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2,001 results for “x-rays”
Data for: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation
<p>Collection of data files used in the paper titled: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation.</p> <p>Additional dataset which covers the noise study CT scans and digital volume correlation noise studies can be found in DOI: <a href="http://www.doi.org/10.5281/zenodo.3352268">10.5281/zenodo.3361832</a></p> <p><strong>Data contains the following:</strong></p> <ul> <li>X-ray CT data for the interrupted direct shear tests of a soil sample containing a Willow plant. The first file is the specimen scanned unloaded, followed by seven incremental shear load steps to 20 mm shear displacement. Files are 8-bit unsigned and 1800x1800x1600px. Voxel resolution is 0.04642 mm. <ol> <li><strong>20180430_HUTCH_1839_DJB_willow_C_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_A_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_B_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_C_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_D_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_E_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_F_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_G_8-bit_1800x1800x1600.raw</strong></li> </ol> </li> <li>Direct shear vs. displacement data is presented in an Excel spreadsheet: <ul> <li><strong>All_load_data_with_reducing_area.xlsx</strong></li> </ul> </li> <li>Normal and shear strain DVC data which has been averaged and plotted against specimen depth is presented in an Excel spreadsheet: <ul> <li><strong>Average_slice_vs_depth_data_all_samples_all_loads3.xlsx</strong></li> </ul> </li> <li>CT scan metadata giving information to the scan settings, voxel resolution, etc. is contained in a .zip file which consists of .xtekct and .XML files generated from the Nikon CT scanner. <ul> <li><strong>CT_Scan_Metadata.zip</strong></li> </ul> </li> <li>Drawings and Solidworks CAD files of the direct shear test rig are contained within the .zip file. There are a number of parts to the assembly. The file SSSB1003-5.SLDASM contains the complete assembly of the direct shear rig and will help identify the part names. The folder CAD_Drawings contains pdf documents of the part drawings. <ul> <li><strong>Direct_Shear_Drawings_Solidworks_Files.zip</strong></li> </ul> </li> <li>Tabulated DVC data taken at 32x32x32px subset size is contained inside a .zip file. These consist of tab separated .dat files from unloaded (data_1.dat) through incremental load steps Load A, Load B... Load G, (data_2.dat, data_3.dat... data_8.dat). The structure of the .dat file contains columns of data with each column number corresponding to the following: (1) x, (2) y (3) z, (4) vx, (5) vy, (6) vz, (7) exx, (8) eyy, (9) ezz, (10) exy, (11) exz, (12) eyz, (13) volumetric strain, (14) is valid. Columns 1-3 are subset positions in mm, 4-6 are displacements in mm, 7-9 are normal strains, 10-12 are shear strains, 13 is volumetric strain and 14 is a binary value indicating if the subset is valid. <ul> <li><strong>DVC_Load_Data_Willow_C.zip</strong></li> </ul> </li> <li>Tabulated DVC data applied to the load step CT data at 32, 48, 64, 96, 128 pixels is presented in the .zip files containing .dat tab separated files. The structure of the .dat files is described in the previous bullet point. These files were used to construct the noise study applied to incrementally loaded CT data by sampling regions away from the shear zone where the strain signals are close to zero. <ul> <li><strong>DVC_Noise_Study_Data_Load_Steps_Willow_C.zip</strong></li> </ul> </li> <li>Processed noise study data which compares the effects of DVC subset size is presented in the .xlsx file. This file contains both the controlled noise experiment data (stationary, magnification and rigid body motion) and noise study data applied to incrementally loaded data using sampled regions away from the shear zone where the strain signals are close to zero. <ul> <li><strong>Willow_C_Processed_Noise_study2.xlsx</strong></li> </ul> </li> <li>Tabulated data which compares the local x displacement vs depth profile from DVC and direct measurement of root position is contained in the following Excel spreadsheet file: <ul> <li><strong>X_Displacement_Root_and_DVC_Comparison_Willow C.xlsx</strong></li> </ul> </li> </ul>
