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395 results for “Aircraft”
Aircraft Aerobatic Manoeuvres
<p>For the context of my master's dissertation, I collected a lot of aerobatic manoeuvres examples from volunteers and decided to publish them. Therefore, the dataset is available for the community if anyone is interested in using the data to train airplane controllers. </p> <p>Note: Not the best version for dataset download. Better version will be available in <a href="https://zenodo.org/record/6804539">here</a>.</p> <p> </p> <p>Currently unavailable.</p>
Soviet military aircraft
Source: Objaverse 1.0 / Sketchfab
Aircraft Painting Contest — Spitfire, Dunkirk
Based on "[Aircraft Painting Contest – Base](https://sketchfab.com/models/e1b4d01dbd7c42bb95c5a0f922747ac2)" by [Renafox](https://sketchfab.com/kryik1023), licensed under CC Attribution-ShareAlike. Scene is inspired by stunning Christopher Nolan's film Dunkirk. Music: Hans Zimmer — Supermarine Made for [Sketchfab Texturing Challenge: Spitfire](https://blog.sketchfab.com/sketchfab-texturing-challenge-spitfire/)   Source: Objaverse 1.0 / Sketchfab
Aircraft Gold
Source: Objaverse 1.0 / Sketchfab
CO2 and NOy aircraft measurement data obtained within the framework of the ECLIF3 campaign
<p>Based on airborne in-situ NOy and CO2 measurements from six flights in 2021 we estimate the NOx emission index of a long-range Airbus aircraft. Measurement flights were conducted with the DLR Falcon within the joint ECLIF3 (Emission and CLimate Impact of alternative Fuels 3) project. </p>
Input files for elPaSo for the paper: Efficient Solution Strategies for Cabin Noise Assessment of wave-resolving Aircraft Fuselage Models
<div> <div>This dataset showcases the input and output data for the paper: "Efficient Solution Strategies for Cabin Noise Assessment of wave-resolving Aircraft Fuselage Models".</div> <div> </div> The model data are made available in the folder "models" and contain the three models corresponding to the frequency-dependent mesh of the aircraft fuselage model. <div>Three frequency-dependent meshes are placed in subfolders, where the number indicates up to which frequency in Hz the respective model can be used. Here, the ".cub5" files are the mesh files created by the meshing tool Coreform Cubit. For the application of the FEM elPaSo is used. Here, the ".hdf5" files serve as the elPaSo input.</div> <div>Once computed, the results are written in the "eGenOutput*.hdf5" files. The results for the degrees of freedom across the frequency domain can be found here.</div> </div>
Dataset: 2023 GPS Anomalies, NOTAMs, and Aircraft Traffic
<h1><strong>Dataset: 2023 GPS Anomalies, NOTAMs, and Aircraft Traffic</strong></h1> <p>The dataset "2023 GPS Anomalies, NOTAMs, and Aircraft Traffic" was collected and generated for the paper "Detecting GPS Anomalies in Aviation Using ADS-B: Correlating Coordinate Gaps and GPS Deviations with NOTAM Warnings."</p> <p>This dataset provides a collection of geospatial and temporal data necessary for analyzing potential GPS anomalies in aviation. The data sources include NOTAMs received from the FAA, and the aircraft traffic and GPS information calculated and extracted from the OpenSky Trino ADS-B database.</p> <p>The FAA_and_ICAO_locations file includes 21,382 records with identifiers, coordinates, and detailed facility information. This dataset serves as a reference for analyzing the geographical distribution of aviation facilities. The Flights_per_Hour_per_Grid file, with 74,219,036 records, provides hourly flight movement counts within specified grids, offering insights into air traffic patterns and potential disruptions. The GPS_Jumps_from_Routes file, comprising 5,878,275 records, documents deviations in flight paths, capturing metrics such as distances, speeds, and timestamps. This data is crucial for identifying potential GPS spoofing incidents by analyzing unusual jumps between consecutive data points.