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338 results for “gps”
GPS time series of CMONOC
<p>GPS time series of CMONOC, which were used in the studying of vertical land motion throughout the Tianshan and forelands. </p>
Plate boundary deformation at the Azores triple junction determined from continuous GPS geodetic measurements, 2000-2017
<p>Ground deformation in the Azores, at the triple junction between the Eurasian, Nubian, and North American plates, has been mapped with continuous GPS (Global Positioning System) geodetic measurements to improve tectonic motion estimates and for understanding volcanic unrest. We compute daily GPS positions, spanning almost 17 years (2000-2017), from 18 continuous GPS stations. The GPS time-series are analyzed by searching for discontinuities and periodic functions. Results show that Flores and Graciosa islands have displacements close to predicted North American and Eurasian plate motions, respectively, while São Miguel, Terceira, São Jorge, Faial and Pico islands have displacements in between predicted Eurasian and Nubian plate motions. The Eurasian-Nubian plate boundary in the Azores behaves as a diffuse ultra-slow oblique spreading center with focused deformation found in the Central Group and São Miguel Island. The velocity field is modeled by approximating segments of the Eurasian-Nubian plate boundary with vertical dislocations with right-lateral motion and opening below a locking depth. Best fitting models have deep motion in the range of 2.4-2.7 mm/yr directed N(76.5-78.8<sup>o</sup>)E. Such displacement accounts for more than half predicted Eurasian-Nubian relative plate motion. The modeling results suggest that the locking depth in the Central Group is about 17 km while in São Miguel is about 2 km. We found transient deformation at Fogo volcano, São Miguel Island, due to unrest activity mainly during 2003–2006 and 2011–2012, and local continuous subsidence in Terceira Island, attributed to a deflation source centered on the island.</p>
Recording of GPS L1 signals
<p>This is a 15 second long recording of GPS L1 C/A signals done on 2022-03-27. To do the recording, a USRP B205 mini and GNU Radio 3.9 were used. The recording has a centre frequency of 1575.42 MHz and 4 Msps IQ 16-bit integer resolution. A small GPS patch antenna was used. The USRP was synchronized to the 1PPS output of a GPSDO connected to the same antenna, but its frequency reference was free-running.</p> <p>The recording was done to show how to process the GPS signals to obtain the timestamp of the recording. This can be seen in the post "<a href="https://destevez.net/2022/03/timing-sdr-recordings-with-gps/(opens in a new tab)">Timing SDR recordings with GPS</a>" by the author.</p> <p>The recording was done using GNU Radio metadata file format, and then converted to <a href="https://github.com/gnuradio/SigMF">SigMF</a> format. The original metadata detached header is included in the .hdr file.</p>
The GPS velocity of Kepingtagh fold-and-thrust belt
<p>Our GPS dataset for the Kepingtagh FTB started in 2008 and ended in 2019, using data from a variety of GPS instrument types. For every GPS campaign survey, we set 30s sampling rates and observed for 48-96 hours every GPS site to ensure that at least one full UTC session (and often two or more) was recorded. Our GPS observation network consists of 73 GPS stations across the entire Kepingtagh FTB (Table S1). We estimated GPS velocities for 32 campaign sites of the enhanced CMONOC project from 2008 to 2019, and 41 sites of our GPS campaigns measured in 2017, 2018, and 2019.</p>
Dataset with square plots across Sierra Nevada (Spain) where the contours of all juniper shrubs were annotated as polygons using centimetric GPS and very high resolution aerial and satellite RGB images
<p><strong>This dataset is a shapefile of 767 polygons describing the contours of Juniperus communis L. and Juniperus sabina L. shrubs for the year 2021 in rectangular plots across Sierra Nevada. The coordinates of the polygons were obtained from a field work campaign with a differential centimetric GPS, and their contours were drawn manually in QGIS using the Google Earth satellite image for 2020 and the PNOA aerial image for the 2020. </strong></p> <p><strong>This dataset also contains an excel file describing the features of each polygon: the polygon centroid coordinates, the type of species, the sexgender, the morphotype, the damage in the vegetation cover estimated in the field and telematically, certainty of the digitalization with QGIS and also if the differential centimetric GPS used belongs to the University of Granada or the University of Almeria. </strong></p>
Distribution. Ogasawara (Bonin) Is (Chichijima and Hahajima) and Iwo Is (Kita-Iwoto, Iwoto, and Minami-Iwoto) of SJapan. Sightings, GPS recordings, or traces of foraging are reported for Mukojima, Nishijima, Anijima, Ototojima, and Higashijima Is. in Pteropodidae
Distribution. Ogasawara (Bonin) Is (Chichijima and Hahajima) and Iwo Is (Kita-Iwoto, Iwoto, and Minami-Iwoto) of SJapan. Sightings, GPS recordings, or traces of foraging are reported for Mukojima, Nishijima, Anijima, Ototojima, and Higashijima Is.
