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559 results for “Track data”
Eye Tracking Data for Research on the Educational Use of Feynman Diagrams
<p>These data were collected within an eye tracking study as part of the PhD Thesis "The Educational Use of Feynman Diagrams: Opportunities, Challenges, Practices". The data were collected on a learning environment published under <a href="https://doi.org/10.5281/zenodo.11486241">this link</a>.</p> <p>Files including "et-data" are raw eye tracking data, mostly used to create and analyse transition-based metrics, files including "metrics" are metrics calculated by the Tobii Pro Lab software mostly used to calculate gaze durations on certain areas of interest. Files containing "questionnaire" are data from different questionnaires. The file"le-questions-analysis.xlsx" contains an item analysis of the answers within the learning environment. </p> <p>Analysis scripts are published <a href="../records/11519651">here</a>.</p> <p>The thesis with all relevant information is published <a href="http://dx.doi.org/10.53846/goediss-10576">here</a>.</p>
Data from: Tracking shifts in forest structural complexity through space and time in human-modified tropical landscapes
<p>Habitat structural complexity is an emergent property of ecosystems that directly shapes their biodiversity, functioning and resilience to disturbance. Yet despite its importance, we continue to lack consensus on how best to define structural complexity, nor do we have a generalised approach to measure habitat complexity across ecosystems. To bridge this gap, here we adapt a geometric framework developed to quantify the surface complexity of coral reefs and apply it to the canopies of tropical rainforests. Using high-resolution, repeat-acquisition airborne laser scanning data collected over 450 km2 of human-modified tropical landscapes in Borneo, we generated 3D canopy height models of forests at varying stages of recovery from logging. We then tested whether the geometric framework of habitat complexity – which characterises 3D surfaces according to their height range, rugosity and fractal dimension – was able to detect how both human and natural disturbances drive variation in canopy structure through space and time across these landscapes. We found that together, these three metrics of surface complexity captured major differences in canopy 3D structure between highly-degraded, selectively logged and old-growth forests. Moreover, the three metrics were able to track distinct temporal patterns of structural recovery following logging and wind disturbance. However, in the process we also uncovered several important conceptual and methodological limitations with the geometric framework of habitat complexity. We found that fractal dimension was highly sensitive to small variations in data inputs and was ecologically counteractive (e.g., higher fractal dimension in oil palms than old-growth forests), while rugosity and height range were tightly correlated (r=0.75) due to their strong dependency on maximum tree height. Our results suggest that forest structural complexity cannot be summarised using these three descriptors alone, as they overlook key features of canopy vertical and horizontal structure that arise from the way trees fill 3D space.</p> <p> </p> <p> </p>
Single-track ATLAS-like Inner Detector hit data for Tracking Studies
<p>A dataset to study the performance of tracking tools. The events contain single tracks with pile-up and detector noise according to Run 3 ATLAS conditions. More details can be found <a href="https://arxiv.org/pdf/2406.16752">here.</a></p> <p>The root files contain two trees: "tree" and "track". The first contains the hit coordinates in mm and labels (1 - signal hit, 0 - pile up, -1 - noise). "Track" contains the truth z0 and pT of the track. The train, validation, and test files contain 398 996, 49 885, and 49878 events respectively. </p>
Rewarding Fitness Tracking – The Communication and Promotion of Health Insurers' Bonus Programs and the Use of Self-Tracking Data
<p>The data set offers additional information for the study on "Rewarding Fitness Tracking – The Communication and Promotion of Health Insurers’ Bonus Programs and the Usage of Self-Monitored Data", to be submitted at HCII 2018.</p> <p>The data set includes the full lists of German and Australian Health Insurers investigated, including a link to their apps.</p> <p>This study aims at giving an overview on the current status quo of health insurances that investigate self-tracking opportunities and possible rewards for customers that share their fitness and health activities. We are interested in how insurers promote their health and well-being programs (intended program goals) and motivate customers to live healthier (incentives). We introduce research in progress while firstly focusing on the countries Germany and Australia. We discuss the current situation of health insurance clients’ data use, data security issues as well as long-term health benefits regarding those programs based on recent research on self-tracking activities. The research questions are: </p> <ol> <li>Which health insurers offer options for client to self-track health and fitness data?</li> <li>How do insurers communicate about the programs?</li> <li>How do those insurers communicate about data security?</li> </ol>
