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338 results for “gps”
Locations of GPS-collared moose and geographic correlates, as well as for random points within study area
<p>Moose are among the many species that are vulnerable to both direcdt and indirect effects of climate change. Habitat selection is one framework to assist investigators in disentangling the various factors (including weather) that ultimately dictate how animals respond to their environment. We investigated patterns of winter habitat selecdtion by adult female moose in southerwestern MOntana, USA, during 2007-2010, and how that selection was affected by snow (quantified by snow water equivalent) and winter temperatures. We used data from GPS colalrs and a suite of environmental covariates to quantify winter habitat selection at both study area (2nd order) and home range (3rd order) spatial scales using resource selection functions. Moose strongly select for the willow (<em>Salix</em> spp.) cover type, and against grassland cover. Moose use of conifer cover at the home range scale increased when either amount of snow or ambient temperature was higher, altough the latter only during periods of the day when conifer pathces were likely to have been cooler than cover types lacking a canopy. Wildlife conservatoin and management naturally focuses on preferred habitats, particularly those that fulfill essentially all forgaing requirements. However, habitats used preferentially under stresful weather conditions, even if used rarely overall, can also form a critical part of a species' overall needs.,</p>
Supporting Information for "Data analysis of the unsteadily accelerating GPS and seismic records at Campi Flegrei caldera from 2000 to 2021". Data Set S1. Extended dataset of all the analyses
<p>This compressed folder contains supporting information related to the Figures in the manuscript: "Data analysis of the unsteadily accelerating GPS and seismic records at Campi Flegrei caldera from 2000 to 2021".</p> <p>Files and folders labeled with G1…n are related to the GPS data, those labeled with H1…n are related to the seismic data.</p> <p>In particular: <br> Subfolder 1_DATA supports Figure 3 – the vertical and the horizontal moduli of ground displacement at all analyzed GPS stations; the logarithmic plots of all seismic events and of their energy. It also shows the complete plot leveling data from 1905 to 2010 (modified from del Gaudio et al., 2010). It also includes Figure 2 and Figure 6a-c.</p> <p>Subfolder 2_AnnualRate supports Figure 4 - the annual rate of the vertical and horizontal moduli of ground displacement at all analyzed GPS stations; the annual rate of all seismic events and of their energy. These detail the 2-year, the 6-month, and the 30-day average results. It also supports Figure 5 with similar data concerning 2018-2020.</p> <p>Subfolder 3_InverseRate supports Figure S3 - the inverse rate of the vertical and the horizontal moduli of ground displacement at all analyzed GPS stations; the inverse rate of all seismic events and of their energy. These detail the 2-year, 6-month, and 30-day average results, including detailed plots of 2018-2020.</p> <p>Subfolder 4_RateChange supports Figure S2 - the daily rate change of the vertical and horizontal moduli of ground displacement at all analyzed GPS stations; the daily rate change of all seismic events and of their energy. These detail the 2-year, the 6-month, and the 30-day average results, including detailed plots of 2018-2020.</p> <p>Subfolder 5_FourierCoef supports Figure 6 - the Fourier spectrum of the vertical and the horizontal moduli of ground displacement at all analyzed GPS stations. These detail the 2-year, 6-month, and 30-day average results obtained in 2000-2020, 2011-2020, 2018-2020. Also, additional plots that detail other combinations of time domain and part of the Fourier spectrum, thus testing the sensitivity of the main harmonics on the time domain selected.</p> <p>Subfolder 6_ FFM_WaitTime supports Figure 11 – waiting time examples based on vertical and horizontal moduli of ground displacement at all analyzed GPS stations; all seismic events, and their energy. These detail the 2-year, 6-month, and 30-day average rate results, and the 10-year, 5-year and 3-year regressions.</p> <p>Subfolder 7_FFM_FailTime also supports Figure 11 – all the results expressed in terms of the failure time t<sub>f</sub> instead of in terms of the waiting time [t<sub>f</sub>(t) - t].</p> <p>Subfolder 8_pFFM_Regression supports Figure 9 - the pFFM examples based on the vertical and the horizontal moduli of ground displacement at all analyzed GPS stations; all seismic events and of their energy. These detail the 2-year, 6-month, and 30-day average rate results, and the 10-year, 5-year and 3-year regression.</p> <p>Subfolder 9_pFFM_Probability supports Figure S4 - pFFM examples based on vertical and horizontal moduli of ground displacement at all analyzed GPS stations; all seismic events, and their energy. These detail the 2-year, 6-month, and 30-day average rates, and the 10-year, 5-year and 3-year regressions.</p> <p>Subfolder 10_BarplotProb supports Figure S5 - results expressed in terms of the mean failure time probability at 2, 5, 10, and 25 years.It also supports Figure S6 - examples based on 6-month, and 30-day average rate results.</p> <p>Subfolder 11_BarplotWaitTime supports Figure S6 - all the results expressed in terms of the waiting time (t<sub>f</sub> – t) barplot. It also includes Figure S5.</p>