Supplementary data for article 'Estimating and abstracting the 3D structure of feline bones using neural networks on X-ray (2D) images'
<p>3D DICOM volumes (CT scans) of feline femora, PNGs generated from them as DRRs using MeVisLab, and STLs generated from the DICOM volumes with MIMICS or MeshLab. Software to work with these files can be found at http://doi.org/10.5281/zenodo.3829423</p>
Data Accompanying "Fracturing process of concrete under uniaxial and triaxial compression: insights from in-situ x-ray mechanical tests"
<p>These are the datasets analysed in "Fracturing process of concrete under uniaxial and triaxial compression: insights from <em>in-situ</em> x-ray mechanical tests" by Stamati <em>et. al.</em> (submitted on June 2020 in Cement and Concrete Research)</p> <p>The datasets contain the reconstructed x-ray 3D images of selected tests and the corresponding DVC analysis.</p> <p>Note that details regarding the DVC analysis can be found in the paper.</p> <ul> <li>Each folder contains a separate test; uniaxial compression ("<em>C-02"</em>) and triaxial compression at 5MPa ("<em>TX5-01"</em>), 10MPa ("T<em>X10-01</em>") and 15MPa ("<em>TX15-01</em>") confining pressures.</li> <li>For each test, folder 01 contains the first scan, where 01 is the reference state for the DVC analyis. Folder 01 also contains the image of the labelled largest aggregates used for the discrete DVC. Folrders 02-XX contain the scans at intermediate loading steps and the corresponding DVC analysis; "<em>reg</em>" contains the registration result in downscaled 2-binning images. "<em>ddic</em>" contains the discrete DVC computation. "<em>ldic</em>" contains the local DVC computation, before and after the merge and filtering of the grid and discrete DVC fields. "<em>strains</em>" contains the strain fields coming from the corrected merged DVC field.</li> <li>Folder "<em>stressStrain</em>" contains the stress-strain curves measured during the tests after subtracting the displacement corresponding to the loading system.</li> <li>Folder "<em>doubleScanUniaxial</em>" contains the two reconstructed images of the "repeated scan" of the uniaxial tests, from which the DVC measurement uncertainties were evaluated.</li> </ul>
Simultaneous three-dimensional vascular and tubular imaging of whole mouse kidneys with X-ray µCT
<p>µCT dataset of a mouse kidney injected with contrast agent XlinCA and scanned with 3.3 µm voxel size. Detailed sample preparation and image acquisition protocols are published as <a href="https://doi.org/10.1017/S1431927620001725">"Simultaneous three-dimensional vascular and tubular imaging of whole mouse kidneys with X-ray µCT"</a> in <em>Microscopy and Microanalysis</em>.</p> <p>Segmentations of the vascular and tubular lumina along with the renal tissue are provided as masks. The three different segmented features were combined into a single dataset and encoded as different gray values:</p> <p>0: Background<br> 51: Tubules<br> 204: Tissue<br> 255: Blood vessels</p> <p>The Supplemental Video features a computer graphics visualization of the segmented masks. Blood vessel lumina are rendered in red, tissue in transparent blue and tubular lumina in yellow.</p>
Image Dataset for 'Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning'
<p>Dataset used in the manuscript 'Digitally Deconstructing Leaves in 3D Using X-ray microcomputed Tomography and Machine Learning'. Please cite the paper presenting this dataset:</p> <p><strong>Citation:</strong> Théroux-Rancourt, G., M. R. Jenkins, C. R. Brodersen, A. McElrone, E. J. Forrestel, and J. M. Earles. 2020. Digitally deconstructing leaves in 3D using X-ray microcomputed tomography<strong> </strong>and machine learning. <em>Applications in Plant Sciences</em> 8(7): .