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MOVING: a Multi-MOdal dataset of EEG signals and VIrtual Glove hand trackING
<p>A new Multi-modal dataset comprising neural EEG signals and kinematic data associated with three hand movements — open/close, finger tapping, and wrist rotation — along with a rest period. The dataset, obtained from eleven subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (~ 1 minute) while introducing more noise than the gold standard devices for data acquisition. The data set, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. </p> <p>For citation please refer to the paper:<br>Mattei, E.; Lozzi, D.; Di Matteo, A.; Cipriani, A.; Manes, C.; Placidi, G. MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking. Sensors 2024,24, 5207. https://doi.org/10.3390/s24165207 </p> <p><strong>References</strong>:</p> <p>Placidi, Giuseppe. "<em>A smart virtual glove for the hand telerehabilitation.</em>" Computers in Biology and Medicine 37.8 (2007): 1100-1107.</p> <p>Placidi, Giuseppe, et al. "<em>Measurements by a LEAP-based virtual glove for the hand rehabilitation.</em>" Sensors 18.3 (2018): 834.</p> <p>Placidi, Giuseppe, et al. "<em>Patient–therapist cooperative hand telerehabilitation through a novel framework involving the virtual glove system.</em>" Sensors 23.7 (2023): 3463.</p>
Rare Diseases hand-annotated news articles and research articles
<p>This dataset was produced in 2023 from the data collected throughout 2022 from MEDLINE (scientific articles) and from Event Registry (news) for the development of the Rare Diseases Mining project (https://idefine-europe.org/medline)</p><p>The data is distributed across 16 diseases supporting the research paper "Automatic text classification and interactive data visualization of published scientific and news articles on Rare Diseases"</p><p>The available data comes in 2 kinds and file formats:<br>CSV - the hand annotation of the news articles in TXT with 5 to 10 MeSH headings<br>JSON - the input file for the evaluation of the classifier, including the title, news article body and MeSH heading IDs (available from https://www.ncbi.nlm.nih.gov/mesh/)</p><p>The CSV files with name starting in "f1_", "pr_", "re_" are the results of the F1/Precision/Recall evaluation for each of the cases.</p><p>This work was prepared by Joao Pita Costa (researcher) and curated by Tanja Zdolšek Draksler (domain expert) </p>
Dataset: Behavior of Participants in Hands-on Cybersecurity Training Suitable for Process Mining
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Radek Ošlejšek, Martin Macák, Karolína Dočkalová Burská.<br><em>Hands-on cybersecurity training behavior data for process mining.</em><br>In Elsevier Data in Brief. 2023.<br>Available as open-access article on <a href="https://doi.org/10.1016/j.dib.2023.109956">https://doi.org/10.1016/j.dib.2023.109956</a></p> <p><strong>Contents</strong></p> <p>Datasets store event logs of trainees participating in hands-on cybersecurity exercises organized in the <a href="https://www.kypo.cz">KYPO Cyber Range</a>. The data includes training scenarios (expected behavior), raw event logs in the JSON format, and aggregated behavioral data suitable for process mining analysis.</p> <ol> <li><strong>Data1:</strong> A dataset of 52 trainees participating in the <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> exercise adapted an insider attack scenario. No time restrictions were posed on playtime. The data file is structured as follows: <ul> <li>training_definition.json: The exercise content – cybersecurity tasks and hints. The training is based on the <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> game adapted to an insider attack scenario.</li> <li>training_events: Recorded progress of trainees within the exercise, i.e., the status of completing tasks.</li> <li>command_histories: Recorded commands executed on network hosts.</li> <li>process_mining.csv: Complete PM-ready dataset suitable for process discovery or conformance analysis.</li> <li>process_mining_simplified.csv : Reduced PM-ready dataset with semantically identical events being removed.</li> </ul> </li> <li><strong>Data2:</strong> A dataset of 48 trainees participating in the original <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> exercise. Three supervised training sessions were restricted to two hours of playtime. The structure follows the structure of Data1.