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89 results for “YouTube”
Dataset del Trabajo Fin de Grado "¿Qué puedo aprender en YouTube sobre microscopía escolar?".
<p>Rivas Brousse, I. & Rams, S. (2020). <em>Dataset del Trabajo Fin de Grado "¿Qué puedo aprender en YouTube sobre microscopía escolar?".</em> [Dataset] Zenodo. DOI: 10.5281/zenodo.4300668</p>
A dataset of media releases (Twitter, News and Comments, Youtube, Facebook) form Poland related to COVID-19 for open research
<p>Social behavior has a fundamental impact on the dynamics of infectious diseases (such as COVID-19), challenging public health mitigation strategies and possibly the political consensus. The widespread use of the traditional and social media on the Internet provides us with an invaluable source of information on societal dynamics during pandemics. With this dataset, we aim to understand mechanisms of COVID-19 epidemic-related social behavior in Poland deploying methods of computational social science and digital epidemiology. We have collected and analyzed COVID-19 perception on the Polish language Internet during 15.01-31.07(06.08) and labeled data quantitatively (Twitter, Youtube, Articles) and qualitatively (Facebook, Articles and Comments of Article) in the Internet by infomediological approach.</p> <p>- manually labelled1,449 articles / Facebook posts from Lower Silesia (facebook_articles_lower_silesia.zip) and 111 texts from outside this region;</p> <p>-manually labelled 1000 most popular tweets (twits_annotated.xlsx) with cathegories is_fake (categorical and numeric) topic and sentiment; </p> <p>-extracted 57,306 representative articles (articles_till_06_08.zip) in Polish using Eventregitry.org tool in language Polish and topic "Coronavirus" in article body;</p> <p>- extracted 1,015,199 (tweets_till_31_07_users.zip and tweets_till_31_07_text.zip) and Tweets from #Koronawirus in language Polish using Twitter API.</p> <p>- collected 1,574 videos (youtube_comments_till_31_07.zip and youtube_movie.csv) with keyword: Koronawirus on YouTube and 247,575 comments on them using Google API;</p> <p>- We supplemented the media observations with an analysis of 244 social empirical studies till 25.05 on COVID-19 in Poland (empirical_social_studies.csv).</p> <p>Reports and analyzes and coding books can be found in Polish at: <a href="http://www.infodemia-koronawirusa.pl/percepcja-koronowirusa-na-dolnym-slasku/">http://www.infodemia-koronawirusa.pl</a></p> <p>Main report (in Polish) https://depot.ceon.pl/handle/123456789/19215 </p>
Live Coding YouTube - PAT showcase 2017 (Screen Recording)
<p>This is a screen recording of the premiere performance <em>Live Coding YouTube</em>, presented at the Performing Arts and Technology annual showcase, March 2017, McIntosh Theatre, University of Michigan, Ann Arbor. The following is a blurb used for the program note.</p> <p>Music listening has changed greatly with the emergence of music streaming services, such as Spotify or Youtube. However, did it inspire us to make new experimental music? <em>Live Coding YouTube</em> is a response to the anticipation of novel performance practices using streaming media. A live coder uses any available video from YouTube, a video streaming service, as source material to perform an improvised audiovisual piece. The challenge is to manipulate the emerging media that are streamed from a networked service given the limited functionality of the API provided. The piece finds parallels in early experimental music that manipulates magnetic tape and vinyl records. On the contrary, the audiovisual space that a musician can explore on the fly is practically infinite. The performance system is built entirely on a web browser and publicly available in the following address: https://livecodingyoutube.github.io/</p>
Brazilian Elections YouTube Comments Dataset (2018-2022)
<p>This dataset provides a comprehensive analysis of YouTube comments related to Brazilian election candidates for the years 2018 and 2022. The dataset is organized into two folders:</p><p><strong>video_gender_mapping_BR_elections</strong>: This folder contains individual files, each corresponding to a YouTube channel analyzed. Each file includes the following fields:</p><ul><li><strong>channelId</strong>: YouTube channel identifier</li><li><strong>channelTitle</strong>: YouTube channel title</li><li><strong>videoId</strong>: YouTube video identifier</li><li><strong>publishedAt</strong>: Datetime of when the video was published</li><li><strong>year_month</strong>: Year and month of when the video was published</li><li><strong>views</strong>: Number of views of the video</li><li><strong>comments</strong>: Number of total comments on the video</li><li><strong>likes</strong>: Number of total likes on the video</li><li><strong>match_type</strong>: Type of