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2,001 results for “x-rays”
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 38-42
<p>This upload contains samples 38 - 42 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 33-37
<p>This upload contains samples 33 - 37 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 25-32
<p>This upload contains samples 25 - 32 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 17-24
<p>This upload contains samples 17 - 24 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 9-16
<p>This upload contains samples 9 - 16 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 1-8
<p>This upload contains samples 1 - 8 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Single-shot multi-keV X-ray absorption spectroscopy using an ultrashort laser wakefield accelerator source
<p>The data contained in this repository was used to generate the figures for the publication "Single-shot multi-keV X-ray absorption spectroscopy using an ultrashort laser wakefield accelerator source".</p>
Ultra-luminous X-ray sources and neutron-star-black-hole mergers from very massive close binaries at low metallicity
<p>MESA inlists, run_star_extras, and data associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017A&A...604A..55M">Marchant et al. (2017)</a>. MESA version 8118.</p> <p>Publication DOI: <a href="https://doi.org/10.1051/0004-6361/201630188">10.1051/0004-6361/201630188</a></p> <p>Files are also available in a gihub repository <a href="https://github.com/orlox/mesa_input_data/tree/master/2016_ULX">here</a></p> <p>Upload includes post-processed simulation output in the files Z-XX.tar.xz, where XX represents the metallicity (Z-25.tar.gz is for a metallicity of log10(Z)=-2.5). Each folder inside the archive corresponds to a single MESA simulation, with the name indicating the value of log10(M_1), q=M2/M1 and the orbital period in days. For example, the directory 1.600_0.500_0.900 corresponds to the simulation with log10(M1/Msun)=1.6, M2/M1=0.5 and Porb=0.9 days. Each folder is also a MESA template folder, containing all input files neccesary to reproduce that individual simulation.</p> <p>The file summary_tables.tar.gz contains summarized information for each simulation in ascii format.</p>
Expression test and purification of SETDB1 catalytic domain constructs' for structural studies by X-ray crystallography.
<p><strong>Experiment: </strong>Expression test and purification of SETDB1 catalytic domain constructs’ for structural studies by X-ray crystallography.</p> <p><strong>Aim:</strong> In the present section of this study, we focused on the development of efficient bacterial expression systems to produce large amounts of soluble SETDB1 catalytic domain for structural studies. This report involves a summary of expression test results of different fragments of SETDB1 and purification of various fusion proteins.</p>
X-ray computed tomography reveals that grain protrusion controls entrainment shear stress for entrainment of fluvial gravels: Dataset
<p>This is the dataset that accompanies a paper in Geology:</p> <p>Hodge RA, Voepel H, Leyland J, Sear DA, Ahmed S (2020). X-ray computed tomography reveals that grain protrusion controls entrainment shear stress for entrainment of fluvial gravels. Geology, 48(2), 149-153.</p> <p>The aim of this work was to understand how the properties of a sediment grain in a river bed affect the forces required to entrain that grain. This dataset presents the properties of 1055 sediment grains, which were meaured using CT scanning.</p>
X-ray Signatures of Lightning-induced Electron Precipitation
<p>Figures, data, and code used in my paper describing X-ray signatures of lightning-induced electron precipitation (LEP)</p>
Energy-dependence of the response of X-ray multimeter for mammography- radiation qualities
