13 datasets found
  1. Freight Analysis Framework (FAF5) Regions

    • catalog.data.gov
    • geodata.bts.gov
    • +1more
    Updated Jul 17, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Bureau of Transportation Statistics (BTS) (Point of Contact) (2025). Freight Analysis Framework (FAF5) Regions [Dataset]. https://catalog.data.gov/dataset/freight-analysis-framework-faf5-regions1
    Explore at:
    Dataset updated
    Jul 17, 2025
    Dataset provided by
    Bureau of Transportation Statisticshttp://www.rita.dot.gov/bts
    Description

    The Freight Analysis Framework (FAF5) - Regions dataset was created from 2017 base year data and was published on April 11, 2022 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 2017 Commodity Flow Survey (CFS) contains 132 zones for U.S. domestic regions, which are directly carried over to the geography definitions for the FAF (Version 5) Regions. These geographic areas can be classified as one of the following three types: (1) Metropolitan Area (MA): The state part of a selected metropolitan statistical area (MSA) or combined statistical area (CSA). (2) The Remainder of State (ROS): The portion of a state containing the counties that are not included in the MA type CFS Areas defined above. (3) Whole State: An entire state where no MA type CFS Areas are defined within the state. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529028

  2. b

    Freight Analysis Framework (FAF5) Network Links

    • geodata.bts.gov
    • s.cnmilf.com
    • +3more
    Updated Jul 1, 2003
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. Department of Transportation: ArcGIS Online (2003). Freight Analysis Framework (FAF5) Network Links [Dataset]. https://geodata.bts.gov/datasets/usdot::freight-analysis-framework-faf5-network-links
    Explore at:
    Dataset updated
    Jul 1, 2003
    Dataset authored and provided by
    U.S. Department of Transportation: ArcGIS Online
    Area covered
    Description

    The Freight Analysis Framework (FAF5) - Network Links dataset was created from 2017 base year data and was published on April 11, 2022 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 FAF (Version 5) Network contains 487,384 link features. All link features are topologically connected to permit network pathbuilding and vehicle assignment using a variety of assignment algorithms. The FAF Link and the FAF Node datasets can be used together to create a network. The link features include all roads represented in prior FAF networks, and all roads in the National Highway System (NHS) and the National Highway Freight Network (NHFN) that are currently open to traffic. Other included links provide connections between intersecting routes, and to select intermodal facilities and all U.S. counties. The network consists of over 588,000 miles of equivalent road mileage. The dataset covers the 48 contiguous States plus the District of Columbia, Alaska, and Hawaii.

  3. Freight Analysis Framework (FAF5) Highway Network Assignments

    • catalog.data.gov
    • geodata.bts.gov
    Updated Apr 2, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Bureau of Transportation Statistics (BTS) (Point of Contact) (2025). Freight Analysis Framework (FAF5) Highway Network Assignments [Dataset]. https://catalog.data.gov/dataset/freight-analysis-framework-faf5-highway-network-assignments1
    Explore at:
    Dataset updated
    Apr 2, 2025
    Dataset provided by
    Bureau of Transportation Statisticshttp://www.rita.dot.gov/bts
    Description

    The Freight Analysis Framework (FAF5) - Highway Network Assignments was created from 2017 base year and 30 year forecast data and was published on April 11, 2022 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). This data package includes tabular files with results from FAF5 2017, 2022, and 2050 baseline assignments to represent freight flows by three separate truck only flows type (Total Truck, Single Unit, and Combination Unit) and three freight flow markets (domestic, import and export). 2017 and 2022 model years contain 6 data tables and 2050 model year contains 11 data tables, representing the truck only flows. Each data table can be linked to the FAF5 network geography to display truck flows by link.

