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559 results for “Track data”
Simulated data for particle tracking and use in TrackMateR package
<p>The purpose of this dataset is to provide some example data for users of TrackMateR to use to become familiar with the package.</p> <p>The dataset contains three elements:</p> <ol> <li>Code to simulate some images (movies) of particle motions in 2D in Fiji. `particleSimulator.ijm` will generate images and ground truth positions of particles moving in six different modes (see below). An example output is given in `particleSimulatorOutput/`</li> <li>Code to automate the tracking of these images using TrackMate in Fiji. An example output is give in `TrackMateOutput/` These XML files can be used as the input in TrackMateR package.</li> <li> Outputs from TrackMate v 0.3.5 in `TrackMateROutput/`</li> </ol> <p>The simulated data is:</p> <ul> <li>Simulation A - particles moving in linear direction, variable but constant direction, high speed</li> <li>Simulation B - particles moving in linear direction, variable but constant direction, slow speed</li> <li>Simulation C - random motion high D (diffusion coefficient)</li> <li>Simulation D - random motion low D</li> <li>Simulation E - random motion, 50:50 mix of high and low D particles</li> <li>Simulation F - random motion, subdiffusive</li> </ul> <p>These TrackMate XML files can be processed using <a href="https://github.com/quantixed/TrackMateR">TrackMateR</a> as described <a href="https://quantixed.github.io/TrackMateR/">here</a>.</p>
UWB Positioning and Tracking Data Set
<p><strong># UWB Positioning and Tracking Data Set</strong></p> <p>UWB positioning data set contains measurements from four different indoor environments. The data set contains measurements that can be used for range-based positioning evaluation in different indoor environments.</p> <p> </p> <p><strong># Measurement system</strong></p> <p>The measurements were made using 9 DW1000 UWB transceivers (DWM1000 modules) connected to the networked RaspberryPi computer using in-house radio board SNPN_UWB. 8 nodes were used as positioning anchor nodes with fixed locations in individual indoor environment and one node was used as a mobile positioning tag.</p> <p>Each UWB node is designed arround the RaspberryPi computer and are wirelessly connected to the measurement controller (e.g. laptop) using Wi-Fi and MQTT communication technologies.</p> <p>All tag positions were generated beforehand to as closelly resemble the human walking path as possible. All walking path points are equally spaced to represent the equidistand samples of a walking path in a time-domain. The sampled walking path (measurement TAG positions) are included in a downloadable data set file under downloads section.</p> <p> </p> <p><strong># Folder structure</strong></p> <p>Folder structure is represented below this text. Folder contains four subfolders named by the indoor environments measured during the measurement campaign and a folder raw_data where raw measurement data is saved. Each environment folder has a anchors.csv file with anchor names and locations, .json file data.json with measurements, file walking_path.csv file with tag positions and subfolder floorplan with floorplan.dxf (AutoCAD format), floorplan.png and floorplan_track.jpg.</p> <p>Subfolder raw_data contains raw data in subfolders named by the four indor environments where the measurements were taken. Each location subfolder contains a subfolder data where data from each tag position from the walking_path.csv is collected in a separate folder. There is exactly the same number of folders in data folder as is the number of measurement points in the walking_path.csv. Each measurement subfolder contains 48 .csv files named by communication channel and anchor used for those measurements. For example: ch1_A1.csv contains all measurements at selected tag location with anchor A1 on UWB channel ch1. The location folder contains also anchors.csv and walking_path.csv files which are identical to the files mentioned previously.