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559 results for “Tracking data”
Depth of the sea floor along the 5-minute resolution cruise track of the Antarctic Circumnavigation Expedition (ACE) derived from GEBCO 2019 bathymetry data.
<p><strong>Dataset abstract</strong></p> <p>Depth of the seabed along the five-minute averaged cruise track (Landwehr et al., 2020; DOI: 10.5281/zenodo.3752691) of the Antarctic Circumnavigation Expedition (ACE) was calculated from the the General Bathymetric Chart of the Oceans (GEBCO; GEBCO Compilation Group, 2019) 2019 gridded 30-arc second bathymetry data. The nearest gridded value from the bathymetry dataset was used to find the depth at the averaged position.</p> <p>Provided within this dataset is the average position of the vessel during a five-minute time period (where the time given is the middle time of the averaging interval).</p> <p><strong>Dataset contents</strong></p> <ul> <li>cruise_track_gebco2019_depth_5min.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> <li>ace_cruise_track_gebco2019_depth_5min_change_log.txt, metadata, text</li> </ul> <p><strong>Change log</strong></p> <p><strong>v1.1</strong> - Added additional data coverage and therefore track coverage from 2016-11-17 - 2016-11-22 inclusive. Updated README.txt with information about data coverage. Added change_log file.</p> <p><strong>v1.0</strong> - Initial release of depth along cruise track data set.</p> <p><strong>Dataset license</strong></p> <p>This GEBCO sea floor depth along the cruise track dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
# Replication code and data for: Tracking green space along streets of world cities
<p># Replication code and data for: Tracking green space along streets of world cities<br>Falchetta, G., & Hammad, A. T. (2025). Tracking green space along streets of world cities. Environmental Research: Infrastructure and Sustainability. https://doi.org/10.1088/2634-4505/add9c4 </p> <p>The file "gvi_358cities_2016_2023_yearly_falchetta_hammad.csv" contains<strong> output data</strong>, reporting sampling-point level data on the yearly (2016-2023) values of the Green View Index for the 190 cities covered in the paper AND an additional number of world cities (for a total of 358 cities). The "README_gvi_358cities_2016_2023_yearly_falchetta_hammad.txt" file contains a dictionary of each column name and units. </p> <p>____<br><br></p> <p>To replicate the analysis, the results, and the figures of the paper:</p> <ul> <li>Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking</li> <li><em>*Optional data extraction steps* </em>(processed output data are already available in the Zenodo repository):<br> <ul> <li>Adjust your working directory</li> <li>Run [lines 4-11] of workflow/sourcer.R</li> <li>Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine (https://code.earthengine.google.com) and complete the export to Drive tasks to generate the output .csv files</li> </ul> </li> <li>Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication)</li> </ul> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div>
10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains 10-day backward trajectories along the ship track of the Antarctic Circumnavigation Expedition from Nov 2016 – April 2017 calculated with the Lagrangian analysis tool LAGRANTO using the 3D-wind fields from the European Centre for Medium Range Weather Forecasts (ECMWF) operational analysis data. The trajectories were started from up to 56 vertical levels between 0 and 500 hPa a.s.l. and various variables were interpolated along the trajectories.</p> <p><strong>Dataset contents</strong></p> <ul> <li>trajs_ACE.zip: lsl_${year}${month}${day}_${hour}, trajectory files (containing all trajectories starting at ${year}${month}${day} ${hour}UTC at the ACE track from different vertical levels), comma-separated values</li> <li>fig_map.zip: map_long10_${year}${month}${day}_${hour}.png, map plots of all trajectories starting at ${year}${month}${day} ${hour}UTC coloured by pressure, portable network graphics</li> <li>fig_cross.zip: cross10_q_${year}${month}${day}_${hour}.png, cross-section plots of all trajectories starting at ${year}${month}${day} ${hour}UTC coloured by specific humidity, portable network graphics</li> <li>data_file_header.txt, metadata for lsl-files, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This 10-day backward trajectory dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Intermediate processing steps of quality-checking of Antarctic Circumnavigation Expedition (ACE) cruise track data.