American Beaver: GPS and VHF tag data from resident and translocated beavers on the Price and San Rafael Rivers, Utah
<p>Wildlife translocations can dramatically alter animal movement behavior. Thus, identifying common movement patterns post-translocation can aid in setting expectations and anticipating animal behavior in subsequent efforts. American and Eurasian beavers (Castor canadensis; C. fiber) are frequently translocated for reintroduction efforts, to mitigate human-wildlife conflict, and for use as an ecosystem restoration tool. However, little is known about movement behavior of translocated beavers post-release, especially in desert rivers where resources are patchy and dynamic. We identified space-use patterns to develop an expectation framework of beaver movement behavior for future beaver-assisted restoration efforts. We captured, tagged, translocated, and monitored 41 nuisance American beavers in desert river restoration sites on the Price and San Rafael Rivers, Utah, USA, and compared their space use to 16 resident beavers. We tracked beavers 2-7 times per week from May through October in 2019 and 2020 via GPS locations and radio-telemetry, and from May 2019 through March 2021 via passive integrated antennae installed in the rivers. Resident adult beavers were detected at a mean maximum distance of 0.86 ± 0.21 river kilometers (km; ±1 SE), while resident subadult (11.00 ± 4.24 km), translocated adult (19.69 ± 3.76 km), and translocated subadult (21.09 ± 5.54 km) beavers were detected at substantially greater maximum distances. Based on coarse-scale movement models, translocated and resident subadult beavers moved substantially farther from release sites and faster than resident adult beavers up to six months post-release. In contrast, based on fine-scale, short-term movement models over 5-minute intervals, we observed similar median distance traveled between resident adult and translocated beavers. Our findings suggest day-to-day activities such as foraging and resting were largely unaltered by translocation, but translocated beavers exhibited coarse-scale movement behavior most similar to dispersal by resident subadults. Coarse-scale movement rates decreased with time since release, suggesting that translocated beavers adjusted to the novel environment over time and eventually settled into a home range similar to resident adult beavers. This is the first study comparing resident and translocated beaver movement behavior in the same system. Understanding translocated beaver movement behavior in response to a novel desert system can help future beaver-assisted restoration efforts to identify appropriate release sites and strategies.</p>
Black-tailed Gull GPS foraging trip data and acceleration raw data
Areas at which seabirds forage intensively can be discriminated by tracking the individuals' at-sea movements. However, such tracking data may not accurately reflect the birds' exact foraging locations. In addition to tracking data, gathering information on the dynamic body acceleration of individual birds may refine inferences on their foraging activity. Our aim was to classify the foraging behaviors of surface-feeding seabirds using data on their body acceleration and use this signal to discriminate areas where they forage intensively. Accordingly, we recorded the foraging movements and body acceleration data from seven and ten black-tailed gulls (Larus crassirostris) in 2017 and 2018, respectively, using GPS loggers and accelerometers. By referring to video footage of flying and foraging individuals, we were able to classify flying (flapping flight, gliding, and hovering), foraging (surface plunging, hop plunging, and swimming), and maintenance (drifting, preening, etc.) behaviors using the speed, body angle, and cycle and amplitude of body acceleration of the birds. Foraging areas determined from acceleration data corresponded roughly with sections of low speed and area-restricted searching (ARS) identified from the GPS tracks. However, this study suggests that the occurrence of foraging behaviors may be overestimated based on low-speed trip sections, because birds may exhibit long periods of reduced movement devoted to maintenance. Opposite, the ARS-based approach may underestimate foraging behaviors since birds can forage without conducting an ARS. Therefore, our results show that the combined use of accelerometers and GPS tracking helps to adequately determine the important foraging areas of black-tailed gulls. Our approach may contribute to better discriminate ecologically or biologically significant areas in marine environments.
