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34 results for “Behavior Tracking”
Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots
<h1>Dataset and code description</h1> <p>This repository contains the codes and data for theScience Robotics paper <strong>Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots</strong>.</p> <p>The codes are in <strong>rr_scirob_analyses</strong> and the datasets are in <strong>rr_scirob_data</strong>.<strong> </strong>If you want to rerun the data processing as presented in the paper, you need both <strong>rr_scirob_analyses</strong> and <strong>rr_scirob_data. </strong>You can copy the contents of <strong>rr_scirob_data </strong>into <strong>rr_scirob_analyses, </strong>as they have the same folder structure. Alternatively, you can run the <strong>download </strong>scripts to obtain the partial datasets relevant for certain subfigures. The file <strong>rr_scirob_data_readmes</strong> contains more detailed README files (rosbag info). You can copy its contents to <strong>rr_scirob_analyses </strong>after copying the contents of the <strong>rr_scirob_data</strong>.</p> <p>The individual datasets are organised into seven folders.</p> <h2>Three Figures with Key Behavioural Metrics </h2> <p>Three of the folders correspond to the Key Behavioural Measures, which are presented in three figures in the paper. These are:</p> <ul> <li>Figure-2-KBM-1-Queen Queen - related Key Behavioural Metrics</li> <li>Figure-3-KBM-2-Workers Worker Bee - related Key Behavioural Metrics</li> <li>Figure-4-KBM-3-Comb Comb and Brood -related Key Behavioural Metrics </li> </ul> <p>Each of these <em>Figure-X</em> folders contains the relevant figure from the paper and four subfolders corresponding to the panels of that figure. These are <strong>macro</strong>, <strong>micro</strong>, <strong>mezo</strong>, <strong>social</strong>, related to the four panels of that figure.<br>Each of these subfolders contains a README file, describing how to process the data and providing further details. <br>Furthermore, there are three additional folders located in each of the 'panel' folder:</p> <ul> <li><strong>data</strong>: this is used to store the data necessary to generate the graphs. You can either populate it with the data from Zenodo, i.e., https://zenodo.org/records/13801588 Alternatively, you can use the `download.sh` script wich will download and extract the necessary data from the RoboRoyale project cloud.</li> <li><strong>tmp</strong>: This folder is used to store intermediate results of the processing scripts</li> <li><strong> output</strong>: This folder is used to store all the generated outputs of the individual scripts. These should be identical with the panels of the figure in the paper. These figures are also provided in the relevant folders.</li> </ul> <p>Running the scripts contained in the micro, mezo, macro and social folders generates images and graphs in the output subfolders. These should be identical to the ones in the panels of Figures 2-4 in the paper.</p> <h2>One Resting Analysis Figure</h2> <p>One folder corresponds to the queen resting analysis figure</p> <ul> <li>Figure-5-Resting : Queen resting time analysis</li> </ul> <p>This folder has three subfolders named <strong>data</strong>, <strong>tmp</strong> and <strong>output</strong> similar to the previous folders. Again, running the scripts will generate the figures and/or run the statistical tests as in the previous case.</p> <h2>Three Performance Assessments: Queen Tracking, Workerbee Localisation and Oviposition Detection</h2> <p>Three other folders are related to performance analysis of the core methods required to calculate the KBMs.</p> <ul> <li>KBM-1-performance evaluation: Provides datasets and scripts to assess the performance of the queen marker detector</li> <li>KBM-2-performance evaluation: Provides datasets and scripts to assess the performance of the worker bee detector</li> <li>KBM-3-performance evaluation: Provides datasets and scripts to assess the performance of the oviposition detector </li> </ul> <p>Each of these folders contains a README file explaining what to run in order to evaluate the performance of the method and to replicate the paper's results.</p> <h2>Additional materials and data</h2> <p>The core data used here is the month-long queen tracking information, consisting of 28 million entries in a file <strong>2023-month-queenpos-short.txt.</strong> <br>A description of the file structure is provided in the README of the relevant KBM folder.</p> <p>Additional data are available in the dataset section of https://roboroyale.eu.