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Dataset of: "The Relativity of Reaching: Motion of the touched surface alters the trajectory of hand movements"
<p>The repository contains the data of the paper:</p> <p>The Relativity of Reaching: Motion of the touched surface alters the trajectory of hand movementsAuthors: Colleen P. Ryan, Simone Ciotti, Priscilla Balestrucci, Antonio Bicchi, Francesco Lacquaniti, Matteo Bianchi, Alessandro Moscatelli</p>
E. coli in handwashing water and hands (Harare, Zimbabwe)
<p>Paired samples of the concentration of E. coli in handwashing water sources and on the hands of volunteers (before and after handwashing) from Harare, Zimbabwe. The data are primary data collected, analyzed, and reported as described in: Navab-Daneshmand, T., Friedrich, M. N., Gächter, M., Montealegre, M. C., Mlambo, L. S., Nhiwatiwa, T., Mosler, H.-J., Julian, T. R. (2018). Escherichia coli Contamination across Multiple Environmental Compartments (Soil, Hands, Drinking Water, and Handwashing Water) in Urban Harare: Correlations and Risk Factors. The American Journal of Tropical Medicine and Hygiene, 98(3), 803, [https://doi.org/10.4269/ajtmh.17-0521](https://doi.org/10.4269/ajtmh.17-0521)</p>
Hand gestures raw images
<p>This dataset includes 4 categories of hand gestures: open hand, fist, right hand, left hand + one negative class</p>
Comparison of artificial hands developed in years 1980-2019
<p>File contains comparison of artificial hands developed in years 1980-03.2019. That was the basis for the review article "An Artificial Hand - Current State of Knowledge in Designing and Forecasting Changes" and includes following features:</p> <p>- article title in which device is described,</p> <p>- hand name,</p> <p>- publication year,</p> <p>- overall hand design,</p> <p>- thumb properties,</p> <p>- number of degrees of motion - number of all moving joints and its axis with not considered transmission and actuation system (DOF includes that)</p> <p>- type of actuator,</p> <p>- number of actuators,</p> <p>- transmission type,</p> <p>- available forces/torques,</p> <p>- operating speed,</p> <p>- sensors,</p> <p>- additional information.</p> <p>All the hands were searched in google schollar with phrases "artificial hand" and "robotic hand". There are included these articles/devices which were available with University access.</p>
Calibrated kinematic Ninapro hand movements data
<p>The kinematic data of the Ninapro (Non Invasive Adaptive Prosthetics) database includes calibrated kinematic data from 77 subjects, performing 40 hand movements and grasps. The whole database aims at allowing worldwide research groups to study hand kinematics. As well the dataset allows also to study the relationship between hand kinematics and muscle activity, since it is linked to sEMG datasets. The final goal of this work is to aid the progress in rehabilitation, physiotherapy, medicine, neuroscience and prosthetics. Therefore, the kinematic database improves the state of the art, being the most accurate, comprehensive and advanced reference for the largest kinematic database existing at the time of writing. <br> <br> <strong>Acquisition protocol</strong>:<br> Hand kinematics was measured using a 22-sensor CyberGlove II dataglove (CyberGlove Systems LLC, <a href="http://www.cyberglovesystems.com/">www.cyberglovesystems.com</a>). The CyberGlove is a motion capture data glove, instrumented with joint-angle measurements. It uses proprietary resistive bend-sensing technology to transform hand and finger motions into real-time digital joint-angle data.<br> <br> The calibrated kinematic Ninapro database includes 40 different movements of 77 intact subjects.<br> The subjects have to repeat several movement represented by movies that are shown on the screen of a laptop.<br> The experiment is divided in two exercises:<br> • Isometric, isotonic hand configurations and basic wrist movements<br> • Grasping and functional movements<br> Joint angles from raw data were calculated according to a very detailed calibration protocol. The protocol was previously applied to 10 different intact subjects and consists in recording 64 different poses or guided movements to obtain the gains and also some corrections because of cross-coupling effects for specific anatomical angles.</p> <p>Gracia-Ibáñez, V., Vergara, M., Buffi, J. H., Murray, W. M. & Sancho-Bru, J. L. Across-subject calibration of an instrumented glove to measure hand movement for clinical purposes. C. Comput. Methods Biomech. Biomed. Eng. 20, 587–597 (2017).