<p>Supplementary data to the manuscript “Energy-dependence of the response of X-ray multimeter for mammography- radiation qualities” by Elisabeth Salomon and Paula Toroi</p>
Data and Scripts for the Article "Structural Descriptors and Information Extraction from X-ray Emission Spectra: Aqueous Sulfuric Acid"
<p>Data and scripts for the article titled "Structural Descriptors and Information Extraction from X-ray Emission Spectra: Aqueous Sulfuric Acid".</p> <p>For further details on the contents, see the "readme.md"-file.</p> <p>Article available at <a href="https://doi.org/10.1039/D4CP02454K">10.1039/D4CP02454K</a>.</p>
X-ray Emission of Nearby Low-mass and Sun-like Stars with Directly Imageable Habitable Zones: X-ray Spectra and Light Curves
<p>This repository contains the X-ray light curves extracted from the XMM-Newton and Chandra observations and the best-fit X-ray spectral models (with and without emission lines) from all stars detected at high significance in Binder et al. (2024), ApJS, ..., ...</p> <p>Description of Files:</p> <h3>LightCurves.zip</h3> <p>Summary: Light curve files (327) for all stars detected at high significance; in FITS (BinTableHDU) format with 7-14 extensions. The following types of data are included:</p> <ul> <li>"*_source.lc" : EPIC/PN source light curves of stars detected with more than ~500 net counts in an XMM-Newton observation</li> <li>"*_bkg.lc" : EPIC/PN background light curves of stars detected with more than ~500 net counts in an XMM-Newton observation</li> <li>"*_sub_lc.fits" : Background-subtracted light curves of stars detected with more than ~50 net counts in a Chandra observation</li> </ul> <h3>Spectra.zip</h3> <p>Summary: Spectra (152) for all stars detected at high significance (with >500 net counts in Chandra observations and >2000 net counts in XMM-Newton observations) in ASCII (ECSV format). We provide both the continuum spectra and spectra containing line emission. For stars that exhibit X-ray count rate variability, we provide spectra for specific variability types (see Binder et al. 2024 for details). The following types of data are included:</p> <ul> <li>"*_continuum.dat" : Continuum-only best-fit spectra</li> <li>"*_lines.dat" : Best-fit spectra containing line emission</li> </ul>
Inlists for "Rethinking Thorne-Żytkow Object Formation: The Fate of X-ray Binary LMC X-4 and Implications for Ultra-long Gamma-ray Bursts"
<div> <div> <div> <p>We use the MESA Isochrones and Stellar Tracks (MIST) package (Dotter 2016; Choi et al. 2016) with MESA v7503 (Paxton et al. 2011, 2013, 2015) and mesasdk x86_64-linux-20141212 revision 245. </p> </div> </div> </div>
Fluid Interfaces in Mixed-Wet Bead Packs: Insights from 3D X-Ray Imaging
<p>Data for beads curvature: The data on mean curvature on a 200×140×170 voxel dry image before and after the removal of any points within 5 voxels of bead contacts. </p> <p> </p> <p>Data for fluid interfacial curvature between oil and brine: The data on mean and Gaussian curvature distribution on a 930×930×860 voxel wet image. The curvature data is provided for both before and after the removal of points within one voxel of the three-phase contact line.</p>
Residual stress in steel benchmark determined by synchrotron X-ray and neutron diffraction
<p>The data presented is the residual stress, determined by synchrotron X-ray diffraction (SXRD) at DESY operated by Hereo (P07 and P61A) and neutron diffraction (ND) at ILL(SALSA). The data is for two benchmark samples having the same geometry (U shape) that can be measured by diffraction techniques and adapted to other material systems to validate RS measurements. One of them was elastically loaded while the other had stresses generated by plastic deformation, while having the same geometry. </p> <p>The U-flexure sample flexure-compression(FC), flexure-tension(FT_ and flexure-neutral(FN) were electrical discharge machine from