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44 results for “visual motion”
Visual-inertial input datasets for SLAM applications containing extreme and human-like motion patterns
<p>Recorded datasets in compressed rosbag format, which contain visual and IMU sensor information that are bearing high resemblance to the movement of a human player with a handheld AR-capable device.</p> <p>For machine learning training and validation tasks, separate dataset are available containing motion patterns in a wide range from steady camera image to extremely challenging movements.</p>
Visual and Auditory vection stimuli reduce motion sickness
<p>This is the raw data and the full data set from all of our participants included in the analysis for this experiment. </p> <p>The raw data represents the data recorded throughout the experience. Motion Sickness scores, performance on reading task, performance on attention task. </p> <p>While the full data set (Final1) additionally includes questionnaire data (SSQ, NASA TLX, IPQ,...) as well as demographic data of the participants. </p>
Dataset: Feedback contribution to surface motion perception in the human early visual cortex
<p><strong>Dataset</strong></p> <p>Dataset accompanying the manuscript "Feedback contribution to surface motion perception in the human early visual cortex" (<a href="https://doi.org/10.1101/653626">biorxiv</a>).</p> <p><strong>Description</strong></p> <p>fMRI data are arrange by subject (following BIDS convention). For each subject, there are subfolders for anatomical and functional MRI data.</p> <p>├── sub-01<br> │ ├── anat<br> │ │ └── ...<br> │ ├── func<br> │ │ └── ...<br> │ ├── func_se<br> │ │ └── ...<br> │ └── func_se_op<br> │ └── ...</p> <p>The subfolder 'anat' contains four images from the MP2RAGE sequence (among these, T1 and proton-density weighted images). The subfolder 'func' contains the functional data (GE EPI, T2* weighted) from the main experiment (i.e. the data from which the haemodynamic response was estimated, and on which statistical analysis was performed). The subfolders 'func_se' and 'func_se_op' contain SE EPI images with opposite phase encode polarity that were used for distortion correction. Moreover, for each image/timeseries there is a json file with metadata.</p> <p>Anatomical images have been masked anteriorly (defaced). Functional images are in coronal oblique orientation, covering early visual cortex.</p> <p>The folder 'stimuli' contains information on the stimuli used for retinotopic mapping, including timecourse models used for population receptive field mapping. (These files are included here because of their relatively large file size, which would make distribution via a git repository impractical.) The software used for the presentation of retinotopic mapping stimuli (and for the corresponding analysis) is available on <a href="https://github.com/ingo-m/pyprf">github</a>.</p> <p>For example videos of the main experimental stimuli, see <a href="https://doi.org/10.5281/zenodo.2583017">zenodo.2583017</a>. If you would like to reproduce the experimental stimuli, the respective PsychoPy code can be found on <a href="https://github.com/ingo-m/PacMan/tree/master/stimuli/experiment">github</a>.</p> <p>The exact timing of events during the experiments (rest & stimulus blocks, target events) can be found in FSL-style design matrices ("3 column format") on <a href="https://github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata">github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata</a>.</p> <p><strong>Analysis</strong></p> <p>The analysis pipeline makes use of several MRI software packages (such as SPM and FSL for preprocessing, and CBS tools for cortical depth sampling). In order to facilitate reproducibility, the entire analysis was containerised using docker. Because of licensing issues, the docker images with the third-party software cannot be directly made available. However, the docker files and detailed instructions for the creation of the docker images are available on <a href="https://github.com/ingo-m/PacMan/tree/master/docker">github</a>.</p> <p>If you would like to reproduce the analysis, the first step will be to create the docker images (which provide an exact copy of the system environment that was used to conduct the published analysis). There are two docker images, one for the main analysis (motion correction, distortion correction, GLM fitting; named "dockerimage_pacman_jessie"), and another one for the depth sampling (named "dockerimage_cbs"). Detailed instructions on how to create the docker images can be found <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_PacMan_Image_Jessie.txt">here</a> and <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_CBS_Image.txt">here</a>.</p> <p>Once you set up the docker images, the analysis can be run automatically. For each subject, there is one parent script for the main analysis (e.g. <a href="http://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_01.sh">~/analysis/20180118/metascript_01.sh</a> for subject 20180118) and a separate script for the depth sampling (e.g. <a href="https://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_03.sh">~/analysis/20180118/metascript_03.sh</a>). The only manual adjustments you should have to perform to reproduce the analysis is to change the file paths in the first section of these scripts ('pacman_anly_path' is the parent directory containing the analysis code, i.e. the git repository, and 'pacman_data_path' is the parent directory containing the MRI data). The main analysis (metascript_01.sh) should take about 24 h per subject on a workstation with 12 cores, and the depth sampling (metascript_02.sh) about 2 h. The analysis can be run on consumer-grade hardware, but some parts of the analysis may not run with less than 16 GB of RAM (recommended: 32 GB).