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218 results for “synchrotron”
Datasets for ``Hemispheric handedness in the Galactic synchrotron polarization foreground''
<pre>This directory contains an index.html file with links to E_lm and B_lm data from WMAP observations in the K and Q bands, denoted by superscripts K and Q, respectively. We also give E_lm and B_lm from numerical simulations of a simple dynamo at two different radii, 8kpc and 3kpc from the galactic center, denoted by superscripts 8kpc and 3kpc, respectively. The corresponding run directories and the idl plotting routines with secondary data for the other figures for the paper "Hemispheric handedness in the Galactic synchrotron polarization foreground" by Axel Brandenburg & Marcus Brueggen with the temporary URL http://norlx51.nordita.org/~brandenb/tmp/brueggen are also given.</pre>
Synchrotron diffraction images for the 2.9 Å crystal structure of L-Selenomethionine labeled human GDAP1
<p>Dataset collected at DLS, I04 beamline 16.5.2019. L-SeMet substituted crystals collected with SAD-method.</p> <ul> <li>Flux: 1.32e+11</li> <li>Ω Start: 0.0°</li> <li>Ω Osc: 0.10°</li> <li>Ω Overlap: 0°</li> <li>No. Images: 3600</li> <li>Resolution: 2.90Å</li> <li>Wavelength: 0.9790Å</li> <li>Exposure: 0.040s</li> <li>Transmission: 100.00%</li> <li>Beam size: 63x50μm</li> <li>Type: SAD</li> <li>Comment: X,Y,Z (-561,302,301), Aperture: Large</li> </ul> <p> </p>
Synchrotron Diffraction During Stress Relaxation in CP Ti (grade 4)
<p>These data support the associated paper:</p> <p><strong>Cold Creep of Titanium: Analysis of stress relaxation using synchrotron diffraction and crystal plasticity simulations</strong><br> Yi Xiong, Phani Karamched, Chi-Toan Nguyen, David M Collins, Christopher M Magazzeni, Edmund Tarleton, Angus J Wilkinson<br> Acta Materialia (2020) vol. 199, 561-577<br> <a href="https://doi.org/10.1016/j.actamat.2020.08.010">https://doi.org/10.1016/j.actamat.2020.08.010</a></p> <p>The Authors' Accepted Manuscript version of the paper is available open access on arXiv:<br> <a href="https://arxiv.org/ftp/arxiv/papers/2003/2003.01682.pdf">https://arxiv.org/ftp/arxiv/papers/2003/2003.01682.pdf</a></p> <p> </p> <p>This dataset arises from an in situ stress relaxation experiment on commercially pure (grade 4) Ti undertaken at the Diamond Light Source, beamline ID12, as part of experiment EE17222. The sample was loaded to just beyond the yield point, and then held at constant strain for 5 minutes over which time the stress relaxed. The sample was then reloaded elastically and a further period of stress relaxation at fixed total strain undertaken. in total five periods of stress relaxation were imposed.</p> <p>Throughout the mechanical testing cycle powder diffraction patterns were recorded in the transmission geometry, at 1 second intervals using a 2d Pixium detector held 1097 mm from the sample. The beam energy was determined to be 79.79 keV. </p> <ul> <li>Diffraction Patterns are contained as 16 bit TIF files bundled into the Patterns_72995.zip file.</li> <li>Macroscopic mechanical test data are in the excel file MechTest_2995.xlsx.</li> <li>Small EBSD map in #.ctf format converted from Bruker #bcf file in CPg4.ctf file.</li> <li>Matlab + MTEX script to load and make simple plots from EBSD data file in CPg4Ti_EBSDmap.m file.</li> </ul> <p> </p> <p>Details of the MTEX orientation analysis package from Matlab can be found and freely downloaded at:<br> <a href="https://mtex-toolbox.github.io/">https://mtex-toolbox.github.io/</a></p> <p> </p> <p> </p>
Raw data for article "In Situ Synchrotron X-Ray Diffraction Characterization of Corrosion Products of a Ti-Based Metallic Glass for Implant Applications" Gostin et al 2018