Single particle tracking data for "Histidine-rich domain of kinases induce phase separation to hyperphosphorylate Pol II CTD"
<p><strong>Experimental single-particle tracking (SPT) data supporting "Histidine-rich domain of kinases induce phase separation to hyperphosphorylate Pol II CTD"</strong></p> <p>This dataset contains all the raw SPT data reported in "Histidine-rich domain of kinases induce phase separation to hyperphosphorylate Pol II CTD" in the form of SPT trajectories. The SPT trajectories are provided in two different formats for convenience: a CSV format and a Matlab format. Both formats are readable by Spot-On: https://spoton.berkeley.edu/</p> <p>The SPT data contains "fast tracking" spaSPT data (Figure 2d) and this data was analyzed using the Matlab version of Spot-On which can be found and downloaded at: https://gitlab.com/tjian-darzacq-lab/spot-on-matlab</p> <p>The SPT data also contains "slow tracking" SPT data (Figure 2e).</p> <p>Full details about the Matlab and CSV formats are provided in the ReadMe files in the associated zip files.</p> <p>Please see the associated manuscript for a detailed description of how the data was acquired and analyzed. For questions about the data please contact Anders Sejr Hansen at anders.sejr.hansen {at} berkeley {dot} edu.</p>
Raw Data for RE18 Data Track (Utilizing Product Usage Data in Communication Services for Evaluating Requirement Offerings)
<p>Title: Raw Data for RE18 Data Track</p> <p>Submitted paper title : Utilizing Product Usage Data in Communication Services for Evaluating Requirement Offerings</p> <p>Submitted paper authors: Ashkan Hemmati, S. M. Didar Al Alam, Chris Carlson</p> <p> </p>
Single-particle tracking data for "CTCF sites display cell cycle dependent dynamics in factor binding and nucleosome positioning"
<p>This dataset contains all the raw SPT data reported in "­­­­CTCF sites display cell cycle dependent dynamics in factor binding and nucleosome positioning" in the form of SPT trajectories. The SPT trajectories are provided in two different formats for convenience: a CSV format and a Matlab format. Both formats are readable by Spot-On: https://spoton.berkeley.edu/</p> <p>The SPT data contains "fast tracking" spaSPT data and this data was analyzed using the Matlab version of Spot-On which can be found and downloaded at: https://gitlab.com/tjian-darzacq-lab/spot-on-matlab</p> <p> </p> <p>Full details about the Matlab and CSV formats are provided in the ReadMe files in the associated zip files.</p> <p>Please see the associated manuscript for a detailed description of how the data was acquired and analyzed.</p>
Gaze cueing by multiple people - averaged eye tracking data by participant
<p>Data for article Gaze cueing by multiple people.</p> <p>Averaged eye tracking data by participant that was included.</p> <p>Task 1 - time to first fixation and total fixation duration for each Area of Interest </p> <p>Task 2 - time to first fixation for Area of Interest as determined by the task </p>
Web tracking data for 500 websites popular among Finnish web users
<p>This dataset includes observations of trackers present on the top 500 pages popular among Finnish web users as per Alexa. The data collection was conducted using TrackerTracker in five separate requests for five subsets of 100 sites each between 19.8.2017 and 20.8.2017. The tool used a tracker database from March 24, 2017. More methodology details are described in the associated journal article <a href="https://doi.org/10.23978/inf.87841">https://doi.org/10.23978/inf.87841</a></p>
Grape Bunch Video Dataset with Berry Annotations and Tracking Data
<p>The dataset consists of videos of Bonarda grape bunches in a mature state. A total of 100 grape bunches were collected from five consecutive rows of the same plot at the "Finca de Sancho" located in Lavalle, Mendoza, Argentina, on March 21, 2023. Each bunch was assigned a unique identifier after collection.</p> <p>For video capture, two setups were built, referred to as setup 1 and setup 2. Each consisted of a stand to hold a bunch against a smooth white background. This background was carefully chosen to facilitate precise segmentation of the grapes in the images, ensuring more efficient and accurate object detection. Additionally, a curved structure with a 10x7 grid of QR codes was placed behind the bunch. These codes were not used in this dataset but were included for potential future studies. The setups were mounted outdoors, taking advantage of natural lighting to obtain a more realistic representation of the grape bunches. Video capture sessions were conducted over three consecutive days, lasting between 4 to 7 hours each day. The first session took place on the same day the bunches were harvested. Throughout the sessions, natural light fluctuated due to intermittent cloud cover, introducing variations in ambient lighting.