</p> <p>Visualisations (e.g. cortical depth profiles and signal timecourses) and group-level statistical tests are implemented in <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">py_depthsampling</a>.</p> <p><strong>Further resources</strong></p> <p>Please refer to the research paper for more details: <a href="https://doi.org/10.1101/653626">https://doi.org/10.1101/653626</a></p> <p>The analysis pipeline can be found on <a href="https://github.com/ingo-m/PacMan">https://github.com/ingo-m/PacMan</a></p> <p>A separate repository contains the code used for visualisation of depth-sampling results: <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">https://github.com/ingo-m/py_depthsampling/tree/PacMan</a></p> <p>Free & open source software package for population receptive field mapping: <a href="https://github.com/ingo-m/pyprf">https://github.com/ingo-m/pyprf</a></p> <p> </p>
The Influence of Active and Passive Motion Experience on Infants' Visual Prediction Ability
<p>Data set of the article: The Influence of Active and Passive Motion Experience on Infants’ Visual Prediction Ability</p>
Hummingbirds use compensatory eye movements to stabilize both rotational and translational visual motion
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Perceiving tempo in incongruent audiovisual presentation of human motion: Evidence for a visual driving effect
<p>The video set includes 81 audiovisual stimuli which served in the bisection experiment of the study "Perceiving tempo in incongruent audiovisual presentation of human motion: Evidence for a visual driving effect". The auditory sound tracks (bass drum) and visual stimuli (point-light displays of biological motions) each cover the tempo range from 60 to 180BPM, 15BPM per step. Coupling of the stimuli from both modalities results in 81 stimuli in total. </p>
Semantic audio-visual congruence modulates visual sensitivity to biological motion across awareness levels
<p>This repository contains all experimental scripts, analysis and data files for the manuscript "Semantic audio-visual congruence modulates visual sensitivity to biological motion across awareness levels".</p>
Perceiving Tempo in Incongruent Audiovisual Presentations of Human Motion: Evidence for a Visual Driving Effect
<p>Data set from the study published in Timing & Time Perception.</p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - C. elegans unc-16 and N2 motion behavior dataset
<p>Dataset contains <em>Caenorhabditis</em> <em>elegans </em>motor behaviors originally used in this publication <a href="https://www.nature.com/articles/nmeth.2560">10.1038/nmeth.2560</a> and downloaded from the available <a href="http://wormbehavior.mrc-lmb.cam.ac.uk/">DB</a> which points to Zenodo. The dataset consists in two worm strains, 20 individuals from a mutant unc-16 strain with reduced mobility and 40 individuals from a control N2 strain. The behavioral measures derived from each individual worm trajectory were available in a HDF5-formatted file (Hierarchical Data Format Version 5) that has been included in this dataset.</p> <p>The data set consist in:</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "N2" folder containing the 40 HDF5 files with the measures derived from the N2 worms.</p> <p>- a "N2" folder containing the 20 HDF5 files with the measures derived from the unc-16 worms.</p>
Head-motion and eye-gaze behavior reveal audio-visual target search strategies - dataset
<p>Participants were tasked with finding a target stimulus in the presence of a number of auditory distractors. </p> <p>The target stimulus (audio-only, audio-visual or visual-only) was presented at the central position for 3 seconds, after which the stimulus was moved to one of 24 positions around the participant. The other 23 positions all contained a visual distractor and 0, 1, 2, 3, 5, 7 or 11 auditory distractors (evenly spaced). </p> <p>Based on the eye and headtracking data, we calculated the FOV and target localization time and the maximum headrotation into the wrong direction. </p> <p>FOV localization time: time it took to bring target within FOV. <br>Target localization time: From FOV to response.</p> <p>These datasets contain both the tracking data and the summarized data. </p> <p>Columns "av_stim_x_y" list for each of the 24 AV stimuli (which both served as the targets and distractors), the id, the angle at which it was present during the trial and the visual and audio status.</p> <p>FUNDING: </p> <p>The research was supported by the Centre for Applied Hearing<br>research (CAHR) through a research consortium agreement with<br>GN Resound, Oticon, and Widex. The funders had no role in<br>study design, data collection and analysis, decision to publish, or<br>preparation of the article.</p>
The AlpArray-based ground motion visualization of teleseismic earthquakes
<p>We present a new comprehensive visual representation of global seismic phases using AlpArray. Our AlpArray-based animations connect spatial-temporal wavefield and time-dependent array method to visualize the evolution of teleseismic phases over time. Here are the animations of a few example teleseismic events occurred during the course of AlpArray (2016-2019).</p>
Visual processing and collective motion-related decision-making in desert locusts
