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138 results for “GPS data”
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>
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>
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>
Physical characteristics and GPS data for 21 permanent plots in Coweeta white pine watershed 1 from 2003 to 2004
A GPS receiver was used to acquire UTM coordinates and elevation for each plot center. Aspect and slope were also measured.
MCR LTER: Coral Reef: Coupled Natural-Human Systems: GPS Benthic Data
This dataset includes cover of benthic organisms and substrates in more than 600,000 photographic samples of Moorea’s lagoon habitats, collected between 2018 and 2021. In summer of each year swimmers towed downward facing cameras, taking 1.5 photos per second of habitats they passed over. The resulting pictures were processed through a computer vision algorithm (CoralNet), resulting in percent cover estimates in each photo of organisms such as coral and algae and substrates such as sand and rubble. These data were georeferenced based on simultaneously collecting GPS data. Data are presented here on the scale of the photograph, and are also aggregated to the scale of a minute of sampling and a transect of swimming. The latter scales are chosen to coincide with fish counting data which was simultaneously collected but is presented in a different data set (EDI data package ID: knb-lter-mcr.4013.1) doi:10.6073/pasta/ff3de88334edda65fef26de45ff26c1d These data were collected as part of CNH-L: Multiscale Dynamics of Coral Reef Fisheries: Feedbacks Between Fishing Practices, Livelihood Strategies, and Shifting Dominance of Coral and Algae (BCS-1714704) with additional support from the Moorea Coral Reef LTER (OCE- 1637396).
In-vehicle Sensing Datasets (e.g., GPS, IMU, and OBD data) In Florida
<p>This data collection and distribution is supported by NSF OAC-1948066. These datasets include a total of 497 trajectory datasets over 2404 km. Each dataset includes 6DOF IMU data (e.g., triaxial acceleration and gyroscope data), GPS data (e.g., latitude, longitude, altitude, speed over ground, the number of connected satellites, Course Over Ground), and OBD data (e.g., rpm, throttle positions, accelerator positions, RPM, air temperature, etc.). The data collection mechanism adopts the asynchronous sampling technologies that make capturing sensor data independent of the recorded signal. Therefore, datasets collected from each sensor are logged in separate files (e.g., time_obd.jsonl, time_gps.jsonl, time_obd.jsonl). By matching the time when each sensor module initiated to log data, one can aggregate/fuse multi-type in-vehicle sensing data.</p><p> </p>
GPS data for COPEX campaign carried out in 2002 in Brazil by INPE (Boa Vista Station)
<p>This repository provides the GPS data acquired in Boa Vista station during the Conjugate Point Equatorial Experiment (COPEX) campaign in 2002. In the folders it can find the raw intensity data (FSL file) and also the summary files (SUM files), containing only with S4 index with time and satellite ID. More information about the campaign can be found in Muella et al. (2008), Abdu et al. (2009) and de Paula et al. (2010). Information about the data recording format and reading extraction procedure of this dataset can be found in Appendix B of Beach (1998). More details regarding the receiver used and extraction tools can be found at: <a href="https://gps.ece.cornell.edu/tools.php">https://gps.ece.cornell.edu/tools.php</a></p> <p>Abdu, M. A., Batista, I. S., Reinisch, B. W., De Souza, J. R., Sobral, J. H. A., Pedersen, T. R., ... & Groves, K. M. (2009). Conjugate Point Equatorial Experiment (COPEX) campaign in Brazil: Electrodynamics highlights on spread F development conditions and day‐to‐day variability. Journal of Geophysical Research: Space Physics, 114(A4).</p> <p>Beach, T. L. (1998). Global Positioning System studies of equatorial scintillations. Cornell University.</p> <p>De Paula, E. R., Muella, M. T. A. H., Sobral, J. H. A., Abdu, M. A., Batista, I. S., Beach, T. L., & Groves, K. M. (2010). Magnetic conjugate point observations of kilometer and hundred‐meter scale irregularities and zonal drifts. Journal of Geophysical Research: Space Physics, 115(A8).</p> <p>Muella, M. T. A. H., De Paula, E. R., Kantor, I. J., Batista, I. S., Sobral, J. H. A., Abdu, M. A., ... & Smorigo, P. F. (2008). GPS L-band scintillations and ionospheric irregularity zonal drifts inferred at equatorial and low-latitude regions. Journal of Atmospheric and Solar-Terrestrial Physics, 70(10), 1261-1272.</p>
GPS data for COPEX campaign carried out in 2002 in Brazil by INPE (Campo Grande Station)