match considering candidate name and video title/description (T for Title, D for Description, TD for both)</li><li><strong>match_result</strong>: Array of JSON containing positive matches based on race, elective office, title/description match, and gender</li><li><strong>match_cargos</strong>: List of elective offices that appear in the match</li><li><strong>match_genero</strong>: Overview of gender types (M for male, F for female, MF for both)</li><li><strong>match_raca</strong>: Overview of races for all matched elective offices (BR for white, AM for yellow, PA for brown, PR for black, IN for Indian, NA for None of the above)</li><li><strong>match_genero_raca</strong>: Array of gender and race concatenated for each matched candidate</li></ul><p><strong>comments_folders</strong>: This file provides a sample of comments collected from the videos analyzed in the first file. It includes the following fields:</p><ul><li><strong>id</strong>: Comment identifier</li><li><strong>videoId</strong>: Video identifier where the comment is published</li><li><strong>textDisplay</strong>: Comment as displayed on the YouTube platform</li><li><strong>textDisplay_len</strong>: Number of characters in the displayed comment</li><li><strong>textOriginal</strong>: Encoded comment text</li><li><strong>textOriginal_len</strong>: Number of characters in the original comment text</li><li><strong>authorDisplayName</strong>: Name of the comment author</li><li><strong>authorChannelId</strong>: Comment author channel identifier</li><li><strong>publishedAt</strong>: Timestamp of when the comment was published</li><li><strong>likes</strong>: Number of likes on the comment</li></ul><p>This dataset offers valuable insights into YouTube interactions surrounding Brazilian elections, including demographic and sentiment analysis based on candidate information, gender, and race. Researchers can utilize this dataset for a detailed examination of public engagement and sentiment on political content during the specified election periods.</p>
Tour de Canoz - Youtube video
La tour de Canoz. Patrimoine Jurasien. Modèle 3D issu d'une vidéo youtube : https://youtu.be/ubGVF2URG28 96 pictures - Agisoft Photoscan Source: Objaverse 1.0 / Sketchfab
Motivations for citing research in comments to YouTube videos
<p><strong>This dataset includes 300 YouTube comments that link to research publications or preprints. Each comment has been assigned into one category that define why individuals mention scholarly publications in comments to YouTube videos. </strong></p> <p>Each row of the file "Categories_300random.xlsx" represents one distinct comment to YouTube video.<br> The file includes the following columns:</p> <ul> <li><strong>Comment_text </strong>- the full text of a comment,</li> <li><strong>YouTube_link </strong>- URL to the video where the comment was left,</li> <li><strong>Comment_link </strong>- URL to the thread with the comment,</li> <li><strong>Category </strong>- the final category that was assigned to the comment,</li> <li>Columns <strong>Category_old_schema_SS</strong>, <strong>Category_old_schema_IP</strong>, <strong>Category_old_schema_OZ </strong>- categories assigned by different researchers according to old categorization schema and were used for validation reasons,</li> <li>Columns <strong>Category_OZ</strong>, and <strong>Caregory_LB </strong>- categories assigned by different researchers according to the final schema and were used for validation.<br> <br> </li> </ul>
Data from: The viewer doesn't always seem to care - response to fake animal rescues on YouTube and implications for social media self-policing policies
<p>Animal-related content on social media is hugely popular but is not always appropriate in terms of how animals are portrayed or how they are treated. This has potential implications beyond the individual animals involved, for viewers, for wild animal populations, and for societies and their interactions with animals. Whilst social media platforms usually publish guidelines for permitted content, enforcement relies at least in part on viewers reporting inappropriate posts. Currently, there is no external regulation of social media platforms. Based on a set of 241 "fake animal rescue" videos that exhibited clear signs of animal cruelty and strong evidence of being deliberately staged (i.e. fake), we found little evidence that viewers disliked the videos and an overall mixed response in terms of awareness of the fake nature of the videos, and their attitudes towards the welfare of the animals involved. Our findings suggest, firstly, that despite the narrowly defined nature of the videos used in this case study, exposure rates can be extremely high (one of the videos had been viewed over 100 million times), and, secondly, that many YouTube viewers cannot identify (or are not concerned by) animal welfare or conservation issues within a social media context. In terms of the current policy approach of social media platforms, our findings raise questions regarding the value of their current reliance on consumers as watch dogs.</p>
