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559 results for “Track data”
(Extended Data) Amplicon deep sequencing of ama1 and mdr1 to track within-host P. falciparum diversity throughout treatment in a clinical drug trial
<p>These extended data accompany the manuscript: Targeted Amplicon deep sequencing of ama1 and mdr1 to track within-host <em>P. falciparum</em> diversity throughout treatment in a clinical drug trial</p> <p><strong>Table S1: Concentration ratios and resulting parasitemia in artificial dna mixtures of P. falciparum Lab Isolates 3D7 and Dd2.</strong> This table presents the parasitemia for the artificial mixtures of P. falciparum lab isolates 3D7 and Dd2. Each mixture was prepared at varying ratios of 3D7 to Dd2, starting from equal proportions to a complete presence of only 3D7. The original concentration of each isolate was approximately 50,000 parasites per microliter (pf/μl), and the table displays the proportion of each strain in the mixture and the resulting total parasitemia concentration.</p> <p><strong>Table S2. List of PCR and deep sequencing primers.</strong> This table shows the list of forward and reverse primers used for deep sequencing. In boldface are the MID tags, while in the regular face are the forward primers</p> <p><strong>Table S3. The relative frequencies of each ama1 variant and the number of samples with each variant.</strong> The relative frequencies (%) of the 33 AMA1 variants in pre-and post-treatment samples (n = 330) are shown as a 33 amino acid sequence. The frequencies were calculated by dividing the number of reads of each microhaplotype by the total number of reads obtained per sample (116,187,131).</p> <p><strong>Table S4. Distribution of microhaplotypes among samples.</strong> This table shows the occurrence of microhaplotypes across all participants, both with monoclonal and multiclonal ama1 infections. It presents the ama1 clonality – monoclonal or multiclonal (column 1) - participant IDs (column 2), microhaplotype IDs (column 3), and the relative frequencies of these microhaplotypes across timepoints from 0 to 1008 hours (day 42) (column 3). Dashes represent time points where microhaplotypes were missing or were not detected.</p> <p><strong>Table S5. Distribution of rare microhaplotypes among samples.</strong> This table shows the occurrence of rare microhaplotypes in various samples. It presents participant IDs (column 1), microhaplotype IDs (column 2), and the relative frequencies of these microhaplotypes across time points from 0 to 1008 hours (day 42) (column 3). Samples containing rare microhaplotypes - specifically from PID10, PID32, PID38, PID40, PID49, PID60, PID63, and PID65 - are shown in orange, along with the corresponding rare microhaplotypes and their time points of occurrence. Furthermore, participants are categorised by shared microhaplotypes to indicate instances of rarity and commonality. Except for one microhaplotype unique to PID30, rare microhaplotypes were detected in several samples, frequently exceeding a 5% relative frequency. Dashes represent time points where microhaplotypes were missing or were not detected.</p> <p><strong>Table S6. The parasitemia levels associated with each ama1 microhaplotype per timepoint.</strong> This table shows the parasitemia for each ama1 microhaplotype per timepoint and each participant. “Patient ID” represents the patient ID, “AMA1 COI at 0h” represents the complexity of infection (COI) for each participant at baseline, based on ama1 while subsequent columns represent the parasitemia for each ama1 microhaplotype from timepoint 0h to 1008h. Parasitemia was back-calculated using the COI and total parasitemia for each time point. For time points with a COI > 1, parasitemia for the respective ama1 microhaplotypes are separated by commas, cells in red indicate timepoints without sequencing data (ND = not determined). In contrast, cells in grey indicate time points where microhaplotypes were detected below 10 parasites/μl, hence at risk of falling below the sampling limit.</p> <p><strong>Figure S1. Performance of AmpSeq in the sequencing controls.