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.
40
datasets available to search
ShareScore release 0.7.1
Dataset results
40 results for “Kinematic modelling”
New insights into crustal deformation of the Indonesia-Australia-New Guinea collision zone from a broad-scale kinematic model: Supplementary Model Files
<p>Supplementary kinematic model input for the JGR: Solid Earth publication "New insights into crustal deformation of the Indonesia-Australia-New Guinea collision zone from a broad-scale kinematic model".</p>
Multi-scale sea ice kinematics modeling dataset with a tripolar grid hierarchy (TS grids) in CESM
<p>1. We design a new tripolar grid generation method for global ocean-sea ice modeling. The generated grid is orthogonal and compatible with ocean and sea ice models. A hierarchy of ocean-sea ice model grid is constructed and incorporated in CESM by using the grid generation method. The generated tripolar grids are regarded as TS045, TS015 and TS005, with the nominal resolution of 0.45<sup>o </sup>(800x560), 0.15<sup>o</sup> (2400x1680) and 0.05<sup>o </sup>(7200x5040), respectively. The resolution range for the grid hierarchy covers climate modeling to sub-mesoscale capable for ocean modeling.</p> <p>2. Atmosphere forced simulations based on CESM D-type experiments are carried out for TS grid. Both TS045 and TS015 experiments start with no sea ice, integrate for 42 years. TS005 starts from the equilibrium state of TS015 result (36 year), runs for another 7 years.</p> <p>3. TS0*.grid files include necessary grid information, such as grid latitude, longitude, etc. TS0*.kmt files denote the deepest level at each grid location. Bilinear interpolators from TS grid to atmosphere T62 grid are provided with map*.nc files. The model outputs at year 42 are provided with T*.nc files. The last two kinds of netcdf files are compressed with Linux command "gzip".</p>
Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system
<p><span><span>In our daily life we often make complex actions comprised of linked movements, such as reaching for a cup of coffee and bringing it to our mouth to drink. Recent work has highlighted the role of such linked movements in the formation of independent motor memories, affecting the learning rate and ability to learn opposing force fields. In these studies, distinct prior movements (lead-in movements) allow adaptation of opposing dynamics on the following movement. Purely visual or purely passive lead-in movements exhibit different angular generalization functions of this motor memory as the lead-in movements are modified, suggesting different neural representations. However, we currently have no understanding of how different movement kinematics (distance, speed or duration) affect this recall process and the formation of independent motor memories. Here we investigate such kinematic generalization for both passive and visual lead-in movements to probe their individual characteristics. After participants adapted to opposing force fields using training lead-in movements, the lead-in kinematics were modified on random trials to test generalization. For both visual and passive modalities, recalled compensation was sensitive to lead-in duration and peak speed, falling off away from the training condition. However, little reduction in force was found with increasing lead-in distance. Interestingly, asymmetric transfer between lead-in movement modalities was also observed, with partial transfer from passive to visual, but very little vice versa. Overall these tuning effects were stronger for passive compared to visual lead-ins demonstrating the difference in these sensory inputs in regulating motor memories. Our results suggest these effects are a consequence of state estimation, with differences across modalities reflecting their different levels of sensory uncertainty arising as a consequence of dissimilar feedback delays. </span></span></p>
RPC-Net Dataset. Simultaneous HD-sEMG Recordings on the Forearm and angles of a 29-DOF Hand Kinematic Model
<p>The dataset in this repository comprises data acquired during the doctoral research project of Giovanni Rolandino at the Nuffield Department of Surgical Sciences, University of Oxford. Five sub-datasets make up the repository:</p> <p>DS1: Simultaneous acquisition of high-density surface electromyography (HD-sEMG) signals from the forearm and hand position kinematics. Data were recorded from 12 healthy subjects while they cycled through 16 hand poses. HD-sEMG was acquired with traditional gel electrode arrays.</p> <p>DS2: A similar protocol to DS1 was followed, but the HD-sEMG was acquired using a novel dry-electrode array. This dataset includes 16 subjects. Whereas DS1 included data from a single session for each subject, DS2 includes two sessions, acquired hours to days apart; these sessions are identified as s1 and s2.