</li> <li><strong>Tool:</strong> A Java application used to aggregate raw JSON data and transform them into a CSV format suitable for process mining techniques.</li> </ol> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original work.</p> <pre><code>@article{Oslejsek2023dataset, author = {Radek O\v{s}lej\v{s}ek and Martin Mac\'{a}k and Karol\'{i}na {Do\v{c}kalov\'{a} Bursk\'{a}}}, title = {Hands-on cybersecurity training behavior data for process mining}, journal = {{Data in Brief}}, publisher = {Elsevier}, issn = {2352-3409}, year = {2023}, volume = {52}, doi = {10.1016/j.dib.2023.109956}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923009873} }</code></pre>
Real testing sets for Visual Affordance Segmentation of hand-occluded objects
<p>[<a href="https://arxiv.org/abs/2308.11233">arXiv</a>] [<a href="https://apicis.github.io/projects/acanet.html">webpage</a>] [<a href="https://github.com/SEAlab-unige/acanet">code</a>] [<a href="https://doi.org/10.5281/zenodo.8364197">trained model</a>][<a href="https://doi.org/10.5281/zenodo.5085800">mixed-reality data</a>]</p> <p>RGB images with the corresponding affordance annotation to test affordance segmentation models. Images are selected from two datasets for hand-object pose estimation: <a href="https://www.tugraz.at/institute/icg/research/team-lepetit/research-projects/hand-object-3d-pose-annotation/">HO-3D</a> and <a href="https://corsmal.eecs.qmul.ac.uk/containers_manip.html">CCM</a>.</p> <p>For HO3D we selected 150 frames from the dataset and enriched the annotation of the hand and object segmentation masks with new annotations specific for the affordance segmentation problem.</p> <p>For CCM we selected 150 frames from the dataset and created the annotation specific for the affordance segmentation problem. The forearms and hands in contact with the offered container are annotated. </p> <p>File names are formatted as: <em><videoname>_<framenumber>.png</em></p> <p>Segmentation classes values:</p> <ul> <li> 0: background</li> <li> 1: graspable</li> <li> 2: contain</li> <li> 3: arm</li> </ul> <p> </p> <p><strong>References. </strong></p> <p><strong>Affordance segmentation of hand-occluded containers from exocentric images</strong><br>T. Apicella, A. Xompero, E. Ragusa, R. Berta, A. Cavallaro, P. Gastaldo<br>IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023</p> <pre><code>@inproceedings{apicella2023affordance, title={Affordance segmentation of hand-occluded containers from exocentric images}, author={Apicella, Tommaso and Xompero, Alessio and Ragusa, Edoardo and Berta, Riccardo and Cavallaro, Andrea and Gastaldo, Paolo}, booktitle={IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}, year={2023}, } </code></pre> <p><strong>HOnnotate: A method for 3D Annotation of Hand and Objects Poses<br></strong>S. Hampali, M. Rad, M. Oberweger, V. Lepetit<strong><br></strong>IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020</p> <pre><code>@inproceedings{hampali2020honnotate, title={Honnotate: A method for 3d annotation of hand and object poses}, author={Hampali, Shreyas and Rad, Mahdi and Oberweger, Markus and Lepetit, Vincent}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={3196--3206}, year={2020} }</code></pre> <p><strong>CORSMAL Containers Manipulation (1.0) [Data set]</strong><br>A. Xompero, R. Sanchez-Matilla, R. Mazzon, and A. Cavallaro<br>Queen Mary University of London. <a href="https://doi.org/10.17636/101CORSMAL1"><u>https://doi.org/10.17636/101CORSMAL1</u></a></p> <p> </p> <p><strong>License. </strong>Creative Commons<strong> </strong>Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)</p> <p><strong>Enquiries, Question and Comments. </strong>For enquiries, questions, or comments, please contact <a href="mailto:tommaso.apicella@edu.unige.it">Tommaso Apicella</a>.</p>
Hands-On! Video demonstration - Paper folding components
<p>This video demonstrates all possible actions for the <strong>Paper folding task</strong>, providing real-time examples to help you accurately identify and assess fine motor performance. Use this video alongside the written descriptions and images from the <strong>Hands-On!</strong> observation tool for a comprehensive understanding of this fine motor task. For more information on the use of this video, please refer to <strong>Hands-On!</strong> via: <a href="https://doi.org/10.5281/zenodo.14185207">https://doi.org/10.5281/zenodo.14185207</a></p>
