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Dataset results
102 results for “Video recording”
Eye Movements Recording Using a Mobile : Comparison to Standard Video-oculography in Young Athletes
ClinicalTrials.gov study NCT05211752. IPD Sharing: NO. Countries: 1. Publications: 0.
Eye Movements Recording Using a Smartphone: Comparison to Standard Video-oculography in Patients With Multiple Sclerosis
ClinicalTrials.gov study NCT04702763. IPD Sharing: NO. Countries: 1. Publications: 0.
Teen-to-Teen Videos to Improve Electronic Medical Record Portal Knowledge and Personal Health Information Engagement
ClinicalTrials.gov study NCT06096987. IPD Sharing: NO. Countries: 1. Publications: 0.
The Effect of Routine Video-recording on Colonoscopy Quality Indicators.
ClinicalTrials.gov study NCT02054923. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Collecting Recorded Videos of Colonoscopy Tests
ClinicalTrials.gov study NCT03937063. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Sperm Video Recording Protocol
ClinicalTrials.gov study NCT06150846. IPD Sharing: YES. Countries: 1. Publications: 0.
The Impact of Resuscitation Quality of CPR Team by Implantation of Electronic Checklist and Alarming System Through Video-recording Analyses
ClinicalTrials.gov study NCT02209870. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Collecting Recorded Videos of Colonoscopy Screening Tests
ClinicalTrials.gov study NCT03891290. IPD Sharing: NO. Countries: 1. Publications: 0.
Collecting Recorded Videos of Colonoscopy
ClinicalTrials.gov study NCT03891251. IPD Sharing: NO. Countries: 1. Publications: 0.
Eye Movements Recording Using a Smartphone: Comparison to Standard Video-oculography and Correlation to Imaging Data in Young Athletes
ClinicalTrials.gov study NCT05385601. IPD Sharing: NO. Countries: 1. Publications: 0.
Spreading Teen-Recorded Inspirational Videos to Engage Schoolmates
ClinicalTrials.gov study NCT02065206. IPD Sharing: Not stated. Countries: 1. Publications: 0.
High-speed video recordings of Müller's larva swimming and feeding
Open the record for dataset details and reuse information.
Experimental recording video
Open the record for dataset details and reuse information.
Video records of experiments for "Layered DNA Data Storage"
<p>These videos recorded our experiments on layered DNA data storage. The first color film “Becky Sharp” (53.59 MB) was encoded into ~10.08 million DNA strands. Simultaneous sequencing and real-time readout were achieved.</p> <p><strong>Supplementary Video 2: <span>The full experiment process of real-time readout of the layered DNA storage with a panoramic view.</span><span> </span></strong><span>This video demonstrates a complete real-time readout experiment, from the start of sequencing until the end of the film showing. It mainly covers three parts: signal acquisition, computing (base-calling and decoding), and film showing. The audio has been redacted.</span></p> <p><strong>Supplementary Video 3: <span>The full experiment process of real-time readout of the layered DNA storage with a close view.</span><span> </span></strong><span>This video illustrates a complete real-time film readout experiment from a close view, which mainly showcases sequencer signal acquisition and film showing. This same experiment process in Video 2 was recorded in Video 3 with another recorder. The audio has been redacted.</span></p> <p><strong>Supplementary Video 4: <span>Film showing and statistics information monitoring (with audio in the recovered film). </span></strong><span>This video showcases the details of the film showing, consisting of playing and monitoring windows. The audio is from the restored film. After approximately 28.2 minutes (film readout latency, reading adapters and indices, refer to statistics information panel), the film began to stream out from DNA and playback. The film file was read out within approximately 16.7 minutes and it can be shown for approximately 30 minutes.</span></p> <p><strong>Supplementary Video 5: <span>Signal acquisition and data transfer (with audio in the recovered film).</span><span> </span></strong><span>This video presents the real-time signal acquisition of the sequencer and the data transfer of the sequencer server. The audio is from the restored film.</span></p> <p><strong>Supplementary Video 6: <span>Flow-wise majority voting, layer-wise decoding, and streaming recovery </span><span>(with audio in the recovered film). </span></strong><span>This video showcases the implementation details of flow-wise majority voting, layer-wise decoding, and streaming recovery in a real-time readout experiment. Meanwhile, the error rates of the recovered base coding layer were presented.</span></p> <p><strong>Supplementary Video 7: <span>Signal analysis and base calling (16 nodes). </span></strong><span>This video illustrates the live signal analysis and incremental base calling. A total of 16 computing nodes equipped with 1,640 CPU cores and 24 NVIDIA A100 GPUs are deployed to balance the workload.</span></p> <p><strong>Supplementary Video 8: <span>Detailed signal analysis and base calling (one node). </span></strong><span>To facilitate clearer illustration, Video 8 was zoomed in to obtain this Video. One of the 16 computing nodes was selected and illustrated.</span></p> <p><strong>Supplementary Video 9</strong><strong>: <span>Automatic template preparation on Ion Chef.</span><span> This video showcases the use of the Ion Chef System for automated template preparation. </span></strong><span>After a total of 25 μL of a 20 pM oligo library was loaded, the instrument was started for automatic operation. Since the entire process was automated, this video only shows the first 2.5 hours.</span></p>
