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165 results for “mechanical properties”
Finite element dataset and Artificial Neural Networks algorithms to predict the mechanical properties of innovative CLT
<p>This folder includes the data collected from the finite element simulations of the innovative CLT to compute its mechanical properties, the error of the closed-form solutions predicting the bending stiffness in the minor direction D22, the variation of the distance between the Reissner Mindlin and Bending Gradient theory in terms of spacing between lateral lamellas, the hyperparameters tuning of several Artificial Neural Networks algorithms with or without prior knowledge, the ML evaluations, the saved artificial neural network algorithms to predict each mechanical property of innovative CLT, and the ML application to use it.</p>
Collected data from finite element simulations to calculate the mechanical properties of innovative CLT using ABAQUS
<p>A dataset is collected from finite element computation using ABAQUS for different configurations of innovative CLT. In the dataset, we have the inputs (w, t1, t2, t3, t4, s, E_L, G_LZ, G_CZ) describing the microstructure of innovative CLT, and the elastic properties (Membrane stiffness: A11, A22, In-plane Poisson effect stiffness: A12, In-plane shear stiffness: A33, Bending stiffness: D11, D22, Out of plane Poisson effect stiffness: D12, Torsional stiffness: D33, out of plane Bending Gradient shear compliances: h11, h12, h16, h22, h26, h33, h34, h35, h44, h45, h55, h66) deriving from the FE computations. We have also the results of closed-form solutions that predicts these elastic properties of innovative CLT. </p>
Schiff Base Crosslinked Hyaluronic Acid Hydrogels with Tunable and Cell Instructive Time-Dependent Mechanical Properties
<p>This folder contains raw data for the paper titled "<strong>Schiff Base Crosslinked Hyaluronic Acid Hydrogels</strong><strong> with Tunable and Cell Instructive Time-Dependent Mechanical Properties</strong>" authored by Taha Behroozi Kohlan, Yanru Wen, Carina Milena Mini, and Anna Finne-Wistrand, published in <em>Carbohydrate Polymers</em> journal. The raw data presented here contains NMR, FTIR, SEC, swelling and stability, and rheology data used to create figures.</p> <p><strong>Abstract:</strong></p> <p>The dynamic interplay between cells and their native extracellular matrix (ECM) influences cellular behavior, imposing a challenge in biomaterial design. Dynamic covalent hydrogels are viscoelastic and show self-healing ability, making them a potential scaffold for recapitulating native ECM properties. We aimed to implement kinetically and thermodynamically distinct crosslinkers to prepare self-healing dynamic hydrogels to explore the arising properties and their effects on cellular behavior. To do so, aldehyde-substituted hyaluronic acid (HA) was synthesized to generate imine, hydrazone, and oxime crosslinked dynamic covalent hydrogels. Differences in equilibrium constants of these bonds yielded distinct properties including stiffness, stress relaxation, and self-healing ability. The effects of degree of substitution (DS), polymer concentration, crosslinker to aldehyde ratio, and crosslinker functionality on hydrogel properties were evaluated. The self-healing ability of hydrogels was investigated on samples of the same and different crosslinkers and DS to obtain hydrogels with gradient properties. Subsequently, human dermal fibroblasts were cultured in 2D and 3D to assess the cellular response considering the dynamic properties of the hydrogels. Moreover, assessing cell spreading and morphology on hydrogels having similar modulus but different stress relaxation rates showed the effects of matrix viscoelasticity with higher cell spreading in slower relaxing hydrogels.</p>
Table with mechanical properties of TPS/PCL blends.
