The Runway Ends Table dataset is updated every 28 days from the Federal Aviation Administration (FAA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Runways database contains runways in the United States and US territories with information on the physical characteristics of the runways. Not all runways in this dataset have geospatial locations associated with them, as this is not a required data element from the airport source. As a result, this database contains a polyline layer, a points layer and a table. The polyline layer contains runways that had corresponding runways ends. The points layer contains mostly heliports, but also non-linear landing areas. The table contains runways where geospatial information was not reported. The runways in the database are associated with the airports dataset on NTAD, showing runways for all official and operational aerodromes. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529790

The dataset provides users with information about airport locations and attributes and can be used for national and regional analysis applications.The Aviation Facilities dataset is updated every 28 days from the Federal Aviation Administration (FAA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Aviation Facilities dataset is a geographic point database of all official and operational aerodromes in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the aerodrome, 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. For more information about these data, please visit: https://www.faa.gov/air_traffic/flight_info/aeronav/Aero_Data/NASR_Subscription.For more information on the coded values in this dataset, please visit: https://nfdc.faa.gov/webContent/28DaySub/extra/30_Nov_2023_APT_CSV.zip

The Federal Aviation Administration (FAA) Regional Offices dataset is as of July 12, 2023 from the Federal Aviation Administration’s (FAA's) Office of National Engagement and Regional Administration (ARA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset shows the location and associated information for each of the nine FAA Regional Offices, which fall under the FAA’s Office of National Engagement and Regional Administration (ARA). More information about the FAA Regions and Regional Offices can be found at: https://www.faa.gov/about/office_org/headquarters_offices/ara

The Airport Runways database is a geographic dataset of runways in the United States and US territories. It contains information about their physical characteristics and is associated to the Airports database. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product (Effective April 2015).

© The Federal Aviation Administration (FAA) develops and maintains the textual data from which this geospatial layer was derived. This layer is sourced from maps.bts.dot.gov.

The Airport Runways database is a geographic dataset of runway ends in the United States and US territories. It contains information about their physical characteristics and is associated with the Airport database.

© The Federal Aviation Administration (FAA) develops and maintains the textual data from which this geospatial layer was derived.

Public Law 111-216, which enacted H.R. 5900, Airline Safety and Federal Aviation Administration Extension Act of 2010, mandated that the FAA create an information system containing FAA, Air Carrier, and National Driver Register (NDR) data for Air Carriers to be used in the pilot hiring decision process by Air Carriers. The PRD project includes business process creation and modification and as well as the complete life cycle of system development. The PRD will provide a centralized reliable source of historical information on pilots to enable Air Carriers to make hiring decisions. In addition to the IT project, a rulemaking project is underway that will issue regulations to protect and secure the personal privacy of any individual, and would require written consent from such individual before anyone accesses the records.

The Airports database 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. Data is downloaded from the National Transportation Atlas Database.

Constraints:
Acknowledgment of the Federal Aviation Administration (FAA) and the Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS) National Transportation Atlas Databases (NTAD) 2007 would be appreciated in products derived from these data. Not to be used for navigation, for informational purposes only. See full disclaimer for more information.

This dataset comprises a comprehensive collection of airplane-wildlife strike incidents involving military, commercial or civil aircraft from 1990 to 2023. The incidents were sourced from the Federal Aviation Administration (FAA) Wildlife Strike Database. The dataset covers a range of information related to these incidents, including details about the aircraft involved, wildlife species, geographical locations and various factors contributing to the occurrences.

Key Features:

• INCIDENT_DATE: Date of the incident.
• AIRPORT: Airport involved in the incident.
• AIRCRAFT: Aircraft information, including type and registration.
• SPECIES: Wildlife species involved in the strike.
• LOCATION: Geographical location of the incident.
• PHASE_OF_FLIGHT: Phase of the flight during which the incident occurred.
• DAMAGE_LEVEL: Level of damage resulting from the incident.
• WEATHER: Information about weather conditions at the time of the incident.