FIGURE. Map of specimens collected for the phylogenetic analysis in this study, excluding Tulipa iliensis and T. altaica, which both lacked GPS information. Populations of the new species T. toktogulica are labelled in order of discovery. in Tulipa toktogulica (Liliaceae), a cryptic, endangered new species from the western Tien-Shan, Kyrgyzstan
FIGURE. Map of specimens collected for the phylogenetic analysis in this study, excluding Tulipa iliensis and T. altaica, which both lacked GPS information. Populations of the new species T. toktogulica are labelled in order of discovery.
The postseismic GPS displacements at different time windows following the 2024 M7.5 Noto Peninsula, Japan Earthquake
<p>The postseismic GPS displacements at different time windows following the 2024 M7.5 Noto Peninsula, Japan Earthquake.</p> <p>This data is based on the GPS time series from Nevada Geodetic Laboratory (NGL <span>http://geodesy.unr.edu/ )</span>.</p> <p>The format of data is:</p> <p>Time, lon, lat, dn, de, du, sign, sige, sigu</p>
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>
GPS and Pore Pressure Data from Oak Ridge Earthflow through April 24, 2024
<ul> <li>Tectonically corrected daily GPS displacement data for two stations (OREO, ORE2) located on Oak Ridge earthflow.</li> <li>Temperature corrected daily pore pressure data from a grouted vibrating wire piezometer at Oak Ridge earthflow that is co-located with the OREO GPS antenna.</li> </ul>
360 street view of ULM germany, timestamped with gps data
Open the record for dataset details and reuse information.
The GPS time series of 2005 Kashmir earthquake
<p>These data includes the near-field GPS data provided by Jouanne et al. (2011), InSAR data provided by Wang & Fialko (2014), and the far-field GPS data processed in this work. Additionally, we have uploaded the coseismic rupture models from Avouac et al. (2006) and Yan et al. (2013). The far-field GPS data includes the original time series, the fitting interseismic velocities, and the fitting time series of interseismic and postseismic. These datasets are organized into corresponding folders, with filenames that clearly describe the contents of each file.</p>
GPS data for 'Low-Latitude Ionospheric Density Irregularities and Associated Scintillations Investigated by Combining COSMIC RO and Ground-Based GPS Observations over a Solar Active Period' by Zhe Yang and Zhizhao Liu
<p>This dataset contains the final derived GPS data reported in the paper 'Low-Latitude Ionospheric Density Irregularities and Associated Scintillations Investigated by Combining COSMIC RO and Ground-Based GPS Observations over a Solar Active Period' by Zhe Yang and Zhizhao Liu.</p>
Supporting Data : Signal outages of GPS from an anomaly crest location
<p>This zipped file contains satellite specific raw phase data from GPS L1 C/A, L2C and L5 signals for vernal equinox of 2014. It also contains the sample datasheet for case study and overall statistical data.</p>
Vehicle CAN bus data (with GPS)
<p>The dataset contains 20Hz sampled CAN bus data from a passenger vehicle, e.g. WheelSpeed FL (speed of the front left wheel), SteerAngle (steering wheel angle), Role, Pitch, and accelerometer values per direction.</p> <p>In contrast to the dataset published at https://zenodo.org/record/2658168#.XMw2m6JS9PY we now have GPS data from the vehicle (see signals 'Latitude_Vehicle' and 'Longitude_Vehicle' in h5 group 'Math') and GPS data from the IMU device (see signals 'Latitude_IMU', 'Longitude_IMU' and 'Time_IMU' in h5 group 'Math') included. However, as it was exported with single_precision, therefore we lost some precision for those GPS values.</p> <p>We are currently looking for a solution and will update the records if possible.</p> <p>For data analysis we use R and R Studio (https://www.rstudio.com/) and the library h5.</p> <p>e.g. check file with R code:</p> <p>library(h5)</p> <p>f <- h5file("file path/20181113_Driver1_Trip1.hdf")</p> <p>summary(f["CAN/Yawrate1"][,])</p> <p>summary(f["Math/Latitude_IMU"][,])</p> <p>h5close(f)</p>
