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.
5,607
datasets available to search
ShareScore release 0.9.0
Dataset results
5,607 results for “Knee”
Twitter Dataset for "Will You Take the Knee? Italian Twitter Echo Chambers' Genesis During EURO 2020"
<p>Echo chambers can be described as situations in which individuals encounter and interact only with viewpoints that confirm their own, thus moving, as a group, to more polarized and extreme positions. Recent literature mainly focuses on characterizing such entities via static observations, thus disregarding their temporal dimension. In this work, distancing from such a trend, we study, at multiple topological levels, echo chambers genesis related to the social discussions that took place in Italy during the EURO 2020 Championship. Our analysis focuses on a well-defined topic (i.e., BLM/racism) discussed on Twitter during a perfect temporally bound (sporting) event. Such characteristics allow us to track the rise and evolution of echo chambers in time, thus relating their existence to specific episodes.</p>
Dataset Effect of hypnotic suggestion on knee extensor neuromuscular properties in resting and fatigued states
<p>The .xlsx file contains individual data from all figures / tables of the associated manuscript and each .csv file contains information from one figure / table.</p> <p> </p> <p><strong>Dataset Fig 2</strong></p> <p>Table 1. Maximal voluntary contraction force (Newton) from the knee extensor muscles measured before (pre) and after (post) control / hypnosis suggestion</p> <p>Table 2. Maximal voluntary activation level (%) from the knee extensor muscles measured before (pre) and after (post) control / hypnosis suggestion</p> <p>Table 3. Peak doublet force (Newton) evoked from 100 Hz paired stimuli at the knee extensor level measured before (pre) and after (post) control / hypnosis suggestion</p> <p> </p> <p><strong>Dataset Fig 4</strong></p> <p>Table 1. Time to task failure (s) of a submaximal isometric contraction performed at 20% maximal voluntary contraction force with the knee extensors for the control session and the hypnosis session</p> <p> </p> <p><strong>Dataset Fig 5</strong></p> <p>Table 1. Maximal voluntary contraction force (Newton) from the knee extensor muscles measured before (pre exercise) and after (post exercise) exercise during the control session and the hypnosis session</p> <p>Table 2. Maximal voluntary activation level (%) from the knee extensor muscles measured before (pre exercise) and after (post exercise) exercise during the control session and the hypnosis session</p> <p>Table 3. Peak doublet force (Newton) evoked from 100 Hz paired stimuli at the knee extensor level measured before (pre exercise) and after (post exercise) exercise during the control session and the hypnosis session</p> <p> </p> <p><strong>Dataset Fig 6</strong></p> <p>Table 1. Electromyographic activity (in %, expressed as root mean square values normalized to maximal electromyographic activity measured during the maximal voluntary contraction performed before exercise) of the vastus lateralis muscle measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session</p> <p>Table 2. Electromyographic activity (in %, expressed as root mean square values normalized to maximal electromyographic activity measured during the maximal voluntary contraction performed before exercise) of the vastus medialis muscle measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session</p> <p>Table 3. Electromyographic activity (in %, expressed as root mean square values normalized to maximal electromyographic activity measured during the maximal voluntary contraction performed before exercise) of the rectus femoris muscle measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session</p> <p> </p> <p><strong>Dataset Fig 7</strong></p> <p>Table 1. Motor evoked potential peak to peak amplitude from the vastus lateralis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 2) and the peak-to-peak M-wave amplitude expressed in mV (Table 3).</p> <p>Table 4. Motor evoked potential peak to peak amplitude from the vastus medialis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 5) and the peak-to-peak M-wave amplitude expressed in mV (Table 6).</p> <p>Table 7. Motor evoked potential peak to peak amplitude from the rectus femoris muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 8) and the peak-to-peak M-wave amplitude expressed in mV (Table 9).</p> <p>Table 10. Short intracortical inhibition peak to peak amplitude from the vastus lateralis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 11) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 12).</p> <p>Table 13. Short intracortical inhibition peak to peak amplitude from the vastus medialis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 14) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 15).