Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
156
datasets available to search
ShareScore release 0.9.0
Dataset results
156 results for “Video dataset”
Wikipedia video games similarity dataset with expert annotations
<p>A video games NLP dataset extracted from Wikipedia.</p> <p>For all articles, the figures and tables have been filtered out, as well as the categories and "see also" sections.</p> <p>The article structure, and particularly the sub-titles and paragraphs are kept in these picese.</p> <p>Provided as well are 90 seeds with recommended articles, annotated by human experts.</p>
Dataset of UAV thermal video sequences with annotations for MOTS benchmarking
<p>Instance segmentation dataset created for the research 'Monitoring Mammalian Herbivores via Convolutional Neural Networks implemented on Thermal UAV imagery'. It comprises 959 frames, 20.647 masks, and 239 tracks, and consists of 7 video sequences depicting aerial thermal imagery of cattle collected with a UAV (Parrot ANAFI Thermal) in two outdoor farms in the Netherlands. Data were acquired at three temperatures (10ºC, 19ºC, and 26.5ºC), under sunny and overcast weather conditions, at various angles of inclination (including nadir), and at heights ranging between 8-28 meters. Ground truth was labeled manually with the Computer Vision Annotation Tool <em>CVAT</em>.</p>
Eye blink events ground truth for UBFC video dataset
<p>In this dataset we annotate the timing of blinking events for all 42 videos in the UBFC dataset (available at https://sites.google.com/view/ybenezeth/ubfcrppg).</p> <p>There is one .txt file for each subject, for which each line corresponds to the frame number of a blinking event (videos @30fps).</p> <p>This dataset was created for testing the performance of blinking detection algorithms and is motivated by the potential of using blinking behaviour to assess the depressive condition through video teleconsultation.</p> <p> </p>
Synthetic river flow videos dataset
<p>###### ######### ########## ###### ######### ##########<br> <strong> Synthetic river flow videos for evaluating image-based velocimetry methods</strong><br> ###### ######### ########## ###### ######### ##########<br> # Year : 2022<br> # Authors : G.Bodart (guillaume.bodart@inrae.fr), J.Le Coz (jerome.lecoz@inrae.fr), M.Jodeau (magali.jodeau@edf.fr), A.Hauet (alexandre.hauet@edf.fr)</p> <p>###<br> <em>This file describes the data attached to the article</em></p> <p>### -><strong> 00_article_cases</strong><br> ########################<br> <em>This folder contains the data used in the case studies: synthetic videos + reference files.</em></p> <p> - 00_reference_velocities<br> -> Reference velocities interpolated on a regular grid. Data are given in conventionnal units, i.e. m/s and m.</p> <p> - 01_XX<br> -> Data of the first case study</p> <p> - 02_XX<br> -> Data of the second case study</p> <p>### -> <strong>01_dev</strong><br> #############<br> <em>This folder contains the Python libraries and Mantaflow modified source code used in the paper. The libraries are provided as is. Feel free to contact us for support or guidelines.</em></p> <p> - lspiv<br> -> Python library used to extract, process and display results of LSPIV analysis carried out with Fudaa-LSPIV</p> <p> - mantaflow-modified<br> -> Modified version of Mantaflow described in the article. Installation instructions can be found at http://mantaflow.com</p> <p> - syri<br> -> Python library used to extract, process and display fluid simulations carried out on Mantaflow and Blender. (Require the lspiv library)</p> <p>### -> <strong>02_dataset</strong><br> #################<br> <em>This folder contains synthetic videos generated with the method described in the article. The fluid simulation parameters, and thus the reference velocities, are the same as those presented in the article. </em></p> <p> - The videos can be used freely. Please consider citing the corresponding paper.</p>
Nantes-MobileHDRVQA Dataset: Video Quality of User Generated Mobile HDR Videos
<div>Nantes-MobileHDRVQA Dataset contains 60 source videos (SRC), each compressed with AV1 codec at different bitrate and resolution pairs. More info in ReadMe file.</div> <div>A subjective experiment with Absolute Category Rating with Hidden Reference (ARC-HR) protocol was conducted to collect video quality ratings in the range of (1, 5) where higher values indicate better video quality.</div> <div>The experiments were conducted in laboratory conditions at the facilities of Nantes University, France. </div> <div>For each playlists, individual subjective opinion scores and MOS, DMOS, and 95% CI of the MOS is given for each playlists in corresponding playlists</div> <div>Moreover, playlists are combined in the plistall_ACR.csv file with their MOS, DMOS, and 95% CI of the MOS scores. </div>