<br> <br> <strong>Data Sets</strong>:<br> For each subject and exercise, the database contains one file in Matlab format (<a href="http://www.mathworks.com/">www.mathworks.com</a>) with synchronized variables. The variables included are the following ones:<br> • subject: subject number;<br> • exercise: exercise number;<br> • glove (22 columns): uncalibrated signal from the 22 sensors of the Cyberglove. Details on the location of the sensors are available at the link: <a href="http://ninapro.hevs.ch/node/123">ninapro.hevs.ch/node/123</a>;<br> • angles(22 columns): calibrated signal from the 22 sensors of the Cyberglove by applying an across-subject calibration<br> • re-stimulus (1 column): the a-posteriori refined label of the movement;<br> • re-repetition (1 column): re-stimulus repetition index;</p> <p> • stimulus (1 column): the label of the movement;<br> • repetition (1 column): stimulus repetition index;<br> • order of angles (22 columns) : name of the angles corresponding to variable “angles”.</p> <p>Raw glove data are also included in the folder "Raw data".</p>
Predictive perception of self-generated movements: Commonalities and differences in the neural processing of tool and hand actions
<p>Dataset relative to the following publication:</p> <p>Pazen, M., Uhlmann, L., van Kemenade, B.M., Steinsträter, O., Straube, B., Kircher, T. Predictive perception of self-generated movements: Commonalities and differences in the neural processing of tool and hand actions. <em>NeuroImage</em>. DOI: <a href="https://doi.org/10.1016/j.neuroimage.2019.116309">10.1016/j.neuroimage.2019.116309</a></p> <p>Details are specified in the "readme.docx" file.</p>
PianoMotion10M: Dataset and Benchmark for Hand Motion Generation in Piano Performance
<p>Recently, artificial intelligence techniques for education have been received increasing attentions, while it still remains an open problem to design the effective music instrument instructing systems. Although key presses can be directly derived from sheet music, the transitional movements among key presses require more extensive guidance in piano performance. In this work, we construct a piano-hand motion generation benchmark to guide hand movements and fingerings for piano playing. To this end, we collect an annotated dataset, PianoMotion10M, consisting of 116 hours of piano playing videos from a bird's-eye view with 10 million annotated hand poses. We also introduce a powerful baseline model that generates hand motions from piano audios through a position predictor and a position-guided gesture generator. Furthermore, a series of evaluation metrics are designed to assess the performance of the baseline model, including motion similarity, smoothness, positional accuracy of left and right hands, and overall fidelity of movement distribution. Despite that piano key presses with respect to music scores or audios are already accessible, PianoMotion10M aims to provide guidance on piano fingering for instruction purposes.</p>
Impact of exposure to second-hand smoke during childhood on blood DNA methylation
<p>Meta-analysis results for CpGs associated with household postnatal exposure to second hand smoke (SHS). Cohorts order are GenR, PIAMA, HELIX, BAMSE_Medall, BAMSE_EpiGene, and ALSPAC. Effect size represents the change in DNAm by number of household smokers (from 0 smokers to 2 smokers or more). Gene and CpG Island relative position are indicated (TSS200 is the region from Transcription start site (TSS) to − 200 nt upstream of TSS; TSS1500 covers − 200 to − 1500 nt upstream of TSS; S_Shore corresponds to the 2 kb regions immediately downstream of the CpG island; N_Shore corresponds to the 2 kb regions immediately upstream of the CpG island). Chr stands for Chromosome, SE stands for standard error, I2 stands for heterogeneity index, and FDR stands for False Discovery Rate. (DNA data obtained through Iluumina 450K array profiling)</p>
RIS Based Hand Gesture Recognition Dataset
<h1><strong>RIS Based Hand Gesture Recognition Dataset</strong></h1> <h2><strong>Overview</strong></h2> <div>This dataset contains images for gesture recognition, divided into two main sets: dataset0608 and data_synthetic_variab. The data was collected using a wooden hand. </div> <div> </div> <h3>dataset0608</h3> <div>This dataset consists of two modes: ris_random and ris_optimized. The main difference between the two subfolders is the configuration of the RIS (random or optimized).</div> <div> </div> <div>This dataset consists of four subfolders: ris_random, ris_random2, ris_optimized, and ris_optimized2. The main difference between the subfolders is the format of the data:</div> <div>- ris_random and ris_optimized: Data is stored in individual files for each frame, named as 'frame_{i}{posture}{n_med}' </div> <div>- ris_random2 and ris_optimized2: Data has already been processed and combined into single files for all frames using the compact_files_frames.txt function, named as 'all_frames_{posture}_{n_med}' </div> <div> </div> <div>For each gestures = {close, two, open}, we have n_med values from 0 to 114 and 10 frames. Therefore, the ris_random and ris_optimized folders contain 10 frames × 115 measurements × 3 gestures = 3450 files, while the ris_random2 and ris_optimized2 folders contain 1 × 115 measurements × 3 gestures = 345 files.