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3,853 results for “Video”
Sparse camera volumetric video applications. A comparison of visual fidelity, user experience , and adaptability. Supplementary Video
<p>This video is a supplementary video material to the paper "Sparse camera volumetric video applications. A comparison of visual fidelity, user experience , and adaptability". It shows a comparision of five volumetric videos scenes, captured with three different sparse volumetric video applications. This video aims to visualize the difference in fidelity and artifacts that each system expresses.</p>
Flow Magnetic Tweezers example video
<p>The example video contains a section of a field-of-view from a force spectroscopy experiment called Flow Magnetic Tweezers (FMT). It shows E. coli DNA Gyrase manipulating DNA topology by relaxing positive and introducing negative coils.</p>
Videos of fluid flow in contact interfaces
<p>These videos demonstrate the capabilities of the computational framework presented in [1] to solve complex coupled problem of viscous thin fluid flow in contact interfaces while handling the possibility of the fluid to be trapped in pockets surrounded by contact zones.</p> <p>[1] Andrei G. Shvarts, Julien Vignollet, Vladislav A. Yastrebov "Computational framework for monolithic coupling for thin fluid flow in contact interfaces" https://arxiv.org/abs/1912.11292v3</p>
InVID Fake Video Corpus v1.0
<p>The InVID TV Fake Video Corpus is a small collection of verified fake videos. It was developed in the context of the InVID project with the aim of gaining a perspective of the types of fake video that can be encountered in the real world.</p> <p>Currently the Corpus consists of 59 videos. For each video, information is provided describing the fake, its original source, and the evidence proving it is a fake. As we do not own the videos, the dataset only provides the video URLs and metadata, in the form of a tab-separated value (TSV) file. See the README file for more information.</p>
Figures and videos for: Patterned invagination prevents mechanical instability during gastrulation
<p>This repository contains the high-resolution figures and videos for the paper:</p> <p>Vellutini, B. C., Cuenca, M. B., Krishna, A., Szałapak, A., Modes, C. D. & Tomancak, P. Patterned invagination prevents mechanical instability during gastrulation. <em>Nature</em> (2025). doi:<a href="https://10.0.4.14/s41586-025-09480-3">10.1038/s41586-025-09480-3</a></p> <p>Please refer to the main repository for more information: <a href="https://doi.org/10.5281/zenodo.7781947">https://doi.org/10.5281/zenodo.7781947</a></p>
Aurora SDG Research Dashboard and Classifier - Instructions Videos
<p>Instruction videos about the Aurora SDG Research Dashboard, SDG Classifier, Badges and API.</p><p><a href="https://zenodo.org/doi/10.5281/zenodo.10040524">Also read the User Guides</a>.</p>
Disinformation on YouTube: A dataset of YouTube comments on videos related to claims made by Trump and Vance on Haitian immigrants
<div> <div> <div> <div> <div> <p>The corpus contains three files. First, the youtube_haitian_disinformation_videos_meta.csv file includes comments and YouTube video metadata. Data is organized around per video information. The columnar values are: </p> </div> </div> </div> <div> <ul> <li> <p>video_id </p> <p> </p> </li> <li> <p>date (video publication date) </p> <p> </p> </li> <li> <p>title </p> <p> </p> </li> <li> <p>description </p> <p> </p> </li> <li> <p>channel_title </p> <p> </p> </li> <li> <p>transcript </p> <p> </p> </li> <li> <p>transcript_str (Video transcript without timestamps) </p> <p> </p> </li> <li> <p>views </p> <p> </p> </li> <li> <p>likes </p> <p> </p> </li> <li> <p>comments (all comments per video) </p> </li> </ul> </div> <div> <div> <div> <p>Second, the youtube_haitian_disinformation_comment_reply_metadata.csv file includes comments, replies, and comment metadata. Each comment occupies its own row in the spreadsheet. The columnar field are: </p> </div> </div> </div> <div> <ul> <li> <p>video_id </p> <p> </p> </li> <li> <p>comment </p> <p> </p> </li> <li> <p>comment_date </p> <p> </p> </li> <li> <p>comment_like_count </p> <p> </p> </li> <li> <p>author </p> <p> </p> </li> <li> <p>comment_id </p> <p> </p> </li> <li> <p>in_reply_to </p> <p> </p> </li> <li> <p>neg, neu, pos, compound (VADER polarity scores) </p> <p> </p> </li> <li> <p>named_entities (spaCy named entity tags with tokens) </p> <p> </p> </li> <li> <p>emoji (spaCy Emojis and token spans) </p> </li> </ul> </div> </div> <div> <div> <div> <div> <p>Comments and associated metadata are represented in individual rows. </p> </div> <div> <p>The third file contains the results of the TFIDF analysis described herein. The TFIDF analysis features the top 5000 terms weights for the comments to each video in a .csv file. </p> </div> </div> </div> <div> <ul> <li> <p>youtube_disinfo_comments_tfidf_results_per_video.csv </p> </li> </ul> </div> </div> </div>
