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422 results for “Texture”
Georgia Salt Marsh: Soil Organic Carbon, Nitrogen, Bulk Density, Moisture, and Texture
As part of project predicting soil carbon at depth from that found at the surface using remote sensing, 28 soil cores were taken from six salt marshes along the Georgia coastline. Cores were taken as deep as possible (25 – 165 cm) and sectioned into 5 cm depths. Soils were analyzed for organic carbon (SOC), total nitrogen (N), bulk density (BD), and particle size (by horizon). Stable carbon isotopes were obtained in three marshes on Sapelo Island; a subset was also analyzed for radiocarbon.
WSC - Gridded sample points at Wibu field site including yield, soil texture, water table depth, and estimated soil water retention parameters
A variety of data from gridded sampling points at the Wibu field site. The gridded sampling scheme is described in the Point Locations dataset. This dataset includes 2012 and 2013 absolute and normalized yield, soil textural characteristics (organic content, porosity, bulk density, particle size metrics, % sand/silt/clay), a variety of water table depth metrics (mean, percentiles, sum exceedance values, moving averages), and soil water retention parameters estimated using the Rosetta pedotransfer function. It was collected as part of a study of the impacts of water table depth, soil texture, and growing season weather conditions on corn production at the Wibu field site, described in Zipper et al. (in review). The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site.
Revealing Hidden Orbital Pseudospin Texture with Time-Reversal Dichroism in Photoelectron Angular Distributions
<p>Angle-resolved photoemission spectroscopy (ARPES) of bulk 2H-WSe2 for different crystal orientations linked to each other by time-reversal symmetry. This dataset supplements a manuscript, and was used to measure a new observable called time-reversal dichroism in photoelectron angular distributions (TRDAD), which quantifies the modulation of the photoemission intensity upon effective time-reversal operation. Experimental results are in quantitative agreement with both tight-binding model and state-of-the-art fully relativistic calculations performed using the one-step model of photoemission, unambiguously demonstrating that TRDAD reveals its orbital pseudospin texture counterpart.</p>
Soil texture classes (USDA system) for 6 soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m
<p>Soil texture classes (USDA system) for 6 standard soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m. Derived from predicted soil texture fractions using the soiltexture package in R. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>texture.class = variable: soil texture class,</li> <li>usda = determination method: USDA texture triangle,</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Effect of Textural Properties and Presence of Co-cation on NH3-SCR Activity of Cu-Exchanged ZSM-5
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If t</strong> <ul> <li>Files in <strong>PARACAT_WP3_20210721_01_CW_Experimental</strong> folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt. formats.</li> <li>Files in <strong>PARACAT_WP3_20210721_02_CW_Simulations</strong> folder includes computer simulations/analyses of the EPR measurements; data are in m and txt formats.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong>exp </strong>– experimental data, <strong>hyd </strong>– cw-EPR spectra related to hydrated state, <strong>dehyd </strong>– cw-EPR spectra related to the dehydrated state, <strong>sim </strong>– simulation data. <strong>Sys </strong>– copper species used for constructing the spin-Hamiltonian in EPR simulations.</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
Textural soil data, Colombia, 0 - 100 cm
The textural soil data is a harmonized and structured data set with information related to soil particle-size fractions (PSF) such as clay, sand, and silt, as transformed data. The transformed data was obtained through additive log-ratio transformation because the PSF are compositional data. The textural soil data was fitted to five standard depths using the quadratic function with equal areas (spline). Standard depths were: 0 - 5 cm, 5 - 15 cm, 15 - 30 cm, 30 - 60 cm, and 60 - 100 cm. The original data was obtained from Sistema de Información de Suelos de Latinoamérica y el Caribe - SISLAC, a soil information system developed by the Food and Agriculture Organization of the United Nations.