</p> <p>Video recording was performed by two individuals referred to as capturer A and capturer B. The recording devices were the cameras of two smartphones, a Samsung Galaxy S20 FE and a Motorola G200. The Open Camera application (<a href="https://opencamera.org.uk/" target="_new" rel="noopener">https://opencamera.org.uk/</a>) was used, configured to automatically capture 5-second videos at a resolution of 720 pixels wide by 1280 pixels high (portrait orientation), at 30 frames per second. A fixed focal distance of 30 cm was maintained, and the white balance was manually adjusted according to the natural light variations to maintain good image quality. </p> <p>Three camera movements were defined for video capture: two systematic movements named "horizontal 180º" and "vertical 180º", and a third movement called "freestyle." The horizontal and vertical movements involved sweeping the camera from left to right and from bottom to top, respectively, while keeping the bunch centered in the frame and at a distance of approximately 30 cm. The freestyle movement consisted of random movements, maintaining the bunch centered as best as possible within the frame.</p> <p>The capture protocol was as follows:</p> <p>First, a grape bunch was placed in each setup. Capturer A at setup 1 captured five videos for each of the three defined camera movements, while capturer B did the same at setup 2. Afterward, the capturers switched positions, with capturer B taking videos at setup 1 and capturer A at setup 2. The bunches were then replaced with new ones, and the process was repeated until all 100 bunches were captured.</p> <p>As a result, the dataset contains five videos for each of the three camera movements, captured by both individuals for each of the 100 bunches. This produces a dataset consisting of 3,000 videos: 1,000 videos for each camera movement and 30 videos per bunch. </p> <p>It is worth noting that, due to the manual nature of the capture process, some variations occurred in the number of videos recorded per bunch. In some cases, 9 videos were recorded instead of 10, in one instance only 8 were captured, and in one case 11 videos were obtained. These minor discrepancies are mainly due to human error in counting the recorded videos, but they do not affect the overall quality or integrity of the dataset.</p> <p> </p> <p>The dataset also includes berry annotations for each video, provided in JSON files. These files specify, for each frame of the video, the pixel coordinates of each berry's center and the radius it occupies in the image, also in pixels. The berry detections were obtained through inference using a deep learning architecture called CircleNet, which was specifically trained for this dataset.</p> <p>In addition, the dataset contains berry tracking data, provided in CSV files. These files indicate which berries are the same across different frames of the video, through a unique berry identifier. The tracking was generated using a custom algorithm developed specifically to produce these tracks.</p> <p> </p>
Design of facilitated dissociation enables control over cytokine signaling duration - SMT raw data - unstimulated - calibration beads - long term tracking - labelled ligand
<p>This dataset contains the raw data for the Single-molecule tracking data of the manuscript: "Design of facilitated dissociation enables control over cytokine signaling duration". Presicely, it contains the calibration beads for all imaging experiments, the long term tracking experiments, the experiments with labelled ligand and the unstimulated probes</p>
Mastodon example data set - 10 tracked phallusia mammillata embryos
<ul> <li>This dataset originates from the Phallusia mammillata embryonic development dataset, which was published on figshare. <ul> <li><a href="https://figshare.com/projects/Phallusiamammillata_embryonic_development/64301" rel="nofollow">https://figshare.com/projects/Phallusiamammillata_embryonic_development/64301</a>.</li> </ul> </li> <li>The dataset is described in the following publication: <ul> <li>Léo Guignard et al., Contact area–dependent cell communication and the morphological invariance of ascidian embryogenesis. Science369, eaar5663(2020). DOI:<a href="https://doi.org/10.1126/science.aar5663" rel="nofollow">10.1126/science.aar5663</a></li> </ul> </li> <li>The dataset has been converted to Mastodon file format using these scripts: <ul> <li>Astec file format to CSV: <a href="https://github.com/GuignardLab/LineageTree/blob/master/src/LineageTree/legacy/export_csv.py">https://github.com/GuignardLab/LineageTree/blob/master/src/LineageTree/legacy/export_csv.py</a></li> <li>CSV to Mastodon file format: <a href="https://github.com/mastodon-sc/mastodon-deep-lineage/blob/master/src/test/java/org/mastodon/mamut/astec/AstecReader.java">https://github.com/mastodon-sc/mastodon-deep-lineage/blob/master/src/test/java/org/mastodon/mamut/astec/AstecReader.java</a></li> </ul> </li> <li>Limitations resulting from the conversion: <ul> <li>Image data is not included in the projects. This would require additional effort. However, it is probably not necessary for the intended use cases (tests of lineage clustering, lineage registration, and Blender visualization).</li> <li>Only datasets Pm01 – Pm10 have been converted.</li> <li>The cell names were missing for Pm11-Pm13. To handle this, the conversion scripts would need to be adapted. Thus, these datasets are not included.</li> <li>The uploaded data for Pm14 is faulty on figshare. Thus it is not included.</li> <li>Half-Pm1, U0126PM1 and U0126PM2 do not contain lineages or coordinates. Thus, these datasets are not included.</li> <li>When opening the Mastodon projects, a message appears stating that the image data is missing. It can be chosen, to open the project without image data ("dummy image data"). This must be confirmed. After that, it should work.</li> </ul> </li> </ul>