</div> <div> </div> <h3>data_synthetic_variab</h3> <div>This dataset consists of two modes: ris_random and ris_optimized. The main difference between the two subfolders is the configuration of the RIS (random or optimized). </div> <div> </div> <div>This dataset consists of four subfolders: ris_random, ris_random2, ris_optimized, and ris_optimized2. The main difference between the subfolders is the format of the data:</div> <div>- ris_random and ris_optimized: Data is stored in individual files for each frame, named as 'frame_{i}{posture}{n_med}' </div> <div>- ris_random2 and ris_optimized2: Data has already been processed and combined into single files for all frames using the compact_files_frames.txt function, named as 'all_frames_{posture}_{n_med}' </div> <div> </div> <div>For each gestures = {close, two, open}, we have n_med values from 0 to 8 and 10 frames. This dataset provides additional synthetic data with variations in hand position to increase the dataset's diversity. Each gesture is represented by 8 different ways, where the hand position was slightly modified between each sample. These real data were used as a basis for generating synthetic data. By using the functions in the files "multiply_files.txt" and "add_gaussian_noise.txt," the dataset was expanded and made more realistic by adding Gaussian noise to the images.</div> <div> </div> <div>Therefore, the ris_random and ris_optimized folders contain 10 frames × 8 measurements × 3 gestures = 240 files, while the ris_random2 and ris_optimized2 folders contain 1 × 8 measurements × 3 gestures = 24 files.</div> <div> </div> <h3>Functions</h3> <div>* **add_gaussian_noise.txt:** This script adds Gaussian noise to the images to simulate real-world conditions and improve the robustness of the model.</div> <div>* **compact_files_frames.txt:** This script combines multiple frames into a single image, which can be useful for certain types of analysis.</div>
IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_Data Fusion and Sensing Technology_characterization of sensing system for grippers_v0
<p><span>The dataset contain the data acquired from the multi-sensorized fingers developed in T5.1 and integrated into grippers used in IntelliMan UC3 and UC4. The data contain tactile data, proximity data and endoscopic camera data for the evaluation of sensor performance with respect to IntelliMan use cases requirements.</span></p>
Dataset for the manuscript "Assessing the Role of Hydrodynamics in Enhancing HAND-Derived Synthetic Rating Curves: A Comparative Study in the Wu River Basin, Taiwan"
<p>This is the dataset for developing the HAND-hd workflow mentione in the manuscript "Assessing the Role of Hydrodynamics in Enhancing HAND-Derived Synthetic Rating Curves: A Comparative Study in the Wu River Basin, Taiwan". In the manuscript, we used the topographical cross-sectional survey data to generate topographical synthetic rating curves (RCb) to validate the performance of the HAND method based synthetic rating curves (HAND-SRC) produced by the HAND-hd workflow. Due to regulatory restrictions under Taiwanese law, public sharing of 5-meter (or finer) DEM data is prohibited. However, researchers interested in accessing processed HAND raster data for research purposes may contact the corresponding author.</p>
Dataset: Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training
<p>This repository contains supplementary materials for the following conference paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Daniel Tovarňák, Pavel Čeleda.<br> <em>Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training.</em><br> In Proceedings of the 51st IEEE Frontiers in Education Conference (FIE 2021).<br> <a href="https://doi.org/10.1109/FIE49875.2021.9637052">https://doi.org/10.1109/FIE49875.2021.9637052</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2112.11118">https://arxiv.org/abs/2112.11118</a></p> <ol> </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 paper (not only this web link).</p> <p>Some of the linked repositories have their separate citation entry; please use that one as well, if possible.</p> <pre><code>@inproceedings{Svabensky2021toolset, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and Tovar\v{n}\'{a}k, Daniel and \v{C}eleda, Pavel}, title = {{Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training}}, booktitle = {Proceedings of the 51st IEEE Frontiers in Education Conference}, series = {FIE '21}, location = {Lincoln, NE, USA}, publisher = {IEEE}, address = {New York, NY, USA}, month = {10}, year = {2021}, pages = {1--9}, numpages = {9}, isbn = {978-1-6654-3851-3}, url = {https://doi.org/10.1109/FIE49875.2021.9637052}, doi = {10.1109/FIE49875.2021.9637052}, }</code></pre> <p><strong>Structure of the repository</strong></p> <p>We share four types of content described below. Each of the four types of materials includes:</p> <ul> <li>a link to an up-to-date GitLab repository, which may contain possible future revisions and error corrections, and</li> <li>a ZIP archive here on Zenodo that serves as a snapshot of the state when the article was published.