AndreaBozzo/ceres-open-data-index huggingface dataset:
National Bridge Inventory

The National Bridge Inventory dataset is as of June 20, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data describes more than 624,000 of the Nation's bridges located on public roads, including Interstate Highways, U.S. highways, State and local roads, as well as publicly-accessible bridges on Federal and Tribal lands. The inventory data presented includes information on the location, description, classification, and general condition for each bridge. The Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation's Bridges (Coding Guide) contains a detailed description of each data element including coding instructions and attribute definitions. The Coding Guide is available at: https://doi.org/10.21949/1519105. For additional questions regarding regulations for the National Bridge Inventory or the Coding Guide please contact the National Bridge and Tunnel Inventory team at NBTIS_Support@dot.gov. For questions on the geospatial component of the dataset, contact the NTAD team at NTAD@dot.gov. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1519105

Tags: fgdc, gda, ngda
Organization: Federal Geographic Data Committee
NGDA transportation

<span style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>Thumbnail image for NGDA Transportation Theme</span>

Tags: fgdc, gda, ngda
Organization: Federal Geographic Data Committee
BETA - Hazard Exposure: Principal Ports

The BETA - Hazard Exposure: Principal Ports dataset was updated on October 16, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The BETA - Hazard Exposure: Principal Ports dataset shows the exposure of Principal Ports to select natural hazards. The dataset combines hazard data from FEMA, USGS, NOAA, and USDA with information for the top 150 Principal Ports as determined by the United States Army Corp of Engineers (USACE). Data as of CY 2023. Hazard data include estimates of annualized frequency of Coastal Flooding, Cold Wave, Drought, Hail, Hurricane, Heat Wave, Ice Storm, Lightning, Strong Wind, Tornado, and Winter Weather from the FEMA National Risk Index. Landslide Index and Sinkhole Susceptibility derive from USGS models. USDA Burn Probability data underlie estimates of wildfire exposure. NOAA SLOSH models estimate storm surge inundation from hurricanes. The data set covers all 50 states of the United States, the District of Columbia, the US Virgin Islands, and Puerto Rico. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/gc9g-2396

Tags: fgdc, gda, ngda
Organization: Federal Geographic Data Committee
BETA - Hazard Exposure: Select North American Rail Network Lines

The BETA - Hazard Exposure: Select North American Rail Network Lines dataset was updated on October 16, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The BETA - Hazard Exposure: Select North American Rail Network Lines dataset shows the exposure of North America's railway system to select natural hazards. The dataset combines hazard data from FEMA, USGS, NOAA, and USDA with information for all mainline or operational rail lines that are represented in the North American Rail Network (NARN) Rail Lines dataset from the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Hazard data include estimates of annualized frequency of Coastal Flooding, Cold Wave, Drought, Hail, Hurricane, Heat Wave, Ice Storm, Lightning, Strong Wind, Tornado, and Winter Weather from the FEMA National Risk Index. Landslide Index and Sinkhole Susceptibility derive from USGS models. USDA Burn Probability data underlie estimates of wildfire exposure. NOAA SLOSH models estimate storm surge inundation from hurricanes. Presence in the floodplain is calculated using data from the FEMA National Flood Hazard Layer. The data set covers the 50 states of the United States and the District of Columbia. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/mrpb-7x82

Tags: fgdc, gda, ngda
Organization: Federal Geographic Data Committee
BETA - Hazard Exposure: National Highway System

The BETA - Hazard Exposure: National Highway System dataset was updated on October 16, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The BETA - Hazard Exposure: National Highway System dataset shows the exposure of North America's roadway system to select natural hazards. The dataset combines hazard data from FEMA, USGS, NOAA, and USDA with information for the National Highway System (NHS) dataset -- comprising roadways important to the nation's economy, defense, and mobility -- from the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Hazard data include estimates of annualized frequency of Coastal Flooding, Cold Wave, Drought, Hail, Hurricane, Heat Wave, Ice Storm, Lightning, Strong Wind, Tornado, and Winter Weather from the FEMA National Risk Index. Landslide Index and Sinkhole Susceptibility derive from USGS models. USDA Burn Probability data underlie estimates of wildfire exposure. NOAA SLOSH models estimate storm surge inundation from hurricanes. The data set covers all 50 states of the United States, the District of Columbia, and Puerto Rico. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/3psn-b298

Tags: fgdc, gda, ngda
Organization: Federal Geographic Data Committee
BETA - Hazard Exposure: National Highway System Bridges

