Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,053
datasets available to search
ShareScore release 0.9.0
Dataset results
1,053 results for “Computed Tomography”
FIGURE 4 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 4. Ordovician bryozoan colony. One-half of hemispherical bryozoan, interior of object, bearing probably oldest boring attributable to ichnogenus Entobia Bronn, 1837. Besides semi-radial tunnels and exploratory threads, three bulbous chambers discovered near the center of the hemisphere. Darriwilian (middle Ordovician), Khrevitsa locality, St. Petersburg Region, Russia. Scale bar equals 1 cm.
FIGURE 7 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 7. Three-dimensional visualization of a shell of the recent Foraminifera Amphistegina sp. illustrating the potential of micro-CT in investigations of recent marine shelled organisms. (A) A surface view of the whole shell. (B) A transversal section through the whole shell (C, D) Details of the shells´s surface.
FIGURE 5 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 5. Minute conulariid specimen. (A) Conulariid specimen of Archaeoconularia fecunda and trepostome bryozoan colony; coated with ammonium chloride, no. NMP L21990, locality Loděnice, Upper Ordovician, Zahořany Formation (lower Katian) (B) Micro-CT visualizing of inner surfaces. Scale bar equals 5 mm.
FIGURE 3 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 3. Siliceous nodules of the Šárka Formation. (A, B) Pricyclopyge binodosa, complete trilobite, no. NMP L 35055, locality Praha-Šárka, Middle Ordovician (Darriwilian), (A) Enrolled trilobite coated with ammonium chloride, exterior of objects. (B) Micro-CT image showing dense burrows, interior of objects. (C, D) Rostrum with eyes of a trilobite P. binodosa, no. NMP L46892, locality Praha-Šárka, Middle Ordovician (Darriwilian). (C) Rostrum coated with ammonium chloride, exterior of objects. (D) Micro-CT visualization of tunnels, interior of objects. (E) Bivalve Redonia deshayesi, micro-CT image showing trace fossils, interior of objects, no. NMP L 51722, locality Osek, Middle Ordovician (Darriwilian). All scale bars equal 5 mm.
FIGURE 2 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 2. Custom-made holders specially adapted for each scanned specimen. (A) Plastic cup. (B) Polystyrene holder. (C) Aluminum holder for small specimens. (D) Plastic tube filled with polystyrene.
FIGURE 1 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 1. (A) Single x-ray projection. Schematic representation of positioning of the investigated object inside x-ray device. (B) Multiple x-ray projections as the object rotates. Positioning of investigated object inside micro-CT device. (C) Example of 3D dataset, i.e., a group of 2D slice images acquired by the MicroCT scanner. (D) Examples of Volume rendering; technique in visualization and computer graphics, used to display object from 3D data set in different aspects and orientations.
FIGURE 6 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 6. Tube fragments of the serpulid polychaete Pyrgopolon (Pyrgopolon) deforme. Left images show exterior of objects; right images show interior of objects. (A) Specimen encrusted with bryozoan colonies and serpulid worms, boreholes assigned to Entobia Bronn, 1837, representing the most common ichnogenus in the examined serpulid tubes, no. MHNLM EMV 2016.3.14. (B) Intensely bored specimen preserving tunnels of ichnogenera Entobia and Trypanites Mägdefrau, 1932, no. MHNLM EMV 2016.3.44. (C) Serpulid tube with Entobia boreholes and encrusting juvenile oyster, no. MHNLM EMV 2016.3.40. Scale bar equals 1 cm.
Dataset related to aticle "Additive Fabrication of a Vascular 3D Phantom for Stereotactic Radiosurgery of Arteriovenous Malformations"The database contains 3D models in STL file format of a patient-specific brain arteriovenous malformation phantom reconstructed from computed tomography scans.