Hands-On! Video demonstration - Writing components
<p><span>This video demonstrates all possible actions for the <strong>Writing task</strong>, providing real-time examples to help you accurately identify and assess fine motor performance. Use this video alongside the written descriptions and images from the <strong><span>Hands-On!</span></strong> observation tool for a comprehensive understanding of this fine motor task. For more information on the use of this video, please refer to <strong><span>Hands-On!</span></strong> via: <a href="https://doi.org/10.5281/zenodo.14185207">https://doi.org/10.5281/zenodo.14185207</a> </span></p>
Hands-On! Video demonstration - Paper cutting components
<p><span>This video demonstrates all possible actions for the <strong>Paper cutting task</strong>, providing real-time examples to help you accurately identify and assess fine motor performance. Use this video alongside the written descriptions and images from the <strong><span>Hands-On!</span></strong> observation tool for a comprehensive understanding of this fine motor task. For more information on the use of this video, please refer to <strong><span>Hands-On!</span></strong> via: <a href="https://doi.org/10.5281/zenodo.14185207">https://doi.org/10.5281/zenodo.14185207</a> </span></p>
Microbial Load on Dental Students' Hands: A Gender and Academic Stage Analysis
<p>This dataset supports a study investigating microbial contamination on the hands of dental students at Al-Hadi University College. The data were collected using impression sampling of the index and thumb fingers of 35 students (17 males, 18 females) from the third, fourth, and fifth academic years. Sampling occurred at the end of academic or clinical activities, and bacterial load was measured using colony-forming units (CFUs) cultured on MacConkey agar. Variables in the dataset include gender, academic stage, handedness, and CFU counts.</p>
Synthetic data (Part 2) for HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the rendered images and the segmentation masks that we use to train our model on HO3Dv2 dataset. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the rendered images for HO3Dv2.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Synthetic data (Part 1) for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed SDF samples. Meanwhile, we also include rendered data for HO3Dv2 here. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 rendered images.</div> <div>├── <a href="../api/records/13228003/draft/files/train_ho3d.zip/content" target="_blank" rel="noopener noreferrer">train_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 training set.</div> <div>├── <a href="../api/records/13228003/draft/files/full_test_dexycb.zip/content" target="_blank" rel="noopener noreferrer">full_test_dexycb.zip</a> - Contains the processed SDF files for DexYCB full test set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Processed data and trained models for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: <a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf">https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</a></p> <p>Link to the Arxiv article: <a href="https://arxiv.org/abs/2402.17062">https://arxiv.org/abs/2402.17062</a></p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed data of the interacting objects and SDF samples. Meanwhile, we also include the trained model weights here.</div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/11668766/draft/files/ckpts.zip/content" target="_blank" rel="noopener noreferrer">ckpts.zip</a> - Contains the trained weights model on different datasets (DexYCB and HO3Dv2)</div> <div>├── <a href="../api/records/11668766/draft/files/annotations.zip/content" target="_blank" rel="noopener noreferrer">annotations.zip</a> - Contains the preprocessed annotations of DexYCB and HO3Dv2 for efficient data loading.</div> <div>├── <a href="../api/records/11668766/draft/files/simple_ycb_models.zip/content" target="_blank" rel="noopener noreferrer">simple_ycb_models.zip</a> - Contains the preprocessed YCB objects for batched evaluation.</div> <div>├── <a href="../api/records/11668766/draft/files/test.zip/content" target="_blank" rel="noopener noreferrer">test.zip</a> - Contains the processed SDF files for DexYCB test set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_render_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_render_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set and rendering set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: <a href="https://github.com/amathislab/HOISDF">https://github.com/amathislab/HOISDF</a></div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines - METC Subset