The BETA - Hazard Exposure: National Highway System Bridges dataset was updated on October 16, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The BETA - Hazard Exposure: National Highway System Bridges dataset shows the exposure of bridges on the National Highway System to select natural hazards. The dataset combines hazard data from FEMA, USGS, NOAA, and USDA with information for relevant bridges in the National Bridge Inventory dataset from the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Hazard data include estimates of annualized frequency of Coastal Flooding, Cold Wave, Drought, Hail, Hurricane, Heat Wave, Ice Storm, Lightning, Strong Wind, Tornado, and Winter Weather from the FEMA National Risk Index. Landslide Index and Sinkhole Susceptibility derive from USGS models. USDA Burn Probability data underlie estimates of wildfire exposure. NOAA SLOSH models estimate storm surge inundation from hurricanes. The data set covers all 50 states of the United States, the District of Columbia, and Puerto Rico. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/bsd7-6743

Tags: fgdc, gda, ngda
Organization: Federal Geographic Data Committee
BETA - Hazard Exposure: Primary Airport Parcels

The BETA - Hazard Exposure: Primary Airport Parcels dataset was updated on October 16, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The BETA - Hazard Exposure: Primary Airport Parcels dataset shows the exposure of Primary Airports to select natural hazards. The dataset combines hazard data from FEMA, USGS, NOAA, and USDA with the "U.S. Airport Areas" geospatial layer maintained by the Environmental Systems Research Institute, Inc. (ESRI). Primary Airports are defined by the Federal Aviation Administration (FAA) as commercial service airports with more than 10,000 annual enplanements and specified in the current National Plan of Integrated Airport Systems (NPIAS). Hazard data include estimates of annualized frequency of Coastal Flooding, Cold Wave, Drought, Hail, Hurricane, Heat Wave, Ice Storm, Lightning, Strong Wind, Tornado, and Winter Weather from the FEMA National Risk Index. Landslide Index and Sinkhole Susceptibility derive from USGS models. USDA Burn Probability data underlie estimates of wildfire exposure. NOAA SLOSH models estimate storm surge inundation from hurricanes. The data set covers all 50 states of the United States, the District of Columbia, the US Virgin Islands, and Puerto Rico. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/65n5-j580

Tags: fgdc, gda, ngda
Organization: Federal Geographic Data Committee
Rail Mileposts

The Rail Mileposts dataset was compiled on June 15, 2025 and was updated on September 01, 2025 by the Federal Railroad Administration (FRA), and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The FRA Mileposts feature layer is a spatial reference file compiled from multiple sources and displays approximate point locations of mileposts along the FRA rail network wiithin the United States.  It is intended for general reference purposes only.  The rail lines and mileposts are privately owned and may be changed without notice. As a result, this file may not reflect current conditions. Users should contact the appropriate railroad to verify milepost numbers and their locations. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1528364

Tags: fgdc, gda, ngda
Organization: Federal Geographic Data Committee
TEAMER: Assessing Structural Integrity of a Self-Reactive Point Absorber Subjected to Extreme Sea Conditions

This submission contains the numerical modeling data and analysis results generated for the structural integrity assessment of a self-reactive two-body point absorber wave energy converter (WEC) designed by the University of Michigan. The dataset includes hydrodynamic outputs from ANSYS AQWA time-domain simulations conducted across nine extreme sea states along the 50-year return-period wave contour for the PACWave South site. These data provide WEC motions, wave-induced forces, and bending moments used to identify the governing survival-mode load cases. High-fidelity hydrodynamic loads were then produced using RANS-based OpenFOAM simulations for nine down-selected extreme wave events, including time histories of free-surface elevation, heave/pitch motions, vertical accelerations, and detailed pressure fields mapped over the WEC geometry. These CFD-generated pressure distributions were subsequently transferred to an NX Nastran/FEMAP finite element model to evaluate yielding and buckling performance under 18 critical load configurations. The submission also includes supporting documentation describing device geometry, mass properties, mooring configuration, mesh discretization, and modeling methodologies.

Use of these data requires several assumptions inherent to the modeling approach. Under extreme conditions, the buoy and spar are assumed to be mechanically locked, eliminating relative motion and representing the WEC's survival mode. Hydrodynamic loads from AQWA are based on potential-flow theory supplemented with Morison drag, while CFD loads come from an implicit RANS solver with a k-w SST turbulence model and a calibrated soft-spring representation of the mooring system. Structural analysis assumes linear static behavior, mild-steel material properties, and symmetry boundary conditions applied to a half-model. Only normal pressures from CFD are mapped to the FEA mesh; shear stresses are neglected due to their comparatively small magnitude. These data are suitable for engineers and analysts conducting WEC survivability assessments, structural design refinement, or validation of numerical modeling tools and should be interpreted within the context of the stated modeling assumptions and the PACWave South site conditions.

