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20 results for “GPS Positioning”
The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh
<p><strong>Note: Downloading the large data file could have a timeout issue. If you cannot directly download it here, please use the following link as a complementary method for getting the data. </strong></p> <p><a href="https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing">https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing</a></p> <p> </p> <p>This dataset contains the dynamic responses (acceleration records) of two passenger trains with corresponding GPS positions, environmental conditions and track maintenance schedules for a light rail network in the city of Pittsburgh, Pennsylvania in the United States of America.</p> <p>In particular, two light rail vehicles were instrumented (identified as LRV4306 and LRV4313): <br> LRV 4306 has 5 acceleration channels, corresponding to the two uni-axial accelerometers inside the train and the three channels of the tri-axial accelerometer on the wheel truck.</p> <p><em>- The last digit of each acceleration file: 1, 2, 3, 4, 5<br> - Corresponding sensor channels: tri-axial x, tri-axial y, tri-axial z, front cabinet uni-axial, back cabinet uni-axial</em></p> <p><br> LRV 4313 has 8 acceleration channels, corresponding to the two uni-axial accelerometer and the two tri-axial accelerometers inside the train.</p> <p><em>- The last digit of each acceleration file: 1, 2, 3, 4, 5, 6, 7, 8<br> - Corresponding sensor channels: front cabinet uni-axial, back cabinet uni-axial, front tri-axial x, front tri-axial y, front tri-axial z, back tri-axial x, back tri-axial y, back tri-axial z.<br> - x longitudinal (vehicle moving direction); y-axis, transverse; z-axis, vertical.</em></p> <p>The dataset contained in this repository is a condensed version of the original raw data. While the accelerometers on the train were sampled continuously, this dataset contains only those measurements for when the train was actually moving along the track (i.e. not idling at a terminal).</p> <p>The data is stored in binary MAT-files (a MATLAB/Octave data format). These files contain MATLAB objects of the class "pass", which is defined in the file pass.m that can be found in the "code" folder. Specifically, two MAT-files named "obj_dic.mat", and found in the "LRV4306" and "LRV4313" folders, contain the "pass" objects of the two trains, respectively.</p> <p>Each category is described in detail. For more detail on the regions of the track, refer to the 'region.fig' file in this folder. The track was divided into distinct regions so that the data over specific sections of track could be compared. These regions were chosen for two reasons: <br> (1) within a region, the train always followed the same track and <br> (2) there are no tunnels in them so the GPS data is relatively consistent. </p> <p>To get started, using MATLAB or Octave try running "main_script.m" in the "code" folder.</p> <p>A data descriptor paper with details of the data collection process was published.</p> <p>Please cite as</p> <p><strong>Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Scientific Data, 6, 146. <a href="https://doi.org/10.1038/s41597-019-0148-9">https://doi.org/10.1038/s41597-019-0148-9</a>(2019)</strong></p> <p><strong>Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Zenodo, <a href="https://doi.org/10.5281/zenodo.1432702">https://doi.org/10.5281/zenodo.1432702</a>(2018).</strong></p> <p>For questions or suggestions please e-mail Jingxiao Liu <liujx@stanford.edu></p>
LoRa signal quality and GPS positioning time series dataset
<p>This time series dataset contains measurements taken by moving devices equipped with LoRa communication and GPS positioning capabilities. The measurements have been captured at the Sálvora Archipelago in Galicia (Spain), which belong to the Atlantic Islands of Galicia National Park, in which we have deployed three LoRa gateways at the following locations (latitude, longitude, altitute):</p> <ul> <li>Gateway 1 (42.46972, -9.01345, 73)</li> <li>Gateway 2 (42.49955, -9.00654, 5)</li> <li>Gateway 3 (42.50893, -9.04902, 31)</li> </ul> <p>The dataset is provided as a single comma-separated values (CSV) file, with the following data in each line:</p> <ul> <li>Device identification</li> <li>Received signal strength indicator (RSSI) from LoRa Gateway 1</li> <li>Signal-to-noise ratio (SNR) from Gateway 1</li> <li>RSSI from LoRa Gateway 2</li> <li>SNR from Gateway 2</li> <li>RSSI from LoRa Gateway 3</li> <li>SNR from Gateway 3</li> <li>LoRa spreading factor</li> <li>Timestamp</li> <li>Device latitude received from GPS</li> <li>Device longitude received from GPS</li> <li>Device altitude received from GPS</li> </ul>
GPS RINEX data and position solutions (BUK1 and ARO2)
<p>*.zip => RINEX data</p><p>*.txt => position solutions in IGS20 reference frame</p><p>anal.m => analysis and plotting script in Matlab</p>
Monitoring mobility in older adults using a global positioning system (GPS) smartwatch and accelerometer: A validation study