<p>This repository contains raw data for the article "In Situ Synchrotron X-Ray Diffraction Characterization of Corrosion Products of a Ti-Based Metallic Glass for Implant Applications" by Gostin et al. 2018 in Advanced Healthcare Materials, 7, 1800338 (https://doi.org/10.1002/adhm.201800338).</p> <p>Most data comes from one beamtime at the Diamond synchrotron in the UK in May 2016. It consists of X-ray diffraction images taken in situ in artificial corrosion pits on a Ti-based metallic glass.</p> <p>Please see the README file for more details.</p>
X-ray diffraction images of bovine trypsin crystals recorded at the FemtoMAX beamline of Max IV synchrotron facility
<p>The deposition concerns bovine trypsin diffraction images in two wedges. Each image is recorded on a still crystal and separated by 0.1 deg rotation. The x4.tar.gz archive contains summed intensities from individual snapshots at the same orientation, whereas x4_single.tar.gz archive contains single snapshots/orientation. </p>
Synchrotron X-ray Computed Tomography scan of a wasp
<h4>Contents:</h4><ul><li><i>bee_yazeed-20231001T170032.h5</i> - SXCT scan of a wasp performed at beamline <a href="https://www.sesame.org.jo/beamlines/beats">ID10-BEATS</a> of SESAME.</li><li><i>SESAME_wasp_yazeed.avi -</i> 3D video rendering of phase-contrast CT reconstruction of <i>bee_yazeed-20231001T170032</i>. The dataset was reconstructed using <a href="https://github.com/gianthk/alrecon/tree/master">alrecon</a>. The video was created using ORS Dragonfly.</li></ul><h4>H5 dataset information:</h4><ul><li>Raw experimental data (sinogram, flat fields and dark fields) and metadata are stored in a common .H5 file.</li><li>The HDF5 file is organized hierarchically following the <a href="https://dxfile.readthedocs.io/en/latest/">Scientific Data Exchange (DXfile)</a> community standard.</li></ul><h4>How to reconstruct:</h4><ul><li>You can use <a href="http://www.silx.org/">Silx</a> to read and explore the .H5 dataset.</li><li>The file can be read within Python using the <a href="https://dxchange.readthedocs.io/en/latest/">DXChange</a> package.</li><li>See the <a href="https://beats.readthedocs.io/reconstruction.html">ID10-BEATS beamline user guide</a> for a detailed description on how to process and reconstruct the scan.</li></ul>
Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation: dataset
<p>This dataset contains images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation, 3D visualization and geometric analysis is presented in the corresponding manuscript. Files are uploaded in 16bit .tif format and are named: mouseid_pressurelevel_stacknumber, with mouseid consisting of either Apoe (Apoe-deficient) or Bl (wild-type) and the mouse number, pressurelevel varies from P0 to P120 and stacknumber indicates which image from the stack has been uploaded.</p>
Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation: 2D segmentations
<p>This dataset contains 2D segmentations of images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation algorithm, 3D visualization and geometric analysis are presented in the corresponding manuscript. Files are uploaded in .jpg format and are named: lamella_slicenumber, with slicenumber varying from 1 to 8100. There is also a Matlab file, UndulationData_Zenodo.mat, in which all the relevant variables post analysis are stored. This file contains a variable called "myFiles", which contains the link between the slicenumbers used here and the original dataset that is published in Zenodo (.tif synchrotron images).</p>
Dataset for "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?"