<p><em>The database contains 3D models in STL file format of a patient-specific brain arteriovenous malformation phantom reconstructed from computed tomography scans.</em></p>
Reflection Ultrasound Computed Tomography (RUCT) Phantom Data
<p>Test Data for Reflection Ultrasound Computed Tomography (RUCT) Delay and Sum Algorithm</p> <p>This data is shared for "pyruct" package tests. "pyruct" package can be found in "https://github.com/berkanlafci/pyruct"</p> <p>If you use this data in your research, please cite the following paper:</p> <p>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>.</p>
Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT) dataset of 23 mandarins moving over a circular trajectory
<p><strong>Summary</strong></p><p>This dataset is a collection of X-ray projection images of 23 mandarins moving over a circular trajectory in such a way that the projections of multiple adjacent mandarins overlap. The dataset was acquired to test out Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT), about which a paper is published in IEEE Transactions on Computational Imaging [Schut 2022].</p><p> </p><p><strong>Description</strong></p><p><i>Sample information</i></p><p>The samples are 23 mandarins. The first 10 are of the Nadorcott cultivar, and the remaining 13 are of the Clemenrubi cultivar. The diameter of the mandarins ranges between 50 and 58 mm. Per sample metadata can be found in the mandarin_metadata.csv file.</p><p><i>Scanner information</i></p><p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p><p><i>Scanning geometry</i></p><p>The mandarins were moved according to a custom scanning protocol, with the intention to simulate a conveyor belt setup. A wooden disk was attached on top of the rotation stage and six evenly spaced object positions were marked on the disk at a fixed distance from the center of rotation. Pieces of cardboard tube were used as sample holders to make sure the mandarins wouldn't roll as the disk would rotate and to raise them from the disk without attenuating too much of the X-ray signal. The rotation stage was positioned in such a way that over a full rotation of the disk, each mandarin would be completely in view of the detector for more than 180 degrees of the rotation, while there would also be a position at which it would be completely out of view. An image illustrating the exact dimensions is included in mandarin_carousel_dimensions.png.</p><p>The scan was performed in phases. Every phase 400 projection images were acquired, while rotating the disk for 60 degrees. This would rotate one of the positions out of view of the scanning setup. Before the first 6 phases a mandarin was added on the position that was out of view of the setup. For the phases after that the position that would be out of view would contain a mandarin that had rotated the full circle so that mandarin was replaced with a new mandarin. At the last 6 phases there would be no new mandarins left to add so the mandarin that was out of view of the setup would only be removed. The projection images acquired from each phase were concatenated resulting in a dataset of 11200 projections. At most 5 mandarins were in view at a given time.</p><p>Note: Due to a small oversight while scanning, the 19th mandarin is not included on projections 9200-9205. This area can be masked out during reconstruction.</p><p><i>Scanning settings</i></p><p>A peak voltage of 90kV was used, the target power was set to 49.5W and the spectrum was pre-filtered using 0.1mm of copper. An exposure time of 200 ms was used for each projection. A start-stop acquisition scheme was used to minimize vibrations and to make adding and removing mandarins easier: After each projection image was acquired, the stage was rotated to a new position and the scanner was paused for 200 ms before acquiring the next projection image. Darkfield and flatfield images were acquired before and after all the mandarins were scanned using the average over 200 images. 2x2 pixel hardware binning was used and all images were cropped to a 500 pixel high region around the center, resulting in 11200 projection images of 956x500 pixels (11.1GB uncompressed). All images are stored in .tif format.</p><p><i>Reconstructing volumes</i></p><p>The repository <a href="https://github.com/D1rk123/top-ct_experiments">https://github.com/D1rk123/top-ct_experiments</a> contains code for TOP-CT simulations and reconstructions. The script mandarin_carousel_experiment.py was specifically written to reconstruct volumes for each separate mandarin from this dataset.