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3,036 results for “ct”
Figure 10 in Mastiglanis durantoni from French Guyana, a second species in the genus (Siluriformes: Heptapteridae), with a CT scan survey of phylogenetically-relevant characters
Figure 10. – Geographical distribution of Mastiglanis durantoni n. sp.
Bone point micro-CT raw data from Abric Romaní (Capellades, Barcelona, Spain)
<p>Raw data from micro-CT scanning of a bone point from Abric Romaní archaeological site (Capellades, Barcelona, Spain).</p>
U-10: United-10 COVID19 CT Dataset
<p>This dataset supports the research detailed in the pre-print "<a href="https://arxiv.org/abs/2308.09730"><strong>Virtual Imaging Trials Improved the Transparency and Reliability of AI Systems in COVID-19 Imaging</strong></a>." The study employs both clinical and simulated CT data to evaluate AI models for COVID-19 diagnosis. By leveraging the Virtual Imaging Trials (VIT) framework, the research addresses reproducibility and generalizability issues prevalent in medical imaging AI models. <br><br><strong>The dataset includes:</strong></p> <ul> <li><strong>Clinical CT Data:</strong> Drawn from 10 publicly available datasets, comprising over 12,000 volumes. These datasets span diverse populations, imaging protocols, and scanner configurations. Each of the 10 zip files contains pre-processed CT TFRecords (Train/Validation/Test) used in the study. Detailed information about the data sources, pre-processing steps, and the inclusion and exclusion criteria can be found in the manuscript (<a href="https://arxiv.org/abs/2308.09730">https://arxiv.org/abs/2308.09730</a>).</li> <li><strong>Simulated CT Data:</strong> Generated using computational anatomical phantoms from the XCAT model and imaged with the DukeSim simulation framework. This synthetic dataset allows controlled experiments that isolate the effects of imaging physics and patient-specific factors. Can be available upon request through Center For virtual Imaging Trial Portal at <a href="https://cvit.duke.edu/">https://cvit.duke.edu/</a></li> </ul> <p>The accompanying study analyzes the performance of lightweight convolutional neural networks on both real and synthetic data, comparing results across multiple internal and external validation scenarios. Insights into factors such as infection severity, imaging dose, and modality type are explored.</p> <p><strong>For further details, visit our Project Page:</strong> <a href="https://fitushar.github.io/ReviCOVID.github.io/">https://fitushar.github.io/ReviCOVID.github.io/</a><br>The full source code is available on gitHub and GitLab<br><strong>GitHub:</strong> <a href="https://github.com/fitushar/CVIT_ReviCOVID19">https://github.com/fitushar/CVIT_ReviCOVID19</a><br><strong>GitLab :</strong> <a href="https://gitlab.oit.duke.edu/cvit-public/cvit_revicovid19">https://gitlab.oit.duke.edu/cvit-public/cvit_revicovid19</a></p> <p><strong>Citation:</strong> When using this dataset, please cite the manuscript () and the original data-source.<br><strong><em>Tushar et al.</em>, "Virtual Imaging Trials Improved the Transparency and Reliability of AI Systems in COVID-19 Imaging", arXiv:2308.09730.</strong></p> <p><strong>Contact:</strong> fakrulislam.tushar@duke.edu</p>
CT dataset for "Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer" Part-I
<p>To develop and validate a radiomics-based method for lung cancer detection, chest CT images of patients with pulmonary nodules from clinical 5 centers were collected. Due to size limit of zenodo.org, we split the whole dataset into 2 parts, and this is the Part 1.</p> <p>We hope this large-scale dataset could facilitate both clinical research for automatic lung cancer detection and diagnoses, and engineering research for 3D detection, segmentation and classification. This dataset is a research effort of thousands of hours by experienced thoracic surgeons, and radiologists. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p> <p> </p>
Confocal and micro-CT data of Toxesbium kundratai, holotype, NIGP203543
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Toxesbium kundratai</em> (NIGP203543) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "<em>Toxesbium</em> gen. nov., the first definitive member of Ernobiinae from mid-Cretaceous amber of northern Myanmar (Coleoptera: Ptinidae)" (DOI:10.1007/s12542-025-00715-2).</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm (Argon) or 561 nm (DPSS 561-10) laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 2.8752 μm; power, 3 W; acceleration voltage, 30 kV; exposure time, 2 s; projections, 3001. The TIFF stack is provided.</p>
Dataset for "Validation of SSDE calculation in a modern CT scanner and correlation with effective dose"
<p>Size-Specific Dose Estimate (SSDE) is a size-adjusted dosimetric index that addresses the limitations of the Computed Tomography Dose Index (CTDIvol). This research aims to verify the SSDE generated by a modern Computed Tomography (CT) scanner and examine its relationship with effective dose (E). Sixty CT scans were performed on anthropomorphic phantoms, including models representing pediatric and obese patients, and then analyzed. SSDE values from the CT scanner were compared with those calculated independently using a Python-based method and Radimetrics, a dose monitoring software.</p> <p>The published dataset contains all the CT images and an Excel file with the main parameters given by the CT scanner, alsongside the ones calculated with Python and Radimetrics. </p>
Data for LSTM-certainty as EWS for CT
<p>Data for the paper "LSTM-certainty as EWS for CT"</p>
An example of critical thinking (CT) snippets usage at Fourah Bay College, University of Sierra Leone
<p>Recorded students & lecturer WhatsApp group conversation while going through the 'CT snippets' which are based on INASP's online course 'Questioning as we learn: An introduction to critical thinking'</p>
Confocal and micro-CT data of Basinosa pengweii, holotype, NIGP173119
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Basinosa pengweii</em> (NIGP173910) from mid-Cretaceous Burmese amber.</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using 488 nm Argon laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 16.919 μm; power, 5 W; acceleration voltage, 60 kV; exposure time, 2 s; projections, 2001. The TIFF stack is provided.</p>
