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2,610 results for “tracking”
Tracking focal adhesions with TrackMate and Weka - tutorial dataset 2
<p>This folder contains data used to illustrate the utility of Weka detector in TrackMate.</p> <p>- classifier.model: trained Weka classifier.<br> - image data: <strong> human dermal microvascular blood endothelial cells expressing GFP-paxillin</strong></p> <p>More detail on using these files can be found here: https://imagej.net/plugins/trackmate/trackmate-weka.</p>
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS
<p>Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction.</p> <p>The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes.</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>
DeepLabCut network trained to track mouse 'front' body parts during rotarod running (front-view)
<p>DeepLabCut (https://github.com/DeepLabCut/) (Mathis et al., 2018; Nath et al., 2019) was used for tracking body parts of mice in an open field arena or in the rotarod. DeepLabCut 2.1.8.2 (local version on Windows with CPU, using the GUI) and 2.1.10.2 (google colab to train the network) were used using default parameters and the pretrained resnet50 network with imgaug augmentation. Frames were extracted with the k-means method and outlier frames with the jump method. <em>Rotarod, front camera: </em>29 frames from 18 videos (10 fps) were extracted for a total of 520 labeled pictures. 12 body parts (left, right and mid snout, left/right top/bottom ears, left/right eyes, headmount, left/right foot), 4 corners of the rotarod and 4 points on the rotarod wheels were manually labeled and linked to each other using skeletons. A neural network was trained using these images for 225K iterations (train error: 1.58, test error: 1.63). 20 outlier frames were extracted from each video and relabeled. An additional 20 images from 20 new videos with different recording conditions were labeled. The network was then refined for 331K iterations (from scratch) (train error: 2.34, test error: 5.49). This process was repeated a second time when adding 20 new videos (400 frames) and the network trained to a final 402K (train error: 2.64, test error: 3.98). For this last batch, brightness/contrast were too low to detect body features; brightness/contrast were thus enhanced using custom-written Python scripts. Relevant videos were analyzed at each of the 3 steps, for a total of 152 videos.</p> <p><em>Used to analyze videos for a publication (Labouesse et al., Nature Communications 2023).</em></p> <p><em>Network not included in the final publication.</em></p>
DeepLabCut network trained to track mouse body parts during open field locomotion (top-down view)
<p>DeepLabCut (https://github.com/DeepLabCut/) (Mathis et al., 2018; Nath et al., 2019) was used for tracking body parts of mice in an open field arena or in the rotarod. DeepLabCut 2.1.8.2 (local version on Windows with CPU, using the GUI) and 2.1.10.2 (google colab to train the network) were used using default parameters and the pretrained resnet50 network with imgaug augmentation. Frames were extracted with the k-means method and outlier frames with the jump method. <em>Open field: </em>20 images from 19 videos (10 or 30 fps) were extracted for a total of 380 labeled pictures. 8 body parts (snout, both ears, body center, both side laterals, tail base and tail end) and the 4 corners of the field arena were manually labeled and linked to each other using skeletons. A neural network was trained using these images for 170K iterations. 20 outlier frames were extracted from each video and relabeled. An additional 20 images from 19 videos with different recording conditions were labeled. The network was then refined for 210K iterations (from scratch), yielding a train error of 3.33 pixels and a test error of 8.83 pixels (with a likelihood p-cutoff of 0.6). This process was repeated a second time (using an additional 20 images from 15 new videos) to improve the pixel error; to a final 400 K iterations (train error: 2.65, test error: 3.71). 67 videos from 5 different experiments were analyzed on the final network.<em> </em></p> <p><em>Used to analyze videos for a publication (Labouesse et al., Nature Communications 2023)</em></p>
DeepLabCut network trained to track mouse 'back' body parts during rotarod running (back-view)