</strong> Six aliquots were prepared for each control set to ensure sufficient control data in case of PCR or sequencing failure. The median read depth in the lab controls was 5,658 (range 4,310 – 12,603) and 704 (291 – 1,676). The x-axis represents the aliquot identifier across the five mixtures, starting from 1 to 6, while the y-axis represents the proportions of each variant across all aliquots. For ama1 (A), two variants (3D7 and Dd2) were detected, whereas in mdr1 (B), two variants were detected YY, FY and NY following amplification of Dd2 Copy I, Dd2 Copy II and 3D7, respectively. For ama1, sequencing failed for aliquot 6 of control set 1, while for mdr1, sequencing failed for aliquot 2 and 6 of control set 3, aliquots 1 and 6 of control set 4 and aliquots 1 and 5 of control set 5. Under the mdr1 control set 4, the Dd2 copy II (86F, 184Y) was not identified, possibly due to having very low concentrations that were not picked up in this aliquot. Based on our control mixtures, the minimum variant frequency we could detect was 5%.</p> <p><strong>Figure S2. Heatmaps of the successfully PCR amplified and sequenced samples for ama1 (A) and mdr1 (B).</strong> The rows represent the study participants, while the columns represent time in hours. Successfully sequenced samples are shown in blue, those that failed PCR are shown in red and those that failed sequencing are in black. The timepoint “ Rec” represents unscheduled visits where a recurrent sample was collected. The unshaded areas with "-" are time points where samples were not collected. For each time point, the number of samples successfully sequenced (n Successful) is indicated in the last row of each panel. The table in panel C shows the groupings of samples based on parasitemia, high (> 5,000), moderate (100-5,000) and low (< 100 parasites per microlitre). Many samples collected between 0h-12h had high parasitemia, samples collected between 18h–30h had moderate parasitemia, while samples collected after 30h were primarily of low parasitemia.</p> <p><strong>Figure S3. The mean complexity of infection (COI) by AMA1 throughout treatment.</strong> The mean COI (red diamonds) appeared to be stable (between 1.5 - 2) from baseline (0h) up to 72h and thereafter fluctuated due to the small sample sizes (<5) in the post-treatment samples. The black dots represent the COI per sample.</p> <p> </p>
Eye tracking data set of academics making an omelette: An egg-breaking work
<p>Just as there are numerous ways to cook an egg, there are numerous ways to recreate a YouTube video of cooking an omelette. We created a dataset of 10 academics replicating a viral video of making an omelette. We evaluated the saccade behavior during the varying subtasks and found differences related to the actions (whisking, sprinkling, etc.) and the objects (eggs, butter, plate, etc.). This dataset can further offer insight into eye movements in complex tasks and is potentially even applicable for task planning and </p>
GPS tracking data for: Male mating season range expansion results from an increase in scale of daily movements for a polygynous-promiscuous bird
<p>Males of species with promiscuous mating systems are commonly observed to use larger ranges during the mating season relative to non-mating seasons, which is often attributed to a change in movements related to reproductive activities. However, few studies link seasonal range sizes to variations in daily space use patterns to provide insight into the behavioral mechanisms underlying mating season range expansion. We studied 20 GPS-tagged male wild turkeys (<em>Meleagris gallopavo</em>), a large upland gamebird, during the mating and summer non-mating seasons to test the hypothesis that larger mating season ranges resulted from male wild turkeys expanding the scale of daily movement activities to locate and court females. We delineated mating and non-mating seasons based on the intensity of gobbling, a vocalization tied to courtship behavior, recorded by autonomous recording units distributed across the study area. Mating season ranges were significantly larger than non-mating season ranges. Daily ranges were larger in the mating season, as were distances between roost sites used on consecutive nights. Variance in daily range size was greater in the mating season, but low temporal autocorrelation suggested considerable daily variability in both seasons. We found no evidence that male wild turkeys changed how they distributed daily movements within seasonal ranges, or differences in habitat use, suggesting larger mating season ranges result from male wild turkeys increasing the scale of their daily movements, rather than a systematic shift to a nomadic movement strategy. Likely, the distribution of females is more dynamic and ephemeral compared to other resources, prompting males to traverse larger daily ranges during the mating season to locate and court females. Our work illustrates the utility of using daily movement to understand the behavioral process underlying larger space use patterns.</p>
