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476 results for “Microstructure”
Dataset for "The influence of the amount of recycled material on the microstructure and properties of the second generation of single-domain YBCO bulks"
<p>The development of a recycling process for various REBCO materials is crucial considering both environmental sustainability and economic efficiency, particularly in light of the upcoming large-scale applications. In this paper, a novel general recycling process based on chemical dissolution was employed to grow REBCO bulks; recycled material obtained by recycling defective YBCO single-domain bulks was added (15 wt. %, 30 wt. % and 45 wt. %) to raw materials to prepare recycled YBCO precursor powder. Subsequently, recycled single-domain YBCO bulks were produced using Top-Seeded Melt Growth. The waste recycling related to of single-domain bulks growth was chosen, as it represents the most challenging form of waste in the context of REBCO superconductor production. The properties and microstructure of recycled bulks were further analyzed to determine the influence of the amount of recycled material used and compared to commercially produced bulks. Single-domain YBCO bulks were grown successfully from the recycled precursor powder. Furthermore, it was found that their properties could be tuned by varying the amount of the added recycled powder, allowing the use of vast amounts of REBCO waste for the preparation of bulks, when achieving the best possible properties is not essential for a given application. Given that the underlying recycling process is designed to work for all REBCO systems and any form of waste, it has significant implications for the sustainability and cost-effectiveness of REBCO superconductor production. </p>
Wind tunnel distributed temperature sensing with actively heated fibers and microstructures for detecting wind direction
<p>Wind tunnel tests were performed using distributed temperature sensing with actively heated fibers that had microstructures attached in opposing directions on neighboring fibers. These microstructures created a temperature difference between fibers that depended on wind speed, providing a prototype for distributed sensing of wind direction. These data are connected to a publication detailing this work and method, <a href="https://www.atmos-meas-tech-discuss.net/amt-2019-188/">"Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables"</a>.</p> <p>Data are stored in a netcdf format and includes the instrument reported temperature ('instr_temp') and calibrated temperature ('cal_temp') with the various parameters tested in the linked paper available as coordinates, labeled along an 'expname' dimension.</p> <p>The included ipython notebooks provide examples and explanations for using these laboratory data.</p>
Data for a publication "Exploring the microstructure, mechanical properties, and corrosion resistance of innovative bioabsorbable Zn-Mg-(Si) alloys fabricated via powder metallurgy techniques"
<p><span><span>These data are published as part of the paper: “</span><span>Exploring the microst</span><span>ructure, mechanical properties, </span><span>and corrosion resistance of innovative bioabsorbable Zn-Mg-(S</span><span>i) alloys fabricated via powder </span><span>metallurgy techniques</span><span>” published in journal: “</span><span>Journal of Materials Research and Technology</span><span>”.</span></span><span> </span></p>
Neurodevelopmental Patterns of Early Postnatal White Matter Maturation Represent Distinct Underlying Microstructure and Histology
<p>This dataset includes:</p> <ol> <li>T2w Template from the dHCP datasets.</li> <li>4D weekly average maps from the dHCP study: i) average T2w; ii) average T2w/T1w signal ratio; iii) average neurite density index [from NODDI]; iv) average free water map [from NODDI].</li> <li>NMF results - NeWMaPs from the dHCP study across multiple resolutions (ranging from 2-20 NMFs).</li> </ol> <p> </p>
Voxel-level summary statistics of hippocampus shape, white matter microstructure, and cortical surface curvature in UK Biobank (n=33,324)
<p>This deposit hosts GWAS summary statistics of hippocampus shape (n=33,324), white matter microstructure (n=33,324), and cortical surface curvature (n=15,752) using UKB unrelated white subjects. The data was generated by using the highly efficient imaging genetics (<a href="https://github.com/Zhiwen-Owen-Jiang/heig">HEIG v1.1.0</a>) framework where only the triplets - summary statistics of low-dimensional representations (LDRs), the functional bases, and the variance-covariance matrix LDRs - are shared, which is sufficient to recover all voxel-variant pairs as well as to conduct voxel-level heritability and (cross-trait) genetic correlation analysis. Check the <a href="https://github.com/Zhiwen-Owen-Jiang/heig/wiki">tutorial</a> and the <a href="../records/13770930">example data</a> used in the tutorial. </p> <p>The shared data includes:</p> <p>1. Triplets for hippocampus shape measured by the radial distance from the medial model for each vertex. The original images contain 30,000 vertices while the shared data contains 49 LDRs. Left and right hemispheres were analyzed separately, each with 15,000 vertices.</p> <p>2. Triplets for 21 white matter tracts measured by fractional anisotropy. The original images contain 32,217 voxels and each tract contains 88 ~ 3503 voxels while the shared data contains 1,034 LDRs. Tracts were analyzed separately.</p> <p>3. Triplets for cortical surface curvature. The original images contain 59,412 vertices while the shared data contains 1,750 LDRs. The entire brain was analyzed as a whole.</p> <p>4. LD matrix and its inverse for 22 chromosomes including 460k genotyped SNPs. LD matrix and its inverse were estimated by using two separate datasets each containing 8.4k white unrelated subjects in UKB. Two regularization levels are provided: {85%, 80%} for heritability and genetic correlations within images and {75%, 70%} for cross-trait genetic correlations.</p> <p>5. LD matrix and its inverse for 22 chromosomes including 1.2 million imputed HapMap3 SNPs. LD matrix and its inverse were estimated by using two separate datasets each containing 42k white unrelated subjects in UKB. Two regularization levels are provided: {98%, 95%} for heritability and genetic correlations within images and {90%, 85%} for cross-trait genetic correlations.</p>