An enhanced integrated water vapour dataset from more than 10,000 global ground-based GPS stations in 2020
<p>This is a 5-min Integrated Water Vapor (IWV) product from 12,552 ground-based GPS stations worldwide in 2020. It contains 1,093,591,492 IWV estimates in total. The dataset is an enhanced version of the existing operational GPS IWV dataset from Nevada Geodetic Laboratory. The enhancement is reached by using accurate meteorological information from ERA5 for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. The dataset is recommended for high-accuracy applications.</p> <p> DESCRIPTION Geodetic Institute, Karlsruhe Institute of Technology, Germany<br> OUTPUT 5-min enhanced GPS Integrated Water Vapour product<br> CONTACT Peng Yuan, pyuan@gfz-potsdam.de; Geoffrey Blewitt, gblewitt@unr.edu <br> INPUT NGL: GPS ZTD; ERA5 pressure level product: pressure and Tm</p> <p> NGL: Nevada Geodetic Laboratory, University of Nevada, http://geodesy.unr.edu<br> ERA5: the fifth generation ECMWF reanalysis, https://www.ecmwf.int<br> Authors: Peng Yuan, Geoffrey Blewitt, Corné Kreemer, William C. Hammond, <br> Donald Argus, Xungang Yin, Roeland Van Malderen, Michael Mayer,<br> Weiping Jiang, Joseph Awange, Hansjoerg Kutterer</p> <p><strong>Detailed descriptions and quality evaluations of the dataset have been published in <em>Earth System Science Data </em>(see below).</strong></p> <p><strong>If you would like to use the dataset, please cite the dataset and the associated paper as follows:</strong></p> <p>[1] Yuan, P., Blewitt, G., Kreemer, C., Hammond, W. C., Argus, D., Yin, X., Van Malderen, R., Mayer, M., Jiang, W., Awange, J., and Kutterer, H.: An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020, <em>Earth System Science Data</em>, 15, 723–743, <a href="https://doi.org/10.5194/essd-15-723-2023">https://doi.org/10.5194/essd-15-723-2023</a>, 2023.</p> <p>[2] Yuan, Peng, Blewitt, Geoffrey, Kreemer, Corné, Hammond, William C., Argus, Donald, Yin, Xungang, Van Malderen, Roeland, Mayer, Michael, Jiang, Weiping, Awange, Joseph, & Kutterer, Hansjörg. (2022). An enhanced integrated water vapour dataset from more than 10,000 global ground-based GPS stations in 2020 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6973528</p> <p>>><br>File: NGL2020_12552_blgph.txt<br>Description: coordinates of the 12552 GPS stations worldwide<br>Format:<br> column #1: Site name<br> column #2: Latitude (degree)<br> column #3: Longitude (degree)<br> column #4: Geopotential altitude (geopotential meter)</p> <p>>><br>ZIP files: data saved according to the first letter of the station names<br>Description: enhanced GPS IWV data product at 12552 stations worldwide<br>Structure: saved as each day for each station, and then all the daily<br> files of each station were saved as respective ZIP file<br> which named as "site"_2020.trop.zip<br>Format: IGS TROP format</p>
Present‑day crustal deformation across the Daliang Shan, southeastern Tibetan Plateau: constrained by a dense GPS network
<p><strong>1. Intensive observations</strong> </p> <p>In this study, we collected and processed GPS data from three sources to obtain a crustal horizontal velocity field. The dataset from the first source was raw GPS observations primarily from Phase I of the Crustal Movement Observation Network of China (CMONOC), which was resurveyed every 2 or 3 years from 1999 to 2007, and Phase II of the CMONOC, which involved campaign surveys every year from 2009 to 2020 and continuous surveys from 2010. The dataset from the second source was obtained from the National Key Research and Development Program of China. This dataset contained data from 31 continuous-measurement sites located close to the Anninghe–Zemuhe–Daliangshan fault zone, which were operated from August 2019 to August 2021, and 38 campaign sites from the National GPS Geodetic Control Network of China (NGGCNC), which were measured in 2014 and 2019. All of the campaign surveys used dual-frequency GPS receivers and choke ring antennas, with an operation of 3–4 consecutive days. The dataset from the third source consisted of published GPS velocities from existing studies of the Daliang Shan and its adjacent areas.In this study, we collected and processed GPS data from three sources to obtain a crustal horizontal velocity field. The dataset from the first source was raw GPS observations primarily from Phase