Data archive for the peer-reviewed journal article "Online measurements during simulated atmospheric aging track the strongly increasing oxidative potential of complex combustion aerosols relative to their primary emissions"
<p>This data archive accompanies the article "Online measurements during simulated atmospheric aging track the strongly increasing oxidative potential of complex combustion aerosols relative to their primary emissions", which was accepted in November 2024 in the peer-reviewed journal Environmental Science and Technology Letters. The data archive contains the processed OP_DTT, PM loading, oxidant level, and elemental ratio measurements presented in this journal article. </p>
Data and software for "Metal Pad Sensing: exploiting the electrical double layer to improve resistance-based microfluidic cell tracking, with applications to label-free mechanophenotyping"
<p>Data and software for "Metal Pad Sensing: exploiting the electrical double layer to improve resistance-based microfluidic cell tracking, with applications to label-free mechanophenotyping"</p>
Data repository for the paper: Sharp front tracking with geometric interface reconstruction
<h1>Data repository for the paper</h1> <h1><em>Sharp front tracking with geometric interface reconstruction</em></h1> <p> </p> <p>This repository consists of the results data for the paper "Sharp front tracking with geometric interface reconstruction" by Christian Gorges, Fabien Evrard, Robert Chiodi, Berend van Wachem and Fabian Denner. The simulation results stored in this repository have the following data format:</p> <ul> <li> <p>.txt files consisting the raw data used for the plots in the results chapter of the paper</p> </li> <li> <p>.pvtu and .vtu files containing the front mesh data for the rising bubble simulations (Paraview is an exemplary software to view the front mesh data)</p> </li> <li> <p>.py files containing python scripts serving as examples on how to use and plot the raw data of the .txt files</p> </li> </ul> <p>The main folders of this repository are named as the sections in the results chapter of the paper. For instance, the folder translating_droplet contains the data of the "Translating droplet" section. Within the main folders, sub folders contain the raw data for the specific simulations. The naming style of the raw data files and the subfolders for each section is explained in the following.</p> <p><em>stationary_droplet</em>: This main folder contains subfolders for all Laplace numbers simulated. "La_120" corresponds to a Laplace number of 120. The file names of the .txt files within the subfolders consist of the Laplace number, followed by the front tracking method and the d/dx ratio. If roughness smoothing is used it also consists of "WithRoughnessSmoothing". For example "La_120_ClassicFT_ddx_52.txt" consists of the data for a Laplace number of 120, the classic front tracking method and a d/dx ratio of 52. The content in the .txt files is the following: "%e,%e,%e,%e,%e,%e,%e\n" which corresponds to "Physical time, Physical time / \tau_{mu}, Kinetic energy, RMS velocity, Max velocity, Ca_{max}, U_sigma".</p> <p><em>translating_droplet</em>: This main folder contains subfolders for all Laplace numbers simulated. "La_120" corresponds to a Laplace number of 120. The file names of the .txt files within the subfolders consist of the Laplace number, followed by the front tracking method and the d/dx ratio. If roughness smoothing is used it also consists of "WithRoughnessSmoothing". For example "La_120_ClassicFT_ddx_52.txt" consists of the data for a Laplace number of 120, the classic front tracking method and a d/dx ratio of 52. The content in the .txt files is the following: "%e,%e,%e,%e,%e,%e,%e\n" which corresponds to "Physical time, Physical time / \tau_{mu}, Kinetic energy, RMS velocity, Max velocity, Ca_{max}, U_sigma".