</p> <p>The GPS_Missing_Coordinates file, with 53,232 records, highlights periods of missing GPS signals, indicating possible GPS jamming events. This file includes start and end times, distances between known coordinates, and Navigation Integrity Category (NIC) values to assess data quality during null periods. The NOTAM_ICAO_GPS and NOTAM_USA files, with 30,160 and 234,205 records respectively, provide detailed information on NOTAM areas, including geographic areas, active periods, and categories. This allows for an analysis of the spatial and temporal correlation between NOTAM warnings and GPS anomalies, facilitating a better understanding of the impact of GPS disruptions on aviation safety and operations.</p> <h1><strong>Summary Table</strong></h1> <table> <tbody> <tr> <td> <p><strong>Category</strong></p> </td> <td> <p><strong>File Names</strong></p> </td> <td> <p><strong>Total Records</strong></p> </td> <td> <p><strong>Columns</strong></p> </td> </tr> <tr> <td> <p><strong>FAA and ICAO Locations</strong></p> </td> <td> <p>FAA_and_ICAO_locations.csv</p> <p>FAA_and_ICAO_locations.dpkg</p> </td> <td> <p>21,382</p> </td> <td> <p>WKT, id, fid, Location_ID, ICAO_ID, IATA_ID, FAA_Location_Code, Facility_Type, Facility_Name, FAA_New_Location_Code, Coordinates, lat, lon, Region, Country_Code, Country, State_Id, State_Name, City, Location, Effective_Date, Site_Id, ADO, ARTCC_Id, ARTCC_Computer_ID, ARTCC_Name, Tie_In_FSS_Id, Tie_In_FSS_Name, NOTAM_Facility_Id, NOTAM_Service</p> </td> </tr> <tr> <td> <p><strong>Flights per Hour per Grid</strong></p> </td> <td> <p>Flights_per_Hour_per_Grid-2023.csv</p> <p>Flights_per_Hour_per_Grid-2023.dpkg</p> </td> <td> <p>74,219,036</p> </td> <td> <p>grid_id, hour, movement_count, geometry</p> </td> </tr> <tr> <td> <p><strong>GPS Jumps from Routes</strong></p> <p><strong>(possible spoofing)</strong></p> </td> <td> <p>GPS_Jumps_from_Routes-2023.csv</p> <p>GPS_Jumps_from_Routes-2023.dpkg</p> </td> <td> <p>5,878,275</p> </td> <td> <p>WKT, id, fid, icao24, callsign, time_before_spoofing, time_of_spoofing, distance, time_difference, speed_m_s, time_start, time_end</p> </td> </tr> <tr> <td> <p><strong>GPS Missing Coordinates</strong></p> <p><strong>(possible jamming)</strong></p> </td> <td> <p>GPS_Missing_Coordinates-2023.csv</p> <p>GPS_Missing_Coordinates-2023.dpkg</p> </td> <td> <p>53,232</p> </td> <td> <p>WKT, id, icao24, callsign, null_start_time, null_end_time, time_of_previous_not_null_coords, time_of_next_not_null_coords, between_coords_distance_m, null_duration_seconds, between_coords_duration_seconds, avg_nic, min_nic, max_nic, start_time, end_time, start_y, end_x, end_y, start_x</p> </td> </tr> <tr> <td> <p><strong>NOTAM ICAO GPS</strong></p> </td> <td> <p>NOTAM_ICAO_GPS-2023.csv</p> <p>NOTAM_ICAO_GPS-2023.dpkg</p> </td> <td> <p>30,160</p> </td> <td> <p>WKT, id, fid, notam_id, category_name, coordinates_center, radius_nm, radius_mod_nm, notam_number, accountability, location_id, icao_id, domestic_text, icao_text, type, category_id, time_start, time_end</p> </td> </tr> <tr> <td> <p><strong>NOTAM USA</strong></p> </td> <td> <p>NOTAM_USA-2023.csv</p> <p>NOTAM_USA-2023.dpkg</p> </td> <td> <p>234,205</p> </td> <td> <p>WKT, id, fid, notam_id, category_name, is_circle, coordinates_polygon, coordinates_center, radius_nm, faa_location_code, is_faa_location, location_id, is_restricted_area, restricted_area_id, restricted_area_code, category_id, message, notam_number, notam_accountability, moa, type, time_start, time_end</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <h1><strong>Details</strong></h1> <h2><strong>1. FAA_and_ICAO_locations.csv </strong>and <strong>FAA_and_ICAO_locations.dpkg</strong></h2> <ul> <li><strong>Total Records</strong>: 21,382</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>WKT</strong>: Well-Known Text representation of a point in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>id</strong>: Unique identifier for each record.