I of the Crustal Movement Observation Network of China (CMONOC), which was resurveyed every 2 or 3 years from 1999 to 2007, and Phase II of the CMONOC, which involved campaign surveys every year from 2009 to 2020 and continuous surveys from 2010. The dataset from the second source was obtained from the National Key Research and Development Program of China. This dataset contained data from 31 continuous-measurement sites located close to the Anninghe–Zemuhe–Daliangshan fault zone, which were operated from August 2019 to August 2021, and 38 campaign sites from the National GPS Geodetic Control Network of China (NGGCNC), which were measured in 2014 and 2019. All of the campaign surveys used dual-frequency GPS receivers and choke ring antennas, with an operation of 3–4 consecutive days. The dataset from the third source consisted of published GPS velocities from existing studies of the Daliang Shan and its adjacent areas.</p> <p><strong>2. Data processing</strong></p> <p>We employed the GAMIT and GLOBK software (Herring et al., 2015a, 2015b) to process the raw GPS data and derived the GPS positioning time series with respect to the international terrestrial reference frame for 2014 (ITRF2014) (Altamimi et al., 2017). We utilized the GAMIT software to process the double-differenced carrier-phase observations and acquired regional daily loosely constrained solutions for the site coordinates and satellite orbits. The geophysical models used have been described by Hao et al. (2021). In addition, we employed the same strategy to process ~70 evenly distributed ITRF core GPS sites to acquire global daily loosely constrained solutions. Then, we employed the GLOBK software to combine the same regional and global daily solutions to obtain a GPS time series.</p> <p>Three large earthquakes occurred in the study area: the 2004 M 9.1 Sumatra earthquake, the 2008 M 8.0 Sichuan Wenchuan earthquake, and the 2013 M 7.0 Sichuan Lushan earthquake. For the GPS time series for the campaign sites, we utilized the coseismic slip model of the 2004 Sumatra earthquake (Chlieh et al., 2007). We interpolated the coseismic displacements of the 2008 Wenchuan earthquake (Shen et al., 2009) to correct the coseismic offsets. We only used the data observed before 2008 for those GPS sites contaminated by significant postseismic deformation related to the 2008 Wenchuan earthquake (Wang & Shen, 2020). For the GPS sites affected by the coseismic deformation caused by the 2013 Lushan earthquake (Jiang et al., 2014), we also used data observed before the mainshock to mitigate the coseismic and postseismic deformation. After removing the transient deformation caused by the earthquakes, we used the weighted least-squares adjustment method to estimate linear trends of the velocities. We used the linear trend, seasonal variations, coseismic offset, and color noise model for the continuous GPS sites to fit the time series. We utilized the maximum likelihood estimation (MLE) technique and the CATS software (Williams et al., 2004; Williams., 2008) to estimate the characteristics of the noise in the residuals of the GPS time series after removing the linear trend and seasonal variations (Hao et al., 2016). Then, we obtained the GPS velocities with respect to the ITRF2014 and applied Euler rotation to transfer it to the Eurasia-fixed frame (Altamimi et al., 2017).</p> <p>The reference frames of the GPS velocities reported in previous studies are different from ours. Therefore, to transfer the latter to our selected frame, we employed the Helmert transformation with four parameters through common sites for our velocities and the published velocities. We only chose spatially uniformly distributed common sites with post-fit residuals of less than 1.0 mm/yr in the north-ward and east-ward components. Finally, we derived the geodetically consistent GPS crustal movement in the Daliang Shan and its adjacent areas with respect to the stable Eurasian Plate. Additionally, in order to reduce the residual rigid motion caused by the far-field reference of the Eurasian Plate, we chose the stable South China block as the near-field reference frame. Subsequently, our derived GPS velocities were translated into the South China block reference frame using the published Euler rotation vectors (Hao et al., 2019).