<p>This dataset contains images that were used in the analysis of the manuscript "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?", that was published in Biomedical Physics and Engineering Express in 2018. Images are uploaded in .tif format. Three different synchrotron-based imaging techniques were compared on the same cardiovascular samples: grating interferometry (GI) and absorption-based phase propagation with and without phase retrieval according to Paganins method. An excel file is provided in which the nomenclature of the files is explained.</p>
Armoricaphyton chateaupannense - Propagation Phase Contrast X-Ray Synchrotron Microtomography Dataset
<p>Includes:</p> <p>1) Propagation phase contrast X-ray synchrotron microtomography (PPC-SRμCT) dataset to study the three-dimensional structure of the permineralized wood from <em>Armoricaphyton chateaupannense</em>, using the ID19 beamline of the European Synchrotron Radiation Facility (ESRF), Grenoble, France.</p> <p><strong>Dataset Information (also see scan_log.xml):</strong></p> <ul> <li>Number of image in dataset: 2159 images</li> <li>Images prefix: plante_</li> <li>Image x/y size: 3763 x 2048 px</li> <li>Image type: 16-bit TIFFs (with Pack Bits compression)</li> <li>Image size on disk: 14.8 MB each</li> <li>Scan date: 31-Oct-2008</li> <li>Scan energy: 30keV </li> <li>Voxels size: 0.551 um</li> <li>Filters: Al_1_mm Al_0.5_mm Diam_U</li> <li>Projection number: 4000</li> <li>Projection rotation: 360 degs</li> <li>Magnification: x20</li> <li>Source-sample distance: 145000</li> <li>Scan type: continuous</li> </ul> <p>Note: these images have been cropped from their original scan output size.</p> <p>2) Two supplemental videos of the 3D model.</p>
A Dataset for In-situ synchrotron tomography experiments to investigate anisotropic damage of line pipe steel
<p>In this study, anisotropic ductility and associated damage mechanisms of a grade X100 line pipe steel were investigated using in-situ synchrotron-radiation computed tomography (SRCT) of notched round bars. Line pipe materials have anisotropic mechanical properties, such as tensile strength, ductility and toughness. Specimens were tested for loading along both rolling (L) and transverse (T) directions. The <em>in-situ</em> data collected allowed quantifying both specimen deformation (evolution of the cross section) and microscopic damage parameters such as porosity, void shape and void orientation. The data sets provide here are related to the paper <em>"On the origin of the anisotropic damage of X100 line pipe steel, Part I: in-situ synchrotron tomography experiments"</em> being published in <a href="https://www.springer.com/journal/40192">Integrating Materials and Manufacturing Innovation</a>. For each testing direction, dataset are provided using hdf5 and xdmf standarded exchange format. A compressed file is also provided in connection with the analyses explained in the article.</p>
Serial synchrotron crystallography dataset and 3D-ED dataset - HEWL crystals obtained by instant crystallization with TbXo4
<h2>Data sets related to the publication "Nucleating Agent Crystallophore Induces Instant Protein Crystallization" by Sauter et al.</h2> <p><em><strong>3D-Electron Diffraction data</strong></em>: The raw data obtained on the protein Hen Egg-White Lysozyme is contained in the file <em>RAW-Data_3D-ED_deposition.tar.bz2</em>.</p> <p>Results of first rounds of model refinement are contained in <em>3D-ED-affi_4-5sets</em> files</p> <p><em><strong>Synchrotron Serial Crystallography data</strong></em>: The raw SSX data obtained on the protein Hen Egg-White Lysozyme can be accessed through <a href="https://doi.org/10.15151/ESRF-DC-1823716276">https://doi.org/10.15151/ESRF-DC-1823716276</a>.</p> <p>Results of first rounds of model refinement are contained in <em>SSX-Xo4-supernatent</em> files</p>
Advancing Vanadium Redox Flow Battery Analysis: A Deep Learning Framework for High-Throughput 3D Visualization and Bubble Quantification via Synchrotron X-ray Tomography