The role of etching anisotropy in the fabrication of freestanding oxide microstructures on SrTiO3(001), SrTiO3 (110), and SrTiO3 (111) substrates
<p>Datafiles for the Figures and Supplementary Material of the article "The role of etching anisotropy in the fabrication of freestanding oxide microstructures on SrTiO3(001), SrTiO3 (110), and SrTiO3 (111) substrates" published in Applied Physics Letters by Alejandro E. Plaza et al. (2021)</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>
Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem - Data
<p>Figures and corresponding data associated with the manuscript 'Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem' by Herbst et al.</p>
Dataset for publication "Enhancing C≥2 product selectivity in electrochemical CO2 reduction by controlling the microstructure of gas diffusion electrodes"
<p>Data used for publication:</p> <p>Broad topic: electrochemical reduction of CO2 using gas diffusion electrodes and neutral electrolyte</p> <p>Data is devided in subfolders named after the figure of the paper.</p> <p>Raw data, processed data, and Origin/Power Point files are all contained in the subfolders </p> <p>A subfolder corresponding to a sample contains: data from a potentiostat, gas and liquid chromatograms, recording of flow, pressure and temperature, tables of calculated Faradaic efficiency (FE), png image of the FE vs t, zipped raw files.</p> <p>.json file was created using a yadg scheme (https://dgbowl.github.io/yadg/master/index.html), and data was processed by a dgpost scheme (<a href="https://pypi.org/project/dgpost/">https://dgbowl.github.io/dgpost/master/index.html</a>)</p>
Data bundle for "Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning"
<p>Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk)</p> <p>This repository is a release of the raw data and analysis results for: 'Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning' </p> <p>The raw data is given as 'yprime.h5' - this contains patterns and metadata in the Bruker-exported format.</p> <p>Scripts for dataset decomposition into latent factors are given in 'Scripts'.</p> <p>Our spherical analysis code is included in 'SphericalAngleDF'.</p> <p>Outputs of our analysis code are contained in 'Analysis'.</p> <p>Figures for the paper are included in 'Figures'.</p> <p> </p>
Supplementary material for "Song et al., Modelling Simul. Mater. Sci. Eng., 2021: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies"
<p>This zip archive contains supplementary material in the form of datasets and jupyter notebooks that are used in the following publication:</p> <ul> <li>authors: Hengxu Song, Nina Gunkelmann, Giacomo Po, and Stefan Sandfeld</li> <li>journal: Modelling Simul. Mater. Sci. Eng.</li> <li>year: 2021</li> <li>title: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies</li> </ul>
Cr[CH(SiMe3)2]3/SiO2 catalysts for ethene polymerization: the correlation at amolecular level between the chromium loading and the microstructure of the produced polymer
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, Computer Simulation and Analysis</li> <li>Files are in <strong>txt</strong>, <strong>spc</strong>, <strong>par</strong>, <strong>opj</strong>, and <strong>m</strong> format</li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements in <strong>spc</strong> and <strong>par</strong> formats</li> <li>EPR spectroscopic simulation and analyses in<strong> m</strong> format</li> <li>UV-vis and IR spectroscopic measurements and analyses; and polymer analyses in <strong>opj</strong> format</li> </ul> </li> <li>Are the data <strong>generated</strong> (e.g. by a machine) or <strong>collected</strong> (e.g. by means of a survey)? <ul> <li>EPR spectroscopic measurements were generated by ELEXYS 580 EPR spectrophotometer equipped with SHQ cavity and ER035 M NMR gaussmeter produced by Bruker.</li> <li>FT-IR spectroscopic measurements were generated by Vertex70 produced by Bruker equipped with an MCT detector.</li> <li>UV-Vis-NIR spectroscopic measurements were generated by Cary5000 spectrophotometer produced by Varian equipped with a reflectance sphere.</li> </ul> </li> <li><strong>If t</strong> <ul> <li>Files in <strong>PARACAT_WP4_20201229_01_EPR</strong> folder includes EPR spectroscopic measurements and computer simulations/analyses, original data are in spc/par formats; files in m format were used to process the data of the same name and saved the results in txt format.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>GC</strong> – Gas Chromatography, <strong>GPC</strong> – Gel Permeation Chromatography</li> <li>definitions of variables: <strong>Magnetic field, Wavenumber, Pressure</strong></li> <li>units of measurement: <strong>Gauss (G), milli Tesla (mT), cm<sup>-1</sup>, milli bar (mbar)</strong></li> <li>abbreviations: <strong>IR/EPR/UV </strong>are data relate to measurements of IR/EPR/UV-Vis spectroscopy.</li> <li>abbreviation: <strong>CO/C2H4</strong> are data relate to methods with CO/ethene dosage.</li> <li>abbreviation: <strong>polym</strong> is data relates to polymer analysis conducted by GC and GPC</li> </ul> </li> </ul>
Numerically predicted permeability of over 6500 artificially generated fibrous microstructures