</p> <p> </p> <p><strong>References </strong></p> <p>Altamimi, Z., Métivier, L, Rebischung, P., Rouby, H., Collilieux, X., 2017. ITRF2014 plate motion model. Geophys. J. Int. 209:1906–1912</p> <p>Chlieh, M., Avouac, J. P. , Hjorleifsdottir, V. , Song, T. , Ji, C. , Sieh, K., Sladen, A., Hebert, H., Prawirodirdjo, L., Bock, Y., Galetzka, J., 2007. Coseismic slip and afterslip of the great <em>M</em>w 9.15 Sumatra-Andaman earthquake of 2004. Bulletin of the Seismological Society of America, 97(1A), 152–173.</p> <p>Hao, M., Freymueller, J. T., Wang, Q. L., Cui, D. X., Qin, S. L. 2016. Vertical crustal movement around the southeastern Tibetan Plateau constrained by GPS and GRACE data. Earth and Planetary Science Letters, 437, 1-8. http://dx.doi.org/10.1016/j.epsl.2015.12.038.</p> <p>Hao, M., Li, Y., Zhuang, W., 2019. Crustal movement and strain distribution in east Asia revealed by GPS observations. Scientific Reports, https://doi.org/10.1038/s41598-019-53306-y, 16797.</p> <p>Hao, M., Wang, Q., Zhang, P., Li, Z., Li, Y., Zhuang, W., 2021. “Frame wobbling” causing crustal deformation around the Ordos block. Geophysical Research Letters 48, e2020GL091008. https://doi.org/10.1029/2020GL091008.</p> <p>Herring, T.A., King, R.W., McClusky, S.C., 2015a. GAMIT reference manual, GPS analysis at MIT, Release 10.6. Massachusetts Institute of Technology, Cambridge.</p> <p>Herring, T.A., King, R.W., McClusky, S.C., 2015b. GAMIT reference manual, global Kalman filter VLBI and GPS analysis program, Release 10.6. Massachusetts Institute of Technology, Cambridge.</p> <p>Jiang, Z., Wang, M., Wang, Y., Wu, Y., Che, S., Shen, Z.K., Bürgmann, R., Sun, J., Yang, Y., Liao, H., Li, Q., 2014. GPS constrained coseismic source and slip distribution of the 2013 Mw6.6 Lushan, China, earthquake and its tectonic implications. Geophysical Research Letters 41, 407–413, doi:10.1002/2013GL058812.</p> <p>Shen, Z.K., Sun, J., Zhang, P., Wan, Y., Wang, M., Bürgmann, R., Zeng, Y.H., Gan, W.J., Wang, Q.L., 2009. Slip maxima at fault junctions and rupturing of barriers during the 2008 Wenchuan earthquake. Nat Geosci 2:718–724.</p> <p>Wang, M., Shen, Z.K., 2020. Present-day crustal deformation of continental China derived from GPS and its tectonic implications. J. Geophys. Res. 125 (2) https://doi. org/10.1029/2019JB018774.</p> <p>Williams, S.D.P., 2008. CATS: GPS coordinate time series analysis software. GPS Solutions, 12, 147–153. <a href="http://dx.doi.org/10.1007/s10291-007-0086-4">http://dx.doi.org/10.1007/s10291-007-0086-4</a>.</p> <p>Williams, S.D.P., Bock, Y., Fang, P., Jamason, P., Nikolaidis, R.M., Prawirodirdjo, L., Miller, M., Johnson, D.J. 2004. Error analysis of continuous GPS position time series. J. Geophys. Res. 109 (B03412). http://dx.doi.org/10.1029/2003JB002741.</p> <p> </p> <p> </p>
Monitoring GPS-collared moose by ground versus drone approaches: efficiency and disturbance effects
<p>Efficient wildlife management requires precise monitoring methods, e.g., to estimate population density, reproductive success, and survival. Here, we compared the efficiency of the drone (equipped with an RGB camera) and ground approaches to detect and observe GPS-collared female moose (<em>Alces alces</em>) and their calves. Moreover, we quantified how drone (n = 42) and ground (n = 41) approaches affected moose behavior and space use (n = 24 individuals). The average time used for drone approaches was 17 minutes compared to 97 minutes for ground approaches, with drone detection probability being higher (95% of adult female moose and 88% of moose calves) compared to ground approaches (78% of adult females and 82% of calves). Drone detection success increased at lower drone altitudes (50-70 m). Adult female moose left the site in 35% of drone approaches (with > 40% of those moose becoming disturbed once the drone hovered < 50 m above ground) compared to 56% of ground approaches. We failed to find short-term effects (3-h after approaches) of drone approaches on moose space use, but moose moved > 4-fold greater distances and used larger areas after ground approaches (compared to before the approaches had started). Similarly, longer-term (24-h before and after approaches) space use did not differ between drone approaches compared to days without known disturbance, but moose moved comparatively greater distances during days of ground approaches. In conclusion, we could show that drone approaches were highly efficient in detecting adult moose and their calves in the boreal forest, being faster and less disturbing than ground approaches, making them a useful tool to monitor and study wildlife.</p>