<p>Dataset and model of UTILE-Redox - Deep Learning based Tool for Autonomous 3D Bubble Analysis of Vanadium Flow Batteries from Synchrotron X-ray Imaging. This project focuses on the deep learning-based automatic analysis of Vanadium Redox Flow Batteries (VRFB) Synchrotron X-ray tomographies. This repository contains the Python implementation of the UTILE-Redox software for automatic volume analysis, feature extraction, and visualization of the results.</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>
Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Analysis Dataset)
<p>A dataset of synchrotron X-ray diffraction (SXRD) analysis files, recording the refinement of crystallographic texture from a number of Ti-6Al-4V (Ti-64) sample matrices, containing a total of 93 hot-rolled samples, from three different orthogonal sample directions. The aim of the work was to accurately quantify bulk macro-texture for both the α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases across a range of different processing conditions.</p> <p><strong>Material </strong></p> <p>Prior to the experiment, the Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling, using a rolling mill at The University of Manchester. Rectangular specimens (6 mm x 5 mm x 2 mm) were then machined from the centre of these rolled blocks, and from the starting material. The samples were cut along different orthogonal rolling directions and 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. Samples of the same orientation were glued together to form matrices for the synchrotron analysis. The material, rolling conditions, sample orientations and experiment reference numbers used for the synchrotron diffraction analysis are included in the data as an excel spreadsheet.</p> <p><strong>SXRD Data Collection </strong></p> <p>Data was recorded using a high energy 90 keV synchrotron X-ray beam and a 5 second exposure at the detector for each measurement point. The slits were adjusted to give a 0.5 x 0.5 mm beam area, chosen to optimally resolve both the α and β phase peaks. The SXRD data was recorded by stage-scanning the beam in sequential X-Y positions at 0.5 mm increments across the rectangular sample matrices, containing a number of samples glued together, to analyse a total of 93 samples from the different processing conditions and orientations. Post-processing of the data was then used to sort the data into a rectangular grid of measurement points from each individual sample.</p> <p><strong>Diffraction Pattern Averaging </strong></p> <p>The stage-scan diffraction pattern images from each matrix were sorted into individual samples, and the images averaged together for each specimen, using a Python notebook <a href="https://github.com/LightForm-group/sxrd-tiff-summer">sxrd-tiff-summer</a>. The averaged .tiff images each capture average diffraction peak intensities from an area of about 30 mm<sup>2</sup> (equivalent to a total volume of ~ 60 mm<sup>3</sup>), with three different sample orientations then used to calculate the bulk crystallographic texture from each rolling condition.</p> <p><strong>SXRD Data Analysis </strong></p> <p>A new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package was used to fit full diffraction pattern ring intensities, using a range of different lattice plane peaks for determining crystallographic texture in both the α and β phases. Bulk texture was calculated by combining the ring intensities from three different sample orientations.</p> <p>A .poni calibration file was created using <a href="http://www.clemensprescher.com/programs/dioptas">Dioptas</a>, through a refinement matching peak intensities from a LaB6 or CeO2 standard diffraction pattern image. Two calibrations were needed as some of the data was collected in July 2022 and some of the data was collected in August 2022. Dioptas was then used to determine peak bounds in 2θ for characterising a total of 22 α and 4 β lattice plane rings from the averaged 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 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 22 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 three 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.