</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>A 'Cabernet Sauvignon' grapevine (<em>Vitis vinifera</em> L.) leaf from a plant of the BOKU experimental vineyard in Tulln, Austria, was scanned using microCT at the Swiss Light Source. The original reconstructions of the scans are using the gridrec (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Gridrec_reconstruction_downsized.zip?versionId=28d98982-f69d-4eac-9dfa-efcc89c6823c">Gridrec_reconstruction_downsized.zip</a>) and the paganin, or phase-contrast, algortithm (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Phase_contrast_reconstruction_downsized.zip?versionId=bef3260d-2865-4c9b-b5e1-e692edefb691">Phase_contrast_reconstruction_downsized.zip</a>). To facilitate automated segmentation, the size of the image in the <em>x </em>and <em>y</em> dimensions have been halved, so that the size of the pixels is 0.325 µm in those dimensions, but 0.1625 µm in the <em>z</em> (slices) dimension.</p> <p>A binary image segmenting the leaf cells and the airspace for each gridrec and phase-contrast stacks are created, and both are combined together (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Binary_stack_for_local_thickness.zip?versionId=165e3938-b490-4e56-9c8e-a2084cb39d49">Binary_stack_for_local_thickness.zip</a>), a map of the local thickness is created (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Local_thickness_map.zip?versionId=ce0a7dc7-5e3f-44a4-8881-cf84b6efd87c">Local_thickness_map.zip</a>). This map gives information on the largest diameter of the pixels labeled as cells in the binary stack.</p> <p>Hand-labeled slices or ground truths were drawn on the following slices: 80, 140, 200, 260, 340, 400, 440, 540, 620, 740, 800, 860, 940, 1060, 1140, 1240, 1300, 1400, 1480, 1540, 1600, 1690, 1740, 1840 (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Hand_labelled_slices.tif?versionId=a21a13ac-fa47-4ef8-a903-ecc433787184">Hand_labelled_slices.tif</a>).</p> <p>Using the hand-labeled slices and the different images, a random-forest model was trained, which allowed to automatically segment the remaining slices of the stack (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Fullstack_Prediction_Example-6_training_slices-6_testing_slices.zip?versionId=02b69e65-da85-492e-9b72-9b2b3ccd085f">Fullstack_Prediction_Example-6_training_slices-6...</a>).</p> <p>The source code for the segmentation program is available <a href="https://github.com/plant-microct-tools/leaf-traits-microct/tree/master">here</a>, and the source code for the testing used in the paper is available <a href="https://github.com/plant-microct-tools/leaf-traits-microct/tree/nb-slices-eval">here</a>.</p>
Data from: Visualizing mineralization processes and fossil anatomy using synchronous synchrotron X-ray fluorescence and X-ray diffraction mapping
<p>Fossils, including those that occasionally preserve decay-prone soft-tissues, are mostly made of minerals. Accessing their chemical composition provides unique insight into their past biology and/or the mechanisms by which they preserve, leading to a series of developments in chemical and elemental imaging. However, the mineral composition of fossils, particularly where soft-tissues are preserved, is often only inferred indirectly from elemental data, while X-ray diffraction that specifically provides phase identification received little attention. Here, we show the use of synchrotron radiation to generate not only X-ray fluorescence elemental maps of a fossil, but also mineralogical maps in transmission geometry using a two-dimensional area detector placed behind the fossil. This innovative approach was applied to millimetre-thick cross-sections prepared through three-dimensionally preserved fossils, as well as to compressed fossils. It identifies and maps mineral phases and their distribution at the microscale over centimetre-sized areas, benefitting from the elemental information collected synchronously, and further informs on texture (preferential orientation), crystallites size and local strain. Probing such crystallographic information is instrumental in defining mineralization sequences, reconstructing the fossilization environment and constraining preservation biases. Similarly, this approach could potentially provide new knowledge on other (bio)mineralization processes in environmental sciences. We also illustrate that mineralogical contrasts between fossil tissues and/or the encasing sedimentary matrix can be used to visualize hidden anatomies in fossils.</p>
GPNp and GPNo glass microluminescence measurement for different X-ray doses and probed at different depth.