</p> <p>DS3: This subset consists of two parts. DS3.a repeats the protocol used in DS2 with 4 subjects, introducing repositioning between trials. DS3.b includes the results of the real-time assessment of RPC-Net, a shallow neural network trained with data from DS3.a to estimate hand position from HD-sEMG activity. DS3.b contains the real-time output recorded during prompt-matching tasks and the corresponding targets.</p> <p>DS4: This subset includes data related to the assessment of RFC-Net, a shallow neural network designed to estimate hand position from neck muscle activation. Experiments were performed on 8 healthy participants and 8 participants with tetraplegia. DS4.a includes the data used for training the network, while DS4.b includes data from the testing phase of the algorithm. DS4.b.s1 includes results from a cursor control task, and DS4.b.s2 includes results from a virtual hand control task.</p> <p>AD1: Additional data related to the electrical validation of the dry-electrode array.</p> <p>Code for processing the data in this repository is available on Dropbox:<br>https://www.dropbox.com/scl/fo/nkvbse7evo0k8ou1utn7i/AMDh_MOZQJ6gCwDXGPadmZ0?rlkey=ynoix3anpc81v24hogn3fymb4&st=xrqx07q2&dl=0</p> <p>For additional information, readers are referred to the original papers detailing acquisition protocols and processing procedures:</p> <p>1) G. Rolandino, M. Gagliardi, T. Martins, G. L. Cerone, B. Andrews, J. J. FitzGerald. Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation. IEEE Transactions on Biomedical Engineering, 71(5):1617-1627, May 2024. doi:10.1109/TBME.2023.3346192.</p> <p>2) G. Rolandino, C. Zangrandi, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. HDE-Array: Development and Validation of a New Dry Electrode Array Design to Acquire HD-sEMG for Hand Position Estimation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32:4004-4013, 2024. doi:10.1109/TNSRE.2024.3490796.</p> <p>3) G. Rolandino, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Performance of a ML-Based 3-DoF Kinematic Model in Estimating Hand Position from High-Density EMG. Presented at IFESS Conference, Bath, UK, September 2024.</p> <p>4) G. Rolandino, L. Lion, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Artificial Neural Networks for HD-sEMG-Based Hand Position Estimation: Addressing Inter- and Intra-Subject Variability. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025.</p> <p>5) G. Rolandino, G. Parisi, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Real-Time Hand Kinematic Estimation with HD-sEMG and Artificial Neural Networks: Feasibility and Effects of Multi-Subject Training and Visual Feedback. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025.</p> <p>6) G. Rolandino, V. Taboni Lisboa, T. Vieira, A. Cliquet Jr., B. Andrews, J. J. FitzGerald. HD-sEMG-Based Control Using Neck Muscles and Shallow Neural Networks: Assessing Performance in Rehabilitation-Oriented Tasks. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025.</p> <p>This dataset benefited from the support of all listed authors and arose from collaborations between the Oxford Neural Interfacing Group; LISiN (Politecnico di Torino, Turin, Italy); the Department of Orthopedics, Rheumatology and Traumatology (University of Campinas, SP, Brazil); and the Oxford Robotics Institute (University of Oxford, Oxford, UK). Part of this work was funded by the John Fell Oxford University Press Research Fund.</p> <p>The corresponding author is available for questions or clarification at g.rolandino@protonmail.com.</p>
Kinematic data and mathematical modeling of sea star locomotion
<p>It is unclear how animals with radial symmetry control locomotion without a brain. Using a combination of experiments, mathematical modeling, and robotics, we tested the extent to which this control emerges in sea stars from the local control of their hundreds of feet and their mechanical interactions with the body. We discovered that these animals (<em>Protoreaster nodosus</em>) compensate for an experimental increase in their submerged weight by recruiting more feet that synchronize in the power stroke of the locomotor cycle. Mathematical modeling replicated this response to loading in the absence of nervous communication and demonstrated how the body weight serves as a regulator of recruitment. We built a robotic sea star with an array of independently-controlled actuators that were also recruited in greater numbers under higher loads due to their collective mechanics. These findings demonstrate that an array of actuators in biological and robotic systems are capable of cooperative transport with dynamic adjustments to loading. This form of distributed control contrasts the conventional view of animal locomotion as governed by the central nervous system and offers inspiration for the design of engineered devices with arrays of actuators.</p>
FIGURE 2 in Achieving kinematic identity across shape diversity in musculoskeletal modeling
FIGURE 2. The top images show an overlay of the reduced-asymmetry australopithecine pelvis (beige) with the ADL australopithecine pelvis (dark green). The bottom color-coded distance map pelvis show the distance between the reduced-asymmetry australopithecine pelvis with the ADL australopithecine pelvis.
