agriculture (USDA), USDA Forest Service, EPA and other organizations, which are further processed using relevant assumptions and conversions." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-nrel-biomethane-gis-data.yaml identifier: doe-nrel-biomethane-gis-data lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: NREL Biomethane GIS Data native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-nrel-biomethane-gis-data url: http://en.openei.org/doe-opendata/dataset/a8a354f3-409c-494e-96ed-e2fbeae323e0/resource/e28e591b-6433-42df-bcd7-a9bf5394f90f/download/biomethane.zip variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'This dataset is a qualitative assessment of geothermal potential for the U.S. using Enhanced Geothermal Systems (EGS) and based on the levelized cost of electricity with CLASS 1 being most favorable and CLASS 5 being least favorable. This dataset does not include shallow EGS resources located near hydrothermal sites or the U.S. Geological Survey assessment of undiscovered hydrothermal resources. The source data for deep EGS includes temperature at depth from 3 to 10 kilometer (km) were provided by the Southern Methodist University Geothermal Laboratory (Blackwell & Richards, 2009) and the analyses for regions with temperatures ≥150°C were performed by NREL (2009). CLASS 999 regions have temperatures less than 150°C at a 10-km depth and were not assessed for deep EGS potential. Temperature at depth data for deep EGS in Alaska and Hawaii are not currently available.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-nrel-geothermal-gis-data.yaml identifier: doe-nrel-geothermal-gis-data lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: NREL Geothermal GIS Data native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-nrel-geothermal-gis-data url: http://en.openei.org/doe-opendata/dataset/c08349a6-9aba-4afd-946c-eac9d560bc4e/resource/5bbbb63f-b5c0-45be-929c-d3f5e5816bfd/download/lower48geothermalhighresolution.zip variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "_Abstract:_ Monthly and annual average solar resource potential for the lower 48 states of the United States of America.\r\n \r\n _Purpose:_ Provide information on the solar resource potential for the United States of America lower 48 states. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented due south at an angle from horizontal equal to the latitude of the collector location.\r\n \r\n _Supplemental Information:_ This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. \r\n Units are in kilowatt hours per meter squared per day.\r\n \r\n OtherCitation Details:\r\n \r\n George, R, and E. Maxwell, 1999: \"High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model\", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME. \r\n \r\n Maxwell, E, R. George and S. Wilcox, \"A Climatological Solar Radiation Model\", Proceedings of the 1998 Annual Conference, American Solar Energy Society, Albuquerque NM. \r\n \r\n Marion, William and Stephen Wilcox, 1994: \"Solar Radiation Data Manual for Flat-plate and Concentrating Collectors\". NREL/TP-463-5607, National Renewable Energy Laboratory, 1617 Cole Boulevard, Golden, CO 80401.\r\n \r\n ### License Info\r\n DISCLAIMER NOTICE\r\n This GIS data was developed by the National Renewable Energy Laboratory (\"NREL\"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy (\"DOE\"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.\r\n \r\n Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.\r\n \r\n THE GIS DATA IS PROVIDED \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.\r\n \r\n The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-nrel-gis-data-continental-united-states-photovoltaic-low-resolution-d2414.yaml identifier: doe-nrel-gis-data-continental-united-states-photovoltaic-low-resolution-d2414 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'NREL GIS Data: Continental United States Photovoltaic Low Resolution' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-nrel-gis-data-continental-united-states-photovoltaic-low-resolution-d2414 url: https://www.nrel.gov/gis/data-solar.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "Biomass resource potential for the lower 48 states of the United States of America.\r\n \r\n Estimated technical biomass resources available in the United States by county. The following feedstock categories are considered for this study: crop residues, methane emissions from manure management, methane emissions from landfills and wastewater treatment facilities, forest residues, primary and secondary mill residues, urban wood waste, and dedicated energy crops. \r\n \r\n Units: MSW is in US wet tons (not dry tons like the rest) Landfill, manure and wastewater are metric tonness of CH4 Crop, forest, primary and secondary mill, and urban wastes are all metric dry tonnes (or Bone Dry Tonnes -- BDT)" description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-nrel-gis-data-united-states-high-resolution-biomass-2008-607c1.yaml identifier: doe-nrel-gis-data-united-states-high-resolution-biomass-2008-607c1 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: United States High Resolution Biomass (2008) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-nrel-gis-data-united-states-high-resolution-biomass-2008-607c1 url: https://www.nrel.gov variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "GIS data for offshore wind speed (meters/second). Specified to Exclusive Economic Zones (EEZ).