Textural soil maps, Colombia, 0 - 100 cm
These are the first texture maps of Colombia, obtained from national and global digital soil mapping products. The maps were developed at five standard depths (0-5, 5-15, 15-30, 30-60, and 60-100 cm) and standardized with Additive log-ratio (ALR) transformation. The maps were harmonized at 1 square km of spatial resolution. The data packages include the following set maps: texture maps obtained through the Ensemble Machine Learning (EML) algorithms called landmap and MACHISPLIN; texture maps obtained from SoilGrids platform; residual maps of the texture of the algorithms referenced above; and finally texture maps obtained through spatial ensemble technique.
Dataset for "Reconfigurable Magnonic Crystals Based on Imprinted Magnetization Textures in Hard and Soft Dipolar-Coupled Bilayers"
<p>The dataset consist of the data of the numerical simulations used to prepare the figures for the manuscript: </p><p>Krzysztof Szulc, Silvia Tacchi, Aurelio Hierro-Rodríguez, Javier Díaz, Paweł Gruszecki, Piotr Graczyk, Carlos Quirós, Daniel Markó, José Ignacio Martín, María Vélez, David S. Schmool, Giovanni Carlotti, Maciej Krawczyk, and Luis Manuel Álvarez-Prado. <i>Reconfigurable Magnonic Crystals Based on Imprinted Magnetization Textures in Hard and Soft Dipolar-Coupled Bilayers</i>. ACS Nano <strong>2022</strong> <i>16</i> (9), 14168-14177.</p><p>Please read README.txt file to see the description of the data in the files.</p>
Influence of cation concentration and valence on the structure and texture of spray-dried supraparticles from colloidal silica dispersions
<p>These datasets display the raw data for the manuscript: Huanhuan Zhou, Philipp Groppe, Thomas Zimmermann, Susanne Wintzheimer, Karl Mandel, Influence of cation concentration and valence on the structure and texture of spray-dried supraparticles from colloidal silica dispersions, Journal of Colloid and Interface Science, Volume 658,<br>2024, Pages 199-208, https://doi.org/10.1016/j.jcis.2023.12.051.</p> <p>The data connection file serves as an explanation for all datasets and their connection to the data displayed in the manuscript.</p>
[[Deprecated]] DIGITAL SOIL TEXTURE MAPS OF ARGENTINA
<p>A new version has been uploaded by Guillermo Schulz.</p>
DIGITAL SOIL TEXTURE MAPS OF ARGENTINA
<p>Soil fractions of Argentina in g/100g, Clay, Silt and Sand, for 4 standard depth intervals (0–15, 15-30, 30–60, 60–100) at 1000 m resolution. Including textural classes for the four standard layers and error estimation using random forest.</p> <p>Global accuracy based on cross-validation</p> <table> <tbody> <tr> <td> <p><strong>sp</strong></p> </td> <td> <p><strong>RMSE</strong></p> </td> <td> <p><strong>Rsquared</strong></p> </td> <td> <p><strong>MAE</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 0-15 cm</strong></p> </td> <td> <p><strong>16.189</strong></p> </td> <td> <p><strong>0.640</strong></p> </td> <td> <p><strong>11.069</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 15-30 cm</strong></p> </td> <td> <p><strong>16.320</strong></p> </td> <td> <p><strong>0.629</strong></p> </td> <td> <p><strong>11.213</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 30-60 cm</strong></p> </td> <td> <p><strong>16.676</strong></p> </td> <td> <p><strong>0.618</strong></p> </td> <td> <p><strong>11.364</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 60-100 cm</strong></p> </td> <td> <p><strong>16.762</strong></p> </td> <td> <p><strong>0.587</strong></p> </td> <td> <p><strong>11.472</strong></p> </td> </tr> <tr> <td> <p><strong>silt 0-15 cm</strong></p> </td> <td> <p><strong>12.011</strong></p> </td> <td> <p><strong>0.638</strong></p> </td> <td> <p><strong>8.352</strong></p> </td> </tr> <tr> <td> <p><strong>silt 15-30 cm</strong></p> </td> <td> <p><strong>11.807</strong></p> </td> <td> <p><strong>0.608</strong></p> </td> <td> <p><strong>8.388</strong></p> </td> </tr> <tr> <td> <p><strong>silt 30-60 cm</strong></p> </td> <td> <p><strong>11.504</strong></p> </td> <td> <p><strong>0.561</strong></p> </td> <td> <p><strong>8.168</strong></p> </td> </tr> <tr> <td> <p><strong>silt 60-100 cm</strong></p> </td> <td> <p><strong>11.728</strong></p> </td> <td> <p><strong>0.583</strong></p> </td> <td> <p><strong>8.263</strong></p> </td> </tr> <tr> <td> <p><strong>clay 0-15 cm</strong></p> </td> <td> <p><strong>8.766</strong></p> </td> <td> <p><strong>0.475</strong></p> </td> <td> <p><strong>5.721</strong></p> </td> </tr> <tr> <td> <p><strong>clay 15-30 cm</strong></p> </td> <td> <p><strong>10.723</strong></p> </td> <td> <p><strong>0.452</strong></p> </td> <td> <p><strong>7.432</strong></p> </td> </tr> <tr> <td> <p><strong>clay 30-60 cm</strong></p> </td> <td> <p><strong>11.211</strong></p> </td> <td> <p><strong>0.557</strong></p> </td> <td> <p><strong>7.842</strong></p> </td> </tr> <tr> <td> <p><strong>clay 60-100 cm</strong></p> </td> <td> <p><strong>11.005</strong></p> </td> <td> <p><strong>0.536</strong></p> </td> <td> <p><strong>7.734</strong></p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
