Find research datasets worth reusing

opencc-by-4.0Jul 2024View details →

Cross-sectional images from x-ray computed tomography (XCT) of conserved archaeological samples

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). 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. 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).

Zinc Doped Zeolite 13X DIAD X-Ray Computed Tomography - 0.54 micron pixel size RAW

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. A detailed data descriptor pre-print can be found at https://arxiv.org/abs/2409.07322#   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: <blockquote> curl dataset_url > 43334_raw.h5 </blockquote> Please replace dataset_url with the url you copied.

Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 1.625 micron pixel size

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. This data is one of four resolutions obtained. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322# 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: <blockquote> curl dataset_url > 169067_recon.h5 </blockquote> Please replace dataset_url with the url you copied.

Zinc Doped Zeolite 13X DIAD X-Ray Computed Tomography - 0.54 micron pixel size

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. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322# 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: <blockquote> curl dataset_url > 43334_recon.h5 </blockquote> Please replace dataset_url with the url you copied.

Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.325 micron pixel size

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. This data is one of four resolutions obtained. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322# 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: <blockquote> curl dataset_url > 169065_recon.h5 </blockquote> Please replace dataset_url with the url you copied.

Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.8125 micron pixel size

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. This data is one of four resolutions obtained. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322# 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: <blockquote> curl dataset_url > 169066_recon.h5 </blockquote> Please replace dataset_url with the url you copied.

Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 2.6 micron pixel size

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. This data is one of four resolutions obtained. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322# 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: <blockquote> curl dataset_url > 169068_recon.h5 </blockquote> Please replace dataset_url with the url you copied.

opencc-by-4.0Nov 2023View details →

Synchrotron X-ray Computed Tomography scan of a wasp

<h4>Contents:</h4><ul><li>bee_yazeed-20231001T170032.h5 - SXCT scan of a wasp performed at beamline <a href="https://www.sesame.org.jo/beamlines/beats">ID10-BEATS</a> of SESAME.</li><li>SESAME_wasp_yazeed.avi - 3D video rendering of phase-contrast CT reconstruction of bee_yazeed-20231001T170032. 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>

opencc-by-nc-sa-4.0Aug 2020View details →

4D cone beam computed tomography phantom data set

This data set accompanies the following Medical Physics publication: <a href="https://doi.org/10.1002/mp.14441">Madesta, F., Sentker, T., Gauer, T., & Werner, R. (2020). Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction. Medical Physics, 47(11), 5619-5631</a>.It comprises 6 time-resolved (4D) cone-beam computed tomography scans with the following scan configurations:<ul><li>4D CBCT Scanner: Varian TrueBeam (the detailed scan geometry and further details can be found in Scan.xml included in each scan)</li><li>Phantom: <a href="https://www.cirsinc.com/products/radiation-therapy/dynamic-thorax-motion-phantom/">Dynamic Thorax Phantom: Model 008A</a></li><li>The following motion patterns are included:<ol><li>SI amplitude of insert: ±10mm, pattern: sin, period: 5.0s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 5.0s</li><li>SI amplitude of insert: ±10mm, pattern: sin, period: 2.5s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 2.5s</li><li>SI amplitude of insert: ±10mm, pattern: sin, period: 7.5s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 7.5s</li></ol></li></ul>

opencc-by-4.0Mar 2022View details →

Data for "Measurement of temperature induced X-ray tube transmission target displacements for dimensional computed tomography"

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> Data is available in tab delimited format (.txt) and in Excel (.xls).

opencc-by-4.0Feb 2022View details →

Simulated X-ray micro-computed tomography based particle tracking velocimetry dataset for validation purposes

Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium) Date: 8th February 2022 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). https://doi.org/10.1063/5.0088000 ----------------------------- Validation dataset for micro-computed tomography based particle tracking velocimetry: a simulated micro-CT based velocimetry experiment with associated ground-truth particle trajectories - 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. - 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. - 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. - 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). - 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/ - 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)

opencc-by-4.0Feb 2022View details →

X-ray micro-computed tomography based X-ray particle tracking velocimetry dataset in a porous glass filter

Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium) Date: 8th February 2022 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). https://doi.org/10.1063/5.0088000 ----------------------------- 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). - 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. - 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. - 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). - 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/ - 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)

opencc-by-4.0Feb 2022View details →

X-ray micro-computed tomography based particle tracking velocimetry dataset in a sandpack

Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium) Date: 8th February 2022 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). https://doi.org/10.1063/5.0088000 ----------------------------- 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). - 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. - 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. - 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). - 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/ - 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)

opencc-by-4.0Apr 2022View details →

HandCT: hands-on computational dataset for X-Ray Computed Tomography

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.

openmit-licenseMay 2022View details →

Reflection Ultrasound Computed Tomography (RUCT) Data

Data for Reflection Ultrasound Computed Tomography (RUCT) Delay and Sum Algorithm Data is shared for "pyruct" package tests and as supporting files of the research article indicated below. "pyruct" package can be found in "https://github.com/berkanlafci/pyruct" If you use this data in your research, please cite the following paper: B. Lafci, J. Robin, X. L. Deán-Ben and D. Razansky, "Expediting Image Acquisition in Reflection Ultrasound Computed Tomography," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, doi: <a href="https://ieeexplore.ieee.org/document/9768674">10.1109/TUFFC.2022.3172713</a>. "nct" means number of consecutive transducer elements used in transmission event. Please use the files with "nct_1" tags for the full acquisition and reconstruction.