<p><strong>Silver-doped phosphate glass - Photosensitivity - Ionizing radiation</strong></p> <p><em><strong>- Glass composition of GPNp: </strong>31% P2O5, 20.6% Ga2O3, 46.4% Na2O, 2% Ag2O</em></p> <p><em><strong>- Glass composition of GPNo: </strong>56% P2O5, 28% Ga2O3, 14% Na2O, 2% Ag2O</em></p> <p>The data presented have been corrected for instrument function. Measurement under excitation at 405 nm (100 mW, TEM00, OBIS COHERENT) - LABRAM 800-HR instrument - Micro objective 100x NA 0.9.</p> <p><strong>-</strong><strong> Measurements performed by</strong> : Théo Guérineau, - Contribution of Anna Vedda & Francesca Cova (University of Milano-Bicocca - Milan, Italy) for glass irradiation</p> <p><strong>-</strong><strong> Measurement Date: </strong>February 2018 at ICMCB, Bordeaux, France.</p>
The anisotropy in the optical constants of quartz crystals for soft X-rays
<p>The refractive index of a y-cut SiO2 crystal surface is reconstructed from polarization dependent soft X-ray reflectometry measurements in the energy range from 45 eV to 620 eV. In the datasets the 1-delta and beta values for the (100) and (001) direction of the SiO2 crystal for the different energies are provided.</p>
X-ray structure ensemble refinement of the second bromodomain of Pleckstrin homology domain interacting protein (PHIP) (space group P21212)
<p>X-ray structure ensemble refinement of the second bromodomain of Pleckstrin homology domain interacting protein (PHIP) (space group P21212). Raw diffraction images are available on Zenodo: 10.5281/zenodo.4086066. A single conformer model was deposited in the Protein Data Bank under accession code <a href="https://www.ebi.ac.uk/pdbe/entry/pdb/7AV8">7AV8</a>. Refinement was carried out with <a href="https://www.phenix-online.org/documentation/reference/ensemble_refinement.html">phenix.ensemble_refinement</a> and the repository contains all input and output files:</p> <p>Input:</p> <ul> <li>mx8421v63_xPHIPAx1521_free.mtz</li> <li>refine9.pdb</li> </ul> <p>Output:</p> <ul> <li>PHIPA-P21212_ensemble_refinement_ensemble.geo</li> <li>PHIPA-P21212_ensemble_refinement_ensemble.pdb</li> <li>PHIPA-P21212_ensemble_refinement_ensemble.mtz</li> <li>PHIPA-P21212_ensemble_refinement_ensemble.log</li> </ul>
Improved small-angle x-ray scattering of nanoparticle self-assembly using a cell with a flat liquid surface
<p>One important way of forming nanostructures entails the assembly of nanoparticle (NP) monolayers at a liquid surface. Probing this assembly of 11.8-nm-diameter iron oxide NPs by small-angle x-ray scattering (SAXS) is studied using cells with walls at angles designed to significantly reduce the size of the meniscus. This enables the collection of much larger signals in the SAXS images of ordered arrays of NPs at liquid/gas interfaces, as is needed for kinetics studies and x-ray exposure minimization, along with the observation of extremely high degrees of order. Meniscus flattening and improved signal collection are demonstrated for the assembly of ordered arrays of iron oxide NP monolayers at a diethylene glycol surface.</p>
Dataset: Aberration characterisation of X-ray optics using multi-modal ptychography and a partially coherent source
<p>These are the ptychography datasets used for the publication "Aberration characterisation of X-ray optics using multi-modal ptychography and a partially coherent source". File are in the HDF format and contain a number of datasets detailed below.</p> <p>If you require more information, please contact the corresponding author of the publication, at thomas.moxham@eng.ox.ac.uk</p> <p>Raw files contain: diffraction intensities, scanning positions in millimeters</p> <p>Recon files contain: fourier error, complex probe function, complex object function, recon pixel size, energy</p> <p>raw_siemens_star_be_lens.hdf, raw_fourier_ring_correlation.hdf, merlin_medipix_detector_mask.hdf, recon_multi-modal_probe.hdf, recon_multi-modal_object.hdf, recon_fourier_ring_correlation.hdf</p>
Data from Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory and machine learning. Parts I and II.