Videos of the processed microscope images and time series of the petrophysical parameters from image processing and geochemical simulation and of the measured induced polarisation [Video][Dataset]
<p>Supporting Information for the manuscript <em>Microfluidics and spectral induced polarization for direct observation and petrophysical modeling of calcite dissolution</em> published in Geophysical Research Letters</p> <ul> <li><strong>Data Set S1.</strong> Porosity, water saturation, and calcite sample perimeter from image<br>processing.</li> <li><strong>Data Set S2.</strong> Porosity, water conductivity, and pH from geochemical simulation.</li> <li><strong>Data Set S3.</strong> Real and imaginary components of the complex electrical conductivity at<br>2.5 Hz and CEC from petrophysical modeling.</li> <li><strong>Movie S1.</strong> Dissolution of the calcite sample with the detected contour superimposed in<br>white on the grayscale images. Time, length scale, and flow direction are indicated. In<br>case of problems launching the file, we recommend using VLC Media Player software.</li> <li><strong>Movie S2.</strong> Segmented images of the CO2 bubbles produced by the calcite dissolution.<br>Time, length scale, and flow direction are indicated. In case of problems launching the<br>file, we recommend using VLC Media Player software.</li> </ul>
Multimodal video and IMU kinematic dataset on daily life activities using affordable devices (VIDIMU)
<p>Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient tracking solutions for remote daily life activities recognition and kinematic analysis. </p> <p>The VIDIMU dataset includes 54 healthy young adults that were recorded on video and 16 of them were simultaneously recorded using custom IMUs. For each subject, 13 activities were registered using a low-resolution video camera and five Inertial Measurement Units (IMUs). Inertial sensors were placed in the lower or the upper limbs of the subject, respectively for activities that involve movement with the lower or the upper body. Video recordings were postprocessed using the state-of-the-art pose estimator <em>BodyTrack</em> (similar to OpenPose, and included in NVIDIA Maxine-AR-SDK) to provide a sequence of 3D joint positions for each movement. Raw IMU recordings were post-processed to compute joint angles by inverse kinematics with <em>OpenSim</em>. For recordings including simultaneous acquisition of video and IMU data types, these signals were used for data file synchronization. Collected data can be further used in applications related to human activity recognition and biomechanics related experiments in simulated home-like settings.</p> <p> </p> <p> </p>
AutoML for Video Analytics with Edge Computing - Dataset
<p>Latency and confidence measurements obtained from an edge-assisted object recognition system.</p> <p>The records are obtained by tuning the image encoding rate and Neural Network input layer size and measuring the latency on completing each system operation, i.e. encoding the image, transmiting it wirelessly, decoding and rotating at the server, and performing object recognition with YOLO on the server's GPU. Moreover, we document the achievable frame rate as a result of the total latency, as well as the object recognition confidence and cumulative confidence for all identified objects of each image.</p>
Data for: What's in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed
<p>The aggregate data in these datasets were used in analyses for "What’s in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed".</p>
First-order electroweak phase transitions: a nonperturbative update, videos