<p><strong>Background</strong></p> <p>There is interest in identifying the most reliable method for detecting early mobility limitations. Accelerometry and Global Positioning System (GPS) could provide insight into declines in mobility, but few studies have used this multi-sensor approach to monitor mobility in older adults. </p> <p><strong>Methods</strong></p> <p>Thirty-two volunteers (66.2±6.3 years) agreed to participate in our validation study. We conducted two experiments to determine the validity of the TicWatch S2 and Pro 3 Ultra GPS models against the Qstarz receiver in measuring life-space mobility, trip frequency, duration, and mode. We also assessed the accuracy of the TicWatch in measuring step count and agreement with the ActiGraph wGT3X-BT for activity counts and sedentary behavior. Participants wore devices simultaneously for three consecutive days and recorded activity and trip information.</p> <p><strong><span>Results</span></strong></p> <p>The TicWatch Pro 3 Ultra GPS performed better than the S2 model and was similar to the Qstarz in all tested trip-related measures, and it was able to estimate both passive and active trip modes. Both models showed similar results to the Qstarz in life-space-related measures. The TicWatch S2 demonstrated good to excellent overall agreement with the ActiGraph algorithms for the time spent in sedentary and non-sedentary activities, with 84% and 87% agreement rates, respectively. Under supervised conditions, the TicWatch Pro 3 Ultra GPS measured step count consistently with the gold standard observer, with a bias of 0.4 steps. The thigh-worn ActiGraph algorithm accurately classified sitting and lying postures (97%) and standing postures (90%).</p> <p><strong>Conclusion</strong></p> <p>Our multi-sensor approach to monitoring mobility has the potential to capture both accelerometer-derived movement data and trip/life-space data only available through GPS. In this study, we found that the TicWatch models are valid devices for capturing GPS and raw accelerometer data, making them useful tools for assessing real-world mobility in older adults and advancing our knowledge of early mobility decline.</p>
GPS position time series data for the Tatun Volcano Group (TVG) region
<p>GPS Time Series Data Used in "Transient Deformation in the Tatun Volcano Group, Taiwan: A Spatiotemporal GPS Analysis" by Chang et al.:</p> <p>1. Time series data of six GPS stations in TVO (YM03, YM05, YM06, YM07, YMN4, and YMSM, see Figure 1 of the main text) are included in the zip file "tvo.final_igb14.pos.tar.gz".</p> <p>2. The files are in plain text with the stardard PBO data format (https://www.unavco.org/data/gps-gnss/derived-products/docs/knowledgetree-docs-old/gps_timeseries_format.pdf), which is also listed and explained at the top of each file.</p>
Monitoring mobility in older adults using a global positioning system (GPS) smartwatch and accelerometer: A validation study
Open the record for dataset details and reuse information.
GPS dataset for 'Global View of Ionospheric Disturbance Impacts on Kinematic GPS Positioning Solutions during the 2015 St Patrick's Day Storm' by Zhe Yang, Y. Jade Morton, Irina Zakharenkova, Iurii Cherniak, Shuli Song, Wei Li
<p>This dataset contains observations of ionospheric plasma irregularities and GPS positioning errors for ~5500 stations reported in the paper 'Global View of Ionospheric Disturbance Impacts on Kinematic GPS Positioning Solutions during the 2015 St Patrick’s Day Storm' by Zhe Yang, Y. Jade Morton, Irina Zakharenkova, Iurii Cherniak, Shuli Song, Wei Li</p>
Supporting data for "The ionospheric effects of the 2022 Hunga Tonga Volcano eruption and the associated impacts on GPS Precise Point Positioning across the Australian region"
<p>This dataset contains the GPS Vertical Total Electron Content (TEC) data, the Rate of TEC, and the GPS Precise Point Positioning convergence time results as presented in Carter et al. (2023). For access to the raw datasets used in the analysis, the reader is referred to details in Carter et al. (2023).</p> <p>Carter, B. A., R. Pradipta, T. Dao, J. L. Currie, S. Choy, P. Wilkinson, P. Maher, R. Marshall, K. Harima, M. LeHuy, T. Nguyen Chien, T. Nguyen Ha, T. J. Harris (2023) The ionospheric effects of the 2022 Hunga Tonga Volcano eruption and the associated impacts on GPS Precise Point Positioning across the Australian region, Submitted to Space Weather, March, 2023.</p>
Global Positioning Satellite and Accelerometry to Assess Human Locomotion (ACTI GPS)
ClinicalTrials.gov study NCT01805219. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Global Positioning System (GPS) Exposure to Environments & Relations With Biomarkers of Cancer Risk
ClinicalTrials.gov study NCT02094170. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Performance and accuracy of lightweight and low-cost GPS data loggers according to antenna positions, fix intervals, habitats and animal movements
Open the record for dataset details and reuse information.