<p>This repository provides the GPS data acquired in Campo Grande station during the Conjugate Point Equatorial Experiment (COPEX) campaign in 2002. In the folders it can be found the raw intensity data (FSL file) and also the summary files (SUM files), containing only with S4 index with time and satellite ID. More information about the campaign can be found in Muella et al (2008), Abdu et al (2009) and de Paula et al (2010). Information about the data recording format and reading extraction procedure of this dataset can be found in Appendix B of Beach (1998). More details regarding the receiver used and extraction tools can be found at: <a href="https://gps.ece.cornell.edu/tools.php">https://gps.ece.cornell.edu/tools.php</a></p> <p>Abdu, M. A., Batista, I. S., Reinisch, B. W., De Souza, J. R., Sobral, J. H. A., Pedersen, T. R., ... & Groves, K. M. (2009). Conjugate Point Equatorial Experiment (COPEX) campaign in Brazil: Electrodynamics highlights on spread F development conditions and day‐to‐day variability. Journal of Geophysical Research: Space Physics, 114(A4).</p> <p>Beach, T. L. (1998). Global Positioning System studies of equatorial scintillations. Cornell University.</p> <p>De Paula, E. R., Muella, M. T. A. H., Sobral, J. H. A., Abdu, M. A., Batista, I. S., Beach, T. L., & Groves, K. M. (2010). Magnetic conjugate point observations of kilometer and hundred‐meter scale irregularities and zonal drifts. Journal of Geophysical Research: Space Physics, 115(A8).</p> <p>Muella, M. T. A. H., De Paula, E. R., Kantor, I. J., Batista, I. S., Sobral, J. H. A., Abdu, M. A., ... & Smorigo, P. F. (2008). GPS L-band scintillations and ionospheric irregularity zonal drifts inferred at equatorial and low-latitude regions. Journal of Atmospheric and Solar-Terrestrial Physics, 70(10), 1261-1272.</p>
GPS data for COPEX campaign carried out in 2002 in Brazil by INPE (Cachimbo Station)
<p>This repository provides the GPS data acquired in Cachimbo station during the Conjugate Point Equatorial Experiment (COPEX) campaign in 2002. In the folders it can find the raw intensity data (FSL file) and also the summary files (SUM files), containing only with S4 index with time and satellite ID. More information about the campaign can be found in Muella et al. (2008), Abdu et al. (2009) and de Paula et al. (2010). Information about the data recording format and reading extraction procedure of this dataset can be found in Appendix B of Beach (1998). More details regarding the receiver used and extraction tools can be found at: <a href="https://gps.ece.cornell.edu/tools.php">https://gps.ece.cornell.edu/tools.php</a></p> <p>Abdu, M. A., Batista, I. S., Reinisch, B. W., De Souza, J. R., Sobral, J. H. A., Pedersen, T. R., ... & Groves, K. M. (2009). Conjugate Point Equatorial Experiment (COPEX) campaign in Brazil: Electrodynamics highlights on spread F development conditions and day‐to‐day variability. Journal of Geophysical Research: Space Physics, 114(A4).</p> <p>Beach, T. L. (1998). Global Positioning System studies of equatorial scintillations. Cornell University.</p> <p>De Paula, E. R., Muella, M. T. A. H., Sobral, J. H. A., Abdu, M. A., Batista, I. S., Beach, T. L., & Groves, K. M. (2010). Magnetic conjugate point observations of kilometer and hundred‐meter scale irregularities and zonal drifts. Journal of Geophysical Research: Space Physics, 115(A8).</p> <p>Muella, M. T. A. H., De Paula, E. R., Kantor, I. J., Batista, I. S., Sobral, J. H. A., Abdu, M. A., ... & Smorigo, P. F. (2008). GPS L-band scintillations and ionospheric irregularity zonal drifts inferred at equatorial and low-latitude regions. Journal of Atmospheric and Solar-Terrestrial Physics, 70(10), 1261-1272.</p>
Spatial behavior and diet data for discrete-choice analyses: data observed and classified from GPS video camera collars worn by female members of the Fortymile Caribou Herd across Alaska, USA, and Yukon, Canada
<p>Competition for resources and space can drive forage selection of large herbivores from the bite through the landscape scale. Animal behavior and foraging patterns are also influenced by abiotic and biotic factors. Fine-scale mechanisms of density-dependent foraging at the bite scale are likely consistent with density-dependent behavioral patterns observed at broader scales, but few studies have directly tested this assertion. Here, we tested if space use intensity, a proxy of spatiotemporal density, affects foraging mechanisms at fine spatial scales similarly to density-dependent effects observed at broader scales in caribou. We specifically assessed how behavioral choices are affected by space use intensity and environmental processes using