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3,853 results for “Video”
VRTogether Pilot 2 connection with the reporter video
<p>Video of the reporter in stereo format 180º.</p> <p>Codec: H264 - MPEG-4 AVC</p> <p>Dimensions: 5760x2880</p> <p>Framerate: 29.97 fps</p> <p>Bitrate: 8805kbps</p> <p>Audio: Stereo 48000 kHz</p>
FT12 Castlas 7-key fagottino: measurements, photos, endoscopic video
<p>Dataset of F12 Castlas 7-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video. </p>
FT10 Bonaccorsi 7-key fagottino: measurements, photos, endoscopic video
<p>Dataset of FT10 Bonaccorsi 7-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video. </p> <p> </p> <p> </p>
FT11 Cahusac (1) 6-key tenoroon: measurements, photos, endoscopic video
<p> Dataset of FT11 Cahusac 6-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video. </p>
FT19 Merklein 8-key tenoroon: measurements, photos, endoscopic video
<p>Dataset of FT19 Merklein 8-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video.</p>
FT34 Tuerlinckx (2) 5-key tenoroon: measurements, photos, endoscopic video
<p>Dataset of FT34 Tuerlinckx 5-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video. </p>
FT20 Müller 4-key fagottino: measurements, photos, endoscopic video
<p>Dataset of F20 Müller 4-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video</p>
FT22 Proff 5-key tenoroon: measurements, photos, endoscopic video
<p>Dataset of FT22 Proff 5-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video. </p> <p> </p>
MHMisinfo - Video-based Mental Health Misinformation Dataset
<p>MHMisinfo-Gold and MHMisinfo-Large datasets, as described in the paper "Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis)Information Content on Video-sharing Platforms" (forthcoming at ICWSM 2025). Videos and comments for each dataset are seperately stored in different .csv files</p> <p><strong>Dataset schema, videos</strong></p> <table> <tbody> <tr> <th><strong>Column Name</strong></th> <th><strong>Description</strong></th> </tr> <tr> <td><strong>video_id</strong></td> <td>ID of the Video, as assigned by their respective platforms</td> </tr> <tr> <td><strong>video_title</strong></td> <td>The title of the video</td> </tr> <tr> <td><strong>video_description</strong></td> <td>The description of the video, given by the video creators</td> </tr> <tr> <td><strong>audio_transcript</strong></td> <td>Text transcription of the video's audio track, as generated by Whisper speech-to-text model</td> </tr> <tr> <td><strong>video_view_count</strong></td> <td>View count of the video, at the time of data collection</td> </tr> <tr> <td><strong>video_like_count</strong></td> <td>Like count of the video, at the time of data collection</td> </tr> <tr> <td><strong>video_comment_count</strong></td> <td>Comment count of the video, at the time of data collection</td> </tr> <tr> <td><strong>label_ioi</strong></td> <td>"Information of Interventions" label of video, annotated by experts. 1 = High-quality information on interventions, -1 = Low-quality information on interventions</td> </tr> <tr> <td><strong>label_ebt</strong></td> <td>"Evidence-based Treatment" label of video, annotated by experts. 1 = Encourages evidence-based treatment, -1 = Discourages evidence-based treatment</td> </tr> <tr> <td><strong>label_aoc</strong></td> <td>"Alignment of Consensus" label of video, annotated by experts, 1 = High Alignment with Consensus, -1 = Low Alignment with Consensus</td> </tr> <tr> <td><strong>label</strong></td> <td>Overall mental health misinformation label of the video. 0 = non-MHMisinfo videos, and -1 = MHMisinfo videos</td> </tr> <tr> <td><strong>platform</strong></td> <td>Platform of the video</td> </tr> </tbody> </table> <p><strong>Dataset schema, comments</strong></p> <table> <tbody> <tr> <th><strong>Column Name</strong></th> <th><strong>Description</strong></th> </tr> <tr> <td><strong>text</strong></td> <td>The raw text of the comment</td> </tr> <tr> <td> <p><strong>commenter_channel_display_name</strong></p> </td> <td>The display name of the user who posted the comment.</td> </tr> <tr> <td> <p><strong>comment_publish_date</strong></p> </td> <td>The time when the comment was orignally published, .</td> </tr> <tr> <td> <p><strong>video_id</strong></p> </td> <td>ID of the Video associated by the platform, as assigned by their respective platforms</td> </tr> <tr> <td> <p><strong>platform</strong></p> </td> <td>Platform of the video associated with the comment</td> </tr> <tr> <td> <p><strong>label</strong></p> </td> <td>Overall mental health misinformation label of the video associated with the comment. 0 = non-MHMisinfo videos, and -1 = MHMisinfo videos</td> </tr> </tbody> </table> <p> </p>