</p> <p>The last folder in the data set is the technical_validation folder, where results of technical validation of the data set are collected. They are separated into 8 subfolders:</p> <p>- cir_min_max_mean</p> <p>- los_nlos</p> <p>- positioning_wls</p> <p>- range</p> <p>- range_error</p> <p>- range_error_A6</p> <p>- range_error_histograms</p> <p>- rss</p> <p> </p> <p>The organization of the data set is the following:</p> <p>data_set</p> <p>+ location0</p> <p>- anchors.csv</p> <p>- data.json</p> <p>- walking_path.csv</p> <p>+ floorplan</p> <p>- floorplan.dxf</p> <p>- floorplan.png</p> <p>- floorplan_track.jpg</p> <p>- walking_path.csv</p> <p>+ location1</p> <p>- ...</p> <p>+ location2</p> <p>- ...</p> <p>+ location3</p> <p>- ...</p> <p>+ raw_data</p> <p>+ location0</p> <p>+ data</p> <p>+ 1.07_9.37_1.2</p> <p>- ch1_A1.csv</p> <p>- ch7_A8.csv</p> <p>- ...</p> <p>+ 1.37_9.34_1.2</p> <p>- ...</p> <p>+ ...</p> <p>+ location1</p> <p>+ ...</p> <p>+ location2</p> <p>+ ...</p> <p>+ location3</p> <p>+ ...</p> <p>+ technical validation</p> <p>+ cir_min_max_mean</p> <p>+ positioning_wls</p> <p>+ range</p> <p>+ range_error</p> <p>+ range_error_histograms</p> <p>+ rss</p> <p>- LICENSE</p> <p>- README</p> <p> </p> <p><strong># Data format</strong></p> <p>Raw measurements are saved in .csv files. Each file starts with a header, where first line represents the version of the file and the second line represents the data column names. The column names have a missing column name. Actual column names included in the .csv files are:</p> <p> </p> <p>TAG_ID</p> <p>ANCHOR_ID</p> <p>X_TAG</p> <p>Y_TAG</p> <p>Z_TAG</p> <p>X_ANCHOR</p> <p>Y_ANCHOR</p> <p>Z_ANCHOR</p> <p>NLOS</p> <p>RANGE</p> <p>FP_INDEX</p> <p>RSS</p> <p>RSS_FP</p> <p>FP_POINT1</p> <p>FP_POINT2</p> <p>FP_POINT3</p> <p>STDEV_NOISE</p> <p>CIR_POWER</p> <p>MAX_NOISE</p> <p>RXPACC</p> <p>CHANNEL_NUMBER</p> <p>FRAME_LENGTH</p> <p>PREAMBLE_LENGTH</p> <p>BITRATE</p> <p>PRFR</p> <p>PREAMBLE_CODE</p> <p>CIR (starts with this column; all columns until the end of the line represent the channel impulse response)</p> <p> </p> <p><strong># Availability of CODE</strong></p> <p>Code for data analysis and preprocessing of all data available in this data set is published on GitHub:</p> <p>https://github.com/KlemenBr/uwb_positioning.git</p> <p>The code is licensed under the Apache License 2.0.</p> <p> </p> <p><strong># Authors and License</strong></p> <p>Author of data set in this repository is Klemen Bregar, klemen.bregar@ijs.si.</p> <p>This work is licensed under a Creative Commons Attribution 4.0 International License.</p> <p> </p> <p><strong># Funding</strong></p> <p>The research leading to the data collection has been partially funded from the European Horizon 2020 Programme project eWINE under grant agreement No. 688116, the Slovenian Research Agency under Grant numbers P2-0016, J2-2507 and bilateral project with Grant number BI-ME/21-22-007.</p> <p> </p>
PyFLEXTRKR Global MCS Tracking Dataset using GPM MergedIR Tb and IMERG precipitation data
<p>This is the global mesoscale convective system (MCS) tracking dataset developed by <a href="https://doi.org/10.1029/2020JD034202">Feng et al. (2021) JGR</a>. It contains the MCS track data (location, time, lifecycle evolution of MCS cloud and precipitation characteristics), monthly mean and 20-year climatological MCS statistics on 0.1 degree x 0.1 degree (lat x lon) grid. All data files are in netCDF format.</p><p>The periods are from June 2000 to December 2020. The geographic coverage is 180°W-180°E, 60°S-60°N. For more detailed documentations, please refer to the README "PyFLEXTRKR_MCS_Tracking_Data_Readme.pdf".</p><p>Due to the large file size of the native 1-hourly resolution pixel-level data on the 0.1 degree x 0.1 degree grid, they are not included in this dataset. Please contact Zhe Feng (<a href="mailto:zhe.feng@pnnl.gov">zhe.feng@pnnl.gov</a>) if you are interested in obtaining the pixel-level data.</p>
Hubbard Brook Experimental Forest: Watershed 5 stem origin tracking data
W5 was experimentally whole-tree harvested in the winter of 1983-84. The initial post-harvest surveys were conducted by Jeffrey Hughes in 1984. The species and stem origin (i.e. stem grew from seed, sprouted from a cut stem or was an established stem post-harvest) for all stems in the 1m2 plots were recorded. This data set summarizes the apparent “winners” in both 1984 and 30 years later in 2014 (some evaluated in 2015). There were a total of 13 surveys in these plots that helped clarify the outcomes over time. This data set summarizes from the larger data set. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Supplementary files for Silva et al. 2020 "Reptiles on the wrong track?": R Code, data and figures