  4. Freight Analysis Framework

    • datalumos.org
    delimited
    Updated May 31, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    United States Department of Transportation. Federal Highway Administration (2025). Freight Analysis Framework [Dataset]. http://doi.org/10.3886/E231642V2
    Explore at:
    delimitedAvailable download formats
    Dataset updated
    May 31, 2025
    Dataset provided by
    Federal Highway Administrationhttps://highways.dot.gov/
    Bureau of Transportation Statisticshttp://www.rita.dot.gov/bts
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    From https://www.bts.gov/faf:The Freight Analysis Framework (FAF) database provides estimates of US freight flows. The FAF provides data for states and metropolitan areas. Flows include all modes of transportation and 42 commodity types. The Bureau of Transportation Statistics (BTS) produces the FAF with support from the Federal Highway Administration (FHWA). BTS builds FAF with data from many sources. Inputs include the Commodity Flow Survey (CFS), foreign trade data, and data from agriculture, extraction, utility, construction, service, and other sectors.FAF5 includes three types of freight flows: weight, value and activity. The FAF provides weight in thousands of tons, value in millions of 2017 constant dollars, and activity in millions of ton-miles. Users can download .csv and Microsoft Access files below.The latest version of FAF (FAF5.6.1) provides estimates of weight, value, and activity by origin and destination regions, commodity type, and mode for:* Base year (2017)* Annual estimates (2018–2022)* Preliminary annual estimates (2023)* Forecast year estimates (2025–2050)* State-level historical trend estimates (1997–2012)* Experimental county-to-county estimates (2022) – the recently released experimental product (county-level flows) and documentationThe experimental county-to-county estimates are uploaded separately at https://www.datalumos.org/datalumos/project/231661/version/V1/view.From https://ops.fhwa.dot.gov/freight/freight_analysis/faf/:The Freight Analysis Framework (FAF), produced through a partnership between Bureau of Transportation Statistics (BTS) and Federal Highway Administration (FHWA), integrates data from a variety of sources to create a comprehensive picture of freight movement among states and major metropolitan areas by all modes of transportation. Starting with data from the 2017 Commodity Flow Survey (CFS) and international trade data from the Census Bureau, FAF version 5 (FAF5) incorporates data from agriculture, extraction, utility, construction, service, and other sectors.The FAF5 provides estimates for tonnage and value by regions of origin and destination, commodity type, and mode for base year 2017 and a 30- year forecasts. FAF5 forecasts provide a range of future freight demands at five-year increments representing three different economic growth scenarios, through 2050, by various modes of transportation.

  5. a

    Freight Analysis Framework (FAF5) Network Nodes

    • data-usdot.opendata.arcgis.com
    • s.cnmilf.com
    • +1more
    Updated Jul 2, 2003
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    U.S. Department of Transportation: ArcGIS Online (2003). Freight Analysis Framework (FAF5) Network Nodes [Dataset]. https://data-usdot.opendata.arcgis.com/datasets/usdot::freight-analysis-framework-faf5-network-nodes/about
    Explore at:
    Dataset updated
    Jul 2, 2003
    Dataset authored and provided by
    U.S. Department of Transportation: ArcGIS Online
    Area covered
    Description

    The Freight Analysis Framework (FAF5) - Network Nodes dataset was created from 2017 base year data and was published on April 11, 2022 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 FAF (Version 5) Network Nodes contains 348,498 node features. All node features are topologically connected to permit network pathbuilding and vehicle assignment using a variety of assignment algorithms. The FAF Node and the FAF Link datasets can be used together to create a network. The link features in the FAF Network dataset include all roads represented in prior FAF networks, and all roads in the National Highway System (NHS) and the National Highway Freight Network (NHFN) that are currently open to traffic. Other included links provide connections between intersecting routes, and to select intermodal facilities and all U.S. counties. The network consists of over 588,000 miles of equivalent road mileage. The dataset covers the 48 contiguous States plus the District of Columbia, Alaska, and Hawaii.

  6. a

    Freight Analysis Framework (FAF) Version 5

    • trip-thrive-geohub.hub.arcgis.com
    Updated Jul 7, 2022
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    ThriveRegionalPartnership (2022). Freight Analysis Framework (FAF) Version 5 [Dataset]. https://trip-thrive-geohub.hub.arcgis.com/datasets/f893cedcf68747a98b0dca690150509f
    Explore at:
    Dataset updated
    Jul 7, 2022
    Dataset authored and provided by
    ThriveRegionalPartnership
    Area covered
    Description