Tags: ansys-aqwa, ansys-aqwa-time-domain-simulations, cfd, computational-fluid-dynamics, device-geometry, energy, extreme-sea-states, fea, hydrodynamic-loads, hydrodynamic-outputs, hydrokinetic, k-w-sst-turbulence-model, marine, mass-properties, mesh-discretization, mhk, modeling-data, mooring-configuration, nx-nastran-femap, openfoam, power, sea-state, self-reactive-two-body-point-absorber, structural, teamer, wave-energy-converter, wec
Organization: Department of Energy
Kuskokwim airborne magnetic and radiometric survey, Kilbuck and Buckstock Mountains, Alaska

Kuskokwim airborne magnetic and radiometric survey, Kilbuck and Buckstock Mountains, Alaska, Geophysical Report 2025-5, covers parts of the Russian Mission, Sleetmute, Bethel, and Taylor Mountains quadrangles, 80-260 kilometers east of Bethel, Alaska. New Resolution Geophysics, USA collected magnetic and radiometric data with a helicopter from May 20th to July 31st, 2025. A total of 49,565 line kilometers were collected, with 24,246 line kilometers collected in block A (Buckstock) and 25,319 line kilometers collected in block B (Kilbuck). In total, this survey covers 15,926 km2 in this region. NRG makes use of the Scintrex CS3 Cesium magnetometers in both the aircraft stinger and ground base stations. The radiometric crystals were located in the aircraft cabin; 33.6 liters of downward-facing and 8.4 liters of upward-facing crystals were used for this survey. The Kilbuck and Buckstock Mountains survey was flown with a line spacing of 400 meters (m) and a nominal ground clearance of 100 m. TThese data support geologic interpretation of the Kuskokwim River region's framework, highlight its mineral potential, and support future resource exploration across southwest Alaska. These data are provided as a Geophysical Report under an open end-user license and are available on the DGGS website https://doi.org/10.14509/31724.

Tags: aeromagnetic, aeromagnetic data, aeromagnetic map, aeromagnetic survey, airborne geophysical survey, analytic signal, aniak mining district, anvil creek, barometer mountain, bedrock geology, bonanza creek, bristol bay region mining district, buckstock mountains, canoe mountain, cascade lake, chikuminuk glacier, chikuminuk lake, chuilnuk mountains, cinnabar creek, cripple mountains, critical minerals, crooked creek, dggs, discovery creek, donlin creek, earth mri, economic geology, economic study, exploration, ganes creek, gemuk river, geology, geophysical map, geophysical survey, geophysics, geophysics search tag, george river, gold creek, gold run, granite creek, great bend, gridded data, helicopter survey, henderson mountain, holokuk river, horn mountains, kiokluk mountains, kipchuk river, kisaralik river, kolmakof river, kuskokwim river, magnetic data, magnetic survey, magnetic susceptibility, magnetics, mineral assessment, mineral development, mineral exploration, mineral resources, minerals, mount hamilton, mount plummer, mount romig, nyac, ongutvak mountain, ophir creek, oskawalik river, otter creek, plutonic rocks, plutons, quartz creek, quicksilver creek, radiometric survey, radiometrics, red devil mine, red mountain, remote sensing, russian mountains, salmon river, slate creek, tikchik lakes, tikchik river, tuluksak river, wood-tikchik state park
Organization: State of Alaska
Current Supervisor Districts (trimmed to remove water and other non-populated City territories)

<strong>A. SUMMARY</strong>
This dataset contains a version of the current San Francisco Board of Supervisor district boundaries that have been trimmed to exclude non-populated City and County territory that is outside of the contiguous land mass of the City and County. This excludes for example City and County land in the Farallon Islands, Angel Island, and Alameda County,

The underlying official current Supervisor District boundaries are available in the <u><a href="https://data.sfgov.org/Geographic-Locations-and-Boundaries/Current-Supervisor-Districts/cqbw-m5m3">Current Supervisor Districts</a></u> dataset.

<strong>B. HOW THE DATASET IS CREATED</strong>
This dataset will be updated to reflect the most current Supervisor District boundaries available. It currently reflects the same boundaries found in <u><a href="https://data.sfgov.org/d/f2zs-jevy">Supervisor Districts (2022)</a></u>. More information on how this was created can be found in the datasets by year: <u><a href="https://data.sfgov.org/d/f2zs-jevy">Supervisor Districts (2022)</a></u> and <u><a href="https://data.sfgov.org/d/keex-zmn4">Supervisor Districts (2012)</a></u>

<strong>C. UPDATE PROCESS</strong>
Supervisor District boundaries are updated every 10 years following the federal decennial census. The Supervisor District boundaries reflected in this dataset will be manually updated after the next decennial census in 2030.

The dataset is also manually updated as new members of the Board of Supervisors take office. The most recent manual update date is reflected in the 'data_as_of' field. 

<strong>D. HOW TO USE THIS DATASET</strong>
This dataset can be joined to other datasets for analysis and reporting at the Supervisor District level. It is meant to facilitate visualization and mapping focussed on the contiguous land mass of the City & County and Treasure Island.

