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2,001 results for “X-Ray”
IODP Expedition 391 X-ray fluorescence (XRF)
Elemental peak intensities in section halves were measured by an Avaatech X-ray fluorescence (XRF) Core Scanner postexpedition. Each measurement position may be measured at multiple XRF conditions in order to excite and measure specific ranges of elements (e.g., 10 kV and no filter for light elements). Peak intensity changes (concentrations not provided) are then used to help recognize and define major chemostratigraphic units without the need for destructive sampling. Data are presented in comma-delimited (CSV) files by section and by energy/instrumental conditions.
IODP Expedition 397T 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).
Extracted Source Properties Catalog for "Monitoring the X-ray Variability of Bright X-ray Sources in M33"
<p>Supplemental data to the article "Monitoring the X-ray Variability of Bright X-ray Sources in M33" accepted for publication in ApJ. Contains all extracted source properties for the 56-source final catalog, including single-ObsID extractions and merged values. See ReadMe for column descriptions and additional comments.</p>
ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset
<p>ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 2 consist of two folders with 300 images in each of them as well as annotations. </p> <p>ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge. The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations, depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD. </p> <p>The dataset structure is as follows: top-level directories "syntax" and "stenosis" contain files for the two dataset objectives, namely: i) vessel branch classification according to the SYNTAX methodology; and ii) stenosis detection. Inside both directories, there are 3 subsets of the dataset, such as "train", "val", and "test". Inside each of those folders, there are 2 lower-level directories - "images", and "annotations". Inside the "images" folder there are images in ".png" format, extracted from DICOM recordings. The "annotations" folders contain single ".JSON" files, which are named in correspondence to the objective, i.e. "train.JSON", "val.JSON", and "test.JSON".</p> <p>The structure of ".JSON" contains three top-level fields: "images", "categories", and "annotations". The "images" field contains the unique "id" of the image in the dataset, its "width" and "height" in pixels, and the "file_name" sub-field, which contains specific information about the image. The "categories" field contains a unique "id" from 1 to 26, and a "name", relating it to the SYNTAX descriptions. The "annotations" field contains a unique "id" of the annotation, "image_id" value, relating it to the specific image from the "images" field, and a "category_id" relating it to the specific category from the "categories" field. The "segmentation" sub-field contains coordinates of mask edge points in "XYXY" format. Bounding box coordinates are given in the "bbox" field in the "XYWH" format, where the first 2 values represent the x and y coordinates of the left-most and top-most points in the segmentation mask. The height and width of the bounding box are determined by the difference between the right-most and bottom-most points and the first two values. Finally, the "area" field provides the total area of the bounding box, calculated as the area of a rectangle.</p> <p> </p> <p>The corresponding Dataset Article will be provided later. </p> <p>[1] Syntax score segment definitions. https://syntaxscore.org/index.php/tutorial/definitions/14-appendix-i-segment-definitions</p>
IODP Expedition 383 Portable X-ray fluorescence (p-XRF)
Energy-Dispersive X-Ray Fluorescence (ED-XRF) is a rapid, non-destructive technique for determining qualitative and quantitative changes in chemical composition. Aboard the JOIDES Resolution, pXRF is used for measuring points on section halves, rock pieces, and sometimes powders. Spots are typically irradiated at multiple conditions to excite and measure a wide range of elements. The peak intensity changes (we do not provide concentrations) are then used to help recognize and define major chemo-stratigraphic units without the need for destructive sampling.
