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451 results for “Elasticity”
Design of an elastic porous injectable biomaterial for tissue regeneration and volume retention: raw dataset
<p>Raw dataset for the publication:</p> <p><strong>Design of an elastic porous injectable biomaterial for tissue regeneration and volume retention</strong></p>
Digital image correlation measurement of linear elastic steel specimen
<p>The dataset comprises the axial and lateral displacements on the surface of a plate with a hole subjected to tensile load. The displacement data are measured by digital image correlation and the material is assumed to behave linear elastic. The material under investigation is a common low-carbon steel alloy of type S235. The displacement data are used for calibration of a linear elastic constitutive model using parametric physics-informed neural networks and finite elements. For that purpose, the dataset comprises both the raw experimental displacement data and displacement data interpolated onto a regular grid using linear interpolation, where the interpolation routine is provided as well.</p>
Market Power / Import demand elasticity faced by an exporter at 6-digit HS level from Solleder (2020)
<p><strong>Description</strong></p> <p>This dataset contains the market power of exporters at the country level for more than 4000 6-digit HS codes (HS 1992 / H0) from Solleder (2020). Market power is proxied by the inverse of the import demand elasticity faced by the exporting country. Elasticities are estimated following the method developed by Kee et al. (2008). For more information, please refer to Solleder (2020).</p> <p>The <em>dta </em>file can be opened with STATA 14 or above. The <em>csv</em> file is a comma-separated value file. The separator is ',', and the first row is variable names. The content is the same in both files. Variables are:</p> <ul> <li><em>exporter</em>: ISO 3166 3-character country codes, string; </li> <li><em>commoditycode</em>: product 6-digit HS codes in HS revision 1992 (H0), string;</li> <li><em>epsilon</em>: import demand elasticity faced by the exporter, numeric;</li> <li><em>epsilon_se</em>: standard error of <em>epsilon</em>, numeric;</li> <li><em>marketpower</em>: market power, inverse of the absolute value of the import demand elasticity faced by the exporter, numeric.</li> </ul> <p> </p> <p><strong>Reference</strong></p> <div> <div>Kee H.L., A. Nicita, M. Olarreaga 2008 'Import demand elasticities and trade distortions' Rev. Econ. Stat., 90 (4), pp. 666-682</div> <div> </div> <div>Solleder J.M. 2020 'Market power and export taxes' European Economic Review, Volume 125, 103425, ISSN 0014-2921, <a href="https://doi.org/10.1016/j.euroecorev.2020.103425">https://doi.org/10.1016/j.euroecorev.2020.103425</a>.</div> </div> <p> </p>
Data of Benchmarking Elasticity of FaaS Platforms
<p>This data is part of the publication "Benchmarking Elasticity of FaaS Platforms as a Foundation for Objective-driven Design of Serverless Applications", it contains all plots and data used for the assessment of FaaS platform quality under volatile workloads from a client-side perspective. The paper is part of SAC'20, Brno, Czech Republic.</p>
Elasticity tensors of 10276 crystals from DFT computations
<h2>Paper introducing this dataset</h2> <p>Wen, M., Horton, M., Munro, J., Huck, P., & Persson, K. (2024). An equivariant graph neural network for the elasticity tensors of all seven crystal systems. <em>Digital Discovery</em>. DOI:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3DD00233K">https://doi.org/10.1039/D3DD00233K</a></p> <p> </p> <p>This dataset consists of three data files in the json format. Each file is explained below.</p> <h2>crystal_elasticity_tensor.json</h2> <p>DFT computed elastic tensors of 10276 crystals used for developing the MatTen model.</p> <p>structure: crystal structure of the material<br>formula_pretty: chemical formula<br>crystal_system: crystal system<br>elastic_tensor: full fourth-rank elastic tensor<br>elastic_tensor_voigt: 6x6 Voigt matrix of the elastic tensor<br>split: split of the data into train, validation, and test subsets for model development</p> <h2><br>max_directional_E.json</h2> <p>New crystals with large maximum directional Young's modulus.</p> <p>material_id: Materials Project identifier<br>formula_pretty: chemical formula</p> <p>structure_original: crystal structure from the Materials Project database<br>elastic_tensor_matten_original: MatTen predicted elastic tensor using `structure_original`<br>max_directional_E_matten_original: MatTen predicted maximum directional Young's modulus using `structure_original`</p> <p>structure: further DFT optimized structure with a tigher criterion<br>elastic_tensor: DFT elastic tensor corresponding to `structure`<br>max_directional_E: DFT maximum directional Young's modulus using `structure`<br>elastic_tensor_matten: MatTen predicted elastic tensor using `structure`<br>max_directional_E_matten: MatTen predicted maximum directional Young's modulus using `structure`</p> <h2><br>elemental_cubic_metal_max_E_along_100_direction.json</h2> <p>New crystals with its maximum directional Young's modulus along the [100] direction.