<p><strong>Dataset abstract</strong></p> <p>The Antarctic Circumnavigation Expedition (ACE), undertaken in the austral summer of 2016/2017 recorded the cruise track using two independent geo-location instruments: one using GLobal NAvigation Satellite Systems (GLONASS; hereafter referred to as GLONASS) and another primarily using the Global Positioning System (GPS; hereafter referred to as the Trimble GPS). Daily log files were recorded in real-time from both instruments during the expedition and added to MySQL database tables. Following the expedition, quality-checking work has been undertaken to provide a one-second resolution set of positions for the cruise track. This dataset presents the intermediate files that were produced during the quality-checking, therefore it could be used to check the processing steps that have been undertaken, but should not be used as a final source of the cruise track data. Both the original raw data files and final quality-checked cruise track can be found in related datasets.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_INSTRUMENT_YYYY-MM-DD.csv – daily files for each instrument that were output from the database, data file, comma-separated values</li> <li>ace_INSTRUMENT_concatenated_YYYY-MM.csv – input files concatenated by month and instrument, data file, comma-separated values</li> <li>flagging_data_ace_INSTRUMENT_YYYY-MM-DD.csv – daily output files for each instrument with flagged data points, data file, comma-separated values</li> <li>track_data_combined_overall_flags_YYYY-MM.csv – instrument data combined with overall data flag for each month, data file, comma-separated values</li> <li>track_data_prioritised_YYYY-MM.csv – prioritised data files with overall data flag for each month, data file, comma-separated values</li> <li>ace_INSTRUMENT_manual_position_errors.csv – files containing the manually-observed errors, metadata, comma-separated values</li> <li>in_port.csv - dates on when the ship was stationary in port, metadata, comma-separated values</li> <li>README.txt – metadata, text file</li> <li>data_file_header.txt – metadata, text file</li> </ul> <p><strong>Dataset license</strong></p> <p>This dataset containing intermediate processing files of the ACE cruise track is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Graphic Illustration of our Digital Collections Data and Tracking Disease Workshop Session: Discussion and Synthesis
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Discussion section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Graphic Illustration of Talks in our Digital Collections Data and Tracking Disease Workshop Section: Case Studies
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Case Studies section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Graphic Illustration of Talks in our Digital Collections Data and Tracking Disease Workshop Section: Museum Perspectives
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Museum Perspectives section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
U.S. household food waste tracking data in support of Li et al. 2023
These data were used to generate the results in the article “Household Food Waste Trending Upwards in the United States: Insights from a National Tracking Survey,” by Ran Li, Yiheng Shu, Kathryn E. Bender & Brian E. Roe, which has been accepted for publication in the Journal of the Agricultural and Applied Economics Association (doi – https://doi.org/10.1002/jaa2.59). The Stata code used to generate results is available from the authors upon request. U.S. residents who participate in consumer panels managed by a commercial vendor were invited by email or text message to participate in a two-part online survey during four waves of data collection: February and March of 2021 (Feb 21 wave, 425 initiated, 361 completed), July and August of 2021 (Jul 21 wave, 606 initiated, 419 completed), December of 2021 and January of 2022 (Dec 21 wave, 760 initiated, 610 completed), and February, March and April of 2022 (Feb 22 wave, 607 initiated, 587 completed), July, August and Septemper of 2022 (Jul 22 wave, 1817 initiated, 1067 completed). We are not able to determine if any respondents participated in multiple waves, i.e., if any of the observations are repeat participants. All participants provided informed consent and received compensation. Inclusion criteria included age 18 years or older and performance of at least half of the household food preparation. No data was collected during major holidays, i.e., the weeks of the Fourth of July (Independence Day), Christmas, or New Years. Recruitment quotas were implemented to ensure sufficient representation by geographical region, race, and age group. Post-hoc sample weights were constructed to reflect population characteristics on age, income and household size. The protocol was approved by the local Internal Review Board. The approach begins with participants completing an initial survey that ends with an announcement that a follow-up survey will arrive in about one week, and that for the next 7 days, participants should pay clo
