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.
48
datasets available to search
ShareScore release 0.9.0
Dataset results
48 results for “dynamic force”
Kinematic and Electromyographic Recordings during Dynamic, Repetitive, Low-Force Movements
<p>Experimental recordings of kinematic (XSens Awinda, Full-Body) and electromyographic (Delsys Trigno, 8 Right Arm Muscles) data during 4 repetive upper limb exercises with a 1.5Kg dumbell: A) elbow counter-gravity flexion and gravity-assisted extension, with torso tilted forwards; B) shoulder counter-gravity abduction and gravity-assisted adduction; C) shoulder counter-gravity flexion and gravity-assisted extensio; D) composite sequence of elbow/shoulder flexion/extension motions, executed until self-reported fatigue.</p> <p>A total of 17 healthy volunteers (11 Male; 6 Female; 23.82 ± 2.79 years old, 69.06 ± 14.75 Kg) were recruited. Each participant willingly agreed to participate in the study and gave their signed, informed consent, following the standard set by the declaration of Helsinki and the Oviedo Conventions.</p> <p>Additionally, self-reported fatigue after each exercise, according to Borg's Perceived Exertion Scale (Borg, 1998), is provided for all subjects.</p> <p>For a detailed description of the experimental protocol and instrumentation, refer to associated research paper (submission under review).</p>
Summary of Input from Stakeholders and other institutions involved in dynamic force applications
<p>Survey data used to create Deliverable 1 of ComTraForce project "Roadmap detailing the future requirements for improved force transfer standards and associated calibration methods for force testing machines taking into account realistic uncertainties".</p>
Demonstrations for imitation learning for the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives"
<p>Demonstrations for the coathanger experiment in the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives". https://elib.dlr.de/205110/</p>
Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity
<p><strong>Citation:</strong> Zhu, J., & Poulsen, C. J. (2021). Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity. <em>Clim. Past</em>, <em>17</em>(1), 253–267. <a href="https://doi.org/10.5194/cp-17-253-2021">https://doi.org/10.5194/cp-17-253-2021</a></p> <p>Casename:</p> <ul> <li>FCM_PI: b.e12.B1850C5.f19_g16.iPI.01</li> <li>FCM_LGM: b.e12.B1850C5.f19_g16.i21ka.03</li> <li>SOM_PI: e.e12.E1850C5.f19_g16.PI.02</li> <li>SOM_GHG: e.e12.E1850C5.f19_g16.PI.21kaGHG.02</li> <li>SOM_ICE: e.e12.E1850C5.f19_g16.PI.21kaICE.02</li> <li>SOM_2CO2: e.e12.E1850C5.f19_g16.PIx2.02</li> <li>ATM_PI: f.e12.F1850C5.f19_g16.iPI.01</li> <li>ATM_GHG: f.e12.F1850C5.f19_g16.iPI.21kaGHG_ERF</li> <li>ATM_ICE: f.e12.F1850C5.f19_g16.iPI.21kaICE_ERF</li> <li>ATM_2CO2: f.e12.F1850C5.f19_g16.iPI.01.x2</li> </ul> <p><strong>Boundary condition files and the restart files are also provided as .zip files (bc.zip & rest.zip).</strong></p> <p><strong>Check out the Github repository for the setup of the LGM simulation</strong> (i.e., the entire CESM case folder): <a href="https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne">https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne</a></p> <p><strong>[NEW IN V3] More monthly data for PMIP4 (cmorized) are provided (files starting with `PMIP4.NCAR.CESM1.2-FV2`).</strong></p>
Molecular dynamics simulations of an Ago2-RNA complex in different force fields
<p>This set of simulations contains 2us of Ago2-RNA complex in Amber ff14SB + OL3, ff19SB + OL3 and Desmond OPLS4 force fields. The polarizable force field AMOEBA has two simulation sets of 10*10ns and 2*100ns. The trajectories have been wrapped in the periodic box, centered around the protein atoms and the water molecules have been stripped out to conserve space using cpptraj. The trajectories are presented in Gromacs xtc-format which can be opened with the corresponding pdb file in multiple software tools such as VMD, PyMol or CaverAnalyst. The simulations are based on the crystal structure PDB ID 4W5O, where the missing loops were modeled using the Schrödinger Suite and missing nucleotides added manually. </p>