Dataset: Zoom Video Communications, Inc. (ZM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Roundhill Video Games ETF (NERD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Global X Video Games & Esports ETF (HERO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Video-Audio Neural Network Ensemble For Comprehensive Screening Of Autism Spectrum Disorder in Young Children (Openpose ADOS Dataset)
<p>Here, we share a de-identify subsample of the data used in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308388">research article</a>, that will allow interested scientists to test the <a href="https://github.com/AutismBrainBehavior/Video-Neural-Network-ASD-screening">shared code</a>, as well as, develop alternatives for achieving better prediction accuracy. We have prepared a subsample of pose estimation videos for the first 10 minutes of ADOS examination videos for each of the 160 children including in the current study (80 ASD and 80 TD, 80 Training set and 80 Testing set).</p> <p>With this subset of the full dataset, our trained model achieved an accuracy of 68.75% over 80 videos (40 ASD & 40 TD) by training the Visual Geometry Group 16 Long short term memory recurrent neural network (VGG16 LSTM RNN) over 80 training videos (40 ASD & 40 TD) at 64 batch size and 120 epochs.</p>
Video-rate multi-color structured illumination microscopy with simultaneous real-time reconstruction (Datasets)
<p>Raw data used for figures in the paper titled "Video-rate multi-color structured illumination microscopy with simultaneous real-time reconstruction"</p>
Robot view — Supplementary dataset of experiment videos, IROS 2019 — Self-organized adaptive paths in multi-robot manufacturing: reconfigurable and pattern-independent fibre deployment
<p>This is a supplementary dataset of experiment videos of self-organized multi-robot fibre deployment. Each video is true speed and shows the full respective experiment. These videos show the <strong>robot view</strong> of each experiment.</p> <p><em>For a 2-minute summary video of these experiments, refer to:</em></p> <pre>https://doi.org/10.5281/zenodo.3357187</pre> <p>This supplementary dataset accompanies a conference paper prepared for IEEE IROS 2019.</p> <p>Using multi-robot systems for autonomous construction allows for parallelization and scalability. Swarm construction furthermore exploits robot interactions and collaboration, such that the robot swarm collectively constructs artifacts beyond what a single comparable robot could achieve. Here we present an alternative concept of swarm construction that is distinct because it uses continuous building material. Our approach is unique in its use of braiding techniques for construction. We deploy fibres that potentially allow for structures that are not possible with building blocks. To achieve maximal scalability we restrict ourselves to a decentralized approach. The main challenges are the local coordination of the robot teams, self-organized task allocation, and the dynamic reconfiguration of the braiding scheme at runtime. We successfully validate our approach in multi-robot experiments that show both braiding and branching of the braid. In addition, we show options for implementing an open system—that is robots can join and leave the braiding process on the fly.</p>
Fibre view — Supplementary dataset of experiment videos, IROS 2019 — Self-organized adaptive paths in multi-robot manufacturing: reconfigurable and pattern-independent fibre deployment
<p>This is a supplementary dataset of experiment videos of self-organized multi-robot fibre deployment. Each video is true speed and shows the full respective experiment. These videos show the <strong>fibre view</strong> of each experiment.</p> <p><em>For a 2-minute summary video of these experiments, refer to:</em></p> <pre>https://doi.org/10.5281/zenodo.3357187</pre> <p>This supplementary dataset accompanies a conference paper prepared for IEEE IROS 2019.</p> <p>Using multi-robot systems for autonomous construction allows for parallelization and scalability. Swarm construction furthermore exploits robot interactions and collaboration, such that the robot swarm collectively constructs artifacts beyond what a single comparable robot could achieve. Here we present an alternative concept of swarm construction that is distinct because it uses continuous building material. Our approach is unique in its use of braiding techniques for construction. We deploy fibres that potentially allow for structures that are not possible with building blocks. To achieve maximal scalability we restrict ourselves to a decentralized approach. The main challenges are the local coordination of the robot teams, self-organized task allocation, and the dynamic reconfiguration of the braiding scheme at runtime. We successfully validate our approach in multi-robot experiments that show both braiding and branching of the braid. In addition, we show options for implementing an open system—that is robots can join and leave the braiding process on the fly.</p>