</p> <p>Table 16. Short intracortical inhibition peak to peak amplitude from the rectus femoris muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 17) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 18).</p> <p><br> <strong>Dataset Fig 8</strong></p> <p>Table 1. Rate of perceived exertion (6-20 Borg scale) measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session</p> <p> </p> <p><strong>Dataset Table 1</strong></p> <p>Table 1. Motor evoked potential peak to peak amplitude from the vastus lateralis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 2) and the peak-to-peak M-wave amplitude expressed in mV (Table 3).</p> <p>Table 4. Motor evoked potential peak to peak amplitude from the vastus medialis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 5) and the peak-to-peak M-wave amplitude expressed in mV (Table 6).</p> <p>Table 7. Motor evoked potential peak to peak amplitude from the rectus femoris muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 8) and the peak-to-peak M-wave amplitude expressed in mV (Table 9).</p> <p>Table 10. Short intracortical inhibition peak to peak amplitude from the vastus lateralis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 11) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 12).</p> <p>Table 13. Short intracortical inhibition peak to peak amplitude from the vastus medialis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 14) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 15).</p> <p>Table 16. Short intracortical inhibition peak to peak amplitude from the rectus femoris muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 17) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 18).</p> <p> </p> <p><strong>Dataset table 2</strong></p> <p>Table 1. M-wave peak to peak amplitude (mV) from the vastus lateralis muscle measured before (pre) and after (post) control / hypnosis suggestion</p> <p>Table 2. M-wave peak to peak amplitude (mV) from the vastus medialis muscle measured before (pre) and after (post) control / hypnosis suggestion</p> <p>Table 3. M-wave peak to peak amplitude (mV) from the rectus femoris muscle measured before (pre) and after (post) control / hypnosis suggestion</p>
Dataset of knee joint contact force peaks and corresponding subject characteristics from 4 open datasets
<p>This dataset contains data from overground walking trials of 166 subjects with several trials per subject (approximately 2900 trials total).</p> <p><strong>DATA ORIGINS & LICENSE INFORMATION</strong></p> <p>The data comes from four existing open datasets collected by others:</p> <p>Schreiber & Moissenet, A multimodal dataset of human gait at different walking speeds established on injury-free adult participants</p> <ul> <li>article: https://www.nature.com/articles/s41597-019-0124-4</li> <li>dataset: https://figshare.com/articles/dataset/A_multimodal_dataset_of_human_gait_at_different_walking_speeds/7734767</li> </ul> <p>Fukuchi et al., A public dataset of overground and treadmill walking kinematics and kinetics in healthy individuals</p> <ul> <li>article: https://peerj.com/articles/4640/</li> <li>dataset: https://figshare.com/articles/dataset/A_public_data_set_of_overground_and_treadmill_walking_kinematics_and_kinetics_of_healthy_individuals/5722711</li> </ul> <p>Horst et al., A public dataset of overground walking kinetics and full-body kinematics in healthy adult individuals</p> <ul> <li>article: https://www.nature.com/articles/s41598-019-38748-8</li> <li>dataset: https://data.mendeley.com/datasets/svx74xcrjr/3</li> </ul> <p>Camargo et al., A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions</p> <ul> <li>article: https://www.sciencedirect.com/science/article/pii/S0021929021001007</li> <li>dataset (3 links): https://data.mendeley.com/datasets/fcgm3chfff/1 https://data.mendeley.com/datasets/k9kvm5tn3f/1 https://data.mendeley.com/datasets/jj3r5f9pnf/1</li> </ul> <p>In this dataset, those datasets are referred to as the Schreiber, Fukuchi, Horst, and Camargo datasets, respectively.<br> The Schreiber, Fukuchi, Horst, and Camargo datasets are licensed under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).</p> <p>We have modified the datasets by analyzing the data with musculoskeletal simulations & analysis software (OpenSim).<br> In this dataset, we publish modified data as well as some of the original data.</p> <p><br> <strong>STRUCTURE OF THE DATASET</strong><br> The dataset contains two kinds of text files: those starting with "predictors_" and those starting with "response_".</p> <p>Predictors comprise 12 text files, each describing the input (predictor) variables we used to train artifical neural networks to predict knee joint loading peaks.<br> Responses similarly comprise 12 text files, each describing the response (outcome) variables that we trained and evaluated the network on.