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Dataset results
67 results for “Video Analysis”
FlyTracker Video Analysis and Data Extraction
<p>Protocol video showing how to use the MATLAB package "FlyTracker" to analyze locomotor behavior in a video and extract the data in a format compatible with any worksheet software.</p> <p>Full uncompressed video made with FinalCut Pro (higher quality than version available at STAR Protocols)</p> <p>MATLAB package: <a href="https://github.com/kristinbranson/FlyTracker/archive/refs/heads/main.zip">FlyTracker</a></p> <p>MATLAB Script: <a href="https://github.com/LaurentSeroude/FlyTrackerExtraction">FlyTracker Extraction</a></p> <p>Peer-reviewed publications:</p> <p>Genome 64,139,2021 <a href="https://github.com/LaurentSeroude/FlyTrackerExtraction/blob/main/Genome%2064%2C139%2C2021.pdf">PDF</a></p> <p><a href="https://star-protocols.cell.com/protocols/2193">STAR Protocols 3,101888,2022</a></p> <p>Peer-reviewed protocol: STAR Protocols in press</p>
Supplemental Material: Analysis of video games on the teaching-learning process in children from 3 to 4 years old
<p>Supplementary material of the results obtained in the paper Analysis of video games on the teaching-learning process in children from 3 to 4 years old.</p>
960 fps Swimming performance videos for analysis
Open the record for dataset details and reuse information.
Supplementary material S11: Video analysis of Varroa jolting pulses on honeycomb.
<p>Video showing the accelerometer data in spectrogram format and synchronous displacements of the <em>Varroa</em> individual on empty, fully built honeycomb. The processing for all panels in this video has been completed in the same way as for S9 and S10, with the video slowed to four times for ease of viewing. The lowest point of acceleration magnitude is here forced to be 1/10<sup>th</sup> of the maximum. The mite in this video pulses 28 times over a period of approximately 4 minutes, so the frame rate did not need to be slowed down to 5 frames per second as with S8, where jolting pulses are rapid and continuous. Due to the less regular production of pulses, there are many stretches of time where no jolting pulses occur. To view the synchronicity quickly and easily between mite displacement and accelerometer trace, instances of jolting have been cut together, thereby removing those time periods where no activity occurred. Overall, this video accumulates approximately 24 seconds of mite activity. The soundtrack (i.e. the signal from the accelerometer) has also been included, as in S9 and S10, also slowed accordingly. The jolting pulses can be heard in some instances as a ‘clapping’ noise.</p>
Supplementary material S10: Video analysis of Varroa jolting pulses on brood-comb.
<p>Video showing the accelerometer data in spectrogram format and synchronous displacements of the <em>Varroa</em> individual on brood-comb. The processing for all panels in this video has been completed in the same way as for S9 and S11, with the video slowed to four times for ease of viewing. The lowest point of acceleration magnitude is here forced to be 1/60<sup>th</sup> of the maximum As the mite jolting pulses are more spread out over time on this substrate and the vibrational pulse is inherently of a lower frequency, the frame rate did not need to be slowed as much as it did for S9. The soundtrack has also been slowed accordingly and still reveals an audible jolting pulse in some instances, this time as a ‘knocking’ noise. Several jolting instances are included in this video from different points in time to showcase occurrences where the jolt produces a measurable vibrational trace. Although this mite jolted frequently throughout the video, detectable instances of vibration occurred at more irregular intervals. Here too, the synchronicity between mite movement and accelerometer trace can be clearly viewed. Overall, this video amounts to approximately 38 seconds of real-time data.</p> <p> </p>
Supplementary material S9: Video analysis of Varroa jolting pulses on petri-dish.
