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2,001 results for “X-Ray”
IODP Expedition 355 X-ray diffraction (XRD)
X-ray diffraction (XRD) is used to identify minerals and their proportions in sediment or hard rock sample powders on a Bruker AXS D4 Endeavor X-ray diffractometer. Results are returned as diffractograms in a viewable format (either PDF or PNG).
X-ray diffraction data for SARS-CoV2 spike glycoprotein N-terminal heptad repeat domain + SARS-CoV2(QEYKKEKE)
<p>X-ray diffraction dataset for SARS-CoV2 spike glycoprotein N-terminal heptad repeat domain + SARS-CoV2(QEYKKEKE) collected at the AMX beamline (17-ID-1) at the National Synchrotron Lightsource II, Brookhaven National Laboratory, Upton, NY, USA.</p> <p>Final XDS.INP file generated by autoPROC.</p> <p>Serialized request document for vector collection from LSDC.</p> <p>KB mirrors</p> <p>Detector: EigerX9M (Si)</p> <p>Approx. photon flux at 13475eV: 4E12 ph/s</p> <p>Approx. beam size: 5 x 7 um</p>
IODP Expedition 356 X-ray diffraction (XRD)
X-ray diffraction (XRD) is used to identify minerals and their proportions in sediment or hard rock sample powders on a Bruker AXS D4 Endeavor X-ray diffractometer. Results are returned as diffractograms in a viewable format (either PDF or PNG).
IODP Expedition 353 X-ray diffraction (XRD)
X-ray diffraction (XRD) is used to identify minerals and their proportions in sediment or hard rock sample powders on a Bruker AXS D4 Endeavor X-ray diffractometer. Results are returned as diffractograms in a viewable format (either PDF or PNG).
IODP Expedition 359 X-ray diffraction (XRD)
X-ray diffraction (XRD) is used to identify minerals and their proportions in sediment or hard rock sample powders on a Bruker AXS D4 Endeavor X-ray diffractometer. Results are returned as diffractograms in a viewable format (either PDF or PNG).
Single-pulse hard x-ray holograms of an exploding water jet
<p>This h5 file contains the holograms of an exploding micro-fluidic jet recorded at the MID setup at EuXFEL. The holograms were recorded with single-pulse illumination of 17.8 keV hard x-rays.</p> <p>The file contains only one group (/frames/pixels) with 4499 frames recorded with the Andor Zyla 5.5 camera used in this experiment.</p> <p>The first 352 frames and frames 4233 to 4499 can be used as empty beam / reference frames. The frames in between are data frames. The microfluidic jet is put in the field of view and is pumped with an IR laser with pumping offset from 5 to -35 ns with respect to the arrival of the XFEL pulse. </p> <p>The data set has been published in:</p> <p>J. Hagemann, M. Vassholz, H. Hoeppe, M. Osterhoff, J. M. Rosselló, R. Mettin, F. Seiboth, A. Schropp, J. Möller, J. Hallmann, C. Kim, M. Scholz, U. Boesenberg, R. Schaffer, A. Zozulya, W. Lu, R. Shayduk, A. Madsen, C. G. Schroer, and T. Salditt, “Single-pulse phase-contrast imaging at free-electron lasers in the hard X-ray regime,” Journal of Synchrotron Radiation 28(1), 52–63 (2021).<br> </p> <p> </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>
X-ray Fluorescence Mapping dataset for use in Heritage Science
<p>The following data sets were collected to support the potential uses of opensource data in the context of digital humanities and heritage sciences. </p> <p>This proposed experiment is conducted by the UCL Institute for Sustainable Heritage in collaboration with the Centre for Digital Humanities. Imaging methods including Photography, Multispectral Imaging, Hyperspectral Imaging and Xray Fluorescence Mapping have been collected along with the complete readout metadata of the instrumentation. </p> <p>We hope that you find the data helpful, and we welcome you to use the data in any way you wish, for all and any analysis development purposes. For us to build upon this research, we ask that in return you would be willing to share in some regard your experiences in using open-source data, using our data, successes and issues. </p> <p>If you would be willing to engage with us in this endeavor, please feel free to contact us so that we may be able to follow up with you. </p> <p>Other Data sets available <a href="https://zenodo.org/record/7319696#.Y3NuOXbP2Uk">Here</a></p> <p>E: <a href="mailto:molly.fort.21@ucl.ac.uk">molly.fort.21@ucl.ac.uk</a> </p> <p>Object Paradata; </p> <ul> <li><strong>Postcard – c. Early 1900's </strong></li> <li><strong>Language – Eng. </strong></li> <li><strong>Materials – colour print on card, metallic leafing. </strong></li> <li><strong>Front transcription - </strong></li> <li><strong> ‘Greetings’ </strong></li> <li><strong> ‘May your Birthday bring you Peace & perfect Happiness, Golden hopes & Love of Friends, And every Happiness this world can send.’ </strong></li> <li><strong>Object Dimensions – 138mm X 88mm</strong></li> </ul> <p>The postcard is an item of ephemera donated to the UCLDH Digitisation Suite by Prof Melissa Terras, for teaching and training purposes in 2015.</p> <p>This folder contains:</p> <p>X-ray fluorescence imaging map collected from a <a href="https://www.bruker.com/en/products-and-solutions/elemental-analyzers/micro-xrf-spectrometers/m4-tornado-plus.html">Bruker M4+ Tornado Micro-XRF System</a></p> <ul> <li>Postcardmap1.bcf - Bruker composite file containing full fluorescence spectral and mapping data with some other information. Can be read by Bruker software or using freely dowloadable <a href="https://hyperspy.org/">Hyperspy</a>.</li> <li>EDX.hdf5 - full fluorescence spectral and mapping data in open format, created and readable via. Hyperspy.</li> <li>Postcard_data.txt - metadata saved in ASCII format by Bruker system.</li> <li>postcardmap1_*.png - individual png images of mapped elements, listed in filename. </li> </ul> <p> </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>
Raw Data: Gold Coated ZnO Microstructures by Bragg Coherent X-Ray Diffraction Imaging
<p>Two sets of raw data from gold coated ZnO microstructure (rod) investigated by Bragg coherent X-ray diffraction imaging used in publication: "Visualizing Intrinsic 3D-Strain Distribution in Gold Coated ZnO Microstructures by Bragg Coherent X-Ray Diffraction Imaging and Transmission Electron Microscopy with Respect to Piezotronic Applications" (<a href="https://doi.org/10.1002/aelm.202100546">https://doi.org/10.1002/aelm.202100546</a>)</p> <p>Included is data from two different spatial positions along the c-axis of the ZnO rod. Futher on called position 1 (P1) and position 2 (P2). For each position there is a .nxs file of a rocking scan around the {10-10} Bragg reflection, collected by a 2D detector and other recorded values, e.g. motor positions, counter values. </p>
Database of small molecule X-ray absorption spectra, featurized structures, and neural network ensembles
<p>Companion data for arXiv preprint <em>Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles</em> (<a href="https://arxiv.org/abs/2210.00336">https://arxiv.org/abs/2210.00336</a>), by Animesh Ghose, Mikhail Segal, Fanchen Meng, Zhu Liang, Mark S. Hybertsen, Xiaohui Qu, Eli Stavitski, Shinjae Yoo, Deyu Lu & Matthew R. Carbone.</p> <p><strong>Included</strong></p> <ul> <li>*-XANES-*.tar.bz2: raw input/output files for all molecular simulations used in the work. These inputs and outputs correspond to the structural data in the QM9 dataset.</li> <li>ml_ready.tar.bz2: machine learning-ready data (featurized spectra). Used as input to the neural network ensembles.</li> <li>XANES-220712-ACSF-*.tar.bz2: neural network ensembles used in this work.</li> </ul> <p><strong>Notes</strong></p> <ul> <li>The FEFF9 code [J. J. Rehr, J. J. Kas, F. D. Vila, M. P. Prange, and K. Jorissen, <em>Phys. Chem. Chem. Phys.</em> <strong>12</strong>, 5503 (2010)] was used to generate all X-ray absorption near-edge structure (XANES) spectra.</li> <li>All molecular structures were sourced from the QM9 database [R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld, <em>Sci. Data</em> <strong>1</strong>, 1 (2014)].</li> </ul> <p><strong>Funding</strong></p> <p>This research is based upon work supported by the U.S. Department of Energy, Office of Science, Office Basic Energy Sciences, under Award Number FWP PS-030. This research also used theory and computational resources of the Center for Functional Nanomaterials, which is a U.S. Department of Energy Office of Science User Facility, and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at Brookhaven National Laboratory under Contract No. DE-SC0012704.</p>
Datasets supplementing journal article "Probing dynamic covalent chemistry in a 2D boroxine framework by in-situ near-ambient pressure X-ray photoelectron spectroscopy" in Nanoscale 2022