<p>This data set was generated in the project "ML4ProcessSimulation - Machine Learning for Simulation Intelligence in Composite Process Design" (Leibniz Collaborative Excellence funding program: K377/2021), at Leibniz-Institut für Verbundwerkstoffe GmbH. The goals were to create a comprehensive data set for training different neural networks and to gain insight into the influence of fiber structure on permeability. The models represent the fiber structure within fiber bundles in fiber-reinforced plastic composites (FRPC). Over 6500 structure models were generated in the software GeoDict® [1] and the permeability tensor of these models was numerically calculated in the GeoDict® module FlowDict [2]. The zip files contain the structure file (<i>gdt</i>), the model generation result file (<i>FiberGeo_[...].gdr</i>) and the flow simulation result file (<i>LIRStokesResult_[...].gdr</i>). For each zip file is a JSON meta data file available and in addition the gdr files contain all input and output data of the model generation and the flow simulation. The file <i>Table_of_Parameter_studies_and_model_pictures.jpg</i> gives an overview of the parameter studies and exemplarily shows two models each.The data set is divided into three parameter studies: </p><ul><li>1_Parameter_study_round_fibers with round fibers by varying the fiber volume content (fvc), fiber diameter (fdia) and fiber orientation (fdir). For each modeling parameter, 5 - 100 models (random seed or RS) were generated, all differing due to the randomized fiber positioning during model generation.</li><li>2_Parameter_study_elliptical_fibers with elliptical fibers that was varied based on different aspect ratios (asp1, asp2, asp3). In addition, fdia and fvc were varied and 5 models (RS) were calculated. </li><li>3_Parameter_study_undulation with elliptical fibers, whose undulation was varied. In addition, fdia and fvc were varied and 12 models (RS) were calculated.</li></ul><p><i>[1] J. Hilden, S. Rief, and B. Planas, GeoDict 2023 User Guide. FiberGeo handbook. DE: Math2Market GmbH, 2023. Accessed: Oct. 26, 2023. [Online]. Available: https://doi.org/10.30423/userguide.geodict</i></p><p><i>[2] J. Hilden, S. Linden, and B. Planas, "GeoDict 2023 User Guide. FlowDict handbook." Math2Market GmbH, 2023. Accessed: Jul. 31, 2023. [Online]. Available: https://doi.org/10.30423/userguide.geodict</i></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>
Infinity Microstructure
<p>This microstructure has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel ( <a href="https://www.cs.technion.ac.il/~irit/.">https://www.cs.technion.ac.il/~irit/ </a>).</p> <p>This specific infinity-like model is a two level recursive function composition of trivariate spline tiles inside a macro trivariate shape of infinity.</p> <p>Model is provided in STL format.</p>
Shoe sole Microstructure
<p>This microstructure has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (<a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/</a>).</p> <p>This specific shoe sole is a functional composition of trivariate spline tiles inside a macro trivariate shape of a sole. Model is provided in IGES format, as B-spline surfaces.</p>
Knot Microstructure
<p>This microstructure has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (<a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/</a>).</p> <p>This specific knot is a two level recursive function composition of trivariate spline tiles inside a macro trivariate shape of a knot.</p> <p>Model is provided in STL format.</p>
Turbine Blade Microstructure
<p>This microstructure has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (<a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/</a>).</p> <p>This turbine is a functional composition of trivariate spline tiles inside a macro trivariate shape of a turbine.</p> <p>Model is provided in IGES format, as B-spline surfaces.</p>
Lamellar and Bi-Modal Ti-64 Microstructure Images
<p>A collection of 40 optical micrographs of Titanium-64, taken from different regions of a forged billet. The images comprise two morphologies, bi-modal and lamellar. The first 20 images (Ti64_0.jpg - Ti64_19.jpg) correspond to the bi-modal morphology. The remaning 20 images (Ti64_20.jpg - Ti64_39.jpg) corrrespond to lamellar.</p> <p>This dataset was comprised to test machine learning algorithms for classification of microstructures.</p>
Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties
<p>This dataset provides data described and used in the article submitted to Journal of Advances in Modeling Earth Systems "Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties".</p> <p>It contains .csv files with different properties computed on outputs of the model Snow3D simulating equi-temperature metamorphism. This micro-scale model was used here with experimental micro-tomographic snow images as input and returns series of 3-D images of snow showing features of equi-temperature metamorphism at different time steps as output.</p> <p>In this dataset, you will find two types of files:</p> <p>- the microstructural properties (density, specific surface area, covariance lengths, mean curvature) computed on the simulated images at different time steps.</p> <p>- the transport properties (effective conductivity, normalizes effective vapor diffusion coefficient, permeability) of the simulated images at different time steps.</p> <p>Finally, metadata_simulations.csv gather the information relative to the simulations.</p>
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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)
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DANDI Archive for NWB datasets
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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.