</p> <h2>Rosbags</h2> <p>The work is based on the Robot Operating System (ROS) and thus, the raw data come in the form of rosbags. We provide a few of the rosbags to allow checking examples of video and other raw data as reported by the system:</p> <ul> <li>2023-10-25-08-42-20-Queen-Feeding.bag - queen feeding (KBM-1 Social)</li> <li>KPI1_2_mezo-queen_walk_sample.bag - queen walk as drawn in (KBM-1 Mezo)</li> <li>2023-10-10-00-04-10-trophylaxis.bag - worker bee trophylaxis (KBM-2 Social)</li> <li>2023-09-19-09-00-20-egg-removal.bag - worker bee removing egg (KBM-2 Social)</li> </ul> <h2>Licence </h2> <p>This data and code are under the Creative Commons Attribution-ShareAlike 4.0 International license. If you use these data in your work, please <strong>cite</strong> the relevant paper, i.e., Ulrich, Stefanec, Rekabi-bana et al.: <strong>Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots</strong>. Science Robotics, 2024.</p> <p> </p>
Is the Gaze Behavior During Stair Walking Affected by Pregnancy?-Figure 2. Eye-tracking glasses image showing the gaze location during stair ascent
<p>At each data collection, participants walked the same U-shaped staircase descending a 22- treads (riser: 0.16 m, run: 0.33 m, and width: 1.15 m), making a short U-turn downstairs and ascending back the staircase, one tread at a time (Figure 1). Only the data of stair walking were taken for further analysis. The staircase was equipped with a handrail on one side but none of the participants used it. To monitor the gaze a SensoMotoric Instruments (SMI) eye-tracking glasses (ETG) system (SMI, Inc.) at a frequency of 60 frames per second and 1280x960 pixel picture was used. Calibration was performed using a matrix of 3 points placed on a board in different highs and different horizontal placement. Mean gaze vectors of the right eye (x, y, z) for stair descent and stair ascent were obtained for each data collection session. Gaze vector x, y, z starts at the eye and heads off in mediolateral, up and down, and anterior-posterior direction, respectively (Figure 2) (Haffegee, Alexandrov, & Barrow, 2007; Scheel, & Staadt, 2015).</p>
Data from: Long-term monitoring of <em>Ziphius cavirostris</em> behavior using 3D tracking from fixed hydrophone arrays off Southern California
Open the record for dataset details and reuse information.
Behavioral account of attended stream enhancement for neural tracking
<p>This dataset provides material and data associated with Huet, Micheyl, Parizet, Gaudrain (<strong>2021</strong>) "Behavioral account of attended stream enhances neural tracking", <em>Frontiers in Neuroscience</em>, <a href="https://doi.org/10.3389/fnins.2021.674112">doi: 10.3389/fnins.2021.674112</a>.</p> <p>The current version of the repository contains only the audio data (temporal envelopes) and behavioral data. The EEG data will be added as it is curated.</p>
"Haptic aesthetics and bodily properties of Ori Gersht's digital art: a behavioral and eye-tracking study." - datasets and script
<p>"Haptic aesthetics and bodily properties of Ori Gersht’s digital art: a behavioral and eye-tracking study."</p> <p>Datasets and R script for analyses of behavioural scores and visual parameter.</p>
Odor plume tracking behavior of walking and flying insects
<p><span></span></p> <p>Many animals locate food, mates, and territories by following plumes of attractive odors. There are clear differences in the structure of this plume tracking behavior depending on whether an animal is flying, swimming, walking, or crawling. These differences could arise from different control rules used by the central nervous system during these different modes of locomotion or one set of rules interacting with the different environments encountered by animals suspended in flow or moving across the ground. Flow speeds and turbulence that characterize the environments where walking and flying insects track plumes may alter the structure of odor plumes in an environment-specific way that results in the same control rules generating behaviors that appear quite different. We tested these ideas by challenging walking male cockroaches, <em>Periplaneta</em> <em>americana</em>, and flying male moths, <em>Manduca</em> <em>sexta</em>, to track plumes of their species' sex-pheromones in low wind speeds characteristic of cockroach experimental environments, higher wind speeds characteristic of moth experimental environments, and conditions ranging from low to high turbulence. Introducing a turbulence-generating structure into the flow significantly improved the flying plume tracker's ability to locate the odor source, and changed the structure of the behavior of both flying and walking plume trackers. Specifically, the walking and flying plume trackers located the odor source more often in experimental conditions characteristic of environments in which they live suggesting differing reliance on spatial and temporal measurements of the odor plume.</p>