</p> <p>material_id: Materials Project identifier<br>formula_pretty: chemical formula</p> <p>structure_original: crystal structure from the Materials Project database<br>elastic_tensor_matten: MatTen predicted elastic tensor using `structure_original`<br>Delta_S_matten: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor_matten`</p> <p>structure: further DFT optimized structure with a tigher criterion<br>elastic_tensor: DFT elastic tensor corresponding to `structure`<br>Delta_S: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor`</p>
A laser-plasma platform for photon-photon physics: the two photon Breit-Wheeler process, and Bounding elastic photon-photon scattering at $\sqrt s \approx 1$\,MeV using a laser-plasma platform
<p>The data contained in this repository was used in the production of the publication "A laser-plasma platform for photon-photon physics: the two photon Breit-Wheeler process" (<a href="https://doi.org/10.1088/1367-2630/ac3048">https://doi.org/10.1088/1367-2630/ac3048</a>) and "Bounding elastic photon-photon scattering at $\sqrt s \approx 1$\,MeV using a laser-plasma platform" (<a href="https://doi.org/10.1016/j.physletb.2025.139247">https://doi.org/10.1016/j.physletb.2025.139247</a>).</p>
Training data for neural network-based determination of nematic elastic constants
<p>Neural network training data packets (<strong><em>intensities_{i}.csv, K1K3_{i}.csv</em></strong>), each consisting of 1000 training data pairs, used in a machine learning-based method for determination of Frank elastic constants of nematic liquid crystals, experimental measurements of time-dependent light intensities (<strong><em>experimental_time</em></strong>_<strong><em>{i}.csv, experimental_intensity_{i}.csv</em></strong>), diode spectrum data (<strong><em>diode_lbd</em></strong><strong><em>.csv, diode_w.csv</em></strong>).</p> <p>These data sets are associated with the paper <a href="https://www.nature.com/articles/s41598-023-33134-x"><strong><em>[Zaplotnik et al. SciRep, 2023]</em></strong></a></p> <p>This is supplementary material for a Jupyter Notebook uploaded on <a href="https://zenodo.org/record/7368828">Zenodo</a>.</p>
nanoindentation data associated with the publication "On the elastic microstructure of bulk metallic glasses" in Materials&Design 2023
<p>This dataset consists of indentation data measured with a conospherical tip in a Hysitron-Bruker TI980 Nanoindenter on the surface of a <100> Silicon wafer and a polished cross-sectional cut of a Zr65Cu25Al10 bulk metallic glass.</p> <p>It is associated with the following publication: <br>Birte Riechers, Catherine Ott, Saurabh Mohan Das, Christian H. Liebscher, Konrad Samwer, Peter M. Derlet and Robert Maass "On the elastic microstructure of bulk metallic glasses" Materials and Design 229, (2023) 111929. https://doi.org/10.1016/j.matdes.2023.111929</p> <p>All experimental information can be found in this paper and in the accompanying supplementary information.</p> <p>This electronic version of the data was published on the "Zenodo Data repository" found at http://zenodo.org/deposit in the community "Bundesanstalt fuer Materialforschung und -pruefung (BAM)".</p> <p>The authors have copyright to these data. You are welcome to use the data for further analysis, but are requested to cite the original publication whenever use is made of the data in publications, presentations, etc. </p> <p>Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.</p> <p>____________________________________________________________________</p> <p>The data format is defined as described below:</p> <p>In total, Fifteen text files exist that result in five different data sets.</p> <p>Two data sets represent measurements on Silicon, these are specifically the topography (mapped height profile) and indentation modulus (Si-topography.txt and Si-modulus.txt). The connected lateral information of these mapped quantities (i.e. Si-X_topography.txt and Si-Y_topography.txt; Si-X_modulus.txt and Si-Y_modulus.txt). This amounts to six .txt files connected to measurements on Silicon.</p> <p>Three data sets represent measurements on the Zr65Cu25Al10 bulk metallic glass. These are specifically the topography (MG-topography.txt), the indentation modulus (MG-modulus.txt), and the curvature-corrected indentation modulus (MG-curvcorr_modulus.txt). The connected lateral information of these mapped quantities (i.e. MG-X_topography.txt and MG-Y_topography.txt; MG-X_modulus.txt and MG-Y_modulus.txt; MG-X_curvcorr-modulus.txt and MG-Y_curvcorr-modulus.txt). This amounts to nine .txt files connected to measurements on the metallic glass.</p> <p>The files are plain text files with the data points separated by commata. Topography data is stated in units of Nanometer, modulus data is stated relative to its mean as unit-less values.</p> <p>Beside the .txt files, one figure (.pdf) with a plot of each data set is provided for reference, and the python code (Riechers_OnTheElasticMicrostructureOfBulkMetallicGlasses_zenodo.ipynb) generating these figures from the data sets is uploaded to this repository as well.</p>