FIGURE 4 in Achieving kinematic identity across shape diversity in musculoskeletal modeling
FIGURE 4. AnyBody australopithecine musculoskeletal model without (left) and with (right) muscle model visualization.
FIGURE 1 in Achieving kinematic identity across shape diversity in musculoskeletal modeling
FIGURE 1. The flowchart shows the major steps required to build the ADL australopithecine model. In the blue boxes, the ADL human model is driven with the Schreiber and Moissenet (2019) human locomotion data. From these ADL human simulations, the dimension of the pelvis and femur can be extracted as well as model motion profiles used at later stages of the process (Figure 5). The gray boxes show the major steps in transforming (TPS-based morphing) the ADL human pelvis to match the australopithecine morphology (A.L. 288-1 reduced-asymmetry pelvis; Australopithecus afarensis), thus creating the ADL australopithecine pelvis. The green boxes show the steps necessary to create the ADL australopithecine (hybrid) femur from the ADL human femur.
FIGURE 5 in Achieving kinematic identity across shape diversity in musculoskeletal modeling
FIGURE 5. This flowchart shows the major steps required to generate the C3D motion file to drive the walking simulations with an australopithecine hip. Blue, light blue, and blue/grey and blue/green dashed boxes are the same boxes from Figure 1. The original ADL human model (blue box) is morphed based on the australopithecine pelvis (blue/grey dashed box) and femur (blue/green dashed box) to create the ADL australopithecine model (orange box). The results from the human walking simulation (blue box) are combined with the L5-sacral offset translation (light blue box) to generate new "experimental marker data" that are combined with the original ground reaction force data from Schreiber and Moissenet (2019) (purple box). The ADL australopithecine model and new motion data are then used to drive the simulations of walking with an australopithecine hip.
FIGURE 6 in Achieving kinematic identity across shape diversity in musculoskeletal modeling
FIGURE 6. Motion of the pelvis and lower limb joints for one individual walking simulation with both human (red lines) and australopithecine (black circles) shaped hips. A. Pelvic rotation (transverse plane), tilt (sagittal plane) and drop (coronal plane). B. Hip flexion-extension, abduction-adduction, and internal-external rotation. C. Knee flexion-extension, ankle dorsi-plantar flexion, subtalar eversion-inversion.
astroneb/WR_PN_Kinematic_Models: WR-PN-Kinematic-Models.1.3
<p><strong>Morpho-kinematic properties of Wolf-Rayet planetary nebulae. Supplementary Data</strong></p> <p><strong><a href="https://astroneb.github.io/WR_PN_Kinematic_Models/figure5/">Figure 5</a>.</strong> The interactive figure of the morpho-kinematic mesh models of the PNe M 3-30, Hb 4, IC 1297, Th 2-A, Pe 1-1, M 1-32, M 3-15, M 1-25, Hen 2-142, K 2-16, MGC 6578, M 2-42, NGC 6567 and NGC 6629.</p> <p><strong>Citation</strong></p> <pre><code class="language-bash">@article{Danehkar2021, author = {{Danehkar}, A.}, title = {Morpho-kinematic properties of Wolf-Rayet planetary nebulae}, journal = {ApJS}, volume = {260}, number = {1}, pages = {14}, year = {2022}, doi = {10.3847/1538-4365/ac5cca} }</code></pre> <p><strong>Learn More</strong></p> <p>GitPage: <a href="https://astroneb.github.io/WR_PN_Kinematic_Models/">https://astroneb.github.io/WR_PN_Kinematic_Models/</a></p>
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS
<p>Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction.</p> <p>The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes.</p>
Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system
Open the record for dataset details and reuse information.
