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3,853 results for “Video”
Kuopio gait dataset: motion capture, inertial measurement and video-based sagittal-plane keypoint data from walking trials
<p>This dataset contains motion capture (3D marker trajectories, ground reaction forces and moments), inertial measurement unit (wearable Movella Xsens MTw Awinda sensors on the pelvis, both thighs, both shanks, and both feet), and sagittal-plane video (anatomical keypoints identified with the OpenPose human pose estimation algorithm) data.<br>The data is from 51 willing participants and collected in the HUMEA laboratory in the University of Eastern Finland, Kuopio, Finland, between 2022 and 2023. All trials were conducted barefoot.</p> <p>The file structure contains an Excel file containing information of the participants, data folders under each subject (numbered 01 to 51), and a MATLAB script.</p> <p>The Excel file has the following data for the participants:</p> <ul> <li><strong>ID</strong>: ID of the participants from 1 to 51</li> <li><strong>Age</strong>: age of the participant in years</li> <li><strong>Gender</strong>: biological sex as M for male, F for female</li> <li><strong>Leg</strong>: the participant's dominant leg, identified by asking which foot the participant would use to kick a football; R for right, L for left</li> <li><strong>Height</strong>: height of the participant in centimeters</li> <li><strong>Invalid_trials</strong>: list of invalid trials in the motion capture data (MOCAP) data, usually classified as such because the participant did not properly step on the middle force plate</li> <li><strong>IAD</strong>: inter-asis distance in millimeters, the distance between palpated left and right anterior superior iliac spine, measured with a caliper</li> <li><strong>Left_knee_width</strong>: width of the left knee from medial epicondyle to lateral epicondyle in millimeters, palpated and measured with a caliper</li> <li><strong>Right_knee_width</strong>: same as above for the right knee</li> <li><strong>Left_ankle width</strong>: width of the left ankle from medial malleolus to lateral malleolus in millimeters, palpated and measured with a caliper</li> <li><strong>Right_ankle_width</strong>: same as above for the right ankle</li> <li><strong>Left_thigh_length</strong>: the distance between the greater trochanter of the left femur and the lateral epicondyle of the left femur in millimeters, palpated and measured with a measuring tape</li> <li><strong>Right_thigh_length</strong>: same as above for the right thigh</li> <li><strong>Left_shank_length</strong>: the distance between the medial epicondyle of the femur and the medial malleolus of the tibia in millimeters, palpated and measured with a measuring tape</li> <li><strong>Right_shank_length</strong>: same as above for the right shank</li> <li><strong>Mass</strong>: mass in kilograms, measured on a force plate just before the walking measurements</li> <li><strong>ICD</strong>: inter-condylar distance of the knee of the dominant leg, measured from low-field MRI</li> <li><strong>Left_knee_width_mocap</strong>: distance between reflective MOCAP markers on the medial and lateral epicondyles of the knee in millimeters, measured from a static standing trial; -1 for missing (subject did not have those markers)</li> <li><strong>Right_knee_width_mocap</strong>: same as above for the right knee</li> </ul> <p>The folders under each subject (folders numbered 01 to 51) are as follows:</p> <ul> <li><strong>imu</strong>: "Raw" inertial measurement unit (IMU) data files that can be read with Xsens Device API (included in Xsens MT Manager 4.6, which may be unavailable these days, not sure). You won't need this if you use the data in the imu_extracted folder.