THOR - complementary video recordings
<p><strong>THÖR</strong> is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for the position, head orientation, gaze direction, social grouping and goals. THÖR contains sensor data collected by a 3D lidar sensor and involves a mobile robot navigating the space. In comparison to other, our dataset has a larger variety in human motion behaviour, is less noisy, and contains annotations at higher frequencies.</p> <p><strong>THOR - complementary video recordings </strong>- contains additional video material presenting the image form the eye tracker and stationary camera. The material is not synchronised with other parts of the data set and should be used only as a visualisation material.</p>
Comparison of Efficacy Between Suggestion Method During Video EEG Recording by Verbal Suggestion in Isolation and Verbal Suggestion Along With Cotton Swab or Tuning Fork for Inducing the Paroxysmal Ev
ClinicalTrials.gov study NCT03551431. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
video recordings_job interviews_performance reviews
<p>Data set including video recordings of organisational practices: job interviews, performance appraisal interviews and job meetings.</p> <p>Data in Italian, Finnish and English.</p> <p>Data recorded in 2019, 2020.</p> <p>File "data management sheet" includes the list of recordings.</p> <p>File "data description" includes description of the data set.</p> <p>Video recordings are not open acess because they contain personal idenitifiers. Access is restricted and the main researcher can be contacted.</p>
Video recordings of "Modular Tangible User Interfaces: Impact of Module Shape and Bonding Strength on Interaction"
<p>We provide the video data recorded during the experiment presented in the publication <a href="https://doi.org/10.1145/3569009.3572731">https://doi.org/10.1145/3569009.3572731</a>.</p> <p>We conducted the first study on how the bonding strength and the shape of modules impact usability during user interaction with modular tangible user interfaces (TUIs). We built six magnetic modular prototypes featuring 6x6x6mm modules with three different levels of bonding strength (low, mid, high) and two different shapes (cubes and rounded cubes). We asked participants to perform eight common tasks found in the HCI literature for (non-)modular TUIs, which are presented in section 3.3 from the publication:</p> <ol> <li>Lift</li> <li>Split one module</li> <li>Split two modules</li> <li>Split a line of modules</li> <li>Split half of the modules</li> <li>Slide parts of the interface</li> <li>Bend the interface</li> <li>Fold the interface</li> </ol> <p><br> Participants performed the manipulations on two types of configuration of modules: a flat configuration (i.e., one layer of 80 modules) or a thick configuration (i.e., two layers on 40 modules each). The video data we provide consists of recordings of the hands of the participants when they perform the tasks with each condition of prototype. The video data we provide consists of recordings of the hands of the participants when they perform the tasks with each condition of prototype.</p> <p>The procedure presented in the video recordings is as follow:<br> <br> The participant sits in front of a table with a delimited manipulation area, with an overhead camera pointing towards the manipulation area. Participants are asked to perform the experiment within the borders of the manipulation area.<br> The experimental software (not shown in the recordings, but in the publication) presents the participants with a picture of a starting State A (e.g., a rectangular block) and a target State B (e.g., the rectangular block split in two halves). Reaching state B only requires the use of one elementary manipulation. There is no indication as to how the UI was grabbed to reach State B, to avoid bias. The experimenter places the first prototype on the manipulation area, mimicking State A. The participant manipulates the prototype to reach State B. The task is repeated three times to allow exploring different strategies. After completion of the task, the participant fills a short questionnaire, which is when a mobile tablet is shown in the recordings. They are then presented with the next task to perform (i.e., new State A and B pictures) while the experimenter presents the next prototype. This procedure is repeated for each manipulation and each condition.</p>
Three-dimensional video recordings: Accuracy, reliability, clinical and research guidelines.
<p>The datasets include positional differences between consecutive frames of three-dimensional video recordings performed by two independent operators.</p>
Video Recordings in WMV Format
Downloadable Presentation Recordings
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.