<br> The file names are of the form "predictors_X" for predictors and "response_X" for responses, where X describes which response (outcome) variable is predicted with them.<br> X can be:<br> - loading_response_both: the maximum of the first peak of stance for the sum of the loading of the medial and lateral compartments<br> - loading_response_lateral: the maximum of the first peak of stance for the loading of the lateral compartment<br> - loading_response_medial: the maximum of the first peak of stance for the loading of the medial compartment<br> - terminal_extension_both: the maximum of the second peak of stance for the sum of the loading of the medial and lateral compartments<br> - terminal_extension_lateral: the maximum of the second peak of stance for the loading of the lateral compartment<br> - terminal_extension_medial: the maximum of the second peak of stance for the loading of the medial compartment<br> - max_peak_both: the maximum of the entire stance phase for the sum of the loading of the medial and lateral compartments<br> - max_peak_lateral: the maximum of the entire stance phase for the loading of the lateral compartment<br> - max_peak_medial: the maximum of the entire stance phase for the loading of the medial compartment<br> - MFR_common: the medial force ratio for the entire stance phase<br> - MFR_LR: the medial force ratio for the first peak of stance<br> - MFR_TE: the medial force ratio for the second peak of stance</p> <p>The predictor text files are organized as comma-separated values. Each row corresponds to one walking trial. A single subject typically has several trials.<br> The column labels are DATASET_INDEX,SUBJECT_INDEX,KNEE_ADDUCTION,MASS,HEIGHT,BMI,WALKING_SPEED,HEEL_STRIKE_VELOCITY,AGE,GENDER.</p> <ul> <li>DATASET_INDEX describes which original dataset the trial is from, where {1=Schreiber, 2=Fukuchi, 3=Horst, 4=Camargo}</li> <li>SUBJECT_INDEX is the index of the subject in the original dataset. If you use this column, you will have to rewrite these to avoid duplicates (e.g., several datasets probably have subject "3").</li> <li>KNEE_ADDUCTION is the knee adduction-abduction angle (positive for adduction, negative for abduction) of the subject in static pose, estimated from motion capture markers.</li> <li>MASS is the mass of the subject in kilograms</li> <li>HEIGHT is the height of the subject in millimeters</li> <li>BMI is the body mass index of the subject</li> <li>WALKING_SPEED is the mean walking speed of the subject during the trial</li> <li>HEEL_STRIKE_VELOCITY is the mean of the velocities of the subject's pelvis markers at the instant of heel strike</li> <li>AGE is the age of the subject in years</li> <li>GENDER is an integer/boolean where {1=male, 0=female}</li> </ul> <p>The response text files contain one floating-point value per row, describing the knee joint contact force peak for the trial in newtons (or the medial force ratio). Each row corresponds to one walking trial.<br> The rows in predictor and response text files match each other (e.g., row 7 describes the same trial in both predictors_max_peak_medial.txt and response_max_peak_medial.txt).</p> <p><br> See our journal article "Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks" (https://doi.org/10.1007/s10439-023-03278-y) for more information.</p> <p>Questions & other contacts: jere.lavikainen@uef.fi</p>
Patient reported outcome measures, load-induced blood marker kinetics, and ambulatory knee load in patients with medial compartment knee osteoarthritis
<p>The goal of this study was (i) to quantify the mechanoresponse of this array of potential blood markers for joint pathology (COMP, MMP-1, MMP-3, MMP-9, CPII, C2C, C2C/CPII, ADAMTS-4, PRG-4, IL-6 and resistin) to a walking stress test in patients with knee OA and to determine the correlation (ii) among the kinetics of these blood markers, (iii) with accumulated knee load during the walking stress, and (iv) with patient reported osteoarthritis outcome and QoL.</p> <p>The dataset includes 24 patients with knee osteoarthritis scheduled to receive high tibial osteotomy. All participants completed questionnaires, and a walking stress test with six blood samples analyzed using enzyme-linked immunosorbent assays for cartilage oligomeric matrix protein (COMP), matrix metalloproteinases (MMP)-1, -3, and -9, epitope resulting from cleavage of type II collagen by collagenases (C2C), type II procollagen (CPII), interleukin (IL)-6, proteoglycan (PRG)-4, A disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS)-4, and resistin, and gait analysis. Joint load was computed from gait analysis data and musculoskeletal modelling in AnyBody Modeling System (AnyBody Technology A/S). Discrete loading parameters were extracted for each step using an inhouse algorithm written in Matlab.</p> <p>The detailed experimental protocol of the umbrella study has been described in Mündermann A, Vach W, Pagenstert G, Egloff C, Nüesch C. Assessing in vivo articular cartilage mechanosensitivity as outcome of high tibial osteotomy in patients with medial compartment osteoarthritis: Experimental protocol. Osteoarthr Cartil Open. 2020 Feb 24;2(2):100043. doi: 10.1016/j.ocarto.2020.100043. PMID: 36474590; PMCID: PMC9718245. The study is registered on clinicaltrials.gov (identifier NCT02622204). The method for computing joint loading has been described in detail in De Pieri E, Nüesch C, Pagenstert G, Viehweger E, Egloff C, Mündermann A. High tibial osteotomy effectively redistributes compressive knee loads during walking. J Orthop Res. 2022 Jun 22. doi: 10.1002/jor.25403. Epub ahead of print. PMID: 35730475.</p>
