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67 results for “Video Analysis”
Player Experience in Video Game Character Analysis: A Study of Female Characters
<h3><span>Overview</span></h3> <p><span>This dataset is part of the study titled "Player Experience in Video Game Character Analysis: A Study of Female Characters", conducted at </span>Mapúa University. The research aims to integrate player experience into an existing framework for video game character analysis. </p> <h3><span>Content</span></h3> <p><span>The dataset includes:</span></p> <ul> <li><span>A partial transcript of 5 semi-structured interviews with the key informants. Originally, 8 interviews were conducted, but the audio/video recordings for 3 interviews were lost and thus their transcripts are not available.</span></li> <li><span>Significant codes presented in tabulated form.</span></li> </ul> <h3><span>Data Collection Method</span></h3> <p><span>Data were collected through in-depth interviews conducted via Facebook Messenger and Discord from March to April 2024. Participants were various video game players from different backgrounds and age groups, ranging from 20 to 40 years old. Due to technical issues, the recordings of 3 interviews were lost, resulting in only 5 available transcripts. </span></p> <h3><span>Data Processing and Analysis</span></h3> <p><span>The 5 available interviews were transcribed verbatim. Data were analyzed </span><span>using thematic analysis, involving initial coding, theme development, and refinement.</span></p> <h3><span>Usage data</span></h3> <p><span>The dataset is organized into several sections within a single Word document (.docx). This word document has headings for navigation and a definition of terms.</span></p> <h3><span>Limitations</span></h3> <p><span>The dataset only includes 5 out of 8 due to technical difficulties encountered after the recording of the interview. This may impact the comprehensiveness of the findings.</span></p> <h3><span>Contextual Reference</span></h3> <p><span>The manuscript associated with this dataset heavily references the works "<span>A Structural Model for Player-Characters as Semiotic Constructs." (DOI: https://doi.org/10.26503/TODIGRA.V2I2.37) and "Object, me, symbiote, other: A social typology of player-avatar relationships." (DOI:https://doi.org/10.5210/FM.V20I2.5433) which explore the foundational frameworks on video game character analysis.</span></span></p> <p><span> For any further information or clarifications, please contact wbdg2000@gmail.com</span></p>
Video Analysis Scale of Engagement (VASE): Initial and Final Protocols
<p>These files contain the initial and final protocols that were used to develop the Video Analysis Scale for Engagement (VASE). The abstract of our article in Wellcome Open Research, 5:230: https://wellcomeopenresearch.org/articles/5-230/v2:</p> <p><strong>Background</strong>: The current study sought to develop a valid, reliable and unobtrusive tablet computer-based observational measure to assess engagement of people with advanced dementia. The Video Analysis Scale of Engagement (VASE) was designed to enable the rating of moment-by-moment changes in engagement during an activity, which would be useful for both future research and current residential care. <strong>Methods:</strong> An initial version of the VASE was tested. Face validity and content validity were assessed to validate an operational definition of engagement and develop an acceptable protocol for the scale. Thirty-seven non-professional and professional volunteers were recruited to view and rate level of engagement in music activities using the VASE. <strong>Results</strong>: An inter-class coefficient (ICC) test gave a high level of rating agreement across professionals and non-professionals. However, the ICC results of within-professionals were mixed. Linear mixed modelling suggested that the types of interventions (active or passive music listening), the particular intervention session being rated, time period of video and the age of raters could affect the ratings. <strong>Conclusions</strong>: Results suggested that raters used the VASE in a dynamic fashion and that the measure was able to distinguish between interventions. Further investigation and adjustments are warranted for this to be considered a valid and reliable scale in the measurement of engagement of people with advanced dementia in a residential care setting.</p>
Extreme to phenomenal storm wave impacts on a steep rocky coast, north Mayo, Ireland: video data, image analysis, runup and flow velocity calculations for waves of storms Fionn and Gareth.