</li> <li><strong>fid</strong>: Feature identifier.</li> <li><strong>Location_ID</strong>: Identifier for the location.</li> <li><strong>ICAO_ID</strong>: ICAO (International Civil Aviation Organization) identifier.</li> <li><strong>IATA_ID</strong>: IATA (International Air Transport Association) identifier.</li> <li><strong>FAA_Location_Code</strong>: FAA location code.</li> <li><strong>Facility_Type</strong>: Type of facility (e.g., airport, heliport).</li> <li><strong>Facility_Name</strong>: Name of the facility.</li> <li><strong>FAA_New_Location_Code</strong>: New location code by FAA.</li> <li><strong>Coordinates</strong>: Coordinates of the location.</li> <li><strong>lat</strong>: Latitude of the location.</li> <li><strong>lon</strong>: Longitude of the location.</li> <li><strong>Region</strong>: Geographical region of the location.</li> <li><strong>Country_Code</strong>: Country code of the location.</li> <li><strong>Country</strong>: Country name of the location.</li> <li><strong>State_Id</strong>: State identifier.</li> <li><strong>State_Name</strong>: Name of the state.</li> <li><strong>City</strong>: City name.</li> <li><strong>Location</strong>: General location information.</li> <li><strong>Effective_Date</strong>: Effective date of the record.</li> <li><strong>Site_Id</strong>: Site identifier.</li> <li><strong>ADO</strong>: Airport District Office.</li> <li><strong>ARTCC_Id</strong>: ARTCC (Air Route Traffic Control Center) identifier.</li> <li><strong>ARTCC_Computer_ID</strong>: ARTCC computer identifier.</li> <li><strong>ARTCC_Name</strong>: Name of the ARTCC.</li> <li><strong>Tie_In_FSS_Id</strong>: Tie-in Flight Service Station identifier.</li> <li><strong>Tie_In_FSS_Name</strong>: Name of the Tie-in Flight Service Station.</li> <li><strong>NOTAM_Facility_Id</strong>: NOTAM (Notice to Airmen) facility identifier.</li> <li><strong>NOTAM_Service</strong>: Indicates if NOTAM service is available (Y/N).</li> </ul> </ul> <h2><strong>2. Flights_per_Hour_per_Grid-2023.csv </strong>and <strong>Flights_per_Hour_per_Grid-2023.dpkg</strong></h2> <ul> <li><strong>Total Records</strong>: 74,219,036</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>grid_id</strong>: Identifier for the grid.</li> <li><strong>hour</strong>: Timestamp for the hour.</li> <li><strong>movement_count</strong>: Number of flights in each 0.5x0.5 degree grid during each hour of year 2023.</li> <li><strong>geometry</strong>: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file.</li> </ul> </ul> <h2><strong>3. GPS_Jumps_from_Routes-2023.csv </strong>and <strong>GPS_Jumps_from_Routes-2023.dpkg</strong></h2> <ul> <li><strong>Total Records</strong>: 5,878,275</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>WKT</strong>: Well-Known Text representation of a linestring in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>id</strong>: Unique identifier for each record.</li> <li><strong>fid</strong>: Feature identifier.</li> <li><strong>icao24</strong>: ICAO 24-bit aircraft address.</li> <li><strong>callsign</strong>: Callsign of the aircraft.</li> <li><strong>time_before_spoofing</strong>: Timestamp before the spoofing event.</li> <li><strong>time_of_spoofing</strong>: Timestamp of the spoofing event.</li> <li><strong>distance</strong>: Distance of the jump in meters.</li> <li><strong>time_difference</strong>: Time difference between two coordinates in seconds.</li> <li><strong>speed_m_s</strong>: Speed in meters per second.</li> <li><strong>time_start</strong>: Start time of the record.