</p> <p><em>oscillating_droplet</em>: This main folder contains subfolders for all droplet viscosities simulated. "mu_d_05" corresponds to a droplet viscosity of 0.5. The file names of the .txt files within the subfolders consist of the droplet viscosity, followed by the front tracking method and the d/dx ratio. If roughness smoothing is used it also consists of "WithRoughnessSmoothing". For example "mu_d_05_ClassicFT_ddx_52.txt" consists of the data for a droplet viscosity of 0.5, the classic front tracking method and a d/dx ratio of 52. The content in the .txt files is the following: "%f,%f,%e\n" which corresponds to "Physical time, \tau, r".</p> <p><em>rising_bubbles</em>: This main folder contains subfolders for all rising bubble cases simulated. "Case_1_Classic" corresponds to a case 1 simulated with the classic front tracking method. The .txt files within the subfolders consist of the physical time, followed by the non-dimensional time and the Reynolds number. The .zip files contain the .pvtu and .vtu files for the front meshes.</p> <p>The python scripts have been tested with Python 3.11.5.</p> <p>This project has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant number 420239128, and from the European Unions's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101026017. This work was supported by the US Department of Energy through the Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218CNA000001).</p>
Data and code for the publication "Tracking microplastics across the streambed interface: Using laser-induced fluorescence to quantitatively analyze microplastic transport in an experimental flume"
<p>This archive contains datasets and codes that were used in the publication "Tracking microplastics across the streambed interface: Using laser-induced fluorescence to quantitatively analyse microplastic transport in an experimental flume".</p>
Census and phenotype data supporting Drosophila adaptive tracking
<p>Direct observation of evolution in response to natural environmental change can resolve <span>fundamental questions about adaptation including its pace, temporal dynamics, and underlying</span> p<span>henotypic and genomic architecture. </span>W<span>e tracked evolution of fitness-associated phenotypes and</span> <span>allele frequencies genome-wide in ten replicate field populations of <i>Drosophila melanogaster</i></span><i> </i><span>over ten generations from summer to late fall. Adaptation </span><span>was evident over each sampling</span> <span>interval (1-4 generations) with exceptionally rapid phenotypic adaptation and large allele frequency shifts at many independent loci. The direction and basis of the adaptive response</span> <span>shifted repeatedly over time, consistent with the action of strong and rapidly fluctuating</span> <span>selection. Overall, we find clear phenotypic and genomic evidence of a</span><span>daptive tracking</span> <span>occurring contemporaneously with environmental change, demonstrating the temporally dynamic</span> <span>nature of adaptation.</span></p>
Honeybee lifetime tracking data 2018
<p>Data from barcode-based tracking of honeybees during summer of 2018, taken in an observation hive located at University of Konstanz, Germany.</p> <p>Associated GitHub repository: <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2018data">https://github.com/jacobdavidson/bees_lifetimetracking_2018data</a></p> <p>Data usage example is on <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2018data/blob/main/Data%20usage%20example.ipynb">Github</a>, or can be run in a browser with <a href="https://mybinder.org/v2/gh/jacobdavidson/bees_lifetimetracking_2018data/HEAD?labpath=Data%20usage%20example.ipynb">Binder</a></p> <p><strong>File contents: </strong></p> <p><strong><a href="https://zenodo.org/record/6045860/files/2018_Quantity_Descriptions.xlsx?download=1">2018_Quantity_Descriptions.xlsx</a></strong></p> <p>List and description of quantities contained in the single-day summary metrics, and the metrics calculated at shorter intervals (hour, 5 minute, 1 minute). Also lists day number definitions, and cohort birthdates.