A glimpse into the foraging and movement behavior of Nyctalus aviator: a complementary study by acoustic recording and GPS tracking
<p>Species of open-space bats that are relatively large, such as bats from the genus <em>Nyctalus</em>, are considered as high-risk species for collisions with wind turbines. However, important information on their behavior and movement ecology, such as the locations and altitudes at which they forage, is still fragmentary, while crucial for their conservation in light of the increasing threat posed by progressing wind turbine construction. We adopted two different methods of microphone array recordings and GPS-tracking capturing data from different spatio-temporal scales in order to gain a complementary understanding of the echolocation and movement ecology of <em>Nyctalus aviator</em>, the largest open-space bat in Japan. Based on microphone-array recordings, we found that echolocation calls during natural foraging are adapted for fast-flight in open space optimal for aerial-hawing. In addition, we attached a GPS tag that can simultaneously monitor feeding buzz occurrence and confirmed that foraging occurred at 300 m altitude and that the flight altitude in mountainous areas is consistent with the turbine conflict zone. Thus, our acoustic GPS survey clearly identified<em> N. aviator</em> as a high-risk species in Japan.</p>
Data for: Tracking and Analysis of the Movement Behavior of European Seabass (Dicentrarchus labrax) in Aquaculture Systems
<p>Data for: Tracking and Analysis of the Movement Behavior of European Seabass (Dicentrarchus labrax) in Aquaculture Systems</p><p>Please read the README.txt file before working with the data.</p>
Odor plume tracking behavior of walking and flying insects
Open the record for dataset details and reuse information.
A glimpse into the foraging and movement behavior of Nyctalus aviator: a complementary study by acoustic recording and GPS tracking
Open the record for dataset details and reuse information.
SpaceAnimal: Pose estimation and tracking dataset for multi-animal behavior analysis on the China Space Station
<p>Pose estimation and tracking dataset for multi-animal behavior analysis on the China Space Station. Scientific Data, 2025</p>
Fig. 4 in Stopped Dead in Their Tracks: The Impact of Railways on Gopher Tortoise (Gopherus polyphemus) Movement and Behavior
Fig. 4. Principal component analysis (PCA) with 95% confidence ellipses comparing tortoise behavior expressed over a one-hour observation period. Confidence ellipse fills are based on railway familiarity in addition to the control group. Control tortoises fall well outside the multivariate space of tortoises placed in the railway, demonstrating the inability of tortoises to cross railways.
Fig. 2 in Stopped Dead in Their Tracks: The Impact of Railways on Gopher Tortoise (Gopherus polyphemus) Movement and Behavior
Fig. 2. (A) The trench dug underneath the rails and between the railway ties. A game camera faces the entrance/exit on the west side of the railway to photograph Gopher Tortoises passing from one side to the other. (B) A series of pictures of a single Gopher Tortoise moving from the east side of the tracks to the west side.
Fig. 1 in Stopped Dead in Their Tracks: The Impact of Railways on Gopher Tortoise (Gopherus polyphemus) Movement and Behavior
Fig. 1. (A) The 20 m railway plot in which Gopher Tortoises were tested for crossing ability and behavioral differences between Habituated (n ¼ 12) and Naïve (n ¼ 12) railway familiarity. (B) The control plot in which tortoises (n ¼ 12) were tested for crossing ability and behavioral differences solely on the presence of a visual barrier.
Fig. 3 in Stopped Dead in Their Tracks: The Impact of Railways on Gopher Tortoise (Gopherus polyphemus) Movement and Behavior
Fig. 3. (A) Three simulated correlated random walks (CRWs) by a single tortoise (ID: 5233) confined to the coastal strand habitat. Each simulation is a different patterned line with the start point designated by the triangle (m) and the stop points designated by squares (&). Each simulation counted the number of times the tortoise crossed the railway (represented by the thick dotted line). (B) Histogram of the number of expected railway crosses based on 1000 simulated CRWs by a single tortoise (ID: 5233). The observed number of crosses is plotted with the dotted line and is significantly below the expected number of crosses.