<p><strong>Overview:</strong> This is a lab-based dataset with videos recording volunteers (medical students) washing their hands as part of a hand-washing monitoring and feedback experiment. The dataset is collected in the Medical Education Technology Center (METC) of Riga Stradins University, Riga, Latvia. In total, 72 participants took part in the experiments, each washing their hands three times, in a randomized order, going through three different hand-washing feedback approaches (user interfaces of a mobile app). The data was annotated in real time by a human operator, in order to give the experiment participants real-time feedback on their performance. There are 212 hand washing episodes in total, each of which is annotated by a single person. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.</p> <p>This dataset is part on three dataset series all following the same format:</p> <ul> <li><a href="https://zenodo.org/record/4537209">https://zenodo.org/record/4537209 </a>- data collected in Pauls Stradins Clinical University Hospital</li> <li><a href="https://zenodo.org/record/5808764">https://zenodo.org/record/5808764</a> - data collected in Jurmala Hospital</li> <li><a href="https://zenodo.org/record/5808789">https://zenodo.org/record/5808789</a> - data collected in the Medical Education Technology Center (METC) of Riga Stradins University</li> </ul> <p><strong>Note #1:</strong> we recommend that when using this dataset for machine learning, allowances are made for the reaction speed of the human operator labeling the data. For example, the annotations can be expected to be incorrect a short while after the person in the video switches their washing movements.</p> <p><strong>Application: </strong>The intention of this dataset is to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control.</p> <p><strong>Statistics:</strong></p> <ul> <li>Frame rate: ~16 FPS (slightly variable, as the video are reconstructed from a sequence of jpg images taken with max framerate supported by the capturing devices).</li> <li>Resolution: 640x480</li> <li>Number of videos: 212</li> <li>Number of annotation files: 212</li> </ul> <p>Movement codes (in JSON files):</p> <ul> <li>1: Hand washing movement — Palm to palm</li> <li>2: Hand washing movement — Palm over dorsum, fingers interlaced</li> <li>3: Hand washing movement — Palm to palm, fingers interlaced</li> <li>4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked</li> <li>5: Hand washing movement — Rotational rubbing of the thumb</li> <li>6: Hand washing movement — Fingertips to palm</li> <li>0: Other hand washing movement</li> </ul> <p><strong>Note #2: </strong>The original dataset of JPG images is available upon request. There are 13 annotation classes in the original dataset: for each of the six washing movements defined by the WHO, "correct" and "incorrect" execution is market with two different labels. In this published dataset, all incorrect executions are marked with code 0, as "other" washing movement.</p> <p><strong>Acknowledgments: </strong>The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.</p> <p><strong>References: </strong>For more detailed information, see this article, describing a similar dataset collected in a different project:</p> <ul> <li> <p>M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. <em>Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines</em>. Data. 2021; 6(4):38. <a href="https://doi.org/10.3390/data6040038">https://doi.org/10.3390/data6040038</a></p> </li> </ul> <p><strong>Contact information: </strong>atis.elsts@edi.lv</p>
Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines - Jurmala Hospital Subset