Additional Information: The dataset includes information essential for understanding the circumstances surrounding airplane-wildlife strikes. For a detailed explanation of each column, aircraft models, engine types and positions, please refer to the accompanying read_me.xls file.

description: The Federal Aviation Administration (FAA) SDRS database contains records of mechanical malfunctions, defects, and failures on civil aviation aircraft.; abstract: The Federal Aviation Administration (FAA) SDRS database contains records of mechanical malfunctions, defects, and failures on civil aviation aircraft.

description:

Downloaded 10/22/07 from http://www.bts.gov/publications/national_transportation
_atlas_database/2007/. Queryed out ND via attributes and reprojected in ArcMap. Brian Bieber - NDDOT The Airport Runways database is a geographic dataset of runways in the United States and US territories containing information on the physical characteristics of the runways. The 5585 runways in the dataset are runways associated with the 20362 airports in the companion airport data set. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product (Effective 18 January 2007).

Constraints:
Acknowledgment of the Federal Aviation Administration (FAA) and the Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS) National Transportation Atlas Databases (NTAD) 2007 would be appreciated in products derived from these data. Not to be used for navigation, for informational purposes only. See full disclaimer for more information.

Downloaded 10/22/07 from http://www.bts.gov/publications/national_transportation
_atlas_database/2007/. Queryed out ND via attributes and reprojected in ArcMap. Brian Bieber - NDDOT The Airport Runways database is a geographic dataset of runways in the United States and US territories containing information on the physical characteristics of the runways. The 5585 runways in the dataset are runways associated with the 20362 airports in the companion airport data set. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product (Effective 18 January 2007).

Constraints:
Acknowledgment of the Federal Aviation Administration (FAA) and the Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS) National Transportation Atlas Databases (NTAD) 2007 would be appreciated in products derived from these data. Not to be used for navigation, for informational purposes only. See full disclaimer for more information.

As new technologies are developed to handle the complexities of the Next Generation Air Transportation System (NextGen), it is increasingly important to address both current and future safety concerns along with the operational, environmental, and efficiency issues within the National Airspace System (NAS). In recent years, the Federal Aviation Administration’s (FAA) safety offices have been researching ways to utilize the many safety databases maintained by the FAA, such as those involving flight recorders, radar tracks, weather, and many other high-volume sensors, in order to monitor this unique and complex system. Although a number of current technologies do monitor the frequency of known safety risks in the NAS, very few methods currently exist that are capable of analyzing large data repositories with the purpose of discovering new and previously unmonitored safety risks. While monitoring the frequency of known events in the NAS enables mitigation of already identified problems, a more proactive approach of finding unidentified issues still needs to be addressed. This is especially important in the proactive identification of new, emergent safety issues that may result from the planned introduction of advanced NextGen air traffic management technologies and procedures. Development of an automated tool that continuously evaluates the NAS to discover both events exhibiting flight characteristics indicative of safety-related concerns as well as operational anomalies will heighten the awareness of such situations in the aviation community and serve to increase the overall safety of the NAS. This paper discusses the extension of previous anomaly detection work to identify operationally significant flights within the highly complex airspace encompassing the New York area of operations, focusing on the major airports of Newark International (EWR), LaGuardia International (LGA), and John F. Kennedy International (JFK). In addition, flight traffic in the vicinity of Denver International (DEN) airport/airspace is also investigated to evaluate the impact on operations due to variances in seasonal weather and airport elevation. From our previous research, subject matter experts determined that some of the identified anomalies were significant, but could not reach conclusive findings without additional supportive data. To advance this research further, causal examination using domain experts is continued along with the integration of air traffic control (ATC) voice data to shed much needed insight into resolving which flight characteristic(s) may be impacting an aircraft's unusual profile. Once a flight characteristic is identified, it could be included in a list of potential safety precursors. This paper also describes a process that has been developed and implemented to automatically identify and produce daily reports on flights of interest from the previous day.