<p>Datasets, R code and figures pertaining to the manuscript: Silva, I., Crane, M., Marshall, B.M., & Strine, C.T. (2020).<em> </em> <em>Reptiles on the wrong track? Moving beyond traditional estimators with dynamic Brownian Bridge Movement Models.</em> Movement Ecology 8, 43 DOI: 10.1186/s40462-020-00229-3</p> <p>Article available at: <a href="https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-020-00229-3">https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-020-00229-3</a></p>
Dataset for the paper: TaskTracker tool: a Toolkit for Tracking of Code Snapshots and Activity Data During Solution of Programming Tasks (SIGCSE Technical Symposium 2021))
<pre>It is a dataset for the <em>TaskTracker-tool: a Toolkit for Tracking of Code Snapshots and Activity Data During Solution of Programming Tasks</em> paper from <a href="https://sigcse2021.sigcse.org/">SIGCSE Technical Symposium 2021</a>. The dataset consists of code snapshots, IDE actions, and demographic information gathered by <a href="https://github.com/JetBrains-Research/codetracker">this</a> tool. We had 148 participants, aged 11 to 40 (mean age is 19 years), take part in the data gathering process. </pre> <pre>During data gathering, solutions were accepted in one of four languages: Python, Java, Kotlin, or C++. However, some of the students chose not to submit tasks or solved some tasks incorrectly. At the same time, some students solved some tasks many times in multiple languages. All submitted solutions are included in the final dataset.</pre> <pre>To get more information see the <em>README</em> file.</pre>
EEG and eye-tracking data from a go/no-go saccadic task based on facial expression cues
<p>Electroencephalographic (EEG) and eye-tracking data from 20 healthy individuals who performed a go/no-go saccadic task based on facial expression cues aimed at studying error monitoring processes.</p> <p> </p> <p>Version 2 includes only the EEG data, but with triggers of correct and erroneous actions. Version 2 has an error that was corrected for version 3.</p> <p> </p> <p>EEG Triggers</p> <table> <tbody> <tr> <td> <p><span>19</span></p> </td> <td> <p><span>Eye tracker starts recording</span></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>Beginning of each trial</span></p> </td> </tr> <tr> <td> <p><span>200</span></p> </td> <td> <p><span>Gap between neutral and instruction</span></p> </td> </tr> <tr> <td> <p><span>99</span></p> </td> <td> <p><span>Fixation cross between instruction and saccade</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Instruction no-go happy</span></p> </td> </tr> <tr> <td> <p><span>21</span></p> </td> <td> <p><span>Target left no-go happy</span></p> </td> </tr> <tr> <td> <p><span>22</span></p> </td> <td> <p><span>Target right no-go happy</span></p> </td> </tr> <tr> <td> <p><span>121</span></p> </td> <td> <p><span>Response period for no-go happy after target left</span></p> </td> </tr> <tr> <td> <p><span>122</span></p> </td> <td> <p><span>Response period for no-go happy after target right</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Instruction no-go sad</span></p> </td> </tr> <tr> <td> <p><span>31</span></p> </td> <td> <p><span>Target left no-go sad</span></p> </td> </tr> <tr> <td> <p><span>32</span></p> </td> <td> <p><span>Target right no-go sad</span></p> </td> </tr> <tr> <td> <p><span>131</span></p> </td> <td> <p><span>Response period for no-go sad after target left</span></p> </td> </tr> <tr> <td> <p><span>132</span></p> </td> <td> <p><span>Response period for no-go sad after target right</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Instruction pro-right</span></p> </td> </tr> <tr> <td> <p><span>42</span></p> </td> <td> <p><span>Target right pro-right</span></p> </td> </tr> <tr> <td> <p><span>142</span></p> </td> <td> <p><span>Response period for pro-right</span></p> </td> </tr> <tr> <td> <p><span>5</span></p> </td> <td> <p><span>Instruction pro-left</span></p> </td> </tr> <tr> <td> <p><span>51</span></p> </td> <td> <p><span>Target left pro-left</span></p> </td> </tr> <tr> <td> <p><span>151</span></p> </td> <td> <p><span>Response period for pro-left</span></p> </td> </tr> <tr> <td> <p><span>6</span></p> </td> <td> <p><span>Instruction anti-left</span></p> </td> </tr> <tr> <td> <p><span>61</span></p> </td> <td> <p><span>Target left anti-left</span></p> </td> </tr> <tr> <td> <p><span>161</span></p> </td> <td> <p><span>Response