</p><p> </p><p><strong>Research group</strong><br>These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p><p><strong>Contact details</strong><br>dirk [dot] schut [at] cwi [dot] nl</p><p><strong>Acknowledgments</strong><br>This work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003). The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p><p><strong>References</strong></p><p>[Schut 2022] D. E. Schut, K. J. Batenburg, R. van Liere, and T. van Leeuwen, "TOP-CT: Trajectory with Overlapping Projections X-ray Computed Tomography", 2022, IEEE Transactions on Computational Imaging<br>[Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, "Explorative imaging and its implementation at the FleX-ray Laboratory," J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p><p>If you use (parts of) this data in a publication, please consider citing the first article.</p>
Supplemental material: Operative videos on application of microscope-based augmented reality with intraoperative computed tomography-based navigation for resection of skull base meningiomas
<p>Supplemental material</p> <p>Operative videos:</p> <p>Patient number 9: Microsurgical resection of medial sphenoid wing meningioma using microscope-based augmented reality and intraoperative computed tomography-based navigation</p> <p>Pt 28:Microsurgical resection of right clinoidal meningioma via fronto-temporal craniotomy with microscope-based augmented reality</p> <p>Pt 31:Microsurgical resection of recurrent sphenoid wing meningioma using microscope-based augmented reality with intraoperative computed tomography</p> <p>Pt 36: Microsurgical resection of giant olfactory meningioma via bifrontal approach with use of augmented reality and intraoperative CT-based navigation</p>
Shedding Light on Metal-Based Nanoparticles in Zebrafish by Computed Tomography with Micrometer Resolution
<p>Supplementary 3D image stacks of microtomography data.</p> <p>100 layer xy, xz, and yz image stacks</p> <p>Publication included as PDF file (open access, DOI: 10.1002/smll.202000746)</p> <p>********************************************</p> <p>Metal-based nanoparticles are clinically used for diagnostic and therapeutic<br> applications. After parenteral administration, they will distribute throughout<br> different organs. Quantification of their distribution within tissues in the 3D<br> space, however, remains a challenge owing to the small particle diameter.<br> In this study, synchrotron radiation-based hard X-ray tomography (SRμCT)<br> in absorption and phase contrast modes is evaluated for the localization of<br> superparamagnetic iron oxide nanoparticles (SPIONs) in soft tissues based<br> on their electron density and X-ray attenuation. Biodistribution of SPIONs<br> is studied using zebrafish embryos as a vertebrate screening model. This<br> label-free approach gives rise to an isotropic, 3D, direct space visualization<br> of the entire 2.5 mm-long animal with a spatial resolution of around 2<br> μm. High resolution image stacks are available on a dedicated internet<br> page (http://zebrafish.pharma-te.ch). X-ray tomography is combined with<br> physico-chemical characterization and cellular uptake studies to confirm the<br> safety and effectiveness of protective SPION coatings. It is demonstrated<br> that SRμCT provides unprecedented insights into the zebrafish embryo<br> anatomy and tissue distribution of label-free metal oxide nanoparticles.</p>
Data: Computed tomography lacks sensitivity to image gold labelled mesenchymal stromal cells in vivo as evidenced by multispectral optoacoustic tomography.
<p>This data set includes all the raw data collected for the following article: "Computed tomography lacks sensitivity to image gold labelled mesenchymal stromal cells in vivo as evidenced by multispectral optoacoustic tomography."</p>
Cone-Beam Computed Tomography Dataset of a Chicken Bone Imaged at 4 Different Dose Levels
<p><strong>Summary</strong></p> <p>This dataset is a collection of X-ray projection images of a chicken leg bone imaged in a cone-beam computed tomography (CBCT) scanner, using four different dose levels. The dataset also includes a metadata file for each of the scans, specifying the scan geometry and other important scan parameters.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample Information</em></p> <p>The sample is a chicken bone obtained from a cooked chicken. The bone was boiled to remove soft tissues, after which it was left to dry in room temperature for several months to remove extra moisture. For the scan the sample was placed directly into the rotation stage and secured with a screw.</p> <p><em>Scanner</em></p> <p>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).