AndreaBozzo/ceres-open-data-index huggingface dataset:
Motor Vehicle Occupant Death Rate, by Age and Sex, 2012 & 2014, Region 3 - Philadelphia

Rate of deaths by age/gender (per 100,000 population) for motor vehicle occupants killed in crashes, 2012 & 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Safety Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

Tags: cdc, centers-for-disease-control-and-prevention
Organization: U.S. Department of Health & Human Services
Motor Vehicle Occupant Death Rate, by Age and Sex, 2012 & 2014, Region 4 - Atlanta

Rate of deaths by age/gender (per 100,000 population) for motor vehicle occupants killed in crashes, 2012 & 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Safety Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

Tags: cdc, centers-for-disease-control-and-prevention
Organization: U.S. Department of Health & Human Services
Impaired Driving Death Rate, by Age and Sex, 2012 & 2014, Region 9 - San Francisco

Rate of deaths by age/gender (per 100,000 population) for people killed in crashes involving a driver with BAC =>0.08%, 2012, 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File. Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

Tags: cdc, centers-for-disease-control-and-prevention
Organization: U.S. Department of Health & Human Services
2024 County-Level Hourly Renewable Capacity Factor Dataset for the ReEDS Model

This dataset contains hourly capacity factors for each renewable resource class and region (in this case, county). Technologies like large-scale utility PV (UPV), onshore (land-based) wind, offshore wind, and concentrating solar power (CSP) are included. Hourly profiles are provided for 15 weather years covering 2007-2013 and 2016-2023 for all technologies except for CSP, which is only provided for 2007-2013 due to the lack of data covering the latter years.

These data are used as inputs to the ReEDS-2.0 model (see the "ReEDS 2.0 GitHub Repository" resource link below), developed by NREL. The weather profiles apply to any capacity that exists or is built in each region and class. This helps calculate the generation that can be provided using these resources. These data are compatible with ReEDs Version 2025.1 and newer. To run county-level with older versions (2024.0-2024.3), use the data posted with the "Link to Data for Older Versions of ReEDS" resource below.

Open, reference, and limited are 3 scenarios based on land-use allowance, derived from the Renewable Energy Potential (reV) model developed by NREL, which helps generate supply curves for renewable technologies and assess the maximum potential of renewable resources in a designated area. Each zipped file in this dataset corresponds to a technology and contains the respective land-use scenario files required to run that technology in ReEDS.

To use this dataset, download and place the extracted files in the locally cloned ReEDS repository inside one of the folders (inputs/variability/multi_year). After completing this copy, upon running the ReEDS model at the county-level spatial resolution for respective analysis purposes, the program will detect the presence of these files and will not fail.

The data provided here correspond to the 2024 supply curves from the reV model; for details see the "Renewable Energy Technical Potential and Supply Curves for the Contiguous United States - 2024 Edition" resource below. For additional details on the profiles see the README below.

Tags: capacity-expansion-model, capacity-factor-data, computational-science, concentrated-solar-power, county-level, energy, high-resolution-capacity-expansion, hourly-generation-data, land-based-wind, model, offshore-wind, onshore-wind, power, processed-data, reeds, regional-energy-deployment-system, renewable, solar-power, utility-scale-pv, wind-power
Organization: Department of Energy
Floodplains from 2023

<div style="text-align:Left;"><div><div><p><span>The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs.</span></p><p><span>The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA.</span></p><p><span>The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set.</span></p><p><span>The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Datum of 1983.For more information, visit </span><a target="_blank" rel="noopener noreferrer" href="https://msc.fema.gov/portal/home"><span><u>https://msc.fema.gov/portal/home</u></span></a><span>.</span></p></div></div></div>

Tags: dc, dc-gis, district-of-columbia, environment, environmental, fema, firm, floodplain, insurance, washington-dc, water
Organization: District of Columbia
ACS 5-Year Social Characteristics DC

<div style='text-align:Left;'><div><div><p><span>Household type, Education, Disability, Language, Computer/Internet Use, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. </span><strong>Current Vintage: 2019-2023. ACS Table(s): DP02. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025</strong><span>. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.</span></p></div></div></div>

Tags: acs, american-community-survey, dc, district-of-columbia, district-wide, social-characteristics, washington-dc
Organization: District of Columbia
ACS 5-Year Social Characteristics DC Census Tract

<div style='text-align:Left;'><div><div><p><span>Household type, Education, Disability, Language, Computer/Internet Use, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: Census Tracts. </span><strong>Current Vintage: 2019-2023. ACS Table(s): DP02. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025</strong><span>. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.</span></p></div></div></div>

Tags: acs, american-community-survey, census-tracts, dc, district-of-columbia, social-characteristics, washington-dc
Organization: District of Columbia
Census Block Groups in 2020