<p>The primary data are video (.mp4) files of extreme storm wave impacts on the sites of high elevation (>=20m above high water mark) coastal boulder deposits, recorded during storms Fionn (16/01/2018) and Gareth (12/03/2019), at (54.320355, -9.569633) on the north Mayo coast of Ireland, while the significant wave height was in the range [11m,14m]. There are also .png and .jpg files derived from frames of some of the videos, relating to the analysis of the impacting wave kinematics (runup/landward propagation and flow velocities), together with physical measurements for scale determination and runup/velocity/measurement uncertainty calculations in Excel. The files EventX.mp4 are the primary data for the wave impacts EventX. The files EventX_Frame_Y.jpg are frames sampled from EventX.mp4 at constant time intervals in the temporal vicinity of the impact. The files EventX_Edges_Y.png are the edges derived from the frames with the Canny edge detector. The files EventX_Registration_Y.jpg are the impacting wavefront edges with topographical edges registered on the file ReferenceImage.jpg The files EventX.jpg are the stacked registrations for all Y, from which the impact kinematics are derived. The file Scale_Registration_Position_Velocity_Measurements_AndUncertainty.xlsx contains physical measurements for scale determination, measurements of registration error, and the calculations of impact runup/landward displacement and flow velocities, with their uncertainties. The files JetX_Leacht_a_Chúil.mp4/g are videos of large jet-producing impacts at another site.</p> <p>The files DSCN0066.MP4-DSC0085.MP4 are the raw video observations of Storm Gareth, recorded from 15:35-18:41 UT on 12 March 2019 with a Nikon Coolpix W100, while the significant wave height increased from 12m to in excess of 14m (the timestamp of these videos in Properties->Details->Media Created is one hour later than the UT of creation, because the camera's clock was set to Irish Summer Time). The file GPO15366.MP4 is an example of the GoPro (Hero 5) videos recorded simultaneously.</p>
Dynamic FRET example videos related to "Mars, a molecule archive suite for reproducible analysis and reporting of single-molecule properties from bioimages"
<p>Videos of dynamic switching between iso-I and iso-II conformations of a holiday junction at 50 mM Magnesium resulting in high and low FRET from Cy3 and Alexa647 labels positioned on the arms. Holiday junctions are surface immobilized through a biotin attachment and imaged using TIRF microscopy. The camera sensor is split using a dual view so that the acceptor emission is on the top and the donor emission is on the bottom. Videos from each position are provided as compressed zip files containing a sequence of tif files and associated metadata text file. Image sequences were collected using Micro-Manager 2.0 using ALEX or alternating laser excitation with alternating 637 and 532 pulses separated as two different channels. Beam profile images are provided for 637 and 532 excitation allowing for correction of the non-uniform beam profiles. The following 2D affine transformation matrix can be used to transform from the top acceptor emission region to the bottom donor emission region during processing.</p> <p>Affine 2D transformation from top to bottom: (m00, m01, m02, m10, m11, m12), (1.00276, 0.000208, 1.01236, 0.000267, 1.00312, 507.21025)</p> <p>A detailed image processing workflow for this dataset using Mars can be found under the example section at <a href="https://duderstadt-lab.github.io/mars-docs/">https://duderstadt-lab.github.io/mars-docs/</a> or directly at <a href="https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/">https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/</a></p>
Analysis of interventions of practice abstracts and videos
<p>This dataset containts the underlying data that was used to analyse the SmartCulTour identified interventions that were described under the production of abstracts and practice videos of D6.2. The data collection established:</p> <ul> <li>Context & background</li> <li>Reason why of the intervention</li> <li>Resources and tools</li> <li>Expected economic impact</li> <li>Expected social impact</li> <li>Expected cultural impact</li> <li>Expected environmental impact</li> <li>Success conditions</li> </ul> <p>These criteria were analysed by the SmartCulTour local experts and meant to feed into the updated taxonomy on cultural tourism interventions of D3.4. The interventions that are described are linked to the practice videos that can be found under the Living Labs section of the SmartCulTour website (<a href="http://www.smartcultour.eu">www.smartcultour.eu</a>) and pertain to:</p> <ul> <li>Rotterdam Living Lab: Planning for the future of Hoek van Holland & Bospolder-Tussendijken</li> <li>Scheldeland Living Lab: Hof van Coolhem: social employment and care project in tourism</li> <li>Scheldeland Living Lab: Bornem Castle: upgrades historical exhibitions & creates visitor centre</li> <li>Scheldeland Living Lab: Steam train Dendermonde-Puurs: volunteers protecting industrial heritage</li> <li>Utsjoki Living Lab: Traces in Utsjoki: inspiring respectful visitor behaviour in nature areas</li> <li>Utsjoki Living Lab: Placemaking as a technique to support meaningful visitor experiences</li> <li>Huesca Living Lab: The Somontano Wine Route: a resilient strategy for Huesca</li> <li>Huesca Living Lab: The Río Vero Cultural Park. From Palaeolithic human history to the present</li> <li>Split Living Lab: Making traditional Easter bread-Sirnica in Solin</li> <li>Split Living Lab: The cultural heritage of Sinj: the story of Alka</li> <li>Vicenza Living Lab: Vicenza: the city of Palladio</li> <li>Vicenza Living Lab: The international library "La Vigna" becomes an open innovation Living Lab</li> </ul>
Bee Tracker – an open-source machine-learning based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees
<p>The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radio-frequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honey bees, behavioral studies on solitary bees still largely depend on direct observations, which is very time-consuming.</p> <p>Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity-nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by a machine learning based software. This <i>Bee Tracker</i> software consists of four trained deep learning networks to detect bees that enter or leave their nest and to recognize individual IDs on the bees' thorax as well as the IDs of their nests according to their positions in the nesting unit.</p> <p>The software is able to identify each nest of each individual nesting bee, which permits to measure individual-based measures of reproductive success. Moreover, the software quantifies the number of cavities a female enters until it finds its nest as a proxy of nest recognition, and it provides information on the number and duration of foraging trips. By training the software on 8 videos recording 24 nesting females per video, the software achieved a precision of 96% correct measurements of these parameters.</p> <p>The software could be adapted to various experimental setups by training it to an according set of videos. The presented method allows to efficiently collect large amounts of data on cavity-nesting solitary bee species and represents a promising new tool for the monitoring and assessment of behavior and reproductive success under laboratory, semi-field and field conditions.</p>
Wind Value: Second Conference 2024 Real Option Analysis Peter Deeney Video
<p>Video 13mins 31 seconds, of the presentation by Peter Deeney on the topic " Planning is Optional". The presentation considers the preparatory work before a wind farm, to be a European call option. It also looks at the decisions at end-of-life to decommission, extend life or repower the wind farm. This took place at the second Wind Value conference on 29th May 2024 in the Ellen Hutchins Building, of the Environmental Research Institute of University College Cork, Ireland.</p>
IoT Forensic Analysis: a Family of Experiments with Amazon Echo Devices (ISP diagrams and Teardown videos)
<p>The two zip files (i.e., ISP Diagrams.zip and Teardown videos.zip) contain the ISP diagrams and teardown videos of the Amazon Echo Show IoT devices used in the experiment that we report in our research paper titled "IoT Forensic Analysis: a Family of Experiments with Amazon Echo Devices."</p>
Bee Tracker – an open-source machine-learning based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees
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Dataset to ACM MMSys'20 paper entitled "Comparing Fixed and Variable Segment Durations for Adaptive Video Streaming – A Holistic Analysis"
<p>Dataset for the ACM MMSys'20 paper entitled "Comparing Fixed and Variable Segment Durations for Adaptive<br> Video Streaming – A Holistic Analysis".<br> The dataset includes</p> <ul> <li>Results from video encoding (using variable and fixed segment durations)</li> <li>Video sequences used for streaming evaluations</li> </ul>
Insights into the operation of the solid Earth system from analysis of compiled geochemical data (Video)
<p>This is the first session video recording of the Goldschmidt 2020 Virtual Workshop: Earth Science meets Data Science - Services & Systems, Policies & Procedures, Tools & Techniques for Geochemistry. Moderated by Kerstin Lehnert (Columbia University)</p>
Videos from "Postural analysis reveals persistent vigilance in paper wasps after conspecific challenge"
