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342 results for “GNSS”
GNSS refractometry data from Davos Weissfluhjoch, Switzerland in 2016/17
<p>This data is collected for the feasibility study on snow water equivalent (SWE) retrieval using the GNSS refractometry method, described in Steiner et al. (2020). </p> <p>GNSS Rinex data (1s sampling interval, multi-system, multi-frequency) from two high-end geodetic receivers (base=WJLR and rover=WJL0) are available for the complete 2016/17 season for the Swiss Alpine test sites Davos Weissfluhjoch, operated by the WSL Institute for Snow and Avalanche Research SLF (WSL SLF), Switzerland. SWE reference data from a snow pillow, snow scale, and manual observations were provided by the WSL SLF. The data processing and the SWE estimation results are revealed in Steiner et al. (2018, 2020, 2022).</p>
GNSS refractometry data from Davos Laret, Switzerland in 2021/22
<p>This data is collected for the feasibility study on (near) real-time snow water equivalent (SWE) retrieval using the GNSS refractometry method, described in Steiner et al., submitted to Sensors, 2022. </p> <p>GNSS baseline solutions, stored in zipped binary python pickle format (ENU.pkl) are available from real-time kinematic (RTK) GNSS processing for the season 2021/22 for the Swiss Alpine test sites Davos Laret, operated by the WSL Institute for Snow and Avalanche Research SLF (WSL SLF), Switzerland. Additionally, the raw GNSS Rinex data (1s sampling interval, multi-system, multi-frequency) are available for the base and rover receiver from the same setup. SWE reference data from a snow scale and manual observations were provided by the WSL SLF. The data processing and the SWE estimation results are revealed in Steiner et al. 2020 and 2022.</p>
Global Ionosphere Maps of vertical electron content combined in real-time from the RT-GIMs of CAS, CNES, UPC-IonSAT, and WHU International GNSS Service (IGS) centers (from Dec 1, 2020, to March 1, 2021)
<p>The datasets consists on 91 daily files, in IONEX format (<a href="http://ftp.aiub.unibe.ch/ionex/draft/ionex11.pdf">http://ftp.aiub.unibe.ch/ionex/draft/ionex11.pdf</a>) , corresponding to three months of global ionospheric maps (GIM) of vertical total electron content (VTEC) computed in real-time from the assessed and combined real-time GIMs generated by four analysis centers. Indeed, the Real-Time Working Group (RTWG) of International GNSS Service (IGS) is dedicated to providing high-quality data, high-accuracy products for Global Navigation Satellite System (GNSS) navigation, positioning, timing, and Earth observations. As one of the important part of real-time products, the IGS combined Real-Time Global Ionosphere Map (RT-GIM) have been generated by real-time weighting technique with the help of RT-GIMs from IGS real-time ionosphere centers including the Chinese Academy of Sciences (CAS), Centre National d’Etudes Spatiales (CNES), Universitat Politècnica de Catalunya (UPC), and Wuhan University (WHU). Compared with IGS rapid Global Ionosphere Maps (GIMs) (corg, ehrg, emrg, esrg, igrg, jprg, uhrg, uprg, uqrg, whrg) and IGS final combined GIM (igsg), the IGS combined RT-GIM (irtg) is equivalent to the post-processed GIMs and even better than some rapid GIMs. The IGS RT-GIMs are reliable sources of real-time global VTEC information and has great potential for real-time applications including range error correction for transionospheric radio signals (such as GNSS positioning, search and rescue, air traffic, radar altimetry, and radioastronomy), the monitoring of space weather (such as geomagnetic and ionospheric storms, ionospheric disturbance) and detection of natural hazards on a global scale (such as hurricanes/typhoons, ionospheric anomalies associated with earthquakes)</p>
GNSS troposphere products from a network of low-cost GNSS receivers, Wroclaw, Poland, March-April 2021