SD4EO: AI-based synthetic satellite multispectral agricultural textures in Spain (Oct 2017 - Sep 2018)
<p>This dataset has been created as part of the deliverables for ESA’s <a title="https://eo4society.esa.int/projects/sd4eo/" href="https://eo4society.esa.int/projects/sd4eo/" target="_blank" rel="noopener">SD4EO project.</a> It consists of textures generated using a multispectral variant of a still unpublished high-order statistical constraint synthesis method for each of the following crop types:</p> <ul> <li> Barley.</li> <li> Wheat.</li> <li> Other grain leguminous.</li> <li> Peas.</li> <li> Fallow & Bare soil.</li> <li> Vetch.</li> <li> Alfalfa.</li> <li> Sunflower.</li> <li> Oats.</li> </ul> <p>The initial data was sampled from satellite images, specifically from Copernicus’ Sentinel-1 and Sentinel-2 satellites. The images were acquired over a period from October 2017 to September 2018 on the central-east region of northern Spain (Castile and León and Catalonia). From these images, the corresponding crops were extracted and used as samples for assembling large puzzles that have been applied as input reference images to generate the synthetic images that make up this dataset.</p> <p>The datasets of assembled crop field "puzzles" used as reference images combine the largest crop areas to create a square multispectral texture of the largest possible size that is a power of 2 (or nearly a power of 2). Each base image combines data from all available Sentinel-2 satellite passes for the same month and a previous monthly composition from Sentinel-1. Due to cloud masks influence, the shape and number of crops vary for each time sample, preventing the reuse of element disposition in the “puzzles” across different months. Therefore, we have a base image (puzzle) for each month and crop type, with a size dependent on the number and area of crops not covered by clouds. These base image sizes range between 256, 384, 512, 768, 1024, 1536, and 2048 pixels per side, influenced by weather conditions and crop type each year season.</p> <p>In <em>this</em> dataset, the synthetic texture sizes match the corresponding base image sizes to facilitate debugging the method implementation and enable subsequent comparisons. For crops with a base image size of 1536 pixels or larger, the generated synthetic images have been reduced to half their size to reduce computational costs and RAM requirements, thereby completing the synthesis faster. Consequently, there remains some diversity in file sizes, generally smaller for crop types with less cultivated area.</p> <p>Additionally, to increase the amount of available data, six variants have been synthesized from each base multispectral image. This number can be arbitrarily increased, as initialization with noise (random numbers) ensures the distinction among the generated data.</p> <p>File names are structured as follows:</p> <ul> <li>Prefix "HO" indicating the synthesis method</li> <li>The crop type name: <ul> <li>Barley</li> <li>Wheat</li> <li>OtherGrainLeguminous</li> <li>Peas</li> <li>FallowAndBareSoil</li> <li>Vetch</li> <li>Alfalfa</li> <li>Sunflower</li> <li>Oats</li> </ul> </li> <li>Year/Month/01 (representing the start of the month period)</li> <li>Side length of the multispectral texture in pixels (based on the highest precision instrument of Sentinel-2: 10m x 10m)</li> <li>Number of the synthesis variant</li> </ul> <p>The generation parameters for all images include:</p> <ul> <li>Normalized and weighted bands (VH band influence increased by a factor of 3 compared to others)</li> <li>4 levels of depth in the Steerable pyramid</li> <li>6 orientations in the Steerable pyramid</li> <li>14 joint statistics of the wavelet coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. This parameter is crucial for capturing local dependencies between wavelet coefficients, essential for the visual perception of texture.