Assessing the impact of central and peripheral obstructions on visual behavior: insights from gaze-contingent eye-tracking studies
<p>This dataset is a collection of image stimuli, as well as gaze tracks collected using visual field masks, such as central and peripheral scotoma, in order to assess the way people with visual field loss process visual stimuli in digital environments.</p> <h3><strong>This dataset is structured as follows :</strong></h3> <ul> <li>Stimuli: 125 fullHD images (1920x1080) used during the experiment trials; 4 fullHD images used during the training phase.</li> <li>Trials_metadata: <ul> <li>Psychopy_outputs: various callbacks and logs files from the Psychopy experiment</li> <li>trials_metadata: .csv files containing metadata such as stimuli order and timestamps.</li> </ul> </li> <li>Raw_eyetracking_outputs: subfolders containing the raw .edf files output by the Eyelink 1000+ eye-tracker. .edf files are divided into groups of 25 successive stimuli displayed.</li> <li>Raw_gaze_points: .csv files, containing the raw gaze points locations for each subject, stimulus and mask. Blinks, invalid coordinates and out-of-bounds gaze points are already removed from these files.</li> <li>Fixations: .csv files (one per image per participant) containing eye fixations locations extracted from the raw gaze points using a I-VT algorithm with a saccade velocity threshold of 45 deg/s.</li> </ul> <h2><br><strong>Experiment</strong></h2> <h3>Images</h3> <p>This database consists of 100 images collected from personal collections and various public image datasets, such as the CityScapes and the KITTI-360 datasets, covering a variety of themes, including landscapes, people, actions, and nature.<br>Images had a 1920 x 1080 pixel resolution (FullHD) and were shown on a screen with the same resolution. </p> <h3>Eye-tracking data collection</h3> <p>The stimuli were presented to a group of 37 observers, with normal or corrected-to-normal vision. We used a table-mounted EyeLink 1000 Plus eye-tracker, working at a fixed rate of 1000Hz, with a chin rest to ensure data accuracy. Participants were informed that they would observe images both with and without simulated visual field impairments, and the two types of masks used were described. They were also told that the only task is to view the images freely. </p> <p>To familiarize participants with the different types of masks, four training images (distinct from the trial dataset) were provided in the training phase, with each image presented in the three conditions -- peripheral mask, foveal mask, and control (no mask) -- with varying mask sizes. <br>In the trial phase, stimuli were displayed in a random order, with a randomly generated playlist ensuring no three consecutive stimuli were of the same image. A uniform gray screen was displayed between each stimulus. </p> <p>Viewing distance was set to be 90cm.<br>Eye-tracker calibration was carried out using a 9-point calibration protocol, i.e., 9 points were sequentially and randomly shown on the screen, where the observer should fixate their gaze. <br>Additionally, calibration was performed after every 25 displayed images.</p> <p>Each image was presented to the observers for a 5-second period.</p> <p>Each image in the database was presented to the observers under different conditions :<br> - The image without any obstruction, referred to as the control condition (C). <br> - Peripheral mask (P) simulating a tunnel vision, with two circular mask size variations (one individual mask for each eye): 1.5° and 4.5° radius of field of view, referred to as P1 and P2, respectively. <br> - Foveal mask (F) simulating a central scotoma, also with two size variations: 1.5° and 4.5° radius of obstruction, referred to as F1 and F2, in that order.</p> <p> </p>
Social structure and individuals' behavior shaped by group composition in long-term tracking of rats
<p>Trajectory data and behavioral metrics from long-term tracking of groups of rats. Experiments performed in 2012 at Eötvös Loránd University, Budapest, Hungary.</p> <p>The data here is used in the following publication. See the publication for information on experimental structure and notation.<br> Nagy, Máté, Jacob Davidson, Gábor Vásárhelyi, Dániel Ábel, Enikő Kubinyi, Ahmed El Hady, and Tamás Vicsek. 2023. “Social Structure and Individuals’ Behavior Shaped by Group Composition in Long-Term Tracking of Rats.” bioRxiv. <a href="https://doi.org/10.1101/2023.03.18.533183">https://doi.org/10.1101/2023.03.18.533183</a>.