<p>This dataset contains multi-GNSS troposphere products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties. These products were obtained using 3 processing strategies:</p> <p>1) real-time (for details see https://link.springer.com/article/10.1007/s10291-020-01014-w, under to "advanced strategy" configuration, with the exception that only GPS and Galileo observations were considered);</p> <p>2) near real-time (NRT, for details see http://egvap.dmi.dk/);</p> <p>3) final (using CSRS online service, https://webapp.geod.nrcan.gc.ca/geod/tools-outils/ppp.php).</p> <p>Products are stored as standard Matlab MAT files. Each file contains a set of table arrays (Matlab format). Each table array contains the selected set of estimated parameters for a single station. Table columns are labeled and self-explanatory. A comma-delimited text file can be obtained using the in-build Matlab function "writetable.m".</p> <p>For convenience, the same information is stored in alternative data formats:</p> <p>1) for NRT and Final products: troposphere SINEX v1 (TRO / TRP)</p> <p>2) for real-time products: semicolon-delimited text files, with a self-explanatory header line; each file contains daily products for one station.</p>
Reflector heights in the Arctic permafrost areas measured by GNSS interferometric reflectometry
<p>This dataset is about the measurements of reflector height, i.e., the vertical distance between receiver antenna and groud surface, at the GNSS sites in the Arctic permafrost areas. Each data file has four columns. The 1st and 2nd show the time as year and doy, respectively. The 3rd and 4th are the reflector height and its uncertainty, respectively.</p>
Multi-GNSS constellation points
<p>50000 multi-GNSS constellation points covering the full constellations of GPS, Galileo, Beidou-2/Compass and Glonass. Generated based on a Spectracom GSC-64 simulator. Research papers this data could cite the following paper, where also benchmark results based on this data are provided:</p> <ul> <li>G.N. Ferrara, J. Nurmi and E.S. Lohan, "Multi-GNSS analysis via Spectracom constellations", in Proc. of the International Conference on Localization and GNSS (ICL-GNSS 2016), Barcelona, Spain, June 2016</li> </ul>
Geospatial Dataset of GNSS Anomalies and Political Violence Events
<p><strong>Geospatial Dataset of GNSS Anomalies and Political Violence Events</strong></p> <p><strong>Overview</strong></p> <p>The <strong>Geospatial Dataset of GNSS Anomalies and Political Violence Events </strong>is a collection of data that integrates aircraft flight information, GNSS (Global Navigation Satellite System) anomalies, and political violence events from the ACLED (Armed Conflict Location & Event Data Project) database.</p> <p><strong>Dataset Files</strong></p> <p>The dataset consists of three CSV files:</p> <ol> <li><strong>Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv</strong></li> <ul> <li><strong>Description:</strong> Contains all grids and dates that had aircraft traffic during 2023.</li> <li><strong>Number of Records:</strong> 6,777,228</li> <li><strong>Purpose:</strong> Provides a complete view of aircraft movements and associated data, including grids without any GNSS anomalies.</li> </ul> <li><strong>Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv</strong></li> <ul> <li><strong>Description:</strong> A filtered version of V1, including only the grids and dates where GNSS anomalies (jumps or gaps) were reported.</li> <li><strong>Number of Records:</strong> 718,237</li> <li><strong>Purpose:</strong> Focuses on areas and times with GNSS anomalies for targeted analysis.</li> </ul> <li><strong>Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv</strong></li> <ul> <li><strong>Description:</strong> Contains aggregated monthly data for each grid cell, combining GNSS anomalies and ACLED political violence events. Summarizes aircraft traffic, anomaly counts, and conflict activity at a monthly resolution.