Data products and software for `X-ray diagnostics of Cassiopeia A's "Green Monster": evidence for dense shocked circumstellar plasma`
<div> <h2>Data Reproduction Package for the publication ‘X-ray diagnostics of Cassiopeia A’s “Green Monster”: evidence for dense shocked circumstellar plasma’</h2> </div> <div> <h3>Authors: Jacco Vink, Manan Agarwal, Patrick Slane, Ilse De Looze, Dan Milisavljevic, Daniel Patnaude, and Tea Temim.</h3> </div> <div> <h3>Link to paper: <a href="https://doi.org/10.3847/2041-8213/ad2fc5">https://doi.org/10.3847/2041-8213/ad2fc5</a> </h3> <p> </p> </div> <div> <h4>This package was prepared by Jacco Vink and Manan Agarwal (University of Amsterdam)</h4> </div> <div> <h3>Summary</h3> </div> <div> <p>This data reproduction package contains the data files in FITS format used to<br>generate the figures in the paper. The data files concern the revised manuscript, which incorporates changes made in response to the journal’s referee report.</p> </div> <div> <p>The paper is based on Chandra X-ray Observatory (CXO) data of Cassiopeia A taken in 2004. The raw archival data used, maintained by the Chandra Data Archive, can be retrieved using the following DOI link: <a href="https://doi.org/10.25574/cdc.209">https://doi.org/10.25574/cdc.209</a>.</p> </div> <div> <p>Additional James Webb Space Telescope (JWST) data are stored at the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute. The data used in the paper can be downloaded through DOI link <a href="https://doi.org/10.17909/szf2-bg42">https://doi.org/10.17909/szf2-bg42</a>.</p> </div> <div> <p>The data produced from the above raw data are stored in the files:</p> </div> <div> <ul> <li>green_monster_image_data.tar.gz</li> <li>spectral_files_and_models.tar.gz</li> <li>imaging_and_pca_code.tar.gz</li> <li>green_monster_pca_input_output.tar.gz</li> </ul> <p>The repository contains JWST/MIRI mosaics of Cassiopeia A which are described in detail in the paper "A JWST Survey of the Supernova Remnant Cassiopeia A", by D. Milisavljevic, T. Temim, I. De Looze, et al.; see https://arxiv.org/abs/2401.02477, to be published in ApJ letters.<br> </p> </div>
X-ray diffraction dataset for experimental noise filtering
<p>X-ray diffraction data set for the training of noise filtering algorithms. The data set contains groups of low- and high-counting statistics pairs. The sampling times are mostly 1 (20) seconds for low (high) counting data. Three files in HDF5 format are provided, corresponding to a training, validation and test data set. Each data group contains sequences of 41 consecutive frames, corresponding to a scan along the reciprocal h-direction. Next to the raw data, sampling times and monitor values are included. The test data set additionally contains denoised low-count frames obtained from a pre-trained neural network.</p> <p>Additionally, files containing the trained model weights are included for two different architectures described in the main article (10.1038/s42256-024-00790-1).</p> <p>The data has been recorded on a La<sub>1.88</sub>Sr<sub>0.12</sub>CuO<sub>4</sub> single crystal at the beamline P21.1 at the PETRA III storage ring at DESY in Hamburg, Germany. The scattering intensities were recorded using Dectris Pilatus 100K CdTe detector. The diffractometer was operated with 100 keV photons and the sample was cooled to T ~ 30 K. The data contains different signals such as weak 2D charge density wave order, fundamental Bragg peaks, powder lines, spurions and dead pixels.</p>
IODP Expedition 378 Portable X-ray fluorescence (p-XRF)
Energy-Dispersive X-Ray Fluorescence (ED-XRF) is a rapid, non-destructive technique for determining qualitative and quantitative changes in chemical composition. Aboard the JOIDES Resolution, pXRF is used for measuring points on section halves, rock pieces, and sometimes powders. Spots are typically irradiated at multiple conditions to excite and measure a wide range of elements. The peak intensity changes (we do not provide concentrations) are then used to help recognize and define major chemo-stratigraphic units without the need for destructive sampling.
IODP Expedition 378 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).
Alpha-Galactosaminidase family GH191 protein from Environmental sample (99.2% identity to Myxococcus fulvus enzyme): X-ray diffraction images
<p><span>This submission includes a zip archive of diffraction images recorded with the Dectris EIGER X 9M detector at the DIAMOND beamline I04-1. The model of the crystal structure and associated information can be found in the Protein Data Bank entry 9EP5. This is a case of crystal pathology – partial disorder. The model has C 2 2 21 symmetry and two molecules per asymmetric unit with occupancies 1 and 1/3. The molecule with partial occupancy overlaps with a symmetry related molecule.</span></p>
IODP Expedition 367 X-ray fluorescence (XRF)
Elemental peak intensities in section halves were measured by an Avaatech X-ray fluorescence (XRF) Core Scanner postexpedition. Each measurement position may be measured at multiple XRF conditions in order to excite and measure specific ranges of elements (e.g., 10 kV and no filter for light elements). Peak intensity changes (concentrations not provided) are then used to help recognize and define major chemostratigraphic units without the need for destructive sampling. Data are presented in comma-delimited (CSV) files by section and by energy/instrumental conditions.
IODP Expedition 367 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).