</p> <p><em>Scan Settings</em></p> <p>The dataset consists of four different scans of the same sample. For each scan 721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source was set at 40 kV with a 0.5 mm aluminum filter. For the different scans, the relative doses, tube currents, and exposure times were:</p> <ul> <li>100 % relative dose: tube current 1 mA, exposure time 2000 ms,</li> <li>50 % relative dose: tube current 1 mA, exposure time 1000 ms,</li> <li>25 % relative dose: tube current 0.5 mA, exposure time 1000 ms,</li> <li>10 % relative dose: tube current 0.2 mA, exposure time 1000 ms.</li> </ul> <p>The scans were made in sequence, proceeding from the lowest dose to the highest dose.</p> <p><em>Data Post-Processing</em></p> <p>Before the scans, two correction images were acquired for each scan setting. 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.</p> <p><em>Data Format</em></p> <p>The X-ray projections are stored in .tif format. The metadata are contained in .txt files with formatting that is both human-readable and machine-readable.</p> <p><em>Notes</em></p> <p>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. It was also observed that the scans are not entirely aligned, with a small angular discrepancy between each reconstruction.</p> <p> </p> <p><strong>Research Group</strong></p> <p>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>.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>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>.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.</p>
X-ray computed tomography dataset of a walnut
<p>walnut_scan:</p> <ul> <li>scan performed with a conventional micro-CT</li> <li>1601 acquired projections as tiff stack</li> <li>info file containing corresponding metadata</li> </ul> <p> </p> <p>walnut_rec:</p> <ul> <li>reconstructed volume as tiff stack</li> <li>info file containing corresponding metadata</li> <li>reconstruction performed with pyXIT (see reference)</li> </ul> <p> </p>
CFRP Micro-computed tomography - twill weave
<p>A dataset containing three micro-computed tomography scans of a carbon fibre-reinforced polymer. The composites consist of twill weave reinforcement and epoxy matrix. Two samples (P1 and P3) have random stacking sequences, sample P5 has controlled stacking sequence. The uploaded dataset contains metadata files.</p>
Multienergy Fan Beam Computed Tomography Dataset of a Bird Chest Imaged with 3 Different X-ray Spectra
<p><strong>Summary</strong></p> <p>This dataset is a collection of X-ray projection data of a biological imaging phantom (a bird chest) imaged in an X-ray microtomography scanner, using three different X-ray spectra. The dataset also includes a metadata file for each of the scans, specifying the scan geometry and other important scan parameters, as well as photographs and example reconstructions. The dataset is designed for use in algorithm development for multienergy computed tomography.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample Information</em></p> <p>The sample is the chest of a common quail (<em>Coturnix coturnix</em>) bird obtained frozen from a local supermarket. The chest section of the frozen bird was removed using a handsaw, and left to melt and settle in a sample holder before imaging.</p> <p><em>Scanner</em></p> <p>The measurement data were acquired using an X-ray microtomography scanner in the University of Helsinki Micro-CT Laboratory. The scanner uses cone beam geometry and it is equipped with an end-window tube with a tungsten target.</p> <p><em>Scan Settings</em></p> <p>The dataset consists of three consecutive scans made using identical geometry but different X-ray spectra and detector exposure times. For each scan, 720 X-ray projections were acquired using an angle increment of 0.5 degrees. Multiple frames were averaged for each projection in order to increase signal-to-noise ratio. The scan geometry and the energy-specific settings are summarized in the following two tables.</p> <p><strong>Table 1.</strong> Imaging geometry used for collecting the data.</p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Value</strong></td> </tr> <tr> <td>Focus-center distance</td> <td>252 mm</td> </tr> <tr> <td>Focus-detector distance</td> <td>420 mm</td> </tr> <tr> <td>Geometric magnification</td> <td>5/2</td> </tr> <tr> <td>Detector pixel size</td> <td>0.200 mm</td> </tr> <tr> <td>Effective pixel size</td> <td>0.120 mm</td> </tr> <tr> <td>Projection size</td> <td>552 x 576 pixels</td> </tr> <tr> <td>Angular range</td> <td>360'</td> </tr> <tr> <td>#projections</td> <td>720</td> </tr> </tbody> </table> <p><strong>Table 2.</strong> Energy-specific settings used for collecting the data.