Can a knee sleeve influence ground reaction forces and knee joint power during a step-down hop in participants following ACL reconstruction? Discrete and time-continuous datasets
<p>Using a cross-over design, we estimated GRF and knee kinematics and kinetics during a step-down hop for 30 participants (age 26.1 [SD 6.7] years, 14 women) following ACL reconstruction (median 16 months post-surgery) with and without wearing a knee sleeve. In a subsequent randomised clinical trial, participants in the ‘Sleeve Group’ (n=9) then wore the sleeve for 6 weeks at least 1 hour daily, while a ‘Control Group’ (n=9) did not wear the sleeve. Statistical parametric mapping (SPM) was used to compare (1) GRF trajectories in the three planes as well as knee joint power between three conditions at baseline (uninjured side, unsleeved injured and sleeved injured side); (2) within-participant changes for GRF and knee joint power trajectories from baseline to follow-up between groups. We also compared discrete peak GRFs and power, rate of (vertical) force development, and mean knee joint power in the first 5% of stance phase. Time-continuous and discrete data are included in this dataset.</p>
KneE-PAD
<p><span>This repository introduces the </span>KneE-PAD <span>(<strong><span>Knee Rehabilitation Exercises for Postural Assessment D</span></strong>ataset), </span>which is a dataset consisting of knee rehabilitation exercises performed by 31 patients suffering from knee pathologies. In particular, a total of 267 patients were monitored over a 6-month period where they were asked to perform in two physiotherapy centers without any supervision 3 common lower limb rehabilitation exercises (squats, leg extension and walking). At each participant a set of 8 EMG and IMU sensors by Delsys was placed at important lower limb muscle groups. Moreover, they were asked to wear a heart rate sensor, a muscle oxygenation sensor and a goniometer to monitor their level of discomfort while an RGB camera was used to record their sessions. After curating and grouping the wrongly executed exercises, 2 common wrong variations for each exercise were identified in 31 participants.</p> <p>The goal of KneE-PAD is to be used for training machine learning algorithms for automatic postural assessment using only wearable sensors (EMG and IMU), which could become a vital part of a virtual coach to supervise the patients and provide useful feedback to them while executing their prescribed rehabilitation exercises remotely.</p>
Continuous monitoring of patient mobility for 18 months using inertial sensors following traumatic knee injury: a case study
<p><strong>This repository contains raw data relating to: </strong>Continuous monitoring of patient mobility for 18 months using inertial sensors following traumatic knee injury: a case study Mueller A., Hoefling H., Nuritdinow T., et al. DOI: 10.1159/000490919</p> <p><strong>Metadata and processed data derived from the raw data deposited here is available here:</strong> https://github.com/Novartis/mueller_et_al_2018</p> <p><strong>Article Abstract</strong></p> <p>Continuous patient activity monitoring during rehabilitation, enabled by digital technologies, will allow the objective capture of real-world mobility and aligning treatment to each individual’s recovery trajectory in real time. To explore the feasibility and added value of such approaches, we present a case study of a 36-year-old male participant monitored continuously for activity levels and gait parameters using a waist-worn inertial sensor following a tibial plateau fracture on the right side, sustained as a result of a high-energy trauma during a sporting accident. During rehabilitation, data were collected for a period of 553 days, with > 80% daytime compliance, until the participant returned to near full mobility. The participant completed a daily diary with the annotation of major events (falls, near falls, cycling periods, or physiotherapy sessions) and key dates in the patient’s recovery, including medical interventions, transitioning off crutches, and returning to work. We demonstrate the feasibility of collecting, storing, and mining of continuous digital mobility data and show that such data can detect changes in mobility and provide insights into long-term rehabilitation. We make both raw data and annotations available as a resource with the aspiration that further methods and insights will be built on this initial exploration of added value and continue to demonstrate that continuous monitoring can be deployed to aid rehabilitation.</p>