Model configuration files and forcing data for Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
<p>This repository includes the model configuration files, input data, and forcing data used for simulations in Bieri et al. (2025) - <em>Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration.</em></p> <ul> <li>forcing.tar.gz - Compressed folder containing HRLDAS Noah-MP model forcing NetCDF files <ul> <li>These forcing files were derived from the NASA Global Land Data Assimilation System (GLDAS; Beaudoing et al. 2020)</li> <li>The compressed file contains 3-hourly forcing files for the entire simulation period (01 Jun 2000 to 31 Dec 2019)</li> </ul> </li> <li>wrfinput_d01 - NetCDF file used as HRLDAS input file in HRLDAS Noah-MP simulations <ul> <li>Generated from WRF WPS (https://github.com/wrf-model/WPS)</li> </ul> </li> <li>Namelist files <ul> <li>namelist.hrldas.ROOT - Model namelist settings used for ROOT experiment</li> <li>namelist.hrldas.SOIL - Model namelist settings used for SOIL experiment</li> <li>namelist.hrldas.GW - Model namelist settings used for GW experiment</li> <li>namelist.hrldas.CONTROL - Model namelist settings used for FD (CONTROL) experiment</li> </ul> </li> </ul>
Spatio-temporal dynamics of the proton motive force on single bacteria - dataset
<p>Data set used in our manuscript "Spatio-temporal dynamics of the proton motive force on single bacteria" [<a href="https://www.biorxiv.org/content/10.1101/2023.04.03.535353v1">Biorxiv</a>].</p> <p> </p> <p>To produce fig1 and fig2, unzip file in bash:</p> <pre><code class="language-bash">$ 7z e data_fig1_fig2.7z</code></pre> <p>Open fig1 data in python:</p> <pre><code class="language-python">> b = pickle.load(open('fig1.p', 'rb')) > b {'speed_Hz': array([-34.01139986, 13.07744575, 79.66060694, ..., -4.79440373, -4.88539275, -0.1366687 ]), 'speed_Hz_f': array([-11.11722186, 15.74953016, 32.24685265, ..., 20.11726694, -3.54263861, -36.89432049]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 5000.0}</code></pre> <p>where</p> <p>b['speed_Hz'] : speed trace in Hz</p> <p>b['speed_Hz_f'] : speed trace in Hz, savgol filtered (5th order, 41 points)</p> <p>b['laser'] : laser trace in arbitrary units</p> <p>b['FramesPerSecond'] : camera frame acquisition rate</p> <p> </p> <p>Open fig2 data:</p> <pre><code class="language-python">> a = pickle.load(open('fig2.p','rb')) > a {11: {'speed_Hz': array([ 16.2828179 , 38.42508915, 94.68772452, ..., -12.24519659, 160.68335892, 114.39589274]), 'speed_Hz_f': array([ 25.8833788 , 31.87792607, 37.33062434, ..., 66.97264585, 91.38908923, 122.73732123]), 'laser': array([555950., 554818., 555193., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 12: {'speed_Hz': array([ 57.41541418, 24.4936895 , 248.68571544, ..., 86.08667522, -46.11733599, 18.16993041]), 'speed_Hz_f': array([102.04557049, 91.82376088, 84.51288552, ..., 33.63965888, 16.31075159, -5.36834171]), 'laser': array([555950., 554818., 555193., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 21: {'speed_Hz': array([ -57.50587524, 74.55546084, 65.87878605, ..., -197.26554361, 140.95754077, 52.72452236]), 'speed_Hz_f': array([-24.36465769, 22.9146126 , 49.48957346, ..., 47.67536653, 43.44702708, 36.80127664]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 22: {'speed_Hz': array([ 56.78413066, -147.32742392, -14.28783413, ..., -29.51455248, 15.66125098, 41.38001828]), 'speed_Hz_f': array([-23.57073686, -18.61154574, -15.85582681, ..., 7.05157621, 18.45809539, 37.52083946]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}} </code></pre> <p>where</p> <p>a[11] : dictionary for motor 1 trace, laser on motor 1, composed as above.</p> <p>a[12] : dictionary for motor 1 trace, laser on motor 2.</p> <p>a[21] : dictionary for motor 2 trace, laser on motor 1.</p> <p>a[22] : dictionary for motor 2 trace, laser on motor 2.</p>
Dynamics of Centipede Locomotion Revealed by Large-Scale Traction Force Microscopy (J. Roy Soc INTERFACE, to be published, march 2024)