</p> <p>Github repository to reproduce results in the paper: <a href="https://github.com/jacobdavidson/ratsocialgroups">github.com/jacobdavidson/ratsocialgroups</a><br> Github repositories used for tracking and computation of behavioral metrics: <a href="https://github.com/vasarhelyi">github.com/vasarhelyi</a></p> <p>The behavioral metrics, approach-avoidance networks, individual labels and group membership information, and behavioral assay data needed to reproduce the results in the publication are included in the zip file <a href="https://zenodo.org/api/files/969c78d4-1d30-4926-ac6e-141136cd3564/metrics-networks-labels.zip">metrics-networks-labels.zip</a>. This includes the following files in .csv format:</p> <p><strong>Naming/labels:</strong></p> <ul> <li><strong>uidlabels.csv</strong>: References the ranked-mass labels used in the paper to the 3-color-code labels that identify each rat from the tracking. "Group" is the membership during the original groups (Period 1), and "GroupG" is membership during the remixed groups (Period 3). <table> <tbody> <tr> <td> </td> <td> <p><strong>masslabel</strong></p> </td> <td> <p><strong>GroupMassRank-Exp7</strong></p> </td> <td> <p><strong>Group</strong></p> </td> <td> <p><strong>GroupG</strong></p> </td> </tr> <tr> <td> <p><strong>GOB</strong></p> </td> <td> <p>a5</p> </td> <td> <p>5.0</p> </td> <td> <p>A1</p> </td> <td> <p>G3</p> </td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> </li> <li><strong>groupnames.csv</strong>: Group membership information for each rat, during each experimental period <table> <tbody> <tr> <td> <p><strong>Experiments</strong></p> </td> <td> <p><strong>GOB</strong></p> </td> <td> <p><strong>OBG</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>1-7</strong></p> </td> <td> <p>A1</p> </td> <td> <p>A1</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> </ul> <p><strong>Behavioral metrics and approach-avoidance networks:</strong></p> <ul> <li><strong>metrics.csv: </strong>Behavioral metrics for each rat, calculated for each experiment. <table> <tbody> <tr> <td> <p><strong>Experiment</strong></p> </td> <td> <p><strong>metric</strong></p> </td> <td> <p><strong>GOB</strong></p> </td> <td> <p><strong>OBG</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p>AA</p> </td> <td> <p>0</p> </td> <td> <p>0.690964</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> <li><strong>Networks-Period1+Period3.csv: </strong>Approach-avoidance interaction networks for the groups of 7 rats in Period 1 and Period 3. <table> <tbody> <tr> <td> <p><strong>Experiment</strong></p> </td> <td> <p><strong>DayNight</strong></p> </td> <td> <p><strong>Group</strong></p> </td> <td> <p><strong>index</strong></p> </td> <td> <p><strong>winner</strong></p> </td> <td> <p><strong>loser0</strong></p> </td> <td> <p><strong>loser1</strong></p> </td> <td> <p><strong>loser2</strong></p> </td> <td> <p><strong>loser3</strong></p> </td> <td> <p><strong>loser4</strong></p> </td> <td> <p><strong>loser5</strong></p> </td> <td> <p><strong>loser6</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p>Day</p> </td> <td> <p>A1</p> </td> <td> <p>0</p> </td> <td> <p>GOB</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> <li><strong>Networks-Experiment8.csv</strong> and <strong>Networks-Experiment9.csv: </strong>Approach-avoidance interaction networks for the larger combined groups in the experiments during Period 2. </li> </ul> <p> </p> <p><strong>Behavioral assays results:</strong></p> <ul> <li><strong>IndividualTests.csv</strong>: Individual behavioral assay test results for the black and white box, elevated plus-maze, and canopy tests. <table> <tbody> <tr> <td> <p><strong>ID</strong></p> </td> <td> <p><strong>BWB-All Frames</strong></p> </td> <td> <p><strong>BWB-Black</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>BGP</strong></p> </td> <td> <p>7500</p> </td> <td> <p>6860</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> <li><strong>PairedTests.csv: </strong>Pairwise social interaction behavioral assay test results for tests with unfamiliar and familiar rats <table> <tbody> <tr> <td> <p><strong>rat_ID</strong></p> </td> <td> <p><strong>rat_group_N</strong></p> </td> <td> <p><strong>freq_standup_unfam1</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>BGP</strong></p> </td> <td> <p>2</p> </td> <td> <p>28</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> </ul> <p> </p> <p><strong>Trajectory data</strong> is included as multiple text files for each experiment in TSV format. #t is frame number, Color_code_name identifies each rat, Real_recognition(1)_or_virtual(0) = 1 is a detection of the rat's color codes while 0 is an interpolated value, x,y, and Orientation are the position and orientation of the rat.</p> <table> <tbody> <tr> <td>#t</td> <td>Color_code_name</td> <td>Real_recognition(1)_or_virtual(0) </td> <td>x</td> <td>y</td> <td>Orientation</td> </tr> <tr> <td>0</td> <td>ORG</td> <td>0</td> <td>872.4000</td> <td>778.7000</td> <td>-79.5000</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p> </p> <p><strong>Time-lapse video of the entire experiment:</strong> <a href="https://zenodo.org/api/files/969c78d4-1d30-4926-ac6e-141136cd3564/ratlab_timelapse_entire_exp.avi?versionId=a58df78d-1291-4301-9763-86a97b9e146c">ratlab_timelapse_entire_exp.avi</a></p>
Tracking behavior in naturalistic situations using artificial intelligence