<p>This deposit contains videos created from Monte-Carlo and Langevin simulations of the SU(2) Higgs model, related to the paper "First-order electroweak phase transitions: a nonperturbative update". There are two videos, both in MPEG-4 format. Both files show data at the parameter point <span class="math-tex">\(x=0.0152473\)</span>, <span class="math-tex">\(y=0.0303620\)</span>, <span class="math-tex">\(ag_3^2= 4/7.332605\)</span>, <span class="math-tex">\(L=60a\)</span>, <span class="math-tex">\(\eta=10\)</span>, using the notation of the paper.</p> <p>The file <em>smoothing.mp4</em> shows the process of smoothing, or coarse-graining, the Higgs field, according to the method described in the section "Visualising bubble nucleation" of the paper. The particular configuration chosen is from the separatrix between phases, i.e. it is a critical bubble.</p> <p>The file <em>trajectory.mp4</em> shows a trajectory of the Langevin time evolution of a configuration starting in the symmetric phase and nucleating into the broken phase. The configurations in this trajectory have been smoothed, or coarse-grained, 10 times.</p>
InVID Fake Video Corpus v2.0
<p>The InVID TV Fake Video Corpus was developed in the context of the InVID project with the aim of gaining a perspective of the types of fake video that can be encountered in the real world. The dataset does not aspire to serve as an exhaustive list of all forgeries that have circulated the Web in the past, but we intend to maintain and extend it throughout the course of the project as new cases arise. </p> <p>The collection is a collaborative effort between AFP and CERTH-ITI. This is the second version of the dataset, containing 117 fake videos and 110 real videos, alongside annotations and descriptions. As we do not own the rights to the videos, the dataset only contains the video URLs and annotations.</p>
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>
Design of an Ontology-Driven Constraint Tester (ODCT) and Application to SAREF & Smart Energy Appliances: Datasets, SHACL Shapes, Demo Video of Web Application, and Detailed Performance Reports
<h2>Description</h2> <p>This repository presents the resources used for validating the compliance of <strong>smart energy appliances</strong> against the <strong>Smart Appliances REFerence (SAREF)</strong> ontology and its extension <strong>SAREF4ENER</strong>, as part of the <strong>Ontology-Driven Constraint Tester (ODCT)</strong> project. The ODCT tool is specifically designed to ensure <strong>semantic interoperability</strong> and adherence to standardized ontological frameworks, which are crucial for integrating smart devices into modern energy management systems.</p> <h2>ODCT Overview</h2> <p>The <strong>Ontology-Driven Constraint Tester (ODCT)</strong> is a robust framework created to validate datasets against ontologies defined by <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, both established under ETSI SmartM2M. This tool has been applied to the <strong>Flexible Start use case</strong> from the <strong>Joint Research Centre’s (JRC) Code of Conduct for Energy Smart Appliances</strong>. The ODCT tool ensures that smart devices like energy-efficient washing machines, thermostats, and connected lighting operate in compliance with established ontologies, thereby enhancing their <strong>interoperability</strong> within energy management systems and smart grids.</p> <h2>Repository Contents</h2> <p>This repository contains essential resources used in the ODCT compliance testing process:</p> <ul> <li> <p><strong>Compliant Dataset</strong>: This dataset represents a fully compliant scenario where no errors are present in the smart energy appliances’ profiles, demonstrating the ODCT’s accuracy under ideal conditions.</p> </li> <li> <p><strong>Modified Datasets</strong>: These datasets introduce various types of errors to showcase ODCT’s ability to handle diverse compliance scenarios:</p> <ol> <li><strong>Modified Dataset 1</strong>: Introduces type mismatches and spelling errors in key attributes.</li> <li><strong>Modified Dataset 2</strong>: Contains extraneous properties and missing required properties, including details about energy consumption and efficiency class.</li> <li><strong>Modified Dataset 3</strong>: Includes both extraneous and missing properties, and additional priority levels for energy profiles.