behavioral state and forage selection data from caribou (<i>Rangifer tarandus granti</i>) observed from GPS video-camera collars using a multivariate discrete-choice modeling framework. We found that the probability of eating shrubs increased with increasing caribou space use intensity and cover of <i>Salix</i> spp. shrubs, whereas the probability of eating lichen decreased. Insects also affected fine-scale foraging behavior by reducing the overall probability of eating. Strong eastward winds mitigated the negative effects of insects and resulted in higher probabilities of eating lichen. Lastly, caribou exhibited foraging functional responses wherein their probability of selecting each food type increased as the availability (% cover) of that food increased. Space use intensity signals of fine-scale foraging were consistent with density-dependent responses observed at larger scales and with recent evidence suggesting declining reproductive rates in the same caribou population. Our results highlight the potential risks of overgrazing on sensitive forage species such as lichen. Remote investigation of the functional responses of foraging behaviors provides exciting future applications where spatial models can identify high-quality habitats for conservation.</p>
The North American Monsoon GPS Hydrometeorological Network 2017: Flux and Precipitation Data
<p>Water, energy and carbon fluxes and ancillary meteorological measurements and precipitation data taken during The North American Monsoon GPS-Hydrometeorological Network 2017. The experiment was carried out during the summer of 2017 in the state of Sonora in northwestern Mexico.</p>
A relative-motion method for parsing spatio-temporal behaviour of dyads using GPS relocation data
<p>In this paper, we introduce a novel method for classifying and computing the frequencies of movement modes of intra- and interspecific dyads, focusing in particular on distance-mediated approach, retreat, following and side by side movement modes. Besides distance, other factors such as time of day, season, sex, or age can be included in the analysis to assess if they cause frequencies of movement modes to deviate from random. By subdividing the data according to selected factors, our method allows us to identify those responsible for (or correlated with) significant differences in the behaviour of dyadic pairs. We demonstrate and validate our method using both simulated and empirical data. Our simulated data were obtained from a relative-motion, biased random-walk (RM-BRW) model with attraction and repulsion components. Our empirical data were GPS relocation data collected from African elephants in Etosha National Park, Namibia. The simulated data were primarily used to validate our method while the empirical data were used to illustrate the types of behavioural assessment that our methodology reveals. Our method facilitates automated, observer-bias-free analysis of the locomotive interactions of dyads using GPS relocation data, which are becoming increasingly ubiquitous as telemetry and related technologies improve. It should open up a whole new vista of behavioural-interaction type analyses to movement and behavioural ecologists.</p>
GPS collar data and social-ecological feature data for examining the movement of coyotes in Los Angeles, California (2019-2021)
Open the record for dataset details and reuse information.
Spatial behavior and diet data for discrete-choice analyses: data observed and classified from GPS video camera collars worn by female members of the Fortymile Caribou Herd across Alaska, USA, and Yukon, Canada
Open the record for dataset details and reuse information.
Test data for Sonic Kayaks particulate matter sensor, temperature and GPS
<p>These data sets are the first trials for adding a particulate matter sensor to the Sonic Kayak project https://fo.am/activities/kayaks/</p> <p>There are three data files - gps.csv is the GPS co-ordinates, pm.csv is the particulate matter data (using a PMS7003), and temp.csv is the temperature data (two separate but identical digital thermometer sensors). Time is included in all datasets and can be used to align them. Together the data can be used to make a fine scale heat line map of particulate matter and temperature.</p>
Analysis of migration patterns of western marsh harriers using GPS tracking data
<p>This repository contains analysis code for Vansteelant et al. (2020, <a href="https://doi.org/10.1007/s10336-020-01785-6">https://doi.org/10.1007/s10336-020-01785-6</a>). See the <code>README.md</code> for more information.</p>
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>
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.