</li> <li><strong>Number of Records:</strong> 25,770</li> <li><strong>Purpose:</strong> Enables temporal trend analysis and spatial correlation studies between GNSS interference and political violence, using reduced data volume suitable for modeling and visualization.</li> </ul> </ol> <p><strong>Data Fields: </strong>Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv and Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv</p> <ol> <li><strong>grid_id</strong></li> <ul> <li><strong>Description:</strong> Unique identifier for a grid cell on Earth measuring 0.5 degrees latitude by 0.5 degrees longitude.</li> <li><strong>Format:</strong> String combining latitude and longitude (e.g., -10.0_-36.0).</li> </ul> <li><strong>day</strong></li> <ul> <li><strong>Description:</strong> Date of the recorded data.</li> <li><strong>Format:</strong> YYYY-MM-DD (e.g., 2023-03-28).</li> </ul> <li><strong>geometry</strong></li> <ul> <li><strong>Description:</strong> Polygon coordinates of the grid cell in Well-Known Text (WKT) format.</li> <li><strong>Format:</strong> POLYGON((longitude latitude, ...)) (e.g., POLYGON((-36.0 -10.0, -35.5 -10.0, -35.5 -9.5, -36.0 -9.5, -36.0 -10.0))).</li> </ul> <li><strong>flights</strong></li> <ul> <li><strong>Description:</strong> Number of aircraft flights that passed through the grid on that day.</li> <li><strong>Format:</strong> Integer (e.g., 28).</li> </ul> <li><strong>GPS_jumps</strong></li> <ul> <li><strong>Description:</strong> Number of reported GNSS "jump" anomalies (possible spoofing incidents) in the grid on that day.</li> <li><strong>Format:</strong> Integer (e.g., 1).</li> </ul> <li><strong>GPS_gaps</strong></li> <ul> <li><strong>Description:</strong> Number of reported GNSS "gap" anomalies, indicating gaps in aircraft routes, in the grid on that day.</li> <li><strong>Format:</strong> Integer (e.g., 0).</li> </ul> <li><strong>gaps_density</strong></li> <ul> <li><strong>Description:</strong> Density of GNSS gaps, calculated as the number of gaps divided by the number of flights.</li> <li><strong>Format:</strong> Decimal (e.g., 0).</li> </ul> <li><strong>jumps_density</strong></li> <ul> <li><strong>Description:</strong> Density of GNSS jumps, calculated as the number of jumps divided by the number of flights.</li> <li><strong>Format:</strong> Decimal (e.g., 0.035714286).</li> </ul> <li><strong>event_id_cnty</strong></li> <ul> <li><strong>Description:</strong> ACLED event ID corresponding to political violence events in the grid on that day.</li> <li><strong>Format:</strong> String (e.g., BRA69267).</li> </ul> <li><strong>disorder_type</strong></li> <ul> <li><strong>Description:</strong> Type of disorder as classified by ACLED (e.g., "Political violence").</li> <li><strong>Format:</strong> String.</li> </ul> <li><strong>event_type</strong></li> <ul> <li><strong>Description:</strong> General category of the event according to ACLED (e.g., "Violence against civilians").</li> <li><strong>Format:</strong> String.</li> </ul> <li><strong>sub_event_type</strong></li> <ul> <li><strong>Description:</strong> Specific subtype of the event as per ACLED classification (e.g., "Attack").</li> <li><strong>Format:</strong> String.</li> </ul> <li><strong>acled_count</strong></li> <ul> <li><strong>Description:</strong> Number of ACLED events in the grid on that day.</li> <li><strong>Format:</strong> Integer (e.g., 1).</li> </ul> <li><strong>acled_flag</strong></li> <ul> <li><strong>Description:</strong> Indicator of ACLED event presence in the grid on that day (0 for no events, 1 for one or more events).</li> <li><strong>Format:</strong> Integer (0 or 1).</li> </ul> </ol> <p><strong> </strong></p> <p><strong>Data Fields: </strong>Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv</p> <p>The file contains monthly aggregated GNSS anomaly and ACLED event data per grid cell. The structure and meaning of each field are detailed below:</p> <ol> <li><strong>grid_id</strong></li> <ul> <li><strong>Description</strong>: Unique identifier for a grid cell on Earth measuring 0.5° latitude by 0.5° longitude.