Multi-resolution X-Ray micro-CT images of Bentheimer Sandstones
<p>This dataset consists of multi-resolution X-Ray micro-tomography images of two Bentheimer sandstone rock cores. The rock cores were first used experimentally in [1] with further modelling in [2]. This new dataset is used directly in the publication [3] - preprint available at https://arxiv.org/abs/2111.01270. </p> <p>The original dataset from [1] (of the same rock cores) is hosted on the BGS National Geoscience Data Centre, ID #130625 at dx.doi.org/10.5285/5f899de8-4085-4370-a45e-e613f27e8f1d and there is also a subvolume image dataset, for easier download available on the Digital Rocks Portal, project 229, DOI:10.17612/KT0B-SZ28 at digitalrocksportal.org/projects/229. </p> <p>The images provided herein are from two distinct Bentheimer rock cores -- core 1 and core 2. The cores have diameter, 12.35mm, lengths 73.2mm and 64.7mm, core-averaged porosities of 0.203 and 0.223 and permeabilities of 1.636D and 0.681D for core 1 and 2, respectively. Core 2 has a clear low permeability lamination occurring at 2/3 of the total core length, whereas core 1 has a general fining towards the outlet of the core creating a reduction in porosity [1].</p> <p>The images were acquired with a Zeiss Versa 510 X-Ray CT scanner. We acquired images of two sub volumes from each core, at locations 1/3rd (subvolume 1) and 2/3rds (subvolume 2) of the way along the core length, at resolutions of 2, 6 and 18 microns. We refer to the 2 micron images as high-resolution (HR), the 6 micron images as low-resolution (LR) and the 18 micron images as very-low-resolution (VLR). There are also super-resolution (SR) images created at 2 micron resolution from the LR images, using a deep-learning algorithm. There are also cubic interpolation images created from the LR image - these are labels bicubic. These have a resolution of 2 microns, and size equal to the HR and SR images. Details of the SR and LR Bicubic generation are found in [3]. The following scanning protocols were used for the direct imaging:</p> <p>2 micron images:<br> --We use a 4x microscope objective, an exposure time of 8s, 2x averaged binning, 9001 projections, a scan voltage of 80kV and a power of 7W. Each scan takes approximately 24 hours.</p> <p>6 micron images:<br> --We use a flat panel detector, an exposure time of 0.7s, 10x repeat frames, 1x averaged binning, 2401 projections, a scan voltage of 80kV and a power of 7W. The cone angle is 14.46 degrees and the fan angle is 22.2 degrees. Each scan takes approximately 1 hour.</p> <p>18 micron images:<br> --We use a 0.4x microscope objective, an exposure time of 1s, 10x repeat frames, 1x averaged binning, 2401 projections, a scan voltage of 80kV and a power of 7W. The cone angle is 12.65 degrees and the fan angle is 12.65 degrees. Each scan takes approximately 2 hours.</p> <p>We present 4 sets of the images with different levels of processing. All images are mutual registered to each other. Each image filename has a Core#_Subvol#_resolution identifier, either with the actual resolution (e.g. 6) or the short form (e.g. LR). The following name endings are used</p> <p>(1) - '_16bit_LE.raw'. These are the .raw images of little-endian format. Preceding this filename is also the cubic image side length in voxels, e.g. _75cube. 12 images in total.</p> <p>(2) - '_16bit_LE_normalised.raw'. These are the .raw images of little-endian format with normalised greyscale values following the procedure in [1]. Preceding this filename is also the cubic image side length in voxels, e.g. _75cube. 12 images in total.</p> <p>(3) - 'Core1_Subvol1_HR' etc. These are the .tiff images of (2) above, which have been converted to 8 bit. Includes bicubic interpolation images and SR images, but no 16 micron images, since these were not used in the analysis of [3]. 16 images in total. </p> <p>(4) - 'Core1_Subvol1_HR_filtered' etc. These are the .tiff images from (3) above, which have filtered using non-local means filtering. More details are found in [3]. Note there are no SR images here since they are already essentially filtered, and included in (3) above. 12 images in total.</p> <p><br> <strong>References</strong><br> <br> [1] Jackson, S.J., Lin, Q. and Krevor, S. 2020. Representative Elementary Volumes, Hysteresis, and Heterogeneity in Multiphase Flow from the Pore to Continuum Scale. Water Resources Research, 56(6), e2019WR026396</p> <p>[2] Zahasky, C., Jackson, S.J., Lin, Q., and Krevor, S. 2020. Pore network model predictions of Darcy‐scale multiphase flow heterogeneity validated by experiments. Water Resources Research, 56(6), e e2019WR026708.</p> <p>[3] Jackson, S.J, Niu, Y., Manoorkar, S., Mostaghimi, P. and Armstrong, R.T. 2021. Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling. Under review, preprint available at https://arxiv.org/abs/2111.01270 </p>