<p>Datasets supporting the Nanoscale journal article "Probing dynamic covalent chemistry in a 2D boroxine framework by in-situ near-ambient pressure X-ray photoelectron spectroscopy".</p> <p>NAP-XPS.zip: Near-ambient pressure X-ray photoelectron spectroscopy, Figures 2, 3. (NEP 101007417)</p> <p>STM.zip: Scanning tunneling microscopy, Figure 5a, inset. (NEP 101007417)</p> <p>TPD.zip: Temperature programmed desorption, Figure 1a.</p> <p>UHV-XPS.zip: X-ray photoelectron spectroscopy, Figure 1b,c.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007417, having benefited from the access provided by by ALBA in Barcelona (Spain) and CNR-IOM in Trieste (Italy) within the framework of the NFFA-Europe Pilot Transnational Access Activity, proposal ID075.</p>
X-ray structures of HIV-1 protease
<p>As of March 1, 2023, there were 233 X-ray structures of HIV-1 protease available in the RCSB PDB database (<a href="https://www.rcsb.org/">https://www.rcsb.org/</a>). Out of these structures, 219 had ligands bound in the active site while 14 did not have any ligands. To prepare the structures for analysis, we removed water, ions, and solvent molecules, extracted the ligands from the receptors, and aligned all the structures. Each HIV-1 protease structure is identified by its PDB ID (<pdbid>.pdb) while the corresponding ligand structures are named as ligs_<pdbdid>.pdb.</p>
X-ray tomographic datasets associated with the article "Pore space of in-situ semi-dense asphalt: A characterization by X-ray tomography" (DOI: 10.1016/j.conbuildmat.2024.139091)
<p>This Zenodo repository provides two sets of 3D images, which constitute part of the dataset base for the article titled "Pore space of in-situ semi-dense asphalt: A characterization by X-ray tomography", written by the same authors cited here, together with other co-authors. The article is published in the journal "Construction and Building Materials". It can be reached <em>via</em> the following URL: <a href="https://doi.org/10.1016/j.conbuildmat.2024.139091" target="_blank" rel="noopener">https://doi.org/10.1016/j.conbuildmat.2024.139091</a>.</p> <p>The core specimens were obtained in 2019 from semi-dense asphalt (SDA) pavement sections located in the Swiss Canton of Zürich. For each of three pavement sections, labelled in the following as SDA4-1yr, SD4-5yr and SDA8, 100 mm diameter cores were extracted, both inside (I) and outside (O) of the wheel path, in order to see the effect of the traffic load on the pore space characteristics. Out of the original cores for the SDA4 pavements, 5 30 mm diameter sub-cores were drilled out of their centers, both in- and out-of the wheel path, and investigated with X-ray tomography. Only 1 30 mm core was analyzed for SDA8, both in- and out- of the wheel path. The asphalt in that pavement type has lower porosity, making it less interesting from the sound absorption viewpoint.</p> <p>The whole dataset consists of .7z archive files. Such files have the following designations: SDA_J_K_L_Tomogram.7z or SDA_J_K_L_PoreSpaceBinTomogram.7z, where J = 1,2, K = I,O and L = 1,2,3,4,5. When referring to the specimen naming within the corresponding article, the first index, J, refers to the specimen "age": J = 1 indicates the 1-year old specimens (called SDA4-1yr within the article); J = 2 refers to the 5-year old ones (SDA4-5yr). The second index, K, refers to the location of the specimen within the pavement section course ("I" for in-wheel path and "O" for out-of-wheel path). The final index L just enumerates the distinct specimens of the same group.</p> <p>There are two additional groups of archive files: LNA_I_Tomogram.7z/LNA_I_PoreSpaceBinTomogram.7z refers to the single in-wheel-path, 7-year old specimen (called SDA8 within the article); LNA_O_Tomogram.7z/LNA_O_PoreSpaceBinTomogram.7z refers to the single out-of-wheel path, 7-year old specimen.</p> <p>The two sets/types of 3D images can be recognized by the different file naming.</p> <p>The first set includes the raw X-ray tomograms of the 22 specimens analyzed. Each tomogram is stored in the form of a "stack" (or series) of 16-bit unsigned integer 2D TIFF image file, being one 2D cross-section (also called "slice", in tomographic jargon) from the "tomographed" volume. Such slices are contained in a folder. The folder was then archived in a .7z archive file.</p> <p>The second set of 3D images is characterized by the filename pattern SDA_J_K_L_PoreSpaceBinTomogram.7z. Each zipped folder contains the slices of the binary tomogram of the whole pore space of the respective specimen, segmented according with the 3d image analysis workflow described within the article. Each slice of such tomogram was stored as a 8-bit unsigned integer 2D TIFF image file, whose pixels can have only two possible values: 255, if the pixel is inside the segmented pore space; 0 if the pixel is outside it.</p> <p>Almost all of the acquired tomograms have an isotropic voxel size of 0.0214 mm, meaning that each slice is separated in space from the next one by such distance. The samples SDA_2_O_1 and SDA_2_I_1 have a voxel size of 0.0220 mm, while the sample LNA_I has a voxel size of 0.0223 mm.</p>