<p>data and matlab code from paper "Dynamics of Centipede Locomotion Revealed by Large-Scale Traction Force Microscopy" (paper submitted to J. Roy Soc INTERFACE, to be published, march 2024)</p> <p><strong><span>Dynamics of Centipede Locomotion Revealed by Large-Scale Traction Force Microscopy</span></strong></p> <p><span> </span><span>J.P. Rieu</span><sup><span>1, *</span></sup><span>, H. Delanoë-Ayari</span><sup><span>1</span></sup><span>, C. Barentin</span><sup><span>1</span></sup><span>, T. Nakagaki</span><sup><span>2</span></sup><span> and S. Kuroda</span><sup><span>3</span></sup><sup><span> ,*</span></sup></p> <p><sup><span>1</span></sup><span> </span><span>Institut Lumière Matière, University of Lyon, Université Claude Bernard Lyon 1, CNRS, F-69622, Villeurbanne, France</span></p> <p><sup><span>2</span></sup><span> Research Institute for Electronic Science, Hokkaido University, N20W10 Kita-ku, Sapporo Hokkaido 001-0020, Japan</span></p> <p><sup><span>3 </span></sup><span>Faculty of Software and Information Technology, Aomori University, Koubata 2-3-1, Aomori, 030-0943, Japan</span></p> <p><span>* Authors for correspondence:<span> </span>Jean-Paul Rieu (e-mail: jean-paul.rieu@univ-lyon1.fr) and Shigeru Kuroda (shigeru-kuroda@aomori-u.ac.jp)</span></p> <p> </p> <p><strong><span>Abstract</span></strong></p> <p><span>We present a novel approach to traction force microscopy (TFM) for studying the locomotion of 10cm-long walking centipedes on soft substrates.</span><span> Leveraging the </span><span>remarkable</span><span> elasticity and ductility of kudzu starch gels, we utilize them as a deformable gel substrate, providing resilience against the centipedes' sharp leg tips. </span><span>Through optimizing fiducial marker size and density and fine-tuning imaging conditions, we enhance measurement accuracy. Our TFM investigation reveals traction forces along the centipede's longitudinal axis that effectively counterbalance inertial forces within the 0-10mN range, providing the first report of non-vanishing inertia forces in TFM studies. Interestingly, we observe waves of forces propagating from the head to the tail of the centipede, corresponding to its locomotion speed. Furthermore, we discover a characteristic cycle of leg clusters engaging with the substrate: forward force (friction) upon leg tip contact, backward force (traction) as the leg pulls the substrate while stationary, and subsequent forward force as the leg tip detaches to reposition itself in the anterior direction. </span><span>This work </span><span>opens perspectives for TFM applications in ethology, tribology, and robotics</span><span>.</span></p>
Data from: Decipher soil organic carbon dynamics and driving forces across China using machine learning
<p><span><span>The dynamics of soil organic carbon (SOC) play a critical role in modulating global warming. However, the long-term spatiotemporal changes of SOC at large scale and the impacts of driving forces remain unclear. In this study, we investigated the dynamics of SOC in different soil layers across China through the 1980s to 2010s using a machine learning approach and quantified the impacts of the key factors based on factorial simulation experiments. Our results showed that the latest (2000-2014) SOC stock in the first meter soil (SOC<sub>100</sub>) was 80.68 ± 3.49 Pg C, of which 42.6% was stored in the top 20 cm, sequestrating carbon with a rate of 30.80 </span><span>± 12.37</span><span> g C m<sup>-2</sup> yr<sup>-1</sup> since the 1980s. Our experiments focusing on the recent two periods (2000s and 2010s) revealed that climate change exerted the largest relative contributions to SOC dynamics in both layers and warming or drying can result in SOC loss. However, the influence of climate change weakened with soil depth, while the opposite for vegetation growth. </span><span>Relationships between SOC and forest canopy height further confirmed this strengthened impact of vegetation with soil depth, and highlighted the carbon sink function of deep soil in mature forest. Moreover, our estimates suggested that SOC dynamics in 71% of topsoil were controlled by climate change and its coupled influence with environmental variation (CE). Meanwhile CE and the combined influence of climate change and vegetation growth dominated the SOC dynamics in 82.05% of the first meter soil. </span><span>Additionally, the national cropland topsoil organic carbon increased with a rate of 23.6 </span><span>± 7.6 </span><span>g C m<sup>-2</sup> yr<sup>-1</sup> since the 1980s, and the widely applied nitrogenous fertilizer was a key stimulus. </span><span>Overall, our study extended the knowledge about the dynamics of SOC and deepened our understanding about the impacts of the primary factors.</span></span></p>