opencc-by-4.0Aug 2022View details →

Cone-Beam Computed Tomography Dataset of a Seashell

Summary This dataset is a collection of X-ray projection images of a seashell imaged in a cone-beam computed tomography (CBCT) scanner. The dataset also includes a metadata file, specifying the scan geometry and other important scan parameters, and photographs of the sample and the measurement setup.   Description Sample Information The sample is an empty seashell of an unknown species, approximately 4.3 cm in length and 2.5 cm in diameter. The sample was placed in a plastic tube filled with cotton wool to prevent unwanted motion during the scan. Scanner The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22). Scan Settings 721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source voltage and tube current were set at 50 kV and 1 mA, respectively. The exposure time of the flat panel detector was set to 1000 ms. Data Post-Processing Two correction images were acquired before scanning the sample. A dark current image was created by averaging 100 images taken with the X-ray source off. A flat-field image was created by averaging 100 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3. Data Format The X-ray projections are stored in .tif format. The metadata is contained in .txt file with formatting that is both human-readable and machine-readable. Notes Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 4 pixels, using circular boundary conditions, before performing any other operations on the projections.   Research Group This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: <a href="https://www2.helsinki.fi/en/researchgroups/inverse-problems">https://www2.helsinki.fi/en/researchgroups/inverse-problems</a>.   Additional Links To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at <a href="https://github.com/Diagonalizable/HelTomo">https://github.com/Diagonalizable/HelTomo</a>   Contact Details For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.

opencc-by-4.0Aug 2022View details →

Cone-Beam Computed Tomography Dataset of a Walnut

Summary This dataset is a collection of X-ray projection images of a walnut imaged in a cone-beam computed tomography (CBCT) scanner. The dataset also includes a metadata file, specifying the scan geometry and other important scan parameters.   Description Sample Information The sample is a walnut in its shell. For the scanning process double-sided tape was used to attach the sample to a plastic tube placed into the rotation stage. Scanner The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22). Scan Settings 721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source voltage and tube current were set at 40 kV and 1 mA, respectively. The exposure time of the flat panel detector was set to 1000 ms. Data Post-Processing Two correction images were acquired before scanning the sample. A dark current image was created by averaging 100 images taken with the X-ray source off. A flat-field image was created by averaging 100 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3. Data Format The X-ray projections are stored in .tif format. The metadata is contained in .txt file with formatting that is both human-readable and machine-readable. Notes Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 5 pixels, using circular boundary conditions, before performing any other operations on the projections.   Research Group This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: <a href="https://www2.helsinki.fi/en/researchgroups/inverse-problems">https://www2.helsinki.fi/en/researchgroups/inverse-problems</a>.   Additional Links This dataset was originally created as part of a tutorial on working with measured X-ray data in computed tomography. A video tutorial on the measurement process can be found on the Inverse Problems Channel on YouTube at <a href="https://www.youtube.com/watch?v=CWUomAmUDys">https://www.youtube.com/watch?v=CWUomAmUDys</a>. To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at <a href="https://github.com/Diagonalizable/HelTomo">https://github.com/Diagonalizable/HelTomo</a>. Please note that this is a an entirely separate dataset from the Walnut dataset accessible at <a href="https://zenodo.org/record/1254206">https://zenodo.org/record/1254206</a>, although both datasets have been created by the same research group.   Contact Details For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.

opencc-by-4.0Aug 2022View details →

Cone-Beam Computed Tomography Dataset of a Pine Cone

Summary This dataset is a collection of X-ray projection images of a pine cone imaged in a cone-beam computed tomography (CBCT) scanner. The dataset also includes a metadata file, specifying the scan geometry and other important scan parameters, and a photograph of the sample.   Description Sample Information The sample is an open cone of a Baltic pine (Pinus sylvestris), approximately 3 cm in diameter. For the scanning process sticky tack was used to attach the sample to a plastic tube placed into the rotation stage. Scanner The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22). Scan Settings 721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source voltage and tube current were set at 40 kV and 1 mA, respectively. The exposure time of the flat panel detector was set to 1000 ms. Data Post-Processing Two correction images were acquired before scanning the sample. A dark current image was created by averaging 100 images taken with the X-ray source off. A flat-field image was created by averaging 100 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3. Data Format The X-ray projections are stored in .tif format. The metadata is contained in .txt file with formatting that is both human-readable and machine-readable. Notes Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 5 pixels, using circular boundary conditions, before performing any other operations on the projections.   Research Group This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: <a href="https://www2.helsinki.fi/en/researchgroups/inverse-problems">https://www2.helsinki.fi/en/researchgroups/inverse-problems</a>.   Additional Links This dataset was originally created as part of a tutorial on working with measured X-ray data in computed tomography. A video tutorial on the measurement process can be found on the Inverse Problems Channel on YouTube at <a href="https://www.youtube.com/watch?v=CWUomAmUDys">https://www.youtube.com/watch?v=CWUomAmUDys</a>. To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at <a href="https://github.com/Diagonalizable/HelTomo">https://github.com/Diagonalizable/HelTomo</a>   Contact Details For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.