</p> <table> <tbody> <tr> <td>Energy label</td> <td><em>U</em> (kV)</td> <td>Filtration</td> <td><em>I</em> (μA)</td> <td>Exposure time (ms)</td> <td>Frame averaging</td> </tr> <tr> <td><em>E1</em></td> <td>50</td> <td>None</td> <td>300</td> <td>125</td> <td>4</td> </tr> <tr> <td><em>E2</em></td> <td>80</td> <td>1 mm Al</td> <td>180</td> <td>125</td> <td>4</td> </tr> <tr> <td><em>E3</em></td> <td>120</td> <td>0.5 mm Cu</td> <td>120</td> <td>250</td> <td>4</td> </tr> </tbody> </table> <p><em>Data Post-Processing</em></p> <p>Before the scans were made, a dark current image and flat-field image were acquired for each scan setting. During the scans, dark current subtraction and flat-field correction were automatically applied to the X-ray projections by the measurement software.</p> <p><em>Data Contents</em></p> <p>This dataset contains the following files:</p> <ul> <li>The raw projection data (.tif format) for each scan and a metadata file (.txt format) describing the measurement setup, with formatting that is both human-readable and machine-readable.</li> <li>Pre-created 2D sinograms for each energy level. The sinograms have been created from the central plane of the cone beam, which reduces to fan beam geometry. The sinograms are stored in Matlab's .mat file format in data structures which also contain metadata on the measurement.</li> <li>Photographs taken during the measurement process.</li> <li>Example filtered backprojection (FBP) reconstructions of the central plane of the phantom for each energy. The reconstructions were computed using the Phoenix datos|x CT software provided with the microtomography scanner</li> </ul> <p> </p> <p><strong>Research Group</strong></p> <p>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://www.helsinki.fi/en/researchgroups/inverse-problems">https://www.helsinki.fi/en/researchgroups/inverse-problems</a>) in collaboration with the Computational Physics and Inverse Problems research group at the University of Eastern Finland, Finland (<a href="https://sites.uef.fi/inverse">https://sites.uef.fi/inverse</a>) and the X-ray Laboratory at the Department of Physics at the University of Helsinki, Finland (<a href="https://www.helsinki.fi/en/researchgroups/x-ray-laboratory">https://www.helsinki.fi/en/researchgroups/x-ray-laboratory</a>).</p> <p> </p> <p><strong>Previous Use</strong></p> <p>This dataset has been used in the following publications:</p> <p>Jussi Toivanen, Alexander Meaney, Samuli Siltanen, Ville Kolehmainen. Joint reconstruction in low dose multi-energy CT. <em>Inverse Problems and Imaging</em>, 2020, 14(4): 607-629. doi: <a href="https://doi.org/10.3934/ipi.2020028" target="_blank" rel="noopener">10.3934/ipi.2020028</a>.</p> <p>E. Cueva, A. Meaney, S. Siltanen, M. J. Ehrhardt. Synergistic multi-spectral CT reconstruction with directional total variation. <em>Philos Trans A Math Phys Eng Sci</em>. 2021 Aug 23;379(2204):20200198. doi: <a href="https://doi.org/10.1098/rsta.2020.0198">10.1098/rsta.2020.0198</a>.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected by the Inverse Problems research group, and available at <a href="https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox">https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox</a>.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We wish to thank laboratory engineer Heikki Suhonen for his guidance and assistance in conducting the measurements.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.</p>
Cone-Beam Computed Tomography Dataset of a Walnut Imaged at 4 Different Dose Levels
<p><strong>Summary</strong></p> <p>This dataset is a collection of X-ray projection images of a walnut imaged in a cone-beam computed tomography (CBCT) scanner, using four different dose levels. The dataset also includes a metadata file for each of the scans, specifying the scan geometry and other important scan parameters.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample Information</em></p> <p>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.</p> <p><em>Scanner</em></p> <p>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).</p> <p><em>Scan Settings</em></p> <p>The dataset consists of four different scans of the same sample. For each scan 360 X-ray projections were acquired using an angle increment of 1 degrees, with one additional frame taken at the end to estimate sample movement. The X-ray source was set at 40 kV with a 0.5 mm aluminum filter. For the different scans, the relative doses, tube currents, and exposure times were:</p> <ul> <li>100 % relative dose: tube current 1 mA, exposure time 2000 ms,</li> <li>50 % relative dose: tube current 1 mA, exposure time 1000 ms,</li> <li>25 % relative dose: tube current 0.5 mA, exposure time 1000 ms,</li> <li>10 % relative dose: tube current 0.2 mA, exposure time 1000 ms.