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559 results for “Tracking Data”
Data for Li et al., Coupling remote sensing and particle tracking to estimate trajectories in large water bodies, International Journal of Applied Earth Observation and Geoinformation, 2022
<p>This data set contains four parts:</p> <p>1) compressed folder with input parameters and results for the hydrodynamic model</p> <p>2) compressed folder with input parameters and results for the particle tracking</p> <p>3) compressed folder with satellite data </p> <p>4) code used in the article for hydrodynamic model, particle tracking and image processing</p> <p>Each folder contains a readme file,</p>
GFDL hurricane model track data associated with "Dynamical downscaling projections of late 21st century U.S. landfalling hurricane activity"
<p>These data include North Atlantic tropical cyclone track and intensity for control and projected late 21st century simulation from the GFDL hurricane model used in a <em>Climatic</em> <em>Change</em> manuscript: </p> <p>Knutson, T., J. Sirutis, M. Bender, R. Tuleya, and B. Schenkel, 2022: Dynamical downscaling projections of late 21st century U.S. landfalling hurricane activity. <em>Clim. Change</em>, <strong>171</strong>, 1–23.<br> <br> A readme file included below describes the variables and format of the tropical cyclone track data. Questions about the dataset may be directed to Ben Schenkel (<a href="mailto:benschenkel@gmail.com">benschenkel@gmail.com</a>) and Tom Knutson (<a href="mailto:tom.knutson@noaa.gov">tom.knutson@noaa.gov</a>). </p>
Data from "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad"
<p>This upload includes data shown in the figures of the Paper "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad".</p>
Multiple Particle Tracking Data from Neonatal Organotypic Rat Brain Slices
<p>The data includes statistical features generated from raw multiple particle tracking data from videos collected during three independent experiments: (1) 5 different brain regions, (2) 3 different treatment conditions in the brain, and (3) 5 different brain ages. </p> <p> </p> <table> <thead> <tr> <th scope="col">Feature</th> <th scope="col">Model Abbreviation</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>alpha</td> <td>alpha</td> <td>Exponent of the anomalous diffusion equation.</td> </tr> <tr> <td>Effective diffusion coefficient</td> <td>D_fit</td> <td>Coefficient of the anomalous diffusion equation</td> </tr> <tr> <td>Kurtosis</td> <td>kurtosis</td> <td>The fourth moment of the projected positions on the dominant eigenvector of the radius gyration tensor (T).</td> </tr> <tr> <td>Asymmetry1</td> <td>asymmetry1</td> <td>Characterizes the asymmetry of the trajectory. Asymmetry1 equals 0 for circularly symmetric trajectories and 1 for linear trajectories.</td> </tr> <tr> <td>Asymmetry2</td> <td>asymmetry2</td> <td>The ratio of the smaller to larger principal radius of gyration.</td> </tr> <tr> <td>Asymmetry3</td> <td>asymmetry3</td> <td>An asymmetry feature that accounts for non-cylindrically symmetric point distributions.</td> </tr> <tr> <td>Aspect ratio</td> <td>AR</td> <td>The ratio of the kong and short side of the trajectory's minimum bounding rectangle. Perfectly symmetric trajectories have an aspect ratio of 1, and aspect ratio increases as trajectories become more elongated. </td> </tr> <tr> <td>Elongation</td> <td>elongation</td> <td>An estimation of amount of extension of the trajectory from its centroid. </td> </tr> <tr> <td>Boundedness</td> <td>boundedness</td> <td>Boundedness quantifies how much a particle with diffusion coefficient <em>D<sub>eff</sub></em> is restricted by a circular confinement of radius <em>r</em> when diffusing for a period of time <span class="math-tex">\(N\Delta t \)</span></td> </tr> <tr> <td>Fractal Dimension</td> <td>fractal_dim</td> <td>Fractal dimension is a measure of how "complicated" a self similar figure is. </td> </tr> <tr> <td>Trappedness</td> <td>trappedness</td> <td>The