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338 results for “GPS”
GPS Locations of Seagrass Sites in Hog Island Bay and South Bay, VA 2010-2021
This dataset contains the GPS coordinates for plots within the Z. marina restoration in the Hog Island Bay and South Bay, VA that are sampled annually as part of the long-term restoration experiment. Plots were seeded between 2001-2008; 64 plots in Hog Island Bay (58 seagrass plots and 6 bare plots) and 12 plots in South Bay (6 seagrass plots and 6 bare plots) were sampled beginning in 2010. The bare sites in South Bay were no longer sampled after 2013 due to colonization by seagrass. Additional sites were identified in 2017 in areas of natural seagrass expansion - 6 in Hog Island Bay and 6 in South Bay.
Unverified GPS track of R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>A Trimble Global Positioning System (GPS) recorded the route undertaken by the R/V Akademik Tryoshnikov during a circumnavigation of the Antarctic as part of the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017. The data provided in this dataset are raw NMEA strings containing date, time, latitude and longitude, with other NMEA variables allowing the accuracy of the location to be ascertained with one-second resolution.</p> <p>The data have not been quality checked or corrected.</p> <p>Data coverage is from 21st December 2016 until 11th April 2017.</p> <p><strong>Dataset contents </strong></p> <ul> <li>gpsdata_YYYYMMDD.log, data file, text</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p>Data files include the date (in UTC) on which the data were recorded in the format YYYYMMDD.</p> <p><strong>Dataset license</strong></p> <p>This unverified GPS track dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
GPS point locations of plots, subplots, itex subplots, transects and soil sensor in the black sand extended growing season experiment, 2018 - 2023.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows in a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot of each block by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to these plots after snow had naturally melted. This dataset includes geolocations of individual subplots and sensors within the experiment, measured in summer 2023.
DEM and associated kinematic GPS coordinates of September 2009 survey of the salar de Uyuni, Bolivia
<p>This dataset consists of two parts: 1) the post-processed kinematic GPS coordinates of a September 2009 survey of a 45 x 54 km region of the salar de Uyuni, Bolivia. 2) a digital elevation model (DEM) of the salar de Uyuni surface derived from those kinematic GPS data.</p> <p>Details of the survey design are identical to that from an earlier survey in 2002 and can be found in the manuscript, "Topography of the salar de Uyuni, Bolivia from kinematic GPS" (doi: 10.1111/j.1365-246X.2007.03604.x). The DEM is described in the manuscript "A Terrestrial Validation of ICESat Elevation Measurements and Implications for Gloval Reanalysis" (doi: 10.1109/TGRS.2019.2909739). The DEM was generated from fitting two-dimensional Fourier basis set with parameters: L_x = L_y = 70000 meters, m = n = 10. This results in a basis set with a nominal resolution of 7 km.</p> <p>The attached "salar_de_uyuni_2009_dem" files duplicate Figure 1 from the authors' "A terrestrial validation of ICESat elevation measurements and implications for global reanalyses," whose caption is: </p> <p>Landsat image of the salar de Uyuni, showing ICESat tracks 85, 241, 360 and 1320 (red) and the GPS-derived DEM from 2009 (color-coded with respect to mean elevation). The portion of each track plotted in Figure 2 is boxed in black. Total relief on the GPS DEM is less than 1 m over 50 km.</p>
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): GPS Elevation, 2009-2024
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This dataset contains elevation measurements made using a differential GPS with real time kinematic correction in early August of each year and additionally in mid-May of 2019.
American alligator GPS tracking study from May 2008 to September 2010 on Sapelo Island, Georgia
We deployed GPS tracking units on seven adult American alligators (two females and five males), for periods ranging from 34 to 100 days from May 2008 to September 2010 on Sapelo Island, Georgia. GPS units were set to record the location of tracked alligators every 1.5 to 2 hours. GPS data were then downloaded and tracks analyzed using GIS software after recapture.
May to July 2018 ground control points GPS coordinates of tidal marsh and tidal forest plant species to be used as ground control points in habitat mapping.
We collected field data from sites distributed in habitats along the salinity axis of the Altamaha River estuary and the Duplin River to be used as ground control points (GCP) and ground reference data for habitat mapping. GCPs for tidal marsh (salt, brackish, tidal fresh) and tidal fresh forest vegetation species were acquired. A real time kinematic (RTK) GPS survey of GPS coordinates and ground elevations for tidal marsh vegetation was carried out in May of June of 2018. A handheld GPS was used to collect GPS coordinates for tidal forest plant species in July of 2018. A total of 101 GCPs were collected in tidal habitats, with 26 in salt, 28 in brackish and 29 in tidal fresh marsh, and another 18 in tidal fresh forest. These observations will be used to create habitat maps from aerial photographs of the Altamaha River estuary, GA taken following Hurricane Irma to better understand how the storm surge affected tidal vegetation and to examine any shifts in vegetation type.