</p> <p><strong>Metadata </strong></p> <p>An accompanying YAML text file contains associated SXRD beamline metadata for each measurement. The raw data is in the form of synchrotron diffraction pattern .tiff images which were too large to upload to Zenodo and are instead stored on The University of Manchester's Research Database Storage (RDS) repository. The raw data can therefore be obtained by emailing the authors.</p> <p>The material data folder documents the machining of the samples and the sample orientations.</p> <p>The associated processing metadata for the Continuous-Peak-Fit analyses records information about the different packages used to process the data, along with details about the different files contained within this analysis dataset.</p>
Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Results Dataset)
<p>A dataset of crystallographic texture results for both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases, measured from 31 different hot-rolled Ti-6Al-4V (Ti-64) materials and 3 differently orientated samples using synchrotron X-ray diffraction (SXRD). The aim of the work was to accurately quantify bulk macro-texture for both the α and β phases across a range of different processing conditions, and to compare results with electron backscatter diffraction (EBSD) measurements. The synchrotron intensities were extracted using a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package, and then directly used to calculate the pole figures, orientation distribution functions (ODFs) and numerical values for the texture indices in <a href="https://mtex-toolbox.github.io">MTEX</a></p> <p><strong>Material </strong></p> <p>The Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling. Three samples of different orientation were cut from the centre of these rolled blocks, and from the starting material. The material and hot-rolling conditions are recorded in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a> as an excel spreadsheet and summarised in the table below.</p> <table align="center"> <caption>A table recording the sample number and associated hot-rolling condition.</caption> <tbody> <tr> <td> <p><em><strong>Sample Number</strong></em></p> </td> <td> <p><em><strong>Rolling Condition</strong></em></p> </td> </tr> <tr> <td>1</td> <td>825ºC, 87.5% Reduction</td> </tr> <tr> <td>2</td> <td>865ºC, 87.5% Reduction</td> </tr> <tr> <td>3</td> <td>895ºC, 87.5% Reduction</td> </tr> <tr> <td>4</td> <td>915ºC, 87.5% Reduction</td> </tr> <tr> <td>5</td> <td>935ºC, 87.5% Reduction</td> </tr> <tr> <td>6</td> <td>950ºC, 87.5% Reduction</td> </tr> <tr> <td>7</td> <td>960ºC, 87.5% Reduction</td> </tr> <tr> <td>8</td> <td>975ºC, 87.5% Reduction</td> </tr> <tr> <td>9</td> <td>1020ºC, 87.5% Reduction</td> </tr> <tr> <td>10</td> <td>β-annealed, 825ºC, 87.5% Reduction</td> </tr> <tr> <td>11</td> <td>β-annealed, 915ºC, 87.5% Reduction</td> </tr> <tr> <td>12</td> <td>β-annealed, 975ºC, 87.5% Reduction</td> </tr> <tr> <td>13</td> <td>Reduced heating from 915ºC, 87.5% Reduction</td> </tr> <tr> <td>14</td> <td>Reduced heating from 975ºC, 87.5% Reduction</td> </tr> <tr> <td>15</td> <td>825ºC, 75% Reduction</td> </tr> <tr> <td>16</td> <td>865ºC, 75% Reduction</td> </tr> <tr> <td>17</td> <td>895ºC, 75% Reduction</td> </tr> <tr> <td>18</td> <td>915ºC, 75% Reduction</td> </tr> <tr> <td>19</td> <td>935ºC, 75% Reduction</td> </tr> <tr> <td>20</td> <td>950ºC, 75% Reduction</td> </tr> <tr> <td>21</td> <td>960ºC, 75% Reduction</td> </tr> <tr> <td>22</td> <td>975ºC, 75% Reduction</td> </tr> <tr> <td>23</td> <td>1020ºC, 75% Reduction</td> </tr> <tr> <td>24</td> <td>β-annealed, 825ºC, 75% Reduction</td> </tr> <tr> <td>25</td> <td>β-annealed, 915ºC, 75% Reduction</td> </tr> <tr> <td>26</td> <td>β-annealed, 975ºC, 75% Reduction</td> </tr> <tr> <td>27</td> <td>Reduced heating from 915ºC, 75% Reduction</td> </tr> <tr> <td>28</td> <td>Reduced heating from 975ºC, 75% Reduction</td> </tr> <tr> <td>29</td> <td>As-received</td> </tr> <tr> <td>30</td> <td>As-received, β-annealed</td> </tr> <tr> <td>31</td> <td>975ºC, 50% Reduction</td> </tr> </tbody> </table> <p><strong>MTEX Data Analysis</strong></p> <p>The lattice plane intensities for 22 α and 4 β phase peaks were extracted from the Continuous-Peak-Fit analysis, also included in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a>, and saved as text files in the form of pole figures. The lattice intensity text files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package, to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima, texture indices and texture component phase fractions. A kernel half-width of 10° was found to produce optimal data fitting, for highly accurate texture strength intensity values.