</li> </ul> <p><em>Data Post-Processing</em></p> <p>Before the scans, two correction images were acquired for each scan setting. A dark current image was created by averaging 255 images taken with the X-ray source off. A flat-field image was created by averaging 255 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.</p> <p><em>Data Format</em></p> <p>The X-ray projections are stored in .tif format. The metadata are contained in .txt files with formatting that is both human-readable and machine-readable.</p> <p><em>Notes</em></p> <p>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. It was also observed that the scans are not entirely aligned, with a small angular discrepancy between each reconstruction.</p> <p> </p> <p><strong>Research Group</strong></p> <p>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://www.helsinki.fi/en/researchgroups/inverse-problems">https://www.helsinki.fi/en/researchgroups/inverse-problems</a>.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>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://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox">https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox</a>.</p> <p>Please note that this is a an entirely separate dataset from the Walnut datasets accessible at <a href="../record/1254206">https://zenodo.org/record/1254206</a> and <a href="https://doi.org/10.5281/zenodo.6986012">https://doi.org/10.5281/zenodo.6986012</a>, although both datasets have been created by the same research group.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.</p>
Translaminar Fracture in a Mini-Protruded Compact Tension Specimen: A Dataset of Micro-Scale Tomograms of a Thin-Ply Carbon Fibre-Epoxy Composite acquired via Synchrotron Radiation Computed Tomography During In-Situ Loading
<p>In this study, we developed a scaled-down “mini-protruded compact tension specimen” to facilitate in-situ tensile testing coupled with synchrotron radiation computed tomography (SRCT). This innovative design provides valuable insights into in-situ translaminar damage mechanisms, significantly enhancing the accuracy of data used in finite element models.</p> <p>The specimen is made of HS40 carbon fibres and ThinPreg<sup>TM </sup>736LT epoxy resin, with the layup of [90<sub>2</sub>/0/90<sub>2</sub>/0/90<sub>2</sub>/0/90<sub>2</sub>]. The translaminar fracture experiments were conducted under continuous loading and scanning using ultra-fast SRCT at the Swiss Light Source (SLS) TOMCAT beamline (Paul Scherrer Institut in Villigen, Switzerland). A polychromatic beam with an energy of 24 keV was used. The achieved voxel size was 800 <em>nm</em>, and 1000 projections per scan and 2 <em>ms</em> exposure time were acquired per scan. The GigaFRoST camera served as the detector. The scans were reconstructed into 3D volumes using the SLS’s in-house absorption-based algorithm (Gridrec) for critical loading steps during a test—both before and after a load drop (detailed in the accompanying Excel file). The tensile loading was exerted on the specimen at a rate of 0.2 <em>mm/min</em> until failure during scanning with the Deben CT500.</p>
Figure 4 in The pros and cons of using micro-computed tomography in gross and micro-anatomical assessments of polychaetous annelids
Figure 4. Pharyngeal anatomy of Syllidae: Syllis gracilis (a-c, PTA stained) and Hediste diversicolor (d-h,). Syllis a) section through body showing the proventriculus; b) surface morphology, lines c where transverse section c image taken, line d where transverse section d image taken; c) TS showing pharyngeal tube; d) TS showing proventricle. Scale bars = 0.5 mm. Hediste e) surface morphology; f) section through pharynx, lines g and h where transverse section images taken; g) TS through anterior pharynx at level of jaws; h) TS through distal pharynx. TS through pharynx indicates that the pharynx is not symmetrical, particularly in the distal part. Scale bars e, f = 5.00 mm, g,h = 1.00 mm. Images 1a–d were produced using the SkyScan 1172 microtomograph at HCMR at 60kV / 167µA, without a filter, no camera binning, full rotation of 360°, tungsten target. Images i-h were produced using the Nikon metrology HMX ST 225 at the NHM (60 KV, 2 sec exposure, molybdenum target). Abbreviations used:; DLM–dorsal longitudinal muscles; J–jaws; M–mouth; P–pharynx; Pr–prostomium; PO–proventricle; PS–proboscidian sheath; VLM–ventral longitudinal muscles.
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.