Cholec80-Boxes: Bounding-Box Labels for Surgical Tools in Five Cholecystectomy Videos
<p> </p> <p>The dataset is descriped in a pending publication titled "Cholec80-Boxes: Bounding-box Labeling Data for Surgical Tools in Cholecystectomy Images". The dataset was used in the following studies titled:</p> <ul> <li>"Surgical tool classification & localisation using attention and multi-feature fusion deep learning approach".</li> <li>"Laparoscopic video analysis using temporal, attention, and multi-feature fusion based-approaches".</li> <li>"Analysing attention convolutional neural network for surgical tool localisation: A feasibility study".</li> </ul> <p>The dataset consists of cholecystectomy images and bounding-box labels for surgical tools. These images were extracted from five videos of the Cholec80 dataset (Twinanda et al., 2016) at a rate of 1 Hz. The images are stored in '.png' format with a resolution of 854*480 pixels. Each video’s images are organized in a separate folder. The labeling data are stored in a CSV file, which contains the region of interest (ROI) labels for each surgical tool visible in the extracted images. Additionally, the CSV file provides information about each labeled image. Table 1 presents a content description of the 'ROI_Labels.csv' file.</p> <p><strong>Table 1:</strong> Description of 'ROI_Labels.csv' file.</p> <table> <tbody> <tr> <td><strong>Column Name</strong></td> <td><strong>Description</strong></td> <td><strong>Type</strong></td> </tr> <tr> <td><em>Surgery_num</em></td> <td>Procedure number in the Cholec80 dataset from which the image was extracted.</td> <td>Integer</td> </tr> <tr> <td><em>Dir</em></td> <td>Directory of the image folder.</td> <td>String</td> </tr> <tr> <td><em>FrameName</em></td> <td>Image name in the format '<em>Video_SS_fffff.png', </em>where <em>SS is the Surgery_num and fffff is the frame number in the video.</em></td> <td>String</td> </tr> <tr> <td><em>NumBBox_inFrame</em></td> <td>The bounding-box number in the image.</td> <td>Integer</td> </tr> <tr> <td><em>ToolName</em></td> <td>Name of the surgical tool.</td> <td>String</td> </tr> <tr> <td><em>BBox</em>_<em>X</em></td> <td>X-coordinate of the top-left corner.</td> <td>Integer</td> </tr> <tr> <td><em>BBox_Y</em></td> <td>Y-coordinate of the top-left corner.</td> <td>Integer</td> </tr> <tr> <td><em>BBox_Width</em></td> <td>Bounding box width.</td> <td>Integer</td> </tr> <tr> <td><em>BBox_Height</em></td> <td>Bounding box height.</td> <td>Integer</td> </tr> </tbody> </table> <p> </p> <p><strong>Citing This Dataset:</strong></p> <p>When using this dataset, please cite the following publications:</p> <ul> <li>Jalal, N. A., Alshirbaji, T. A., Docherty, P. D., Arabian, H., Laufer, B., Krueger-Ziolek, S., Neumuth, T. & Moeller, K. (2023). Laparoscopic video analysis using temporal, attention, and multi-feature fusion based-approaches. <em>Sensors</em>, <em>23</em>(4), 1958.<br><br></li> <li>Jalal, N. A., Alshirbaji, T. A., Docherty, P. D., Arabian, H., Neumuth, T., & Möller, K. (2023). Surgical tool classification & localisation using attention and multi-feature fusion deep learning approach. IFAC-PapersOnLine, 56(2), 5626-5631.</li> <li> <p>Abdulbaki Alshirbaji, T., Arabian, H., Jalal, N. A., Battistel, A., Docherty, P. D., Neumuth, T., & Moeller, K. Cholec80-Boxes: Bounding-box labeling data for surgical tools in cholecystectomy images. (<em>to be submitted</em>). </p> </li> <li>Twinanda, A. P., Shehata, S., Mutter, D., Marescaux, J., De Mathelin, M., & Padoy, N. (2016). Endonet: a deep architecture for recognition tasks on laparoscopic videos. <em>IEEE transactions on medical imaging</em>, <em>36</em>(1), 86-97.</li> </ul>
La piattaforma di riviste Open Access dell'Università degli Studi di Milano. [Video]