\r \r Wind resource based on NOAA blended sea winds and monthly wind speed at 30km resolution, using a 0.11 wind sheer to extrapolate 10m - 90m. Annual average >= 10 months of data, no nulls." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-nrel-global-offshore-wind-gis-data.yaml identifier: doe-nrel-global-offshore-wind-gis-data lat_max: 77.46673759525017 lat_min: -60.24315809822545 lon_max: 176.3486519999998 lon_min: -157.8093750000001 name: NREL Global Offshore Wind GIS Data native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-nrel-global-offshore-wind-gis-data url: http://en.openei.org/doe-opendata/dataset/b072d774-4df5-4489-8b6c-a35d2bdaa78e/resource/efede2a1-aa2e-489e-89c0-c8e424da199f/download/globaloffshorewind.zip variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "This dataset contains information about the biomass resources generated by county in the United States. It includes the following feedstock categories: crop residues, forest residues, primary mill residues, secondary mill residues, and urban wood waste.\r \r The estimates are based on county-level statistics and/or point-source data gathered from the U.S. Department of Agriculture (USDA), USDA Forest Service, EPA and other organizations, which are further processed using relevant assumptions and conversions." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-nrel-solid-biomass-gis-data.yaml identifier: doe-nrel-solid-biomass-gis-data lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: NREL Solid Biomass GIS Data native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-nrel-solid-biomass-gis-data url: http://en.openei.org/doe-opendata/dataset/2c9715e5-fdb9-431c-ac7a-c64da599d5de/resource/4fa4254c-bbc6-4b9e-9fd9-5127398c3426/download/solidbiomass.zip variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "This shapefile represents annual average net power estimates.\r \r The OTEC Plant model predicts the net power production at a specific location, given three inputs: surface temperature (°C), depth (m), and difference between warm surface water temperature and cold deep sea water temperature (ΔT in °C) at the given depth, relative to the surface temperature.\r \r In order to normalize values for the purposes of visualization of the OTEC resource around the world, a baseline plant design was used. The baseline 100MW Net Power design has been optimized for conditions indicative of the Hawai‘i OTEC resource. As such, power output as described by the results of this study is not optimized for local conditions (except in parts of Hawai’i), but does provide guidance for site selection. Given the nominal plant power output of 100MW based on a competitive cost of electricity (Hawai’i), any output exceeding this value represents significant potential. A large area of predicted 100 MW+ net power exists in many locations around the world, especially in areas with high energy costs.\r \r Data were processed and converted to shapefile format by NREL for the [Ocean Thermal Extractable Energy Visualization](http://www.osti.gov/bridge/purl.cover.jsp?purl=/1055457/1055457.pdf)\r \r ### License Info\r This GIS data was developed by the National Renewable Energy Laboratory (\"NREL\"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy (\"DOE\"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.\r \r Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.\r \r THE GIS DATA IS PROVIDED \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.\r \r The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-ocean-thermal-energy-conversion-otec-net-power-annual-average-e7f3c.yaml identifier: doe-ocean-thermal-energy-conversion-otec-net-power-annual-average-e7f3c lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'Ocean Thermal Energy Conversion (OTEC) - Net Power (Annual Average)' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-ocean-thermal-energy-conversion-otec-net-power-annual-average-e7f3c url: http://www.nrel.gov variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "This shapefile represents seasonal summer average net power estimates.\r \r The OTEC Plant model predicts the net power production at a specific location, given three inputs: surface temperature (°C), depth (m), and difference between warm surface water temperature and cold deep sea water temperature (ΔT in °C) at the given depth, relative to the surface temperature.\r \r In order to normalize values for the purposes of visualization of the OTEC resource around the world, a baseline plant design was used. The baseline 100MW Net Power design has been optimized for conditions indicative of the Hawai‘i OTEC resource. As such, power output as described by the results of this study is not optimized for local conditions (except in parts of Hawai’i), but does provide guidance for site selection. Given the nominal plant power output of 100MW based on a competitive cost of electricity (Hawai’i), any output exceeding this value represents significant potential. A large area of predicted 100 MW+ net power exists in many locations around the world, especially in areas with high energy costs.