</p> <p><strong><a href="https://zenodo.org/record/6045860/files/beetrajectories_days_000_to_049.zip?download=1">beetrajectories_days_000_to_049.zip</a>, <a href="https://zenodo.org/record/6045860/files/beetrajectories_days_050_to_085.zip?download=1">beetrajectories_days_050_to_085.zip</a></strong></p> <p>Single-day hdf files, each containing x-y trajectory data in the form:</p> <table> <thead> <tr> <th>daynum</th> <th>framenum</th> <th>uid</th> <th>x</th> <th>y</th> <th>camera</th> <th>theta</th> </tr> </thead> </table> <ul> <li>daynum: the day number of the observation period. See 'Day numbers and cohorts' sheet in 2018_Quantity_Descriptions.xlsx</li> <li>framenum: using the camera frame rate of 3fps, the frame number with respect to that particular day</li> <li>uid: the Unique ID of each bee</li> <li>x,y: pixel coordinate values of each bee. Conversion is 80 pixels/cm</li> <li>camera: which camera the bee was detected on: 0=exit side (with dance floor - shown at right in comb map images), 1=back side (show at left in comb map images)</li> <li>theta: orientation of the bee in the hive </li> </ul> <p><strong><a href="https://zenodo.org/record/6045860/files/comb-contents2018.zip?download=1">comb-contents2018.zip</a></strong></p> <p>Comb content color-coded png image files, as well as pkl files for using the comb data type (see <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2018data/blob/main/Data%20usage%20example.ipynb">Data Usage Example</a>)</p> <p><strong><a href="https://zenodo.org/record/6045860/files/daydatamat.csv?download=1">daydatamat.csv</a></strong></p> <p>Summary behavioral metrics calculated for each tracked bee on each day of the experiment. See 2018_Quantity_Descriptions.xlsx for full list and detail of quantities. Data is in the form:</p> <table> <thead> <tr> <th>Age</th> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th><metric1></th> <th><metric2></th> <th>...</th> </tr> </thead> </table> <p><strong><a href="https://zenodo.org/record/6045860/files/df_day1min_alldays.zip?download=1">df_day1min_alldays.zip</a>, <a href="https://zenodo.org/record/6045860/files/df_day5min_alldays.zip?download=1">df_day5min_alldays.zip</a>, <a href="https://zenodo.org/record/6045860/files/df_dayhour_alldays.zip?download=1">df_dayhour_alldays.zip</a> </strong></p> <p>Behavioral metrics calculated over different time intervals: 1 minute, 5 minute, or 1 hour divisions. See 2018_Quantity_Descriptions.xlsx for full list and detail of quantities. Files are organized as a single .hdf file for each day. Each file contains data in the form:</p> <p>Per-hour data:</p> <table> <thead> <tr> <th>Age</th> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th>Hour</th> <th><metric1></th> <th><metric2></th> <th>...</th> </tr> </thead> </table> <p>5-minute or 1-minute data: </p> <table> <thead> <tr> <th>Age</th> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th>timedivision</th> <th><metric1></th> <th><metric2></th> <th>...</th> </tr> </thead> </table> <p><strong> <a href="https://zenodo.org/record/6045860/files/dfxy_dayhour_alldays.zip?download=1">dfxy_dayhour_alldays.zip</a></strong></p> <p>x-y histogram data, calculated by using a 2cmx2cm grid and binning for each hour of each day. One file per day, and each file contains data in the form:</p> <table> <thead> <tr> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th>Hour</th> <th>hist0</th> <th>hist1</th> <th>...</th> <th>hist1434</th> </tr> </thead> </table> <p>Convert the flattened histogram structure into a shape of [41,35] in order to view as shown in the paper or in the <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2018data/blob/main/Data%20usage%20example.ipynb">Data Usage Example</a></p>
Data and Code for: Validating ATLAS: a regional-scale high-throughput tracking system
<p>Data and code for the manuscript: <strong>Validating ATLAS: a regional-scale high-throughput tracking system</strong></p>
Tracking magma spine extrusion from space: Implications for conduit and topography complexity at Shiveluch volcano, Kamchatka - Webcam data repository
<p>Webcam data showing the growth of a spine at Shieveluch volcano.</p> <p>The data is provided in three formats.</p> <p>First the original images with clear visibility are provided.</p> <p>Second a time lapse movie is provided with images co-aligned to reduce shaking of the camera.</p> <p>Third, a zoom in of the time lapse movie is provided.</p> <p>For more information we refer to the publication with the title "Tracking magma spine extrusion from space: Implications for conduit and topography complexity at Shiveluch volcano, Kamchatka" published in Nature Communications E Env</p>
Data from: Tracking population genetic signatures of local extinction with herbarium specimens
