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.
471
datasets available to search
ShareScore release 0.9.0
Dataset results
471 results for “Proprioception”
Dataset from: Correlation between proprioception, functionality, patient-reported knee condition and joint acoustic emissions
<p>Measures of functionality, proprioception, self reported status and joint acoustic emissions (AE) were recorded for a sample of general population. Specifically, threshold to detect passive motion (TTDPM), Knee Osteoarthritis Outcome Scores (KOOS) and 5 times sit-to-stand test (5STS) were collected from 51 participant. Knee AE were recorded using two sensors in different frequency ranges and three modes of AE event detection were investigated during cycling with 30 and 60 rpm cadences.</p>
Task-driven neural network models predict neural dynamics of proprioception: Synthetic muscle spindle datasets
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article: </p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the synthetic spindle datasets of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the synthetic generated training dataset of simulated muscle spindles during arm passive movements generated with either character writing (PCR) or with 3D target reaching using reinforcement learning (RL).</p> <p>The overall structure of the data is:</p> <p>└── spindle_datasets<br> ├── pcr_dataset - Contains PCR synthetic training dataset<br> └── rl_dataset - Contains RL-generated synthetic training dataset</p> <p>The code to generate the PCR synthetic spindle dataset is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/PCR-data-generation">https://github.com/amathislab/Task-driven-Proprioception/tree/master/PCR-data-generation</a></p> <p>The code to generate the RL-generated synthetic spindle dataset is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/RL-data-generation">https://github.com/amathislab/Task-driven-Proprioception/tree/master/RL-data-generation</a></p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article: </p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the trained model checkpoints for all tasks of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains 300 temporal convolutional networks (TCNs) and 50 LSTM models trained on 16 tasks as well as the untrained initialization. </p> <p>The overall structure of the data is:</p> <p>└── models<br> ├── deepdraw_models - Contains networks hyperparameters<br> │ ├── template_models - Contains the default parameters<br> │ ├── torque - Contains network hyperparameters for the torque task<br> │ └── ... <br> ├── experiment_*** - Contains checkpoint of trained and untrained models <br> ├── ... <br> └── ... </p> <p>--------------------------------</p> <p>The checkpoints are stored in experiment folders (experiment_***) that follow this scheme:<br>- Task: shallow exp id, deep TCNs exp id, LSTM id.</p> <p>Experiment IDs for each task:</p> <p>- Untrained: 15, 115, 45<br>- Classification: 4015, 5015, 4045</p> <p>- Torque: 8015, 8030, 8045</p> <p>- Regress joint pos: 17016, 17031, 17046<br>- Regress joint vel: 17216, 17231, 17246<br>- Regress joint pos & vel:: 17416, 17431, 17446<br>- Regress joint pos & vel & acc:: 20516, 20531, 20546</p> <p>- Regress hand pos: 4016, 5016, 4046<br>- Regress hand vel: 17316, 17331, 17346<br>- Regress hand pos & vel: 17516, 17531, 17546<br>- Regress hand pos & vel & acc: 20416, 17831, 17846</p> <p>- Regress hand and elbow pos: 20016, 20031, 20046<br>- Regress hand and elbow vel: 20916, 20931, 20946<br>- Regress hand and elbow pos & vel: 20616, 20631, 20646<br>- Regress hand and elbow pos & vel & acc: 20816, 20831, 20846</p> <p>- Redundancy reduction - task transfer (AR): 10020, 10035, 10050<br>- Redundancy reduction - task transfer (HP): 10021, 10036, 10051<br>- Autoencoder 20716 & 20717, 20731 & 20732, X</p> <p>The code to load, evaluate and train the models is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/nn-training">https://github.com/amathislab/Task-driven-Proprioception/tree/master/nn-training</a></p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article:</p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception".</p> <p>It contains the behavioral and neural experimental data (cuneate nucleus and somatosensory recordings from the Miller Lab, Northwestern University), the result dataframes for task-driven and untrained models, the activations and predictions for the *best models for all tasks* for active and passive movements and the predictions for linear models for active and passive movements. </p> <p>Note, the predictions of other models can be computed from the network weights that were deposited for all trained models. </p> <p>The overall structure of the data is:</p> <p>└── exp_analysis<br> ├── results - Contains the result dataframe of the predictions for all models, tasks and primates<br> ├── activations<br> │ ├── active - Contains activations related to active movements<br> │ └── passive - Contains activations related to passive movements<br> ├── predictions<br> │ ├── active - Contains predictions related to active movements<br> │ └── passive - Contains predictions related to passive movements<br> └── beh_exp_datasets<br> ├── matlab_data - Contains raw behavioral and neural data<br> ├── MonkeyAlignedDatasets_new - Contains padded test behavioral input for generating network activations<br> ├── MonkeyDatasets - Contains not aligned padded test behavioral input for generating network activations<br> ├── MonkeySpikeRegressDatasets - Contains datasets for training data-driven models<br> ├── MonkeySpikeRegressDatasets_new - Contains trial index for regression splits <br> └── new_beh_exp_dataframe - Contains pre-processed behavioral and neural data</p> <p>--------------------------------</p> <p>The activations and predictions for the best 3 models and for all tasks are stored in experiments folder (in .h5 format) that follows the same name convention of the checkpoints.</p> <p>The checkpoints are stored in experiment folders (experiment_***) that follow this scheme:<br>- Task: shallow exp id, deep TCNs exp id, LSTM id.</p> <p>Experiment IDs for each task:</p> <p>- Untrained: 15, 115, 45<br>- Classification: 4015, 5015, 4045</p> <p>- Torque: 8015, 8030, 8045</p> <p>- Regress joint pos: 17016, 17031, 17046<br>- Regress joint vel: 17216, 17231, 17246<br>- Regress joint pos & vel:: 17416, 17431, 17446<br>- Regress joint pos & vel & acc:: 20516, 20531, 20546</p> <p>- Regress hand pos: 4016, 5016, 4046<br>- Regress hand vel: 17316, 17331, 17346<br>- Regress hand pos & vel: 17516, 17531, 17546<br>- Regress hand pos & vel & acc: 20416, 17831, 17846</p> <p>- Regress hand and elbow pos: 20016, 20031, 20046<br>- Regress hand and elbow vel: 20916, 20931, 20946<br>- Regress hand and elbow pos & vel: 20616, 20631, 20646<br>- Regress hand and elbow pos & vel & acc: 20816, 20831, 20846</p> <p>- Redundancy reduction: 10020, 10035, 10050<br>- Autoencoder 20716 & 20717, 20731 & 20732, X</p> <p> </p> <p>The code to process the behavioral data is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing">https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing</a><br>The code to load and use the models to generate activations and predictions is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction">https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction</a></p> <p>To reproduce the results, it is possible to reproduce the main figures using the result dataframe. See our repository for more details. </p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Cervical Proprioception, Postural Control, and Pain
<p>The dataset comprises demographic and clinical characteristics of two groups: 82 Rheumatoid Arthritis (RA) patients and 82 asymptomatic individuals. The RA patients have a mean age of 55.25 years (SD = 10.02), while the asymptomatic individuals have a mean age of 54.18 years (SD = 10.15), with both groups having an age range of 40-70 years. Gender distribution is identical, with 20% males and 80% females in each group. Marital status differs slightly; 36% of RA patients are single, and 66% are married, compared to 39% and 61% in the control group. Educational attainment shows 27% of RA patients completed high school, 49% have college degrees, and 24% hold graduate degrees, compared to 30%, 43%, and 27% in the control group. Employment status reveals 61% of RA patients are employed, 24% are unemployed, and 15% fall into other categories, similar to 63%, 22%, and 15% in the control group. </p>
Data from: A proprioceptive feedback circuit drives C. elegans locomotor adaptation through dopamine signaling
Open the record for dataset details and reuse information.
Peripheral anatomy and central connectivity of proprioceptive sensory neurons in the Drosophila wing
Open the record for dataset details and reuse information.
Minimal impact of chronic proprioceptive loss on implicit sensorimotor adaptation and perceived movement outcome
Open the record for dataset details and reuse information.