<p>Videos of field assays demonstrating persistent effects of a simulated social challenge on the vigilance behavior of wild northern paper wasp foundresses, <em>Polistes fuscatus</em>. Untracked and tracked ("predictions.cleaned.slp") videos included.</p>
An Analysis of the Current Bibliographical Data Landscape in the Humanities. A Case for the Joint Bibliodata Agendas of Public Stakeholders - video presentation
<p>A video presenting the DARIAH's Bibliographical Data Working Group entitled <em>An Analysis of the Current Bibliographical Data Landscape in the Humanities. A Case for the Joint Bibliodata Agendas of Public Stakeholders. </em>The report is freely available on Zenodo: <a href="https://zenodo.org/record/6559857#.Y0XDo3ZBy5f">https://zenodo.org/record/6559857#.Y0XDo3ZBy5f</a>. </p> <p>This presentation aims to present the original work - co-authored by 18 WG's members - in a condensed manner.</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>
DIVERSE: Deciphering Internet Views on the U.S. Military Through Video Comment Stance Analysis: A Novel Benchmark Dataset for Stance Classification
<p>Paper citation: Cruickshank, Iain J., and Lynnette Hui Xian Ng. "DIVERSE: Deciphering Internet Views on the US Military Through Video Comment Stance Analysis, A Novel Benchmark Dataset for Stance Classification." <em>arXiv preprint arXiv:2403.03334</em> (2024).</p> <p>Link to paper: https://arxiv.org/abs/2403.03334</p>
Mocap video examples for the analysis of Sign Language movements
<p>These mocap videos support my PhD thesis "Extracting human characteristics from motion: the case of identity in Sign Language" carried out from October 2018 to October 2021. The original mocap data is taken from the <a href="https://www.ortolang.fr/market/corpora/mocap1/">MOCAP1</a> corpus of French Sign Language. The videos have been generated using Python code available as part of the <a href="https://github.com/felixbgd/PLmocap">PLmocap</a> library.</p> <p> </p> <p> </p>
Mocap video examples for the analysis of Sign Language motion
<p>These mocap videos support my PhD thesis "Extracting human characteristics from motion: the case of identity in Sign Language" carried out from October 2018 to October 2021. The original mocap data is taken from the <a href="https://www.ortolang.fr/market/corpora/mocap1/">MOCAP1</a> corpus of French Sign Language. The videos have been generated using Python code available as part of the <a href="https://github.com/felixbgd/PLmocap">PLmocap</a> library.</p>
Mocap video examples for the analysis of Sign Language motion
<p>These mocap videos support my PhD thesis "Extracting human characteristics from motion: the case of identity in Sign Language" carried out from October 2018 to October 2021. The original mocap data is taken from the <a href="https://www.ortolang.fr/market/corpora/mocap1/">MOCAP1</a> corpus of French Sign Language. The videos have been generated using Python code available as part of the <a href="https://github.com/felixbgd/PLmocap">PLmocap</a> library.</p>
Mocap video examples for the analysis of Sign Language motion
<p>These mocap videos support my PhD thesis "Extracting human characteristics from motion: the case of identity in Sign Language" carried out from October 2018 to October 2021. The original mocap data is taken from the <a href="https://www.ortolang.fr/market/corpora/mocap1/">MOCAP1</a> corpus of French Sign Language. The videos have been generated using Python code available as part of the <a href="https://github.com/felixbgd/PLmocap">PLmocap</a> library.</p> <p> </p>
High-speed video microscopy analysis of cilia before and after airway cell culture
<p>High-speed video microscopy analysis (HSVA) is a diagnostic tool used within the UK Primary Ciliary Dyskinesia (PCD) Service to access airway ciliary function on nasal brushing biopsies. The Southampton PCD group is based at the University of Southampton, Faculty of Medicine and the University Hospital Southampton NHS Foundation Trust and is led by Professor Jane Lucas. We also use air-liquid interface (ALI) culture to differentiate airway epithelial cells to regrow healthy cilia to repeat standard PCD tests (including HSVA, immunofluoresence labelling of cilia proteins, transmission electron microscopy and functional genomics) and provide PCD research samples, which also allow us to develop new diagnostic approaches. ALI-culture can restore normal ciliary movement when secondary damage (due to infection or poor cell health) temporarily causes of abnormal cilia movement or a lack of cilia. ALI-culture can also re-confirm when ciliary defects and abnormal ciliary function are permanent and cause by inherited PCD (a ciliopathy). </p>
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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.