Raw SNR data for Manuscript "GPS Interferometric Reflectometry : Using a Low Cost Antenna to Measure Water Levels"
<p>Raw GPS L1 SNR (and ancillary) data for an experiment to use a low-cost GPS antenna/receiver to measure water levels using the GNSS - Interferometric Reflectometry technique.</p> <p>The data were recorded at the RNLI lifeboat station in Sligo, Ireland (N 54<sup>o </sup>18' 17.8'', W 8<sup>o</sup> 34' 5.4'' ) using a Globalsat BU353S4 USB puck that uses a SirfStar IV receiver with patch antenna (2018 data) and a Maestro A2200A SirfStar IV module (2019 data). Both systems were mounted to a radio mast at around 16m above sea level.</p> <p>The data are stored in daily files with the naming convention sligDDD0.YY.TNR.gz where DDD is the Day of Year and YY is the year in short format (18,19). Each file is gzipped. </p> <p>The files are flat text files with fixed width columns in the following order</p> <p>1) PRN GPS satellite code</p> <p>2) Elevation (degrees)</p> <p>3) Azimuth (degrees)</p> <p>4) Seconds of Day</p> <p>5) change in elevation angle with time (degrees/second) : needed for reflector height change corrections</p> <p>6) Blank</p> <p>7) S1 SNR signal (dB-Hz)</p> <p>8) Blank reserved for S2 SNR signal</p> <p>9) Blank reserved for S5 SNR signal</p>
Private vehicles GPS data
<p>The dataset provided here is an output of the Track & Know project, shared with the scientific community. It is an anonymized dataset of private vehicles. The dataset, containing anonymous GPS traces of private vehicles, was made accessible by the data owner to the partners of the Track & Know project, for activities relevant to the project. The proprietary dataset is not accessible to the public. It includes vehicle engine status. </p>
Attika GPS data
<p>The dataset provided here is an output of the Track & Know project, shared with the scientific community. It is an anonymized dataset of private vehicles. The dataset, containing anonymous GPS traces of private vehicles, was made accessible by the data owner to the partners of the Track & Know project, for activities relevant to the project. The proprietary dataset is not accessible to the public.</p>
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>
Fatiando a Terra Data: Alps - 3D GPS velocities
<p>This is a compilation of 3D GPS velocities for the Alps. The horizontal velocities are reference to the Eurasian frame. All velocity components and even the position have error estimates, which is very useful and rare to find in a lot of datasets.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's mean for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made:</strong> Combined the data from 3 different files, keeping the 3-component velocities in the Eurasion frame, coordinates, uncertainties, and station ID; exported to a compressed CSV file.</p> <p><strong>Source:</strong> Sánchez, Laura; Völksen, Christof; Sokolov, Alexandr; Arenz, Herbert; Seitz, Florian (2018): Present-day surface deformation of the Alpine Region inferred from geodetic techniques (data). PANGAEA, <a href="https://doi.org/10.1594/PANGAEA.886889">https://doi.org/10.1594/PANGAEA.886889</a></p> <p><strong>Source license:</strong> <a href="https://doi.org/10.1594/PANGAEA.886889">CC-BY-3.0</a></p> <p><strong>Repository:</strong> <a href="https://github.com/fatiando-data/alps-gps-velocity">https://github.com/fatiando-data/alps-gps-velocity</a></p>
Data from: "Using low-fix rate GPS telemetry to expand estimates of ungulate reproductive success"
<p>Secondary datasets used for analysis in "Using low-fix rate GPS telemetry to expand estimates of ungulate reproductive success". Raw GPS relocation data are not publicly available due to potential ethical implications but are available from the corresponding author (Nathan Hooven, nathan.d.hooven@gmail.com) upon reasonable request. Datasets include:</p> <p>elk_days_part.csv: generated movement metrics and days from parturition for all cow elk for which reproductive success was confirmed</p> <p>elk_np.csv: generated movement metrics for non-parturient cow elk</p> <p>elk_unknowns.csv: generated movement metrics for cows with unknown reproductive status, but were confirmed pregnant in mid-winter</p> <p>elk_thisyear.csv: generated movement metrics for 2020 Vectronic cows monitored in 2021</p> <p>Part_dates.csv: Confirmed and predicted dates of parturition for all elk in training and testing sets</p> <p>all_prob_summary_parturient.csv: Confirmed and predicted dates of parturition and differences for confirmed successful elk</p> <p>Decision rules 1.xlsx: Spreadsheet with classification accuracy based upon varying decision rules</p> <p>Decision rules 2.csv: Plottable summary of classification accuracy based upon varying decision rules</p> <p> </p>
Understanding the heterogeneous rheologic structure across the Longmenshan fault from ten-year postseismic GPS observations
<p>The two datasets are the 10-year cumulative displacements following the 2008 Wenchuan earthquake, GPS time-series observations for all sites, GPS time-series simulation for all sites and the secular velocity corresponding to the interseismic tectonic response, respectively.</p>
DEM and associated kinematic GPS coordinates of September 2002 survey of the salar de Uyuni, Bolivia