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>
Grood and Suntay joint coordinate system for knee kinematics
<p>Grood and Suntay joint coordinate system for description of knee kinematics</p> <p>This software implements the Grood and Suntay joint coordinate system for the clinical description of the three-dimensional motion of the knee. The Grood and Suntay joint coordinate system is described in the research paper :</p> <p>Grood E.S., Suntay W.J., “A joint coordinate system for the clinical description of three-dimensional motions: application to the knee”, J Biomech Eng., 1983 May; 105(2), 136-44, 1983.</p> <p>The software was used in producing the numerical results shown in various research papers such as :</p> <p>Arsene, CT, Gabrys, B., “Probabilistic finite element predictions of the human lower limb model in the total knee replacement”, Med Eng Phys, 2013 Aug; 35(8): 1116-32, 2013.</p> <p>Description of the Matlab files:</p> <p>Uncerpasl.m – starts the process of calculating the knee joint coordinate system;</p> <p>Anglesn.m – calculates the angles and the displacements of interest for the tibio-femoral component</p> <p>Anglespf.m – calculates the angles and the displacements of interest for the patello-femoral component</p> <p>Readinput.m – reads the text files containing the raw data obtained from the Finie Element simulations such as :</p> <p>a) femur_COG_I_X - means the X coordinate of the I point which defines a coordinate system together with points J and K with respect to the center of gravity (COG) of the femoral component.</p> <p>b) femur_COG_X - means the X coordinate of the center of gravity (COG) of the femoral component which together with points I, J , K forms a sort of local coordinate system attached to the femoral component.</p> <p>All the text files such as femur_COG_I_X_1.txt, femur_COG_I_Y_1.txt, etc were produced with the finite element software PAM-CRASH/PAMOPT from the company ESI, Paris, France (<a href="https://www.esi-group.com/">https://www.esi-group.com</a>). The text files contain a single result consisting of 0.12 seconds of the passive flexion cycle and therefore are of no real use but only to verify that the Grood and Suntay Matlab software code works.</p> <p>Please acknowledge the project financed by the European Union Framework Programme 6 (FP6) entitled Decision Support Software for Orthopaedic Surgery and Mr Corneliu T.C. Arsene if you are going to use this software anywhere in your work. The license for this software is a Creative Commons Attribution 4.0 International License (CCL). This project has also a DOI : 10.5281/zenodo.2136822</p> <p>It is provided here with no warranty. Direct all questions and requests to <a href="mailto:galenpalimpsestproject@gmail.com">galenpalimpsestproject@gmail.com</a>.</p>
Data from: Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study
<p>This dataset accompanies the following article: "Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study," in <em>IEEE Transactions on Biomedical Engineering</em>, doi: 10.1109/TBME.2023.3263388.</p> <p>Knee acoustic emissions (AE) recorded in the 100-450 kHz and 15-200kHz frequency ranges from a cadaver specimen knee in flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated. </p> <p><em><strong>Stages of artificially inflicted cartilage damage:</strong></em> the cartilage surface damage on the medial compartment, KL III; the cartilage surface damage on the medial compartment plus patellofemoral surface, KL III; the cartilage surface damage on the medial compartment plus on the patellofemoral surface KL IV; the cartilage surface damage on the medial compartment plus on the patellofemoral surface and lateral compartment.</p> <p><strong><em>Sensor positions</em></strong>: medial and lateral knee</p>
Subject-specific knee models, data, and results for specimen S192803
<p>This dataset is part of an ongoing manuscript to validate that sources of data from currently available in vivo methods are sufficient to create computational models of the knee compared with existing in vitro techniques. The data included in this repository is for the S192803 specimen of that dataset and includes experimental data, working models, code, and results obtained for that model and used in that manuscript.</p>
Data/Code: Objective monitoring of functional recovery after total knee and hip arthroplasty using sensor-derived gait measures