period for anti-left</span></p> </td> </tr> <tr> <td> <p><span>7</span></p> </td> <td> <p><span>Instruction anti-right</span></p> </td> </tr> <tr> <td> <p><span>72</span></p> </td> <td> <p><span>Target right anti-right</span></p> </td> </tr> <tr> <td> <p><span>172</span></p> </td> <td> <p><span>Response period for anti-right</span></p> </td> </tr> <tr> <td> <p><span>199</span></p> </td> <td> <p><span>Final fixation period (1.5s black screen after last saccade)</span></p> </td> </tr> <tr> <td> <p><span>190</span></p> </td> <td> <p><span>Eye tracker stops recording</span></p> </td> </tr> <tr> <td> <p><span>proCorr</span></p> </td> <td> <p><span>Beginning of saccade in the correct direction following a pro-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>proErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a pro-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>antiCorr</span></p> </td> <td> <p><span>Beginning of saccade in the correct direction following a anti-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>antiErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a anti-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>nogoErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a no-go intruction</span></p> </td> </tr> </tbody> </table>
MCIT storm tracks with updraft radar data for large hail cases in the United States (2013 - 2023)
<p>Storm tracking data from 114 hail days in the United States between 2013 and 2023. Cases were selected based on the availability of abundant Storm Prediction Center (SPC) hail reports (either at least 20 reports of hail > 1 cm or five reports > 10 cm) in a range between 20 and 90 km from a NEXRAD radar site. Only cases in a geographic area from 28-48°N and 105-90°E, roughly corresponding to the U.S. Great Plains, were considered. Cells are quality filtered based on these criteria: (1) Cell center within 120 km of the radar range, and (2) continuous tracking for at least 6 timesteps (approx. 30 minutes, depending on radar scan rate). SPC hail data is matched to tracking timesteps. We used this data to train a Random Forest model for hail size nowcasting.</p> <p>Further metadata can be requested from the authors.</p>
Data from: Deep learning-assisted near-Earth asteroid tracking in astronomical images
<p>This repository is the data release of our paper <em>Deep learning-assisted near-Earth asteroid tracking in astronomical images</em>. There are two categories in this repository:</p> <ul> <li>Simulated training dataset for training the star segmentation network. </li> </ul> <p>The dataset consists of two folders: image (grayscale images) and mask (binary images). The size of each image is 256*256.</p> <ul> <li>Example data for testing asteroid tracking algorithm.<br><br></li> </ul> <p>If you find this work useful, please cite our paper:</p> <div> <div>@article{du2024ASR,</div> <div>title = {Deep learning-assisted near-Earth asteroid tracking in astronomical images},</div> <div>journal = {Advances in Space Research},</div> <div>volume = {73},</div> <div>number = {10},</div> <div>pages = {5349-5362},</div> <div>year = {2024},</div> <div>issn = {0273-1177},</div> <div>doi = {https://doi.org/10.1016/j.asr.2024.02.048},</div> <div>url = {https://www.sciencedirect.com/science/article/pii/S0273117724001911},</div> <div>author = {Zhenhong Du and Hai Jiang and Xu Yang and Hao-Wen Cheng and Jing Liu},</div> <div>keywords = {Near-Earth asteroid, Deep learning, Convolutional neural network, Faint object extraction, Moving object linking},</div> <div>}</div> </div>
Data from: Satellite tracking of American Woodcock reveals a gradient of migration strategies
<p>Diversity in behavior is important for migratory birds in adapting to dynamic environmental and habitat conditions and responding to global change. Migratory behavior can be described by a variety of factors that comprise migration strategies. We characterized variation in migration strategies in American Woodcock (<em>Scolopax minor</em>), a migratory gamebird experiencing long-term population decline, using GPS data from approximately 300 individuals tracked throughout eastern North America. We classified woodcock migratory movements using a step-length threshold, and calculated characteristics of migration related to distance, path, and stopping events. We then used principal components analysis (PCA) to ordinate variation