    The Freight Analysis Framework (FAF), produced through a partnership between BTS and FHWA, integrates data from various sources to create a comprehensive picture of freight movement among states and major metropolitan areas by all modes of transportation. The 2017 Commodity Flow Survey (CFS) and international trade data from the Census Bureau serve as the backbone of FAF and are integrated with ancillary data sources that capture goods movement in agriculture, resource extraction, utility, construction, retail, services, and other sectors.The current version of FAF5 (FAF5.3) provides estimates for tonnage (unit: thousand tons), value (unit: million dollars), and ton-miles (unit: million ton-miles) by origin-destination pair of FAF regions, commodity type, and mode for the base year (2017), the recent years (2018 - 2019), the forecast year estimates (2020 - 2050), and the state level historical trend estimates (1997-2012). The information may be accessed through the Data Tabulation Tool and downloaded as either a complete database or in summary files.This dataset represents the FAF5 network and has been subset to the greater Chattanooga region.

  7. a

    Wisconsin FAF5

    • hub.arcgis.com
    Updated Mar 5, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    ECWRPC (2021). Wisconsin FAF5 [Dataset]. https://hub.arcgis.com/documents/4be17af8cb9a46438fb20a44f8d8de12
    Explore at:
    Dataset updated
    Mar 5, 2021
    Dataset authored and provided by
    ECWRPC
    Area covered
    Wisconsin
    Description

    The creation of the fifth generation of FAF (FAF5) is underway, and the regional database for the benchmark year of 2017 is now available. Future releases of FAF5 will include annual estimates, historical time series, forecasts, tabulation and visualization tools, and network files based on the 2017 Commodity Flow Survey.The FAF5 Regional Database of tonnage, value, and ton-miles by FAF regions of origin and destination, commodity type, and mode, benchmarked to the 2017 Commodity Flow Survey is available. Weights are in thousands of tons, activity is in millions of ton-miles, and values are in millions of 2017 constant dollars. Files are available in .csv and MS Access formats.https://www.bts.gov/faf

  8. D

    Freight Analysis Framework: Experimental County-Level Data

    • datalumos.org
    Updated Jun 1, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    United States Department of Transportation. Research and Innovative Technology Administration. Bureau of Transportation Statistics (2025). Freight Analysis Framework: Experimental County-Level Data [Dataset]. http://doi.org/10.3886/E231661V1
    Explore at:
    Dataset updated
    Jun 1, 2025
    Dataset provided by
    United States Department of Transportation. Federal Highway Administration
    United States Department of Transportation. Research and Innovative Technology Administration. Bureau of Transportation Statistics
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2022
    Area covered
    United States
    Description

    From https://www.bts.gov/faf/county:The Freight Analysis Framework (FAF) database provides estimates of the weight and value of shipments throughout the United States for all commodity types and forms of transportation using a geographic system of 132 FAF zones. The Bureau of Transportation Statistics (BTS) developed an experimental county-to-county commodity flow product to provide the user community with more geographically granular commodity flow data to support planning, policymaking, and operational decisions at the state and local levels. Users can download state-specific files or the entire set of disaggregation factors to create customized queries. This experimental product provides flows for five commodity groups and five mode categories (see documentation for more details). BTS welcomes users to email FAF@dot.gov with feedback on this experimental product.The state FIPS code is also shown next to the state. Each zip file contains four tables with 1) county-level OD flows for the state of interest and every adjacent state, 2) county-to-FAF OD flows from the multi-state area to all other FAF zones, 3) FAF-to-county OD flows from all other FAF zones to the multi-state area, and 4) FAF-to-FAF OD flows from all other FAF zones to all other FAF zones. The files use county-level geography for the state of interest and states adjacent to this state. FAF zones represent flows outside of this area.The main Freight Analysis Framework files are loaded to Data Lumos separately here: https://www.datalumos.org/datalumos/project/231642/version/V1/view. Additional documentation is available at that link.The faf5_county_readme.txt and faf5_county_readme.xlsx were created for this upload and were not created by the DOT. The direct url to download each state-level dataset is in faf5_county_readme.xlsx.

