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89 results for “YouTube”
Use of YouTube for Nursing Students to Learn Empathy
ClinicalTrials.gov study NCT04441853. IPD Sharing: YES. Countries: 1. Publications: 0.
Anesthesia Awareness on YouTube: Content and Quality Analysis
ClinicalTrials.gov study NCT07323537. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Dataset for: "Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children"
<p>Dataset for paper: Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children</p> <p>The dataset consists of five files:<br> 1. groundtruth_videos.json: This is the ground truth dataset. We have 4797 manually annotated videos (1513 suitable, 929 disturbing, 419 restricted, and 1936 irrelevant). You can distinguish among the different labels by observing the 'classification_label' field.<br> 2. elsagate_related_videos.json: Contains the data for 233K elsagate-related YouTube videos (1K seed and 232K recommended) that were obtained as described in the paper.<br> 3. other_child_related_videos.json: Contains the data for 155K other child-related YouTube videos (2K seed and 153K recommended) that were obtained as described in the paper.<br> 4. random_videos.json: Contains the data for 482K random YouTube videos (8K seed and 474K recommended) that were obtained as described in the paper.<br> 5. popular_videos.json: Contains the data for 11K popular YouTube videos (500 seed and 10.5K recommended) that were obtained between November 18 and November 21, 2018, as described in the paper.</p> <p>For each video in all sets, you can check the predicted label of our classifier by observing the 'prediction' field.</p>
Ben Shapiro YouTube Comments
<div> <div> <div> </div> </div> </div> <div> <p>This dataset was complied as a <span>resource for analyzing viewer engagement, sentiment, and discussion trends on the Ben Shapiro YouTube channel over the specified period. It </span>comprises user-generated comments extracted from the Ben Shapiro YouTube channel. The collection process involved first cataloging a comprehensive list of all videos published on the channel. Subsequently, these videos were categorized into three distinct time frames. From each time frame, the ten videos that garnered the highest number of comments were identified for detailed comment extraction. The extraction of videos and their associated comments was conducted utilizing YouTube Data Tools (Rieder, 2015). The dataset was finalized on September 12, 2022, and encompasses 711,909 comments ranging from September 1, 2020, to September 12, 2022. This dataset was uploaded and analyzed in the 4CAT: Capture & Anlysis Toolkit (Peeters & Hagen, 2022). </p> <p>References:</p> <div> <div>Peeters, S., & Hagen, S. (2022). The 4CAT Capture and Analysis Toolkit: A Modular Tool for Transparent and Traceable Social Media Research. <em>Computational Communication Research</em>, <em>4</em>(2), 571–589. <a href="https://doi.org/10.5117/CCR2022.2.007.HAGE">https://doi.org/10.5117/CCR2022.2.007.HAGE</a></div> <div> </div> <div> <div> <div>Rieder, B. (2015). <em>YouTube Data Tools</em> (1.11) [Computer software].</div> </div> </div> </div> </div>
YouTube Radicalization (2008 - 2018)
<p>This dataset was created by Dutch journalists at De Correspondent and De Volkskrant in order to investigate YouTube Radicalization. Their work is detailed in various Dutch articles and presented as a case-study in the <a href="https://gijn.org/stories/how-they-did-it-exposing-right-wing-radicalization-on-youtube/"><em>Global Network of Investigative Journalism</em></a> in English. In their work they describe that the dataset contains approximatetly: </p> <ul> <li>600,000 videos</li> <li>450,000 transcripts of those videos (by using YouTube’s automatic closed-captioning service, which is not available for all videos)</li> <li>120 million comments on those videos</li> <li>20 million recommendations automatically generated from viewing those videos</li> </ul> <p>The dataset has also been used for academic research at the department of Mediastudies at the University of Amsterdam. Notably for the publication of '<a href="https://academic.oup.com/dsh/article-abstract/37/4/949/6445201?redirectedFrom=fulltext&login=true">A free market in extreme speech: Scientific racism and bloodsports on YouTube</a>' in the journal <em>Digital Scholarship in the Humanties</em>. The dataset is managed by Ivan Kisjes as part of the Public Data Lab.</p> <p>As of October 2022, the dataset is also used by Daniel Jurg, who now manages this version of the dataset for his PhD project: 'Broadcasting the Culture War - Audience Engagement in YouTube’s Alternative Community,' as funded by the FWO (<a href="https://researchportal.vub.be/en/projects/broadcasting-the-culture-war-audience-engagement-in-youtubes-alte">FWOTM1133</a>). In the final SQL version of the dataset that is captured in this repository, there is the following data:</p> <ul> <li>channels: 1738</li> <li>videos: 626110</li> <li>comments: 109,315,583</li> </ul> <p>Given the sensitivity of this data it cannot yet be widely shared and we are looking working on ways to acrhive a public version.</p>
Dataset for "The Good, the Bad and the Bait: Detecting and Characterizing Clickbait on YouTube"
<p>This is the dataset used for the research "The Good, the Bad and the Bait: Detecting and Characterizing Clickbait on YouTube", with DOI: <a href="http://10.1109/SPW.2018.00018">10.1109/SPW.2018.00018</a>.</p> <p>The dataset consists of three files:</p> <p>1. groundtruth.json: This is the groundtruth dataset. We have 3443 manually annotated videos (we manually annotated more after the acceptance of the paper), and 17,648 videos that were obtained from channels that post clickbait or not. You can distinguish the method of annotation by observing the field "comments" in "clickbaitClassification" (the ones that have the comment "channels" are the ones obtained from the channels).</p> <p>2. videos.json: Contains the data for 206K videos that were obtained as described in the paper.</p> <p>3. predictions.json: It contains the mapping between the video id and the probability of our classifier. In our paper, we treat a video as clickbait if the probability is larger than 0.5.</p> <p> </p> <p>The related software produced for this study may be found <a href="https://zenodo.org/record/3405484#.XXk7bigzaUk">here</a>.</p>
YouTube recommended videos network from the Olavo de Carvalho vídeo "Globalismo e poder"
<p>This dataset structures a network of YouTube video recommendations from the Olavo de Carvalho conspiracy theory vídeo "Globalismo e poder". The data is structured as a graph, which could be constructured through the parameter "parentId". The graph includes 50,000 nodes, with id, title, thumbnail link and channel name.</p>
Dataset of Ainu-related videos in YouTube
<p>Anonymized open data pertaining 428 YouTube videos related to Ainu Indigenous people. Curated and verified by humans.</p>
mPCNL youtube data
<p>mPCNL youtube videos data were collected by authors.</p>
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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)
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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.