Hare disturbance GPS data
<p>Capture and handling of wildlife is an important component of wildlife studies, and hunting can be a central tool for wildlife management. However, human-caused disturbance of animals can cause various negative effects on individuals. Thus, an increased understanding of different disturbances on animals will allow improved mitigation of human stressors for wildlife, and provides the basis for data‐censoring when using information obtained from captured individuals. Here, we investigated the effects of capture and handling, as well as experimental disturbance, on the movement behavior of GPS-collared European hares (<i>Lepus europaeus</i>). Of 28 hares captured in box traps, 3 died during handling to fit GPS collars, likely due to acute stress. Apart from an 11% decrease in activity in both sexes the first 4 days post-capture compared to later, capture events had no significant effects on subsequent movement behavior. Hares that were disturbed experimentally, i.e. flushed with or without a shotgun shot fired, moved on average (± SD) 422 ± 206 m directly subsequent to the disturbance, leading to a spatial displacement of their short-term home range and an increased daily home range size on the disturbance day. Home range sizes returned to their pre-disturbance size on the following days, but hares remained further from field edges and spent more time in short vegetation in the days after simulated hunting, though this effect was comparatively small. Overall, our findings indicate that hares only marginally changed their movement behavior in response to short-term disturbances. Therefore, capture and hunting disturbance should not have severe negative effects on the movement behavior of individuals, but future studies should aim to reduce acute capture-related stress to avoid mortalities. We recommend that researchers should censor the first 4 days post-capture from their analyses to avoid using potentially biased data.</p>
Google Earth trace and GPS coordinates of Mongolian Great Wall
<p>Google Earth trace and GPS coordinates of Mongolian Great Wall.</p>
GPS data of CMONOC (1)
<p>GPS data of CMONOC, which were used in the studying of vertical land motion throughout the Tianshan and forelands.</p>
GPS data set used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations"
<p>Data set of continuous GPS observations at CVAN site in Gran Canaria (Canary Islands, Spain). The period of observation spans from July 3, 2013 to November 30, 2015. Data was acquired during the execution of the research project GCL2011-25494 of the Spanish Research Agency. </p> <p>This data set belongs to the Research Group ‘Geodesia’ of the University Complutense of Madrid, Spain, and has been used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations", by Jose Arnoso, Machiel S. Bos, Maite Benavent, Nigel T. Penna, Sergio Sainz-Maza, submitted to Geophysical Journal International, 2022.</p>
GPS data of CMONOC
<p>GPS data of CMONOC, which were used in the studying of vertical land motion throughout the Tianshan and forelands.</p>
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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)
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DANDI Archive for NWB datasets
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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.