<p>Folder "Data for analysis" :<br> - Folder "Extracted_Data_For_Preprocessing" : raw data after extraction for all subjects (1 folder by subs) that contains OpenPose folder (json file for each frame) and OpenFace csv with the extracted metrics of a whole video. It also contains 1 file .tps / 1 file .syncIN / 1 file .txt (SliderRes) for the script 01_loader (see on the associated github repository) and 1 file .tps (logs of OpenFace).<br> - File "Data_For_Analysis_Michelot_2023.csv" : entire data after preprocessing used for analysis</p> <p>Folder "Examples" :<br> - Folder "Example Data To Preprocess" : raw data after extraction for one subject (test data) with OpenPose folder containing a json file for each frame and OpenFace csv containing the extracted metrics of a whole video. It also contains 1 file .tps / 1 file .syncIN / 1 file .txt (SliderRes) for the script 01_loader (see on the associated github repository) and 1 file .tps (logs of OpenFace).<br> - Example_Data_For_Analysis.csv : test data for analysis with reduce amount of data in all subjects.</p> <p> </p> <p><em><strong>Abstract</strong> : Environmental context, e.g. social interactions, can shape our behavior, due to the range of stimuli it entails. Current research on this topic often relies on controlled laboratory settings, which may not fully capture the complexity and richness of real-life social situations.</em></p> <p><em>To overcome this limitation, we propose a new approach that leverages artificial intelligence (AI) tools, specifically computer vision, machine learning algorithms and explainability methods to extract, classify and highlight behaviors in naturalistic situations.<br> To validate our methodology, we conducted a study with sixteen participants in various naturalistic situations that varied socially and emotionally, including periods of silence, listening to music or neutral sounds, and listening to someone speaking about autobiographical or insignificant content in front of them.</em></p> <p><em>We adapted two open-source computer vision models, OpenFace and OpenPose, to extract objective whole-body metrics, such as skeletal and facial points, gaze, and facial action unit activations.<br> Applying two different machine learning models on the extracted behavioral metrics, extreme gradient boosting (XGBoost) and a long short-term memory neural network (LSTM), we accurately classified the situations up to around 90% accuracy for the social vs non-social situations.<br> Using advanced state-of-the-art explainability methods in conjunction with hypothesis-driven analysis, we identified distinct clusters or specific behaviors that were influenced by the social and emotional aspects of the situations. These behaviors included movements related to gaze, limbs, facial features, and facial expressions activations.<br> Our study highlights the potential of AI methodology to track behavior precisely and objectively in naturalistic contexts, paving the way for further research.</em></p>
America Walks: An Observational Study of Walking Behavior Based on Passive Step Tracking
ClinicalTrials.gov study NCT02792439. IPD Sharing: YES. Countries: 1. Publications: 4.
Data from: Influence of preexisting preference for color on sampling and tracking behavior in bumble bees
Animals reduce uncertainty in their lifetime by using information to guide decision making. Information available can be inherited from the past or gathered from the present. Therefore, animals must balance inherited biases with new information that may be in conflict with those potential biases. In our study, we set up color pairings such that an arbitrarily chosen focal color, human-orange, would result in an inherent bias in comparison to three other colors tested resulting in equal, medium, and strong preference differences. We chose color pairings through a series of preferences tests across 8 colonies of bumblebees. We subsequently used these pairings with rewards that varied in quality (good or bad states) and consistency (steady and fluctuating) in order to investigate how inherited biases affect the foraging choices of bumblebees when new information is gathered. We found that the pre-existing color biases within our bees were only maintained when the reward associated with those colors was steady, even if paired with mediocre sugar concentrations. When maintained, we observed that other aspects of bee choice also reflected this bias, including increased sampling for the preferred color and an increased likelihood of choosing that color in a subsequent choice. Thus, environmental change and reward differences interact with the level of pre-existing bias to determine whether inherited information is more heavily weighted than newly gathered information, and even a strong pre-existing bias can be quickly erased with experience under some conditions.
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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.