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2,000 results for “Hand”
Dataset: Hand-pose estimation videos
<p>These digital artifacts provide a set of videos used as an input dataset for a qualitative evaluation of the pieces of software developed in the context of the Gesture Tracking for Low-End Virtual Reality Systems (GT4LEVRS) project. Each video shows a user making different sorts of gestures: static gestures, slow-moving gestures, and fast-moving gestures. The following files is provided</p> <ol> <li>"2022-05-25 18-57-07.mp4" shows a demonstration of the prototype</li> <li>"Cenario estatico-20220624T180204Z-001.zip" provides five set of videos of static gestures as follows: <ol> <li>Affirmative/OK signal (Folder: Afirmativo)</li> <li>Claw (Folder: Garra)</li> <li>Hang loose gesture (Folder: Hang loose)</li> <li>Opened Hand (Folder: Palma)</li> <li>Positive gesture (Folder: Positivo)</li> </ol> </li> <li>"cenario lento-20220624T180237Z-001.zip" provides five set of videos of low-moving gestures as follows: <ol> <li>Touching hands (Folder: maos encostando)</li> <li>Moving little finger (Folder: mindinho)</li> <li>Holding an object and moving it (Folder: objeto)</li> <li>Moving peace signal (Folder: paz)</li> <li>Moving fists (Folder: punho)</li> </ol> </li> <li>"Cenario rapido-20220624T180245Z-001.zip" provides five set of videos of fast-moving gestures as follows: <ol> <li>Goodbye (Folder: acenar)</li> <li>Moving indication (Folder: apontar)</li> <li>Throwing an object (Folder: arremessar objeto)</li> <li>Crossing hands (Folder: cruzar maos)</li> <li>Clapping hands (Folder: palmas)</li> </ol> </li> </ol> <p>Source code and other design artifacts can be found on Github: <a href="https://github.com/lesc-utfpr/gt4levrs">https://github.com/lesc-utfpr/gt4levrs</a></p> <p>This dataset was created in the context of a bachelor's final thesis in the Computer Engineering course of Federal University of Technology - Paraná (UTFPR), campus Curitiba, by Lucas Kuttner Amin and Rafael Hideo Toyomoto supervised by Prof. Marco Wehrmeister.</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - Kashtanka Pets (400 Hand-labelled Images - Cats & Dogs, Single Folder)
<p>400 images (200 cats, 200 dogs) hand-labelled by Maria E. with head and body bounding box labels in YOLOv5 format. Images are in a single folder, no separate folders for cats and dogs.</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - Kashtanka Pets (200 Hand-labelled Images, Cats and Dogs, Separate Folders)
<p>400 images (200 cats, 200 dogs) hand-labelled by Maria E. with head and body bounding box labels in YOLOv5 format. Images for cats, for dogs are in a separate folders.</p>
PNS-Cyst-Count (Part2): hand-picked nematode cysts without organic debris, annotated with cyst count
<p>Data collected by the PheNeSens (Phenotyping of Nematodes with Sensors) project. The images recorded cysts of sugar beet nematode, together with organic debris from the soil sample, left after the soil processing.</p> <p>A total of 48 soil samples are recorded (30 images per sample) and number of cysts are manually counted. This is the second part, containing images of hand-picked cysts on filter papers. The cysts before hand-picking (with large amount of organic debris) is available under: https://zenodo.org/record/6861775</p> <p>For details of the data collection and deep learning model training, refer to our paper:</p> <p>Chen L, Daub M, Luigs H-G, Jansen M, Strauch M and Merhof D (2022) High-throughput phenotyping of nematode cysts. Front. Plant Sci. 13:965254. doi: 10.3389/fpls.2022.965254 [<a href="https://www.frontiersin.org/articles/10.3389/fpls.2022.965254/full?&utm_source=Email_to_authors_&utm_medium=Email&utm_content=T1_11.5e1_author&utm_campaign=Email_publication&field=&journalName=Frontiers_in_Plant_Science&id=965254">link</a>]</p>