Dataset: 2023 Aircraft traffic and GPS anomalies aggregated per hexbins
<p>We divided the globe into hexbins, each with an average area of 385 square kilometers. Once the hexbin grid was established, data from the GPS gaps, GPS deviations, and Traffic Density datasets were used to populate these hexbins with relevant information. On average, each hexbin has around 23,478 flights passing through it.</p> <ul> <li><strong>Total Records</strong>: 14,117 - total number of hexbin on a map, where number of flights > 0</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>id:</strong></li> <li><strong>WKT</strong>: Well-Known Text representation of a POINT (senter of each hexbin) in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>flights</strong>: number of flights traveled trough that hexbin in 2023</li> <li><strong>gaps</strong>: number of GPS gap incidents registered in that hexbin in 2023</li> <li><strong>deviations</strong>: number of GPS deviation incidents started in that hexbin in 2023</li> </ul> </ul>
360 Recording of downtown ULM Germany, with time aligned gps data
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The burden of research: Effects of GPS tags on metabolic physiology of a small passerine
<p>Statistical code & corresponding datasets for "The burden of research: Effects of GPS tags on metabolic physiology of a small passerine" submitted to <em>Ornithilogical Applications.</em></p>
GPS, InSAR, and seismic waveform data for study of 2014 South Napa, California, earthquake
<p>GPS_Brocher_et_al2015.txt : Observed static offsets at CGPS and SGPS sites, respectively, presented by Brocher et al. (2015) determined using GPS time series up to several days after the event</p> <p>napa_CSK_20140619_20140903_asc.grd : Observed unwrapped COSMO-SkyMed ascending interferogram spanning June 19 - September 3, 2014</p> <p>napa_CSK_20140726_20140827_desc.grd : Observed unwrapped COSMO-SkyMed descending interferogram spanning July 26 - August 27, 2014</p> <p>napa_sentinel_20140807_20140831_desc.grd : Observed unwrapped Sentinel descending interferogram spanning August 7 - August 31, 2014</p> <p>seismic_waveforms.tar.gz : Three-component seismic waveforms in (time (s after origin time), velocity (m/s)) format for 16 stations bandpass filtered between 0.067 and 1.5 Hz. Filenames indicate which velocity component (East, North, or Up=Z) and station name.</p> <p>Study: "Coseismic slip and early after slip of the M6.0 August 24, 2014 South Napa, California, earthquake" by Fred F. Pollitz, Jessica R. Murray, Sarah E. Minson, Charles W. Wicks, and Jerry L. Svarc. Journal of Geophysical Research, <em>in press</em></p>
GPS timeseries raw and filtered
<p>The data files include the raw and ICA filtered GPS timeseries for the five-minute (early postseismic of the five six days), and daily timeseries (the first two years).</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>
GPS-synchronized recordings of 10 MHz during October 2, 2024 eclipse from Tortel, Aysén, Chile
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GPS-synchronized recordings of 15 MHz during October 2, 2024 eclipse from Tortel, Aysén, Chile
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Data from: An inexpensive and open-source method to study large terrestrial animal diet and behavior using time-lapse video and GPS
1. The behavior of free-ranging animals is difficult to study, especially on the large spatial and temporal scales relevant to long-lived large species. Animal-borne video and environmental data collection systems (AVEDs) record behavior and other data in real time as animals conduct daily activities. However, few studies have combined systematically collected, long term AVED foraging data with environmental and movement data to test hypotheses on animal foraging. Additionally, AVEDs are often either prohibitively expensive, or require extensive fabrication and programming knowledge. 