<p>Abstract</p> <p>Background: Inertial sensors hold the promise to objectively measure functional recovery after total knee (TKA) and hip arthroplasty (THA), but their value in addition to patient-reported outcome measures (PROMs) has yet to be demonstrated. This study investigated recovery of gait after TKA and THA using inertial sensors, and compared results to recovery of self-reported scores of pain and function.</p> <p>Methods: PROMs and gait parameters were assessed before and at two and fifteen months after TKA (n=24) and THA (n=24). Gait parameters were compared with healthy individuals (n=27) of similar age. Gait data were collected using inertial sensors on the feet, lower back, and trunk. Participants walked for two minutes back and forth over a 6m walkway with 180° turns. PROMs were obtained using the Knee Injury and Osteoarthritis Outcome Scores and Hip Disability and Osteoarthritis Outcome Score.</p> <p>Results: Gait parameters recovered to the level of healthy controls after both TKA and THA. Early improvements were found in gait-related trunk kinematics, while spatiotemporal gait parameters mainly improved between two and fifteen months after TKA and THA. Compared to the large and early improvements found in of PROMs, these gait parameters showed a different trajectory, with a marked discordance between the outcome of both methods at two months post-operatively.</p> <p>Conclusion: Sensor-derived gait parameters were responsive to TKA and THA, showing different recovery trajectories for spatiotemporal gait parameters and gait-related trunk kinematics. Fifteen months after TKA and THA, there were no remaining gait differences with respect to healthy controls. Given the discordance in recovery trajectories between gait parameters and PROMs, sensor-derived gait parameters seem to carry relevant information for evaluation of physical function that is not captured by self-reported scores.</p>
Surface electromyography data set of knee osteoarthritis patients undergoing hydrotherapy and physiotherapy rehabilitation routines
<p>Osteoarthritis is one of the most prevalent degenerative diseases in the elderly that affects the structural and functional integrity of the musculoskeletal system. Currently, hydrotherapy is taking relevance for osteoarthritis treatment due to its possible efficacy on pain relief and functionality. This therapy can be evaluated by means of an electromyographic analysis. The objective is to show a database of lower limbs muscles electrical activity in charge of postural stability, comparing the difference between hydrotherapy and conventional therapy through surface electromyography. A signal acquisition methodology was developed measuring electromyographic activity of the Tibialis Anterior, Soleus, Vastus Medialis, Biceps Femoris, Medial Gastrocnemius and Lateral Gastrocnemius muscles, before and after applying a specific exercise routine for knee osteoarthritis. This routine consisted of 12 therapy sessions spread over 4 weeks and could be done in water and on land. </p> <p>The data is organized according to the attendance of 28 patients to the therapy sessions. There are two records per person, before and after each hydrotherapy or physiotherapy session, obtaining a total of 1344 records at the end of the 12 sessions, and 56 data for each session. However, a total of 864 complete records were obtained for both therapies, withdrawing those who missed any of the sessions. There is a personal information database in .CSV format and electromyographic records with a .MAT extension.</p>
Dronabinol in Total Knee Arthroplasty (TKA)
ClinicalTrials.gov study NCT04734080. IPD Sharing: YES. Countries: 1. Publications: 0.
Implementing Group Physical Therapy (PT) for Veterans With Knee Osteoarthritis (Group PT): Function QUERI 2.0
ClinicalTrials.gov study NCT05282927. IPD Sharing: YES. Countries: 1. Publications: 3.
Randomized Study of the Efficacy and Safety of a Single Dose of Synvisc-One® in Chinese Patients With Symptomatic Osteoarthritis of the Knee
ClinicalTrials.gov study NCT03190369. IPD Sharing: YES. Countries: 1. Publications: 1.
A Study to Evaluate the Safety and Efficacy of CNTX-6970 in Subjects With Knee Osteoarthritis Pain.
ClinicalTrials.gov study NCT05025787. IPD Sharing: YES. Countries: 1. Publications: 58.
Study of Efficacy, Safety, and Tolerability of LNA043 in Patients With Knee Osteoarthritis
ClinicalTrials.gov study NCT04864392. IPD Sharing: YES. Countries: 15. Publications: 1.
A Trial to Learn How Well REGN7508 Works for Preventing Blood Clots After a Knee Replacement in Adult Participants
ClinicalTrials.gov study NCT06454630. IPD Sharing: YES. Countries: 5. Publications: 1.
A Trial to Learn How Well REGN9933 Works for Preventing Blood Clots After Knee Replacement Surgery in Adult Participants
ClinicalTrials.gov study NCT05618808. IPD Sharing: YES. Countries: 7. Publications: 1.
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.