</li> <li><strong>imu_extracted</strong>: IMU data extracted from those data files using the Xsens Device API, so you don't have to. <ul> <li>The data is saved as MATLAB structs where the fields are named as a sensor ID (e.g., "B42D48"). The sensor IDs and their corresponding IMU locations are as follows: <ul> <li>pelvis IMU: B42DA3</li> <li>right femur IMU: B42DA2</li> <li>left femur IMU: B42D4D</li> <li>right tibia IMU: B42DAE</li> <li>left tibia IMU: B42D53</li> <li>right foot IMU: B42D48</li> <li>left foot IMU: B42D51 (except for subjects 01 and 02, where left foot IMU has the ID B42D4E)</li> </ul> </li> <li>Some of the data are just zeros as they couldn't be read from these sensors, but under each sensor, the fields "calibratedAcceleration", "freeAcceleration", "time", "rotationMatrix", and "quaternion" contain usable data. <ul> <li>time: Contains time stamps of the measurement at each frame recorded at 100 Hz, so if you remove the first value from all values in the time vector and divide the result by 100, you will get the time in seconds from the beginning of the walking trial.</li> <li>calibratedAcceleration and freeAcceleration: Contain triaxial acceleration data from the accelerometers of the IMU. freeAcceleration is just calibratedAcceleration without the effect of Earth's gravitational acceleration.</li> <li>rotationMatrix: Orientations of the IMU as rotation matrices.</li> <li>quaternion: Orientations of the IMU as quaternions.</li> </ul> </li> </ul> </li> <li><strong>openpose</strong>: Trajectories of the keypoints identified from sagittal plane video frames, saved as json files. <ul> <li>The keypoints are from the BODY_25 model of OpenPose (https://cmu-perceptual-computing-lab.github.io/openpose/web/html/doc/md_doc_02_output.html).</li> <li>Each frame in the video has its own json file.</li> <li>You can use the function in the script "OpenPose_to_keypoint_table.m" in the root folder to read the keypoint trajectories and confidences of all frames in a walking trial into MATLAB tables. The function takes as argument the path to the folder containing the json files of the walking trial.</li> </ul> </li> <li>Note that some subjects (11, 14, 37, 49) do not have keypoint and IMU data.</li> </ul> <p>The folders under each subject are divided into three ZIP archives with 17 subjects each.</p> <p>The script "OpenPose_to_keypoint_table.m" is a MATLAB script for extracting keypoint trajectories and confidences from JSON files into tables in MATLAB.</p> <p><br><strong>Publication in Data in Brief</strong>: <a href="https://doi.org/10.1016/j.dib.2024.110841" target="_blank" rel="noopener">https://doi.org/10.1016/j.dib.2024.110841</a></p> <p><br><strong>Contact</strong>: Jere Lavikainen, jere.lavikainen@uef.fi</p>
Video 1 - Open Science, why do we need it?
<p>Interview with York Sure-Vetter, Director of NFDI, Germany and Professor at Karlsruhe Institute of Technology; Eva Maria Méndez, PhD in Library and Information Science; Joaquín Tintoré, Professor of Physical Oceanography; Michael Arentoft, Head of Unit, Open Science, DG R&I, European Commission; and Iryna Kuchma, Open Access Programme Manager for EIFL on the necessity of funding Open Science infrastructure and Open Science in general in order to accelerate the shift towards more openness and higher quality in science.</p> <p>Open Science is an attitude of collaboration, transparency, and equitability. SOCIB is one research organisation which makes research data available, which in turn triggers a new understanding in oceanography and allows for faster responses to societal needs. Funding Open Science means funding higher quality, faster, and more impactful science. Funders can also fund Open Science infrastructure to facilitate the shift to Open Science.</p>
Video 4 - Open Science: equitable access for everyone.
<p>An interview about the necessity of funding Open Science platforms as a means of achieving equitable science with Iryna Kuchma, Open Access Programme Manager for EIFL; Ana María Cetto, Professor of Physics at Universidad Nacional Autónoma de México; Yensi Flores, Postdoctoral researcher at the Cancer Research Centre, University College Cork; and <span>Bregt Saenen, Senior Policy Officer for Open Science at Science Europe</span>.<br><br></p> <p><span>Science is a global enterprise targeting global problems – limiting access to science defeats this purpose. Open Science platforms are necessary for researchers from all countries and organizations to be able to participate in the global scientific effort. Funding bodies can support Open Science platforms as a way of ensuring equitable and trustworthy science. </span></p> <p> </p>
Video 6 - Open Science: science for and with citizens.