</li> </ul> <p><strong>Attached content</strong></p> <ol> <li><strong>Logging toolset.</strong> It is implemented in the form of <a href="https://www.ansible.com/">Ansible</a> roles and consists of three separate projects: for <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-bash">Bash logging</a>, <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-msf">Metasploit logging</a>, and <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-forward">log forwarding within the sandbox</a>.</li> <li><strong>Sample training.</strong> To quickly test the toolset, instantiate the exemplary cybersecurity game called <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/junior-hacker">Junior hacker training</a>. It already deploys the logging; no further setup is needed.</li> <li><strong>Dataset.</strong> The data collected with the toolset are available at <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/datasets/commands">this repository</a>, which is being gradually updated with new data. Attached is a subset of this repository that includes the data analyzed in the paper.</li> <li><strong>Analytical tools.</strong> To analyze the data, you can either use the attached Python scripts, or instantiate <a href="https://gitlab.ics.muni.cz/muni-kypo/tools/commands-elk">this project for ELK</a>.</li> </ol>
Towards safe human-to-robot handovers of unknown containers: pre-trained models and 3D hand keypoints annotations
<p>This repository contains additional data to be used with the implementation of the real-to-simulation framework of the paper <em>Towards safe human-to-robot handovers of unknown containers</em>. The data include pre-trained models and annotations of the 3D hand poses for selected recordings from the public training and testing sets of <a href="http://corsmal.eecs.qmul.ac.uk/containers_manip.html">CORSMAL Container Manipulation (CCM) dataset</a>. The pre-trained models are used for classifying the filling type and filling level of a container. 3D hand poses are annotated as 21 keypoints based on the <a href="https://github.com/CMU-Perceptual-Computing-Lab/openpose">OpenPose</a> format.</p>
Experimental data for "A Novel Clinical-Driven Design for Robotic Hand Rehabilitation: Combining Sensory Training, Effortless Setup and Large Range of Motion in a Palmar Device"
<p>Experimental data for the interaction force benchmark test of the PRIDE haptic hand rehabilitation device.</p>
Labelled and unlabelled hand acceleration data captured unobtrusively from PD patients and Healthy Controls
<p>The dataset contains acceleration signals captured in-the-wild via the IMU sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). It contains two different groups of subjects:</p> <ul> <li>tremor_sdata.pickle --> A group of 45 subjects that have been subjected to neurological examination (the same dataset as https://zenodo.org/record/3519213)</li> <li>tremor_gdata.pickle --> A group of 454 subjects who just self-reported their PD status</li> </ul> <p>All subjects contributed accelerometer data using their personal smartphones, for a period spanning many months. Tri-axial acceleration values were recorded automatically whenever a phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article.</p> <p>The data is presented as a python dictionary, indexed by the subject ids. Each element of the dictionary is a list with the following significance:</p> <table> <thead> <tr> <th scope="col">Index</th> <th scope="col">Meaning</th> </tr> </thead> <tbody> <tr> <td>0</td> <td>List of np.arrays, Each array contains the power spectral density for an acceleration segment of 5s duration</td> </tr> <tr> <td>1</td> <td>Dictionary, Denotes subject updrs</td> </tr> <tr> <td>2</td> <td>List of str containing a unique identifier of the acceleration session that each segment in the other lists belongs to</td> </tr> <tr> <td>3</td> <td>List of np.arrays, Each array contains the pre-processed acceleration values for a 5s segment</td> </tr> </tbody> </table> <p> </p><p>The subject updrs is represented as dictionary containing the following tremor-related annotation values (FOR THE FIRST GROUP ONLY):<br> * updrs16: scalar int<br> The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject.</p> <p></p> <p>* updrs20_right: scalar int in range [0, 4]<br> The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs20_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* updrs21_right: scalar int in range [0, 4]<br> The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs21_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* sp_expert: scalar int in range [0, 1]<br> A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article.<br> A '1' value indicates that the subject has tremor.<br> A '0' value indicates that the subject doesn't have tremor.</p> <p>* pd_status: scalar int in range [0, 1]<br> A '1' value indicates that the subject is a PD patient.<br> A '0' value indicates that the subject is a Healthy Control</p> <p>Note: Each annotation value refers to the subject as a whole, and not in any one session.</p>