<p>Table containing micro- and macromechanical properties of TPS/PCL polymer blends.</p> <p>Format: Zipped MS Excel file; description of the data is directly inside the file.</p>
Frictional Properties of Fault Rocks and Creep Mechanism for the Laohushan Segment of the Haiyuan Fault, Northeastern Tibet
<p>All raw data of XRD analyses of fault rocks, particle size analyses of fault gouges, and mechanical data of fault gouge at different temperature are related to this paper.</p>
STUDY OF PHYSICAL-CHEMICAL AND MECHANICAL PROPERTIES OF NEW DENTAL SEALANTS FORMULATIONS
<p><span><span><span>Data from the tests reported in the article "STUDY OF PHYSICAL-CHEMICAL AND MECHANICAL PROPERTIES OF NEW FORMULATIONS OF DENTAL SEALANTS".</span></span> Revista Cubana de Estomatología<span><span>.</span></span></span></p> <p>Datos de los ensayos reportados en el artículo "ESTUDIO DE PROPIEDADES FÍSICO-QUÍMICAS Y MECÁNICAS DE NUEVAS FORMULACIONES DE SELLANTES DENTALES". Revista Cubana de Estomatología.</p>
Supporting data for "Neural Network-Based Interatomic Potential for the Study of Thermal and Mechanical Properties of Siliceous Zeolites"
Open the record for dataset details and reuse information.
Gas permeability and mechanical properties of dust grains at hyper- and zero-gravity
<p>Two-phase flow experimental data obtained from the TEMPusVoLA facility over the course of three Zero-gravity parabolic flight campaigns (<strong>PFCs</strong>). The experiment, designated 'System 1' , contains low-pressure gas and analogue dust particles (of diverse compositions, shapes, and sizes) that experience a low-pressure gas cross-wind normal to the gravitational force vector. During steady flight, the particles pack into the lower end of the chamber and the layer of granular media creates a pressure differential, which is used to extract permeability coefficients in both the continuum and molecular-diffusion dominated regimes. We compare the permeability both with and without the effect of gravity and make comparisons with the conditions on low-gravity objects in the solar system, such as planetesimals and the dust particles that comprise them. In particular, we are able to compare and contrast the cohesiveness of the dust-regolith analogue types, which has consequences for formation theories of Solar-system bodies. </p> <p> </p> <p><strong>Data products: </strong></p> <p><strong>Pressure, acceleration</strong></p> <p>Each file corresponds to a particular parabolic flight campaign and day of flight. The 4th Swiss PFC lasted 1 day in 2020. The 75 ESA PFC lasted 3 days in 2021, and the the 78th ESA PFC lasted 3 days in 2022. Data was obtained in System 1 during all flights of all campaigns, and for a set of 12-16 parabolas. On each parabola, we studied the sample in one out of three chambers and changed the gas mass flux rate after cycling through all three samples. The below files were used to correlate changes in sample gas permeability with changes in gravitational loading. </p> <p>-- Raw pressure files output from 6-channel pressure logger measuring pressure as function of time as .csv files: DATALOG_DATE. </p> <p>-- Raw acceleration files output from accelerometer measuring gravitational load on aircraft.</p> <p><strong>Imaging data</strong></p> <p>-- Movie sequences obained with high-resolution thor-labs camera. All three chambers were imaged, despite only one chamber being subject to active gas flow at any given time. movie1 and movie 2 are referenced in the associated publication. </p> <p><strong>Multimeter data</strong></p> <p><strong>-- </strong>Output from photo-diodes measured across 9 channels using a Keithley Multimeter/Multiplexor. Acquisition speed was 27 Hz. </p>
Dataset for the paper 'Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning'
<div> <div>This dataset contains the data produced for the above paper. The dataset consists of:</div> <div> </div> <div>- input nanopillars before deformation (molecular_dynamics/nanopillars)</div> <div>- stress-strain curves acquired by deforming the nanopillars (molecular_dynamics/stress_strain_curves)</div> <div>- weights of the CNNs trained to predict mechanical properties of the nanopillars (machine_learning/train_CNN)</div> <div>- Grad-CAM fields of the predictions (machine_learning/train_CNN)</div> <br> <div>Codes used for creating and analyzing the dataset are available at https://github.com/tekoivisto/nanopillar-ML</div> </div>
Dataset of Mechanical Properties WH Composites
<p>Dataset tensile and impact strength of water hyacint reinforced polymer composite</p>
Characterisation of mechanical properties of materials used in 3D printing