in migration characteristics along axes that explained different fundamental aspects of migration, and tested effects of body condition, age-sex class, and starting and ending location on PCA results. The PCA did not show evidence for clustering, suggesting a lack of discrete strategies among groups of individuals; rather, woodcock migration strategies existed along continuous gradients driven most heavily by metrics associated with migration distance and duration, departure timing, and stopping behavior. Body condition did not explain variation in migration strategy during the fall or spring, but during spring adult males and young females differed in some characteristics related to migration distance and duration. Starting and ending latitude and longitude, particularly the northernmost point of migration, explained up to 61% of the variation in any one axis of migration strategy. Our results reveal gradients in migration behavior of woodcock, and this variability should increase the resilience of woodcock to future anthropogenic landscape and climate change.</p>
Data from: Making better use of tracking data can reveal the spatiotemporal and intraspecific variability of species distributions
<p>Understanding geographic ranges and species distributions is crucial for effective conservation, especially in the light of climate and land use change. However, the spatial, temporal and intraspecific resolution of digital accessible information on species distributions is often limited. Here, we suggest to make better use of high-resolution tracking data to address existing limitations of occurrence records such as spatial biases (e.g. lack of observations in parts of the geographic range), temporal biases (e.g. lack of observations during a certain period of the year), and insufficient information on intraspecific variability (e.g. lack of population- or individual-level variation). Addressing these gaps can improve our knowledge on geographic ranges, intra-annual changes in species distributions, and population-level differences in habitat and space use. We demonstrate this with tracking data and species distribution models (SDMs) of the Barnacle Goose, a migratory bird species wintering in western Europe and breeding in the Arctic. Our analyses show that tracking data can (1) supplement occurrence records from the Global Biodiversity Information Facility (GBIF) in remote areas such as the European and Russian Arctic, (2) improve information on the temporal use of wintering, staging and breeding areas of migratory species, and (3) provide insights into the differences of population-level responses to environmental variables. We recommend a broader use of tracking data to address the Wallacean shortfall (i.e. the incomplete knowledge on the geographic distribution of species) and to improve forecasts of biodiversity responses to climate and land use change (e.g. species vulnerability assessments). To avoid common pitfalls, we provide six recommendations for consideration during the research cycle when using tracking data in species distribution modelling, including steps to assess biases and integrate information on intraspecific variability in modelling approaches.</p>
Web browser useragent and activity tracking data
<p>600 000 000 web traffic records normalized into MySQL tables using TokuDB storage, complete with original web server response codes. Suitable for browser data and trend analysis as well as AI training of exploit and bot detection algorithms. The data had been collected from multiple Apache 2.x web servers across 8000+ domain names with special care for GDPR compliance.</p> <p> </p>
Data from: An Easily Compatible Eye Tracking System for Free-moving Small Animals
<p>These datasets are associated with human labled eye tracking datasets in DLC formate and pixel formate from the paper Huang et. al., <em>An Easily Compatible Eye Tracking System for Free-moving Small Animals, </em>2021.<em> </em></p>
Source data for the publication "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time", Nature Communications, 2021