2. The video and coordinate animal-mounted system (VACAMS) is an animal-mounted data collection system based on a modified GoPro® action camera platform that records short, first "person" perspective videos of animal behavior on an automated time-lapse schedule. As most videos are georeferenced, researchers can return to the locations of specific behaviors and collect accurate, fine-grained data on non-woody vegetation and other habitat characteristics that may influence animal behavior. Moreover, VACAMS are inexpensive and easy to use. 3. This study describes VACAMS preliminary data on cattle foraging and a hypothesis exploring free-ranging cattle browsing habits throughout the rainy season in the tropical dry forest of Sonora, Mexico. I generated a database of vegetation types consumed by cows each month (Annual, Woody, and Leaf litter) and compared actual vegetation type frequencies to a priori assumptions based on seasonal patterns of forage availability. During the monsoons, when palatable vegetation was abundant, frequencies of annual and woody perennial vegetation in cattle diets did not differ from month to month. When the rains ceased and palatable vegetation became scarce, cows switched to leaf litter, dead annual vegetation, twigs, and dried leguminous fruits. 4. Open source software and commercially available hardware make VACAMS financially attainable for many researchers, land managers, students, and other user groups. VACAMS could be used on a range of domestic and semi-domestic free-ranging animals, particularly in dense forests where conventional observations are impossible. With improvements to GPS battery life and durability, the weakest points of the system, VACAMS could also potentially apply to studies of other large terrestrial animals.
Very Early Postseismic Deformation following the 2015 Mw 8.3 Illapel Earthquake, Chile Revealed from High-rate GPS
<p>Daily.zip: daily GPS solutions at 12 Chilean stations</p> <p>Subdaily.zip: 1-Hz GPS Solutions at 12 Chilean stations.</p>
Data for: Simultaneous GPS-tracking of parents reveals a similar parental investment within pairs, but no immediate co-adjustment on a trip-to-trip basis
<p>This repository contains data for the paper: Kavelaars et al. 2021. Simultaneous GPS-tracking of parents reveals a similar parental investment within pairs, but no immediate co-adjustment on a trip-to-trip basis. <strong>Movement Ecology</strong>. https://doi.org/10.1186/s40462-021-00279-1</p>
GPS data of little owls
<p>Advances in bio-logging technology for wildlife monitoring have expanded our ability to study space use and behavior of many animal species at increasingly detailed scales. However, such data can be challenging to analyze due to autocorrelation of GPS positions. As a case study, we investigated spatiotemporal movements and habitat selection in the little owl (<i>Athene noctua</i>), a bird species that is declining in central Europe and verges on extinction in Denmark. We equipped 6 Danish food-supplemented little owls and 6 non-supplemented owls in the Czech Republic with high-resolution GPS loggers that recorded one position per minute. Nightly home ranges, measured as 95% kernel density estimates, of Danish male owls were on average 62 ha (± 64 SD, larger than any found in previous studies) compared to 2 ha (± 1) in females, and to 3 ± 1 ha (males) versus 3 ± 5 ha (females) in the Czech Republic. Foraging Danish male owls moved on average 4-fold further from their nest and at almost double the distance per hour than Czech males. To create availability data for the habitat selection analysis, we accounted for high spatiotemporal autocorrelation of the GPS data by simulating correlated random walks with the same autocorrelation structure as the actual little owl movement trajectories. We found that habitat selection was similar between Danish and Czech owls, with individuals selecting for short vegetation and areas with high structural diversity. Our limited sample size did not allow us to infer patterns on a population level, but nevertheless demonstrates how high-resolution GPS data can help to identify critical habitat requirements to better formulate conservation actions on a local scale.</p>
GPS velocities in North China from a combination of published results
<p>GPS velocities in North China from a combination of published results ( e.g., Wang & Shen, 2020,<a href="https://doi.org/10.1029/2019JB018774">https://doi.org/10.1029/2019JB018774</a> ; Hao et al., 2021, <a href="https://doi.org/10.1029/2020GL091008">https://doi.org/10.1029/2020GL091008</a>)</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.