  9. a

    Wisconsin FAF5

    • data-ecwrpc.opendata.arcgis.com
    Updated Mar 5, 2021
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    ECWRPC (2021). Wisconsin FAF5 [Dataset]. https://data-ecwrpc.opendata.arcgis.com/maps/ECWRPC::wisconsin-faf5
    Explore at:
    Dataset updated
    Mar 5, 2021
    Dataset authored and provided by
    ECWRPC
    Area covered
    Wisconsin
    Description

    The creation of the fifth generation of FAF (FAF5) is underway, and the regional database for the benchmark year of 2017 is now available. Future releases of FAF5 will include annual estimates, historical time series, forecasts, tabulation and visualization tools, and network files based on the 2017 Commodity Flow Survey.The FAF5 Regional Database of tonnage, value, and ton-miles by FAF regions of origin and destination, commodity type, and mode, benchmarked to the 2017 Commodity Flow Survey is available. Weights are in thousands of tons, activity is in millions of ton-miles, and values are in millions of 2017 constant dollars. Files are available in .csv and MS Access formats.Website - https://www.bts.gov/fafAttributes - dms_dest = FAF region or state where a freight movement ends the domestic portion of shipment. For exports, this is the US exit region where an export leaves the United States.

    fr_dest = Foreign region of shipment destination

    fr_inmode = Mode used between a foreign region and the US entry region for the imported goods

    dms_mode = Mode used between domestic origins and destinations

    fr_outmode = Mode used between the US exit region and foreign region for the exported goods

    sctg2 = 2-digit level of the Standard Classification of Transported Goods

    trade_type = Type of trade

    value = Total value (in 2017 dollar) of commodities shipped (unit: Million $)

    tons = Total weight of commodities shipped (unit: Thousand Tons)

  10. GeoJSON files for the MCSC's Trucking Industry Decarbonization Explorer...

    • zenodo.org
    • data.niaid.nih.gov
    zip
    Updated May 28, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Danika Eamer; Danika Eamer; Micah Borrero; Micah Borrero; Noman Bashir; Noman Bashir (2025). GeoJSON files for the MCSC's Trucking Industry Decarbonization Explorer (Geo-TIDE) [Dataset]. http://doi.org/10.5281/zenodo.13207716
    Explore at:
    zipAvailable download formats
    Dataset updated
    May 28, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Danika Eamer; Danika Eamer; Micah Borrero; Micah Borrero; Noman Bashir; Noman Bashir
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Summary

    Geojson files used to visualize geospatial layers relevant to identifying and assessing trucking fleet decarbonization opportunities with the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE) tool.

    Relevant Links

    Link to the online version of the tool (requires creation of a free user account).

    Link to GitHub repo with source code to produce this dataset and deploy the Geo-TIDE tool locally.

    Funding

    This dataset was produced with support from the MIT Climate & Sustainability Consortium.

    Original Data Sources

    These geojson files draw from and synthesize a number of different datasets and tools. The original data sources and tools are described below:

    Filename(s)Description of Original Data Source(s)Link(s) to Download Original Data
    License and Attribution for Original Data Source(s)

    faf5_freight_flows/*.geojson

    trucking_energy_demand.geojson

    highway_assignment_links_*.geojson

    infrastructure_pooling_thought_experiment/*.geojson

    Regional and highway-level freight flow data obtained from the Freight Analysis Framework Version 5. Shapefiles for FAF5 region boundaries and highway links are obtained from the National Transportation Atlas Database. Emissions attributes are evaluated by incorporating data from the 2002 Vehicle Inventory and Use Survey and the GREET lifecycle emissions tool maintained by Argonne National Lab.

    Shapefile for FAF5 Regions

    Shapefile for FAF5 Highway Network Links

    FAF5 2022 Origin-Destination Freight Flow database

    FAF5 2022 Highway Assignment Results

    Attribution for Shapefiles: United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Available at: https://geodata.bts.gov/search?collection=Dataset.

    License for Shapefiles: This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.

    Attribution for Origin-Destination Freight Flow database: National Transportation Research Center in the Oak Ridge National Laboratory with funding from the Bureau of Transportation Statistics and the Federal Highway Administration. Freight Analysis Framework Version 5: Origin-Destination Data. Available from: https://faf.ornl.gov/faf5/Default.aspx. Obtained on Aug 5, 2024. In the public domain.