\r \r Data were processed and converted to shapefile format by NREL for the [Ocean Thermal Extractable Energy Visualization](http://www.osti.gov/bridge/purl.cover.jsp?purl=/1055457/1055457.pdf)\r \r ### License Info\r This GIS data was developed by the National Renewable Energy Laboratory (\"NREL\"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy (\"DOE\"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. \r \r Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.\r \r THE GIS DATA IS PROVIDED \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.\r \r The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-ocean-thermal-energy-conversion-otec-net-power-summer-average-dfb1b.yaml identifier: doe-ocean-thermal-energy-conversion-otec-net-power-summer-average-dfb1b lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'Ocean Thermal Energy Conversion (OTEC) - Net Power (Summer Average)' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-ocean-thermal-energy-conversion-otec-net-power-summer-average-dfb1b url: http://www.nrel.gov variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "This shapefile represents seasonal winter average net power estimates.\r \r The OTEC Plant model predicts the net power production at a specific location, given three inputs: surface temperature (°C), depth (m), and difference between warm surface water temperature and cold deep sea water temperature (ΔT in °C) at the given depth, relative to the surface temperature.\r \r In order to normalize values for the purposes of visualization of the OTEC resource around the world, a baseline plant design was used. The baseline 100MW Net Power design has been optimized for conditions indicative of the Hawai‘i OTEC resource. As such, power output as described by the results of this study is not optimized for local conditions (except in parts of Hawai’i), but does provide guidance for site selection. Given the nominal plant power output of 100MW based on a competitive cost of electricity (Hawai’i), any output exceeding this value represents significant potential. A large area of predicted 100 MW+ net power exists in many locations around the world, especially in areas with high energy costs.\r \r Data were processed and converted to shapefile format by NREL for the [Ocean Thermal Extractable Energy Visualization](http://www.osti.gov/bridge/purl.cover.jsp?purl=/1055457/1055457.pdf)\r \r ### License Info\r This GIS data was developed by the National Renewable Energy Laboratory (\"NREL\"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy (\"DOE\"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. \r \r Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.\r \r THE GIS DATA IS PROVIDED \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.\r \r The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-ocean-thermal-energy-conversion-otec-net-power-winter-average-37233.yaml identifier: doe-ocean-thermal-energy-conversion-otec-net-power-winter-average-37233 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'Ocean Thermal Energy Conversion (OTEC) - Net Power (Winter Average)' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-ocean-thermal-energy-conversion-otec-net-power-winter-average-37233 url: http://www.nrel.gov variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "Open Energy Information (OpenEI) is a knowledge-sharing online community dedicated to connecting people with the latest information and data on energy resources from around the world. Created in partnership with the United States Department of Energy and federal laboratories across the nation, OpenEI offers access to real-time data and unique visualizations that will help you find the answers you need to make better, more informed decisions with structured linked open data and information in widely-used formats such as API, CSV, XML, and XLS. OpenEI is making a profound impact on the world’s energy transformation by providing data access, generative data use, key knowledge derivation tools, and synthetic datasets that will help inform policy, purchase, build, and business decisions. This community-based platform is a core competency for the U.S. Department of Energy and its laboratories, providing a high-degree of value for building knowledge and datasets, connecting and structuring data via linked open data standards, and serving as the place for the world to contribute and utilize energy data, APIs and web-services.\r \r OpenEI is the backbone to the DOE Data Catalog and federates all DOE-sponsored data upwards to Data.gov in order to enable data transparency and access." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-open-energy-information-openei-org-29f7e.yaml identifier: doe-open-energy-information-openei-org-29f7e lat_max: 83.63846772837344 lat_min: -73.4303467637361 lon_max: 174.9423999999999 lon_min: -165.5437300000001 name: Open Energy Information (OpenEI.org) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-open-energy-information-openei-org-29f7e url: http://en.openei.org/wiki/Main_Page variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Total population (in millions) by country, 1980 to 2010. Compiled by Energy Information Administration (EIA).
' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-population-by-country-1980-2010-d0250.yaml identifier: doe-population-by-country-1980-2010-d0250 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'Population by Country (1980 - 2010)' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-population-by-country-1980-2010-d0250 url: http://en.openei.org/doe-opendata/dataset/a7fea769-691d-4536-8ed3-471e993a2445/resource/86c50aa8-e40f-4859-b52e-29bb10166456/download/populationbycountry19802010millions.csv variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Provides annual renewable energy consumption (in quadrillion btu) for electricity generation in the United States by energy use sector (commercial, industrial and electric power) and by energy source (e.g. biomass, geothermal, etc.) This data was compiled and published by the Energy Information Administration (EIA).' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-renewable-energy-consumption-for-electricity-generation-by-energy-use-sector-and-energy-so-aca54.yaml identifier: doe-renewable-energy-consumption-for-electricity-generation-by-energy-use-sector-and-energy-so-aca54 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'Renewable Energy Consumption for Electricity Generation by Energy Use Sector and Energy Source, 2004 - 2008' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-renewable-energy-consumption-for-electricity-generation-by-energy-use-sector-and-energy-so-aca54 url: http://en.openei.org/doe-opendata/dataset/cc09e11d-dbcb-42e2-bb07-5836e4d245f4/resource/eb240021-43bc-4baf-aa3d-98b818ed343a/download/2008re.consumption.for.elec.geneia.aug.2010.xls variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The Solar Energy Environmental Mapper is a web-based application that displays environmental data for the southwest U.S. in the context of utility-scale solar energy development. It provides access to screening-level data about resources and constraints, with analysis tools to help improve siting decisions.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-solar-energy-environmental-mapper-a8f51.yaml identifier: doe-solar-energy-environmental-mapper-a8f51 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Solar Energy Environmental Mapper native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-solar-energy-environmental-mapper-a8f51 url: http://solarmapper.anl.gov variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "These estimates are derived from the best available solar resource data available to NREL. Resources are organized by class and country. Resolution varies spatially from 1 km to 1 degree (approximately 100 km) depending on the data source. High spatial resolution datasets (1 km to 40 km cells) were modeled to support country or regional projects. Where high resolution datasets were not available, data from NASA’s Surface Meteorology and Solar Energy (SSE) version 6 database were used. \r \r The data represent total potential solar energy per year as a function of land area per solar class (`KWh/m²/day`). Each solar class correlates to a specific `0.5 kWh/m²/day` range. Energy is calculated by multiplying the productive land by the class, conversion efficiency and number of days per year. In this case, a standard calendar year of 365 days was used. The conversion efficiency rate applied was 10%.\r \r `E = Productive Land * kWh/m²/day * 365 days * 10% efficiency`\r \r The solar data has been derived from solar data measured or modeled between 1961 and 2008, depending on the dataset." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-solar-resources-by-class-and-country-87c7a.yaml identifier: doe-solar-resources-by-class-and-country-87c7a lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Solar Resources by Class and Country native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-solar-resources-by-class-and-country-87c7a url: http://www.nrel.gov variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Total annual biofuels consumption (Thousand Barrels Per Day) for 2005 - 2009 for over 230 countries and regions.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-total-biofuels-consumption-2005-2009-b27a2.yaml identifier: doe-total-biofuels-consumption-2005-2009-b27a2 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'Total Biofuels Consumption (2005 - 2009)' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-total-biofuels-consumption-2005-2009-b27a2 url: https://www.eia.gov/totalenergy/data/annual/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "Renewable energy technical potential as defined in this report represents the achievable energy generation of a particular technology given system performance, topographic limitations, environmental, and land-use constraints.