<p><strong>Overview:</strong> This is a large-scale real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Jurmala Hospital located in Jurmala, Latvia. There are 2427 hand washing episodes in total, almost all of which are annotated by two persons. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.</p> <p>This dataset is part on three dataset series all following the same format:</p> <ul> <li><a href="https://zenodo.org/record/4537209">https://zenodo.org/record/4537209</a> - data collected in Pauls Stradins Clinical University Hospital</li> <li><a href="https://zenodo.org/record/5808764">https://zenodo.org/record/5808764</a> - data collected in Jurmala Hospital</li> <li><a href="https://zenodo.org/record/5808789">https://zenodo.org/record/5808789</a> - data collected in the Medical Education Technology Center (METC) of Riga Stradins University</li> </ul> <p><strong>Applications: </strong>The intention of this dataset is twofold: to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control, and to allow to investigate the real-world quality of washing performed by working medical staff.</p> <p><strong>Statistics:</strong></p> <ul> <li>Frame rate: 30 FPS</li> <li>Resolution: 320x240 and 640x480</li> <li>Number of videos: 2427</li> <li>Number of annotation files: 4818</li> </ul> <p>Movement codes (both in CSV and JSON files):</p> <ul> <li>1: Hand washing movement — Palm to palm</li> <li>2: Hand washing movement — Palm over dorsum, fingers interlaced</li> <li>3: Hand washing movement — Palm to palm, fingers interlaced</li> <li>4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked</li> <li>5: Hand washing movement — Rotational rubbing of the thumb</li> <li>6: Hand washing movement — Fingertips to palm</li> <li>7: Turning off the faucet with a paper towel</li> <li>0: Other hand washing movement</li> </ul> <p><strong>Acknowledgments: </strong>The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.</p> <p><strong>References: </strong>For more detailed information, see this article, describing a similar dataset collected in a different project:</p> <ul> <li> <p>M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. <em>Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines</em>. Data. 2021; 6(4):38. <a href="https://doi.org/10.3390/data6040038">https://doi.org/10.3390/data6040038</a></p> </li> </ul> <p><strong>Contact information: </strong>atis.elsts@edi.lv</p>
Raw data of: "Controlling Hand Movements Relying on Tactile Illusions: A Model Predictive Control Framework"
<p>in Fig4_a.txt: raw the data for the plot of Fig4_a (x and y of the first simulated trajectory from trajectory 1 to 50)</p> <p>in Fig4_b.txt: raw the data for the plot of Fig4_b </p> <p>in Fig4_c.txt raw the data for the plot of Fig4_b. Each column corresponds to the optimal angle of the plate for each of the 50 trajectories simulated in Fig4_a</p>
Jewelry segmentation masks for the 11k Hands dataset
<p>We provide an additional set of segmentation masks for jewelry in the 11K Hands dataset [1]. We filtered out a total of 3179 hands <br> with jewelry and were manually annotated using CVAT. For ease of use, the maks have the same size and filename as the original images and are exported in png format. The pixel value represents whether jewelry exists, being 0 background and 1 jewelry.</p> <p>The 11k Hands [1] dataset is a collection of 11,076 hand photos (1600 × 1200 pixels) from 190 people aged 18 to 75 years old. Each hand was shot from both the dorsal and palmar sides, on a uniform white background, at roughly the same distance from the camera. Each image has a metadata record that includes the following information: the subject ID, gender, age, skin color, and a set of information about the captured hand, such as right- or left-hand, hand side (dorsal or palmar), and logical indicators indicating whether the hand image contains accessories, nail polish, or irregularities. You can download <a href="https://drive.google.com/open?id=1KcMYcNJgtK1zZvfl_9sTqnyBUTri2aP2">here</a> the original 11K Hands dataset and the <a href="https://drive.google.com/file/d/1RC86-rVOR8c93XAfM9b9R45L7C2B0FdA/view?usp=sharing">metadata</a>.</p> <p>In the future, we will add our paper if accepted. In the meantime, if you use the masks provided on this webpage, please cite our DOI: <em>10.5281/zenodo.6541286</em> and the original 11K Hands paper.</p> <p>[1] Mahmoud Afifi, "11K Hands: Gender recognition and biometric identification using a large dataset of hand images." Multimedia Tools and Applications, 2019.</p>
