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.
306
datasets available to search
ShareScore release 0.9.0
Dataset results
306 results for “mechanical modeling”
Data from: Model sensitivity and use of the comparative finite element method in mammalian jaw mechanics: mandible performance in the Gray Wolf
Finite Element Analysis (FEA) is a powerful tool gaining use in studies of biological form and function. This method is particularly conducive to studies of extinct and fossilized organisms, as models can be assigned properties that approximate living tissues. In disciplines where model validation is difficult or impossible, the choice of model parameters and their effects on the results become increasingly important, especially in comparing outputs to infer function. To evaluate the extent to which performance measures are affected by initial model input, we tested the sensitivity of bite force, strain energy, and stress to changes in seven parameters that are required in testing craniodental function with FEA. Simulations were performed on FE models of a Gray Wolf (Canis lupus) mandible. Results showed that unilateral bite force outputs are least affected by the relative ratios of the balancing and working muscles, but only ratios above 0.5 provided balancing-working side joint reaction force relationships that are consistent with experimental data. The constraints modeled at the bite point had the greatest effect on bite force output, but the most appropriate constraint may depend on the study question. Strain energy is least affected by variation in bite point constraint, but larger variations in strain energy values are observed in models with different number of tetrahedral elements, masticatory muscle ratios and muscle subgroups present, and number of material properties. These findings indicate that performance measures are differentially affected by variation in initial model parameters. In the absence of validated input values, FE models can nevertheless provide robust comparisons if these parameters are standardized within a given study to minimize variation that arise during the model-building process. Sensitivity tests incorporated into the study design not only aid in the interpretation of simulation results, but can also provide additional insights on form and function.
Data from: Biophysically inspired model for functionalized nanocarrier adhesion to cell surface: roles of protein expression and mechanical factors
In order to achieve selective targeting of affinity–ligand coated nanoparticles to the target tissue, it is essential to understand the key mechanisms that govern their capture by the target cell. Next-generation pharmacokinetic (PK) models that systematically account for proteomic and mechanical factors can accelerate the design, validation and translation of targeted nanocarriers (NCs) in the clinic. Towards this objective, we have developed a computational model to delineate the roles played by target protein expression and mechanical factors of the target cell membrane in determining the avidity of functionalized NCs to live cells. Model results show quantitative agreement with in vivo experiments when specific and non-specific contributions to NC binding are taken into account. The specific contributions are accounted for through extensive simulations of multivalent receptor–ligand interactions, membrane mechanics and entropic factors such as membrane undulations and receptor translation. The computed NC avidity is strongly dependent on ligand density, receptor expression, bending mechanics of the target cell membrane, as well as entropic factors associated with the membrane and the receptor motion. Our computational model can predict the in vivo targeting levels of the intracellular adhesion molecule-1 (ICAM1)-coated NCs targeted to the lung, heart, kidney, liver and spleen of mouse, when the contributions due to endothelial capture are accounted for. The effect of other cells (such as monocytes, etc.) do not improve the model predictions at steady state. We demonstrate the predictive utility of our model by predicting partitioning coefficients of functionalized NCs in mice and human tissues and report the statistical accuracy of our model predictions under different scenarios.
Data from: Metabolite profile of a mouse model of Charcot-Marie-Tooth type 2D neuropathy: implications for disease mechanisms and interventions
Charcot-Marie-Tooth disease encompasses a genetically heterogeneous class of heritable polyneuropathies that result in axonal degeneration in the peripheral nervous system. Charcot-Marie-Tooth type 2D neuropathy (CMT2D) is caused by dominant mutations in glycyl tRNA synthetase (GARS). Mutations in the mouse Gars gene result in a genetically and phenotypically valid animal model of CMT2D. How mutations in GARS lead to peripheral neuropathy remains controversial. To identify putative disease mechanisms, we compared metabolites isolated from the spinal cord of Gars mutant mice and their littermate controls. A profile of altered metabolites that distinguish the affected and unaffected tissue was determined. Ascorbic acid was decreased fourfold in the spinal cord of CMT2D mice, but was not altered in serum. Carnitine and its derivatives were also significantly reduced in spinal cord tissue of mutant mice, whereas glycine was elevated. Dietary supplementation with acetyl-L-carnitine improved gross motor performance of CMT2D mice, but neither acetyl-L-carnitine nor glycine supplementation altered the parameters directly assessing neuropathy. Other metabolite changes suggestive of liver and kidney dysfunction in the CMT2D mice were validated using clinical blood chemistry. These effects were not secondary to the neuromuscular phenotype, as determined by comparison with another, genetically unrelated mouse strain with similar neuromuscular dysfunction. However, these changes do not seem to be causative or consistent metabolites of CMT2D, because they were not observed in a second mouse Gars allele or in serum samples from CMT2D patients. Therefore, the metabolite 'fingerprint' we have identified for CMT2D improves our understanding of cellular biochemical changes associated with GARS mutations, but identification of efficacious treatment strategies and elucidation of the disease mechanism will require additional studies.