</li> <li><strong>Format</strong>: String combining latitude and longitude (e.g., -0.5_-79.0).</li> </ul> <li><strong>year_month</strong></li> <ul> <li><strong>Description</strong>: Month and year of the aggregated data.</li> <li><strong>Format</strong>: String in Mon-YY format (e.g., Jan-23).</li> </ul> <li><strong>geometry</strong></li> <ul> <li><strong>Description</strong>: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.</li> <li><strong>Format</strong>: POLYGON((longitude latitude, ...))<br>(e.g., POLYGON((-79.0 -0.5, -78.5 -0.5, -78.5 0.0, -79.0 0.0, -79.0 -0.5))).</li> </ul> <li><strong>flights</strong></li> <ul> <li><strong>Description</strong>: Total number of aircraft flights that passed through the grid cell during the month.</li> <li><strong>Format</strong>: Integer (e.g., 1230).</li> </ul> <li><strong>GPS_jumps</strong></li> <ul> <li><strong>Description</strong>: Total number of GNSS "jump" anomalies (possible spoofing events) in the grid cell during the month.</li> <li><strong>Format</strong>: Integer (e.g., 13).</li> </ul> <li><strong>GPS_gaps</strong></li> <ul> <li><strong>Description</strong>: Total number of GNSS "gap" anomalies, indicating interruptions in aircraft routes, during the month.</li> <li><strong>Format</strong>: Integer (e.g., 0).</li> </ul> <li><strong>event_id_cnty</strong></li> <ul> <li><strong>Description</strong>: Semicolon-separated list of ACLED event IDs associated with the grid cell during the month.</li> <li><strong>Format</strong>: String (e.g., ECU3151;ECU3158;ECU3150).</li> </ul> <li><strong>disorder_type</strong></li> <ul> <li><strong>Description</strong>: Semicolon-separated list of disorder types (e.g., "Political violence", "Demonstrations") reported by ACLED in that grid cell during the month.</li> <li><strong>Format</strong>: String.</li> </ul> <li><strong>event_type</strong></li> <ul> <li><strong>Description</strong>: Semicolon-separated list of high-level ACLED event types (e.g., "Riots", "Protests").</li> <li><strong>Format</strong>: String.</li> </ul> <li><strong>sub_event_type</strong></li> </ol> <ul> <li><strong>Description</strong>: Semicolon-separated list of detailed subtypes of ACLED events (e.g., "Mob violence", "Armed clash").</li> <li><strong>Format</strong>: String.</li> </ul> <ol> <li><strong>acled_count</strong></li> </ol> <ul> <li><strong>Description</strong>: Total number of ACLED conflict events in the grid cell during the month.</li> <li><strong>Format</strong>: Integer (e.g., 2).</li> </ul> <ol> <li><strong>acled_flag</strong></li> </ol> <ul> <li><strong>Description</strong>: Conflict presence indicator: 1 if any ACLED event occurred in the grid cell during the month, otherwise 0.</li> <li><strong>Format</strong>: Integer (0 or 1).</li> </ul> <ol> <li><strong>gaps_density</strong></li> </ol> <ul> <li><strong>Description</strong>: Monthly density of GNSS gaps, calculated as GPS_gaps / flights.</li> <li><strong>Format</strong>: Decimal (e.g., 0.0).</li> </ul> <ol> <li><strong>jumps_density</strong></li> </ol> <ul> <li><strong>Description</strong>: Monthly density of GNSS jumps, calculated as GPS_jumps / flights.</li> <li><strong>Format</strong>: Decimal (e.g., 0.0106).</li> </ul> <p><strong> </strong></p> <p><strong>Data Sources</strong></p> <ul> <li><strong>GNSS Anomalies Data:</strong></li> <ul> <li>Calculated from ADS-B (Automatic Dependent Surveillance-Broadcast) messages obtained via the OpenSky Network's Trino database.</li> <li>GNSS anomalies include "jumps" (potential spoofing incidents) and "gaps" (interruptions in aircraft route data).</li> </ul> <li><strong>Political Violence Events Data:</strong></li> <ul> <li>Sourced from the ACLED database, which provides detailed information on political violence and protest events worldwide.</li> </ul> </ul> <p><strong>Temporal and Spatial Coverage</strong></p> <ul> <li><strong>Temporal Coverage:</strong></li> <ul> <li>From January 1, 2023, to December 31, 2023.