<p>The mechanical properties of 19 materials used in 3D printing were experimentally tested. Mechanical properties (mostly elastic modulus and strength, but not only) were obtained and compared for filaments, single-extruded fibres, and dog-bone type specimens printed with printing angles 0, 90° and intermediate angles. Thus, anisotropy of properties was considered. The density, modulus of elasticity, and tensile strength values were compared for all mentioned types of specimens.</p>
Database of the mechanical properties of FRP composites subjected to different environmental effects
<p>The database is established by collecting FRP aging test data from various published literature, with a uniform record format to ensure consistency. The database comprises two main categories: accelerated aging and natural aging. The recorded accelerated aging data were further sorted into five types: water immersion aging, alkaline solution aging, acidic solution aging, high-temperature aging, and ultraviolet radiation aging. </p>
Mechanical properties on Metal Additive manufacturing
<p><strong>Mechanical properties on Metal Additive manufacturing</strong></p> <p><strong>Abstract</strong></p> <p>This dataset contains the results on the experimental procedure to gather data on the manufacturing of Additive Manufacturing (AM) pieces for Laser Metal Deposition (LMD) processes.To create this dataset, a set of 37 pieces were manufactured using the same material and procedure but different manufacturing parameters and strategies. The process was monitored and recorded, capturing thermal images and sensor readings on each point of the manufacturing. Each one of the pieces was divided in 4 coupons, that were tested to obtain mechanical properties of resistance and hardness.</p> <p> </p> <p><strong>Contents</strong></p> <p>This dataset contains data related to 37 coupons (named T1 - T37), including the monitoring data gathered during manufacturing, the process parameters, results of tensile testing, results of hardness testing and pictures of the coupons.</p> <p>The dataset is structured as follows:</p> <ul> <li>[Piece ID] <ul> <li>Hdf5 file</li> <li>Piece picture</li> <li>Piece crosssection</li> </ul> </li> <li>Testing <ul> <li>Parameters.csv: tabular data with process parameters and piece measurements as a direct result of the manufacturing</li> <li>Coupons.csv: tabular data with tensile and hardness metrics on the tested coupons for each piece</li> </ul> </li> <li>README: explicative document</li> </ul> <p> </p> <p><strong>Formats</strong></p> <p>The data is provided in the following formats:</p> <ol> <li>Monitoring: HDF5 file format. This format allows the recording of multimodal manufacturing data.</li> <li>Pictures: jpg format</li> <li>Parameters: csv file containing process parameters</li> <li>Coupons: csv file with the material testing results.</li> </ol> <p>More information on the contents and formats of the dataset is contained in the attached README document.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This work has been funded under the "Red de Excelencia en Fabricación Aditiva" (READI) with the support of the Spanish Ministry of Science and Innovation and the Centre for the Development of Industrial Technology (CDTI), under the program Cervera (CER-20191020).</p> <p> </p>
Effect of Dry Needling on Muscle Mechanical Properties and Muscle Contractility in Latent Trigger Points
ClinicalTrials.gov study NCT04466813. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of Foam Rolling on Muscle Mechanical Properties
ClinicalTrials.gov study NCT04210947. IPD Sharing: NO. Countries: 1. Publications: 7.
Strengthening Effect on Hamstring's Passive Mechanical Properties
ClinicalTrials.gov study NCT03884738. IPD Sharing: NO. Countries: 1. Publications: 6.
The Acute Effect of Cold Spray Application on the Mechanical Properties of the Quadriceps Muscle in Athletes
ClinicalTrials.gov study NCT04277494. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Investigation of Effects of Physiotherapy Interventions on Mechanical Properties of Muscle in Head and Neck Cancer
ClinicalTrials.gov study NCT05399953. IPD Sharing: NO. Countries: 1. Publications: 12.
3D-Printed Vs Thermoformed Retainers: Comparison of Post-Treatment Stability, Changes in Mechanical Properties and Patients' OHRQoL
ClinicalTrials.gov study NCT05968625. IPD Sharing: NO. Countries: 1. Publications: 15.
Ex Vivo Lung Perfusion : Effect of Cellular Perfusate on Mechanical Properties and Gas Exchange Function of Donor Lungs
ClinicalTrials.gov study NCT02953483. IPD Sharing: NO. Countries: 1. Publications: 2.
ScienceDex guides
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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.