\r \r The primary benefit of assessing technical potential is that it establishes an upper-boundary estimate of development potential. It is important to understand that there are multiple types of potential—resource, technical, economic, and market—each seen in *Figure 1* (below), with key assumptions." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-united-states-renewable-energy-technical-potential-77f43.yaml identifier: doe-united-states-renewable-energy-technical-potential-77f43 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: United States Renewable Energy Technical Potential native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-united-states-renewable-energy-technical-potential-77f43 url: http://en.openei.org/doe-opendata/dataset/5346c5c2-be26-4be7-9663-b5a98cbb7527/resource/01fe78a8-77b6-4c59-bc36-cae177ee86c3/download/usretechpotential.pdf variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "Distribution tables of oil and gas wells by production rate for all wells, \r\n including marginal wells, are available from the EIA for most states for the years 1919 to 2009. Graphs displaying historical behavior of well production rate are also \r\n available. The quality and completeness of data is dependent on update lag times and the \r\n quality of individual state and commercial source databases. Undercounting of \r\n the number of wells occurs in states where data is sometimes not available at \r\n the well level but only at the lease level. States not listed below will be \r\n added later as data becomes available." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-u-s-distribution-and-production-of-oil-and-gas-wells-24b0f.yaml identifier: doe-u-s-distribution-and-production-of-oil-and-gas-wells-24b0f lat_max: 49.3845 lat_min: 24.52006292188697 lon_max: -66.93157003173832 lon_min: -124.76249999999999 name: U.S. Distribution and Production of Oil and Gas Wells native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-u-s-distribution-and-production-of-oil-and-gas-wells-24b0f url: https://openei.org/doe-opendata/dataset/51d0b90a-4bfb-4f40-a323-b2c2fe7f01e5/resource/91e504bb-f128-40c5-b106-53f851db2ef3/download/allyearsstates.xls variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Provides total annual net electric summer capacity (in megawatts) for the United States, broken down by renewable energy source (e.g. biomass, solar thermal/pv) and the nonrenewable total.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-u-s-electric-net-summer-capacity-2004-2008-51372.yaml identifier: doe-u-s-electric-net-summer-capacity-2004-2008-51372 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'U.S. Electric Net Summer Capacity, 2004 - 2008' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-u-s-electric-net-summer-capacity-2004-2008-51372 url: https://www.eia.gov/totalenergy/data/annual/showtext.php?t=ptb0811a variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "NREL's Geographic Information System (GIS) team offers both a national wind resource assessment of the United States and high-resolution wind data. The national wind resource assessment was created for the U.S. Department of Energy in 1986 by the Pacific Northwest Laboratory and is documented in the Wind Energy Resource Atlas of the United States, October 1986. This national wind resource data provides an estimate of the annual average wind resource for the conterminous United States, with a resolution of 1 3 degree of latitude by 1 4 degree of longitude. The wind resource assessment was based on surface wind data, coastal marine area data, and upper-air data, where applicable. In data-sparse areas, three qualitative indicators of wind speed or power were used when applicable: topographic meteorological indicators (e.g. gorges, mountain summits, sheltered valleys); wind deformed vegetation; and eolian landforms (e.g. playas, sand dunes). The data was evaluated at a regional level to produce 12 regional wind resource assessments; the regional assessments were then incorporated into the national wind resource assessment. The conterminous United States was divided into grid cells 1 4 degree of latitude by 1 3 degree of longitude. Each grid cell was assigned a wind power class ranging from 1 to 6, with 6 being the windiest. The wind power density limits for each wind power class are shown in Table 1-1. Each grid cell contains sites of varying power class. The assigned wind power class is representative of the range of wind power densities likely to occur at exposed sites within the grid cell. Hilltops, ridge crests, mountain summits, large clearings, and other locations free of local obstruction to the wind will be well exposed to the wind. In contrast, locations in narrow valleys and canyons, downwind of hills or obstructions, or in forested or urban areas are likely to have poor wind exposure." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-wind-energy-resource-data.yaml identifier: doe-wind-energy-resource-data lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Wind Energy Resource Data native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-wind-energy-resource-data url: http://www.nrel.gov/gis/wind.