</li> <li>Daily records provide temporal granularity for time-series analysis.</li> </ul> <li><strong>Spatial Coverage:</strong></li> <ul> <li>Global coverage with grid cells measuring 0.5 degrees latitude by 0.5 degrees longitude.</li> <li>Each grid cell represents an area on Earth's surface, facilitating spatial analysis.</li> </ul> </ul> <p><strong>Usage and Applications</strong></p> <ul> <li><strong>Security Analysis:</strong></li> <ul> <li>Assess potential correlations between GNSS anomalies and political violence events.</li> <li>Identify regions with increased risk of GNSS spoofing or signal disruption.</li> </ul> <li><strong>Research and Development:</strong></li> <ul> <li>Develop models to predict socio-political events based on GNSS anomalies.</li> <li>Study the impact of political instability on aviation safety.</li> </ul> <li><strong>Policy and Decision Making:</strong></li> <ul> <li>Inform aviation authorities and policymakers about regions requiring enhanced navigation security measures.</li> <li>Support conflict analysis and monitoring efforts.</li> </ul> </ul> <p> </p> <p> </p>
Precision viticulture dataset for detailed vineyard mapping composed of geotagged smartphone ground images, phytosanitary status, UAV orthomosaics, 3D point clouds, and RTK GNSS data - Northern Spain, July 2022
<p>This dataset offers a rich multimodal collection of data from vineyards, designed to enhance agricultural research with a focus on vineyard management and disease monitoring. It includes geotagged smartphone ground images in ".7z" format for detailed plant-level analysis, a ".csv" file detailing plants' phytosanitary status for health assessment, UAV-derived 3D Point Clouds and orthomosaics in ".las" and ".tiff" formats for aerial landscape views, and RTK GNSS data in ".shp" format for precise plant geolocations.</p> <p>This dataset can be combined with other datasets to enable a comprehensive view of the vineyards and improve its value:</p> <div> <ul> <li>Ariza-Sentís, Mar, Sergio Vélez, and João Valente. ‘Dataset on UAV RGB Videos Acquired over a Vineyard Including Bunch Labels for Object Detection and Tracking’. <em>Data in Brief</em> 46 (February 2023): 108848. <a href="https://doi.org/10.1016/j.dib.2022.108848">https://doi.org/10.1016/j.dib.2022.108848</a>.</li> <li>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘VineLiDAR: High-Resolution UAV-LiDAR Vineyard Dataset Acquired over Two Years in Northern Spain.’ <em>Data in Brief</em>, October 2023, 109686. <a href="https://doi.org/10.1016/j.dib.2023.109686">https://doi.org/10.1016/j.dib.2023.109686</a>.</li> <li> <div> <div>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘Dataset on Unmanned Aerial Vehicle Multispectral Images Acquired over a Vineyard Affected by Botrytis Cinerea in Northern Spain’. <em>Data in Brief</em> 46 (February 2023): 108876. <a href="https://doi.org/10.1016/j.dib.2022.108876">https://doi.org/10.1016/j.dib.2022.108876</a>.</div> <div> </div> </div> </li> </ul> </div>
Seasonal Terrestrial Water Load Modulation of Seismicity at the Southeastern Margin of the Tibetan Plateau Constrained by GNSS and GRACE Data
<p>Data Set S1. The earthquake catalog is used to decluster aftershocks and background events, and the time range is from July 2004 to July 2021. This data set includes 672585 events in the study area.</p> <p>Data Set S2. Focal mechanism solutions of M ≥ 4 earthquakes at the southeastern margin of the Tibetan Plateau. The data set includes 634 solutions of earthquakes M ≥ 4, and the time range is from 2009 to 2017.</p>
RINEX files from low-cost GNSS receivers in Wrocław, Poland; January - March, 2021
<p>Daily RINEX files with multi-GNSS (GPS, GLONASS, Galileo) observations at 30 sec. interval obtained with low-cost GNSS receiver u-blox ZED-F9P and u-blox patch antennas (except BX02 - ArduSimple survey antenna). Time period (depending on stations): 27.02.2021 - 28.03.2021.</p>
GATEMAN project, GNSS raw data in presence of spoofing