Data and code from "Black-throated blue warblers (Setophaga caerulescens) exhibit diet flexibility and track seasonal changes in insect availability" Kaiser et al. 2024 Ecology and Evolution
Changes in leaf phenology from warming spring and autumn temperatures have lengthened the temperate zone growing ‘green’ season and breeding window for migratory birds in North America. However, the fitness benefits of an extended breeding season will depend, in part, on whether species have sufficient dietary flexibility to accommodate seasonal changes in prey availability. We used fecal DNA metabarcoding to test the hypothesis that seasonal changes in the diets of the insectivorous, migratory black-throated blue warbler (Setophaga caerulescens) track changes in the availability of arthropod prey at the Hubbard Brook Experimental Forest, New Hampshire, USA. We examined changes across the breeding season and along an elevation gradient encompassing a two-week difference in green season length. From 98 fecal samples, we identified 395 taxa from 17 arthropod orders; 242 were identified to species, with Cecrita guttivitta (saddled prominent moth), Theridion frondeum (eastern long-legged cobweaver), and Philodromus rufus (white-striped running crab spider) occurring at the highest frequency. We found significant differences in diet composition between survey periods and weak differences among elevation zones. Variance in diet composition was highest late in the season, and diet richness and diversity were highest early in the season. Diet composition was associated with changes in prey availability surveyed over the green season. However, several taxa occurred in diets more or less than expected relative to their frequency of occurrence from survey data, suggesting that prey selection or avoidance sometimes accompanies opportunistic foraging. This study demonstrates that black-throated blue warblers exhibit diet flexibility and track seasonal changes in prey availability, which has implications for migratory bird responses to climate-induced changes in insect communities with longer green seasons. These data were gathered as part of the Hubbard Brook Ecosystem Study (HB
Weddell seal tracking data from the Weddell Sea (2011)
<p>This animation (.gif) comprises locations from 19 adult Weddell seals (<em>Leptonychotes weddellii</em>) instrumented in February 2011 with telemetry devices. These devices were Sea Mammal Research Unit Conductivity-Temperature-Depth Satellite Relay Data Loggers. These are animal-attached loggers that are manufactured and programmed by the Sea Mammal Research Unit Instrumentation Group at the University of St Andrews, Scotland. Ethical approval for this work was obtained from the University of St Andrews Animal Welfare and Ethics Committee, and from the corresponding committee at the British Antarctic Survey.</p> <p>This dataset is linked to the manuscript Photopoulou et al. 2020 "Sex-specific variation in the use of vertical habitat by a resident Antarctic top predator" Proceedings of the Royal Society B (http://dx.doi.org/10.1098/rspb.2020.1447) and a preprint on bioRxiv (https://doi.org/10.1101/2020.06.15.152009).</p> <p>The locations are the product of fitting a statistical model to the observed ARGOS location data, taking account of the error class. This animation does not show the originally observed locations. The model used was a continuous time state-space model implemented in the R package foieGras by Ian Jonsen and Toby Patterson (2019) foieGras: Fit Continuous-Time State-Space and Latent Variable Models for Filtering Argos Satellite (and Other) Telemetry Data and Estimating Movement Behaviour. R package version 0.4.01. <https://cran.r-project.org/package=foieGras></p> <p>The animation was created in R (R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/) using the moveVis package by Jakob Schwalb-Willmann (2019). moveVis: Movement Data Visualization. R package version 0.10.2. https://CRAN.R-project.org/package=moveVis</p> <p>The background satellite image is generated by selecting the option [map_service = "mapbox"] in the moveVis function frames_spatial() that was used to generate the .gif file. This uses satellite maps from <a href="https://www.mapbox.com/maps/">https://www.mapbox.com/maps/</a></p> <p>Please note: the background satellite image is static and does not represent the sea ice conditions experienced by the seals in this dataset.</p>