<p>DeepLabCut (https://github.com/DeepLabCut/) (Mathis et al., 2018; Nath et al., 2019) was used for tracking body parts of mice in an open field arena or in the rotarod. DeepLabCut 2.1.8.2 (local version on Windows with CPU, using the GUI) and 2.1.10.2 (google colab to train the network) were used using default parameters and the pretrained resnet50 network with imgaug augmentation. Frames were extracted with the k-means method and outlier frames with the jump method. <em>Rotarod, back camera:</em> 20 images from 9 videos (10 fps) were extracted for a total of 180 labeled pictures. 5 body parts (2 paws, 2 ankles, tail base), 4 corners of the rotarod, 2 points on the rotarod wheels and 4 points in a flashing LED (indicating timestamps) were manually labeled. A neural network was trained using these images for 80K iterations. 20 images from 14 videos with different recording conditions were labeled. The network was trained to 200K iterations (from scratch) (train error: 3.00, test error: 3.75). Relevant videos were analyzed at each step, for a total of 152 videos.</p> <p><em>Used to analyze videos for a publication (Labouesse et al., Nature Communications 2023)</em></p>
Uruguayan candombe drumming - beat and downbeat tracking data set
<p>Uruguayan Candombe drumming - beat and downbeat data set</p> <p><br>===========<br>Description<br>===========</p> <p>This dataset includes more than 2 hours of Candombe recordings, with annotated beats and downbeats. It features 35 complete performances by renowned players, playing in groups of three to five drums. A total of 26 tambor players from various generations took part, representing the three key traditional Candombe styles. These recordings were created in a studio setting over a period of more than two decades as part of musicological research. The sessions from 1992 and 1995 were produced by Luis Jure, and the session from 2014 was produced by Luis Jure and Martín Rocamora.</p> <p>The audio files are in stereo with a sampling rate of 44.1 kHz and 16-bit precision. An expert annotated the location of beats and downbeats, totaling more than 4700 downbeats. The audio is provided in flac format and the annotations are in .csv files. The values in the first column of the CSV file represent the time instants of the beats. The numbers in the second column indicate both the bar number and the beat number within the bar. For example, 1.1, 1.2, 1.3, and 1.4 represent the four beats of the first bar. Therefore, each label ending in .1 indicates a downbeat. Another set of annotations is provided as .beats files in which the bar numbers are removed.</p> <p><br>========<br>Citation<br>========</p> <p>This dataset was released with the publication of the following paper. So if you find the dataset useful and want to reference it in your publications, please cite it.</p> <p>"Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming”. Leonardo Nunes, Martín Rocamora, Luis Jure, Luiz W. P. Biscainho. Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), pages 264-270, Málaga, Spain, 26-30 October, 2015.</p> <p>@inproceedings{Nunes2015,<br> author = {Leonardo Nunes and Martín Rocamora and Luis Jure and Luiz W. P. Biscainho},<br> title = {{Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming}},<br> booktitle = {Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015)},<br> month = {Oct.},<br> address = {Málaga, Spain},<br> pages = {264--270},<br> year = {2015}<br>}</p> <p><br>========<br>Candombe<br>========</p> <p>Candombe is a vital part of Uruguayan popular culture, with thousands of practitioners and its rhythm influencing various genres of popular music. In 2009, UNESCO recognized it as part of the Intangible Cultural Heritage of Humanity. While it originated in Uruguay, Candombe has its roots in the culture brought by African slaves in the 18th century. Over time, it has evolved to incorporate the descendants of European immigrants and has become a part of the entire society. Candombe drumming, with its unique rhythm, is the essential element of this tradition, which also includes dancing, symbolic characters, and costumes.</p> <p>The drum used in Candombe is called tambor, which is Spanish for "drum." There are three different sizes: chico (small), repique (medium), and piano (big). Each size has its own unique sound, ranging from high to low frequency, and its own specific rhythmic patterns. All three drums are played with a stick in the dominant hand and the other one hitting the skin directly. The stick is also used to hit the shell when playing the clave or madera pattern. A minimal ensemble of drums (cuerda de tambores) must have at least one of each of the three drums. During a llamada de tambores, the ensemble usually consists of around 20 to 60 drums. When marching, the players walk forward with short steps synchronized with the beat, which is important for embodying the rhythm, even though it is not audible.