Prosthetic Hand Landmark dataset
<p>This is a dataset of 7164 prosthetic hand images with corresponding landmark labels. The dataset is partially made of synthetic images made using unity. The goal is to address the lack of representation of prosthetic devices in modern machine learning models. Particularly in the field of segmentation and landmark identification. </p>
Rhyme annotation evaluation: a Hand-Annotated Sample of the Quan Tang Shi and Quan Song Shi
<p>This dataset consists of 3 files:</p> <ul> <li>hand_annotated_sample.json contains a sample of 444 poems from the Quan Tang Shi and Quan Song Shi; these poems were pre-annotated by a Community annotator and manually reviewed / amended by the main author.</li> <li> <p>hand_annotated_subsample_(author).json contains a sub-sample of the previous file, containing 44 poems (10%); the annotations therein are identical to the annotations in the file above.</p> </li> <li> <p>hand_annotated_subsample_(colleague).json contains the same sub-sample of 44 poems, but annotated by a colleague of the author. The aim is to assess human inter-annotator agreement for this type of poetry.</p> <p> </p> <p> </p> <p> </p> </li> </ul> <p> </p>
Text-fig. 15. Photomicrographs of thin sections of holotype BP/16/1738, Sorindeioxylon gorongosense gen. et sp. nov. from Muaredzi site 5, Gorongosa, Mozambique. a: TS, note the irregularly spaced and very narrow bands of parenchyma and mostly solitary vessel elements; b: TS at higher magnification with narrow rays; c: radial longitudinal section (RLS), rather oblique but shows the alternate, small-to-medium inter-vessel pits; d: tangential longitudinal section (TLS), rays are 1–3 cells wide but maintain the same width. Small arrow towards the right hand ray indicates a prismatic crystal in the ray cell; e: TLS rays with fibres in between; f: RLS showing mixed ray cells (upright, square and procumbent) poorly preserved. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 15. Photomicrographs of thin sections of holotype BP/16/1738, Sorindeioxylon gorongosense gen. et sp. nov. from Muaredzi site 5, Gorongosa, Mozambique. a: TS, note the irregularly spaced and very narrow bands of parenchyma and mostly solitary vessel elements; b: TS at higher magnification with narrow rays; c: radial longitudinal section (RLS), rather oblique but shows the alternate, small-to-medium inter-vessel pits; d: tangential longitudinal section (TLS), rays are 1–3 cells wide but maintain the same width. Small arrow towards the right hand ray indicates a prismatic crystal in the ray cell; e: TLS rays with fibres in between; f: RLS showing mixed ray cells (upright, square and procumbent) poorly preserved.
Hedera sinensis (Tobl.) Hand.-Mazz. (BR0000009355934)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Measurement verification data for AutoMorph vs. hand measurements
<p>These data include comparisons between 2D and 3D measurements automatically extracted by the AutoMorph software (https://github.com/HullLab/AutoMorph) and those collected manually (<em>i.e</em><em>.,</em> using calipers, ImageJ, or VGSStudio Max for the 2D measurements, or using CT scans for the 3D measurements). These measurements are intended to demonstrate the breadth of AutoMorph's applicability, and help users determine whether the accuracy of AutoMorph is suitable for their needs. 2D measurements (specifically major and minor axis length and/or squared area) have been collected and presented here for fossil patellogastropods (limpets), extant bivalves, and ichthyoliths (<em>i.e.</em>, fish teeth). 3D volume and surface area data have been collected for a sample of planktonic foraminifera.</p> <p>More information about how these data were collected, and discussion about the measurement comparisons, can be found in the manuscript describing AutoMorph (Hsiang et al. 2018).</p> <p><strong>References</strong></p> <p>Hsiang AY, Nelson K, Elder LE, Sibert EC, Kahanamoku SS, Burke JE, Kelly A, Liu Y, and Hull PM (2018) <em>AutoMorph</em>: Accelerating morphometrics with automated 2D and 3D image processing and shape extraction. <em>Methods in Ecology and Evolution. </em>9(3):605-612 (<a href="https://doi.org/10.1111/2041-210X.12915">https://doi.org/10.1111/2041-210X.12915).</a></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.