<p><span>An interview on Open Science funding with Ignasi Labastida, director of the Office for the Dissemination of Knowledge, University of Barcelona; Bregt Saenen, Senior Policy Officer for Open Science at Science Europe; Victoria Tsoukala, Policy Officer – Open Science at the European Commissions, DG-Reearch and Innovation; and Sumithra Vellupilai, Senior Research Officer at the Swedish Research Council.</span></p> <p><span>Universities should fund Open Science as a means of sharing knowledge, and as a means of providing tools for all of society to access knowledge. All parts of society should be able to benefit from the knowledge produced through the scientific process. Making openness the norm is a way for including society’s stakeholders in the research process. This level of openness and transparency requires funding infrastructure for sharing articles, data and other research results.</span></p> <p><span> </span></p>
Video 5 - Open Science: why do we need data stewards.
<p><span>An interview on the need of data professionals and Open Science skills with York Sure-Vetter, Director of NFDI, Germany and Professor at Karlsruhe Institute of Technology; Jessica Lindvall, Head of Training at SciLifeLab Training Hub; Anne Sophie Fink, Head of Data Management at DeiC (Denmark); and Sally Chambers, Director at DARIAH-EU.</span></p> <p><span>Modern research and technology can require not only large amounts of data, but also good data quality. Ensuring good data quality requires specialized expertise. Data stewards and other data management professionals support researchers with providing and working with good quality data which are also FAIR. Open software, open infrastructures are also important bits in the Open Science puzzle, all of which require funding. The uptake of Open Science depends on widespread Open Science awareness and skills. These require outreach, training and formal education.</span></p>
Video 2 - Open Science to enable collaboration.
<p><span>Interview with Maria Bellantone, PhD in materials science; Bregt Saenen, Senior Policy Officer for Open Science at Science Europe; Pilar Rica Castro, Senior project officer for Open Access, Spanish Foundation for Science & Technology; and Iryna Kuchma, Open Access Programme Manager for EIFL on the importance of policies supporting Open Science infrastructures as a tool for implementing and promoting Open Science.</span></p> <p><span>Open Science fosters inter- and transdisciplinarity. This requires open data infrastructure and interoperability. Open Science policies can be a tool for changing how research is performed and assessed. The Spanish Foundation for Science and Technology funds such infrastructures at a local level. This provides digital infrastructures and expertise to make it possible to share interoperable data. This interoperability also makes it possible for infrastructures to collaborate. Funders have a responsibility to ensure that the research they fund makes an impact, and Open Science infrastructure increases the impact potential of research.</span></p>
Video 3 - Open Science: a better return on investment.
<p><span>An interview with Roberto Sabatino, Research Engagement Officer at HEAnet, Dublin Ireland; Nadia Tonello, Data Management Manager at the Barcelona Supercomputing Centre; and Eva Mendes, PhD in Library and Information Science on the potential of Open Science to enhance humanity’s ability to respond to crises and to provide a better return on investment.</span></p> <p><span>Science is increasingly collaborative, and this includes sharing data. Funding needs to include data management, sharing, and infrastructure. Increased interoperability in research can help humanity collaboratively face challenges such as climate change. The response to the Covid-19 pandemic was also facilitated by data sharing, showing the positive societal impact and net benefit of Open Science.</span></p>
Eye tracking videos and raw data of breathing recognition attempts in simulated out-of-hospital cardiac arrest
<div> <div> <div> <p>This dataset comprises eye tracking videos and raw data documenting attempts to recognize breathing in simulated out-of-hospital cardiac arrest scenarios.</p> <p>The data were recorded using an Ergoneers Dikablis head-mounted eye tracker.</p> <p>Our analysis of this data resulted in the publication of two studies: Study 1, available at <a href="https://doi.org/10.1097/SIH.0000000000000617" target="_blank" rel="noopener">https://doi.org/10.1097/SIH.0000000000000617</a>, and Study 2, accessible at <a href="https://doi.org/10.25894/ijfae.2307" target="_blank" rel="noopener">https://doi.org/10.25894/ijfae.2307</a></p> <p> </p> <p>Version 2 is up-to-date.</p> <p>In Version 1:</p> <ul> <li>the doi for Study 2 was incorrect</li> <li>data for participant #51 of Study 1 were missing</li> </ul> </div> </div> </div>