<p>GNSS raw data generated during the in-lab validation activities of spoofing detection and localization performed in the frame of the <strong>GATEMAN</strong> project. These files are grouped for each type of validation scenario defined. A word file describing the test setup is included.</p>
Integrated DInSAR + GNSS example data sets
<p>This data repository contains sample datasets of raw DInSAR time series (NSBAS_PARAMS.h5), raw, interpolated GNSS time series maps (GPS_East/North/Up.h5) , errors associated with the GNSS data (GPS_East/North/Up_sigma.h5), and integrated DInSAR + GNSS time series (fused.h5). Details about the data can be read about in the following publication: [Corsa, B. "Integration of DInSAR Time Series and GNSS data for Continuous Volcanic Deformation Monitoring and Eruption Early Warning Applications" <em>Remote Sens.</em> <strong>2022</strong>, <em>14</em>(3), 784; <a href="https://doi.org/10.3390/rs14030784">https://doi.org/10.3390/rs14030784</a>]. The raw DInSAR time series spans 245 dates between 2015-11-11 to 2021-04-13 over the Big Island of Hawaii. The current raw GPS data and fused time series used 22 data points between those same dates. </p>
HR-GNSS data used in Neuro-Fuzzy Kinematic Finite-Fault Inversion: 2. Application to the Mw6.2, 24/August/2016, Amatrice Earthquake
<p>Here are the high-rate GNSS data we used to infer the low-frequency components of seismic source radiation within the M 6.2, 24/August/2016, Amatrice Earthquake. In particular, the traces are used to constrain frequencies between 0.03-0.06 Hz. This data has been used to evaluate the performance of the method, in a train/test split procedure, described in the manuscript. We upload data here to comply with AGU Fair data policy (https://www.agu.org/Publish-with-AGU/Publish/Author-Resources/Policies/Data-policy)</p> <p>Please find the pre-print of the manuscript from the ESSOAR (<a href="https://doi.org/10.1002/essoar.10504341.1">https://doi.org/10.1002/essoar.10504341.1</a>).</p> <p>Notice that the complete set of data are reposited on INGV FTP server: ftp://gpsfree.gm.ingv.it/amatrice2016/</p> <p>The data is originally processed by Avallone et al. (2016), and the detailed analysis procedure has been explained there. In the case where you used this data, please cite the original articles: </p> <p>Avallone, A., Latorre, D., Serpelloni, E., Cavaliere, A., Herrero, A., Cecere, G., ... & Selvaggi, G. (2016). Coseismic displacement waveforms for the 2016 August 24 Mw 6.0 Amatrice earthquake (central Italy) carried out from High-Rate GPS data. Annals of Geophysics, 59. (<a href="https://doi.org/10.4401/ag-7275">https://doi.org/10.4401/ag-7275</a>)</p> <p>Avallone, A., Selvaggi, G., D'Anastasio, E., D'Agostino, N., Pietrantonio, G., Riguzzi, F., ... & Zarrilli, L. (2010). The RING network: improvement of a GPS velocity field in the central Mediterranean. Annals of Geophysics, 53(2), 39-54. (<a href="https://doi.org/10.4401/ag-4549">https://doi.org/10.4401/ag-4549</a>)</p> <p> </p>
GNSS training - dataset - Wrocław - 1st GATHERS Summer School
<p>The files in the collection contain a 3D time-series of displacements in [m]. Data were collected before 1st GATHERS Summer School in Wrocław as a reference data. The observations were taken at the roof of Wrocław University of Environmental and Life Sciences - Institute of Geodesy and Geoinformatics: <a href="https://goo.gl/maps/W94mr2UhJKn7XH1z9">Location in Google Maps</a></p> <p>Please note that each file is containing 3 time-series in North, East and Up direction.</p> <p>There are three headers beginning each of the time-series.</p> <p>The GNSS_PPP.ascii contains the results of kinematic PPP (Precise Point Positioning)</p> <p>The GNSS_VAD.ascii contains the results of a kinematic variometric approach to GNSS data processing (from VADASE software).</p> <p>The ACC_DISPL.ascii contains the displacements calculated from accelerations measured with GeoTiny!AC accelerometer.</p> <p>The GNSS antenna and the accelerometer were co-located with a distance of 10 cm and rigidly connected.</p> <p>If some questions arise, please send a message to:</p> <p><a href="mailto:jan.kaplon@upwr.edu.pl">jan.kaplon@upwr.edu.pl</a>, </p> <p> </p>