</li> </ol> </li> <li> <p><strong>SHACL Shapes</strong>: The SHACL shapes used in the compliance testing for both SAREF and SAREF4ENER ontologies are included in this repository to allow reproducibility of the validation process.</p> </li> </ul> <ul> <li> <p><strong>Error Detection Results and Performance Reports</strong>: After conducting compliance tests using ODCT we got the Results and Performance Reports, the repository includes comprehensive reports detailing the results. These reports highlight the types of errors detected and provide a performance analysis of the tool under various scenarios.</p> </li> <li> <p><strong>Demonstration Video</strong>: A video is provided to guide users through the <strong>ODCT web application</strong>, showcasing how the tool detects errors and generates detailed compliance reports based on smart energy appliance datasets.</p> </li> </ul> <h2>Background</h2> <p>The integration of smart energy appliances into modern power grids is key to improving <strong>energy management</strong> and supporting <strong>sustainability goals</strong> like the <strong>European Green Deal</strong>. However, ensuring that these devices communicate effectively and conform to <strong>standardized protocols</strong> is a challenge. The <strong>ODCT</strong> tool addresses this challenge by providing a rigorous, ontology-based validation framework that is both <strong>protocol-agnostic</strong> and <strong>technology-flexible</strong>.</p> <p>This work is grounded in the broader context of <strong>global warming</strong> and the need for <strong>energy efficiency</strong> and <strong>demand-side flexibility</strong> in energy systems. By ensuring compliance with <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, ODCT supports the EU’s ambitions for <strong>carbon neutrality</strong> by 2050, contributing to a connected, efficient, and sustainable energy ecosystem.</p> <h2>Methodology</h2> <p>ODCT uses a structured methodology that involves:</p> <ol> <li><strong>Generating relevant datasets</strong> for validation.</li> <li><strong>Defining SHACL shape constraints</strong> based on ontologies.</li> <li><strong>Developing a user-friendly web application</strong> to facilitate compliance testing.</li> <li><strong>Performing compliance tests</strong> that validate datasets against SHACL shapes, ensuring interoperability and adherence to energy management standards.</li> </ol> <h2>Why It Matters</h2> <p>Researchers and developers working on smart energy appliances will benefit from ODCT by:</p> <ul> <li>Ensuring their devices meet standardized ontological requirements for <strong>interoperability</strong>.</li> <li>Reducing <strong>compliance issues</strong> in the development phase, leading to smoother integration into energy management systems.</li> <li>Supporting the <strong>sustainability efforts</strong> by enhancing device communication in <strong>smart grids</strong>.</li> </ul> <p>This repository showcases the potential of ODCT in fostering <strong>data accuracy</strong>, <strong>semantic interoperability</strong>, and <strong>compliance</strong> with essential energy standards. It offers comprehensive resources for furthering research and development in the field of smart energy appliances and energy management.</p>
Videos, audio transcriptions and closed captions for the workshop: Introduction to Wikidata for Maastricht University, Theory and Pratice - 15 October 2024
<h1><strong>Videos, audio transcriptions and closed captions for the workshop: Introduction to Wikidata for Maastricht University, Theory and Pratice - 15 October 2024</strong></h1> <h3><a href="https://theplant.maastrichtuniversity.nl/event/navigating-the-world-of-wikidata-for-research-science-and-cultural-heritage-2"><em>Navigating the World of Wikidata for Research, Science and Cultural Heritage</em></a></h3> <h3><em><a href="https://www.wikidata.org/wiki/Wikidata:Twelfth_Birthday/Workshop_in_Maastricht" target="_blank" rel="noopener">Wikidata's Twelfth Birthday: Workshop in Maastricht </a></em></h3> <p>Wikidata is a free, collaborative, multilingual database, collecting structured open data for anyone in the world to use. It also plays a crucial role in supporting Wikimedia projects, such as Wikipedia and Wikimedia Commons. Over the last 12 years it has strongly increased in popularity among the scientific and cultural heritage communities.