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "#Abstract\r\n This dataset describes the global ocean wind speed and wind power density maps created at the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) as part of the Solar and Wind Energy Resource Assessment (SWERA) project for the United Nations Environment Programme.\r\n \r\n The data includes raster GIS ASCII data files of wind speed and wind power density at 10 and 50 m heights with 0.25 degree resolution. Global data of offshore wind resource as generated by NASA's QuikScat SeaWinds scatterometer.\r\n \r\n #Purpose\r\n To provide information on the wind resource potential of offshore areas." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doe-wind-wind-speed-and-wind-power-density-gis-data-at-10m-and-50m-above-surface-and-0-25-degr-734f6.yaml identifier: doe-wind-wind-speed-and-wind-power-density-gis-data-at-10m-and-50m-above-surface-and-0-25-degr-734f6 lat_max: 70.37895123632465 lat_min: -55.780365322033184 lon_max: 174.76875399999983 lon_min: -162.5575940000001 name: GIS wind speed and wind power density data for global oceans native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doe-wind-wind-speed-and-wind-power-density-gis-data-at-10m-and-50m-above-surface-and-0-25-degr-734f6 url: https://www.nrel.gov/gis/data-wind.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "After water quality results are received by DOH, it takes about a week before the data is posted to this site. This is to provide staff the opportunity to conduct Quality Assurance/Quality Control reviews. If you identify data that you believe to be inaccurate or incorrect, please contact the Department of Health's Office of Drinking Water using the contact information listed below. This website is intended to provide summary information pertaining to public Water Systems. Use or distribution of the data contained in these web pages in a form other than that in which it is presented may inaccurately portray the data. Sentry Internet was built to be viewed with a monitor resolution setting of 1024x768 pixels." description_attribution: https://fortress.wa.gov/doh/eh/portal/odw/si/Intro.aspx doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doh-wa-sentry-internet-database.yaml identifier: doh-wa-sentry-internet-database lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Washington State DOH Sentry Internet Database native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doh-wa-sentry-internet-database url: https://www.doh.wa.gov/DataandStatisticalReports/EnvironmentalHealth/DrinkingWaterSystemData/SentryInternet variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The National Map includes lower 48 states coverage of hydrography, orthoimagery, elevation, geographic place names, landcover, structures, boundaries, and transportation at 1:24,000 scale accuracy and better. Some data over Alaska and the US Terratories also exists. Data can be viewed and downloaded at viewer.nationalmap.gov.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doi-108.yaml identifier: doi-108 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: The National Map Viewer and Download Platform native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doi-108 url: http://nationalmap.gov/viewers.html variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: Integrated and documented small-scale geospatial and geostatistical data from more than two dozen Federal organizations and the Library of Congress. The National Atlas was intended for the use of America's citizens and its data offerings are supplemented with multimedia articles and many types of maps. description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doi-118.yaml identifier: doi-118 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 1997-2014 Edition of The National Atlas of the United States native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doi-118 url: https://nationalmap.gov/small_scale/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'The Bureau of Reclamation provides monthly net hydropower generation data on a per facility basis for the past 10 years. The data will be updated monthly with a quarterly delay to allow for data verification; i.e., October generation data would be available the following January.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doi-1985.yaml identifier: doi-1985 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'Monthly hydropower generation data by facility, US Bureau of Reclamation.' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doi-1985 url: http://www.usbr.gov/power/data/10%20Year%20Rolling%20Monthly%20Generation%20by%20Facility.xls variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Datasets used in the analysis of the Colorado Plateau (COP) Rapid Ecoregion Assessment (REA).They can be downloaded via a layer package (lpk, similar to a zip file and contains layer files) that can be used in ESRI software. The Colorado Plateau REA has been peer reviewed. The Colorado Plateau ecoregion is located Utah and Colorado with extensions in New Mexico and Arizona. It has an area of 32,387 square miles and includes all or portions of 16 BLM field offices. The Colorado Plateau is an uplifted, eroded, and deeply dissected tableland. Its benches, mesas, buttes, salt valleys, cliffs, and canyons are formed in and underlain by thick layers of sedimentary rock. The ecoregion has a broad latitudinal range, from the Uinta Basin in the north to the arid canyonlands along the border of Arizona and New Mexico.' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doi-25871.yaml identifier: doi-25871 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Colorado Plateau Rapid Ecoregion Assessment Data Catalog native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doi-25871 url: https://landscape.blm.gov/geoportal/catalog/REAs/REAs.page variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: Near real-time earthquake information for a variety of time windows in a variety of formats. description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doi-34512.yaml identifier: doi-34512 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: Earthquake Feeds native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doi-34512 url: http://earthquake.usgs.gov/earthquakes/feed/v1.0/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "WaterWatch (http://waterwatch.usgs.gov) is a U.S. Geological Survey (USGS) World Wide Web site that displays maps, graphs, and tables describing real-time, recent, and past streamflow conditions for the United States. The real-time information generally is updated on an hourly basis. WaterWatch provides streamgage-based maps that show the location of more than 3,000 long-term (30 years or more) USGS streamgages; use colors to represent streamflow conditions compared to historical streamflow; feature a point-and-click interface allowing users to retrieve graphs of stream stage (water elevation) and flow; and highlight locations where extreme hydrologic events, such as floods and droughts, are occurring. \r\n \r\n The streamgage-based maps show streamflow conditions for real-time, average daily, and 7-day average streamflow. The real-time streamflow maps highlight flood and high flow conditions. The 7-day average streamflow maps highlight below-normal and drought conditions. \r\n \r\n WaterWatch also provides hydrologic unit code (HUC) maps. HUC-based maps are derived from the streamgage-based maps and illustrate streamflow conditions in hydrologic regions. These maps show average streamflow conditions for 1-, 7-, 14-, and 28-day periods, and for monthly average streamflow; highlight regions of low flow or hydrologic drought; and provide historical runoff and streamflow conditions beginning in 1901.\r\n \r\n WaterWatch summarizes streamflow conditions in a region (state or hydrologic unit) in terms of the long-term typical condition at streamgages in the region. Summary tables are provided along with time-series plots that depict variations through time. WaterWatch also includes tables of current streamflow information and locations of flooding." description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doi-4515.yaml identifier: doi-4515 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: 'WaterWatch -- Current Water Resources Conditions' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doi-4515 url: https://waterwatch.usgs.gov/new/?id=ww_current variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: Ten million archive images of the Earth's surface are available for immediate selection and free download via the USGS Earth Resources Observation and Science (EROS) Center's Global Visualization Viewer at http://glovis.usgs.gov/. Users can preview thumbnail browse images and download full-image selections from 1.5 million aerial photos of U.S. sites and 8.5 million images captured worldwide by U.S. Earth-observing satellites. description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doi-93.yaml identifier: doi-93 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: USGS Global Visualization Viewer for Aerial and Satellite Data native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doi-93 url: http://earthexplorer.usgs.gov/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'National scope of NAWQA water-quality sample- and laboratory-result data and other supporting information obtained from NWIS systems hosted by individual Water Science Centers as well as national BioTDB system for aquatic biological data on communities and habitats and stored in a centralized NAWQA Data Warehouse (DWH)' description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/doi-95.yaml identifier: doi-95 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ name: National Water Quality Assessment (NAWQA) Program native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/doi-95 url: http://cida.usgs.gov/nawqa_public/apex/f?p=136:1:0 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "The Airports database (NTAD 