probability (<span class="math-tex">\(\textit{P}_{\textit{t}} \)</span>) that a particle with duffusion coefficient <em>D<sub>eff</sub></em> is trapped in a region (<em>r<sub>0</sub></em>) for a period of time <span class="math-tex">\(N\Delta t \)</span>. </td> </tr> <tr> <td>Efficiency</td> <td>efficiency</td> <td>The ratio of the squared net displacement to the sum of step lengths. </td> </tr> <tr> <td>Straightness</td> <td>straightness</td> <td>The ratio of the net displacement to the sum of step lengths. </td> </tr> <tr> <td>MSD Ratio</td> <td>MSD_ratio</td> <td>MSD ratio characterizes the shape of the MSD curve. For Brownian motion, it is 0; For restricted motion it is < 0; For directed motion it is > 0. </td> </tr> <tr> <td>Frames</td> <td>frames</td> <td>The total number of frames the trajectory spans. </td> </tr> <tr> <td>Effective Diffusion Coefficient 1</td> <td>Deff1</td> <td>Effective diffusion coefficient at 0.33 s.</td> </tr> <tr> <td>Effective Diffusion Coefficient 2</td> <td>Deff2</td> <td>Effective diffusion coefficient at 3.3s. </td> </tr> </tbody> </table> <p>Mean values were calculated based on surrounding datapoints for alpha, D_fit, kurtosis, asymmetry1, asymmetry2, asymmetry3, AR, elongation, boundedness, fractal_dim, trappedness, efficiency, straightness, MSD_ratio, Deff2, and Deff2. </p> <p> </p>
Tracking Selection using Temporal Population Genomics Data
<p>This repository contains the implementation of a pipeline to run the simulations and to produce a reference table for the ABC-RF inference of demography and selection. In its new release, this repository contains the whole-genome polymorphism of contemporary and museum specimens of <em>Apis mellifera</em> feral populations analyzed by Cridland et al. (2018).</p>
Data: multimodal cell tracking from systemic administration to tumour growth by combining gold nanorods and reporter genes
<p>This data set includes multispectral optoacoustic tomography images supporting an article on cell tracking (preprint: bioRxiv 199836; https://doi.org/10.1101/199836). The corresponding bioluminescence results are included too, as well as the spectra used for the multispectral processing. </p>
Hailstorm Identification and Tracking over Brazil (HIToB): A Storm Polygons Database From GOES ABI Data from 2018 to 2023
<p>This dataset comprises a detailed record of deep convective storm events tracked across South America from 2018 to 2023, utilizing brightness temperature (BT) data from Channel 13 of the GOES-16 Advanced Baseline Imager (ABI) and the TATHU (Tracking and Analysis of Thunderstorms) toolset, that caused hail-fall over Brazil. The database includes storm identification, tracking details, and associated meteorological variables such as brightness temperature statistics inside the storm polygon at each scene and event classifications (e.g., spontaneous generation, continuity, split, merge). The storms were detected and tracked based on brightness temperature threshold of 235 K, with tracking data refined by a 10% overlap criterion between sequential scenes. The tracked convective systems were filtered for intersections in space and time with verified hail reports from Prevots group. The whole family of storm polygons that matched the reports were exported to this database with SpatiaLite enabled dtaa format, in order to make it easier for spatial data queries and analysis. Some example queries using Python library SQLAlchemy are displayed in the code repository as well as the process of creating the tables in the database.<br><br>The data is organized in three tables: "storms", "storm_events" and "intersections". In table "storms" are the records of storm families identifier. Each identifier represents a sequence of storm polygons tracked over subsequent satellite scenes. Table "storm_events" holds the evolution of the storm's geometry through its lifecycle, including BT's mean, minimum and standard deviation inside the storm polygon; as well as storm's pixel count (i.e. storm size). Intersections table stores every instance where a storm event polygon intersects with a hailstorm report's buffer at the corresponding time. In total, there are 9893 intersections belonging to 2172 unique storm families.</p>
Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research
<p><strong>This is the dataset of the report: Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research</strong></p> <p>It contains the following information of all the papers from ASE, FSE, and ICSE in 2023:</p> <ul> <li>Paper title</li> <li>Keyword</li> <li>Is the source data available and accessible in the paper?</li> <li>If the source data is not available, do the authors explain why?</li> <li>Hosting platforms</li> <li>Access mode</li> <li>License</li> <li>Is their experiment data reused from previous work, or newly generated specifically for this study, or combination of both? </li> <li>Do the authors change/modify their experiment data before experiment?</li> <li>What modifications do they perform?</li> <li>Does the link provide detailed instructions about how to replicate their paper?</li> <li>Does the link contains their complete experiment data, their source code or other materials that are necessary to replicate their experiments?</li> <li>What's the data format inside the link?</li> <li>What's the content of the link?</li> </ul> <p> </p> <p>This is a course project and I collect the data in a rush.</p> <p>If you want to use this dataset and find any error, please contact me ;-)</p> <p>My email: echo.xiangchen@gmail.com</p>
Data from: Tracking the popularity and outcomes of all bioRxiv preprints
<p>The data used to generate figures in the manuscript titled <a href="https://www.biorxiv.org/content/early/2019/01/13/515643">"Tracking the popularity and outcome of all bioRxiv preprints,"</a> posted to bioRxiv 13 Jan 2019.</p> <ul> <li><strong>22 Mar 2019:</strong> PDFs of each figure from the paper have been added to the repository. In addition, the license has been changed from CC-BY-NC to CC0.</li> </ul>
Rhodamine fluorometry tracking data Alfacs Bay
<p>Rhodamine dye was released from a waste water outfall in Alfacs Bay, Catalonia. This dye has a strong red/pink colour and can be used to trace the movement of the waster water plume. These fluorimetry data are combined with GPS geolocation data and were generated using a fluorometer towed from a boat. They show the progression of the plume over the course of a day.</p> <p>Times are Central European Time (GMT+1)</p>
Food Tracking Device data
<p>This dataset includes data from the Food Tracking Device from several pilot cases within the URBAN-WASTE project. In particular, a few hotels and restaurants were supplied with a food waste tracking device where the kitchen could record their food waste data. The statistics would then be transformed into simple figures to give the kitchen feedback on their development in terms of food waste generation.</p>
PEPT data for Understanding the effect of fluid viscosity in Vertical Stirred Mills using the Positron Emission Particle Tracking (PEPT) approach
<p>The raw PEPT data collected for the paper "Understanding the effect of fluid viscosity in vertical stirred mills using the positron emission particle tracking (PEPT) approach." The paper is the first to use the PEPT technique to investigate the effect of fluid viscosity on the efficiency of the grinding process.</p> <p>This data can be post-processed using the PEPT-ML library and used in isolation or it can be used to calibrate an equivalent simulation. The simulation template is available on GitHub and the link to this is under the Software tab. Each file is labelled by the fluid viscosity and attritor speed used in the experiment, The data for a single run is often split across files but can be combined by the PEPT-ML library.</p>
Data supporting "Burn Period: A use-inspired metric to track wildfire risk across the southwest U.S."