<p>Video showing the accelerometer data in spectrogram format and synchronous displacements of the <em>Varroa</em> individual on petri-dish. The excerpt demonstrates approximately 10 seconds of continuous jolting behaviour slowed down ten times for ease of viewing and hearing of the rapid jolting behaviour. The soundtrack of this video, also slowed by a factor ten, results in the ultra-high frequency (23 kHz) jolting pulses to be heard at 2200 Hz. Not all pulses produce an audible signature, but for those that do, the sound could be described as a quiet ‘clinking’ noise. The spectrogram shows acceleration magnitude in logarithmic (to the base 10) scale, with dark red showing the highest magnitude and dark blue as the lowest at 1/70<sup>th</sup> of the maximum. The maximum acceleration magnitude is forced to be that of the <em>Varroa </em>jolting pulses for better viewing. The original video data is shown in panel ‘a’. Panel ‘b’ is a replica of this data, further cropped on the mite and demonstrate simple edge detection by means of the spatial gradient of the pixel intensity. Panel ‘c’ further shows the temporal changes in pixel intensity in two consecutive frames seen in panel ‘b’. When motionless the mite is mostly seen as dark blue in ‘c’, but when moving the pixels flash red. The sum of the pixel intensities in this panel are then displayed as the white line superimposed on the spectrogram data, demonstrating the remarkable synchronicity between video-detected mite displacement and accelerometer trace.</p>
Supplementary video for Hegwood, Langendorf, and Burgess tradeoff analysis
<p>Video walk-through of methods for: Hegwood, Langendorf, and Burgess (2022). Why win-wins are rare in complex environmental management. Narration is provided by Margaret Hegwood and Ryan Langendorf.</p>
Video: Pandemic and Social Media Textual Sentiment Analysis of the Indonesian Goverment Policy in Facing the Thitd Wave of Covid-19 Attack
<p>This material has presented on 1st International Conference on Advance Research in Social and Economic Science. October 25, 2022</p>
Video Coding and Data Analysis-Discover the unseen
<p>InterAct: Discover the Undiscovered</p> <p>Workshop is suitable for anyone who wants to conduct research and analysis on video or audio material. The workshop will teach you how to use InterAct to get the most of your audio and video file observations, and how to best discover and communicate behavioral associations and interaction patterns in your data. </p> <p><br> This workshop present and teach participants how to: </p> <p>Get the most of your observations:<br> Create new Codes based on your observations (Co-occurrences, Contingencies, Pre- and postevents)<br> Identify complex behavior from a combination of simple Codes<br> Discovering behavioral associations & interaction patterns:<br> Uncover the invisible through intelligent data analysis<br> Smart data merging of and Codes, pre- and post-event analysis, data restructuring, Co-occurrences, Contingencies, Create pattern segments, statistical analysis, and sequence analysis<br> Visualize behavior before and after an intervention as well as all overlaps between two classes in the State-Space-Grid<br> Combine separate routines into a single Workflow that any user can execute.<br> User the power of Python to run any of the available analysis routines – short key-click<br> Samples of predefined implementations for observation (coding) systems like PLATO but also the availability of coding systems<br> WOW, Showing a moving State-Space-Grid, showing a box-plot from the Python-based analysis. </p>
Supplementary Videos to "Anatomic and neurochemical analysis of the palpal olfactory system in the red flour beetle Tribolium castaneum, HERBST"
<p>Supporting Videos to Figures 4, 5, and 6 of the peer-reviewed paper supplemented by this upload.</p>
Pain Detection Through Automated Video Analysis
ClinicalTrials.gov study NCT04011189. IPD Sharing: NO. Countries: 1. Publications: 0.
Analysis of Video Imaging in Newborns
ClinicalTrials.gov study NCT03004482. IPD Sharing: NO. Countries: 1. Publications: 14.
Deep Learning-Based Intraoperative Dual-tracer Video Analysis of Sentinel Lymph Node Mapping for Metastasis Prediction in cN0 Papillary Thyroid Carcinoma
ClinicalTrials.gov study NCT07391514. IPD Sharing: UNDECIDED. Countries: 1. Publications: 46.
Video Analysis of Prehospital Emergency Intubations
ClinicalTrials.gov study NCT03929796. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
A Comparative Analysis of the Efficacy of Instructional Videos and Live Demonstrations in Crown Preparation Training for Preclinical Dental Students
ClinicalTrials.gov study NCT06426095. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Analysis of the Effect of Pre-course Videos on Improving Clinical Skills in Emergency Ultrasound Training
ClinicalTrials.gov study NCT06947733. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of Harmonicity in Running Gait by the Use of Innovative Techniques of Video Analysis
ClinicalTrials.gov study NCT04103437. IPD Sharing: UNDECIDED. Countries: 1. Publications: 15.
Video Analysis of Errors and Technical Performance Within Minimally Invasive Surgery Short Title: Video Analysis in Minimally Invasive Surgery (VAMIS)
ClinicalTrials.gov study NCT05279287. IPD Sharing: NO. Countries: 1. Publications: 10.
Using a new video rating tool to crowd-source analysis of behavioural reaction to stimuli
Open the record for dataset details and reuse information.
Teahing method analysis - lecture video dataset
<p>Lecture Videos dataset, these videos are collected using CCTV camera, later these are split into 3 seconds clips and stored in folders based on action performed in the clips. each folder represent sest of 2 actions seperated by _ underscore sign.</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.