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243 results for “X-ray tomography”
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 8-bit Sub-Volumes
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of each dataset is 500x1000x1000. Below is a summary of the pixel-sizes and associated datasets on Zenodo.</p> <blockquote> <p>Key:</p> <ul> <li>160695 = 0.3125 Micron = https://zenodo.org/records/13327692</li> <li>169066 = 0.8125 Micron = https://zenodo.org/records/13327682</li> <li>169067 = 1.625 Micron = https://zenodo.org/records/13327651</li> <li>169068 = 2.6 Micron = https://zenodo.org/records/12206815</li> </ul> </blockquote> <p>The purpose of this dataset is to provide an easy to download sub-volumes of the larger (>50GB) datasets in the above Zenodo entries.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p>
Slice-by-Slice X-ray Tomography dataset of Dog Toy
<p>This submission contains a dataset used in the paper</p> <p>"Ajinkya Kadu, Felix Lucka, and K. Joost Batenburg. "Single-shot Tomography of Discrete Dynamic Objects." <em>arXiv preprint <a href="https://arxiv.org/abs/2311.05269">arXiv:2311.05269</a></em> (2023)."</p> <p>The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT). To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector, which results in 956 detector pixel with an effective length of 149.6 <strong>μ</strong>m each. Between source and detector there is a rotation stage, upon which the sample was mounted. The sample that we imaged was a dog toy in a shape of a bone made of a rubber. The X-ray tube voltage was 90kV and a copper filter was used to block the low-energy part of the spectrum to limit beam-hardening artifacts. The source-to-detector distance was 487.9 mm, while the source-to-origin of the sample was 374.5 mm in a fan-beam geometry. We acquired 673 z-slices with 0.25 mm distance between slices. Further information about the technical details of X-ray CT can be found in the <a href="https://arxiv.org/abs/2311.05269">above paper</a> and in </p> <p>Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6"><em>Sci Data</em> <strong>10</strong>, 576 (2023)</a> or <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a></p> <p>The upload consists of two files, namely:</p> <ol> <li>GrayBone90kV4Filter.zip: contains the raw measurement data.</li> <li>GrayBone90kV4FilterPreprocessed.mat: contains preprocessed data to be used in the MATLAB script provided to do pseudo-dynamic tomography. It also contains reference reconstruction obtained via Filtered Back Projection (FBP) algorithm. </li> </ol> <p>In the Github repository <a href="https://github.com/ajinkyakadu/DynamicXRayCT">https://github.com/ajinkyakadu/DynamicXRayCT</a>, we provide the scripts to read and process the raw data. The Github repository also contains all the scripts to reconstruct the dynamic solution using advanced algorithms. Furthermore, the raw data formats are described in great details in <a href="https://www.nature.com/articles/s41597-023-02484-6">Kiss et al 2023</a> paper referenced above. </p>
Cross-sectional images from x-ray computed tomography (XCT) of conserved archaeological samples
<p>The repository contains cross-sections of 83 wood samples derived from X-ray computed tomography (CT) data. The samples are a part of the LEIZA reference collection, which were created within the framework of the project "Mass Finds in Archaeological Collections", which was funded by the "Kulturstiftung des Bundes" and the "Kulturstiftung der Länder" from 15.04.2008 to 31.12.2011 as part of the "Program for the Conservation and Restoration of Mobile Cultural Property" (KUR, see www.rgzm.de/kur).</p> <p>Around 10 years later, during the CuTAWAY project (ConservaTion And Wod AnalYses), the wood samples were digitized using an in-house laboratory X-ray CT system (Diondo d2, Germany) at HSLU with a nominal voxel size between 27 and 44 μm in order to analyse the structure of the interior. You can download the cross-sectional images of the data here. The 3D data acquisition was carried out during November 2019 - April 2021.</p> <p>The CuTAWAY project was funded by the German Research Association (DFG) and the Swiss National Science Foundation (SNSF) from 2019 to 2023 (CuTAWAY - Conservation and Wood Analyses, DFG - 416877131 and SNSF - 200021E_183684).</p>
Zinc Doped Zeolite 13X DIAD X-Ray Computed Tomography - 0.54 micron pixel size RAW
<p>This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the DIAD beamline at Diamond Light Source. Data is stored as a .nxs file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 0.54 microns. A script containing the savu process list and code used to perform the 3D reconstruction is provided.</p> <p>A detailed data descriptor pre-print can be found at https://arxiv.org/abs/2409.07322#</p> <p> </p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 43334_raw.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 1.625 micron pixel size
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 1.625 microns.</p> <p>This data is one of four resolutions obtained.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 169067_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X DIAD X-Ray Computed Tomography - 0.54 micron pixel size
<p>This repository contains processed data for the zinc-doped zeolite 13X sample imaged on the DIAD beamline at Diamond Light Source. Data is stored as a .nxs file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 0.54 microns. A script containing the savu process list and code used to perform the 3D reconstruction is provided.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 43334_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.325 micron pixel size
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 0.325 microns.</p> <p>This data is one of four resolutions obtained.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 169065_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.8125 micron pixel size