<p>L’intervento percorre i passi fondamentali della creazione della piattaforma di riviste open access dell’Università degli Studi di Milano, come modello in cui una istituzione che produce conoscenza decide di assumersi la responsabilità di validare e diffondere questa conoscenza ad un pubblico che sia il più ampio possibile, liberandosi da scelte e vincoli imposti dagli editori commerciali e dalle logiche editoriali e riportando nelle mani dei ricercatori le attività che da tempo erano state consegnate agli editori.</p>
Infrared Video of Bone Drilling - Supplementary material for article "Thermal Evaluation of Bone Drilling: Assessing Drill Bits and Sequential Drilling"
<p>Video 1 shows sequential bone drilling with 5 drill bits (⌀2.0 mm, ⌀2.5 mm, ⌀3.2 mm, ⌀3.7 mm, and ⌀4.1 mm) used in series following the manufacturer's recommended spindle speeds.</p> <p>Video 2 shows bone drilling with a single drill bit (⌀2.0 mm) with a spindle speed of 1500 rpm.</p> <p>These videos are supplementary to the article, "Thermal Evaluation of Bone Drilling: Assessing Drill Bits and Sequential Drilling" published in the journal <em>Bioengineering. </em></p>
Eye image data with gaze labels recorded using custom video-oculography hardware at 120Hz
<p>The repository of eye image data with corresponding gaze labels collected from 40 subjects. The preview contains a collage of random image samples, one per subject. </p> <p>All recorded subjects gave informed consent under an experimental protocol approved by the Institutional Research Board of Texas State University (approval code 2018044) and their data were anonymized prior to public release.</p> <p>The data were recorded using the custom video-oculography (VOG) desktop hardware setup at 120Hz. The full description of this eye-tracking system's capabilities is provided at https://doi.org/10.48550/arXiv.1904.07361.</p> <p>This VOG set contains recordings of the random oblique saccades task. It is comprised of 174 on-screen fixation targets that densely cover the range of ±20.51° horizontally and ±16.7° vertically (in degrees of visual angle). More detail on the presented stimuli can be found at https://doi.org/10.1145/3379156.3391370.</p> <p>The data were also used in Dmytro Katrychuk's Ph.D. thesis "Generating Realistic Eye Images to Evaluate Photosensor Oculography Eye-Tracking for Portable Headsets" (https://hdl.handle.net/10877/19437); with the release for public use in the upcoming publication "An appearance-based gaze estimation as a benchmark for eye image data generation methods" accepted to MDPI Journal of Applied Sciences. </p> <p>Each .zip archive represents a recording from one subject, which includes:</p> <ul> <li>Video of the close eye capture in ".avi" format</li> <li>Calibration data in ".xml" format</li> <li>Gaze data in ".tsv" format</li> <li>On-screen target stimulus position in ".tsv" format</li> </ul> <p>The "src.zip" provides a Python script to unpack each ".avi" video recording to a set of ".png" images. The direct playback of ".avi"s may require special codecs and is not supported. </p> <p>Any additional code will be uploaded to https://github.com/dkatrychuk/psog-eval-diss2023</p> <p>The authors can be contacted at their corresponding emails: Dmytro Katrychuk - d_k139@txstate.edu; Oleg Komogortsev - ok@txstate.edu.</p>
Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability: Subjective fidelity study data
<p>Supplementary Material to the Paper: <em>Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability</em></p> <p>This folder contains all collected data and scripts that were used to analyze the subjective fidelity study.</p>
Protests Ukraine Covid 2020-22: YouTube Videos
The collection "YTV Protests Ukraine Covid 2020-22" contains 146 videos (mp4) on protests relating to government measures due to Covid-19. We have downloaded all data in October 2024 and made screenshots (pdf) of websites so that the discussion and comments on the single video posts can be followed. All data is processed in an MS Excel database with metadata. We collect all videos that are 1) event related, 2) show actions of this event, 3) we can find with our search words during a particular period. We strictly aim at a systematic and objective selection and organized storage of protest-related videos. The collection is based on extensive