SWOT 2019-2020 Prelaunch Oceanography Field Campaign JPL Global Positioning Systems (GPS)
This dataset provides the 1Hz time series of the sea surface height measured by a surface buoy equipped with a Global Position System (GPS). The GPS-mooring was deployed by the SWOT prelaunch field campaign conducted near the SWOT CalVal crossover location, about 300 kilometers west of Monterey, California between September, 2019 and January, 2020. The GPS measurements represent the total sea surface height including the Inverted barometer component. The same mooring also carries fixed-depth CTD sensors https://doi.org/10.5067/SWTPR-CTD11. They were used together with atmospheric pressure and bottom pressure measurements to close the sea surface equation (Wang et al., 2022). The campaign also deployed another two CTD moorings, a slocum glider, and a Pressure Inverted Echo Sounder (PIES). Details can be found in the user guide and the journal reference given in the documentation section.
Dataset accompanying article: On Linear and Circular Approach to GPS Data Processing: Analyses of the Horizontal Positioning Deviations Based on the Adriatic Region IGS Observables
<p>The dataset accompanies journal article: On Linear and Circular Approach to GPS Data Processing: Analyses of the Horizontal Positioning Deviations Based on the Adriatic Region IGS Observables published on 21 January 2021 in Data. </p> <p>SINEX, RINEX, position solution files, logs used for positional accuracy and deviation distribution of IGS observables and additional figures with results are included. Dataset covers period from 10th to 20th of March 2017. </p> <p>IGS SINEX combined weekly station position/velocity solutions were retrieved from the online archives of the Crustal Dynamics Data Information System (CDDIS), NASA Goddard Space Flight Center, Greenbelt, MD, USA, available at: ftp://cddis.nasa.gov/gnss/products/. Please note that from from October 31, 2020 anonymous ftp service has been discontinued. Access with examples is available via HTTPS or ftp-ssl from <a href="https://cddis.nasa.gov/Data_and_Derived_Products/CDDIS_Archive_Access.html">CDDIS Archive</a></p> <p>RINEX observation and navigation files were retrieved from IGS repository available at: ftp://igs.ign.fr/pub/igs/data/</p> <p>Position solution files were determined with <a href="https://github.com/tomojitakasu/RTKLIB_bin/">RTKLIB</a>: An Open Source Program Package for GNSS Positioning, version 2.4.3 b33 available</p> <p>Please cite respective sources accordingly.</p> <p> </p>
IceBridge UAF GPS/IMU L1B Corrected Position and Attitude Data, Version 1
This data set contains Global Positioning System (GPS) and Inertial Measurement Unit (IMU) positioning and orientation measurements for the University of Alaska Fairbanks Glacier lidar system campaigns. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.
Differences in GPS variables according to playing formations and playing positions in U19 male soccer players
<p>The aims of this study were 1) to investigate Global Positioning System (GPS)-based match physical performance according to players’ playing position in three different playing formations (4-4-2, 3-5-2, 4-3-3) and 2) to analyse the differences in match performance between 1st and 2nd half. Twenty-three U19 elite male soccer players (age: 18 ± 1 year, height: 1.80 ± 0.04 m, body mass: 70.65 ± 6.02 kg), categorized as Central Back (CB, n = 5), Full Back (FB, n = 4), Central Midfielders (CM, n = 4), Wingers (W, n = 3), Strikers (S, n = 7), were monitored using 10 Hz GPS during 31 competitive matches. The results showed that FB and W always had the highest very high-speed running distance and number of sprints in all playing formations. Significant decrease in all GPS variables was observed in the 2nd half of the match for all playing positions. Strength coaches should adopt specific training regimes in accordance with players’ playing position.</p>
Geolocation Positional System (GPS) Experience
ClinicalTrials.gov study NCT05991713. IPD Sharing: NO. Countries: 1. Publications: 0.
GPS: Adaptation Trial of an HIV Prevention Counselling Program for HIV-positive and HIV-negative Gay and Bisexual Men
ClinicalTrials.gov study NCT03186183. IPD Sharing: NO. Countries: 1. Publications: 0.
IceBridge GPS L1B Time-Tagged Real-Time Position and Attitude Solution, Version 1
This data set contains elevation, roll, pitch, and heading measurements over Antarctica using the Ashtech GG24 GPS+ receiver and the Systron and Donner MMQ-50 Inertial Measurement Unit (IMU). The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC), with additional support from NASA Operation IceBridge.
IceBridge GPS/IMU L1B Primary Position and Attitude Solution, Version 1
This data set contains elevation, roll, pitch, heading, north-south acceleration, east-west acceleration, and vertical acceleration measurements over Antarctica using the Systron and Donner MMQ-50 Inertial Measurement Unit (IMU) and the Topcon GB-1000 GPS+ receiver. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC), with additional support from NASA Operation IceBridge.
Aviation Safety Reporting System: Global Positioning (GPS) Reports
A variety of incident reports involving the use of GPS devices.
ScienceDex guides
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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.