</li> <li><strong>time_end</strong>: End time of the record.</li> </ul> </ul> <h2><strong>4. GPS_Missing_Coordinates-2023.csv </strong>and <strong>GPS_Missing_Coordinates-2023.dpkg</strong></h2> <ul> <li><strong>Total Records</strong>: 53,232</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>WKT</strong>: Well-Known Text representation of a linestring in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>id</strong>: Unique identifier for each record.</li> <li><strong>icao24</strong>: ICAO 24-bit aircraft address.</li> <li><strong>callsign</strong>: Callsign of the aircraft.</li> <li><strong>null_start_time</strong>: Start time of missing GPS coordinates.</li> <li><strong>null_end_time</strong>: End time of missing GPS coordinates.</li> <li><strong>time_of_previous_not_null_coords</strong>: Time of the last known good GPS coordinates before the null period.</li> <li><strong>time_of_next_not_null_coords</strong>: Time of the first known good GPS coordinates after the null period.</li> <li><strong>between_coords_distance_m</strong>: Distance between the previous and next known good coordinates in meters.</li> <li><strong>null_duration_seconds</strong>: Duration of the null period in seconds.</li> <li><strong>between_coords_duration_seconds</strong>: Duration between the previous and next known good coordinates in seconds.</li> <li><strong>avg_nic</strong>: Average Navigation Integrity Category (NIC) during the period.</li> <li><strong>min_nic</strong>: Minimum NIC during the period.</li> <li><strong>max_nic</strong>: Maximum NIC during the period.</li> <li><strong>start_time</strong>: Human-readable start time of the null period.</li> <li><strong>end_time</strong>: Human-readable end time of the null period.</li> <li><strong>start_y</strong>: Latitude of the start point.</li> <li><strong>end_x</strong>: Longitude of the end point.</li> <li><strong>end_y</strong>: Latitude of the end point.</li> <li><strong>start_x</strong>: Longitude of the start point.</li> </ul> </ul> <h2><strong>5. NOTAM_ICAO_GPS-2023.csv </strong>and <strong>NOTAM_ICAO_GPS-2023.dpkg</strong></h2> <ul> <li><strong>Total Records</strong>: 30,160</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>WKT</strong>: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>id</strong>: Unique identifier for each record.</li> <li><strong>fid</strong>: Feature identifier.</li> <li><strong>notam_id</strong>: NOTAM identifier.</li> <li><strong>category_name</strong>: Name of the NOTAM category.</li> <li><strong>coordinates_center</strong>: Center coordinates of the NOTAM area.</li> <li><strong>radius_nm</strong>: Radius in nautical miles.</li> <li><strong>radius_mod_nm</strong>: Modified radius in nautical miles.</li> <li><strong>notam_number</strong>: NOTAM number.</li> <li><strong>accountability</strong>: Accountability of the NOTAM.</li> <li><strong>location_id</strong>: Location identifier.</li> <li><strong>icao_id</strong>: ICAO identifier.</li> <li><strong>domestic_text</strong>: Text of the NOTAM for domestic purposes.</li> <li><strong>icao_text</strong>: Text of the NOTAM for ICAO purposes.</li> <li><strong>type</strong>: Type of NOTAM.</li> <li><strong>category_id</strong>: Identifier for the NOTAM category.</li> <li><strong>time_start</strong>: Start time of the NOTAM.</li> <li><strong>time_end</strong>: End time of the NOTAM.</li> </ul> </ul> <h2><strong>6. NOTAM_USA-2023.csv </strong>and <strong>NOTAM_USA-2023.dpkg</strong></h2> <ul> <li><strong>Total Records</strong>: 234,205</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>WKT</strong>: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>id</strong>: Unique identifier for each record.</li> <li><strong>fid</strong>: Feature identifier.</li> <li><strong>notam_id</strong>: NOTAM identifier.</li> <li><strong>category_name</strong>: Name of the NOTAM category.