Dataset for the article EDM-GNSS distance comparison at the EURO5000 calibration baseline: preliminary results
<p>GNSS RINEX observation files for the observation campaign used in the article "EDM-GNSS distance comparison at the EURO5000 calibration baseline: preliminary results" by Kinga Wezka, Luis García-Asenjo, Dominik Próchniewicz, Sergio Baselga, Ryszard Szpunar, Pascual Garrigues, Janusz Walo and Raquel Luján, Journal of Applied Geodesy https://doi.org/10.1515/jag-2022-0049. The work leading to this paper was performed within the 18SIB01 GeoMetre project of the European Metrology Programme for Innovation and Research (EMPIR). This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme, funder ID: 10.13039/100014132. Raquel Luján acknowledges the funding from the Programa de Ayudas de Investigación y Desarrollo (PAID-01-20) de la Universitat Politècnica de València.</p>
Dataset - Simulation results GNSS raytracing INTOMO
<p>The 3D ray-tracing algorithm is based on the reconstruction of the signal path within the grid model (3D refractivity field). For our studies, an extension of the original 3D ray-tracing module from ATom (Atmospheric Tomography) GNSS software package [37] is used. The necessary modifications were inspired by Javaherian et al. [44]. The first step of ray-tracing in our approach (shooting<br> technique) is based on the prior knowledge of transmitter and receiver positions, as well as information about weather parameters and thus, refraction at nodal points of the grid. The transmitter and receiver are the GPS satellite and the LEO satellite, respectively. In the current version, coordinates are delivered by atmPhs files in an earth-centered inertial (ECI) coordinate system [45].</p>
Additional results for article "A new approach for the generation of real-time GNSS low-latitude ionospheric scintillation maps"
<p>Complete set of interpolation error and correlation metrics for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options using the SSS cross-validation scheme for the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Complete table of interpolation errors and correlation.csv”.</p> <p>Complete set of scintillation maps for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options covering the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Scintillation maps for the 10-hour dataset.zip”.</p> <p>Comparison plots of the scintillation maps generated by the approaches GDA, IDW, RBF and GPR with the pre-processing options SAR, SMR and VQI for each of the 40 intervals of time of 16 minutes covering the 10-hour dataset - file “Set of maps for all 4 approaches with the SAR, SMR and VQI sets of options.zip”.</p> <p>Sequence of scintillation maps for the 8-hour dataset generated by the GPR(VQI) approach for the three time resolutions (1, 2, and 16-minute) - file “Scintillation maps for the 8-hour comparison dataset.zip”.</p> <p>Animations corresponding to the sequence of maps generated by the GPR(VQI) approach for the 8-hour dataset, and for the three time resolutions (1, 2, and 16-minute) - file “Animations of GPR(VQI) maps for the 8-hour comparison dataset.zip”.</p>
Datasets of the work named Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform
<pre>- 1_Motion_Simulator/ - IMU_results/ - 20211028101756.csv - 20220114101543.csv - 20220117000000.csv - Rotary_Table/ - 15/ - rover_20220105.nav - rover_20220105.obs - solution_20220105_CAS.log - 360/ - rover_20211221.nav - rover_20211221.obs - solution_20211221_CAS.log - 360-15/ - rover_20220202_CAS.nav - rover_20220202_CAS.obs - solution_20220202_CAS.log - Static_Tests/ - solution_SSRA00CAS0 - solution_SSRA00WHU0 - 2_GNSS_Signal_Simulator/ - platformmov_C1.xtd - platformmov_C2.xtd - platformmov_C3.xtd - TestBetaNoneMov_C1 - TestBetaNoneMov_C1.nav - TestBetaNoneMov_C1.obs - TestBetaNoneMov_C1.ubx - TestBetaNoneMov_C2 - TestBetaNoneMov_C2.nav - TestBetaNoneMov_C2.obs - TestBetaNoneMov_C2.ubx - TestBetaNoneMov_C3 - TestBetaNoneMov_C3.nav - TestBetaNoneMov_C3.obs - TestBetaNoneMov_C3.ubx - 3_Test_Sea/ - 20220503000000.xlsx - solution_28.nav - solution_28.obs - solution_28.ubx Background: {Journal Article using this dataset} 'Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform' Paper DOI: <a href="https://doi.org/10.3390/s23020925">https://doi.org/10.3390/s23020925</a> Abstract: a low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved. </pre>
The GNSS time series along the northern coastline of Java, Indonesia
<p>This repository contains the GNSS time series along the northern coastline of Java, both in .rneu and the GAGE's .pos format (https://www.unavco.org/data/gps-gnss/derived-products/docs/NOTICE-TO-DATA-PRODUCT-USERS-GPS-2013-03-15.pdf). The repository also contains the stations' coordinates. </p> <p>Notes:<br> In the .rneu format of the GNSS time series:<br> 1. The outliers have been removed.<br> 2. The offsets due to instrument changes at CGON in mid-2016 and at CSIT in late 2015 have been corrected.</p> <p>Please refer to:<br> Susilo, S., Salman, R., Hermawan, W. <em>et al.</em> GNSS land subsidence observations along the northern coastline of Java, Indonesia. <em>Sci Data</em> <strong>10</strong>, 421 (2023). https://doi.org/10.1038/s41597-023-02274-0</p>
Towards the Future Generation of Railway Localization Exploiting RTK and GNSS
<p>This repository contains the datasets acquired by ETH-PBL in conjunction with Unibo and SADEL during two days of testing in October 2022 near Modena, Italy.</p> <p>The data were acquired using two sensor nodes developed by ETH Zurich running a <a href="https://www.st.com/en/microcontrollers-microprocessors/stm32l452ce.html">STM32L452CEU6</a> MCU.<br> Each node collected data on the motion of the train using an <a href="https://www.st.com/en/mems-and-sensors/asm330lhh.html">ST ASM330LHH</a> automotive grade IMU as well as a <a href="https://www.u-blox.com/en/product/zed-f9p-module">u-blox ZED-F9P</a> GNSS module fed with live RTCM-data from a closeby RTK base station provided by SADEL. The base station utilized another ZED-F9P GNSS module connected to a Raspberry Pi which transmitted the generated RTCM correction packages over a raw TCP socket.<br> The data was then received using a <a href="https://www.u-blox.com/en/product/sara-r4-series">u-blox SARA-R4</a> cellular network module.</p> <p>The track was chosen as it exposes a variety of interesting GNSS environments. Encountered environments are ranging from urban over suburban to open field environments as well as one tunnel. Due to this composition, the availability of cellular connection and thus RTK correction data was patchy but mostly stable.</p> <p>The two sensor nodes were fixed to the Train Chassis, one centered in the train and the other positioned on the left side in driving orientation. Node 1 was placed on the floor in front of the driver's seat and positioned to be aligned with the center of the train in the lateral direction. A TOPGNSS TOP106 L1/L2 multi-band antenna was placed below the rear-facing windscreen also aligned with the same axis. Node 2 was mounted on a window on the left side of the train when facing in the direction of travel. This is approximately 1m above the floor and 1.4m left to the lateral center of the train. An ANN-MB00 L1/L2 antenna was attached to the outside frame of the train above the window.</p> <p>This dataset is linked with the GitHub repository at <a href="https://github.com/ETH-PBL/Railway-Precise-Localization">Railway-Precise-Localization</a> where the data format description and the pre-processing scripts are provided.</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)
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.