The detection of radio emission from known X-ray flaring star EXO 040830−7134.7
<p>This is the radio light curve of known X-ray flaring star EXO 040830−7134.7 observed by MeerKAT as part of ThunderKAT. These data are part of a publication in the Monthly Notice of the Royal Astronomical Society (Driessen et al., Accepted 2021 November 25. Received 2021 November 25; in original form 2021 August 25).</p> <p>The light curve is from the full-time-integration, full-frequency-integration images of VW Hyi, as processed by the LOFAR Transients Pipeline (<a href="https://tkp.readthedocs.io/en/latest/introduction.html">TraP</a>).</p> <p>The columns in the file are:</p> <ul> <li>mjd: the modified Julian Date (MJD) of the observation. The MJD is given by MJD=JD-2400000.5 where JD is the Julian Date</li> <li>f_int_Jy: the integrated flux density of the source in Jansky (Jy) determined by the LOFAR TraP</li> <li>f_int_err_Jy: the uncertainty on f_int_Jy in Jansky determined by the LOFAR TraP</li> <li>freq_eff_Hz: the effect frequency in Hertz (Hz) as determined by the LOFAR TraP</li> <li>taustart_ts: the ISO 8601 time of the observation in Coordinated Universal Time (UTC)</li> </ul> <p>The files were made using the Pandas package, so we recommend Python users load them using</p> <pre><code>import pandas as pd pd.read_csv(filename, comment='#')</code></pre> <p>If you use the data shared here please ensure that you cite the MNRAS paper (Driessen at al. 2021) and the Zenodo DOI: 10.5281/zenodo.5084298.</p> <p>The MeerKAT telescope is operated by the South African Radio Astronomy Observatory, which is a facility of the National Research Foundation, an agency of the Department of Science and Innovation.<br> LND acknowledges support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 694745).</p>
Dataset: Environment effects on X-ray absorption spectra with quantum embedded real-time Time-dependent density functional theory approaches
<p>This dataset collects the outputs from real-time TDDFT simulation of X-ray absorption of halides in model systems, using the frozen density embedding (FDE) and block-orthogonalized Manby-Miller embedding (BOMME), as well as processing tools and scripts used to carry out the calculations.</p>
Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455
<p>This is a basic reproduction package for the paper "Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455" by <a href="https://doi.org/10.1093/mnras/stab2202">N. Degenaar et al. (2021)</a>. It provides reduced data products, simulated data 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>
Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot
<p>Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot</p> <blockquote> <p><strong>X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil </strong>- [<a href="https://doi.org/10.1002/ppj2.20036">https://doi.org/10.1002/ppj2.20036</a>]</p> </blockquote> <p>The ZIP file contains:</p> <ul> <li><code>MCT1_Rcode.R</code> - Statistics script for candidate single-timepoint experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.</li> <li><code>MCT1... .csv</code> - 3 CSV data files required by the R script.</li> <li><code>MCT2_Rcode.R</code> - Statistics script for time-series experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.</li> <li><code>MCT2... .csv</code> - 3 CSV data files required by the R script.</li> <li><code>R_RooThProcessing.R</code> - R code for aggregating root traits from RooTh software.</li> <li><code>Modelling folder</code> - OpenSimRoot with model parameters and root data used in manuscript.</li> </ul>
Data for "Measurement of temperature induced X-ray tube transmission target displacements for dimensional computed tomography"
<p>Raw data used to create figures for the paper "Measurement of temperature induced X-ray tube transmission target displacements for dimensional computed tomography" <a href="https://doi.org/10.1016/j.precisioneng.2021.06.002">https://doi.org/10.1016/j.precisioneng.2021.06.002</a></p> <p>Data is available in tab delimited format (.txt) and in Excel (.xls).</p> <p> </p>
Single-crystal X-ray diffractometry data for a sample of [Cu(HF₂)(pyrazine)₂]PF₆ collected on beamline I19-2 at Diamond Light Source