Data samples for Flow-matching -- efficient coarse-graining molecular dynamics without forces
<p>CG samples generated during the training and validation processes in the flow-matching project. Accompanying the preprint "Flow-matching -- efficient coarse-graining molecular dynamics without forces": https://arxiv.org/abs/2203.11167. Detailed descriptions can be found in the preprint as well as the included README.</p>
Data from: Linking in vivo muscle dynamics to in situ force-length and force-velocity reveals that guinea fowl lateral gastrocnemius operates at shorter than optimal lengths
<p>Force-length (F-L) and force-velocity (F-V) properties characterize skeletal muscle's intrinsic properties under controlled conditions, and it is thought that these properties can inform and predict <em>in vivo</em> muscle function. Here, we map dynamic <em>in vivo</em> operating range and mechanical function during walking and running, to the measured <em>in situ</em> F-L and F-V characteristics of guinea fowl (<em>Numida meleagris</em>) lateral gastrocnemius (LG), a primary ankle extensor. We use <em>in vivo</em> patterns of muscle (tendon) force, fascicle length, and activation to test the hypothesis that muscle fascicles operate at optimal lengths and velocities to maximize force or power production during walking and running. Our findings only partly support our hypothesis: <em>in vivo</em> LG velocities are consistent with optimizing power during work production, and economy of force at higher loads. However, LG does not operate at lengths on the force plateau (±5% Fmax) during force production. LG length was near L<sub>0</sub> at the time of EMG onset but shortened rapidly such that force development during stance occurred almost entirely on the ascending limb of the F-L curve, at shorter than optimal lengths. These data suggest that muscle fascicles shorten across optimal lengths in late swing, to optimize the potential for rapid force development near the swing-stance transition. This may provide resistance against unexpected perturbations that require rapid force development at foot contact. We also found evidence of passive force rise (in absence of EMG activity) in late swing, at lengths where passive force is zero <em>in situ</em>, suggesting that history dependent and viscoelastic effects may contribute to <em>in vivo</em> force development. Direct comparison of<em> in vivo </em>work loops and physiological operating ranges to traditional measures of F-L and F-V properties suggests the need for new approaches to characterize dynamic muscle properties in controlled conditions that more closely resemble <em>in vivo </em>dynamics.</p>
Developing and Benchmarking Sulfate and Sulfamate Force Field Parameters via Ab Initio Molecular Dynamics Simulations to Accurately Model Glycosaminoglycan Electrostatic Interactions
<p>To cite and for more details: Riopedre-Fernandez et al. <em>J. Chem. Inf. Model.</em> <strong>2024</strong>, 64 (18), 7122–7134. DOI: <a href="https://doi.org/10.1021/acs.jcim.4c00981">https://doi.org/10.1021/acs.jcim.4c00981</a></p> <p>The dataset includes molecular dynamics simulations of sulfated saccharides and their sulfated analogs in the presence of calcium cations in aqueous solution. Several force field parameter sets were compared (CHARMM36, GLYCAM06, AMOEBA, Drude) and new have been developed (prosECCo75 and GLYCAM-ECC75).</p> <p>The uploaded files contain the following simulation input files or/and simulation trajectories:</p> <p>1) Sulfated_Molecules_Umbrella_Sampling_AIMD: Umbrella sampling ab initio molecular dynamics simulations of calcium-methylsufate and calcium N-methylsulfamate ion pairs in water.