EMG hand gesture dataset
<p>This dataset contains surface electromyography (sEMG) data of 5 different hand gestures performed by eight subjects. The data is store in a folder for each one of the subjects, each folder, contains 5 files, one for each gesture. The files contain the sEMG data of 4 sEMG channels placed on the forearm. The gestures are: open hand, closed hand, lateral pinch, signaling sign, rock sign. A file with a demo Recurrent Neural Network is also included.</p> <p>The reader may refer to the following articles for more information about the dataset: <a href="https://doi.org/10.3390/app12199700">LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals</a> and <a href="https://doi.org/10.3390/biomimetics8010029">A Proposal of Bioinspired Soft Active Hand Prosthesis</a>.</p>
Toward Early and Objective Hand Osteoarthritis Detection by using EMG during grasps
<p>Dataset analyzed in the study "Toward Early and Objective Hand Osteoarthritis Detection by using EMG during grasps". Use of the data requires proper reference to [1].</p> <p>Dataset contains Electromyographic data from forearm, recorded with an 8-channel sEMG Biometrics Ltd. device. The fields contained in the structure are those detailed in the following scheme::</p> <ul> <li>Group: 0 for healthy subjects; 1 for HOA patients</li> <li>Subject: subject ID;</li> <li>Grasp: grasp ID, according to Figure 1 [1];</li> <li>Raw EMG data (7 columns): Raw sEMG data without any filter and not resampled, for the seven representative spot areas according to [2].</li> </ul> <p>[1] Jarque-Bou, N.J.; Gracia-Ibáñez, V.; Roda-Sales, A.; Bayarri-Porcar, V.; Sancho-Bru, J.L.; Vergara, M. Toward Early and Objective Hand Osteoarthritis Detection by Using EMG during Grasps. <em>Sensors</em> <strong>2023</strong>, <em>23</em>, 2413. https://doi.org/10.3390/s23052413</p> <p>[2] Jarque-Bou, N. J., Vergara, M., Sancho-Bru, J. L., Alba, R.-S. & Gracia-Ibáñez, V. Identification of forearm skin zones with similar muscle activation patterns during activities of daily living. <em>J. NeuroEngineering Rehabil. </em>(2018).</p> <p> </p>
Epistemic Insight Initiative Data set: Findings from a hands-on science intervention to build school students' agency and epistemic insight into the power and limitations of science.
<p>Data set that reports on the Findings from a hands-on science intervention to build school students’ agency and epistemic insight into the power and limitations of science.</p> <p>We report on an empirical study that sought to address three questions:</p> <ul> <li>A study to extend the cognitive science of insight into the field of children’s developing understanding of the power and limitations of science</li> <li>A study to examine the effectiveness of the Epistemic Insight (EI) strategy which is designed to give each child hands-on, minds-on science</li> <li>A study to examine the effectiveness of the EI strategy to sustain science activity in schools during the challenging conditions of the pandemic</li> </ul> <p>1919 children and 63 teachers from 24 schools in England took part in the project. This includes both primary and secondary schools. The data reported here are taken from ten primary schools representing 1045 children aged 8-12 in years 4 to 7.</p> <p> </p>
Biface Hand Axe
Chunky chert biface, possibly a hand axe. Found in Tensas Parish, Louisiana 62mm x 32mm x 40mm, 52.4g Source: Objaverse 1.0 / Sketchfab
Hand Painted WW2 JU 88 Bomber
This Plane model was done as part of a college piece of mine based on the topic of "Forgotten" I have one more plane to upload as this is the first of a mini scene. The tail texture did slightly self destruct during the exporting process needs to be fixed after the rest of my project is completed. All of the textures are hand painted with a select few being helped with the use of masking etc. Any and all feedback is welcome. The assembly line of the Junkers Ju 88 ran constantly from 1936 to 1945 and more than 16,000 Ju 88s were built in dozens of variants. Links to facts etc here : https://en.wikipedia.org/wiki/Junkers_Ju_88 Source: Objaverse 1.0 / Sketchfab
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.