</li> <li><strong>is_circle</strong>: Indicates if the NOTAM area is a circle (1) or not (0).</li> <li><strong>coordinates_polygon</strong>: Coordinates of the polygon vertices.</li> <li><strong>coordinates_center</strong>: Center coordinates of the NOTAM area.</li> <li><strong>radius_nm</strong>: Radius in nautical miles.</li> <li><strong>faa_location_code</strong>: FAA location code.</li> <li><strong>is_faa_location</strong>: Indicates if it is an FAA location (1) or not (0).</li> <li><strong>location_id</strong>: Location identifier.</li> <li><strong>is_restricted_area</strong>: Indicates if it is a restricted area (1) or not (0).</li> <li><strong>restricted_area_id</strong>: Restricted area identifier.</li> <li><strong>restricted_area_code</strong>: Code for the restricted area.</li> <li><strong>category_id</strong>: Identifier for the NOTAM category.</li> <li><strong>message</strong>: NOTAM message.</li> <li><strong>notam_number</strong>: NOTAM number.</li> <li><strong>notam_accountability</strong>: NOTAM accountability.</li> <li><strong>moa</strong>: Military Operations Area (MOA) identifier.</li> <li><strong>type</strong>: Type of NOTAM.</li> <li><strong>time_start</strong>: Start time of the NOTAM.</li> <li><strong>time_end</strong>: End time of the NOTAM.</li> </ul> </ul> <p> </p> <p>Note that all the files are zipped as CSV. The GPKG version is also available where geographical information is present.</p> <p>Due to the size of the file <strong>Flights_per_Hour_per_Grid-2023</strong>, the command line program ogr2ogr may be the best choice to upload the data into a database. Below is an example of a SQL script and a command to upload this file into a PostgreSQL table. Update the placeholders your_DB_table, your_DB_name, your_DB_user, your_DB_password, your_DB_hostname with the actual information.</p> <p>CREATE TABLE your_DB_table (grid_id TEXT, date TIMESTAMP, movement_count INTEGER, geometry GEOMETRY(POLYGON, 4326));</p> <p>"C:\Program Files\QGIS 3.36.2\bin\ogr2ogr" -f "PostgreSQL" PG:"dbname=your_DB_name user=your_DB_user password=your_DB_password host=your_DB_hostname port=5432" C:\Flights_per_Hour_per_Grid.gpkg -nln your_DB_table -a_srs EPSG:4326 -dim 2 -progress -append</p> <h1><strong>Author</strong></h1> <p>Eugene Pik</p> <p><a href="https://orcid.org/0000-0001-6296-919X">https://orcid.org/0000-0001-6296-919X</a></p> <p><a href="https://www.linkedin.com/in/eugene/">https://www.linkedin.com/in/eugene/</a></p> <p>eugene.pik@mevocopter.com</p> <h1><strong>DOI</strong></h1> <p><a href="https://doi.org/10.5281/zenodo.11411991">https://doi.org/10.5281/zenodo.11411991</a></p> <h1><strong>References</strong></h1> <p><strong>Following references were used to update NOTAMs with WKT polygons:</strong></p> <p>airport-data.com. (n.d.). <em>USA airports by FAA code</em>. https://www.airport-data.com/usa-airports/faa-code/A.html</p> <p>DoD. (2019). <em>Flight information publication area planning special use airspace</em>. NATIONAL GEOSPATIAL-INTELLIGENCE AGENCY. https://www.cnatra.navy.mil/assets-global/docs/area-planning-1A-20190815.pdf</p> <p>FAA. (n.d.-a). <em>Airport Data and Information Portal</em>. https://adip.faa.gov/agis/public/#/airportSearch/advanced</p> <p>FAA. (n.d.