<p>Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning; Parts I and II.</p> <p>This data-set is published in Refs. [1-2] and it is now made openly accessible. The data-set consists of computational X-ray spectroscopy fingerprints of plain and functionalized amorphous carbon. This data can be used in interpretation of experimental spectroscopy data (XAS and XPS). The spectra are averages of certain atomic environments, that are described in the publications. Standard deviation is included in the third column. Please, feel free to use the data-set, and if you do so, remember to cite Refs. [1-2] and this source. If there are any questions, please contact the corresponding author. </p> <p> </p> <p>[1] A. Aarva, V. L. Deringer, S. Sainio, T. Laurila, andM. A. Caro, “Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning. Part I: Fingerprint spectra,” Chem. Mater. 31, 9243–9255 (2019).</p> <p>[2] A. Aarva, V. L. Deringer, S. Sainio, T. Laurila, andM. A. Caro, “Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning. Part II: Quantitative fitting of spectra,” Chem. Mater. 31, 9256–9267(2019).</p> <p> </p> <p>Funding and resources for the work are acknowledged as follows:</p> <p>Funding from the Academy of Finland (project no.285526) and the computational resources provided for this project by CSC – IT Center for Science are gratefully acknowledged. M. A. C. acknowledges personal funding from the Academy of Finland under project no. 310574.V. L. D. acknowledges a Leverhulme Early Career Fellowship and support from the Isaac Newton Trust. M. A. C.and V. L. D. are grateful for travelling support from the HPC-Europa3 program under the auspices of the European Union’s Horizon 2020 framework (grant agreement no. 730897). Use of the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under contract no. DE-AC02-76SF00515. S. S. acknowledges personal funding from Instrumentarium Science Foundation and the Walter Ahlström Foundation.</p> <p> </p>
Data from: X-ray microtomography for ant taxonomy: an exploration and case study with two new Terataner (Hymenoptera, Formicidae, Myrmicinae) species from Madagascar
We explore the potential of x-ray micro computed tomography (μCT) for the field of ant taxonomy by using it to enhance the descriptions of two remarkable new species of the ant genus Terataner: T. balrog sp. n. and T. nymeria sp. n.. We provide an illustrated worker-based species identification key for all species found on Madagascar, as well as detailed taxonomic descriptions, which include diagnoses, discussions, measurements, natural history data, high-quality montage images and distribution maps for both new species. In addition to conventional morphological examination, we have used virtual reconstructions based on volumetric μCT scanning data for the species descriptions. We also include 3D PDFs, still images of virtual reconstructions, and 3D rotation videos for both holotype workers and one paratype queen. The complete μCT datasets have been made available online (Dryad, https://datadryad.org) and represent the first cybertypes in ants (and insects). We discuss the potential of μCT scanning and critically assess the usefulness of cybertypes for ant taxonomy.
Simultaneous X-ray diffraction from multiple single crystals of macromolecules
<p>X-ray diffraction datasets from the<br /> publication: K. S. Paithankar, H. O. Sørensen, J. P. Wright, S. Schmidt, H. F. Poulsen and E. F. Garman*</p> <p>Acta Cryst. (2011). D67, 608-618 doi:10.1107/S0907444911015617</p> <p>The potential in macromolecular crystallography for using multiple crystals to collect X-ray diffraction data simultaneously from assemblies of up to seven crystals is explored. The basic features of the algorithms used to extract data and their practical implementation are described. The procedure could be useful both in relation to diffraction data obtained from intergrown crystals and to alleviate the problem of rapid diffraction decay arising from the effects of radiation damage.</p>
Crystallization and X-ray diffraction studies of a complete bacterial fatty-acid synthase type I