Moisture-Precipitation Couplings for Mesoscale Convective Systems in Tracking Data and Idealized Simulations
<p>Morphological properties, collocated synoptic conditions, and collocated rainfall for mesoscale convective systems in 1) the ISCCP Convective Tracking (CT) dataset with coincident data from the ERA-Interim (ERA-I) reanalysis and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) product and 2) long-channel radiative-convective equilibrium (RCE) simulations in the System for Atmospheric Modeling (SAM).</p> <p><strong>ISCCP_tracking_colloc.tar.gz </strong>- NetCDF files by year from 2000 to 2004 inclusive including ISCCP-CT morphological properties of MCSs, a series of collocated synoptic variables from ERA-5 (including specific humidity, temperature, vertical velocity, and cloud condensate profiles), and collocated precipitation intensity and accumulation from MSWEP.</p> <p><strong>RCE_colloc_execution1.tar.gz</strong> - NetCDF files including RCE-SAM extent of MCSs (RCE_COL_cluster-sizes_*.nc), as well as collocated synoptic variables. Collocation of synoptic variables is performed for these files by extracting and averaging the variables over grid cells where the precipitation is greater than either its mean (RCE_COL_MEAN_*.nc) or its 99th percentile (RCE_COL_99_*.nc).</p> <p><strong>RCE_colloc_execution2.tar.gz</strong> - NetCDF files including RCE-SAM extent of MCSs (RCE_COL_cluster-sizes_*.nc), as well as collocated synoptic variables. Collocation of synoptic variables is performed for these files by taking either the mean (RCE_COL_MEAN_*.nc) or the 99th percentile (RCE_COL_99_*.nc) value over all grid cells within the MCS.<br><br>For the NetCDF files from RCE output, the numeric value in the file name is the corresponding sea surface temperature from 280 to 310 K.</p>
Research Data Alliance Interest Group Professionalising Data Stewardship Career Tracks Survey Dataset
<p>This is the final dataset resulting from the data steward Career Tracks survey that the Reseach Data Alliance (RDA) Interest Group Professionalising Data Stewardship carried out in 2022. Data stewards were defined as professionals who aim at guaranteeing that data is appropriately treated in all stages of the research cycle (i.e., design, collection, processing, analysis, preservation, data sharing and reuse); we invited responses from participants who either now or in the past carried out data stewardship functions, regardless of their job title. The survey asked respondents about their job titles, the organizational context in which they work(ed) including contract types and domains, their educational background, and how they perceive their professional future. </p><p>This dataset publication includes:</p><ol><li>Survey response data in CSV format. The file includes data from 241 respondents who consented to participate in the survey and share the data via a repsoitory, who indicated that they either currently work or have worked in the past in a data stewardship role, and who responded to at least one further question.</li><li>Thematic analysis of the qualitative questions Q11 and Q12 in PDF format.</li></ol>
Eye tracking videos and raw data of breathing recognition attempts in simulated out-of-hospital cardiac arrest
<div> <div> <div> <p>This dataset comprises eye tracking videos and raw data documenting attempts to recognize breathing in simulated out-of-hospital cardiac arrest scenarios.</p> <p>The data were recorded using an Ergoneers Dikablis head-mounted eye tracker.</p> <p>Our analysis of this data resulted in the publication of two studies: Study 1, available at <a href="https://doi.org/10.1097/SIH.0000000000000617" target="_blank" rel="noopener">https://doi.org/10.1097/SIH.0000000000000617</a>, and Study 2, accessible at <a href="https://doi.org/10.25894/ijfae.2307" target="_blank" rel="noopener">https://doi.org/10.25894/ijfae.2307</a></p> <p> </p> <p>Version 2 is up-to-date.</p> <p>In Version 1:</p> <ul> <li>the doi for Study 2 was incorrect</li> <li>data for participant #51 of Study 1 were missing</li> </ul> </div> </div> </div>
Data supporting tracking changes in wetlandscape properties of the Lake Winnipeg Watershed using Landsat inundation products (1984–2020)
<p>The workbook contains time series of wetlandscape properties, climate variables, and climate oscillation indices for 1984–2020, and land cover statistics for 1992–2020 in the Lake Winnipeg Watershed. The wetlandsacpe properties were generated as part of a study by Fendereski, Ma, Mohammady, Spence, Trick, and Creed ("Tracking changes in wetlandscape properties of the Lake Winnipeg Watershed using Landsat inundation products (1984–2020)") submitted<span> </span>to the International Journal of Applied Earth Observation and Geoinformation. The use of the data is subject to citing the paper.</p>
Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments"
<p>Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments."</p> <p>Data includes representative real and simulated bead trajectories used in the manuscript.</p> <p>Code includes all simulations, analysis, and plot details for the Figures in the manuscript. </p> <p>See included README.txt for more details.</p>
Data and Videos for Argos: a toolkit for tracking multiple animals in complex visual environments
<p>Original videos used and data generated for the article "Argos: a toolkit for tracking multiple animals in complex visual environments".</p> <p>The data contains original videos used as input to the Argos Tracking tool, the generated raw tracks in Pandas-HDF5 format, and the corrected tracks after processing with Argos Review tool.</p> <p>It also includes a zip archive with ground truth tracks along with tracks detected from two videos by Argos and several other tracking tools for comparison using the HOTA metric organized in a folder structure suitable for the TrackEval tool.</p>
Real-Time Frequency Tracking of an Electro-Thermal Piezoresistive Cantilever Resonator with ZnO Nanorods for Chemical Sensing (Data)
<p>Origin projects, figures and COMSOL simulation used for the article "Real-Time Frequency Tracking of an Electro-Thermal Piezoresistive Cantilever Resonator with ZnO Nanorods for Chemical Sensing", published in <em>Chemosensors</em> on 03 Jan 2019.</p>
Raw data and code for "Addressing gaps in small-scale fisheries: a low-cost tracking system"
<p>This repository contains the raw data and code to reproduce results and plots presented in: "Addressing gaps in small-scale fisheries: a low-cost tracking system". The release contains:</p> <ul> <li>ssf_function.R. The R function developed for the analysis</li> <li>ssf_workflow.R. The R scripts to reproduce the analysis and the results.</li> <li>gps_data.csv. Raw data used in the paper</li> </ul>
A consistent discretization of the single-field two-phase momentum convection term for the unstructured finite volume Level Set / Front Tracking method - data
<p>Research data from the rhoLENT unstructured Level Set / Front Tracking method for simulating two-phase flows with large density ratios. </p>
Satellite tracking data of white sharks in the southwest Indian Ocean (2012-2014)
<p>These data comprise locations and individual metadata from 34 white sharks (<em>Carcharodon carcharias</em>) instrumented March-May 2012 with telemetry devices along the coast of South Africa. These devices were SPOT5 transmitters (SPOT-257, SPOT-258; Wildlife Computers) which transmit locations via ARGOS CLS. All research methods were approved and conducted under the South African Department of Environmental Affairs: Oceans and Coasts permitting authority.</p> <p>This dataset is linked to the manuscript Kock et al. 2021 "Sex and size influence the spatiotemporal distribution of white sharks, with implications for interactions with fisheries and spatial management in the southwest Indian Ocean".</p> <p>The data are structured in long format, so that each row in the dataset represents an observation. The columns in the data are as follows.</p> <p>DeployID: This a factor variable identifying each individual shark. It has 34 levels.</p> <p>SPOT: This is a numeric variable identifying the tag number unique to each shark.</p> <p>Date: This is a date variable (POSIXct) that gives the date and time of a geographic location record in UTC time.</p> <p>Type: This is a character variable identifying the type of location record.</p> <p>Quality: This is a character variable made up of numbers and letters giving the location error associated with each location as provided by ARGOS.</p> <p>Latitude: This is a numeric variable and gives the latitude of the shark at the time of each record.</p> <p>Longitude: This is a numeric variable and gives the longitude of the shark at the time of each record.</p> <p>Area_tagged: This is a character variable that gives the area where the shark was tagged.</p> <p>Sex: This is a character variable identifying the sex of the shark, either "F" or "M" for female and male.</p> <p>TL: This is a numeric variable giving the total length of the shark in centimetres.</p> <p>Maturity: This is a character variable giving the maturity of the shark based on its total length following Malcolm et al. 2001: juveniles (male and female: 175-300 cm TL), sub-adults (male: >300-360 cm TL; females: >300-480 cm TL) and adults (male: >360 cm TL; female: >480 cm TL).</p> <p> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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