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2,001 results for “x-rays”
Supporting data for "Understanding the impact of precipitation kinetics on the electrochemical performance of lithium–sulfur batteries by operando X-ray diffraction"
<p>This is the dataset of electrochemical and operando X-ray diffraction measurements for our publication "Understanding the impact of precipitation kinetics on the electrochemical performance of lithium–sulfur batteries by operando X-ray diffraction". This archive contains the raw data and scripts written in R used in the analysis and presentation of the results in this manuscript.</p> <p>Abstract of the manuscript:</p> <p>The complex reaction mechanism of the lithium–sulfur battery system consists of repetitive dissolution and precipitation of the sulfur-containing species in the positive electrode. In particular, the precipitation of lithium sulfide (Li<sub>2</sub>S) during discharge has been considered a crucial factor for obtaining a high degree of active material utilization. Here, the influence of electrolyte amount, electrode thickness, applied current and electrolyte salt on the formation of Li<sub>2</sub>S is systematically investigated in a series of operando X-ray diffraction experiments. Through a combination of simultaneous diffraction and resistance measurements, the evolution of Li<sub>2</sub>S is directly correlated to the variation in internal resistance and transport properties inside the positive electrode. The correlation indicates that at different stages, the Li<sub>2</sub>S precipitation both facilitates and impedes the discharge process. This information on the kinetics of Li<sub>2</sub>S formation offers mechanistic explanations for the strong impact of different electrochemical cell parameters on the cell performance and thus, directions for holistic optimizations to achieve high sulfur utilization.</p> <p> </p>
X-ray database: pneumonia and norm
<p>X-rays result of patients with pneumonia and normal chest radiographs. X-rays were taken in frontal and lateral projections using Apollo DRF (VILLA SISTEMI MEDICALI).</p>
Self-synchronized cost-effective time-resolved measurements at x-ray free-electron lasers with femtosecond resolution
<p>In compliance with guidelines from SNSF (Swiss National Science Foundation), we upload the raw data that belongs to a publication we submitted to <em>Physical Review Research</em>.<br> The dataset also contains the analysis scripts used to produce the plots we show in the publication.</p> <p>Our software is attached to the dataset. An installation of the well known ELEGANT code is required to run it.</p> <p>Shortly after upload of the first version of this dataset, a bug was found in the analysis software.<br> The second version contains the corrected software.</p>
Hyperspectral X-ray CT dataset of a single, iodine-stained lizard head sample
<p><strong>General Data description:</strong></p> <p>This is a hyperspectral (energy-resolved) X-ray CT projection dataset of a lizard head sample, stained with an iodine contrast agent. It was acquired in a custom-built, laboratory micro-CT scanner with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The following data contains all the files necessary for reconstruction, after a hyperspectral scan was taken of a single, iodine-stained lizard head sample. The iodine contrast agent provided a spectral marker, measured by an energy-sensitive detector, which may be used for spatial mapping and segmentation of stained soft tissue regions.</p> <p><strong>File descriptions:</strong></p> <p>Contained are four MATLAB (.mat) data files, as well as a single text (.txt) file.</p> <p>Lizard_head_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition.</p> <p>lizard_180Proj_noSupp_1_180.mat contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data. The 4D array contains the total number of energy channels acquired during scanning, vertical and horizontal pixel number, and total projections angles acquired. The data provided is prior to application of any post-processing filters. The first 180 energy channels are included.</p> <p>lizard_180Proj_Supp_1_180.mat contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data. This dataset is identical to the .mat file above, however here we have also applied a ring-reduction filter, using a wavelet-based Fourier filter which suppresses the presence of ring artefacts in every energy channel. The first 180 energy channels are included.</p> <p>Energy_axis.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning.</p> <p>FF.mat contains the 4D flatfield data acquired when no sample was present. This data was used to normalise the projection datasets, as the sinogram was constructed. The first 180 energy channels are included.</p>