    Attribution for the 2022 Vehicle Inventory and Use Survey Data: United States Department of Transportation Bureau of Transportation Statistics. Vehicle Inventory and Use Survey (VIUS) 2002 [supporting datasets]. 2024. https://doi.org/10.21949/1506070

    Attribution for the GREET tool (original publication): Argonne National Laboratory Energy Systems Division Center for Transportation Research. GREET Life-cycle Model. 2014. Available from this link.

    Attribution for the GREET tool (2022 updates): Wang, Michael, et al. Summary of Expansions and Updates in GREET® 2022. United States. https://doi.org/10.2172/1891644

    grid_emission_intensity/*.geojson

    Emission intensity data is obtained from the eGRID database maintained by the United States Environmental Protection Agency.

    eGRID subregion boundaries are obtained as a shapefile from the eGRID Mapping Files database.

    eGRID database

    Shapefile with eGRID subregion boundaries

    Attribution for eGRID data: United States Environmental Protection Agency: eGRID with 2022 data. Available from https://www.epa.gov/egrid/download-data. In the public domain.

    Attribution for shapefile: United States Environmental Protection Agency: eGRID Mapping Files. Available from https://www.epa.gov/egrid/egrid-mapping-files. In the public domain.

    US_elec.geojson

    US_hy.geojson

    US_lng.geojson

    US_cng.geojson

    US_lpg.geojson

    Locations of direct current fast chargers and refueling stations for alternative fuels along U.S. highways. Obtained directly from the Station Data for Alternative Fuel Corridors in the Alternative Fuels Data Center maintained by the United States Department of Energy Office of Energy Efficiency and Renewable Energy.

    US_elec.geojson

    US_hy.geojson

    US_lng.geojson

    US_cng.geojson

    US_lpg.geojson

    Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy. Alternative Fueling Station Corridors. 2024. Available from: https://afdc.energy.gov/corridors. In the public domain.

    These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.

    daily_grid_emission_profiles/*.geojson

    Hourly emission intensity data obtained from ElectricityMaps.

    Original data can be downloaded as csv files from the ElectricityMaps United States of America database

    Shapefile with region boundaries used by ElectricityMaps

    License: Open Database License (ODbL). Details here: https://www.electricitymaps.com/data-portal

    Attribution for csv files: Electricity Maps (2024). United States of America 2022-23 Hourly Carbon Intensity Data (Version January 17, 2024). Electricity Maps Data Portal. https://www.electricitymaps.com/data-portal.

    Attribution for shapefile with region boundaries: ElectricityMaps contributors (2024). electricitymaps-contrib (Version v1.155.0) [Computer software]. https://github.com/electricitymaps/electricitymaps-contrib.

    gen_cap_2022_state_merged.geojson

    trucking_energy_demand.geojson

    Grid electricity generation and net summer power capacity data is obtained from the state-level electricity database maintained by the United States Energy Information Administration.

    U.S. state boundaries obtained from "https://www.sciencebase.gov/catalog/item/52c78623e4b060b9ebca5be5">this United

  11. f

    Baseline profiles of study patients (n = 27).

    • figshare.com
    xls
    Updated Jun 1, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Ari Shinojima; Miki Sawa; Ryusaburo Mori; Tetsuju Sekiryu; Yuji Oshima; Aki Kato; Chikako Hara; Masaaki Saito; Yukinori Sugano; Masayuki Ashikari; Yoshio Hirano; Hitomi Asato; Mayumi Nakamura; Kiyoshi Matsuno; Noriyuki Kuno; Erika Kimura; Takeshi Nishiyama; Mitsuko Yuzawa; Tatsuro Ishibashi; Yuichiro Ogura; Tomohiro Iida; Fumi Gomi; Tsutomu Yasukawa (2023). Baseline profiles of study patients (n = 27). [Dataset]. http://doi.org/10.1371/journal.pone.0229694.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Ari Shinojima; Miki Sawa; Ryusaburo Mori; Tetsuju Sekiryu; Yuji Oshima; Aki Kato; Chikako Hara; Masaaki Saito; Yukinori Sugano; Masayuki Ashikari; Yoshio Hirano; Hitomi Asato; Mayumi Nakamura; Kiyoshi Matsuno; Noriyuki Kuno; Erika Kimura; Takeshi Nishiyama; Mitsuko Yuzawa; Tatsuro Ishibashi; Yuichiro Ogura; Tomohiro Iida; Fumi Gomi; Tsutomu Yasukawa
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Baseline profiles of study patients (n = 27).