If you are building an automated reporting pipeline using Socrata API access, we recommend using this dataset if you would like the boundaries to automatically update after each decennial census to reflect the most recent Supervisor District boundaries. If you'd like your boundaries to remain static, see the <u><a href="https://data.sfgov.org/d/f2zs-jevy">Supervisor Districts (2022)</a></u> dataset. 

<strong>E. RELATED DATASETS</strong>
<u><a href="https://data.sfgov.org/Geographic-Locations-and-Boundaries/Current-Supervisor-Districts/cqbw-m5m3">Current Supervisor Districts</a></u>
<u><a href="https://data.sfgov.org/d/f2zs-jevy">Supervisor Districts (2022)</a></u>
<u><a href="https://data.sfgov.org/d/keex-zmn4">Supervisor Districts (2012)</a></u>

Tags: city-reference-data, map, mapping, supervisor-district
Organization: City of San Francisco
Current Supervisor Districts

<strong>A. SUMMARY</strong>
This dataset contains the current San Francisco Board of Supervisor district boundaries approved by the <u><a href="https://sf.gov/public-body/2020-census-redistricting-task-force#:~:text=The%20Redistricting%20Task%20Force%20consists,following%20each%20federal%20decennial%20census.">San Francisco Redistricting Task Force</a></u>. This dataset was created in April 2022 following redistricting based on the 2020 Decennial Census. 

<strong>B. HOW THE DATASET IS CREATED</strong>
This dataset will be updated to reflect the most current Supervisor District boundaries available. It currently reflects the same boundaries found in <u><a href="https://data.sfgov.org/d/f2zs-jevy">Supervisor Districts (2022)</a></u>. More information on how this was created can be found in the datasets by year: <u><a href="https://data.sfgov.org/d/f2zs-jevy">Supervisor Districts (2022)</a></u> and <u><a href="https://data.sfgov.org/d/keex-zmn4">Supervisor Districts (2012)</a></u>

<strong>C. UPDATE PROCESS</strong>
Supervisor District boundaries are updated every 10 years following the federal decennial census. The Supervisor District boundaries reflected in this dataset will be manually updated after the next decennial census in 2030.

The dataset is also manually updated as new members of the Board of Supervisors take office. The most recent manual update date is reflected in the 'data_as_of' field. 

<strong>D. HOW TO USE THIS DATASET</strong>
This dataset can be joined to other datasets for analysis and reporting at the Supervisor District level. 

If you are building an automated reporting pipeline using Socrata API access, we recommend using this dataset if you would like the boundaries to automatically update after each decennial census to reflect the most recent Supervisor District boundaries. If you'd like your boundaries to remain static, see the <u><a href="https://data.sfgov.org/d/f2zs-jevy">Supervisor Districts (2022)</a></u> dataset. 

<strong>E. RELATED DATASETS</strong>
<u><a href="https://data.sfgov.org/d/f2zs-jevy">Supervisor Districts (2022)</a></u> - current Supervisor District Boundaries as of 2022
<u><a href="https://data.sfgov.org/d/keex-zmn4">Supervisor Districts (2012)</a></u>
<u><a href="https://data.sfgov.org/Geographic-Locations-and-Boundaries/Current-Supervisor-Districts-trimmed-to-remove-wat/hcgx-vtsb">Current Supervisor Districts (trimmed to remove water and non-populated areas)</a></u>
<u><a href="https://sfgov.maps.arcgis.com/home/item.html?id=7181401605d545f99e787682f6653be1">View this data on ArcGIS Online</a></u>

Tags: supervisor-district
Organization: City of San Francisco
Workforce 1 for NYCHA Residents by City Council District - Local Law 163

This dataset contains information about the Workforce 1 service, a service offered by the Department of Small Business Services (SBS) that connects New Yorkers to job opportunities. Each row in the dataset represents the number of public housing residents on a City Council District-level who receive or utilize this service.

For datasets related to other services provided to NYCHA residents, view the data collection “Services available to NYCHA Residents - Local Law 163”.

Tags: councildistrict, ll163, locallaw163, nycha, public-housing, sbs, workforce1
Organization: City of New York
Summer Youth Employment Program (SYEP) for NYCHA Residents by Borough- Local Law 163

This dataset contains information about <a href="https://www1.nyc.gov/site/dycd/services/jobs-internships/summer-youth-employment-program-syep.page">Summer Youth Employment Program</a> (SYEP), a service offered by the Department of Youth and Community Development (DYCD) aimed at getting young New Yorkers paid work experience and career exploration opportunities. Each row in the dataset represents the number of public housing residents on a Borough-level who receive or utilize this service.

For datasets related to other services provided to NYCHA residents, view the data collection “Services available to NYCHA Residents - Local Law 163”.

Tags: dycd, ll163, locallaw163, nycha, public-housing, syep
Organization: City of New York