Data Appendix for Lack, P., "Using Word Analysis to Track the Evolution of Emotional Well-being in Nineteenth-Century Industrializing Britain", Historical Methods (forthcoming)
<p>This file contains the data associated with the publication Lack, P., "Using Word Analysis to Track the Evolution of Emotional Well-being in Nineteenth-Century Industrializing Britain", <em>Historical Methods</em> (forthcoming). It quantifies the trend in emotional well-being expressed in a corpus of British pamphlets published between 1800 and 1900. The first page of the excel document presents this key data on the trend in emotional well-being. Sheet 1A presents summary statistics on the trend in emotional well-being and its correlation with GDP per capita and real wages. </p>
Data for: Simultaneous GPS-tracking of parents reveals a similar parental investment within pairs, but no immediate co-adjustment on a trip-to-trip basis
<p>This repository contains data for the paper: Kavelaars et al. 2021. Simultaneous GPS-tracking of parents reveals a similar parental investment within pairs, but no immediate co-adjustment on a trip-to-trip basis. <strong>Movement Ecology</strong>. https://doi.org/10.1186/s40462-021-00279-1</p>
Tracking Data II/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><strong>PLEASE NOTE: this repository contains only the folder compare_postprocessing_synth_bm </strong></p> <p><strong>All other datasets and files are provided in 10.5281/zenodo.5227595 due to size restrictions.</strong></p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p> </p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [will be stored in 10.5281/zenodo.5227610 due to size restrictions]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p> <p> </p> <p> </p>
Surface and subsurfaca data collected during artificial rainfall experimetns on tilled soil with wheel tracks
<p>This dataset contains data measured during the artificial rainfall experiment conducted within the project LTC18030 - The effects of land use changes on soil erosion, sediment transport, water quality and runoff conditions in 2018 and 2019 at experimental site Řisuty, Czech Republic.</p> <p>This dataset contains raw data (xlsx, csv) and images of more qualitative data such as electrical resistivity tomography profiles.</p> <p>For further information about the experiments and analysis please check: Jeřábek, J., Zumr, D., Laburda, T., Krása, J., Dostál, T., 2022. Soil surface connectivity of tilled soil with wheel tracks and its development under simulated rainfall. J. Hydrol. 613. https://doi.org/10.1016/j.jhydrol.2022.128322</p> <p><strong>The dataset consists of following files: </strong></p> <ul> <li>experimental_plots_overview.pdf: overview of the setup and map of the experimental site</li> <li>electrical_resistivity_tomography.pdf: overview of the ERT measurement is provided in this file</li> <li>penetrometry.pdf: penetrometry was done in and in the vicinity of the experimental plot</li> <li>basic_variables_of_experiments.xlsx: basic information about each of the experiments</li> <li>measuted_soil_hydraulic_properties.xlsx: summarised the measured soil hydraulic propertie for all used method and for each plot</li> <li>optimized-parameters-all.xlsx: optimal parameters for all optimized scenarios</li> <li>optimized-parameters-stats.xlsx: statistics of optimal parameters for each soil layer and plot</li> <li>vol1-soil-water-retention-data.xlsx: soil water retention data from the sand tank and pressure chamber measurement of the vol1 experiment</li> <li>vol2-soil-water-retention-data.xlsx: soil water retention data from the sand tank and pressure chamber measurement of the vol2 experiment</li> <li>soil_loss.csv: soil loss time series</li> <li>surface_runoff.csv: surface runoff time series</li> <li>soil_water_pressure.csv: soil water pressure time series</li> <li>soil_water_pressure_positions.csv: position of tensiometers on each plot</li> <li>volumetric_water_content.csv: volumetric water content time series</li> <li>volumetric_water_content_positions.csv: position of vwc probe on each plot</li> <li>start_stop_rainfall_info.csv: information about the time span of each experiment</li> </ul>
Data: What is conservation paleobiology? Tracking 20 years of development
<p>Survey and literature review data supporting the manuscript:</p> <p>Dillon et a (in review) What is conservation paleobiology? Tracking 20 years of development, <em>Frontiers in Ecology and Evolution</em>.</p>