</p> <p><strong>Metadata </strong></p> <p>An accompanying YAML text file contains associated processing metadata for the SXRD analysis, recording information about the packages used to process the data, along with details about the different files contained within this results dataset.</p>
Sandstone rock tomographic data, i12 beamline, DLS synchrotron
<p>This data compliments a paper <i>Conditional generative adversarial networks for stripe artefact removal in high-resolution X-ray tomography </i>by D. Kazantsev, L. Beveridge, V. Shanmugasundar and O. Magdysyuk to be published in the <strong>Tomography of Materials and Structures</strong> journal in 2023 (to be updated). This data alongside the data from <a href="https://zenodo.org/records/1443568#.ZBREbUjP1qN">here</a> was used to train the network to remove stripe artefacts. </p><p>The code is written in Python and located at: https://github.com/dkazanc/NoStripesNet</p>
Self-absorption of synchrotron radiation in a laser-irradiated plasma
<p>Data and code required to reproduce the simulation results in T. G. Blackburn, A. J. MacLeod, A. Ilderton, B. King, S. Tang and M. Marklund, "Self-absorption of synchrotron radiation in a laser-irradiated plasma", <a href="https://arxiv.org/abs/2005.00302">arXiv:2005.00302</a> [physics.plasm-ph].</p>
Text-fig. 7. Scanning electron micrographs (a, b, d, e, g–k), X-ray microtomographic orthoslices (c) and synchrotron radiation X-ray tomographic microscopy orthoslices (f) of fruits and endocarps of uncertain affinity from Zliv-Řídká Blana locality. a–c: Trebecenia sarcocalis, a – tricarpellate fruit, no. NM-F 3637, b – fruits supported by pentamerous and persistent calyx, no. NMF 3637, c – fruit almost circular in transverse section, no. NM-F 3637; d: Taxon 17, small fruit with slightly sunken stylar region, no. NM-F 3201; e: Taxon 19, spherical fruit, the fruit wall composed of large isodiametric, thick walled cells, no. NM-F 3181; f: Taxon 19, single-seeded fruit, no. NM-F 3621; g: Taxon 20, syncarpous, multicarpellate fruit of ten carpels, no. NM-F 3200; h: Taxon 22, syncarpous, multicarpellate fruit of seven carpels, no. NM-F 3159; i: cf. Sabia menispermoides, endocarp of drupaceous fruits, no. NM-F 4624; j: Taxon 25, endocarp triangular in cross-section, no. NM-F 3218; k: Taxon 24, endocarp spherical in cross-section with a distinctly ribbed and foveolate surface, no. NM-F 3217. in Plant Mesofossils From The Late Cretaceous Klikov Formation, The Czech Republic
Text-fig. 7. Scanning electron micrographs (a, b, d, e, g–k), X-ray microtomographic orthoslices (c) and synchrotron radiation X-ray tomographic microscopy orthoslices (f) of fruits and endocarps of uncertain affinity from Zliv-Řídká Blana locality. a–c: Trebecenia sarcocalis, a – tricarpellate fruit, no. NM-F 3637, b – fruits supported by pentamerous and persistent calyx, no. NMF 3637, c – fruit almost circular in transverse section, no. NM-F 3637; d: Taxon 17, small fruit with slightly sunken stylar region, no. NM-F 3201; e: Taxon 19, spherical fruit, the fruit wall composed of large isodiametric, thick walled cells, no. NM-F 3181; f: Taxon 19, single-seeded fruit, no. NM-F 3621; g: Taxon 20, syncarpous, multicarpellate fruit of ten carpels, no. NM-F 3200; h: Taxon 22, syncarpous, multicarpellate fruit of seven carpels, no. NM-F 3159; i: cf. Sabia menispermoides, endocarp of drupaceous fruits, no. NM-F 4624; j: Taxon 25, endocarp triangular in cross-section, no. NM-F 3218; k: Taxon 24, endocarp spherical in cross-section with a distinctly ribbed and foveolate surface, no. NM-F 3217.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.