<p>These are X-ray diffraction data from the publication<br> Enderle, M.E, McCarthy, A, Paithankar, K. S, and Grininger, M</p> <p>Crystallization and X-ray diffraction studies of a complete bacterial fatty-acid synthase type I.</p> <p>Acta Crystallogr F Struct Biol Commun. 2015 Nov;71(Pt 11):1401-7</p> <p>CC-BY-SA license</p> <p>MD5SUMS</p> <p>a774aabcd316b5b200ef5c08b109ba9a crystal-form-II_part-1.tar.lzma</p> <p>3596da75621648cc0ac5ee84b26deab0 crystal-form-II_part-2.tar.lzma</p> <p>8a5410ce3178c814d7a127491e68ca0a crystal-form-I_part-1.tar.lzma</p> <p>259614e1b2cfe089141fb7baa846af7e crystal-form-I_part-2.tar.lzma</p>
X-ray diffraction images for DPF3 tandem PHD fingers co-crystallized with an acetylated histone-derived peptide
<p>This submission includes a tar archive of bzipped diffraction images recorded with the ADSC Q315r detector at the Advanced Photon Source of Argonne National Laboratory, Structural Biology Center beam line 19-ID. Relevant meta data can be found in the headers of those diffraction images.</p> <p>Please find below the content of an input file XDS.INP for the program XDS (Kabsch, 2010), which may be used for data reduction. The "NAME_TEMPLATE_OF_DATA_FRAMES=" item inside XDS.INP may need to be edited to point to the location of the downloaded and untarred images.</p> <p>!!! Paste lines below in to a file named XDS.INP</p> <p>DETECTOR=ADSC MINIMUM_VALID_PIXEL_VALUE=1 OVERLOAD= 65000<br /> DIRECTION_OF_DETECTOR_X-AXIS= 1.0 0.0 0.0<br /> DIRECTION_OF_DETECTOR_Y-AXIS= 0.0 1.0 0.0<br /> TRUSTED_REGION=0.0 1.05<br /> MAXIMUM_NUMBER_OF_JOBS=10<br /> ORGX= 1582.82 ORGY= 1485.54<br /> DETECTOR_DISTANCE= 150<br /> ROTATION_AXIS= -1.0 0.0 0.0<br /> OSCILLATION_RANGE=1<br /> X-RAY_WAVELENGTH= 1.2821511<br /> INCIDENT_BEAM_DIRECTION=0.0 0.0 1.0<br /> FRACTION_OF_POLARIZATION=0.90<br /> POLARIZATION_PLANE_NORMAL= 0.0 1.0 0.0<br /> SPACE_GROUP_NUMBER=20<br /> UNIT_CELL_CONSTANTS= 100.030 121.697 56.554 90.000 90.000 90.000<br /> DATA_RANGE=1 180<br /> BACKGROUND_RANGE=1 6<br /> SPOT_RANGE=1 3<br /> SPOT_RANGE=31 33<br /> MAX_CELL_AXIS_ERROR=0.03<br /> MAX_CELL_ANGLE_ERROR=2.0<br /> TEST_RESOLUTION_RANGE=8.0 3.8<br /> MIN_RFL_Rmeas= 50<br /> MAX_FAC_Rmeas=2.0<br /> VALUE_RANGE_FOR_TRUSTED_DETECTOR_PIXELS= 6000 30000<br /> INCLUDE_RESOLUTION_RANGE=50.0 1.7<br /> FRIEDEL'S_LAW= FALSE<br /> STARTING_ANGLE= -100 STARTING_FRAME=1<br /> NAME_TEMPLATE_OF_DATA_FRAMES= ../x247398/t1.0???.img</p> <p>!!! End of XDS.INP</p> <p> </p> <p> </p>
Experimental data for the publication: "Evaluating scintillator performance in time-resolved, hard X-ray studies at synchrotron light sources"
<p>In accordance with the expectations outlined in <em><strong>Clarifications of EPSRC expectations on research data management</strong></em> (09/10/14) this data has been made publicly available to complement the open access publication "Evaluating scintillator performance in time-resolved, hard X-ray studies at synchrotron light sources". </p> <p>There are six data sets, corresponding to the six experimental data sets presented in the article. In each data set, which may be identified by their file names and reference to the article, the 1st column is the RF trigger - to - ICCD exposure delay in [ns], and the second column in the intensity recorded on the ICCD in [counts]. This intensity accounts for any online and offline processing outlined in the article, such as on-CCD exposures, dark frame correction etc. </p>
Low dose, high multiplicity thermolysin X-ray diffraction data from Diamond Light Source beamline I03
<p>Low dose, high multiplicity X-ray diffraction data recorded from a thermolysin crystal prepared according to standard protocols as part of ongoing research. The data were recorded with low transmission to ensure minimal radiation damage, with the side-effect that the individual reflections are exceedingly weak even at low resolution, and the majority of background pixels have no counts.</p>
Atomic resolution X-ray crystal structure of cisplatin bound to hen egg white lysozyme stored for 5 years ‘on the shelf’
<p>These are the raw diffraction images for crystals 1 and 2 underpinning PDBe code 5LXW.</p>
High resolution X-ray diffraction images for yeast 5-aminolevulinic acid dehydratase complexed with levulinic acid.
<p>X-ray diffraction images collected at DESY Hamburg in June 1998 using beamline BW7B. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.