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 0.8125 microns.</p> <p>This data is one of four resolutions obtained.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 169066_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 2.6 micron pixel size
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 2.6 microns.</p> <p>This data is one of four resolutions obtained.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 169068_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
STEMPO - dynamic X-ray tomography phantom
<p>The Spatio-TEmporal Motor-Powered (<strong>STEMPO</strong>) phantom is a physical phantom designed for collecting dynamic X-ray tomography data. The dynamic part of the phantom is computer controlled allowing for wide variety of different measurements and sampling setups to be used. The primary goal is to help mathematical community test and validate novel dynamic tomography reconstruction methods.</p> <p>Detailed documentation of the phantom, the included data (volume 1 only) and some examples can be found on the related publication: <a href="https://doi.org/10.1007/978-981-97-6769-4_1">https://doi.org/10.1007/978-981-97-6769-4_1</a> (available as an arXiv preprint: <a href="http://arxiv.org/abs/2209.12471">http://arxiv.org/abs/2209.12471</a>).</p> <p>This data set can be appended with new data in the future. Current version (<strong>1.2</strong>) includes<br> <strong>Data - vol.1 (v1.0)</strong></p> <ul> <li>stempo_static_2d_b*.mat</li> <li>stempo_static_3d_b*.mat</li> <li>stempo_cont360_2d_b*.mat</li> <li>stempo_cont360_3d_b*.mat</li> <li>stempo_seq8x45_2d_b*.mat</li> <li>stempo_seq8x45_3d_b*.mat</li> <li>stempo_data_geometries.csv</li> </ul> <p> <strong>Data - vol.2 (added in v1.2)</strong></p> <ul> <li>stempo_seq8x180_2d_b*.mat</li> <li>stempo_seq8x180_3d_b*.mat</li> </ul> <p>where b* denotes downsampling or binning of the data by a factor of (4, 8, 16 or 32). These are 2D and 3D data collected from a <em>static</em> object for reference, or from a dynamic target in a <em>continuous</em> 360 projection scan or <em>sequence</em> of 8 rotations, each consisting of 45 or 180 projections (with <strong>seq8x45</strong> and <strong>seq8x180</strong> data respectively). Finally stempo_data_geometries.csv is a simple table containing the key parameters of the measurement geometry in text format. Note that the height of the phantom for volume 2 data is slightly different compared to volume 1, including the static scan (mostly relevant for comparing 3D reconstructions).</p> <p>In addition the data set contains</p> <p> <strong>Additional files</strong></p> <ul> <li>stempo_ground_truth_2d_b4.mat</li> </ul> <p>which is an approximation of the true motion obtained from a single static FBP reconstruction which has been interpolated to match the location of the moving block during the <em>cont360</em> and <em>seq8x45</em> scans. Finally there are</p> <p> <strong>Example algorithms</strong></p> <ul> <li>stempo_fbp_example.m</li> <li>stempo_fdk_example.m</li> <li>stempo_pdfp_wavelet_2d_example.m</li> <li>stempo_LplusS_2d_example.m</li> </ul> <p>which are short example algorithms of well know analytic (<em>FBP</em> and <em>FDK</em>) and iterative methods. <em>stempo_pdfp_wavelet_2d.m<strong> </strong></em>uses variational regularization and wavelet transform of the 2D + time object to reach a suitable solution. The codes are adapted from [<a href="https://doi.org/10.1088/1361-6501/aa9260">1</a>,<a href="https://doi.org/10.1088/1361-6420/ab9c15">2</a>]. <em>stempo_LplusS_2d_example.m</em> attempts to split the reconstruction into low-rank component <em>L</em> and a sparse dynamic component <em>S</em>. This code is adapted from [<a href="https://doi.org/10.1002/mrm.25240">3</a>]. These are meant to give users ideas how the data can be used in different applications to match the requirements of different methods.</p> <p>Easiest way to utilize the data is with the <a href="http://www.astra-toolbox.com/">ASTRA Toolbox</a> and the <a href="https://github.com/Diagonalizable/HelTomo">HelTomo Toolbox</a>. Some of the example codes also require <a href="https://www.cs.ubc.ca/labs/scl/spot/">Spot Linear Operator Toolbox</a> (highly recommended) and the Wavelet Toolbox. However none of these are mandatory and any method (including programming languages other than MATLAB) are fine as long as the measurement geometry is respected.</p> <p><br>The author is supported by the Emil Aaltonen Foundation junior researcher grant no. 200029 and the Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish Academy of Science and Letters. The author also acknowledges the support of Academy of Finland through the Finnish Centre of Excellence in Inverse Modelling and Imaging 2018–2025, decision number 312339. Finally the author would like to thank E. Heikkilä, T. Heikkilä, A. Meaney and F.S. Moura for all their technical expertise and help in developing, building and imaging the mechanism.</p> <p>The author also thanks O. Tapaninen for helping measure the data for <strong>vol.2</strong>.</p>
Synchrotron X-ray Computed Tomography scan of a wasp
<h4>Contents:</h4><ul><li><i>bee_yazeed-20231001T170032.h5</i> - SXCT scan of a wasp performed at beamline <a href="https://www.sesame.org.jo/beamlines/beats">ID10-BEATS</a> of SESAME.</li><li><i>SESAME_wasp_yazeed.avi -</i> 3D video rendering of phase-contrast CT reconstruction of <i>bee_yazeed-20231001T170032</i>. The dataset was reconstructed using <a href="https://github.com/gianthk/alrecon/tree/master">alrecon</a>. The video was created using ORS Dragonfly.</li></ul><h4>H5 dataset information:</h4><ul><li>Raw experimental data (sinogram, flat fields and dark fields) and metadata are stored in a common .H5 file.</li><li>The HDF5 file is organized hierarchically following the <a href="https://dxfile.readthedocs.io/en/latest/">Scientific Data Exchange (DXfile)</a> community standard.</li></ul><h4>How to reconstruct:</h4><ul><li>You can use <a href="http://www.silx.org/">Silx</a> to read and explore the .H5 dataset.</li><li>The file can be read within Python using the <a href="https://dxchange.readthedocs.io/en/latest/">DXChange</a> package.</li><li>See the <a href="https://beats.readthedocs.io/reconstruction.html">ID10-BEATS beamline user guide</a> for a detailed description on how to process and reconstruct the scan.</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>