</p> <p>===============<br>Acknowledgments<br>===============</p> <p>This work was partially supported by the funding agency Comisión Sectorial de Investigación Científica, Universidad de la República, Uruguay, and by the Department of Culture of the Municipality of Montevideo.</p> <p>This is the complete list of performers, in alphabetical order: Mariano Barroso, Eduardo 'Cacho' Giménez, Eduardo 'Malumba' Giménez, Francisco Giménez, José Luis Giménez, Jorge 'Foqué' Gómez, José Pedro 'Perico' Gularte, Luis 'Pocholo' Maciel, Julio Magariños, Raúl 'Neno' Magariños, Marcelo Magariños, Javier 'Cerdo' Martirena, Wilson Martirena, Eduardo 'Tierra' Nilo, Sergio Ortuño, Fernando 'Lobo' Núñez, Edinson 'Palo' Oviedo, Gustavo Oviedo, Egdardo Pintos, Luis 'Mocambo' Quiroz, Rodolfo 'Pelado' Rodríguez, Fernando 'Hurón' Silva, Juan Silva, Raúl Silva, Waldemar 'Cachila' Silva, and Héctor Manuel Suárez.</p>
Data from: Hidden in plain sight: migration routes of the elusive Anadyr bar-tailed godwit revealed by satellite tracking
<p><strong>Abstract</strong></p> <p>Satellite and GPS tracking technology continues to reveal new migration patterns of birds which enables comparative studies of migration strategies and distributional information useful in conservation. Bar-tailed godwits in the East Asian–Australasian Flyway <em>Limosa lapponica baueri </em>and <em>L. l. menzbieri</em> are known for their long non-stop flights, however these populations are in steep decline. A third subspecies in this flyway, <em>L. l. anadyrensis</em>, breeds in the Anadyr River basin, Chukotka, Russia, and is morphologically distinct from <em>menzbieri</em> and <em>baueri</em> based on comparison of museum specimens collected from breeding areas. However, the non-breeding distribution, migration route and population size of <em>anadyrensis </em>are entirely unknown. Among 24 female bar-tailed godwits tracked in 2015–2018 from northwest Australia, the main non-breeding area for <em>menzbieri</em>, two birds migrated further east than the rest to breed in the Anadyr River basin, i.e. they belonged to the <em>anadyrensis </em>subspecies. During pre-breeding migration, all birds staged in the Yellow Sea and then flew to the breeding grounds in the eastern Russian Arctic. After breeding, these two birds migrated southwestward to stage in Russia on the Kamchatka Peninsula and on Sakhalin Island en route to the Yellow Sea. This contrasts with the other 22 tracked godwits that followed the previously described route of <em>menzbieri</em>, i.e. they all migrated northwards to stage in the New Siberian Islands before turning south towards the Yellow Sea, and onwards to northwest Australia. Since the Kamchatka Peninsula was not used by any of the tracked <em>menzbieri</em> birds, the 4 500 godwits counted in the Khairusova–Belogolovaya estuary in western Kamchatka may well be <em>anadyrensis</em>. Comparing migration patterns across the three bar-tailed godwits subspecies, the migration strategy of <em>anadyrensis </em>lies between that of <em>menzbieri </em>and <em>baueri</em>. Future investigations combining migration tracks with genomic data could reveal how differences in migration routines are evolved and maintained.</p> <p> </p> <p><strong>Data set</strong></p> <p>Stopping sites and migration timing of satellite-tracked bar-tailed godwits in the East Asian-Australasian Flyway</p> <p>file name: Chan et al. 2022 BARG_Stops_Timing.xlsx</p> <p>The sheet 'stopping_sites' contains stopping sites of bar-tailed godwits tracked with solar Argos satellite transmitters, and their respective arrival and departure times at each site. The sheet 'timing' contains departure and arrival times at the non-breeding and breeding sites in 2017. The transmitters were deployed in Roebuck Bay and Eighty Mile Beach, Australia, and were operating on an 8 h on and 25 h off duty cycle. </p> <p> </p> <p>Measurements of satellite-tracked bar-tailed godwits in the East Asian-Australasian Flyway</p> <p>file name: Chan et al. 2022 BARG_measurements.csv</p> <p>The datafile contains bill, wing and tarsus lengths and sex of bar-tailed godwits tracked with solar Argos satellite transmitters. The birds were captured in Roebuck Bay and Eighty Mile Beach, Australia. </p> <p> </p> <p><strong>Journal Article</strong></p> <p>Chan, Y.-C., Tibbitts, T. L., Dorofeev, D., Hassell, C. J. and Piersma T. (2022) Hidden in plain sight: migration routes of the elusive Anadyr bar-tailed godwit revealed by satellite tracking. J Avian Biol e02988. <a href="https://doi.org/10.1111/jav.02920">https://doi.org/10.1111/jav.02988</a></p>