2015) is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product." description_attribution: http://maps.bts.dot.gov/services/rest/services/appServices/Airports/MapServer/info/iteminfo doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/dot-airports-national-national-geospatial-data-asset-ngda-airports.yaml identifier: dot-airports-national-national-geospatial-data-asset-ngda-airports lat_max: 76.533333 lat_min: -14.33166 lon_max: 174.113619 lon_min: -177.381308 name: 'Airports (National) - National Geospatial Data Asset (NGDA) Airports' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/dot-airports-national-national-geospatial-data-asset-ngda-airports url: https://www.bts.gov/maps/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "The Rail Network (NTaD 2015) is a comprehensive database of the nation's railway system at 1:24,000 to 1:100,000 scale. The data set covers all 50 States plus the District of Columbia." description_attribution: http://maps.bts.dot.gov/services/rest/services/NTAD/Railroad_Lines/MapServer/info/iteminfo doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/dot-amtrak-rail-lines-national.yaml identifier: dot-amtrak-rail-lines-national lat_max: 48.999834 lat_min: 25.805273 lon_max: -69.922208 lon_min: -123.201588 name: Amtrak Rail Lines (National) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/dot-amtrak-rail-lines-national url: https://www.bts.gov/maps/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: Updated database of the Federal Railroad Administration's (FRA) Amtrak Station database. This database is a geographic data set containing Amtrak intercity railroad passenger terminals in the United States and Canada. Attribute data include services and passenger amenities provided at the station. description_attribution: ~ doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/dot-amtrak-rail-stations-national.yaml identifier: dot-amtrak-rail-stations-national lat_max: 48.720486 lat_min: 25.849848 lon_max: -68.670621 lon_min: -124.281564 name: Amtrak Rail Stations (National) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/dot-amtrak-rail-stations-national url: https://www.bts.gov/maps/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'Border Crossing Ports (NTAD 2015) are points of entry for land modes along the U.S. - Canadian and U.S.- Mexcian borders. The ports of entry are located in 15 states along the U.S. borders. The nominal scale of the data set is 1:1000,000 with a maximal positional error of +- 10 meters.' description_attribution: https://maps.bts.dot.gov/services/rest/services/NTAD/Border_Crossings/MapServer doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/dot-border-crossings-national.yaml identifier: dot-border-crossings-national lat_max: 64.085516 lat_min: 25.883416 lon_max: -66.980076 lon_min: -141.001444 name: Border Crossings (National) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/dot-border-crossings-national url: https://www.bts.gov/maps/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'This map layer portrays major dams of the United States, including Puerto Rico and the U.S. Virgin Islands (NTAD 2015). The map layer was created by extracting dams 50 feet or more in height, or with a normal storage capacity of 5,000 acre-feet or more, or with a maximum storage capacity of 25,000 acre-feet or more, from the 79,777 dams in the U.S. Army Corps of Engineers National Inventory of Dams. This is a replacement for the April 1994 map layer.' description_attribution: https://maps.bts.dot.gov/services/rest/services/NTAD/Dams/MapServer doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/dot-dams-national.yaml identifier: dot-dams-national lat_max: 68.069399 lat_min: 18.017303 lon_max: -66.014998 lon_min: -162.877839 name: Dams (National) native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/dot-dams-national url: https://www.bts.gov/maps/ variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "Version 2004 of the Fixed-Guideway Transit Network is a network database of the nation's fixed-guideway transit systems (NTAD 2015). The data set covers systems in cities defined as FTA's universe of cities and includes heavy rail, light rail, monorail, cable car, inclined plane, and automated guideway." description_attribution: https://maps.bts.dot.gov/services/rest/services/NTAD/Transit_Stations/MapServer doi: ~ end_time: ~ href: https://data.globalchange.gov/dataset/dot-fixed-guideway-transit-lines-national-national-geospatial-data-asset-ngda-transit-lines.yaml identifier: dot-fixed-guideway-transit-lines-national-national-geospatial-data-asset-ngda-transit-lines lat_max: 47.979935 lat_min: 25.680243 lon_max: -70.62638 lon_min: -122.991273 name: 'Fixed-Guideway Transit Lines (National) - National Geospatial Data Asset (NGDA) Transit (Lines)' native_id: ~ processing_level: ~ publication_year: ~ release_dt: ~ scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: ~ temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/dot-fixed-guideway-transit-lines-national-national-geospatial-data-asset-ngda-transit-lines url: https://www.bts.gov/maps/ variables: ~ version: ~ vertical_extent: ~