Central processing of leg proprioception in Drosophila: Physiology and behavior data
<p>Proprioception, the sense of self-movement and position, is mediated by mechanosensory neurons that detect diverse features of body kinematics. Although proprioceptive feedback is crucial for accurate motor control, little is known about how downstream circuits transform limb sensory information to guide motor output. Here, we investigate neural circuits in <i>Drosophila </i>that process proprioceptive information from the fly leg. We identify three cell-types from distinct developmental lineages that are positioned to receive input from proprioceptor subtypes encoding tibia position, movement, and vibration. 13Bα neurons encode femur-tibia joint angle and mediate postural changes in tibia position. 9Aα neurons also drive changes in leg posture, but encode a combination of directional movement, high-frequency vibration, and joint angle. Activating 10Bα neurons, which encode tibia vibration at specific joint angles, elicits pausing in walking flies. Altogether, our results reveal that central circuits integrate information across proprioceptor subtypes to construct complex sensorimotor representations that mediate diverse behaviors, including reflexive control of limb posture and detection of leg vibration.</p>
Kinesthetic information facilitates saccades towards proprioceptive-tactile targets
<p>Saccades to somatosensory targets have longer latencies and are less accurate and precise than saccades to visual targets. Here we examined how different somatosensory information influences the planning and control of saccadic eye movements. Participants fixated a central cross and initiated a saccade as fast as possible in response to a tactile stimulus that was presented to either the index or the middle fingertip of their unseen left hand. In <em>a static condition</em>, the hand remained at a target location for the entire block of trials and the stimulus was presented at a fixed time after an auditory tone. Therefore, the target location was derived only from proprioceptive and tactile information. In a <em>moving</em> <em>condition</em>, the hand was first actively moved to the same target location and the stimulus was then presented immediately. Thus, in the <em>moving condition</em> additional kinesthetic information about the target location was available. We found shorter saccade latencies in the moving compared to the <em>static condition</em>, but no differences in accuracy or precision of saccadic endpoints. In a second experiment, we introduced variable delays after the auditory tone (<em>static condition</em>) or after the end of the hand movement (<em>moving condition</em>) in order to reduce the predictability of the moment of the stimulation and to allow more time to process the kinesthetic information. Again, we found shorter latencies in the <em>moving</em> compared to the <em>static condition</em> but no improvement in saccade accuracy or precision. In a third experiment, we showed that the shorter saccade latencies in the <em>moving condition</em> cannot be explained by the temporal proximity between the relevant event (auditory tone or end of hand movement) and the moment of the stimulation. Our findings suggest that kinesthetic information facilitates planning, but not control, of saccadic eye movements to proprioceptive-tactile targets.</p>
Data from: Muscle force-length dynamics during walking over obstacles indicates delayed recovery and a shift towards more strut-like function in birds with proprioceptive deficit
<p>Recent studies of in vivo muscle function in guinea fowl revealed that distal leg muscles rapidly modulate force and work to stabilize running in uneven terrain. Previous studies focused on running only, and it remains unclear how muscular mechanisms for stability differ between walking and running. Here we investigate in vivo function of the lateral gastrocnemius (LG) during walking over obstacles. We compare muscle function in birds with intact (iLG) versus self-reinnervated LG (rLG). Self-reinnervation results in proprioceptive feedback deficit due to loss of monosynaptic stretch reflex. We test the hypothesis that proprioceptive deficit results in decreased modulation of EMG activity in response to obstacle contact, and a delayed obstacle recovery compared to iLG. We found that total myoelectric intensity (Etot) of iLG increased by 68% in obstacle strides (S 0) compared to level terrain, suggesting a substantial reflex-mediated response. In contrast, Etot of rLG increased by 31% in S 0 strides compared to level, but also increased by 43% in the first post-obstacle (S+1) stride. In iLG, muscle force and work differed significantly from level only in the S 0 stride, indicating a single-stride recovery. In rLG, force increased in S 0, S+1, and S+2 compared to level, indicating three-stride obstacle recovery. Interestingly, rLG showed little variation in work output and shortening velocity obstacle terrain, indicating a shift towards near isometric strut-like function. Reinnervated birds also adopt a more crouched posture across level and obstacle terrains compared to intact birds. These findings suggest gait specific control mechanisms in walking and running.</p>
Rehabilitation of the Upper Extremity With Enhanced Proprioceptive Feedback Following Incomplete Spinal Cord Injury
ClinicalTrials.gov study NCT00833105. IPD Sharing: Not stated. Countries: 1. Publications: 1.
"Effectiveness of Mulligan Mobilization Technique and Proprioceptive Exercises Among Patients With Knee Osteoarthritis"
ClinicalTrials.gov study NCT06971016. IPD Sharing: NO. Countries: 1. Publications: 1.
Exercise Based on Proprioceptive Neuromuscular Facilitation in Older Individuals
ClinicalTrials.gov study NCT06690944. IPD Sharing: YES. Countries: 1. Publications: 3.
The Effect of Shoulder Mobilization on Muscle Strength and Proprioception: a Randomized Double-blind Study
ClinicalTrials.gov study NCT06910332. IPD Sharing: YES. Countries: 1. Publications: 0.
Proprioception Testing in Persons With Sensorimotor Impairment
ClinicalTrials.gov study NCT01974635. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Proprioceptive Neuromuscular Facilitation Combined With Spiral Muscle Chain Training for Pediatric Scoliosis
ClinicalTrials.gov study NCT07319702. IPD Sharing: NO. Countries: 1. Publications: 3.
Effects of Transcranial Direct Current Stimulation With Proprioceptive Training in Blind People
ClinicalTrials.gov study NCT03173105. IPD Sharing: YES. Countries: 1. Publications: 29.
Kinesio Taping for the Knee and Ankle: Acute Impact on Balance, Proprioception, and ACL Risk
ClinicalTrials.gov study NCT07138573. IPD Sharing: NO. Countries: 1. Publications: 11.
Comparison of the Proprioceptive Neuromuscular Facilitation and Mobilization In Subacromial Impingement Syndrome
ClinicalTrials.gov study NCT03735485. IPD Sharing: UNDECIDED. Countries: 1. Publications: 22.
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.