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.
94
datasets available to search
ShareScore release 0.9.0
Dataset results
94 results for “Haptics”
Measurement dataset for a 3D-printed fingertip haptic device
<p>The device's raw position tracking, blocked force, and frequency data.</p>
Humans flexibly use visual priors to optimize their haptic exploratory behaviour
<p>datasets of the first, second and third experiment</p> <p>for each subject, there is one .raw file and one folder containing the .trj movement data files of each trial.</p> <p>the .trj files contain the variables (columns left to right): time in ms, x, y, z, force, velocity, position (-1 not on stim, 0 left, 1 right), inStimPos (-1 not, 0 outerstim, 1 innerstim)</p> <p>the .raw files contain the variables (columns left to right): block nr, trial nr, randomization nr, stimulus orientation, visual quality, startstim (0 left 1 right), ref stim, comp stim (always < ref), chosenStim, force. in exp2&3, ref stim and comp stim have the numbers 1xx for the "hard" block and 2xx for the "easy" block.</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>
Neural correlates of top-down modulation of haptic shape versus roughness perception
<p>Exploring an object’s shape by touch also renders information about its surface roughness. It has been suggested that shape and roughness are processed distinctly in the brain, a result based on comparing brain activation when exploring objects that differed in one of these features. To investigate the neural mechanisms of top-down control on haptic perception of shape and roughness, we presented the same multidimensional objects but varied the relevance of each feature. Specifically, participants explored two objects that varied in shape (oblongness of cuboids) and surface roughness. They either had to compare the shape or the roughness in an alternative-forced-choice-task. Moreover, we examined whether the activation strength of the identified brain regions as measured by functional magnetic resonance imaging (fMRI) can predict the behavioral performance in the haptic discrimination task. We observed a widespread network of activation for shape and roughness perception comprising bilateral pre- and postcentral gyrus, cerebellum, and insula. Task-relevance of the object’s shape increased activation in the right supramarginal gyrus (SMG/BA40) and the right precentral gyrus (PreCG/BA44) suggesting that activation in these areas does not merely reflect stimulus-driven processes, such as exploring shape, but also entails top-down controlled processes driven by task-relevance. Moreover, the strength of the SMG/PreCG activation predicted individual performance in the shape but not in the roughness discrimination task. No activation was found for the reversed contrast (roughness > shape). We conclude that macrogeometric properties, such as shape, can be modulated by top-down mechanisms whereas roughness, a microgeometric feature, seems to be processed automatically.</p>
Piezo-Ionic Actuator for Haptic Feedback
<p>This upload contains the dataset for the paper: 'Piezo-Ionic Actuator for Haptic Feedback'. The description of the data can be found in the ReadMe file in the folder.</p>
Haptics and Targeted Box and Blocks Data for Evaluating Reach and Grasp Segmentation Methods
<p>Data associated with <em>Approaches for Segmenting the Reaching and Targeting Motion Primitives in Functional Upper Extremity Reaching Tasks. </em>Pre-print available at: https://doi.org/10.36227/techrxiv.22672312.v1</p> <p>Code associated with these data available at <a href="https://github.com/kjacks21/UE-reach-grasp-seg">kjacks21/UE-reach-grasp-seg: Segmenting reach and grasp phases of reaching motions (github.com)</a>.</p> <p>Data include kinematics from performing a task in a haptics virtual environment and optical motion capture of participants completing the targeted Box and Blocks test. Please reference the paper for additional information.</p> <p>For questions, please contact kjacks21 [at] gmu [dot] edu or zduric [at] gmu [dot] edu.</p>
A Prospective Observational Case Series of a Four-ring-haptic Acrylic One-piece IOL
ClinicalTrials.gov study NCT01244373. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Innovative Hand Rehabilitation System With Synchronized Contralateral Haptic Feedback
ClinicalTrials.gov study NCT07077460. IPD Sharing: UNDECIDED. Countries: 1. Publications: 83.
HAptic Neurofeedback Design for Stroke
ClinicalTrials.gov study NCT04130711. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Immersive Virtual Environments and Wearable Haptic Devices in Rehabilitation of Children With Neuromotor Impairments
ClinicalTrials.gov study NCT03353623. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Effect of Haptic Cueing on Long-Range Autocorrelations in Parkinson's Disease Gait Variability
ClinicalTrials.gov study NCT05790759. IPD Sharing: YES. Countries: 1. Publications: 1.
The Effect of Haptic and Highly Fidelity Simulator in Cardiopulmonary Resuscitation Training
ClinicalTrials.gov study NCT05343221. IPD Sharing: NO. Countries: 1. Publications: 4.
BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Feedback With Stroke Survivors
ClinicalTrials.gov study NCT04995367. IPD Sharing: NO. Countries: 1. Publications: 9.
The Effect of Haptic Interactive Virtual Reality on Nasogastric Tube Practice Teaching
ClinicalTrials.gov study NCT05040191. IPD Sharing: NO. Countries: 1. Publications: 1.
Development and Implementation Haptic VR Simulation
ClinicalTrials.gov study NCT07175467. IPD Sharing: YES. Countries: 1. Publications: 3.
Assessment of Upper Limb Motor Performance Using an Interface With Haptic Feedback
ClinicalTrials.gov study NCT05819047. IPD Sharing: YES. Countries: 1. Publications: 14.
Movement Characterization in Spastic/Dystonic Cerebral Palsy Using Haptic Feedback in Virtual Reality
ClinicalTrials.gov study NCT03744884. IPD Sharing: NO. Countries: 1. Publications: 54.
Developments of Novel Virtual Visual and Haptic Stimulation Systems for the Elderly
ClinicalTrials.gov study NCT06435078. IPD Sharing: Not stated. Countries: 1. Publications: 47.
Effectiveness of Haptic Technology in Teaching Urinary Catheterization Skill
ClinicalTrials.gov study NCT04653233. IPD Sharing: NO. Countries: 1. Publications: 2.
Changing Finger Movement Perception: Influence of Active Haptics on Visual Dominance
<p>The perception of one’s own body is a complex mechanism that can be disturbed by conflicting sensory information and lead to illusory (mis-) perceptions. Prominent models of multisensory integration propose that sensory streams are integrated according to their reliability by approximating Bayesian inference. As such, when considering self-attribution of seen motor actions, previous works argue in favor of visual dominance over other sensations, and internal cues. In the present work, we use virtual reality and a haptic glove to investigate the influence of an active haptic feedback on one’s visual and agency judgments over a performed finger action under experimentally manipulated visual and haptic feedbacks. Data overall confirm that vision dominates for agency judgment in conditions of multisensory conflict. Interestingly, we also show that participants’ visual judgment over their finger action is sensitive to multisensory conflicts (vision, proprioception, motor afferent signals, and haptic perception), thus bringing an important nuance to the widely accepted view on a general visual dominance.</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.