-b). <em>US ICAO location finder</em>. https://www.notams.faa.gov/common/icao/USA.html</p> <p>FAA. (2017a, September 30). <em>Encodes/decodes—Aeronautical data</em> [Template]. https://www.faa.gov/air_traffic/flight_info/aeronav/aero_data/loc_id_search/Encodes_Decodes/</p> <p>FAA. (2017b, October 12). <em>Aeronautical information manual—Official guide to basic flight Information and ATC procedures</em>. https://www.faa.gov/air_traffic/publications/media/AIM_Basic_dtd_10-12-17.pdf#page=142</p> <p>FAA. (2021, July 26). <em>Order JO 7350.9Z - Location identifiers</em> [Template]. https://www.faa.gov/regulations_policies/orders_notices/index.cfm/go/document.information/documentID/1040529</p> <p>FAA. (2023a). <em>Pilot’s handbook of aeronautical knowledge</em>. https://www.faa.gov/regulations_policies/handbooks_manuals/aviation/phak</p> <p>FAA. (2023b, June 1). <em>Airport Data</em> [Template]. https://www.faa.gov/air_traffic/flight_info/aeronav/aero_data/Airport_Data/</p> <p>FAA. (2024, February 16). <em>Order JO 7400.10F - Special Use Airspace</em> [Template]. https://www.faa.gov/documentLibrary/media/Order/Order_7400.10F_2024_-_final_-signed.pdf</p> <p>ICAO. (2022). <em>North Atlantic (NAT) air navigation plan Volume I (Doc 9634)</em>. https://www.icao.int/EURNAT/EUR%20and%20NAT%20Documents/NAT%20Documents/_eANP%20NAT%20Doc9634/Doc9634%20NAT%20eANP%20Vol%20I.pdf</p> <p>ProAirPilot.com. (2024). <em>Complete list of NOTAM abbreviations</em>. https://proairpilot.com/notam-abbreviations.html</p> <p>SkyVector. (n.d.). <em>Search for Airports by ICAO ID or name</em>. https://skyvector.com/airports</p> <p> </p> <p><strong>Below is the reference to our source of the aircraft traffic and GPS anomalies data, the OpenSky ADS-B database.</strong></p> <p>Schäfer, M., Strohmeier, M., Lenders, V., Martinovic, I., & Wilhelm, M. (2014). Bringing up OpenSky: A large-scale ADS-B sensor network for research. <em>IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks</em>, 83–94. https://doi.org/10.1109/IPSN.2014.6846743</p> <p> </p>
Airborne measurements of aerosols, trace gases and meteorological variables on board a Cessna 172 aircraft (2015 and 2017): dataset
<p>The dataset contains airborne measurements of aerosols, trace gases and meteorological variables on board an instrumented Cessna 172 aircraft (see the included readme files). The measurements were done in May–June 2015, August 2015 and April–May 2017. The flights took off from Tampere-Pirkkala airport (IATA: TMP, ICAO: EFTP) and the measurement profiles profiles were flown over Hyytiälä (61.85N, 24.28E) in southern Finland.</p>
Data for Aircraft observations of aerosol and microphysical quantities of stratocumulus in autumn over Guangxi Province, China: Diurnal variation, vertical distribution and aerosol-cloud relationship
<p>Data for Aircraft observations of aerosol and microphysical quantities of stratocumulus in autumn over Guangxi Province, China: Diurnal variation, vertical distribution and aerosol-cloud relationship</p>
Synthetic Aircraft Landing Trajectory Dataset for Oslo Airport
<p>This dataset is <span>a collection of synthetically generated landing trajectories for Oslo airport.</span></p>
Four-dimensional aircraft emission inventory dataset of Landing and take-off cycle in China from 2019 to 2023
<p>Aircraft emissions during landing and takeoff (LTO) having unique three-dimensional spatial characteristics and typical hourly temporal variations. In order to further investigate the adverse effects of aircraft emissions, the adverse effects for aircraft emissions, this study integrated the emission calculation and flight trajectory recognition methods to establish a four-dimensional aircraft emission inventory dataset of China’s LTO cycle (4D-LTO emission inventory dataset) from 2019 to 2023. The dataset has a high spatial-temporal resolution (hourly, 0.03° × 0.03° × 34 height layers).</p> <p>Information for 4D-LTO emission inventory dataset during 2019-2023:</p> <p>Species: NOx.</p> <p>Number of airports included in different years: 2019 (237 airports), 2020 (239 airports), 2021 (248 airports), 2022 (254 airports), 2023 (257 airports).</p> <p>Temporal information: 2019 (8760 hours), 2020 (8784 hours), 2021 (8760 hours), 2022 (8760 hours), 2023 (8760 hours).