a rolled plate. The U-bend(B) sample was obtained by three-point bending a cuboidal blank. The thicknes of the machine part is close to 10 mm and runs parallel to the z-direction. The measurement line runs from the centre of the top surface near the bend (defined at the origin) down into the bend (positive z-direction). The FN samples acted as stress-free reference for the U-flexures and a pin extracted from the bend was used for the U-bend. For the U-flexures, a finite element analysis (FEA) model was developed to account for the slight variations in loading conditions observed in the samples used for ND and SXRD. The results of the FEA model is also presented for comparison.</p> <p>The gauge volume sizes and shapes were dependent on the technique and research facility. The gauge volumes were: a) P07 – 0.2 x 0.2 x ~2.0 mm<sup>3 </sup>(b) P61A- 0.15 x 0.15 x ~3.4 µm<sup>3 </sup>(c) SALSA- 0.6 x 0.6 x 2 mm<sup>3</sup>. </p>
RODIN X-ray Diffraction Data 2334961
<p>This dataset is part of the Resource of Diffraction Images Newcastle (RODIN), intended for the teaching of crystallography. More information about RODIN can be found on the <a title="Link to RODIN webpage on CCDC website" href="https://www.ccdc.cam.ac.uk/community/education-and-outreach/education/rodin/" target="_blank" rel="noopener">CCDC website</a>, as well as in the following article: </p> <p>J. Chem. Educ. 2024, 101, 10, 4276–4281 DOI: <a href="https://doi.org/10.1021/acs.jchemed.4c00797">10.1021/acs.jchemed.4c00797</a> </p> <p>RODIN was created for educational purposes and is ideal for teaching at secondary school and University level. We also encourage its use for teaching early-stage researchers crystallographic data processing and training new diffractometer users. The diffraction images from this project can be downloaded from Zenodo and used in teaching. You can use this resource to help students understand how to go from diffraction images to a solved crystal structure. Please note that structures from these datasets are already in the Cambridge Structural Database (CSD) and you should not submit your final structure solution to the CSD or publish them, they are intended for teaching purposes only. The diffraction images are provided under a CC-BY license. </p> <p>The zip file contains X-ray diffraction images for 1-phenyl-3,3-bis(phenylsulfanyl)prop-2-en-1-one collected for a crystal exhibiting non-merohedral twinning. A final structure solution for this data can be found in the CSD. This structure is published in the CSD as a CSD Communication and data shared with permission of the authors: Roly J. Armstrong, Aaron D. G. Campbell, Paul G. Waddell, CSD Communication, 2024</p> <p>The tables below summarise the data collection parameters for the experiment.</p> <p> </p> <p><strong>General information</strong></p> <table> <tbody> <tr> <td> <p>Project</p> </td> <td> <p>Repository of Diffraction Images Newcastle (RODIN)</p> </td> </tr> <tr> <td> <p>Collection Site</p> </td> <td> <p>Newcastle University, UK</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p>rja230015_2</p> </td> </tr> <tr> <td> <p>Linked CCDC number</p> </td> <td> <p>2334961</p> </td> </tr> <tr> <td> <p>CSD Refcode</p> </td> <td> <p>GOLSOT</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Instrument information</strong></p> <table> <tbody> <tr> <td> <p>Instrument</p> </td> <td>four-circle diffractometer</td> </tr> <tr> <td> <p>Instrument type</p> </td> <td>XtaLAB Synergy Single source at home/near HyPix-Arc 100</td> </tr> <tr> <td> <p>Instrument manufacturer</p> </td> <td>Rigaku</td> </tr> <tr> <td> <p>Detector</p> </td> <td>Hybrid Pixel Array Detector</td> </tr> <tr> <td> <p>Detector type</p> </td> <td>HyPix-Arc 100</td> </tr> <tr> <td> <p>X-ray source</p> </td> <td>fine-focus sealed X-ray tube</td> </tr> <tr> <td> <p>X-ray source type</p> </td> <td>Enhance (Cu) X-ray