A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and other sources about the 2024 outbreak of Measles
<p><strong>Please cite the following paper when using this dataset:</strong></p> <p>N. Thakur, V. Su, M. Shao, K. Patel, H. Jeong, V. Knieling, and A. Bian “A labelled dataset for sentiment analysis of videos on YouTube, TikTok, and other sources about the 2024 outbreak of measles,” Proceedings of the 26th International Conference on Human-Computer Interaction (HCII 2024), Washington, USA, 29 June - 4 July 2024. (Accepted as a Late Breaking Paper, Preprint Available at: <a href="https://doi.org/10.48550/arXiv.2406.07693" rel="nofollow">https://doi.org/10.48550/arXiv.2406.07693</a>)</p> <p><strong>Abstract</strong></p> <p>This dataset contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. The paper associated with this dataset (please see the above-mentioned citation) also presents a list of open research questions that may be investigated using this dataset.</p>
YouTube Music Artists Data
<p>This dataset was collected using the YouTube Data API v3, covering various artist channels from the YouTube Music app. This dataset provides insight into artists' digital presence and engagement on the YouTube platform, including metrics such as number of subscribers, number of presentations, and engagement rate. Researchers, data analysts, and music fans can use this dataset to explore trends in the music industry, study audience behavior, and analyze the impact of online content on artists' popularity.</p>
[LS2N_IPI_YouTube_UGC] A DATASET FOR UNDERSTANDING OPEN UGC VIDEO DATASETS
<p>User Generated Content (UGC) video streaming is a major application on the Internet. Even small bitrate savings can have large network impacts at this scale. In order to achieve improvements without sacrificing experience, the quality of UGC videos needs to be better understood. In recent years video quality evaluation models designed for the evaluation of UGC videos have received a lot of attention. However, considering that these models are learning-based models, they heavily depend on the training data that has been used. </p> <p>In this paper, a new dataset is introduced that allows studying the differences in characteristics between existing UGC video datasets. It reveals the range of quality that was covered by existing UGC video datasets, and the implication of these quality ranges on training and validation performance of UGC video quality prediction models. Furthermore, this work demonstrates that dataset alignment enables existing UGC models to achieve higher performance.</p>
YA Domain Dataset: Dataset of scholarly bibliographic references on YouTube videos
<p><strong>Abstract</strong></p> <p>Scholarly communication through YouTube videos has been increasing. Although Altmetric (<a href="https://altmetric.com/">https://altmetric.com/</a>) provides the dataset on such references, its coverage is unclear, and it does not contain the original external links in each video. Considering this background, we built and published a dataset of scholarly bibliographic references on YouTube videos by using YouTube Data API v3, targeting six types of domain names: "doi.org," "ncbi.nlm.nih.gov," ieeexplore.ieee.org," "link.springer.com," "onlinelibrary.wiley.com," and "sciencedirect.com." As a result, we identified approximately 480,000 references associated with Crossref DOIs among 230,000 videos published by December 31, 2023, posted on 55,000 channels. Notably, over half of these references were not covered by the Altmetric dataset, resulting in a 150% increase in the number of references when combining the dataset constructed by the proposed method with the Altmetric dataset, compared to the Altmetric dataset alone. Regarding external links, PubMed and DOI links were prominent; however, a substantial number of direct links to publisher platforms were observed. Most channels and videos contained external links to a single platform, scattered across each platform. This dataset is helpful for identifying and analyzing scholarly references on YouTube.<br>As for the original paper related to this dataset, please refer to the references section.</p> <p> </p> <p><strong>Data Records</strong></p> <p>The data format of the dataset is JSON lines, where each line is a single record. The data is split into files by DOI Registration Agencies. A sample of the record is as follows:</p> <table> <tbody> <tr> <td>{<br> "channel_id": "UCEfEi-IMiB87UsxY3765P6w",<br> "video_id": "e7YmyVd4uOE",<br> "is_covered_by_altmetric_com": false,<br> "youtube_data_api_search": [<br> {<br> "query": "doi.org",<br> "uri": "http://dx.doi.org/10.1145/2807442.2814654"<br> }<br> ],<br> "doi": "10.1145/2807442.2814654",<br> "doiRA": "Crossref"<br>}</td> </tr> </tbody> </table> <ul> <li>channel_id (String) -- Channel ID of the YouTube channel that uploaded the video.