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>
X-ray micro-computed tomography based X-ray particle tracking velocimetry dataset in a porous glass filter
<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>Dataset of a micro-computed tomography based particle tracking velocimetry experiment performed on a glass filter (ROBU P0; sample size 4 mm diameter by 1 cm).</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 59 time steps (35 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 a high-quality pre-scan taken before the main experiment, which was registered and resampled to 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 20 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
X-ray micro-computed tomography based particle tracking velocimetry dataset in a sandpack
<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>Dataset of a micro-computed tomography based particle tracking velocimetry experiment performed on a sand pack (grainsize 500-710 µm; sample size 4 mm diameter by 2 cm).</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 79 time steps (35 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 a high-quality pre-scan taken before the main experiment, which was registered and resampled to 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 20 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
HandCT: hands-on computational dataset for X-Ray Computed Tomography
<p>HandCT is a computational dataset to train machine-learning models for X-Ray Computed Tomography (CT). It consists of a meshed hand model, of which pose and anatomical properties are computed at run-time from a script. As such, it is an accurate modeling of anatomical phantoms of only 1.35 mB, and reproducibility is ensured using random seeds. It allows the user to have full control over the imaging chain, from projection to reconstruction, and over the X-Ray interaction with the different parts of the model by a simple variable editing. This open-source solution relies on the freeware Blender for the modelling and Python for the computations. The first deals with modelling, rigging and deformations, whilst the later ensures transformations such as scaling, translation, or else forward projection. This dataset can be used to train and evaluate regularisation procedures for low-energy, dual-energy and scarce-view CT.</p>
Code and Data from: Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets
<p>This record contains code and data for segmentation using a three-dimensional level-set method, written by Amy Tabb in C++. The record also contains two datasets of root systems in media imaged with X-Ray CT, and the results of running the code on those datasets. The code will also perform a pre-processing task in three-dimensional image sets, and a dataset for that purpose is included as well. This work is a companion to the paper : "Segmenting root systems in X-ray computed tomography images using level sets" (WACV 2018) by the authors or this record, and and open-access version of the paper is here -- https://arxiv.org/abs/1809.06398 . The code is also available from Github: https://github.com/amy-tabb/tabb-level-set-segmentation , using a DOI and stable releases https://doi.org/10.5281/zenodo.3344906.</p> <p>Format of the data:</p> <p>Three input datasets are provided; two for the segmentation functionality of the code, and one to test the pre-processing functionality. The two segmentation sets are the same as were used in the paper, and are CassavaDataset, and SoybeanDataset. The pre-processing set is CassavaSlices. The output set for Soybean is SoybeanResultsJul11. The Cassava result set is large, so I broke it into three compressed folders, CassavaResultsJul12_A, _B, _C. _B is the largest, and only contains the results overwritten on the original X-Ray images. Unless your connection to Zenodo is extremely fast, it will be faster to compute the result than to download it.</p> <p> </p> <p> </p><p> </p><p> </p> <p></p> <p></p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.325 micron pixel size RAW
<p>This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Raw data at a pixel-size of 0.325 microns is stored as a .nxs file, and a savu process list is provided to perform the reconstruction we used to reproduced the reconstructed data.</p> <p>This data is one of four resolutions obtained.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .hdf file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 169065_raw.hdf</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 1.625 micron pixel size RAW
<p>This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Raw data at a pixel-size of 1.625 microns is stored as a .nxs file, and a savu process list is provided to perform the reconstruction we used to reproduced the reconstructed data.</p> <p>This data is one of four resolutions obtained.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .hdf file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 169067_raw.hdf</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 2.6 micron pixel size RAW
<p>This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Raw data at a pixel-size of 2.6 microns is stored as a .nxs file, and a savu process list is provided to perform the reconstruction we used to reproduced the reconstructed data.</p> <p>This data is one of four resolutions obtained.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .hdf file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 169068_raw.hdf</p> </blockquote> <p>Please replace dataset_url with the url you copied.</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.