ARIVVD: Aberystwyth Robot Infant Vision Video Dataset
<p>This is the Aberystwyth Robot Infant Vision Video Dataset, created for developmental robotics research at Aberystwyth University. This dataset was created for an internally funded research pilot project (“Babyvision: a robotic investigation into early development of colour constancy”). </p>
Acoustic video cameras multi-species multi-cameras Training Dataset (TD) for Deep Learning applications
<p>This images dataset, called also TD (Training Dataset), is designed to train/test/validate deep learning models to identify fish species in sonar camers video flux. It includes data from two different type of cameras (ARIS and DIDSON), two sites (Touques and Selune rivers in Normandy, France), 6 different fishes classes (Atlantic Salmon, European Eel, Sea Lamprey, Allis Shad, European Catfish and generic unidentified fish). This dataset, formatted in the yolo-darknet format as explained in <a href="https://github.com/AlexeyAB/darknet">https://github.com/AlexeyAB/darknet</a>, includes also a large number of images without any fish passage, in order to test the effect of negative data on the trainings.</p>
Dataset for Reproduce "v2e: From Video Frames to Realistic DVS Events"
<p>This dataset release is meant for reproducing the results in our paper "v2e: From Video Frames to Realistic DVS Events".</p> <p>The paper is published in The Third International Workshop on Event-Based Vision.</p> <p>Please check out DATASET_README.md for more information. The code that accompanies the dataset is published <a href="https://github.com/SensorsINI/v2e_exps_public">here</a>.</p> <p>If you use this dataset, please cite:</p> <ul> <li>Y. Hu, S-C. Liu, and T. Delbruck. v2e: From Video Frames to Realistic DVS Events. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021.</li> <li>Y. Hu, T. Delbruck, S-C. Liu, "Learning to Exploit Multiple Vision Modalities by Using Grafted Networks" in The 16th European Conference on Computer Vision (ECCV), Online, 2020.</li> </ul>
Dataset for Real and Virtual Backgrounds of Video Calls
<p>Video conferencing applications play an important role in our day-to-day life. They enable people to meet, work, and collaborate remotely, especially in circumstances where physical meetings are not possible (e.g., pandemic scenarios, long distances, etc.). However, such applications might invade people's privacy, such as disclosing their sensitive information. In this dataset, we recorded different video conferencing with diverse real and virtual backgrounds, changing subjects, lighting, and so on.</p> <p>If you want to use these datasets for non-commercial purposes, please cite the following papers:</p> <p>@article{nowroozi2020survey,<br> title={A survey of machine learning techniques in adversarial image forensics},<br> author={Nowroozi, Ehsan and Dehghantanha, Ali and Parizi, Reza M and Choo, Kim-Kwang Raymond},<br> journal={Computers \& Security},<br> pages={102092},<br> year={2020},<br> publisher={Elsevier}<br>}</p> <p> </p>
Dataset for Adaptive Light-Sheet Fluorescence Microscopy with a Deformable Mirror for Video-Rate Volumetric Imaging
<p>1. Underlying data of figures in the paper </p> <p>2. Background images used to process the experimental data</p> <p>3. image stack of 250 nm beads</p> <p>4. image stack of sunflower pollen grains</p> <p>5. image stacks and videos of Fluo-4 labelled cells</p> <p>6. image stacks and videos of CMO-labelled cells</p> <p>The data is organised according to the figures they are related to in the following publication:</p> <p> </p> <p><a href="https://aip.scitation.org/author/Hong%2C+Wenzhi">Wenzhi Hong</a><em>, </em><a href="https://aip.scitation.org/author/Wright%2C+Terry">Terry Wright</a><em>, </em><a href="https://aip.scitation.org/author/Sparks%2C+Hugh">Hugh Sparks</a><em>, </em><a href="https://aip.scitation.org/author/Dvinskikh%2C+Liuba">Liuba Dvinskikh</a><em>, </em><a href="https://aip.scitation.org/author/MacLeod%2C+Ken">Ken MacLeod</a><em>, </em><a href="https://aip.scitation.org/author/Paterson%2C+Carl">Carl Paterson</a><em>, and </em><a href="https://aip.scitation.org/author/Dunsby%2C+Chris">Chris Dunsby</a> </p> <p>, "Adaptive light-sheet fluorescence microscopy with a deformable mirror for video-rate volumetric imaging", Appl. Phys. Lett. 121, 193703 (2022) <a href="https://doi.org/10.1063/5.0125946">https://doi.org/10.1063/5.0125946</a></p>
ArabicSL-Net: A Benchmark Video Dataset for Arabic Words Sign Language