<div style='text-align:Left;'><div><div><p><span>Standard block groups are clusters of blocks within the same census tract that have the same first digit of their 4-character census block number (e.g., Blocks 3001, 3002, 3003 to 3999 in census tract 1210.02 belong to block group 3). Current block groups do not always maintain these same block number to block group relationships due to boundary and feature changes that occur throughout the decade. For example, block 3001 might move due to a change in the census tract boundary. Even if the block is no longer in block group 3, the block number (3001) will not change. However, the GEOID for that block, identifying block group 3, would remain the same in the attribute information in the TIGER/Line Shapefiles because block GEOIDs are always built using the decennial geographic codes.</span></p><p><span>Block groups delineated for the 2020 Census generally contain 600 to 3,000 people. Local participants delineated most block groups as part of the Census Bureau's PSAP. The Census Bureau delineated block groups only where a local or tribal government declined to participate or where the Census Bureau could not identify a potential local participant.</span></p><p><span>A block group usually covers a contiguous area. Each census tract contains one or more block groups and block groups have unique numbers within census tract. Within the standard census geographic hierarchy, block groups never cross county or census tract boundaries, but may cross the boundaries of county subdivisions, places, urban areas, voting districts, congressional districts, and AIANNH areas.</span></p><p><span>Block groups have a valid range of zero (0) through nine (9). Block groups beginning with a zero generally are in coastal and Great Lakes water and territorial seas. Rather than extending a census tract boundary into the Great Lakes or out to the 3-mile territorial sea limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore.</span></p></div></div></div>

Tags: age, census, census-blocks-groups, census2020, dc, dc-gis, demographic, demographics, district-of-columbia, housing-units, people, population, race, tract, washington-dc
Organization: District of Columbia
Census Blocks in 2020

<div style='text-align:Left;'><div><div><p><span>Census blocks are statistical areas bounded on all sides by visible features (e.g., streets, roads, streams, and railroad tracks), and by non-visible boundaries (e.g., city, town, township, county limits, and short line-of-sight extensions of streets and roads). Generally, census blocks are small in area (e.g., a block in a city). Census blocks in suburban and rural areas may be large, irregular, and bounded by a variety of features (e.g., roads, streams, and/or transmission line rights-of-way). In remote areas, census blocks may encompass hundreds of square miles. Census blocks cover all territory in the United States, Puerto Rico, and the Island areas. Blocks do not cross the boundaries of any entity for which the Census Bureau tabulates data.</span></p><p><span>Census Block Numbers—Census blocks are numbered uniquely within the boundaries of each state, county, census tract with a 4-character census block number. The first character of the tabulation block number identifies the block group. A block number can only be unique by using the decennial census state (STATEFP&lt;YR&gt;), county (COUNTYFP&lt;YR&gt;), census tract (TRACTCE&lt;YR&gt;), and block (BLOCKCE&lt;YR&gt;)+. There is no consistency in block numbers from census to census.</span></p></div></div></div>

Tags: 1-percent-public-use-microdata-area-code, 106th-congressional-district-code, 108th-congressional-district-code, 11001, 3-digit-zip-code-tabulation-area-code, 5-digit-zip-code-tabulation-area-code, 5-percent-public-use-microdata-area-code, age, alaska-native-regional-corporation-fips-code, american-indian-alaska-native-native-hawaiian-areas-census-code, block-group, boundaries, census-blocks, census-tract-number, census2020, consolidated-city-fips-code, county-fips-code, county-or-equivalent-entity, county-subdivision-fips-code, dc-gis, demographic, demographics, district-of-columbia, elementary-school-district-local-education-agency-code, fips-55-place-code-for-all-places, housing-units, land-water-flag, legislative-session-year, metropolitan-statistical-area-consolidated-metropolitan-statistical-area-fips-code, new-england-county-metropolitan-area-fips-code, number, open-data, people, population, primary-metropolitan-statistical-area-fips-code, race, rreservation-or-off-reservation-trust-land-indicator, secondary-school-district-local-education-agency-code, state-fips-code, state-legislative-district-lower-chamber-code, state-legislative-district-upper-chamber-code, sub-minor-civil-division-fips-code-in-puerto-rico, table, tabulation-block-number, topological-faces, traffic-analysis-zone-code, tribal-subdivision-code, u-s, unified-school-district-local-education-agency-code, united-states, urban-area-code, urban-growth-area-code, voting-district-code, washington-dc
Organization: District of Columbia
ACS 5-Year Economic Characteristics DC Census Tract

<div style='text-align:Left;'><div><div><p><span>Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: Census Tracts. </span><strong>Current Vintage: 2019-2023. ACS Table(s): DP03. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025</strong><span>. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.</span></p></div></div></div>

Tags: acs, american-community-survey, census-tracts, dc, district-of-columbia, economic-characteristics, washington-dc
Organization: District of Columbia
ACS 5-Year Housing Characteristics DC Ward

<div style='text-align:Left;'><div><div><p><span>Occupancy status, Units, Rooms, Year built, Owner/Renter (Tenure), Mortgage/Rent costs, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: 2022 Wards (State Legislative Districts [Upper Chamber])</span><strong>Current Vintage: 2019-2023 ACS Table(s): DP04. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025</strong><span>. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.</span></p></div></div></div>

Tags: acs, american-community-survey, dc, district-of-columbia, housing-characteristics, wards, washington-dc
Organization: District of Columbia