As currently delineated, this is a complex comprising at least two species, as dem- onstrated by the work of J. M. Lamb and colleagues in 2014 and A. Monadjem and colleagues in 2015. Large gray-bellied specimens attributed to M. triton form two highly divergent molecular and chromosomal clades: on the one hand, from Tanzania, Malawi, and Mozambique (with chromosomal complement of 2n = 20-22, FN = 34); and, on the other, from DR Congo (with 2n = 32, FN = 34. Monotypic. Distribution. Extreme S South Sudan, S Ethiopia (Bale Mts), NE DR Congo, and from Uganda and Kenya SW & S to Angola, Zambia, WC Mozambique, and Malawi. in Muridae
As currently delineated, this is a complex comprising at least two species, as dem- onstrated by the work of J. M. Lamb and colleagues in 2014 and A. Monadjem and colleagues in 2015. Large gray-bellied specimens attributed to M. triton form two highly divergent molecular and chromosomal clades: on the one hand, from Tanzania, Malawi, and Mozambique (with chromosomal complement of 2n = 20-22, FN = 34); and, on the other, from DR Congo (with 2n = 32, FN = 34. Monotypic. Distribution. Extreme S South Sudan, S Ethiopia (Bale Mts), NE DR Congo, and from Uganda and Kenya SW & S to Angola, Zambia, WC Mozambique, and Malawi.
The intra- and inter-rater reliability of a variety of testing methods to measure shoulder range of motion, hand-behind-back and external rotation strength in healthy participants
<p>Dataset for a intra- and inter-rater reliability study of a variety of testing methods to measure shoulder range of motion, hand-behind-back and external rotation strength in healthy participants</p>
AR Colossal Hand of Amenhotep III, Egypt
Colossal Hand of Amenhotep III in the blockyard at Khonsu Temple, Karnak Temple Complex, Luxor, Egypt. Scanned as part of a training session in 3D photogrammetric scanning I conducted for trainees from the Ministry of Antiquities. These individuals were participating in larger training program being conducted by the American Research Center in Egypt. Created from 69 photographs (Canon EOS Rebel T5i) using Agisoft Photoscan. Photographed in January 2018. This scan has been optimized for augmented reality. Source: Objaverse 1.0 / Sketchfab
Hand Painted Creamer - Preview
For more information about this item visit: https://bhpsite.org/ Source: Objaverse 1.0 / Sketchfab
Senegal Hand Carved Mask
Photogrametry of a Senegal wood carved mask Unknown artist. Source: Objaverse 1.0 / Sketchfab
Acheulean Hand Axe (506a1)
**Acheulean hand axe** Location: Caubert Somme, France. Period: Lower Paleolithic (before 300,000 years ago). Material: flint. Dimensions: length, 107.0 mm; width, 66.3 mm; thickness, 36.2 mm. Notes: Catalog no. 506a1. This specimen is part of a larger collection of artifacts that was acquired in 1929 by James Bell Bullitt during a tour of western Europe and later donated to the University of North Carolina. Specimen resides in the North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Kay Waller. Source: Objaverse 1.0 / Sketchfab
FLIM Data and analysis of the hands-on sessions "CF44-1 Label-free metabolic FLIM with 2 photon excitation"
<p>This FLIM dataset on HeLa cells and human astrocytes was acquired during the hands-on sessions <em>CF44-1 Label-free metabolic FLIM with 2 photon excitation</em> during the <a href="https://www.bioimaging.bmc.med.uni-muenchen.de/gerbiflim2024/flimprogram/index.html">German BioImaging </a><a href="https://www.bioimaging.bmc.med.uni-muenchen.de/gerbiflim2024/flimprogram/index.html">workshop on FLIM in Munich</a>.</p> <p>The FLIM data are analyzed with the open source software FLUTE available on GitHub: <a href="https://github.com/LaboratoryOpticsBiosciences/FLUTE"><strong><em>https://github.com/LaboratoryOpticsBiosciences/FLUTE</em></strong></a></p> <p>and published on Biological imaging Journal: <a href="https://www.cambridge.org/core/journals/biological-imaging/article/flute-a-python-gui-for-interactive-phasor-analysis-of-flim-data/862F290EC14187741BDA6B58E9868FA2"><strong><em>Gottlieb, D., Asadipour, B., Kostina, P., Ung, T., & Stringari, C. (2023). FLUTE: A Python GUI for interactive phasor analysis of FLIM data. Biological Imaging, 1-22. doi:10.1017/S2633903X23000211</em></strong></a></p>
Resources for BMF Collaborative Project 64: Predictors of premium for bio-based clothes and discount for second-hand clothes
Open the record for dataset details and reuse information.