Kinematic data and mathematical modeling of sea star locomotion
Open the record for dataset details and reuse information.
The MATLAB code for "A kinematic model for understanding rain formation efficiency of a convective cell"
<p>Please check the code for the 1D model and the plotting commands. Please start from main.m </p>
Foot kinematics and kinetics data for different static foot posture collected using a multi-segment foot model
<p>Dataset presented in the paper <em>"Foot kinematics and kinetics data for different static foot posture collected using a multi-segment foot model".</em></p> <p>This dataset contains human foot joints kinematics and kinetics data collected during walking, classified depending on their static foot posture. The kinematics data were recorded using a three-dimensional motion analysis system, and kinetics data were recorded through a pressure platform. The data was collected considering a multi-segment foot model that considers the ankle, midtarsal and first metatarsophalangeal joint. A total of 70 healthy subjects with different static posture (highly pronated, highly supinated and normal, as classified by the foot posture index) participated in the experiments. This dataset contains a total of 350 continuous recordings of anatomical angles and joint moments of the ankle, midtarsal, and first metatarsophalangeal joints of the right foot during walking, as well as the right foot contact pressures recorded. The recordings were collected at 100 Hz, and the resulting data are provided filtered and resampled to 100 frames evenly distributed along the stance phase. Participants’ descriptive data are also provided: age, weight, height, and foot anthropometric data and foot posture index for both feet. The data are presented as a spreadsheet file (.xlsx) and a Matlab structure file (.mat), with contact pressures provided only in the .mat file. Further details and data validation are provided in the main paper.</p>
FIGURE 3 in Achieving kinematic identity across shape diversity in musculoskeletal modeling
FIGURE 3. Control landmarks for the AnyBody TPS-morphing on the ADL human femur and pelvis.
Supplementary dataset for " Kinematic rupture modeling of broadband ground motion from the 2022 MS6.9 Menyuan earthquake"
<p>This is the data used in " Kinematic rupture modeling of broadband ground motion from the 2022 MS6.9 Menyuan earthquake". The paper is currently under review.</p>
Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS
<p>InSAR Line-of-Sight (LOS) velocities and their associated uncertainties in the southeastern Tibetan Plateau, along with the strain rate fields.</p> <p><br>Citations:</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS. Geophysical Research Letters.</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13731812</p>
Supplemental material to 'A variational rigid-block modelling approach to nonlinear elastic and kinematic analysis of failure mechanisms in historic masonry structures subjected to lateral actions'
<p>This repository contains the data necessary to reproduce the content of the article:</p> <blockquote> <p>A variational rigid-block modelling approach to nonlinear elastic and kinematic analysis of failure mechanisms in historic masonry structures subjected to lateral actions (2021). Earthquake Engineering & Structural Dynamics, 1–23. <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/eqe.3512">https://doi.org/10.1002/eqe.3512</a></p> </blockquote> <p>The file <strong>01_Dataset.zip</strong> contains the dataset. The companion document <strong>00_Dataset_description.pdf </strong>describes the content of the dataset, guiding the analyst to its use in order to (i) reproduce the article's results and (ii) compare the article's results to new results brought by the analyst, e.g. by comparison with other numerical models.</p> <p>Version history</p> <p>v2: updated references in 00_dataset description.pdf </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.