</p> <p>In this 2,5 hours workshop you will learn the basics of working with Wikidata, both in theory and practice. You will learn</p> <ol> <li>The basics of Wikidata: A first look at what Wikidata is and how it works, both technically and socially (Wikidata community)</li> <li>How Wikidata can be relevant for research, science and cultural heritage (GLAM), and</li> <li>First steps in contributing to Wikidata yourself, with a focus on the topic of UM professors from past and present.</li> </ol> <p>As part of the <a title="Wikidata:Twelfth Birthday" href="https://www.wikidata.org/wiki/Wikidata:Twelfth_Birthday">Wikidata 12th Birthday celebrations</a> this workshop is open to academics, researchers, students, and professionals interested in working with Wikidata in the intersection of open data, research, and science. Whether you are new to Wikidata or looking to deepen your understanding, this session will provide valuable insights for improving your work.</p> <h2><strong>Workshop outline</strong></h2> <h3><strong>Part 1: Theory, Wikidata basics (45-60 minutes) </strong></h3> <ul> <li><strong><a href="https://zenodo.org/records/13984149/files/Wikidata%20Workshop%20-%20Theoretical%20part%20-%20Maastricht%20University%20-%2015%20October%202024.webm" target="_blank" rel="noopener">Video</a> (.webm) including <a href="https://zenodo.org/records/13984149/files/WikidataWorkshop_MaastrichtUniversity_15October2024_TheoreticalPart.txt?download=1" target="_blank" rel="noopener">audio transcription </a>(.txt) and <a href="https://zenodo.org/records/13984149/files/Wikidata%20Workshop%20-%20Theoretical%20part%20-%20Maastricht%20University%20-%2015%20October%202024.webm.en.srt?download=1" target="_blank" rel="noopener">closed captions</a> (.srt) are available below</strong></li> </ul> <p><strong>Additional materials</strong></p> <ul> <li><strong>Slides in <a href="https://zenodo.org/records/13837957/files/WikidataWorkshop_MaastrichtUniversity_15October2024_TheoreticalPart.pptx?download=1" rel="nofollow">PowerPoint</a> or <a href="https://zenodo.org/records/13837957/files/Wikidata%20Workshop%20-%20Theoretical%20part%20-%20Maastricht%20University%20-%2015%20October%202024.pdf?download=1" rel="nofollow">PDF</a> are available from <a href="https://zenodo.org/records/13837957" target="_blank" rel="noopener">https://zenodo.org/records/13837957</a></strong></li> </ul> <p><em>1) Wikidata basics</em></p> <ul> <li>What is Wikidata?</li> <li>What are the principles of Wikidata?</li> <li>How are things described in Wikidata?</li> <li>Who builds Wikidata? - The Wikidata community</li> </ul> <p><em>2) Wikidata for research, science and cultural heritage</em></p> <ul> <li>To what extent is Wikidata used throughout science, research and GLAM?</li> <li>Six anecd<em>a</em>tic cases <ol> <li>Scientometrics - Scholia</li> <li>Life and biomedical sciences</li> <li>Astronomy</li> <li>Language technology / AI / LLMs</li> <li>GLAM – KB collection highlights</li> <li>Representation of (female) scientists</li> </ol> </li> </ul> <h3><strong>Break (15 minutes)</strong></h3> <h3><strong>Part 2: Practice, contributing to Wikidata (75-90 minutes) </strong></h3> <ul> <li><strong><a href="https://zenodo.org/records/13984149/files/Wikidata%20Workshop%20-%20Practical%20part,%20UM%20professors%20-%20Maastricht%20University%20-%2015%20October%202024.webm?download=1" target="_blank" rel="noopener">Video</a> (.webm) including <a href="https://zenodo.org/records/13984149/files/WikidataWorkshop_MaastrichtUniversity_15October2024_PracticalPart_UMprofessors.txt?download=1" target="_blank" rel="noopener">audio transcription </a>(.txt) and <a href="https://zenodo.org/records/13984149/files/Wikidata%20Workshop%20-%20Practical%20part,%20UM%20professors%20-%20Maastricht%20University%20-%2015%20October%202024.webm.en.srt?download=1" target="_blank" rel="noopener">closed captions</a> (.srt) are available below</strong></li> </ul> <p><strong>Additional materials</strong></p> <ul> <li><strong>Slides in <a href="https://zenodo.org/records/13837957/files/WikidataWorkshop_MaastrichtUniversity_15October2024_PracticalPart_UMprofessors.pptx?download=1" rel="nofollow">PowerPoint</a> or <a href="https://zenodo.org/records/13837957/files/Wikidata%20Workshop%20-%20Practical%20part,%20UM%20professors%20-%20Maastricht%20University%20-%2015%20October%202024.pdf?download=1" rel="nofollow">PDF</a> are available from <a