</li> <li>video_id (String) -- Video ID.</li> <li>is_covered_by_altmetric_com (Boolean) -- Whether this reference is covered by altmetric.com or not.</li> <li>youtube_data_api_search (Array) <ul> <li> query (String) -- The query used in the search:list of YouTube Data API v3. (<a href="https://developers.google.com/youtube/v3/docs/search/list?hl=en">https://developers.google.com/youtube/v3/docs/search/list?hl=en</a>)</li> <li> uri (String)-- The original external links written in the description text or video title in each video.</li> </ul> </li> <li>doi (String) -- DOI corresponding to the bibliographic reference in the video.</li> <li>doiRA (String) -- DOI registration agency for the DOI. We obtained this data using the WhichRA? API (<a href="https://www.doi.org/the-identifier/resources/factsheets/doi-resolution-documentation#4-which-ra">https://www.doi.org/the-identifier/resources/factsheets/doi-resolution-documentation#4-which-ra</a>).</li> </ul> <p>We note that the altmetric dataset obtained from Altmetric Explorer in this study is not included in this dataset.</p> <p><strong>References</strong></p> <ul> <li>Kikkawa, Jiro; Takaku, Masao; Yoshikane, Fuyuki: "Enhancing Identification of Scholarly Reference on YouTube: Method Development and Analysis of External Link Characteristics", <em>Proceedings of the 28th International Conference on Theory and Practice of Digital Libraries (<a href="https://tpdl2024.nuk.si/">TPDL 2024</a>)</em>, Ljubljana, Slovenia, Lecture Notes in Computer Science (LNCS), Vol.15178, 2024.09. (in press).</li> </ul> <p><strong>Fundings</strong></p> <p>JSPS KAKENHI Grant Numbers <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K18147/">JP22K18147</a>, <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-23K11761">JP23K11761</a>, and <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-24K15652">JP24K15652</a>.</p>
Dataset Abusive YouTube Comments
<p>Sexually Abusive Comments and specific words collection from popular youtube videos such as music videos and cartoons (Peppa Pig)</p>
Commonfare videos on YouTube data (until September 30, 2019)
<p>Views of the PIE News project 16 videos in the "COMMONFARE videos" YouTube channel to September 30, 2019.</p>
R code and dataset to "Monetizing Spillover Effects in the Creative Industries: the Impact of Live Music Performances on Youtube Searches"
<p>Content:</p> <ol> <li>The script<strong> main_script.R</strong> includes code to run a regression discontinuity (RD) design and validation and falsification of estimated results</li> <li>The folder <strong>data</strong> contains two files: <ol> <li>bands_2016_2019.csv: a dataset of performers with additional information for each one.</li> <li>festivals_2016_2019.csv: a dataset of video search activity (as retrieved from Google Trends) for performers in file bands_2016_2019.csv</li> </ol> </li> <li>The folder <strong>source</strong> contains two additional R scripts: <ol> <li>data_preparation.R: generates the long dataset used to estimate RD effects</li> <li>status_simulation.R: randomly assigns treattment status to performers and estimates RD effects. Note this may take a long time to run. Parallel code is used: the number of cores has been set to 4. </li> </ol> </li> <li>The folder simulation_results contains simulated data after running the script status_simulation.R.</li> </ol>
Building better conservation media for primates and people: A case study of orangutan rescue and rehabilitation YouTube videos
<p>1. Conservation organizations rely on social/internet media platforms to raise awareness and fundraise. Social media is a double-edged sword: it can be a wide-reaching and effective tool for education and fundraising, but can also have counter-productive impacts on public views toward wildlife and understanding of wildlife conservation.</p> <p>2. For example, depicting humans interacting with wildlife in media may increase video popularity, but animals shown in anthropogenic contexts are also viewed as appealing pets. We are interested in understanding whether this is true for social media posts (YouTube videos) by orangutan rescue and rehabilitation organizations, which rely on social media for fundraising and awareness-raising. Our goal is to provide data and recommendations to guide these organizations in building media with positive conservation impact while minimizing potential negative effects.</p> <p>3. Using YouTube analytics and sentiment analysis of comments on 118 videos, we ask how viewer responses to videos vary with 1) the amount of human-orangutan interaction depicted, 2) the ages of the orangutans featured, and 3) the mention of threats to orangutans.