This dataset represents the location of public, private and military airports and heliports in Kentucky as derived from the Federal Aviation Administration Airport Data & Contact Information online database.Data Download: https://ky.box.com/s/c0k7h4jwz8u5d5e7fwmlbzq9qgqz11lb

TIAS 11754 cover memo

The Runways dataset is updated every 28 days from the Federal Aviation Administration (FAA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Runways database contains runways in the United States and US territories containing information on the physical characteristics of the runways. This data layer contains runways that have corresponding and reported runways ends, and the linear feature of the runway could be created based off these points. The runways in the dataset are associated with the airports dataset on NTAD, showing runways for all official and operational aerodromes. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529077

Abstract: Data downloaded from the USDOT Bureau of Transportation Statistics Open Data Catalog and clipped to Volusia County plus the surrounding area. It was then saved as a local file in SPC FL E NAD83. A Coast Guard facility in Ponce Inlet and a private airport in Osteen were added as they were not included. Some attribute fields were removed, and some points were moved based on visual inspection of aerial photography.The Airports dataset includes all official and operational aerodromes as of July 16, 2020 and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of official operational aerodromes in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the aerodrome, 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.Copyright Text: Credit the Assistant Secretary for Research and Technology/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Federal Aviation Administration (FAA) develops and maintains the textual data from which this geospatial layer was derived.

This dataset provides locations and technical specifications of wind turbines in the United States, almost all of which are utility-scale. Utility-scale turbines are ones that generate power and feed it into the grid, supplying a utility with energy. They are usually much larger than turbines that would feed a house or business. The regularly updated database contains wind turbine records that have been collected, digitized, and locationally verified. Turbine data were gathered from the Federal Aviation Administration's (FAA) Digital Obstacle File (DOF) and Obstruction Evaluation Airport Airspace Analysis (OE-AAA), American Clean Power (ACP) Association (formerly American Wind Energy Association (AWEA)), Lawrence Berkeley National Laboratory (LBNL), and the United States Geological Survey (USGS), and were merged and collapsed into a single dataset. Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. A locational error of plus or minus 10 meters for turbine locations was tolerated. Technical specifications for turbines were assigned based on the wind turbine make and models as provided by manufacturers and project developers directly, and via FAA datasets, information on the wind project developer or turbine manufacturer websites, or other online sources. Some facility and turbine information on make and model did not exist or was difficult to obtain. Thus, uncertainty may exist for certain turbine specifications. Similarly, some turbines were not yet built, not built at all, or for other reasons cannot be verified visually. Location and turbine specifications data quality are rated, and confidence is recorded for both. None of the data are field verified.

This includes the December 2023 release of three related tools: PaperBLAST, Curated BLAST for Genomes, and SitesBLAST. PaperBLAST links protein sequences to papers about them and to curated annotations. Curated BLAST for Genomes uses the characterized subset of PaperBLAST's database to find candidates with a function of interest within a genome. SitesBLAST links protein sequences to functional sites, such as catalytic residues or substrate-binding residues.Most of the files are gzipped:litsearch.db -- the sqlite3 database for PaperBLAST and SitesBLAST. Also see the schema.litsearch.faa -- sequences for all of the proteins mentioned in the Gene or CuratedGene tables, in fasta formatuniq.faa -- unique sequences from litsearch.faa, in fasta format. (Also see the SeqToDuplicate table.)stats -- some statistics on the PaperBLAST databasecurated.faa -- the curated subset of PaperBLAST's database, in fasta formathassites.faa -- sequences for all the proteins mentioned in the HasSites table, in fasta format.To run the tools from these downloaded databases, you need to gunzip the files and format the BLAST databases. For more information see here.References:PaperBLAST: Text Mining Papers for Information about Homologs (mSystems, 2017)Curated BLAST for Genomes (mSystems, 2020)Interactive Analysis of Functional Residues in Protein Families (mSystems, 2022)

Abstract

Dataset quality ***: High quality dataset that was quality-checked by the EIDC team

The United States Wind Turbine Database (USWTDB) provides the locations of land-based and offshore wind turbines in the United States, corresponding wind project information, and turbine technical specifications. The creation of this database was jointly funded by the U.S. Department of Energy (DOE) Wind Energy Technologies Office (WETO) via the Lawrence Berkeley National Laboratory (LBNL) Electricity Markets and Policy Group, the U.S. Geological Survey (USGS) Energy Resources Program, and the American Clean Power Association (ACP). The database is being continuously updated through collaboration among LBNL, USGS, and ACP. Wind turbine records are collected and compiled from various public and private sources, digitized or position-verified from aerial imagery, and quality checked. Technical specifications for turbines are obtained directly from project developers and turbine manufacturers, or they are based on data obtained from public sources.