Salinity measurements recorded at Lehanagh Pool using hand-held refractometers_IMTA lab Ireland
Open the record for dataset details and reuse information.
Arquivo Raster do Mapeamento HAND (Height Above Nearest Drainage) do Estado do Espírito Santo, Brasil
<p><strong>Descrição em português:</strong></p> <p>O arquivo compactado (HAND_ES.zip) contém arquivo raster (HAND_ES.tif) com o mapeamento do HAND para o estado do Espírito Santo, Brasil.</p> <p>O HAND foi obtido com o software TerraHidro/INPE, a partir do modelo digital de terreno de 30 metros ANADEM (v018).</p> <p><strong>Description in english:</strong></p> <p><span>The compressed file (HAND_ES.zip) contains a raster file (HAND_ES.tif) with the HAND mapping for the state of Espírito Santo, Brazil.</span></p> <p><span>The HAND was obtained using the TerraHidro/INPE software, based on the 30-meter ANADEM (v018) digital terrain model.</span></p>
One Hand project - Transfer of prosthesis control skill after training in VR, data set pre-test post-test
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HAND IN HAND: Empowering Teachers Across Europe to Deal with Social, Emotional and Diversity Related Career Challenges - International Dataset
<p><span>HAND IN HAND: Empowering teachers across Europe to deal with social, emotional and diversity related career challenges (HAND:ET) is a European (Erasmus K3) policy experimentation project that brings together 11 partners and 13 associated partners from seven countries (Austria, Croatia, Denmark, Germany, Portugal, Slovenia, and Sweden).</span></p> <p><span>The project focuses on teachers by supporting their development of social and emotional competencies as well as their diversity awareness (SEDA) to empower them for the complexity of everyday working life with increasingly diverse classrooms and enable them to deal flexibly with new challenges. It also puts the teachers' well-being at the centre by highlighting how developing SEDA competencies simultaneously fosters self-care for teachers, giving a central role to the voices of teachers.</span></p> <p><span>Information on the project can be accessed at: <a href="https://www.handinhand.si/en/" target="_blank" rel="noopener">https://www.handinhand.si/en/</a></span></p> <p><span>Further information on the project and the evaluation results can be found in the following publication: Kozina, A. (2024). Empowering Teachers Across Europe to Deal with Social, Emotional and Diversity Related Challenges, Volume 1: Experimentation Perspectives. Waxman.</span></p> <p><span>Information on the instruments used in the evaluation is provided in the attached document "HAND_ET_Scale_Documentation.pdf".</span></p> <p><span>Design: All countries implementing the experiment (Austria, Croatia, Portugal, Slovenia and Sweden) invited schools to participate. The schools participating in the experiment were randomly allocated to either the experimental or the control group. Central to the experiment’s design, schools had to agree to their participation in either condition (experimental or control). The intention was to test the effectiveness of the HAND:ET system in promoting SEDA competencies: to compare the changes in the same competencies from before implementing the HAND:ET system (pre-test) to after implementing the system (post-test) in a group of teachers (and other school staff) who took part in the experiment (experimental group) with a group of teachers (and other school staff) who did not participate (the control group). </span></p> <p><span> </span><span>Data collection period: T1 (pre-test) data was collected August to September 2022, T2 (post-test) data was collected Mai to July 2023.</span></p> <p><span> </span><span>Sample: The HAND_ET datafile includes primary and/or secondary teachers from Austria, Croatia, Portugal, Slovenia, and Sweden, who responded to the questionnaire either at first time point or second time point or both. In Austria, teachers teaching students in grades 1 to 4, in Croatia those teaching students in grades 1 to 8, in Portugal those teaching students in grades 1 to 12, in Slovenia those teaching students in grades 1 to 9, while in Sweden teachers teaching students in grades 4 to 9 participated.