<p><strong><span>Background and Aims </span></strong><span>Habitat degradation and landscape fragmentation dramatically lower population sizes of rare plant species. Decreasing population sizes may, in turn, negatively affect genetic diversity and reproductive fitness which can ultimately lead to local extinction of populations. Although such extinction vortex dynamics have been postulated in theory and modelling for decades, empirical evidence from local extinctions of plant populations is scarce. In particular, comparisons between current vs. historical genetic diversity and differentiation are lacking despite their potential to guide conservation management.</span></p> <p><strong><span>Methods </span></strong><span>We studied the population genetic signatures of the local extinction of <em>Biscutella laevigata</em> subsp. <em>gracilis </em>populations in Central Germany. We used microsatellites to genotype individuals from 15 current populations, one ex-situ population, and 81 herbarium samples from five extant and 22 extinct populations. In the current populations, we recorded population size and fitness proxies, collected seeds for a germination trial and conducted a vegetation survey. The latter served as surrogate for habitat conditions to study how habitat dissimilarity affects functional connectivity among the current populations. </span></p> <p><strong><span>Key Results </span></strong><span>Bayesian clustering revealed similar gene pool distribution in current and historical samples but also indicated that a distinct genetic cluster was significantly associated with extinction probability. Gene flow was affected by both spatial distance and floristic composition of population sites, highlighting the potential of floristic composition as powerful predictor of functional connectivity which may promote decision making for reintroduction measures. For an extinct population, we found a negative relationship between sampling year and heterozygosity. Inbreeding negatively affected germination. </span></p> <p><strong><span>Conclusions</span></strong><span> Our study illustrates the usefulness of historical DNA to study extinction vortices in threatened species. Our novel combination of classical population genetics together with data from herbarium specimens, an ex-situ population and a germination trial underscores the need for genetic rescue measures to prevent extinction of <em>B. laevigata</em> in Central Germany. </span></p>
Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis - Single molecule tracking - raw data
<p>This dataset contains the raw image data that was analyzed in the manuscript "Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis"</p>
Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis - Single molecule tracking - raw data - calibration images
<p>This dataset contains the images used for channel calibration for the single molecule data that was analyzed in the manuscript "Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis"</p>
Cell-ACDC: segmentation, tracking, annotation and quantification of microscopy imaging data (dataset)
<p>This repository includes all the data generated or analysed during the preparation of Cell-ACDC publication, including test datasets for testing the software.</p> <p>Cell-ACDC is open-source software available on GitHub <a href="https://github.com/SchmollerLab/Cell_ACDC">here</a>.</p>
Data and scripts for: Satellite-tracking reveals sex-specific migration distance in green turtles (Chelonia mydas)
<p>Data derivates and analysis scripts (in R) used for the paper "Satellite-tracking reveals sex-specific migration distance in green turtles (<em>Chelonia mydas</em>)", published in Biology Letters, on analyzing male and female green turtle movements in West Africa.</p>
Odor trail tracking data from DeepLabCut preprocessed for I-MuPPET
<p>This data entry contains <strong>annotated mouse data </strong>from the<strong> <a href="https://www.nature.com/articles/s41593-018-0209-y">DeepLabCut Nature Neuroscience paper</a></strong> preprocessed for <a href="https://urs-waldmann.github.io/i-muppet/"><strong>I-MuPPET</strong></a>.</p> <p>Code for I-MuPPET available at <a href="https://github.com/urs-waldmann/i-muppet/">https://github.com/urs-waldmann/i-muppet/</a>.</p>
FESOM model data and particle tracking data used in publication 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean'