</li> <li>30 iterations</li> </ul> <p>A significant effort has been made to stabilize the algorithm, and to eliminate artifacts in the generated textures, resulting in much more robust outcomes. However, in rare cases, the initial white noise distribution can be statistically unfavorable, leading to instabilities. Files have been left as generated, without correcting these effects, to make them visible despite their low frequency. Specifically, among the 657 generated multispectral textures, this phenomenon has occurred prominently in only two and is relatively noticeable in another two, leaving the rest free of this effect (affecting less than 1% of the syntheses).</p> <p>Thus, the following files can be considered partially failed syntheses:</p> <ul> <li>HO_Alfalfa_20180801_768_1.nc</li> <li>HO_FallowAndBareSoil_20180101_768_3.nc</li> <li>HO_OtherGrainLeguminous_20171201_256_4.nc</li> <li>HO_Vetch_20180301_384_3.nc</li> </ul> <p>Files are encoded in the standardized net4CDF format [<a href="https://unidata.github.io/netcdf4-python/">link</a>], each containing a single xarray with metadata corresponding to a 3D array with the synthesized texture of the indicated crop type and satellite passes for the regions of Castilla y León and Catalonia for the corresponding monthly period.</p> <p>The most important data structure is the 3D array, where the first two dimensions correspond to the pixel extent indicated in the file name as square textures ('x' and 'y' labels in the xarray). The third dimension denotes the spectral band of the satellite, ordered by constellation and pixel size:</p> <ul> <li>'B02' 10m (Sentinel-2)</li> <li>'B03' 10m (Sentinel-2)</li> <li>'B04' 10m (Sentinel-2)</li> <li>'B08' 10m (Sentinel-2)</li> <li>'B05' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B06' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B07' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B11' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B12' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B8A' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'VH' also resampled to 10m (Sentinel-1)</li> </ul> <p>The original dynamic range is preserved in all bands, and they have been synthesized together using our multispectral algorithm variant. The new band combination may result in slightly unusual values in vegetation indices since restrictions were not considered in their transformed space, but in the latent space of the decorrelated Steerable pyramid.</p> <p>Additionally, the following metadata are stored as xarray attributes:</p> <ul> <li>"long_name": corresponding to the crop type name</li> <li>"date": the period of the original data used as the base image for synthesis</li> <li>"dataset": denotes the combination of the initial Castilla y León dataset and the extended 6 Tiles from Catalonia</li> <li>"synthetic_method": corresponds to the high-order constrained method</li> <li>"max_visible_value": a reference value to maintain the same dynamic range when comparing with base images, avoiding distortions in color space and contrast</li> </ul> <p>A total of:</p> <p><strong> 9</strong> types of crops x <strong>12</strong> months x <strong>6</strong> variants = <strong>648</strong> synthetized multispectral textures</p> <p>occupying <strong>34.5</strong>GB, have been organized and uploaded into 9 ZIP files (one per crop type) on the Zenodo website for distribution under Creative Commons Attribution 4.0 International license.</p> <p>The SD4EO Project is funded by the ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and supervised by ESA Φ-lab.</p> <p> </p>
Stress-Strain Analysis of Polycrystalline Copper with Goss Texture Using Crystal Plasticity FEM
<pre>Stress-strain analysis of single-phase polycrystalline copper with a Goss texture using a cubic representative volume element (RVE) and periodic boundary conditions, performed with Abaqus through the crystal plasticity finite element method.</pre>
Demoulding process assessment of elastomers in micro-textured moulds