Reference videos for: Multi-track bottom-up synthesis from non-flattened AZee scores
<p>The upload contains videos referenced in the paper/poster "Multi-track bottom-up synthesis from non-flattened AZee scores"</p>
Data from: Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing
<p>This dataset contains the data for the publication "Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing".</p>
Above-anvil Cirrus Plume Producing Storm Tracks
<p>Two separate CSV files containing all warm and cold above-anvil cirrus plume-producing storm tracks used in Murillo and Homeyer (under review). These files contain the hourly storm number, binary above-anvil cirrus plume flag, date and time in UTC, longitude, and latitude positions of the overshooting top associated with the above-anvil cirrus plume-producing storm. The first and second rows contain the variable names and units, respectively, with the remaining rows containing the variable values.</p> <p> </p>
Carbon dioxide and blood-feeding shift visual cue tracking during navigation in Aedes aegypti mosquitoes
<p>Hematophagous mosquitoes need a blood meal to complete their reproductive cycle. To accomplish this, female mosquitoes seek vertebrate hosts, land on them, and bite. As their eggs mature, they shift attention away from hosts and towards finding sites to lay eggs. We asked whether females were more tuned to visual cues when a host-related signal, carbon dioxide, was present, and further examined the effect of a blood meal, which shifts behavior to ovipositing. Using a custom, tethered-flight arena that records wing stroke changes while displaying visual cues, we found the presence of CO2 enhances visual attention towards discrete stimuli and improves contrast sensitivity for host-seeking <em>Aedes aegypti</em> mosquitoes. Conversely, intake of a blood meal reverses vertical bar tracking, a stimulus that non-fed females readily follow. This switch in behavior suggests that physiological status modulates visual attention in mosquitoes, a phenomenon that has been described before in olfaction but not in visually-driven behaviors.</p>
Tagging and tracking information for radiotagged Chinook Salmon in the Copper River, Alaska 2021
<p>The first worksheet (2021 Raw Data) consists of each radiotagged fish and its relevant information including date of capture, the frequency and code of the transmitter, length (MEF) and age. Subsequent columns are Julian dates when they passed fixed tracking stations. The final 4 columns are fate columns. The last column is a general description of the general fate of each fish.</p> <p> </p> <p>The final worksheet (2021 summary) summarizes fates of all fish by tagging date. This is the primary input file for the Program R which has a code written to do the data analyses for this study.</p>
Mappability tracks for human assemblies (hg19 and GRCh38)
<p>They were created by using the GEM mapper aligner (Derrien et al., 2012) allowing up to two mismatches and considering sliding windows of 100-mer. They are exploited by the EXCAVATOR2 tool for reducing technical biases of Read Count measure in WES/TS experiments.</p>
Ship tracks detected using machine learning algorithm
<p>The filtered, vector ship tracks detected using the linked machine learning algorithm and derived from the linked segmentation masks. Each dataset contains the date and other related data for each shiptrack polygon. The `_geo` dataset contains the polygons on a lat/lon coordinate system while the other provides the polygons on the MODIS swath (pixel) indices.</p>
Tracking the photomineralization mechanism in irradiated lab-generated and field-collected brown carbon samples and its effect on cloud condensation nuclei abilities
<p>Data set of the data presented in figures and tables in our manuscript on the photomineralization of brown carbon samples: ammonium sulfate-methylglyoxal solutions, Suwannee River fulvic acid isolates, firewood smoke and ambient aerosols from Padua, Italy.</p>
OpenDRIVE dataset of 5G Living Lab research track in Wolfsburg
<p>This OpenDRIVE dataset models a road network excerpt of the city of Wolfsburg, Lower Saxony, Germany. Main application scopes of this OpenDRIVE data are in the domain of automated driving: simulation, verification and validation. The data has been acquired in context of the research project <a href="https://verkehrsforschung.dlr.de/en/projekte/5g-reallabor">5G Living Lab in the Mobility Region Braunschweig-Wolfsburg</a>.</p>
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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.