COHERENT Collaboration data release from the first detection of coherent elastic neutrino-nucleus scattering on argon
<p>Release of COHERENT collaboration data from the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on argon. This data release corresponds with the results of "Analysis A" published in arXiv:2003.10630[nucl-ex]. The data release enables further studies of CEvNS.</p> <p>Use of the data release is presented in the accompanying pdf document within this submission. Example code is included within the release as part of this submission. The materials here are also available at http://coherent.ornl.gov/data/, which preserves the directory structure used within the accompanying document. Note the use of the example code in this release expects the directory structure written within the accompanying pdf document.</p>
Deliverable 2.1 Aero-hydro-elastic model definition - SOFTWIND 10 MW FOWT (wave-tank SIL version)
<p>For the detailed validation and verification of the capabilities of QBladeOcean in work package 2 of FLOATECH, a detailed definition of the models is needed. This database presents the QBladeOcean model of the DTU 10MW Reference Wind Turbine mounted on the SOFTWIND floater.</p> <p>Update V2.0.0: <br>Structure files are modified according to the requirements of the QBladeCE version</p> <p>Update V3.0.0:<br>- Added controller from SOFTWIND experiments (Modified from DTU 10MW to have oO star controller parameters)<br>- Modified mooring line length<br>- Shifted platform COG slightly towards centerline<br>- Modified blade definition to AD14 blade def.<br>- Included STATICBUOYANCY flag</p> <p>Update V3.1.0:<br>- Included ADVANCEDBUOYANCY flag<br>- Corrected excitation file (.3), previously: incorrect assignment of wave headings and excitation force coefficients<br>- Addition of mean drift file (.8)<br>- Corrected error in added mass matrix entry [4,2] (sway-roll coupling)</p> <p>Update V3.2.0:<br>- DELTA_DIR_DIFF 1-->20<br>- STATICBUOYANCY --> true</p> <p>Update V3.3.0:<br>- updated Substructure .txt file to format compatible with new QBlade version 2.0.6.4+<br>- extrapolation stretching activated<br>- depth dependent drag coefficient of 0.6 until z = -4m<br>- adjusted "DAMP_[-]" paremeter in the "MOORELEMENTS" table of ths Substructure .dat file to be zero due to numerical instabilities</p>
Deliverable 2.1 Aero-hydro-elastic model definition - OC5 5MW MSWT
<p>For the detailed validation and verification of the capabilities of QBladeOcean in work package 2 of FLOATECH, a detailed definition of the models is needed. This database presents the QBladeOcean model of the MARIN Stock Wind Turbine mounted on the OC5 floater.</p> <p>Update V2.0.0; Structure files are modified according to the requirements of the QBladeCE version</p> <p>Update V3.0.0; Bugfix mooring line 3 definition</p> <p>Update V4.0.0;<br>- TOWERDRAG activated and set to cd = 0.5<br>- Inlcusion of Non-linear QTF forces<br>- Modified RAYLEIGHDMP 0.05 --> 0.01</p> <p>Update V5.0.0;<br>- updated Substructure .dat file to a format compatible with QBlade version 2.0.6.4+<br>- extrapolation stretching activated<br>- depth dependent drag coefficient of 0.6 until z = -4m<br>- enhanced model to improve low frequency pitch excitation --> additional parameters in HYDROJOINTCOEFF table<br>- adjusted "DAMP_[-]" paremeter in the "MOORELEMENTS" table of the Substructure .dat file to be zero due to numerical instabilities</p>
Data for: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling
<h2>Description</h2> <p>DATA REPOSITORY FOR</p> <p>Title: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and <br> multi-scale material modeling<br>By: Eva Jägle, Jithender J. Timothy, Daniel Jansen, Alisa Machner<br>Accepted by: Cement and Concrete Research</p> <p>This dataset presents the data of the paper 'Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling' submitted to and accepted by Cement and Concrete Research. The dataset follows the structure of the paper such that the calculations described therein can be reproduced.</p> <p>Data is available on three types of cement: Two ordinary Portland cements of different grinding fineness (CEM I 42.5 R und CEM I 52.5 R) and one limestone-containing blended cement (CEM II/A-LL 42.5 R). The data refer to the first 24 hours of hydration and temperature conditions of 20°C (for CEM I 42.5 R, CEM I 52.5 R, CEM II/A-LL 42.5 R) and 35°C (for CEM I 52.5 R). All data were retrieved for cement pastes with a water-to-cement ratio of 0.45.</p> <p>The dataset contains raw and processed data from quantitative X-ray diffraction, 5PL cement dissolution fitting, thermodynamic simulation with GEMS, multi-scale material modeling, ultrasonic testing and Vicat penetration tests. The data is mainly available in .xlsx files together with short descriptions in ReadMe.txt files.</p>