<p>Collectively moving groups of animals rely on decision-making of locally interacting individuals in order to maintain swarm cohesion. However, the complex and noisy visual environment poses a major challenge to the extraction and processing of relevant information. We addressed this challenge by studying swarming-related decision-making in desert locust nymphs. Controlled visual stimuli, in the form of random dot kinematograms, were presented to tethered locust nymphs in a trackball setup, while monitoring movement trajectory and walking parameters. In a complementary set of experiments, the neurophysiological basis of the observed behavioral responses was explored. Our results suggest that locusts utilize filtering and discrimination upon encountering multiple stimuli simultaneously. Specifically, we show that locusts are sensitive to differences in speed at the individual conspecific level, and to movement coherence at the group level, and may use these to filter out non-relevant stimuli. The locusts also discriminate and assign different weights to different stimuli, with an observed interactive effect of stimulus size, relative abundance, and motion direction. Our findings provide insights into the cognitive abilities of locusts in the domain of decision-making and visual-based collective motion and support locusts as a model for investigating sensory-motor integration and motion-related decision-making in the intricate swarm environment.</p>
Data from: Building variation in visual displays through discrete modifications of motion
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Ventral motion parallax enhances fruit fly steering to visual sideslip
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Visual processing and collective motion-related decision-making in desert locusts
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Data for: Distinguishing externally- from saccade-induced motion in visual cortex
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Video Data: Optic flow in the natural habitats of zebrafish supports spatial biases in visual self-motion estimation
<p>Video dataset accompanying "Spatial Biases in Optic-Flow Sampling for Self-Motion Estimation in Natural Environments." See accompanying <a href="https://github.com/eacooper/AlexanderOpticFlow">Github repository</a> for more documentation and analysis code.</p>
Dataset and code for "Tiltable objective microscope visualizes selectivity for head motion direction and dynamics in zebrafish vestibular system", Nat Commun 13, 7622 (2022). https://doi.org/10.1038/s41467-022-35190-9
<p>Dataset and code for "Tiltable objective microscope visualizes selectivity for head motion direction and dynamics in zebrafish vestibular system", Tanimoto, Watakabe, and Higashijima.</p> <p> </p> <p>The spreadsheet files contain source data for the figures.</p> <p>The file named "register_rotated_images_demo.zip" contains the MATLAB code, example image data, and instruction text data. To use the code, unzip the file and follow the instructions in the "readme.txt" file. The file named "register_rotated_images_demo_output.zip" contains expected output image data.</p> <p> </p>
Data from Schmitt et al. 2021: Preattentive processing of visually guided self-motion in humans and monkeys. Progress in Neurobiology
<p><strong>Dataset associated with the following publication:</strong></p> <p>Constanze Schmitt, Jakob C.B. Schwenk, Adrian Schütz, Jan Churan, André Kaminiarz, Frank Bremmer.<br> Preattentive processing of visually guided self-motion in humans and monkeys. Progress in Neurobiology,<br> 2021,102117. https://doi.org/10.1016/j.pneurobio.2021.102117. Published 2021 July 2.</p> <p><strong>Description of dataset:</strong></p> <p>In our study we presented an optic flow stimulus simulating forward self-motion across a ground plane in an oddball EEG paradigm to 12 human participants and 2 macaque monkeys. We simulated two different headings (forward-left vs. forward-right) presented either as standard or deviant trials and tested for the occurrence of a visual mismatch negativity (vMMN) by comparing the visual-evoked potentials (VEPs). </p> <p>Human data:</p> <p>The dataset contains the preprocessed VEPs of all 12 human participants already averaged over all trials per participant. It contains data from all electrodes used for analysis (P1, P2, O1, O2, PO3, PO4, CP1, CP2) divided into data recordings in standard ('_std') or deviant ('_odd') trials. Each of these files consists of four subfiles representing the presented combinations of heading (forward to the right: '_r' or forward to the left: '_l') and attention condition (attention towards fixation target: 'fix' or attention towards the ground plane: 'plane'). The data matrices in these subfiles are sorted as [participants x time-points].</p> <p>Monkey data:</p> <p>Each .mat file contains the preprocessed VEPs from one monkey ('data'), the corresponding electrode labels ('elec') and the common time vector in seconds ('time'). The data struct contains VEPs for left- and rightwards heading in separate subfields, which again contain standard ('stan') and deviant ('odd') presentations of that heading. Within each subcondition, the actual data matrices are sorted as [electrodes x time-points x trials]. Here, the numbering of electrodes corresponds to the electrode labels contained in the 'elec' variable.</p>
Effect of Transcutaneous Vagal Nerve Stimulation on Reducing Visually Induced Motion Sickness in Healthy Volunteers
ClinicalTrials.gov study NCT02177890. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.