href="https://zenodo.org/records/13837957" target="_blank" rel="noopener">https://zenodo.org/records/13837957</a></strong></li> <li><strong>Handout for participants in <a href="https://zenodo.org/records/13837957/files/WikidataWorkshop_MaastrichtUniversity_15October2024_PracticalPart_HandoutForParticipants.docx?download=1" rel="nofollow">Word</a> or <a href="https://zenodo.org/records/13837957/files/WikidataWorkshop_MaastrichtUniversity_15October2024_PracticalPart_HandoutForParticipants.pdf?download=1" rel="nofollow">PDF</a></strong> <strong>are available from <a href="https://zenodo.org/records/13837957" target="_blank" rel="noopener">https://zenodo.org/records/13837957</a></strong></li> </ul> <p><strong> </strong>The goals of this hands-on part are:</p> <ul> <li>Get familiar with basic data editing via the Wikidata interface</li> <li>Understand WD data models and structures related to professors (of Maastricht University)</li> <li>Extend existing <a title="Wikidata:Wiki-wetenschappers/Universiteit Maastricht/hoogleraren" href="https://www.wikidata.org/wiki/Wikidata:Wiki-wetenschappers/Universiteit_Maastricht/hoogleraren">Wikidata items about UM professors</a>, based on information in public sources.</li> <li>If time allows: Create new Wikidata items about UM professors</li> </ul> <p>The visual slides and the textual handout explain the same content, blocks and exercises, albeit in a slightly different order.<strong> </strong></p> <h2><strong>Required preparation</strong></h2> <p>To make optimal use of our time, participants must create a Wikidata account in the weeks before the workshop. See <a href="https://www.wikidata.org/w/index.php?title=Special:CreateAccount" target="_blank" rel="noopener">https://www.wikidata.org/w/index.php?title=Special:CreateAccount</a>.</p> <p>This is important because very fresh accounts may have limited editing rights. Furthermore only 6 Wikidata accounts can be created per day from UM IP addresses, so creating a lot of new accounts during the workshop might overstretch this limit.</p> <h2><strong>Workshop leader</strong></h2> <p>This workshop was given by <a href="https://www.kb.nl/over-ons/experts/olaf-janssen">Olaf Janssen</a>, the Wikimedia coordinator of the <a href="https://www.kb.nl/over-ons/experts/olaf-janssen">Koninklijke Bibliotheek</a>, the national library of the Netherlands.</p> <p>In this role he stimulates and facilitates collaboration between the collections, knowledge, open data and staff of the KB on the one hand, and the projects of the Wikimedia movement, such as Wikipedia, Wikimedia Commons and Wikidata on the other. He is also active as a volunteer within the community. Feel free to contact Olaf via olaf.janssen(at)<a href="http://kb.nl">kb.nl</a></p> <h2><strong>Materials on Wikimedia Commons</strong></h2> <p>Photos , videos and presentations related to this event can be found on Wikimedia Commons: <a title="c:Category:Wikidata Workshop at Maastricht University, 15 October 2024" href="https://commons.wikimedia.org/wiki/Category:Wikidata_Workshop_at_Maastricht_University,_15_October_2024">Category:Wikidata Workshop at Maastricht University, 15 October 2024</a></p> <h2>Relevant URLs </h2> <ul> <li><a href="https://www.wikidata.org/wiki/Wikidata:Twelfth_Birthday/Workshop_in_Maastricht">https://www.wikidata.org/wiki/Wikidata:Twelfth_Birthday/Workshop_in_Maastricht </a></li> <li><a href="https://theplant.maastrichtuniversity.nl/event/navigating-the-world-of-wikidata-for-research-science-and-cultural-heritage-2" target="_blank" rel="noopener">https://theplant.maastrichtuniversity.nl/event/navigating-the-world-of-wikidata-for-research-science-and-cultural-heritage-2</a> + <a href="https://web.archive.org/web/20240926154021/https://theplant.maastrichtuniversity.nl/event/navigating-the-world-of-wikidata-for-research-science-and-cultural-heritage-2/">archived version</a></li> <li><a href="https://www.linkedin.com/feed/update/urn:li:activity:7244614063102529536/">https://www.linkedin.com/feed/update/urn:li:activity:7244614063102529536/</a></li> <li><a href="https://www.linkedin.com/feed/update/urn:li:activity:7245329234385059843/" target="_blank" rel="noopener">https://www.linkedin.com/feed/update/urn:li:activity:7245329234385059843/</a></li> </ul> <p>Earlier LinkedIn posts (April-May 2024, before rescheduling the worlshop to October)</p> <ul> <li><a href="https://www.linkedin.com/feed/update/urn:li:activity:7188470390858428416/" target="_blank" rel="noopener">https://www.linkedin.com/feed/update/urn:li:activity:7188470390858428416/</a></li> <li><a href="https://www.linkedin.com/feed/update/urn:li:activity:7188180802021543936/" target="_blank" rel="noopener">https://www.linkedin.com/feed/update/urn:li:activity:7188180802021543936/</a></li> <li><a href="https://www.linkedin.com/feed/update/urn:li:activity:7189276985267826691/" target="_blank" rel="noopener">https://www.linkedin.com/feed/update/urn:li:activity:7189276985267826691/</a></li> </ul> <h3> </h3>