Mechanism of receptor assembly via the pleiotropic adipokine Leptin - single molecule tracking - raw data
<p>This dataset contains the raw single-molecule image data that was analyzed in the manuscript "Mechanism of receptor assembly via the pleiotropic adipokine Leptin"</p>
Data from an eye-tracking based study of solution strategies in similar mathematical and physical tasks
<p>Eye-tracking data:</p> <p>The .nas file "ET_data" contains the eyetracking project in software tobii studio with gaze data of all 131 participants.<br> To open this project, the software tobii studio is required.</p> <p>----------------------------------------<br> Interview data:</p> <p>All .txt files contain transcribed interview data in German language. <br> The files are labeled by subject number (e.g., P01 for the first participant) and the item for which the interview was recorded (e.g., M8).<br> The files contain interview data of participants explaining their solution strategy for the given item.</p> <p>----------------------------------------<br> Descriptive data:</p> <p>The file descriptive_data.csv contains the descriptive data of all participants (N=131).<br> The column labels are as follows:</p> <p>code: code of participant, p01-p131 <br> survey_period: survey period, summer_2021 / autumn_2020 / spring_2020<br> age: age of participant in years <br> grade: grade of participant <br> gen: gender, male (m) / female (f) / diverse (d) <br> ger_nat_speak: German native speaker, yes / no <br> repeater: Repeater of the last grade?, yes / no<br> math_course: basic course (basic) or advanced course (advanced) in mathematics<br> physics_course: basic course (basic) or advanced course (advanced) in physics<br> grade_math: last grade in mathematics, 1-6 <br> grade_phy: last grade mark in physics, 1-6 <br> grade_ger: last report mark in german, 1-6 <br> glasses: wearer of glasses?, yes / no<br> motivation: four-point lickert scale, very motivated (1) to not motivated at all (0) <br> test: physics tasks before math tasks (pm) or vice versa (mp) <br> For each test item 2 columns are created. Example Item k1:<br> ac_k1: answer correctness of item k1, not correct and/or guessed (0), correct (1). <br> cf_k1: confidence of item k1, four-point lickert scale, very confident (1) to guessed (0) </p>
Honeybee lifetime tracking data 2019
<p>Data from barcode-based tracking of honeybees during summer of 2019, taken in an observation hive located at University of Konstanz, Germany.</p> <p>See <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2019data">table of experiments and manipulations performed</a></p> <p>Associated GitHub repositories:<br> <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2019data">https://github.com/jacobdavidson/bees_lifetimetracking_2019data</a><br> <a href="https://github.com/jacobdavidson/bees_drones_2019data">https://github.com/jacobdavidson/bees_drones_2019data</a></p> <p>Data usage example is on <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2019data/blob/main/Data%20usage%20example%20-%20updated.ipynb">Github</a></p> <p><strong>File contents: </strong></p> <p><strong><a href="https://zenodo.org/record/7298798/files/2019_Quantity_Dates_Cohorts_DaysLived.xlsx?download=1">2019_Quantity_Dates_Cohorts_DaysLived.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, cohort birthdates, and estimated days lived per bee.</p> <p><strong><a href="https://zenodo.org/record/7298798/files/trajectories_000-019.zip?download=1">trajectories_000-019.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_020-039.zip?download=1">trajectories_020-039.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_040-059.zip?download=1">trajectories_040-059.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_060-079.zip?download=1">trajectories_060-079.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_080-099.zip?download=1">trajectories_080-099.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_100-114.zip?download=1">trajectories_100-114.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' sheet in <a href="https://zenodo.org/record/7298798/files/2019_Quantity_Dates_Cohorts_DaysLived.xlsx?download=1">2019_Quantity_Dates_Cohorts_DaysLived.xlsx</a></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/7298798/files/comb-contents-images2019.zip?download=1">comb-contents-images2019.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_2019data/blob/main/Data%20usage%20example%20-%20updated.ipynb">Data Usage Example</a>)</p> <p><strong><a href="https://zenodo.org/record/7298798/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/7298798/files/df_day1min.zip?download=1">df_day1min.zip</a>, <a href="https://zenodo.org/record/7298798/files/df_day5min.zip?download=1">df_day5min.zip</a>, <a href="https://zenodo.org/record/7298798/files/df_dayhour.zip?download=1">df_dayhour.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/7298798/files/dfxy_dayhour.zip?download=1">dfxy_dayhour.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_2019data/blob/main/Data%20usage%20example%20-%20updated.ipynb">Data Usage Example</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.