Mechanical data of rotary shear experiments, temperature measurements, and temperature numerical models for the manuscript: "Mechanical energy dissipation during seismic dynamic weakening in calcite-bearing faults"
<p>All data included in this data repository is ancillary to the manuscript "Energy dissipation during dynamic weakening in calcite-bearing fault rocks", submitted to Journal of Geophysical Research: Solid Earth. </p><p>The data consists in time series of high velocity friction experiments run with SHIVA (INGV, Rome), time series acquired from a two-color pyrometer (UC3M), the synchronization of the two, and numerical models. The data format is .mat, proprietary to Matlab, but they can be easily accessed with Python (see <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">link</a>). Each .mat contains vector of the measured variables when opened from Matlab, or dictionaries when opened using the scipy.loadmat() function. </p><p>SHIVA and PYRO red data (calibrated data), fin data (synchronized data), and shivaRED vect data (numerical model data) are included in this data repository in separate folders. Numerical models are grouped in subfolder by type of model (the relation fin data to model is 1:n). We included the scripts to convert SHIVA raw data into SHIVA red data (<a href="https://github.com/aretu/shivaUNIX">link to shivaUNIX</a>), SHIVA and PYRO red data into fin data (/scripts/syncing2021.m), to obtain numerical models from fin data (<a href="https://github.com/aretu/shivaRED">link to shivaRED</a>), and to plot data (/scripts/making plots.ipynb).</p>
Data from: A multifactor coupling prediction model for the failure depth of floor rocks in fully mechanized caving mining: a numerical and in situ study
To study the mining-induced failure depth of floor rocks in a fully-mechanized mining caving field affected by different coal seam pitches, mining face lengths, burial depths and aquifer water pressures, multifactor coupled orthogonal numerical tests on the failure depth of floor rocks were conducted. The numerical results show that the failure depth of floor rocks increases with increasing mining face length, coal seam pitch and burial depth. According to the relationship between failure depth and these impact factors, a multifactor coupled prediction model for the failure depth of floor rocks was established. In addition, the in-situ measurement of the failure depth of floor rocks in the Yitang Coal Mine in Huoxi coal field in Shanxi Province, China, was performed, and the in-situ failure depths of floor rocks in the 100502 (80 m) and 100502 (180 m) mining faces were approximately 12.50~14.65 m and 17.50~19.20 m, in good agreement with the results of the multifactor prediction model. Furthermore, the sensitivity of each impact factor in the prediction model of the floor failure depth was further analysed by F-test and range analysis, and the impact order of studied factors on the floor failure depth is coal seam pitch>mining face length>burial depth>aquifer water pressure.
Laboratory evidence supporting a mechanical model describing the dynamic formation of fault damage asymmetry [DATA]
<p>Data and metadata for the publication "Laboratory evidence supporting a mechanical model describing the dynamic formation of fault damage asymmetry", published in Geophysical Research Letters.</p>
Circuit mechanism underlying fragmented sleep and memory deficits in 16p11.2 deletion mouse model of autism
Open the record for dataset details and reuse information.
DATA for Evaluating Scale-Aware Boundary Layer Similarity Functions and Their Mechanisms in Tropical Cyclone Modeling Using Idealized Large-Eddy Simulations
<p>data.xlsx has the data to make Figs.1-4</p> <p> </p>
Phosphorylation Regulation Mechanism of β2 Integrin for the Binding of Filamin Revealed by Markov State Model
<p>Datasets of MD and MSM trajectories</p>
Fig. 5 in Modeling the peroxyl radical scavenging behavior of Carnosic acid: Mechanism, kinetics, and effects of physiological environments
Fig. 5. ΔG values in kcal/mol of CA + HOO• reaction in the gas phase, pentyl ethanoate, and water.
Fig. 1 in Modeling the peroxyl radical scavenging behavior of Carnosic acid: Mechanism, kinetics, and effects of physiological environments
Fig. 1. Molecular structure of Carnosic acid (CA).
Micromechanical Modeling Using Low Magnitude Mechanical Stimulation
ClinicalTrials.gov study NCT01921517. IPD Sharing: NO. Countries: 1. Publications: 0.
A Comparative Study to Evaluate the Effects and Mechanism of Action of Dysport®, Botox® and Xeomin® in the Extensor Digitorum Brevis Model in Healthy Adult Male Participants
ClinicalTrials.gov study NCT04970407. IPD Sharing: NO. Countries: 1. Publications: 0.
Research on the Complex Scientific Mechanisms Underlying Exercise and Health: Construction and Application of a Mechanistic Model and Public Database（CISS）
ClinicalTrials.gov study NCT06936982. IPD Sharing: NO. Countries: 1. Publications: 0.
Defibrotide in the Human Endotoxemia Model --- an Exploratory Trial Investigating the Effects and the Mechanisms of Defibrotide
ClinicalTrials.gov study NCT02876601. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Pathogen growth in insect hosts: inferring the importance of different mechanisms using stochastic models and response time data
Open the record for dataset details and reuse information.
Data from: A cell-based mechanical model of coronary artery tunica media
Open the record for dataset details and reuse information.
Data from: Biophysically inspired model for functionalized nanocarrier adhesion to cell surface: roles of protein expression and mechanical factors
Open the record for dataset details and reuse information.
Data from: A multifactor coupling prediction model for the failure depth of floor rocks in fully mechanized caving mining: a numerical and in situ study
Open the record for dataset details and reuse information.
Data from: Morphological datasets fit a common mechanism much more poorly than DNA sequences and call into question the Mkv model
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.