  12. Characteristics of eight eyes with progression to exudative AMD.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Ari Shinojima; Miki Sawa; Ryusaburo Mori; Tetsuju Sekiryu; Yuji Oshima; Aki Kato; Chikako Hara; Masaaki Saito; Yukinori Sugano; Masayuki Ashikari; Yoshio Hirano; Hitomi Asato; Mayumi Nakamura; Kiyoshi Matsuno; Noriyuki Kuno; Erika Kimura; Takeshi Nishiyama; Mitsuko Yuzawa; Tatsuro Ishibashi; Yuichiro Ogura; Tomohiro Iida; Fumi Gomi; Tsutomu Yasukawa (2023). Characteristics of eight eyes with progression to exudative AMD. [Dataset]. http://doi.org/10.1371/journal.pone.0229694.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Ari Shinojima; Miki Sawa; Ryusaburo Mori; Tetsuju Sekiryu; Yuji Oshima; Aki Kato; Chikako Hara; Masaaki Saito; Yukinori Sugano; Masayuki Ashikari; Yoshio Hirano; Hitomi Asato; Mayumi Nakamura; Kiyoshi Matsuno; Noriyuki Kuno; Erika Kimura; Takeshi Nishiyama; Mitsuko Yuzawa; Tatsuro Ishibashi; Yuichiro Ogura; Tomohiro Iida; Fumi Gomi; Tsutomu Yasukawa
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Characteristics of eight eyes with progression to exudative AMD.

  13. f

    Median of each examiner’s scoring and Fleiss’ kappa coefficients of FAF...

    • plos.figshare.com
    xls
    Updated Feb 19, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Taro Kominami; Tien-En Tan; Hiroaki Ushida; Kanika Jain; Kensuke Goto; Yasmin M. Bylstra; Ai Fujita Sajiki; Ranjana S. Mathur; Junya Ota; Weng Khong Lim; Koji M Nishiguchi; Beau J. Fenner (2025). Median of each examiner’s scoring and Fleiss’ kappa coefficients of FAF image parameters. [Dataset]. http://doi.org/10.1371/journal.pone.0318857.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Feb 19, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Taro Kominami; Tien-En Tan; Hiroaki Ushida; Kanika Jain; Kensuke Goto; Yasmin M. Bylstra; Ai Fujita Sajiki; Ranjana S. Mathur; Junya Ota; Weng Khong Lim; Koji M Nishiguchi; Beau J. Fenner
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Median of each examiner’s scoring and Fleiss’ kappa coefficients of FAF image parameters.

  14. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Bureau of Transportation Statistics (BTS) (Point of Contact) (2025). Freight Analysis Framework (FAF5) Regions [Dataset]. https://catalog.data.gov/dataset/freight-analysis-framework-faf5-regions1
Organization logo

Freight Analysis Framework (FAF5) Regions

Explore at:
Dataset updated
Jul 17, 2025
Dataset provided by
Bureau of Transportation Statisticshttp://www.rita.dot.gov/bts
Description

The Freight Analysis Framework (FAF5) - Regions dataset was created from 2017 base year data and was published on April 11, 2022 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 2017 Commodity Flow Survey (CFS) contains 132 zones for U.S. domestic regions, which are directly carried over to the geography definitions for the FAF (Version 5) Regions. These geographic areas can be classified as one of the following three types: (1) Metropolitan Area (MA): The state part of a selected metropolitan statistical area (MSA) or combined statistical area (CSA). (2) The Remainder of State (ROS): The portion of a state containing the counties that are not included in the MA type CFS Areas defined above. (3) Whole State: An entire state where no MA type CFS Areas are defined within the state. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529028

Search
Clear search
Close search
Google apps
Main menu