Time-lapse 3D confocal microscopy videos of mitochondrial dynamics in human alveolar epithelial cells (A549-DsRed) infected with Mycobacterium marinum (Mmar) strains
<div>The dataset consists of time-lapse, 3D confocal images of mitochondrial dynamics in human alveolar epithelial cells (A549-DsRed) infected with Mycobacterium marinum (Mmar) strains. Images were captured at 60X magnification in an environmental chamber at 35°C for live-cell imaging. Host cell mitochondria were labeled with red fluorescent protein (RFP) and infected with both wildtype (wt) and ESAT-6 operon knockout mutant labeled with green fluorescent protein (GFP) at MOI of 100 for 24 hours at 35°C. Infected cells were identified and analyzed to explore the effect of pathogenic mycobacteria on mitochondrial morphology over time.</div> <div> </div> <div>More details available in this preprint: <a href="https://doi.org/10.48550/arXiv.2411.06035">https://doi.org/10.48550/arXiv.2411.06035</a></div>
The Great Ape Dictionary Video Data Ark
<p>We study the behaviour and cognition of wild apes and other species (elephants, corvids, dogs). Our video archive is called the Great Ape Dictionary, you can find out more here <a href="https://greatapedictionary.ac.uk/">www.greatapedictionary.com</a> or about our lab group here <a href="https://www.wildminds.ac.uk/">www.wildminds.ac.uk</a> We consider these videos to be a data ark that we would like to make as accessible as possible. While we are unable to make the original video files open access at the present time you can search this database to explore what is available, and then request access for collaborations of different kinds by contacting us directly or <a href="https://greatapedictionary.ac.uk/video-resources/request-access/">through our website</a>.</p> <p>We label all videos in the Great Ape Dictionary video archive with basic meta-data on the location, date, duration, individuals present, and behaviour present. Version 1.0.0 contains current data from the Budongo East African chimpanzee population (n=13806 videos). These datasets are being updated regularly and new data will be incorporated here with versioning. As well as the database there is a second read.me file which contains the ethograms used for each variable coded, and a short summary of other datasets that are in preparation for subsequent version(s). If you are interested in these data please contact us. Please note that not all variables are labeled for all videos, the detailed Ethogram categories are only available for a subset of data. All videos are labelled with up to 5 Contexts (at least one, rarely 5). If you are interested in finding a good example video for a particular behaviour, search for 'Library' = Y, this indicates that this clip contains a very clear example of the behaviour.<br><br>March 17th 2025: Version 1.1.0 contains added data from the Bwindi mountain gorilla chimpanzee population (n=3537 videos).</p>
Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments"
<p>Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments."</p> <p>Data includes representative real and simulated bead trajectories used in the manuscript.</p> <p>Code includes all simulations, analysis, and plot details for the Figures in the manuscript. </p> <p>See included README.txt for more details.</p>
Data and Videos for Argos: a toolkit for tracking multiple animals in complex visual environments
<p>Original videos used and data generated for the article "Argos: a toolkit for tracking multiple animals in complex visual environments".</p> <p>The data contains original videos used as input to the Argos Tracking tool, the generated raw tracks in Pandas-HDF5 format, and the corrected tracks after processing with Argos Review tool.</p> <p>It also includes a zip archive with ground truth tracks along with tracks detected from two videos by Argos and several other tracking tools for comparison using the HOTA metric organized in a folder structure suitable for the TrackEval tool.</p>
SMA-TB Clinical trial: video & electronic informed consent