research into Covid-related protest events in Ukraine, which made it possible to identify relevant search words. According to the snowball principle, we then start the collection of videos with the help of these search words and try to download as much relevant content as possible. However, we cannot guarantee the completeness of protest videos on the particular event. We search the videos and include them into the collection until a particular degree of saturation has been reached. Due to copyright restrictions, we are only allowed to give access to the database of the collected video files including the hyperlinks with its metadata and not to the videos themselves. The videos have been posted mainly by TV channels and news outlets. Therefore, the material is only an extract and biased by the perspective of the single creator/creating institution. The collection is part of a larger and ongoing collection of videos on protest events in the post-Soviet region.
Protests Kazakhstan 2022: YouTube Videos
The collection "Protests Kazakhstan 2022" contains 315 videos (mp4) on protests in mainly January 2022 triggered by a sharp increase in gas prices. We have downloaded all data in September 2024 and made screenshots (pdf) of websites so that the discussion and comments on the single video posts can be followed. All data is processed in an MS Excel database with metadata. We collect all videos that are 1) event related AND show actions of this event, 2) downloadable, 3) we can find with our search words during a particular period. We strictly aim at a systematic and objective selection and organized storage of protest-related videos. We identify particular event-related search words after intense research on the event. According to the snowball principle, we then start the collection of videos with the help of these search words and try to download as much relevant content as possible. However, we cannot guarantee the completeness of protest videos on the particular event. We search the videos and include them into the collection until a particular degree of saturation has been reached. Due to copyright restrictions, we are only allowed to give access to the database of the collected video files including the hyperlinks with its metadata and not to the videos themselves. The videos have been posted mainly by the participants of the events. Therefore, the material is only an extract and biased by the perspective of the single creator. The collection is part of a larger and ongoing collection of videos on protest events in the post-Soviet region.
Video Series: Integrated Pest Management focusing on disease control in cereals
<p>Welcome to this video series on IPM, focusing on disease control in cereals. </p> <p>The challenge of IPM is to make the control methods we use appropriate to the circumstances, and to balance between the productivity of the crop and minimising the impact on the environment. The control decisions we make on one field or in one season may not be appropriate in another set of circumstances – there is no ‘blue print’. In these videos we delve into the physiology of the crop, the epidemiology of the diseases and how different control methods work. By understanding the biology of the systems we're trying to control, we're better equipped to make appropriate decisions. Going into depth means we can’t cover all aspects of IPM. In practice, decisions about disease control are being made alongside decisions about invertebrate pests and weeds, and in the wider context of integrated crop management. In the UK, information on those topics is available from organisations such as LEAF, AHDB and the Voluntary Initiative. Nevertheless, disease control is still a big topic, so we have broken it down into bite size chunks - although each video is still a pretty substantial bite and will need some digesting.</p> <p>The videos can be viewed in any order that interests you, but they'll make most logical sense viewed in the order in the menu.</p> <p>Links to the videos can be found in the summary document. </p> <p>PDF versions of the video presentations are provided. </p>
Modelling of excitation propagation on computer models of insoles colonised by fungal mycelium. Videos and potential difference recordings.