<p>Single-crystal X-ray diffractometry data for a sample of [Cu(HF₂)(pyrazine)₂]PF₆.</p> <p>These data were collected at Diamond Light Source, on beamline I19 (experiments hutch 2), on 2022-01-30, and are particularly useful for testing data reduction routines. They are known to produce good merging statistics and final structure refinement.</p> <p>The sample was prepared as follows:<br> Ammonium hexafluorophosphate (NH₄PF₆) (0.310 g, 1.9 mmol), ammonium hydrogen difluoride ((NH₄)HF₂) (0.109 g, 1.9 mmol) and pyrazine (C₄H₄N₂) (0.300 g, 3.7 mmol) were dissolved in 5 mL of deionised water. The obtained colourless solution was slowly added to a blue solution of copper(II) nitrate prepared by dissolving copper(II) nitrate hemipentahydrate (Cu(NO₃)₂ · 2.5(H₂O)) (0.425 g, 1.8 mmol) in 5 mL of deionised water. The solutions were mixed in a plastic beaker at room temperature. The formation of blue crystals of [Cu(HF₂)(pyrazine)₂]PF₆ on the side of the beaker started after few seconds and continued for about 24 hours during which the sealed beaker was not moved.</p> <p>The sample was measured at room temperature and the illuminating beam had a wavelength of 0.4859 Å (25.52 keV).</p> <p>Beamline I19-2 at Diamond Light Source, a four-circle κ-geometry diffractometer (see <a href="https://onlinelibrary.wiley.com/doi/10.1107/97809553602060000936">[Kern 2019]</a>) with an undulator source, is described in <a href="https://doi.org/10.1107/S0909049512008801">[Nowell 2012]</a> but has since been upgraded to use a Dectris Eiger2 X 4M CdTe hybrid photon counting detector. The data are written in the <a href="https://manual.nexusformat.org/classes/applications/NXmx.html">NXmx variant</a> of the <a href="https://www.nexusformat.org/">NeXus format</a>, and so include metadata with a functionally complete description of the diffractometer.</p> <p>Inventory of data:</p> <ul> <li><strong><code>01_CuHF2pyz2PF6b_Phi.tar.xz</code></strong><br> A single 1750-image 350° φ rotation scan from -175° to 175° with 0.2° rotation per image, an exposure time of 0.1 s per image, ω = -90°, κ = 0° and 2θ = 0°.</li> <li><strong><code>02_CuHF2pyz2PF6b_2T.tar.xz</code></strong><br> A single 1750-image 350° φ rotation scan from -175° to 175° with 0.2° rotation per image, an exposure time of 0.1 s per image, ω = -90°, κ = 0° and 2θ = 20°.</li> <li><strong><code>03_CuHF2pyz2PF6b_P_O.tar.xz</code></strong><br> Two sequential rotation scans: <ul> <li><strong><code>CuHF2pyz2PF6b_P_O_01.nxs</code></strong><br> A 1750-image 350° φ scan from -175° to 175° with ω = -90°, κ = 0° and 2θ = 0°.</li> <li><strong><code>CuHF2pyz2PF6b_P_O_02.nxs</code></strong><br> A 600-image 120° ω scan from -125° to -5° with φ = -90°, κ = 45° and 2θ = 0°.</li> </ul> Both scans had 0.2° rotation per image and an exposure time of 0.1 s per image.</li> </ul> <p>The same sample was used for all these measurements. Throughout, the sample-to-detector distance was 85 mm and the beam was attenuated to 0.2% of its full intensity.</p> <p>For each rotation scan, the data comprise a single top-level NXmx-format NeXus file named <code><filename>.nxs</code>, one or more image files named <code><filename>_00000n.h5</code>, where <code>n</code> is a numeral, and a single detector metadata file named <code><filename>_meta.h5</code>. The NeXus file contains an HDF5 virtual data set that links to the data in the image file(s), and several HDF5 external links to data in the detector metadata file.</p> <p>For internal reference of Diamond Light Source staff, these data were collected as part of commissioning visit CM31144-1. Some file names and corresponding HDF5 link targets have been altered from their original names for consistency with the file contents.</p>
Simulated X-ray micro-computed tomography based particle tracking velocimetry dataset for validation purposes
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Validation dataset for micro-computed tomography based particle tracking velocimetry: a simulated micro-CT based velocimetry experiment with associated ground-truth particle trajectories</p> <p>- The ground truth trajectories were based on randomly dropping virtual particles in the pore space, and tracking their movement through a CFD-based velocity field (see below). The positions were calculated for the time corresponding to each radiograph of a micro-CT experiment. The folder "GroundTruthData" contains the locations of all particles at the central time of each micro-CT scan, as well as their radii. Check the associated readme file to read the data file.</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 7 time steps (70 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains an image of the pore space without particles, matching with the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 6 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
ScienceDex guides
Understand access before you commit
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.