<p>The first SMA-TB video has been produced aiming a better understanding of SMA-TB Randomized Clinical Trial (RCT) objectives and procedures. This RCT is work package 1 of SMA-TB project. It is entitled <em>"Phase 2b Randomized double-blind, placebo controlled trial to estimate the potential efficacy and safety of two repurposed drugs, acetylsalicylic acid and ibuprofen, for use as adjunct therapy added to, and compared with, the standard WHO recommended TB regimen (SMA-TB)”, </em>and is registered in ClinicalTrials.gov data under identifier NCT04575519</p> <p>This video has been conceptualized as an e-informed consent for TB patients aiming their participation in SMA-TB CTs in Georgia and South Africa, as well as for anyone wanting to learn about the SMA-TB CT. The video explains in a very comprehensive way the rationale of SMA-TB concept, how the CT will be performed, what is expected from the patient, which are potential benefits, as well as side effects. The message is produced both in English and in Georgian.</p>
Supplementary videos for "A second fossil species of the enigmatic rove beetle genus Charhyphus in Eocene Baltic amber, with implications on the morphology of the female genitalia (Coleoptera: Staphylinidae: Phloeocharinae)"
<p><strong>Original figures used in this study:</strong></p> <p>The holotype of <em>Charhyphus serratus </em>sp. nov. and four extant <em>Charhyphus </em>species.</p> <p> </p> <p><strong>Supplementary Videos 1–3:</strong></p> <p><strong>Supplementary Videos 1</strong> <em>Charhyphus serratus </em>sp. nov., 001 DUBC, holotype, habitus, movie of X-ray micro-CT volume renderings.</p> <p><strong>Supplementary Videos 2</strong> <em>Charhyphus serratus </em>sp. nov., 001 DUBC, holotype, habitus, movie of X-ray micro-CT volume renderings using different parameters from Supplementary Videos 1.</p> <p><strong>Supplementary Videos 3</strong> <em>Charhyphus serratus </em>sp. nov., 001 DUBC, holotype, female genitalia, movie of X-ray micro-CT volume renderings.</p>
Qualitative coding of brief videos that teach about the h-index
<p><strong>Dataset of qualitative coding of 31 Youtube videos on the h-index. </strong>The study aimed to characterize educational videos about the h-index to understand available resources and provide recommendations for future educational initiatives.</p> <p><em>Data.csv</em>: contains the metadata and qualitative coding for 31 videos.</p> <p><em>ReadMe.csv</em>: contains the codebook including a description of variables.</p> <p><strong>Abstract. </strong>The authors analyzed videos on the h-index posted to YouTube. Videos were identified by searching YouTube and were screened by two authors. To code the videos the authors created a coding sheet, which assessed content and presentation style with a focus on the videos’ educational quality based on Cognitive Load Theory. Two authors coded each video independently with discrepancies resolved by group consensus. Thirty-one videos met inclusion criteria. Twenty-one videos (68%) were screencasts and seven used a “talking head” approach. Twenty-six videos defined the h-index (83%) and provided examples of how to calculate and find it. The importance of the h-index in high-stakes decisions was raised in 14 (45%) videos. Sixteen videos (52%) described caveats about using the h-index, with potential disadvantages to early researchers the most prevalent (n=7; 23%). All videos incorporated various educational approaches with potential impact on viewer cognitive load. Most videos (n=21; 68%) displayed amateurish production quality. The videos featured content with potential to enhance viewers’ metrics literacies such that many defined the h-index and described its calculation, providing viewers with skills to recognize and interpret the metric. However, less than half described the h-index as an author quality indicator, which has been contested, and caveats about h-index use were inconsistently presented, suggesting room for improvement. While most videos integrated practices to facilitate balancing viewers’ cognitive load, few (32%) were of professional production quality. Some videos missed opportunities to adopt particular practices that could benefit learning. </p>
IN_CERT- The work process video