Pyrene-Based Macrocrosslinkers with Supramolecular Mechanochromism for Elastic Deformation Sensing in Hydrogel Networks
<p>Primary data used in the manuscript (NMR, MS, fluorescence, GPC) sorted after Figure and associated panel in manuscipt and supporting information.</p>
Global map of elastic thickness on Venus
<p>This map is Figure 14 from Anderson, F. S., and S. E. Smrekar (2006), Global mapping of crustal and lithospheric thickness on Venus, J. Geophys. Res., 111, E08006, doi:10.1029/2004JE002395. The location labels have been removed. Estimates of elastic thickness have an error of ±10 to 15 km. Caveats in Anderson and Smrekar (2006) should be carefully understood prior to use. No value of elastic thickness was obtained in areas in white. </p>
Lattice diagrams of elastic maps
<p>This collection of figures is a supplement to the presentation by Gupta and Tape (2024) and builds upon the work of Tape and Tape (2021, 2022, 2024). The collection contains this file, 28 composite pdf files, and three additional composite pdf files. We examine 28 elastic maps, each of which is represented by a 6 x 6 symmetric matrix having 21 parameters (in general). For each map we calculate the closest elastic map in each of 8 symmetry classes, and we depict these 8 elastic maps within a lattice diagram.</p>
Forearc faults in northern Cascadia do not accommodate elastic strain driven by the megathrust seismic cycle: Dataset
<p>Input files and codes for Harrichhausen, N., Morell, K.D., Regalla, C. Inner forearc faults in northern Cascadia do not accommodate elastic strain driven by the megathrust seismic cycle. Submitted to Seismica. 2024.</p> <p>See Readme.md for more information</p> <p>Version 1.1: Updated author list and funding information.</p> <p>Version 1.2: Updated matlab codes to work on Mac.</p>
COHERENT Collaboration data release from the first observation of coherent elastic neutrino-nucleus scattering
<p>Release of COHERENT Collaboration data associated with the first observation of coherent elastic neutrino-nucleus scattering (CEvNS), as published in Science (DOI: <a href="http://dx.doi.org/10.1126/science.aao0990">10.1126/science.aao0990</a>) and also available as arXiv:1708.01294[nucl-ex].</p> <p>This data set should enable researchers to extend the study of CEvNS as desired. Future COHERENT Collaboration results will have similar data releases.</p> <p>Example code can be accessed at https://code.ornl.gov/COHERENT/codeExamples_dataRelease_april2018.<br> The full data-release package, including data, code examples, and a descriptive accompanying document can be found at http://coherent.ornl.gov/data.</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 9)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>GBCurvApprx_175_to_5200.zip<br> Single-grain-boundary-contour-point-resolved curvature estimation tracking data for time step 175 to 5200 with 1 time step resolution and all grains</p> <p>CurvatureEvolution.zip,<br> CurvatureEvolutionAverage.zip<br> TopologyTracer analysis capillary driving force evolution with 100 time step resolution based on the<br> GBContour* data --- not the GBCurvApprx* data.</p> <p>CurvatureEvolutionSuccessful.zip<br> TopologyTracer analysis matrix of segment-length-averaged capillary migration speed for the surviving<br> grains at 1 time step temporal resolution based on the GBCurvApprx* data</p> <p>CurvatureEvolutionUnsuccessful.zip<br> TopologyTracer analysis matrix of segment-length-averaged capillary migration speed for the false<br> positive sub-grains, see paper for details, based on the GBCurvApprx* data</p> <p>UnbiasedGrowthMeasures.zip<br> TopologyTracer analysis temporal evolution of long-range characterisation with the xi metric, see paper for details</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 7)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_450_to_5296.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 450 to 5296 with 1 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 5)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_171_to_259.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 171 to 259 with 1 time step resolution</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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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.