Magritte Sphere Video
<p><strong> # Magritte-Sphere Video sequence by LISA ULB</strong></p> <p><br> The test sequence "Magritte Sphere Video" is provided by Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Universite Libre de Bruxelles), Belgium.</p> <p><strong> # License:</strong></p> <p><br> CC BY-NC-SA</p> <p><strong> # Terms of Use:</strong></p> <p><br> Anykind of publication or report using this sequence should refer to the following references.</p> <p>[1] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, "Magritte Sphere Video Test Sequence", 2021.</p> <p><em>@misc{fachada_magrittevideo_2021,<br> title = {{Magritte} {Sphere} {Video} {Test} {Sequence}},<br> author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},<br> month = feb,<br> year = {2021},<br> doi = {</em>10.5281/zenodo.5048270<em>}<br> }</em></p> <p>[2] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, and Gauthier Lafruit, "Light Field Rendering for non-Lambertian Objects," presented at the Electronic Imaging, 2021.</p> <p><em>@inproceedings{fachada_light_2021,<br> title = {Light {Field} {Rendering} for non-{Lambertian} {Objects}},<br> booktitle = {Electronic {Imaging}},<br> author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},<br> year = {2021}<br> }</em></p> <p><strong> # Production:</strong></p> <p><br> Laboratory of Image Synthesis and Analysis, LISA department, EPB, Universite Libre de Bruxelles, Belgium.</p> <p><strong> # Content:</strong></p> <p><br> This dataset contains a test scene created and rendered with Blender [1] and the addon script [2] extended for Blender 2.8. We provide the Blender file and the rendered scene.</p> <p>The scene contains a non-Lambertian (transparent-refractive (T) or mirror-specular (M)) sphere rendered in a regular camera array of 21x21 cameras. It describes a spiral around a central initial position within 17 frames.</p> <p>In addition to the 3D model, we provide the rendered images : resolution of 2000x2000, the cameras are parallel, with a principal point at the center of the image.<br> We provide 17 frames of:<br> - a regular subarray of 5x5 cameras (cameras number 66, 70, 74, 78, 82, 50, 154, 158, 162, 166, 234, 238, 242, 246, 250, 318, 322, 326, 330, 334, 402, 406, 410, 414, 418)<br> - the central horizontal line of 21 cameras (cameras 210 to 230)</p> <p><br> The dataset contains:<br> - a `camera.json` file in OMAF coordinates system (Camera position: X: forwards, Y:left, Z: up, Rotation: yaw, pitch, roll) [3],<br> - a `parameters.cfg` generated with [2],<br> - a `texture_M` folder containing the rendered views in yuv420p10le format for the mirror object,<br> - a `texture_T` folder containing the rendered views in yuv420p10le format for the transparent object,<br> - a `mask` folder containing the mask indicating the sphere in yuv420p format,<br> - a `depth_gt` folder containing the associated ground truth depth maps yuv420p16le format,<br> - a `depth_estimated_M` folder containing the associated estimated depth maps yuv420p16le format,<br> - a `depth_estimated_T` folder containing the associated estimated depth maps yuv420p16le format.<br> <br> <br> <strong> # References and links:</strong><br> <br> [1] Blender Online Community, "Blender - a 3D modelling and rendering package." Blender Institute, Amsterdam: Blender Foundation, 2020.</p> <p>[2] K. Honauer, O. Johannsen, D. Kondermann, and B. Goldluecke, "A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields" in Asian Conference on Computer Vision, 2016,<br> https://github.com/lightfield-analysis/blender-addon<br> https://github.com/dbonattoj/blender-addon</p> <p>[3] B. Kroon, "Reference View Synthesizer (RVS) manual [N18068]," ISO/IEC JTC1/SC29/WG11, Macau SAR, China, p. 19, Oct. 2018.<br> https://mpeg.chiariglione.org/standards/mpeg-i/omnidirectional-media-format</p> <p> </p> <p> </p>