<p>IN_CERT is an initiative lead by the <a href="https://alella-cat.translate.goog/canmanye?_x_tr_sl=auto&_x_tr_tl=en&_x_tr_hl=en">Can Manyé Art Center</a> that arose in a context of pandemic and health crisis unprecedented in our contemporaneity, which aims to make visible the links between science, art and health, the transformative capacity of art and also its social and humanitarian dimensions for general public. The Germans Trias i Pujol Hospital (HUGTIP) and the Germans Trias i Pujol Research Institute (IGTP), through the <a href="https://www.smatb.eu/">SMA-TB project</a>, have participated in IN_CERT. <a href="https://www.smatb.eu/">SMA-TB</a> and <a href="https://www.mistral-hiv.eu/">MISTRAL</a> project coordinators were 2 of the 3 members of the stable group of scientists involved in the initiative. IN_CERT generated several outputs: debates, a book, this video and conferences. </p> <p>This video shows the work process over the course of a year, with regular meetings, made up of a stable group of artists, scientists and people linked to the art world. </p>
SCALIBUR video 1: From household food waste to bioplastics and biopesticides
<p>This video is part of a 3 part series explaining the innovative technologies being developed in the SCALIBUR project.</p> <p>The script is as follows:</p> <p>Each of us throws away a whopping 200 kilograms of food and organic waste each year. More and more cities separately collect this bio-waste. But what to do with it all? The SCALIBUR project is developing innovative technologies to convert household organic waste into valuable products. Where we see waste, SCALIBUR partners see a resource. One approach uses novel biochemical conversion, combining enzymatic hydrolysis and fermentation processes, to transform organic waste into sustainable bio-based products... Like bio-pesticides for more ecological agriculture, or biodegradable and compostable biopolymers for sustainable bioplastics. These technologies will help cities manage waste in a more sustainable and cost efficient way. And contribute to the creation of a truly circular bioeconomy in Europe.</p>
SCALIBUR video 3: From sewage sludge to biofertilisers, bioplastics and compounds
<p>This video is part of a 3 part series explaining the innovative technologies being developed in the SCALIBUR project.</p> <p>The script is as follows:</p> <p>Ever wondered what happens next? Normally wastewater is cleaned up, contaminants removed, and returned to the water cycle. The sweet brown residue is known as sewage sludge. Yum. The SCALIBUR project is developing innovative technologies to convert urban sewage sludge into valuable products. Where we see waste SCALIBUR partners see a resource. Two systems are under development. A new start-to-end valorisation process will turn sludge into bio-fertilisers for agriculture, and biogas, which is converted into high value compounds through bioelectrochemical systems. Additionally a novel demo plant is being built for the production of PHA bioplastics from sludge. These technologies will help cities manage waste in a more sustainable and cost efficient way. And contribute to the creation of a truly circular bioeconomy in Europe.</p>
SCALIBUR video 2: From retail food waste to protein, lipids, and chitin
<p>This video is part of a 3 part series explaining the innovative technologies being developed in the SCALIBUR project.</p> <p>The script is as follows: </p> <p>Eyes bigger than your stomach? Hotels and restaurants make a big contribution to the 100 million tonnes of organic waste produced each year in the EU. The SCALIBUR project is developing innovative technologies to convert waste from the food service industry into valuable products. Where we see waste SCALIBUR partners see a resource. Insects like black soldier flies love leftovers, efficiently converting food scraps into a rich biomass. New processes are being developed to extract the valuable materials like proteins, lipids and chitin: raw materials for bioplastics, and food and feed products. These technologies will help cities manage waste in a more sustainable and cost efficient way. And contribute to the creation of a truly circular bio-economy in Europe.</p>
Single molecule videos related to "MCM complexes are barriers that restrict cohesin-mediated loop extrusion" Part 2/3
<p>Videos of cohesin translocation and collisions between translocating cohesin and MCMs under physiological salt conditions collected with MicroManager 1.4 as tif image sequences. Vidoes of DNA stained with SYTOX Orange after collection of cohesin translocation are included as separate image sequences.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.