<p>The archives contain the raw data used to generate the transient absorption spectra for uridine (Figure 1) and 5-methyluridine (Figure 2) presented in the main paper, as well as the trajectory plots and auxiliary spectra presented in the Supplementary Information of the paper "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time" authored by R. Borrego-Varillas et al. published in Nature Communications, 2021. Specifically:</p> <p><strong>URD</strong>: folder with raw data from the uridine trajectories (56 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p><strong>5mURD</strong>: folder with raw data from the 5-methyluridine trajectories (57 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p>The raw data of each trajectory is inside a folder named <em>geom_XXX</em> where <em>XXX</em> stands for a 3-digit label of the trajectory. The trajectories have been selected out of a pool of 500 trajectories according to the S0-S1 vertical gap so that only trajectories whose energy gap falls under the envelope of the pulse are selected</p> <p><strong>URD</strong>: 003 005 006 011 015 023 039 040 054 056 060 083 098 104 112 114 116 121 122 147 152 158 161 171 173 175 177 186 189 200 204 211 219 223 225 232 234 235 236 246 251 252 257 259 265 268 271 272 279 286 287 289 305 313 318 336</p> <p><strong>5mURD</strong>: 010 044 045 048 052 057 065 074 085 094 097 099 100 105 110 112 113 121 131 137 138 140 144 145 159 164 170 179 182 183 184 186 189 199 203 205 209 214 219 220 221 239 243 250 251 273 284 290 295 301 302 320 325 327 328 333 334</p> <p>In each geom_XXX folder there are following files:</p> <p><strong>S1-S<em>Y</em>.dat</strong>: ASCII files () in which the individual columns correspond to </p> <p>col1: time [fs] </p> <p>col2: transition energy of state S<em>Y</em> with respect to S1 [cm-1] where S0 is the ground state</p> <p>col3-5: X, Y and Z components of the transition dipole moment between S1 and S<em>Y</em> [a.u.]</p> <p>col6: magnitude of the transition dipole moment between S1 and S<em>Y</em> [a.u.] </p> <p>col7: angle between transition dipole moment at time t and t=0 [deg]</p> <p>Note that in URD S1-S0.dat contains in most cases about 500 data points (0-500 fs), in 5mURD S1-S0.dat contains 1000 data points (0-1000 fs) except for a few cases in which the trajectories were interrupted earlier. This data has been used to simulate the stimulated emission before the hopping event and the hot ground state photoinduced absorption after hopping. S1-S<em>Y</em>.dat () contain only data points until the hopping event which have been used to simulate the excited state photoinduced absorption.</p> <p>The spectra reported in the main article (Figs 1 & 2) as well as in the SI can be reproduced following eq. 13-18 in the Supplementary Information.</p> <p> </p> <p><strong>HighMediumLayer_traj.xyz.zip</strong>: archived Cartesian coordinates of the High Layer (nucleobase) and Medium Layer (sugar and waters within 5 Å distance from nucleobase) along the dynamics</p> <p>Note that due to the different number of waters in each trajectory the size of the Medium layer (and thus the size of the system) may vary from trajectory to trajectory.</p> <p>Note that due to the different duration of each trajectory the number of geometries may vary from trajectory to trajectory.</p> <p><strong>LowLayer.xyz:</strong> Cartesian coordinates of the Low Layer (waters > 5 Å from the nucleobase); the coordinates of these waters are kept fixed along the trajectory.</p> <p>The coordinates of High, Medium and Low layers can be used to reproduce the QMMM calculations (energies, gradients and transition dipole moments along each trajectory) with the official COBRAMM release (<a href="https://gitlab.com/cobrammgroup/cobramm.git">https://gitlab.com/cobrammgroup/cobramm.git</a>) following the parameters provided in Supplementary Note 2 of the Supplementary Information.</p>
Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic (Data)
<p>Origin projects and figures used for the article "Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic", published in the proceedings of the 29th Micromechanics and Microsystems Europe Workshop; 26.08.2018 to 29.08.2018; Smolenice Castle, Slovakia.</p>
Supplementary Material 1: Original dataset collected during the tracking and mark-release-recapture study and R script used to analyse the data
<p>The original dataset collected in northern Serbia during butterfly behavioural study on two species, <em>Phengaris teleius</em> and <em>Polyommatus icarus</em>. The dataset is provided in two separate CSV files for mark-release-recapture study and for butterfly tracking study. In addition, R script used to preopare the dataset and fit the models is given.</p>