Source</td> </tr> <tr> <td> <p>X-ray monochromator</p> </td> <td>graphite</td> </tr> </tbody> </table> <p> </p> <p><strong>Experimental information</strong></p> <table> <tbody> <tr> <td> <p>Collection probe</p> </td> <td>x-ray</td> </tr> <tr> <td> <p>Radiation type</p> </td> <td>CuK\a</td> </tr> <tr> <td> <p>Wavelength (Å)</p> </td> <td>1.54184</td> </tr> <tr> <td> <p>Collection temperature (K)</p> </td> <td>150.0(2)</td> </tr> <tr> <td> <p>Collection pressure</p> </td> <td>Sample was collected at atmospheric pressure</td> </tr> </tbody> </table> <p> </p> <p><strong>Crystal information</strong></p> <table> <tbody> <tr> <td> <p>Chemical name</p> </td> <td>1-phenyl-3,3-bis(phenylsulfanyl)prop-2-en-1-one</td> </tr> <tr> <td> <p>Chemical formula</p> </td> <td> <p>C21 H16 O S2</p> </td> </tr> <tr> <td> <p>Crystal max size (mm)</p> </td> <td> <p>0.20</p> </td> </tr> <tr> <td> <p>Crystal mid size (mm)</p> </td> <td> <p>0.11</p> </td> </tr> <tr> <td> <p>Crystal min size (mm)</p> </td> <td> <p>0.04</p> </td> </tr> <tr> <td> <p>Crystal colour</p> </td> <td> <p>yellow</p> </td> </tr> <tr> <td> <p>Crystal habit</p> </td> <td> <p>block</p> </td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td> <p>Re-crystallisation from solvent: Chloroform</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Software information</strong></p> <table> <tbody> <tr> <td> <p>Software for data collection</p> </td> <td>CrysAlisPro 1.171.42.73a (Rigaku OD, 2022)</td> </tr> </tbody> </table> <p> </p> <p><strong>File information</strong></p> <p>The .par file in the folder has been modified from the original version (the unit cell is set to 10 10 10 90 90 90 to require the user to determine the unit cell when processing the data). The original .par file (along with *_peakhunt.tabbin and *_proffitpeak.tabbin files) can be found in the folder named ‘original’.</p> <table> <tbody> <tr> <td> <p>Image folders</p> </td> <td> <p>.frames/xxx.rodhypix</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p>.rodhypix (Rigaku proprietary image format)</p> </td> </tr> <tr> <td> <p>Additional files</p> </td> <td> <p>CrysAlisPro input files:</p> <p>./expinfo/</p> <p>./CrysalisExpSettings.ini</p> <p>./rja230015_2.par</p> <p>./rja230015_2.run</p> <p> </p> <p>Crystal Images:</p> <p>./movie/</p> </td> </tr> </tbody> </table>
RODIN X-ray Diffraction Data 2360262
<p>This dataset is part of the Resource of Diffraction Images Newcastle (RODIN), intended for the teaching of crystallography. More information about RODIN can be found <span>on the <a title="Link to RODIN webpage on CCDC website" href="https://www.ccdc.cam.ac.uk/community/education-and-outreach/education/rodin/" target="_blank" rel="noopener">CCDC website</a></span>, as well as in the following article: </p> <p>J. Chem. Educ. 2024, 101, 10, 4276–4281 DOI: <a href="https://doi.org/10.1021/acs.jchemed.4c00797"><span>10.1021/acs.jchemed.4c00797</span></a><span> </span></p> <p>RODIN was created for educational purposes and is ideal for teaching at secondary school and University level. We also encourage its use for teaching early-stage researchers crystallographic data processing and training new diffractometer users. The diffraction images from this project can be downloaded from Zenodo and used in teaching. You can use this resource to help students understand how to go from diffraction images to a solved crystal structure. Please note that structures from these datasets are already in the Cambridge Structural Database (CSD) and you should not submit your final structure solution to the CSD or publish them, they are intended for teaching purposes only. The diffraction images are provided under a CC-BY license. </p> <p>The zip file contains X-ray diffraction images of Coumarin. A final structure solution for this data can be found in the CSD.</p> <p>The tables below summarise the data collection parameters for the experiment.