Dataset on UAV RGB videos acquired over a vineyard property of Bodegas Terras Gauda at an early stage of Botrytis cinerea infection in 2021
<p>The videos were collected in a vineyard owned by Bodegas Terras Gauda, in June 2021. The videos were collected with a DJI Matrice 210 RTK UAV, which had a DJI Zenmuse X5S sensor onboard. A total of 4 rows were recorded with side videos. The flights were carried out on a sunny day with wind velocity lower than 0.5 m/s. Annotations of the grape clusters in the MOTS style are provided. </p>
Validation Videos and eXplainable levels used in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)
<p>This dataset contains the <strong>explaination levels and the video </strong>used in the validation of the Conflict Detection and Resolution (CD&R) use case.</p> <p>The solution are computed by a Genetic Algorithm developped by Nicolas Durand.<br> <br> Inside the dataset, one can find:</p> <p>-One archive, " Validation_Videos_Traffic.zip ", containing the video of traffic of every scenario.</p> <p>-One archive, " Validation_XAI_levels.zip", containing the Blackbox, Heatmap, and Storyboard eXplainable levels for each scenario.</p> <p>The videos and XAI levels are used in the validation exercice.</p>
Dataset of a 5G RTSP video streaming use case
<p><strong>About the project: monitoring 5G RTSP video streaming</strong></p> <p>This dataset collects data from a 5G video streaming use case. A video is streamed by a cvlc server (realized as a Kubernetes pod) through RTSP to a variable number of 5G UE clients that activate according to a daily traffic pattern. The values of the 4 dataset features (number of active UEs, gNB's downlink bit rate, pod's outbound traffic, and pod's CPU usage) are collected by a custom monitoring system deployed in the context of the MONB5G project.</p> <p><strong>Setup/Equipment</strong></p> <p>The Kubernetes cluster, including the server pod, runs in a COTS server. The 5G core and gNB is realized through Amarisoft Callbox Ultimate. The UEs are emulated through Amarisoft Simbox. In order to display the video in ffplay clients, we use the Remote UE from Amarisoft, so traffic from Simbox is forwarded to an external VM with GUI.</p> <p><strong>Video</strong></p> <p>The streamed video is Big Buck Bunny at 30 FPS from <a href="https://peach.blender.org/">https://peach.blender.org/</a>.</p> <table> <tbody> <tr> <td> <p>Video codec </p> </td> <td> <p>Advanced Video Codec (AVC) </p> </td> </tr> <tr> <td> <p>Width </p> </td> <td> <p>1920 pixels </p> </td> </tr> <tr> <td> <p>Height </p> </td> <td> <p>1080 pixels </p> </td> </tr> <tr> <td> <p>Display aspect radio </p> </td> <td> <p>16:9 </p> </td> </tr> <tr> <td> <p>Duration </p> </td> <td> <p>10 min 34 s </p> </td> </tr> <tr> <td> <p>Max Bitrate </p> </td> <td> <p>16.7 Mb/s </p> </td> </tr> <tr> <td> <p>Frame rate </p> </td> <td> <p>30 FPS </p> </td> </tr> </tbody> </table> <p><strong>What does this Zenodo project contain?</strong></p> <ol> <li>The csv file of the dataset (dataset.csv)</li> <li>A picture displaying an overview of the setup (overview.png)</li> <li>A picture displaying Grafana charts for each featuer (grafana.png)</li> <li>A picture displaying a screenshot of the Remote UE VM with multiple UEs playing the video (ues.png)</li> </ol> <p><strong>Dataset</strong></p> <p>The dataset has 5 columns (time + 4 features). Features:</p> <ol> <li><em>Time</em>: timestamp in epoch format.</li> <li><em>Number of active UEs (N)</em>: number of UEs that are currently downloading more than 100 kbps. No unit.</li> <li><em>gNB's downlink bit rate (R)</em>: aggregate downlinkg bitrate from the gNB to all the UEs. In Mbps.</li> <li><em>Outbound traffic (O)</em>: outboun traffic at the pod's interface, transmitting the video(s) packets. In Mbps.</li> <li><em>CPU (C)</em>: CPU usage at the server pod. In millicores (mc). Each iteration represents a whole day, composed of 24 "demand periods". Each demand period takes 2 minutes and is given by the number of active UEs consuming the video stream (N). N is included for informative reasons. Sampling rate is 10 seconds, but some parameters are refreshed at a lower frequency given monitoring limitations. This means that some parameters repeat the same value in consecutive measurements.</li> </ol>
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.