<p>This dataset consists of two parts: 1) the post-processed kinematic GPS coordinates of a September 2002 survey of a 45 x 54 km region of the salar de Uyuni, Bolivia. 2) a digital elevation model (DEM) of the salar de Uyuni surface derived from those kinematic GPS data.</p> <p>Details of the survey and DEM generation can be found in the manuscript, "Topography of the salar de Uyuni, Bolivia from kinematic GPS" (doi: 10.1111/j.1365-246X.2007.03604.x). The only difference between this dataset and one described is that the DEM was generated from fitting two-dimensional Fourier basis set with parameters: L_x = L_y = 70000 meters, m = n = 10. This results in a basis set with a nominal resolution of 7 km, which is almost identical to that used in the dataset shown in the manuscript.</p>
Ground Vertical and east Velocities for the Western Gulf of Corinth, Greece, combining InSAR and GPS
<p><strong>Data group</strong> :<br> Ground Vertical and East Velocities for the Western Gulf of Corinth combining InSAR and GPS, for the period 2002-2010</p> <p><strong>Data identifiers</strong> : </p> <ol> <li>Best constrained 951 vertical and east PS-SBAS velocities for pixels of 200m</li> <li>Best constrained 4391 vertical and east PS-SBAS velocities for pixels of 200m</li> </ol> <p><strong>Version</strong> : 1.0</p> <p><strong>Coordinate Reference System</strong>: Geographic WGS84, EPSG:4326</p> <p><strong>Citation</strong>: Elias & Briole, 2018, Ground deformations in the Corinth rift, Greece, investigated through the means of SAR multi-temporal interferometry<br> </p>
Terrestrial water storage changes across the contiguous United States from GPS and GRACE, 2007–2017
<p>In this dataset, we provide terrestrial water storage anomalies (TWSA) from 2007-2017 at weekly time scales derived using Global Positioning System (GPS) displacements, further constrained by lower-resolution TWSA observations from the Gravity Recovery and Climate Experiment (GRACE).</p> <p>There are six fields in the HDF5 product provided here:</p> <ol> <li>'/cmwe', which provides terrestrial water storage in units of cm. of water equivalent.</li> <li>'/latitude', latitude at the center of each 0.5 degree grid cell</li> <li>/longitude', longitude at the center of each 0.5 degree grid cell</li> <li>'/time', time in days since January 1st, 2007. The resolution of our time series is weekly, and the first day in our record is January 3rd, 2007.</li> <li>'/signal_to_noise_ratio', the variance of the signal divided by variance of noise for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> <li>'/uncertainty', 95% confidence interval for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> </ol> <p>As a condition of using these data, we request that you acknowledge the authors of this data set by citing the following peer-reviewed publication. </p> <p>Adusumilli, S., Borsa, A. A., Fish, M. A., McMillan, H. K., & Silverii, F. (2019). A decade of water storage changes across the contiguous United States from GPS and Satellite Gravity. <em>Geophysical Research Letters</em>, 46, 13006-13015. <a href="https://doi.org/10.1029/2019GL085370">https://doi.org/10.1029/2019GL085370</a></p>
Data and Code for "Extracting reproductive parameters from GPS tracking data for a nesting raptor in Europe"
<p>Understanding population dynamics requires estimation of demographic parameters. We build on existing approaches to develop a new tool that uses GPS tracking data to estimate breeding propensity and breeding success, and show that this tool yielded accurate predictions for two red kite populations in Central Europe. The tool is available as an R package at <a href="https://github.com/Vogelwarte/NestTool">https://github.com/Vogelwarte/NestTool</a> and will facilitate the estimation of demographic parameters from tracking data to inform population assessments. The files in this repository contain the data and analytical code to replicate the results of the publication in the Journal of Avian Biology (DOI: 10.1111/jav.03246). The version contained in this repository does not include updates and improvements that occurred after the 29 August 2024.</p>
25 years of high-frequency ground penetrating radar measurements of snow studies in Svalbard - metadata and GPS tracks
<p><strong>Surveys by ground penetrating radar (GPR) are accurate and cost-efficient, and have been conducted on Svalbard for more than 25 years, thus permitting the assessment of long term changes. The campaigns so far have covered various areas and the data is dispersed. The purpose of this report is to collect information about the conducted GPR snow cover measurements. The activities initiated in this project will be continued in the coming years and extended with a comprehensive data analysis.</strong></p> <p><strong>The dataset includes a description of metadata from GPR snow cover measurements in 1997-2022 (.CSV file) and GPS traces (.SHP files) of measurements taken in Svalbard.</strong></p> <p><strong>This study is part of the State of Environmental Science in Svalbard Report 2022 published by Svalbard Integrated Arctic Earth Observing System (SIOS).</strong></p>
UAV multispectral imagery dataset over a vineyard affected by Botrytis in 'Tomiño', Pontevedra, Spain. It includes GPS location of vine trunks, diseases and GCP points.
<p>This dataset contains a set of ground data and four flights captured on grape harvest over a vineyard affected by Botrytis cinerea. UAV flights took place on 16 September 2021, at 30 m height and using different angles (0, 30, 45 degrees). Pictures were taking using a Micasense RedEdge 3 sensor and were calibrated using the provided Micasense reflectance panel. The flight path was programmed to fly in autonomously, following manufacturer’s instructions (DJI). The dataset includes a shapefile with the GPS location of vine trunks, bunches affected by Botrytis and GCP points.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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