Dataset for article "From X-rays to physical parameters: a comprehensive analysis of thermal tidal disruption event X-ray spectra"
<p>This repository contains the data used in the modeling of TDE X-ray emission within the article: Mummery et al. 2023, " From X-rays to physical parameters: a comprehensive analysis of thermal<br> tidal disruption event X-ray spectra" published as Mummery et al. 2023, MNRAS, 519, 5828</p>
Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study
<p>Open data for "Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study" Angew. Chem.Int. Ed. 2023,62, e202214032(1 of 11) <a href="https://doi.org/10.1002/anie.202214032">https://doi.org/10.1002/anie.202214032</a></p>
X-ray Fluorescence Ghost Imaging - CuSn mask - Three Wires (Fe & Cu)
<p>X-ray Fluorescence Ghost Imaging (XRF-GI) dataset of three wires (one Fe, and two Cu) in a plastic capillary. The capillary contains trace elements like Zn, Zr, etc.</p> <p>The GI scan is presented in the following article <a title="Synchrotron-based x ray fluorescence ghost imaging" href="https://doi.org/10.1364/OL.499046">10.1364/OL.499046</a>. A total of 896 GI realizations were taken, organized into 16 vertical translations and 56 horizontal translations of the structuring element (CuSn mask).<br>The dataset contains both the sample transmission images and the masks plus sample transmission images. No images of the masks are provided (they need to be computed).</p> <p>The data is organized in an HDF5 file, under the following structure:</p> <pre><code>dataset_CuSn-mask_3wires.h5 │ ├data │ ├flat_panel │ │ ├dark [float32: 16 × 170 × 350] │ │ ├empty_beam [float32: 170 × 350] │ │ ├sample [float32: 16 × 170 × 350] │ │ └sample_and_masks [float32: 16 × 56 × 170 × 350] │ └xrf [float32: 16 × 56 × 4096] │ └metadata └xrf ├bias_keV [float64: scalar] ├gain_keV [float64: scalar] └ranges ├Ca [int64: 2] ├Cu [int64: 2] ├Fe [int64: 2] ├Si [int64: 2] ├Ti [int64: 2] ├Zn [int64: 2] └Zr [int64: 2] </code></pre> <p>The meaning of the paths is:</p> <ul> <li><code>/data/xrf</code> contains the XRF spectra for each GI realization</li> <li><code>/data/flat_panel/dark</code> contains the dark images of each scan line (no beam)</li> <li><code>/data/flat_panel/empty_beam</code> contains the empty beam (no sample & no masks) intensity distribution</li> <li><code>/data/flat_panel/sample</code> contains the transmission images of the sample at each scan line</li> <li><code>/data/flat_panel/sample</code>_and_masks contains the transmission images of the sample and masks at each GI realization</li> <li><code>/metadata/xrf/bias_keV</code> contains the bias in keV of the XRF spectrum</li> <li><code>/metadata/xrf/gain_keV</code> contains the gain in keV of each XRF energy bin</li> <li><code>/metadata/xrf/ranges/</code> contains the bin ranges for interesting K<sub>alpha</sub> elemental emission lines in the XRF spectrum</li> </ul> <p>For further information we refer to the associated publication.</p> <p>The data can be processed with structured illumination routines of the code at: <a href="https://github.com/cicwi/PyCorrectedEmissionCT">https://github.com/cicwi/PyCorrectedEmissionCT</a>.</p>
Probability of Detection applied to X-ray inspection using numerical simulations
<p>In this work, we apply and adapt established Probability of Detection (POD) methods on inline inspection of aluminium cylinder heads using X-ray computed tomography. The CT simulation tool SimCT [4] is used to acquire virtual images of the specimens including artificial defects, which avoids the manufacturing of calibrated defects of known type (e.g., pore, inclusion, crack etc.), size and location. One of the exemplary defects is discussed as representative result together with the generated POD curves as well as its characteristics (i.e., the minimum detected defect, the maximum missed defect, POD(a90) =0.90 and a90/95).</p>
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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.