<p>Micro-texturing is an increasingly used technique that aims at improving the functional behaviour of components during their useful life and it is applied in different industrial manufacturing processes for different purposes, such as reducing friction on dynamic rubber seals for pneumatic equipment, among others. Micro-texturing is produced on polymer components by transfer from the mould and might critically increase the adhesion and friction between the moulded rubber part with the mould, provoking issues during demoulding, both in the mould itself and in the rubber part. The mould design, the coating release agent applied to the mould surface and the operational parameters of the moulding/demoulding process are fundamental aspects to avoid problems and guarantee a correct texture transfer during the demoulding process. In this work, the lack of knowledge about demoulding processes is addressed with an in-house test rig and a robust experimental procedure to measure demoulding forces (DF) as well as the final quality of the moulded part between thermoset polymers and moulds. After the characterization of several Sol-Gel coatings formulations (inorganic; hybrid) the influence of several parameters is analysed experimentally, i.e.: Sol-Gel efficiency, texture effects, pattern geometry, roughness and material compound. The results obtained from the experimental studies reveal that texture depth is the most critical geometrical parameter showing high scatter among the selected compounds. Finally, the experimental results are used to compute a model through Reduced Order Modelling (ROM) technique for the prediction of DF.</p>
Synchrotron X-ray Diffraction Analysis - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples
<p>A dataset of synchrotron X-ray diffraction (SXRD) analysis files, recording the refinement of crystallographic texture from six differently orientated Ti-6Al-4V (Ti-64) samples. Two different refinement methods were used to fit a range of diffraction pattern ring intensities, for determining crystallographic texture in both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases. The first procedure was based on an established Rietveld refinement method, using the software package <a href="https://maud.radiographema.eu">MAUD (Materials Analysis Using Diffraction)</a>. The second procedure uses a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package. Both methods were used to calculate texture from each of the six different sample orientations, a combination of the six sample orientations, and in a batch processing method for calculating spatially-resolved texture variation from 387 individual X-Y stage-scan SXRD measurements across one of the samples.</p> <p><strong>Material</strong></p> <p>The Ti-64 material used in this study was pre-rolled to 87.5% reduction at 915ºC and then air-cooled to develop a characteristic texture. Six different rectangular samples were cut from this material and are referenced according to alignment with the original rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction), and alignment with the horizontal (X) and vertical (Y) axes of the synchrotron detector;</p> <table align="center"> <caption>A table recording the SXRD run number and sample orientation analysed.</caption> <thead> <tr> <th scope="col"><em>Run Number</em></th> <th scope="col"><em>Sample Orientation Reference</em></th> <th scope="col"> <p><em>Sample Orientation (Horizontal - Vertical)</em></p> </th> </tr> </thead> <tbody> <tr> <td>103840</td> <td>Sample 6</td> <td>TD45ºRD - ND</td> </tr> <tr> <td>103841</td> <td>Sample 5</td> <td>RD - TD45ºND</td> </tr> <tr> <td>103842</td> <td>Sample 4</td> <td>TD - RD45ºND</td> </tr> <tr> <td>103843</td> <td>Sample 3</td> <td>RD - TD</td> </tr> <tr> <td>103844</td> <td>Sample 2</td> <td>RD - ND</td> </tr> <tr> <td>103845</td> <td>Sample 1</td> <td>TD - ND</td> </tr> </tbody> </table> <p><strong>Diffraction Pattern Averaging </strong></p> <p>The .cbf images found in the <a href="https://doi.org/10.5281/zenodo.7311306">raw dataset</a> were first converted into .tiff images. The stage-scan images were then averaged together for each of the different sample orientations, using a Python notebook <a href="https://github.com/LightForm-group/sxrd-tiff-summer">sxrd-tiff-summer</a>, to produce six averaged .tiff images. These averaged .tiff image capture average diffraction peak intensities from an area of about 96.75 mm<sup>2</sup> (equivalent to a total volume of around 193.5 mm<sup>3</sup>) from each piece, which is therefore representative of bulk crystallographic texture from six different sample orientations.