A collection of X-ray projections of 131 pieces of modeling clay containing stones for machine learning-driven object detection
<p><strong>Summary</strong></p> <p>This submission contains a collection of 235800 X-ray projections of 131 pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as an extensive and easy-to-use training dataset for supervised machine learning driven object detection. The ground truth locations of the stones are included. The data is supplementary material to the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022].</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections have been corrected with flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images). Both the X-ray projections and the ground truth images are resized to 128x128 pixels. The raw data is made available in another (larger) submission for complete reproduction (<a href="https://zenodo.org/record/5866228">https://zenodo.org/record/5866228</a>). All images are stored in .tif format. The data for samples with 5-8 stones are put in a separate folder from the data with 0-3 stones. The size of the completely unpacked dataset is 19.6 GB.</p> <p><strong>NOTE</strong>: Because the dataset consists of 471600 files, fully extracting the dataset may take a while. Therefore, an additional and significantly smaller zip-file is included for previewing the data, with one X-ray projection for each sample.</p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow enabling deep learning for X-ray based foreign object detection", 2022 (in preparation)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
Reproduction package for the paper "Completing the X-ray view of the recently discovered supernova remnant G54.41+0.03"
<p>This is a basic reproduction package for the paper "Completing the X-ray view of the recently discovered supernova remnant G54.41+0.03" by V. Domček et al. (2021). It provides raw, intermediate and final data sets, including figures and scripts to allow the reproduction of the work performed in this paper. It also lists software used and data archives containing the public observational data. </p> <p>An open access version of the paper can be found at https://arxiv.org/abs/</p>
Pultruded carbon fiber profiles - 3D x-ray tomography data-sets for two different pultruded profiles
<p>3D X-ray scan on Zeiss Xradia 520</p> <table> <tbody> <tr> <td> <table> <tbody> <tr> <td> </td> <td>A1</td> <td>A2</td> <td>A2S</td> <td>B1</td> <td>B1S</td> <td>B2</td> <td>B3</td> </tr> <tr> <td>Scanning Voxel size [μm]</td> <td>1.9767</td> <td>1.97</td> <td>1.9731</td> <td>1.9751</td> <td>1.9753</td> <td>1.9771</td> <td>1.9752</td> </tr> <tr> <td>FoV: (x; y; z) [mm]</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> <td>2.0x2.0</td> </tr> <tr> <td>Accelerating Voltage [kV]</td> <td>30</td> <td>30</td> <td>30</td> <td>30</td> <td>30</td> <td>30</td> <td>30</td> </tr> <tr> <td>Power [W]</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> </tr> <tr> <td>Filter</td> <td>Air</td> <td>LE1</td> <td>LE1</td> <td>LE1</td> <td>LE1</td> <td>Air</td> <td>LE1</td> </tr> <tr> <td>Optical magnification</td> <td>4x</td> <td>4x</td> <td>4x</td> <td>4x</td> <td>4x</td> <td>4x</td> <td>4x</td> </tr> <tr> <td>Detector to sample distance [mm]</td> <td>25.5</td> <td>25.52</td> <td>26.82</td> <td>25.4</td> <td>26.75</td> <td>25.5</td> <td>25.8</td> </tr> <tr> <td>Source to sample distance [mm] </td> <td>10.6</td> <td>10.61</td> <td>11.11</td> <td>10.5</td> <td>11.1</td> <td>10.6</td> <td>10.7</td> </tr> <tr> <td>Exposure time [s]</td> <td>15</td> <td>18</td> <td>20</td> <td>18</td> <td>18</td> <td>15</td> <td>18</td> </tr> <tr> <td>No. of projections</td> <td>5201</td> <td>5201</td> <td>5201</td> <td>5201</td> <td>5201</td> <td>5201</td> <td>5201</td> </tr> <tr> <td>Rotation</td> <td>360</td> <td>360</td> <td>360</td> <td>360</td> <td>360</td> <td>360</td> <td>360</td> </tr> <tr> <td>Binning</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> </tr> <tr> <td>Total scanning time [h]</td> <td>26</td> <td>33</td> <td>33</td> <td>32</td> <td>32</td> <td>26</td> <td>29</td> </tr> <tr> <td>File-size [GB]</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> <td>2</td> </tr> </tbody> </table> </td> </tr> </tbody> </table>
Raw output data for "Covalency in actinide(IV) hexachlorides in relation to the chlorine K-edge X-ray absorption structure", DOI: 10.1039/D1SC06454A
<p>This repository contains raw output files related to the following article:</p> <p>Authors: Dumitru-Claudiu Sergentu and Jochen Autschbach<br> Title: Covalency in Actinide(IV) Hexachlorides in Relation to Chlorine K-Edge X-ray Absorption Structure<br> DOI: https://doi.org/10.1039/D1SC06454A</p> <p>Each archive: 1) ThCl6.rar 2) UCl6.rar 3) NpCl6.rar and 4) PuCl6.rar<br> contains output data produced by ADF (raw DFT outputs associated with the article, the ADF program can be found at: https://www.scm.com/product/adf/) and OpenMolcas (raw RASSCF and RASSI outputs associated with the article, the OpenMolcas program can be found at: https://gitlab.com/Molcas/OpenMolcas). Each folder in the archives contains 00README.txt files describing its content(s).</p> <p> </p> <p> </p>
miRNA profiling of human nasopharyngeal carcinoma cell lines HONE1 and CNE2 after X-ray therapy
<p>The package contains two file:</p> <p>Supplementary Table 1. Kruskal Wallis test for cell viability rate</p> <p>Supplementary Table 2. Kruskal Wallis test for apoptosis rate</p> <p>The HONE1 and CNE2 cells were irradiated with X-rays at doses of 4 Gy, 8 Gy, 16 Gy and 20 Gy for 24 h. The cell viability rate and apoptosis rate were compared.</p>
Example data for "Calibration of X-ray computed tomography for surface topography measurement using metrological characteristics"
<p>Example raw data used to create figures during the conference paper "<a href="https://www.euspen.eu/knowledge-base/ICE21165.pdf"><em>Calibration of X-ray computed tomography for surface topography measurement using metrological characteristics</em></a>", presented at euspen’s 21st International Conference & Exhibition 2020</p> <p>Data in the ".datx" format are acquired using a ZYGO NexView NX2 coherence scanning interferometer.</p> <p>Data in the ".stl" format are acquired using a Nikon MCT 225 X-ray computed tomography instrument.</p>
X-ray powder diffraction data of a dried Pepto-Bismol suspension (bismuth subsalicylate) collected at ambient temperature
<p>PXRD data of dried Pepto-Bismol suspension loaded in a Kapton tube. Data collected under ambient conditions. Calibrated wavelength = 0.458092 Å.</p> <p>Use of the Advanced Photon Source at Argonne National Laboratory was supported by the U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.</p>
The scale of a Martian hydrothermal system explored using combined neutron and X-ray tomography
<p>Neutron and X-ray tomography data of MIL 03346,230 and MIL 03346,231</p> <p> </p> <p>Neutron data was acquired at the Institut Laue-Langevin, https://doi.ill.fr/10.5291/ILL-DATA.UGA-480 79 </p> <p>X-ray data was acquired at the 4D Imaging Lab at Lund University, Sweden</p>
Optical and X-ray data on V496 UMa
<p>The optical light curves and X-ray spectra and light curves presented in Kennedy et al. 2022 are given here. The code to reproduce the MCMCs and plots from the paper is given in a github repo.</p>
Appendix 1. Revelieria groehni Sergi, Perkovsky et Reike, 2013, female, Baltic amber, JDC-9116 [JDC], X-ray microtomography volume rendering of the habitus.
<p>X-ray microtomography volume rendering of the habitus of Revelieria groehni Sergi, Perkovsky et Reike, 2013, female, Baltic amber, JDC-9116 [JDC].</p>
Appendix 2. Revelieria groehni Sergi, Perkovsky et Reike, 2013, female, Baltic amber, JDC-9116 [JDC], X-ray microtomography volume rendering of the forebody.