</span></p> <p><span> </span><span>The HAND_ET datafile is structured as follows: </span></p> <p><span>·</span><span> </span><span>Variables from 'IDS' to 'Gender' include general information about the participant (e.g group, participation in pre- or post-test, gender)</span></p> <p><span>·</span><span> </span><span>Variables from 'TC06exp' to 'TCN22_TCN2204' capture responses from questionnaire items administered during the pre-test.</span></p> <p><span>·</span><span> </span><span>Variables from 'TC02_2' to 'TCN30_TCN30Q04_TCN30G03_2' contain responses to questionnaire items used in the post-test data collection.</span></p> <p><span>·</span><span> </span><span>Variables from 'observe' to 'coopteach' include scale scores for the scales administered in pre-test.</span></p> <p><span>·</span><span> </span><span>Variables from 'observe_2' to 'coopteach_2' include scale scores for the scales administered in post-test.</span></p> <p><span>·</span><span> </span><span>Variables from 'observe_D' to 'CloseC_D' include scale scores calculated as differences between measurements at Time 1 (T1) and Time 2 (T2) for all questionnaire scales.</span></p> <p><span>·</span><span> </span><span>Variables from 'observe_D.1' to 'CloseS_D.5' contain scale score differences (T1-T2) with missing values imputed. As we did 5 imputations, there are five variables for each scale (as indicated at the end of the variable name).</span></p> <p><span> </span><span>The scale score for each participant at each point in time was computed as the arithmetic mean of responses to the items of a scale measuring a SEDA construct. A scale value was only computed if responses for at least half of the items of a scale were available. No overall scale score was computed for multidimensional constructs. Subscales were treated as separate scales in the analysis. The difference score was computed as the T2 minus T1 scale score. A positive value corresponds to an increase of the scale score and a negative value indicates a decrease in the scale score of T2 compared to T1.</span></p> <p><span> </span><span>The dataset includes variables with imputed scale scores. We only imputed the difference score for each of the outcome variables five times. The imputation was carried out separately for each of the scales based on the three variables: group, T1, and T2 scale score. We used predictive mean matching with the R package mice (Buuren & Groothuis-Oudshoorn, 2011) for imputation. The teachers that have a missing in the group variable have no imputed values.</span></p>
User Study Data from "HaptiGlow: Helping Users Position their Hands for Better Mid-Air Gestures and Ultrasound Haptic Feedback"
<p>This dataset contains user study data from the experiment reported in our IEEE World Haptics Conference 2019 paper.</p>
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Hand Screw Clamp
<p>@article{schieber2024asdf,<br> title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br> author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br> journal={arXiv preprint arXiv:2403.16400},<br> year={2024}<br>}</p>
Dataset - Hand-inspired flying grasper
<p>Dataset for manuscripts "Hand-inspired flying grasper".</p> <p>The file 'data_analysis.zip' contains the experimental data packages (rosbag format) for all the autonomous experiments in the the paper, which are recorded in the ROS system under Linux. By running the provided MATLAB-based code, all the data curves in the paper can be reproduced.</p> <p> </p>
Dataset for "Open Educational Resources for distributed hands-on teaching in molecular biology"
<p>This is the raw data for an article that describes novel OER consisting of low cost and locally produced public domain biological reagents, open source hardware and free software collaborative notebooks to teach LAMP DNA amplification, RT-PCR RNA detection, enzyme kinetics and fluorescence imaging. </p>
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Allen Brain Atlas
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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.