<p>FESOM model data and particle tracking data used in the paper 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean" by Andreas Rogge at al.</p> <p>1) FESOM velocity fields averaged over the top 200 m water depth, averaged over the time period 2015-2018.</p> <p>2) FESOM transect at 95°E in the Arctic Ocean (temperature, salinity and velocity), averaged over the time period 2015-2018.</p> <p>3a) Particle back-tracking data based on daily FESOM velocity fields in netcdf format. Particles were released at 95°E every 14 days during the year 2018 and tracked until they reached the surface. Three different constant sinking velocities were used, representative for small and large non-ballasted particles and small ballasted particles.</p> <p>3b) Distribution of particles at the surface for the experiments with three different sinking velocities as mat files. </p>
Data from: Thermal habitat fragmentation in stratified lakes induces resource waves that brook charr track across seasons
<p>The spatial configuration of thermal habitats constrains the thermoregulatory performance of ectotherms. Thermal landscapes also vary through time, which is particularly relevant in seasonal environments such as temperate lakes. Indeed, elevated temperatures in the epilimnion of dimictic lakes during summer could substantially reduce the use of this habitat by cold-stenothermic fish during the stratified period. The main objective of this study was to evaluate whether thermal habitat fragmentation in stratified lakes modulates accessibility to resources that brook charr, <em>Salvelinus fontinalis</em>, which is a mobile consumer, can track across seasons. More specifically, we hypothesize that reduced access to the littoral habitat during summer enhances foraging opportunities in this habitat during winter. We used an automatic acoustic telemetry system offering full coverage of the lake to continuously record brook charr locations across seasons, and we estimated zoobenthos abundances in the littoral habitat using image processing and semi-automatic classification. While brook charr concentrate in the metalimnion of the pelagic habitat in summer, most individuals in winter shift to a shallow bay that is unexploited in summer due to thermal constraints. In this habitat, zoobenthos abundance is more than twice as high at the end of the summer compared to littoral habitats close to the thermal refuge in the pelagic habitat. Surprisingly, brook charr showed strong within-lake site fidelity between two consecutive summers, which suggests that spatial memory could be a key driver of seasonal habitat use in this lacustrine population. Overall, our results suggest that thermal barriers create fragmentation between littoral and pelagic habitats that in turn produces resource opportunities that brook charr can track across seasons.</p>
Data: UTrack3D: 3D Tracking Using Ultra-wideband (UWB) Radios
<p>"# UTrack3D"</p> <p><strong>Environments</strong>: Python3.7 & Matlab2021</p> <p><strong>System</strong>: Windows 11</p> <div> <h2>Install prerequisites</h2> <a href="https://github.com/yifeng361/UTrack3D#install-prerequisites"></a></div> <ul> <li> <p>Python: We test our code using Python3.7. Advanced Python versions work as well. <a href="https://www.python.org/downloads/" rel="nofollow">https://www.python.org/downloads/</a></p> </li> <li> <p>Matlab: We test our code using Matlab2021. Advanced Matlab versions work as well. The matlab is only used for performance evaluation in this code. In case you prefer not installing Matlab, several pre-generated examples are provided.</p> </li> <li> <p>Python libraries: <code>pip install -r requirements.txt</code></p> </li> </ul> <div> <h2>Running</h2> <a href="https://github.com/yifeng361/UTrack3D#running"></a></div> <ul> <li>Run script <code>run_offline_analysis.py</code> for tracking.</li> </ul> <p><code>python run_offline_track.py</code></p> <p>This reads pre-stored CIR data (./raw_data) and generates a file <code>tracking_results.mat</code> in ./output which stores the estimated trajectory and ground-truth trajectory. We provide three examples (test1, test2, test3). One can modify the following line to test a specific example.</p> <p><code>file_dir = "./raw_data/test1/"</code></p> <ul> <li>Run script <code>./matlab_analysis_scripts/evaluate_accuracy_ae.m</code> to compute error and perform visualization. This script takes <code>tracking_results.mat</code> as inputs and generates CDF error plot and trajectory visualization in the current folder.</li> </ul> <p>The CDF error plot and trajectories of three examples have already been pre-generated and put in <code>./matlab_analysis_scripts/</code>.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.