</p> <p><strong>MAUD Analysis </strong></p> <p>To process data using MAUD the diffraction pattern images must first be caked, which converts the data into .dat files of intensity versus 2θ profiles, using 72 azimuthal cakes, each of 5° azimuthal width. Although MAUD has an in-built function to cake data, using ImageJ, it is not possible to cake data in MAUD with ImageJ in an automated way. Therefore, caking was done using <a href="https://pyfai.readthedocs.io/en/master/">pyFAI</a>, an open-source Python package, with the caking procedure recorded in a separate Python notebook, <a href="https://github.com/LightForm-group/pyFAI-integration-caking">pyFAI-integration-caking</a>. The caking was applied to each of the six averaged tiff images, as well as being applied to 387 individual X-Y stage-scan tiff images from Sample 1 (103845). The caking procedure was also applied to the CeO2 calibrant diffraction pattern, creating a .dat file that could be used for calibration of the instrument parameters within MAUD, before fitting the experimental data from the different samples.</p> <p>A separate package <a href="https://github.com/LightForm-group/MAUD-batch-analysis">MAUD-batch-analysis</a> was used to record the setup of the files and details of the refinement procedure. Details about the refinement procedure are also recorded in an accompanying paper reporting on these results. A number of refinement steps were used to fit the caked data from the six different sample orientations, and calculate texture. Texture was also calculated from a .dat file that combined all six sample orientations together. The crystallographic texture was refined using the E-WIMV algorithm, which was found to best reproduce quantitative texture intensity values with an orientation distribution function (ODF) resolution of 15º.</p> <p>The MAUD-batch-analysis package also contains details about how to setup and run MAUD in an automated batch processing mode. MAUD's batch mode was used to calculate texture from a series of 387 individual stage-scan diffraction patterns from Sample 1 (103845). A MAUD-batch-analysis script was first used to substitute caked data from the 387 diffraction patterns into template .par files, which contained an initial refinement of the volume fraction, crystal sizes and micro-strain, as a starting point. Both the crystal parameters and texture were then iteratively refined, in MAUD, using a .ins batch analysis script launched from the terminal. This was done to refine both α and then β phase texture.</p> <p>The texture data from the MAUD analysis was recorded as an ODF, with 15º resolution over all Euler space, and extracted in text format using a script from MAUD-batch-analysis. These text files can be loaded into <a href="https://mtex-toolbox.github.io">MTEX</a>, for plotting and analysing both the α and β phase crystallographic texture.</p> <p><strong>Continuous-Peak-Fit Analysis </strong></p> <p>A .poni calibration file was created using <a href="https://www.clemensprescher.com/programs/dioptas">Dioptas</a>, through a refinement matching peak intensities from a CeO2 standard diffraction pattern image. Dioptas was then used to determine peak bounds in 2θ for characterising a total of 21 α and 4 β lattice plane rings from the Ti-64 diffraction pattern images, which were recorded in a .py input script. Using these two inputs, Continuous-Peak-Fit automatically converts full diffraction pattern rings into profiles of intensity versus azimuthal angle, for each 2θ section, which can also include multiple overlapping α and β peaks.</p> <p>The Continuous-Peak-Fit refinement can then be launched in a notebook or from the terminal, to automatically calculate a full mathematical description, in the form of Fourier expansion terms, to match the intensity variation of each individual lattice plane ring. The results for peak position, intensity and half-width for all 21 α and 4 β lattice plane peaks were recorded at an azimuthal resolution of 1º and stored in a .fit output file. Details for setting up and running this analysis can be found in the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package. This package also includes a Python script for extracting lattice plane ring intensity distributions from the .fit files, matching the intensity values with spherical polar coordinates to parametrise the intensity distributions from each of the six different sample orientations, in the form of pole figures. The script can also be used to combine intensity distributions from different sample orientations. The final intensity variations are recorded for each of the lattice plane peaks as text files, which can be loaded into MTEX to plot and analyse both the α and β phase crystallographic texture. This method was also used to analyse all 387 individual diffraction patterns recorded across Sample 1 (S1 – 103845), to quantify the texture variation across the piece.