<p>We used an artistic image of the mycelium network projected onto a $364 \times 985$ nodes grid. <br> The original image $M=(m_{ij})_{1 \leq j \leq n_i, 1 \leq j \leq n_j}$, $m_{ij} \in \{ r_{ij}, g_{ij}, b_{ij} \}$, where $n_i=364$ and $n_j=985$, and $1 \leq r, g, b \leq 255$, was converted to a conductive matrix $C=(m_{ij})_{1 \leq i,j \leq n}$ derived from the image as follows: $m_{ij}=1$ if $r_{ij}>170$, $g_{ij}>170$ and $b_{ij}<200$; a dilution operation was applied to $C$. </p> <p>FitzHugh-Nagumo (FHN) equations is a qualitative approximation of the Hodgkin-Huxley model of electrical activity of living cells:<br> \begin{eqnarray}<br> \frac{\partial v}{\partial t} & = & c_1 u (u-a) (1-u) - c_2 u v + I + D_u \nabla^2 \\<br> \frac{\partial v}{\partial t} & = & b (u - v),<br> \end{eqnarray}<br> where $u$ is a value of a trans-membrane potential, $v$ a variable accountable for a total slow ionic current, or a recovery variable responsible for a slow negative feedback, $I$ {is} a value of an external stimulation current. The current through intra-cellular spaces is approximated by<br> $D_u \nabla^2$, where $D_u$ is a conductance. The term $D_u \nabla^2 u$ governs a passive spread of the current. The terms $c_2 u (u-a) (1-u)$ and $b (u - v)$ describe the ionic currents. The term $u (u-a) (1-u)$ has two stable fixed points $u=0$ and $u=1$ and one unstable point $u=a$, where $a$ is a threshold of an excitation.</p> <p>We integrated the system using the Euler method with the five-node Laplace operator, a time step $\Delta t=0.015$ and a grid point spacing $\Delta x = 2$, while other parameters were $D_u=1$, $a=0.13$, $b=0.013$, $c_1=0.26$. We controlled excitability of the medium by varying $c_2$ from 0.05 (fully excitable) to 0.015 (non excitable). Boundaries are considered to be impermeable: $\partial u/\partial \mathbf{n}=0$, where $\mathbf{n}$ is a vector normal to the boundary. </p> <p>To record dynamics of excitation in the network, as if in laboratory experiments, we simulated electrodes by calculating a potential $p^t_x$ at an electrode location $x$ as $p_x = \sum_{y: |x-y|<2} (u_x - v_x)$. Configuration of electrodes $1, \cdots, 16$ is shown in Fig.~\ref{fig:mycelium}c. Time-lapse snapshots provided in the paper were recorded at every 100\textsuperscript{th} time step, and we display sites with $u >0.04$; videos and figures were produced by saving a frame of the simulation every 100\textsuperscript{th} step of the numerical integration and assembling the saved frames into the video with a play rate of 30 fps. </p> <p>Insole_01: Excitation started at electrode E2</p> <p>Insole_10: Excitation started at electrode E1</p> <p>Insole_11: Excitation started at electrodes E1 and E2</p> <p> </p>
Visualization and perception of data gaps in the context of Citizen Science projects: Video tutorial support
<p>Online experiment about the influence of the availability of a video tutorial on proportion of correct responses and subjective evaluation of the task (NASA-TLX). Two different tasks were given. The evaluation of statements on a map and the selection of grid fields that met a given requirement.</p>
TraceVis: Visualization for DSMC: tool, demonstration video, data
<p>Tool demonstration video and source code of <em>TraceVis</em>, the visualization tool for Deep Statistical Model Checking, presented in the paper <em>TraceVis: Towards Visualization for Deep Statistical Model Checking</em>, published at ISoLA 2020 (9th International Symposium On Leveraging Applications of Formal Methods, Verification and Validation).</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.