Methodology

The USWTDB combines a 2014 USGS data set (48,956 wind turbines, including decommissioned and duplicate turbines) with a 2017 LBNL data set (43,827 wind turbines) and includes regular updates from ACP's WindIQ as well as the Federal Aviation Administration (FAA) Digital Obstacle File (DOF) and Obstacle Evaluation Airport Airspace Analysis (OE-AAA). The USWTDB is updated as frequently as quarterly as new data become available and will lag installations by approximately one quarter.

All turbine points in the data set are visually verified using high-resolution aerial imagery in ESRI ArcGIS Desktop, and X/Y locations are manually moved to the base of the turbine with an estimated locational tolerance of 10 meters. Visual verification also enables identification and removal of duplicate turbine points and decommissioned turbines from the database, although some decommissioned turbines likely have not yet been identified and thus remain in the data set. Moreover, because of a lag in obtaining up-to-date aerial imagery, some turbine locations have not been visually verified.

Technical specifications for turbines are assigned based on turbine make and model as described in literature, specifications listed in the FAA DOF, and collected via ACP, LBNL, and turbine manufacturer websites. Because some make and model information does not exist or is difficult to obtain, uncertainty may exist for certain turbine specifications.

The uncertainties associated with location and attribute data quality are rated, and a confidence level is recorded. None of the data in the USWTDB are field verified.

Related Literature

Walker, C. (2020). Using the United States Wind Turbine Database to identify increasing turbine size, capacity and other development trends. https://doi.org/10.4236/epe.2020.127025

Miller, L. M., & Keith, D. W. (2018). Observation-based solar and wind power capacity factors and power densities. Environmental Research Letters, 13(10), 104008. https://doi.org/10.1088/1748-9326/aae102

Preziuso, D., Orrell, A., & Lantz, E. (2022). Categorizing distributed wind energy installations in the United States to inform research and stakeholder priorities. Energy, Sustainability and Society, 12(1), 31. https://doi.org/10.1186/s13705-022-00357-1

Diffendorfer, J. E., Vanderhoof, M. K., & Ancona, Z. H. (2022). Wind turbine wakes can impact down-wind vegetation greenness. Environmental Research Letters, 17(10), 104025. https://doi.org/10.1088/1748-9326/ac8da9

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This data set provides industrial-scale onshore wind turbine locations, corresponding facility information, and turbine technical specifications, in the United States to March 2014. The database has nearly 49,000 wind turbine records that have been collected, digitized, locationally verified, and internally quality assured and quality controlled. Turbines from the Federal Aviation Administration Digital Obstacle File, product date March 2, 2014, were used as the primary source of turbine data points. Verification of the position of turbines was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. Turbines without Federal Aviation Administration Obstacle Repository System (FAA ORS) numbers were visually identified and supplemental points were added to the collection. A locational error of plus or minus 10 meters for turbine positions was estimated. Wind farm facility names were identified from publicly available facility data sets. Facility names were then used in a web search of additional industry publications and press releases to attribute additional turbine information (such as manufacturer, model, and technical specifications of wind turbines). Wind farm facility _location data from various wind and energy industry sources were used to search for and digitize turbines not in existing databases. Technical specifications assigned to were based on the make and model as described in literature, in the Federal Aviation Administration Digital Obstacle File, and information from the turbine manufacturers' websites. Some facility and turbine information did not exist or was difficult to obtain. Thus, uncertainty may be present. That uncertainty was rated and a confidence was recorded for both _location and attribution data quality.

USDOT BTS Airports 2022

The Aviation Facilities dataset was compiled on July 16, 2020, is from the Federal Aviation Administration(FAA), and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics’ (BTS') National Transportation Atlas Database (NTAD). The Airports dataset includes all official and operational aerodromes and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of official operational aerodromes in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the aerodrome, 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.