<p>X-ray microtomography volume rendering of the forebody of Revelieria groehni Sergi, Perkovsky et Reike, 2013, female, Baltic amber, JDC-9116 [JDC].</p> <p> </p>
Statistical analyses of the impact of X-ray damage effects on conformational heterogeneity in room temperature (277 K) and cryo-cooled (100 K) protein crystals
<p>These data are published in support of the manuscript "Evaluating the impact of X-ray damage on conformational heterogeneity in room temperature and cryo-cooled protein crystals" submitted to Acta Crystallographica section D, authors:</p> <p>Filip Yabukarski<sup>1,</sup><sup>#,</sup>*, Tzanko Doukov<sup>5</sup>, Daniel A Mokhtari<sup>1</sup>, Siyuan Du<sup>1</sup>, Daniel Herschlag<sup>1,2,3,4</sup><sup>,</sup>*</p> <p><sup>1</sup>Department of Biochemistry, <sup>2</sup>Department of Chemistry, <sup>3</sup>Department of Chemical Engineering, <sup>4</sup>Stanford ChEM-H, Stanford University, Stanford, California 94305, United States, <sup>5</sup>Stanford Synchrotron Radiation Light Source, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States.</p> <p># Current address: Bristol Myers Squibb, San Diego, California 92121, United States.</p> <p>* Correspondence: fyabukar@stanford.edu, herschla@stanford.edu</p>
X-ray computed tomography aided engineering approach for non-crimp fabric reinforced composites [Data set]
<p>The finite element models behind the publication</p> <p>Auenhammer, R.M., Jeppesen, N., Mikkelsen, L.P., Dahl, V.A., Blinzler, B.J., Asp, L.E. Robust numerical analysis of fibrous composites from X-ray computed tomography image data enabling low resolutions, <em>Composites Science and Technology, </em><strong>224</strong>, 109458, <a href="https://doi.org/10.1016/j.compscitech.2022.109458">https://doi.org/10.1016/j.compscitech.2022.109458</a>, 2022. </p> <p>The x-ray scan data which the model is based on can be found in the following publication:</p> <p>Jeppesen, N., V.A. Dahl, A.N. Christensen, A.B. Dahl, L.P. Mikkelsen, Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor. IOP Conf. Ser.: Mater. Sci. Eng. 942, 012037, <a href="https://doi.org/10.1088/1757-899X/942/1/012037">https://doi.org/10.1088/1757-899X/942/1/012037</a> 2020</p> <p>and data-set</p> <p>Jeppesen N, Dahl V A, Christensen A N, Dahl A B and Mikkelsen L P 2020 Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor [data set] Zenodo. <a href="http://dx.doi.org/10.5281/zenodo.3877522">http://dx.doi.org/10.5281/zenodo.3877522.</a></p> <p> </p>
Data for "Direct Observation of Ultrafast Exciton Localization in an Organic Semiconductor with Soft X-ray Transient Absorption Spectroscopy"
<p>Underlying data for figures 1-3 and supplementary figures S1-S7 for the paper entitled 'Direct Observation of Ultrafast Exciton Localization in an Organic Semiconductor with Soft X-ray Transient Absorption Spectroscopy'.</p>
Ptychographic X-ray speckle tracking (PXST) scan of the Siemens star test sample
<p>The PXST scan of the Siemens star test sample measured at P11 beamtime of the PETRA III synchrotron radiation facility. The beam was focused with a pair of MLLs with focal lengths of 1.25 mm and 1.15 mm and numerical apertures of 0.014 and 0.015, in the vertical and horizontal directions, respectively. The X-ray beam photon energy was 17.5 keV.</p>
X-ray driven peanut trait estimation-Plant Methods paper
<p>The ground-truth dataset linked to the research article "X-ray driven peanut trait estimation: Computer vision aided agri-system transformation" of Plant Methods is available in this data repository.</p> <p>Description of the headers:</p> <ol> <li>Peanut variety name [39 peanut varieties were used]</li> <li>Variety number [number linked to variety]</li> <li>Variety Type [Breeding lines, Released popular cultivar, or Farmers' produce]</li> <li>Rep [number of replicates per sample used in the study]</li> <li>Corresponding TIFF image number</li> <li>kernel_weight_(g) [unit = g]</li> <li>shell_weight_(g) [unit = g]</li> <li>percentage_shelling_(%) [unit = %]</li> </ol> <p>For additional information please contact the corresponding author Jana Kholova (j.kholova@cgiar.org)</p>
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.