</p> <p>Spatial information: The horizontal resolution of the 4D-LTO emission inventory is 0.03° × 0.03° with the latitude and longitude range of 3.40°N–53.56°N and 73.44°E–135.09°E, respectively. The altitude resolution was divided into 34 layers from 0 m to 15668 m (0.0 m–38.3 m, 38.3 m–76.7 m, 76.7 m–115.3 m, 115.3 m–154 m, 154 m–231.8 m, 231.8 m–310.3 m, 310.3 m–389.3 m, 389.3 m–469 m, 469 m–549.3 m, 549.3 m–630.3 m, 630.3 m–711.9 m, 711.9 m–794.2 m, 794.2 m–960.7 m, 960.7 m–1130.1 m, 1130.1 m–1302.3 m, 1302.3 m–1477.6 m, 1477.6 m–1656.0 m, 1656.0 m–1929.7 m, 1929.7 m–2211.1 m, 2211.1 m–2599.3 m, 2599.3 m–3107.2 m, 3107.2 m–3643.1 m, 3643.1 m–4210.5 m, 4210.5 m–4813.9 m, 4813.9 m–5458.5 m, 5458.5 m–6151.2 m, 6151.2 m–6900.4 m, 6900.4 m–7717.4 m, 7717.4 m–8617.3 m, 8617.3 m–9621.2 m, 9621.2 m–10759.7 m, 10759.7 m–12080.6 m, 12080.6 m–13664.8 m, 13664.8 m–15668 m.).</p>
Aircraft Aerosol Data and MATLAB Script for Visualisation on 17/06/22
<p>A dataset containing all data from an ultralight aircraft over several flights between Feb 21 and June 22, including flight log data, MCPC, STAP and OPC data. Also includes a data visualisation script used for the manuscript "Evolution of the planetary boundary layer over Copenhagen investigated using aircraft aerosol measurements and the model DEHM".</p>
A Generic Model for Benchmark Aerodynamic Analysis of Fifth-Generation High-Performance Aircraft (CGNS grid files)
<p>Openly available supplementary data to accompany paper https://doi.org/10.3390/aerospace10090746. This data set includes unstructured CGNS grid files to facilitate code comparison. When using this data, please cite:</p> <p>Giannelis, N.F.; Bykerk, T.; Vio, G.A. A Generic Model for Benchmark Aerodynamic Analysis of Fifth-Generation High-Performance Aircraft. Aerospace 2023, 10, 746.</p>
Anomaly Detection dataset for the fuselage of an aircraft
<p>If you use the dataset, please cite:</p> <p><em>Siddhant Shete, Dennis Mronga</em></p> <p><strong>"Adaptive Online Anomaly Detection using Transfer Learning"</strong></p> <p>About the dataset: The dataset is basically used for anomaly detection in the fuselage of an aircraft manufacturing company. We captured the data on the mockup of the fuselage with several iterations at different distances away from the mockup. The dataset is basically the scans of mockup from top to bottom with and without anomalies. The dataset has been segregated into two panels.</p> <p>Contents of <em><strong> AircraftFuselageMockupDataset.zip </strong></em></p> <ol> <li>Nomal_panel1 </li> <li>Nomal_panel2</li> <li>Anomaly_panel1</li> <li>Anomaly_panel2</li> </ol> <p>Every folder has data at 3 distances 15cm, 25cm, 35cm.</p> <p> </p> <p><em>This dataset is provided by the Robotics Innivation Center, DFKI GmbH.</em></p> <p><em>The grant was provided by Federal Ministry for Economic Affairs and Climate Action </em></p> <p><em>Grant number: 20W1922F</em></p>
The Influence of Aircraft Noise Exposure on Renal Hemodynamic in Healthy Individuals
ClinicalTrials.gov study NCT02783456. IPD Sharing: NO. Countries: 1. Publications: 1.
Surgical and Medical Emergencies on Board of European Aircraft Carriers
ClinicalTrials.gov study NCT00713102. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Nanoparticles Emitted by Aircraft Engines, Impact on the Respiratory Function:
ClinicalTrials.gov study NCT02872727. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: Aircraft sound exposure leads to song frequency decline and elevated aggression in wild chiffchaffs
Open the record for dataset details and reuse information.
Data from: How to map forest structure from aircraft, one tree at a time
Open the record for dataset details and reuse information.
Data from: Aeroecology meets aviation safety: early warning systems in Europe and the Middle East prevent collisions between birds and aircraft
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.