</p> <p>2) Sulfated_Molecules_Umbrella_Sampling_FFMD: Umbrella sampling force field molecular dynamics simulations of calcium-methylsufate and calcium N-methylsulfamate ion pairs in water.</p> <p>3) Sulfated_Molecules_AWH_FFMD: Accelerated weight histogram force field molecular dynamics simulations of calcium interacting with both methylsufate and N-methylsulfamate in water.</p> <p>4) Disaccharides_FFMD: Unbiased force field molecular dynamics simulations of calcium-sulfated disaccharide aqueous solutions.</p> <p>UPD. Version 2.0 has updated one of the disaccharide-containing simulations (GLYCAM06, N-sulfation) due to incorrect calcium LJ parameters in the original upload.</p>
A new parameterisation for homogeneous ice nucleation driven by highly variable dynamical forcings
<p>This archive contains the data associated with the preprint of the article <em>"A New Parameterization for Homogeneous Ice Nucleation Driven by Highly Variable Dynamical Forcings."</em> The collection includes datasets used to construct the initial conditions for forcing an air parcel model with ice physics, outputs produced by the parcel model, and data necessary for generating the plots presented in the paper. A README file is provided, offering a detailed explanation of the contents and structure of the archive,</p>
Dataset for Journal of Applied Physics 130, 124502 (2021) - Force microscopy cantilevers locally heated in a fluid: temperature fields and effects on the dynamics
<p><strong>"Fig7.fig"</strong>: Matlab figures including all the measured and treated data used to plot figure 7 of the article.</p> <p><strong>"Fig7_data.mat "</strong>: Matlab files containing the data to plot figure 7 of the article.</p> <p><strong>"Fig7_plot.m "</strong>: Matlab scripts to plot the data of the Matlab files "Fig7_data.mat"</p> <p><strong>"Fig10.fig"</strong>: Matlab figures including all the measured and treated data used to plot figure 10 of the article.</p> <p><strong>"Fig13.fig"</strong>: Matlab figures including all the measured and treated data used to plot figure 13 (in the appendix) of the article.</p> <p> </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>
Molecular dynamics simulation trajectories for the GB99dms implicit solvent force field
<p>Molecular dynamics simulation trajectories used in training and validating the GB99dms implicit solvent protein force field. See the paper:</p> <ul> <li>Greener JG. Differentiable simulation to develop molecular dynamics force fields for disordered proteins, <a href="https://doi.org/10.1039/D3SC05230C" target="_blank" rel="noopener">Chemical Science</a> 15, 4897-4909 (2024)</li> </ul> <p>For more information, including structure files for these trajectories, see https://github.com/greener-group/GB99dms.</p>
Data supplement for "Stick-slip Dynamics in the Forced Wetting of Polymer Brushes"
<p>This dataset contains supplementary data for the following publication:</p> <p>Greve, D., Hartmann, S. & Thiele, U.<br> Stick-slip Dynamics in the Forced Wetting of Polymer Brushes<br> Soft Matter, 2023, 19, 4041-4061</p> <p>We provide the source files and data for all figures as well as a video of the stick-slip motion of a liquid front on a polymer brush substrate corresponding to the data shown in Figs. 10 & 11 of the paper.</p>
Future land cover and land cover dynamic trajectories in China under anthropogenic and climate forcing in 8 SSP-RCP scenarios
<p>The projection of future land cover in 21st century in China and land cover dynamic trajectories in 8 SSP-RCP scenarios</p>
Data from: Decipher soil organic carbon dynamics and driving forces across China using machine learning
Open the record for dataset details and reuse information.
Data from: Linking in vivo muscle dynamics to in situ force-length and force-velocity reveals that guinea fowl lateral gastrocnemius operates at shorter than optimal lengths
Open the record for dataset details and reuse information.
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.