<p>The data was captured by mobile camera in four main organization namely Bank , Cafe , Hospital , and Train station. The ArabicSL-Net initially consists of 307 words recorded in approximately 30,000 videos. For each organization, we capture the most representative words that are used in those places. For Bank data, we have a total of 76 of words, while Cafe data contains 54 words. For Hospital, we collects videos for 102 words, and collects videos for 71 words in Train station.</p>
WormSwin: C. elegans Video Datasets
<p>Data used for our paper "WormSwin: Instance Segmentation of C. elegans using Vision Transformer".<br>This publication is divided into three parts:</p> <ol> <li>CSB-1 Dataset</li> <li>Synthetic Images Dataset</li> <li>MD Dataset</li> </ol> <p>The CSB-1 Dataset consists of frames extracted from videos of Caenorhabditis elegans (C. elegans) annotated with binary masks. Each C. elegans is separately annotated, providing accurate annotations even for overlapping instances. All annotations are provided in binary mask format and as COCO Annotation JSON files (see <a href="https://cocodataset.org/#format-data">COCO website</a>).</p> <p>The videos are named after the following pattern:</p> <pre><code><"worm age in hours"_"mutation"_"irradiated (binary)"_"video index (zero based)"></code></pre> <p>For mutation the following values are possible: </p> <ol> <li>wild type</li> <li>csb-1 mutant</li> <li>csb-1 with rescue mutation</li> </ol> <p>An example video name would be <em>24_1_1_2</em> meaning it shows C. elegans with csb-1 mutation, being 24h old which got irradiated.</p> <p>Video data was provided by M. Rieckher; Instance Segmentation Annotations were created under supervision of K. Bozek and M. Deserno.<br><br>The Synthetic Images Dataset was created by cutting out C. elegans (foreground objects) from the CSB-1 Dataset and placing them randomly on background images also taken from the CSB-1 Dataset. Foreground objects were flipped, rotated and slightly blurred before placed on the background images.<br>The same was done with the binary mask annotations taken from CSB-1 Dataset so that they match the foreground objects in the synthetic images. Additionally, we added rings of random color, size, thickness and position to the background images to simulate petri-dish edges.</p> <p>This synthetic dataset was generated by M. Deserno.<br><br>The Mating Dataset (MD) consists of 450 grayscale image patches of 1,012 x 1,012 px showing C. elegans with high overlap, crawling on a petri-dish.<br>We took the patches from a 10 min. long video of size 3,036 x 3,036 px. The video was downsampled from 25 fps to 5 fps before selecting 50 random frames for annotating and patching.<br>Like the other datasets, worms were annotated with binary masks and annotations are provided as COCO Annotation JSON files.</p> <p>The video data was provided by X.-L. Chu; Instance Segmentation Annotations were created under supervision of K. Bozek and M. Deserno.</p> <p><br>Further details about the datasets can be found in our <a href="https://doi.org/10.1038/s41598-023-38213-7" target="_blank" rel="noopener">paper</a>.</p>
Yoga for all: A Comprehensive Collection of Yoga Images and Videos dataset
<p>The dataset comprises both images and videos depicting right and wrong postures for a variety of Yoga asanas. The focus of the dataset is on 10 specific Yoga postures, namely Anantasana, Ardhakati Chakrasana, Bhujangasana, Kati Chakrasana, Marjariasana, Parvatasana, Sarvangasana, Tadasana, Vajrasana, and Viparita Karani.</p> <p>The Image dataset comprises a total of 11,344 images and is organized into 10 subfolders, each corresponding to a specific Yoga asana. Within each subfolder, there are two additional folders labeled "Right Steps" and "Wrong Steps". The "Right Steps" folder contains several subfolders, each representing a specific step in the right sequence of the Yoga asana and displaying the corresponding images. On the other hand, the "Wrong Steps" folder includes multiple subfolders, each showing images of an wrong steps in the sequence of the Yoga asana.</p> <p>The Yoga asana video dataset consists of 8 videos for each posture, comprising 4 videos demonstrating the right posture from 4 different angles and 4 videos exhibiting the wrong posture from 4 different angles. This dataset includes a total of 80 videos for 10 Yoga asanas, with 40 videos demonstrating the right postures captured from 4 different angles, and 40 videos illustrating the wrong postures from 4 different angles.</p> <p>The dataset has advantages for various groups, such as app developers, machine learning researchers, Yoga instructors, and Yoga practitioners. Machine learning researchers can utilize this dataset to train computer vision algorithms in recognizing and categorizing various yoga postures automatically. App developers can use the dataset to generate yoga apps that present users with visual guidance on executing each posture and keeping track of their progress.</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.