Small mammal ARTS: Orion receiver data for site radiomapping and vole tracking, and scripts and results for localization and activity estimates
<p class="FirstParagraph">This data set accompanies "An Automated Radio-Telemetry System (ARTS) for Monitoring Small Mammals". </p> <p class="FirstParagraph">The behavior of small fossorial mammals, such as voles, is extremely difficult to observe in natural environments. Small mammals were traditionally studied with labor intensive methods such as trapping and recapture or radio telemetry via homing, which require week/months of work and produce static home range estimates.</p> <p class="FirstParagraph">In pursuit of better understanding natural history and behavioral ecology we implemented an automated radio telemetry system (ARTS) to continuously monitor small mammals. We used an isotropic antenna array coupled with broadband receivers to estimate animal positions with nonlinear least squares, nonparameteric, and Bayesian trilateration methods. We then used Lomb-Scargle periodograms to estimate activity patterns of freely-behaving Prairie voles.</p>
Data from: Integrating tracking and resight data enables unbiased inferences about migratory connectivity and winter range survival from archival tags
<p>Archival geolocators have transformed the study of small, migratory organisms but analysis of data from these devices requires bias correction because tags are only recovered from individuals that survive and are re-captured at their tagging location. Data and code provided in this repository can be used to replicate the simulation and Painted Bunting case study results presented by Rushing et al. (2021) showing that integrating geolocator recovery data and mark–resight data enables unbiased estimates of both migratory connectivity between breeding and nonbreeding populations and region-specific survival probabilities for wintering locations.</p>
European plant-based foods sales data 2017-2020 (Nielsen Market Track)
<ul> <li>The dataset consists of Excel (.xlsx) files with data on sales of plant-based food products between 2017 and 2020 in a number of European countries (i.e. Austria, Belgium, Denmark, France, Germany, Italy, the Netherlands, Poland, Romania, Spain and the UK.)</li> </ul> <ul> <li>The data are clearly labelled within each file. The key variables (common across datasets) are Value in Euros, Volume in KG/LIT and Volume in Selling Units for a number of meat and dairy substitute food products specific to the retail region.</li> </ul> <ul> <li>The data were originally collected by Nielsen Market Track. They were analysed on the <a href="http://www.smartproteinproject.eu">Smart Protein project</a> in 2021 and used to publish an extensive <a href="https://smartproteinproject.eu/plant-based-food-sector-report/">market data report</a> and to host a <a href="https://www.youtube.com/watch?v=dsIJqvpXXgw">public webinar</a>, both entitled <em>Plant-based foods in Europe: how big is the market?</em></li> </ul> <p> </p>
Tracking magma spine extrusion from space: Implications for conduit and topography complexity at Shiveluch volcano, Kamchatka - Photogrammetric data repository
<p>This is a dataset relevant for a paper on lava spine extrusion at Shieveluch volcano, Kamchatka. Data was used to show that the spine elongates along a previously identified fracture line and bends to a preferred northerly direction. By repeated morphology analysis and feature tracking, we constrain a spine diameter of ~300 m, extruding at a velocity of 1.7 m/day and discharge rate of 0.3-0.7 m³/s. Results are relevant for understanding the growth and collapse hazards of spines and provide unique insights into the hidden magma-conduit architecture.</p> <p>The data consists of three parts. First, we provide the filtered and corrected three dimensional point clouds generated from Pleiades tristereo data. These 3D point clouds were co-aligned and now allow analysing subtle changes. Point clouds are provided in .las format. Second, we provide the filtered and corrected digital elevation models generated from the point cloud data, these DEMs are provided in geotiff format. The name of the files indicates the dates of their acquisition. Third and lastly, we provide an orthomap stack used to estimate displacements by tracking offsets.</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.