</p> <p> </p> <p><strong>General information</strong></p> <table> <tbody> <tr> <td> <p>Project</p> </td> <td> <p>Resource of Diffraction Images Newcastle (RODIN)</p> </td> </tr> <tr> <td> <p>Collection Site</p> </td> <td> <p> Newcastle University, UK</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td>pgw240007</td> </tr> <tr> <td> <p>Linked CCDC number</p> </td> <td>2360262</td> </tr> <tr> <td> <p>CSD Refcode</p> </td> <td> <p>COUMAR23</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Instrument information</strong></p> <table> <tbody> <tr> <td> <p>Instrument</p> </td> <td>four-circle diffractometer</td> </tr> <tr> <td> <p>Instrument type</p> </td> <td>XtaLAB Synergy Single source at home/near HyPix-Arc 100</td> </tr> <tr> <td> <p>Instrument manufacturer</p> </td> <td> <p>Rigaku</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td>Hybrid Pixel Array Detector</td> </tr> <tr> <td> <p>Detector type</p> </td> <td>HyPix-Arc 100</td> </tr> <tr> <td> <p>X-ray source</p> </td> <td>fine-focus sealed X-ray tube</td> </tr> <tr> <td> <p>X-ray source type</p> </td> <td>Enhance (Cu) X-ray Source</td> </tr> <tr> <td> <p>X-ray monochromator</p> </td> <td>graphite</td> </tr> </tbody> </table> <p> </p> <p><strong>Experimental information</strong></p> <table> <tbody> <tr> <td> <p>Collection probe</p> </td> <td>x-ray</td> </tr> <tr> <td> <p>Radiation type</p> </td> <td>CuK\a</td> </tr> <tr> <td> <p>Wavelength (Å)</p> </td> <td>1.54184</td> </tr> <tr> <td> <p>Collection temperature (K)</p> </td> <td>150.0(2)</td> </tr> <tr> <td> <p>Collection pressure</p> </td> <td> <p>Sample was collected at atmospheric pressure</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Crystal information</strong></p> <table> <tbody> <tr> <td> <p>Chemical name</p> </td> <td>Coumarin</td> </tr> <tr> <td> <p>Chemical formula</p> </td> <td>C9 H6 O2</td> </tr> <tr> <td> <p>Crystal max size (mm)</p> </td> <td>0.18</td> </tr> <tr> <td> <p>Crystal mid size (mm)</p> </td> <td>0.05</td> </tr> <tr> <td> <p>Crystal min size (mm)</p> </td> <td>0.03</td> </tr> <tr> <td> <p>Crystal colour</p> </td> <td>colourless</td> </tr> <tr> <td> <p>Crystal habit</p> </td> <td>block</td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td>Re-crystallisation from solvent: Diethyl Ether</td> </tr> </tbody> </table> <p> </p> <p><strong>Software information</strong></p> <table> <tbody> <tr> <td> <p>Software for data collection</p> </td> <td>CrysAlisPro 1.171.42.90a (Rigaku OD 2023)</td> </tr> </tbody> </table> <p> </p> <p><strong>File information</strong></p> <p>The .par file in the folder has been modified from the original version (the unit cell is set to 10 10 10 90 90 90 to require the user to determine the unit cell when processing the data). The original .par file (along with *_peakhunt.tabbin and *_proffitpeak.tabbin files) can be found in the folder named ‘original’.</p> <table> <tbody> <tr> <td> <p>Image folders</p> </td> <td> <p>.frames/xxx.rodhypix</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p>.rodhypix (Rigaku proprietary image format)</p> </td> </tr> <tr> <td> <p>Additional files</p> </td> <td> <p>CrysAlisPro input files:</p> <p>./expinfo/</p> <p>./CrysalisExpSettings.ini</p> <p>./pgw240007.par</p> <p>./pgw240007.run</p> <p> </p> <p>Crystal Images:</p> <p>./movie/</p> </td> </tr> </tbody> </table> <p> </p>
Investigating Discontinuous X-ray Irradiation as a Damage Mitigation Strategy for [M(COD)Cl]2 Catalysts - Data
<div> <p>This Origin file contains the supporting processed data for all of the figures from the main paper from the publication 'Investigating Discontinuous X-ray Irradiation as a Damage Mitigation Strategy for [M(COD)Cl]2 Catalysts. </p> </div>
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)
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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.