</p> <p><strong>Metadata </strong></p> <p>An accompanying YAML text file contains associated processing metadata for both the MAUD and the Continuous-Peak-Fit analyses, recording information about the different packages used to process the data, along with details about the different files contained within this analysis dataset.</p>
Synchrotron X-ray Diffraction Dataset - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples
<p>A dataset of raw synchrotron X-ray diffraction (SXRD) images, recording crystallographic texture from two different pre-processed Ti-6Al-4V (Ti-64) materials, analysing six differently orientated samples from each material. The aim of the work was to provide a large dataset for testing and improving crystallographic texture refinement from SXRD patterns, with the use of different computational fitting methods.</p> <p>Prior to the experiment, the Ti-64 materials had been pre-rolled and then air-cooled to develop the microstructure, rolling to 50% and 87.5% reduction at 915ºC using a rolling mill at The University of Manchester. Rectangular samples (2 mm thick) were then machined from these rolled blocks. The samples were cut along different directions, three samples along different orthogonal rolling directions, and three at different angles to the rolling directions. The samples are referenced according to alignment of the rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction) with the long horizontal (X) axis and short vertical (Y) axis of the rectangular specimens. </p> <p>Data was recorded using a high energy 99.8 keV synchrotron X-ray beam and a 5 second exposure at the detector. The slits were adjusted to give a 0.5 x 0.5 mm beam area, chosen to optimally resolve both the α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phase peaks. The SXRD data was recorded across each of the specimens by stage-scanning the beam in sequential X-Y positions at 0.5 mm increments, forming a rectangular grid of measurement points across each sample. A powder Ti-64 sample was also measured as a random texture standard.</p> <p>As well as the main experiment, 3 samples (sample 1, 2 and 3) were held together in different orders (1, 2, 3 ; 2, 1, 3 ; 2, 3, 1) and analysed through-thickness, to measure how beam attenuation might affect the bulk texture measurement. In addition, different detector exposure times (1 to 0.04 seconds) were also tested to analyse the impact of exposure time on overall intensity, to see how well the α and β peaks could be resolved from background noise at very fast acquisition frequencies.</p> <p>The raw data is in the form of synchrotron diffraction pattern images which has been separated according to experiment type. An accompanying YAML text file contains associated beamline metadata for each measurement. Further details of the experimental setup can be found in a pdf document.</p> <p>The material data folder contains further details about the material and sample orientations, including an electron backscatter diffraction (EBSD) map that can be used to verify the crystallographic texture.</p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 2 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the second part of 14 parts of the full dataset (2/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 15ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 3 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the third part of 14 parts of the full dataset (3/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 20ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 12 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the twelfth part of 14 parts of the full dataset (12/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 30ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 5 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the fifth part of 14 parts of the full dataset (5/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 30ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p>
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.