<p>Comma delimited data file of derived daily meteorological metrics from hourly, gap filled and quality controlled Remote Automated Weather Station (RAWS) data for Arizona and New Mexico (southwest U.S.) provided by the Climate, Ecosystems, and Fire Applications (CEFA) program at the Desert Research Institute (Brown, 2022, unpublished data). Data file contains daily average dewpoint temperature, air temperature, maximum Hot-Dry-Windy Index, maximum Fosberg Fire Weather Index, maximum vapor pressure deficit, and total number of hours/day with relative humidity below 20% for 124 RAWS from 2000-2022.</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>
Feral Muscovy and Mallard Tracking Data at USF
<p>Tracking data collected as a part of published study. </p> <p> </p> <p>Joni Downs, Mehrdad Vaziri, Lucy Deba Enomah, and Zachary J. Smith. 2021. Habitat use and movements of feral Mallards (Anas platyrhynchos) and invasive Muscovy ducks (Cairina moschata) in Tampa, Florida. Florida Field Naturalist 49(2): 35-45.</p>
Data for the "Does prognostic seeding along flight tracks produce the desired effects of cirrus cloud thinning?" manuscript
<p>Tar file of the data used to prepare the plots and write the text in: "Does prognostic seeding along flight tracks produce the desired effects of cirrus cloud thinning?" manuscript for submission to ACP.</p> <p>A description of each netcdf file is provided in the README file. The format of each file is in netcdf4</p>
Tracking data for breeding Curlew (Numenius arquata) in north Wales, UK
<p>This dataset of GPS tracking data for Eurasian Curlew (Numenius arquata) forms the basis of the analysis carried out in Bowgen, K. M., Dodd, S. G., Lindley, P., Burton, N. H. K., & Taylor, R. C. (2022). Curves for Curlew: Identifying Curlew breeding status from GPS tracking data. Ecology and Evolution, 12, e9509. https://doi.org/10.1002/ece3.9509.</p> <p>This paper details a new method to assign breeding status to individual birds during their full breeding season. All points are spatially anonymised but the analyses in the associated paper are replicable with this dataset as it only requires spatial and temporal relationships between the points rather than to a specific landscape.</p> <p>Data permissions are held jointly between BTO, National Resources Wales and Royal Society for the Protection of Birds as the funders and collaborators of the project. The study is open to collaboration with the full spatially referenced dataset following application to the BTO via the lead authors.</p> <p>........................................................................................</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
CS Track Citizen Science Survey Data 2021
<p>CS Track is launching a survey to gather citizen scientists’ (16 year old and older) perspectives on activities and forms of participation, learning and knowledge-building in citizen science (CS) projects. The aim of CS Track is to broaden our knowledge about CS and the impact CS activities can have. CS Track will do this by investigating a large and diverse set of CS activities, disseminating best practices and formulating knowledge-based policy recommendations in order to maximise the potential benefits of CS activities for individual citizens, organisations and society. This multi-perspective approach will allow us to shed light on the role of citizen science in society and social attitudes and emerging cultures in communities that engage with science and technology challenges.</p> <p><strong>CSTrack_Citizen_Science_Survey_Data_Final_Anon.csv</strong>: CSV File. CS Track Citizen Science Survey Data in a CSV file.</p> <p><strong>CSTrack_Citizen_Science_Survey_Data_Final_Anon.xlsx</strong>: Excel datasheet. Same file as previous in an Excel datasheet.</p> <p><strong>CSTrack_Citizen_Science_Survey_Final.pdf</strong>: PDF-file. CS Track Citizen Science Survey.</p> <p> </p> <p> </p>
Single particle tracking data for Sittewelle & Royle (2023)
<p>A project to analyse intracellular vesicle mobility.</p> <p>A preprint of the manuscript is available at <a href="https://doi.org/10.1101/2023.05.10.540182">https://doi.org/10.1101/2023.05.10.540182</a></p> <p>Code associated with the manuscript can be found at <a href="https://github.com/quantixed/p063p036">https://github.com/quantixed/p063p036</a> </p> <p>TrackMate XML files for:</p> <ul> <li>ATG9A, Clathrin, EB3, LAMP1, ML1N, Rab5, Rab11, Rab30, Rab35, SCAMP1, SCAMP3, TPD54</li> <li>TPD54 (pre and post bleach)</li> </ul> <p>are available here together with some of the larger outputs from the code.</p> <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> or using the code associated with the manuscript. The folders contain calibration csv files which will correct the scaling where required during processing with TrackMateR. </p>
EyeLink 1000 raw eye tracking data - Reading numbers is harder than reading words: An eye-tracking study
<p>Reading Arabic numerals is a fundamentally different activity compared to word reading. This study aimed to investigate the eye movements of normal-reading adults when reading aloud short and long Arabic numerals (with or without a thousand separator) compared to matched-in-length words and pseudowords.</p> <p>This dataset contains the raw data of the article "Reading numbers is harder than reading words: An eye-tracking study" published in the journal <em>Acta Psychologica</em> (https://doi.org/10.1016/j.actpsy.2023.103942)</p>
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OpenNeuro
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