</p> <p>4. Videos with longer human-orangutan interaction time were viewed more, but comments on them were significantly more likely to be negative toward Indonesian/Malaysian people. Comments on orangutan rescue/rehabilitation videos were more likely to be categorized as negative for orangutan conservation compared to videos about orangutans generally, and within these, so were comments on videos featuring infant and juvenile orangutans.</p> <p>5. Based on our findings, we recommend that orangutan rescue and rehabilitation organizations feature adult and mixed age groups of orangutans rather than infants and juveniles, minimize the amount of human-orangutan interaction shown, and talk about conservation threats to orangutans in their videos. We also recommend that, as a precaution, other primate rescue and rehabilitation groups also abide by these suggestions.</p>
Verification of the effects of a YouTube-based home-based (self-managed intervention) training system developed for frailty prevention―A pilot study ―
<p><em>Background and Objectives</em>: Resistance training is considered the most effective intervention for increasing older people’s muscle mass and strength. We devised a self-administered training system (squat + balance training, sukubara®) that incorporates a new low-load exercise. This study hypothesizes that introducing sukubara® affects skeletal muscle mass and physical function positively, and we first conducted a preliminary verification in healthy non-elderly participants.</p> <p><em>Materials and Methods</em>: This study’s participants were non-elderly healthy hospital personnel. Applicants were randomly assigned to two groups, a resistance training group that performed an exercise program (sukubara®) and a control group that did not, and they received a 12-week intervention. This study’s primary endpoint was change in skeletal muscle mass; the secondary endpoints were knee extension strength and one-leg standing time with eyes closed.</p> <p><em>Results</em>: An analysis of Tthe 18 participants (10 in the resistance training group and 8 in the control group), who were 18 analyzed this study’s results was performed. The results of changes in variables between both groups during the intervention period were as follows: skeletal muscle mass, knee extension strength, and one-leg standing time were significantly improved or tended to be significantly higher in a resistance training group than in a control group. <em>Conclusions</em>: A self-administered training system (sukubara®) incorporating low-load exercise resulted in muscle hypertrophy and improvement in physical function.</p>
SHS-YouTube1300: A YouTube-based Cover Song Dataset (CQT Spectograms)
<p>These are the CQT Spectograms for our SHS-YouTube-1300 dataset. This is a subset of our crawl based on the SHS100K dataset and contains YouTube videos of which a subset was annotated by crowd-workers and in-house annotators.</p>
SHS-YouTube1300: A YouTube-based Cover Song Dataset
<p>This is the repository for the SHS-YouTube1300 dataset. A dataset of cover versions from YouTube.</p>
Technical Land-Sea Spaces. Impacts of the Port Clusterization Phenomenon on coasts, cities and architectures [YouTube Video Version]
<p>Beatrice Moretti lectures on the phenomenon of spatial stretching that is imposing a profound evolution, both formal and institutional, in the sphere of contemporary port cities and regions, by giving first insights about the research methodology oriented in this phase to the definition of a indicator systems of the cluster dimension. The presentation questions the spatial impacts introduced by port clusters in the field of architectural design.</p> <p>[YouTube Video Version]<br> <br> <a href="https://www.iccaua.com/page/conference-brochure">6th International Conference of Contemporary Affairs in Architecture and Urbanism - ICCAUA2023</a><br> Alanya Hamdullah Emin Paşa University, Istanbul (Turkey)<br> Chairman of the Conference:<strong> </strong>Dr. <a href="https://arch.alanyahep.edu.tr/en/akademik-kadro">Hourakhsh A. Nia</a>, AHEP University, Alanya, Antalya, TR.<br> Special Session "Coastal and Maritime Spaces", proposed by The University IUAV, Venice (IT)<br> Chairs: Paolo De Martino and Fabio Carella (IUAV).</p>
Pre-release Consumer Buzz on YouTube Trailers
<p>Consensus and valence of pre-release comments on YouTube trailers for movies released between 2015-2017. Analysis of 1.4 million YouTube comments on 146 movies. Information on opening weekend box office, sequel, MPA, star buzz, budget and distribution. </p>
ScienceDex guides
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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.