Confocal and micro-CT data of Mesonosa scandens, holotype, NIGP173928
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Mesonosa scandens</em> (NIGP173928) from mid-Cretaceous Burmese amber.</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using 488 nm Argon laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 6.7745 μm; power, 4 W; acceleration voltage, 50 kV; exposure time, 2 s; projections, 2501. The TIFF stack is provided.</p>
Confocal and micro-CT data of Archaenosodendron spp.
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices for holotypes of <em>Archaenosodendron cretaceum</em> (CNU-COL-MA-0321), <em>Archaenosodendron explanatum</em> (NIGP177613), <em>Archaenosodendron remotidens </em>(NIGP177614) and <em>Archaenosodendron angulare</em> (NIGP177615), and X-ray microtomography (micro-CT) slices for the holotype of <em>Archaenosodendron explanatum</em>. This dataset is associated with the publication "On the Nosodendridae from mid-Cretaceous amber of northern Myanmar (Coleoptera: Nosodendroidea)" (DOI:10.11646/zootaxa.5082.3.2).</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm Argon laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 4.4634 μm; power, 3 W; acceleration voltage, 40 kV; exposure time, 4 s; projections, 2501. The TIFF stack is provided.</p>
Hyperspectral X-ray CT dataset of a single, iodine-stained lizard head sample
<p><strong>General Data description:</strong></p> <p>This is a hyperspectral (energy-resolved) X-ray CT projection dataset of a lizard head sample, stained with an iodine contrast agent. It was acquired in a custom-built, laboratory micro-CT scanner with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The following data contains all the files necessary for reconstruction, after a hyperspectral scan was taken of a single, iodine-stained lizard head sample. The iodine contrast agent provided a spectral marker, measured by an energy-sensitive detector, which may be used for spatial mapping and segmentation of stained soft tissue regions.</p> <p><strong>File descriptions:</strong></p> <p>Contained are four MATLAB (.mat) data files, as well as a single text (.txt) file.</p> <p>Lizard_head_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition.</p> <p>lizard_180Proj_noSupp_1_180.mat contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data. The 4D array contains the total number of energy channels acquired during scanning, vertical and horizontal pixel number, and total projections angles acquired. The data provided is prior to application of any post-processing filters. The first 180 energy channels are included.</p> <p>lizard_180Proj_Supp_1_180.mat contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data. This dataset is identical to the .mat file above, however here we have also applied a ring-reduction filter, using a wavelet-based Fourier filter which suppresses the presence of ring artefacts in every energy channel. The first 180 energy channels are included.</p> <p>Energy_axis.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning.</p> <p>FF.mat contains the 4D flatfield data acquired when no sample was present. This data was used to normalise the projection datasets, as the sinogram was constructed. The first 180 energy channels are included.</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 5 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a> <strong>(this upload)</strong></p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></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><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</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> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<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, we would appreciate it if you would refer to the first article.</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 4 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a> <strong>(this upload)</strong><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a></p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></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><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</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> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<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, we would appreciate it if you would refer to the first article.</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 2 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a> <strong>(this upload)</strong><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a> </p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></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><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</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> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<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, we would appreciate it if you would refer to the first article.</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 1 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><strong> (this upload)</strong><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a></p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></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><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</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> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<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, we would appreciate it if you would refer to the first article.</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 3 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a> <strong>(this upload)</strong><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a></p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></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><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</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> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<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, we would appreciate it if you would refer to the first article.</p>
CT-EBM-SP - Corpus of Clinical Trials for Evidence-Based-Medicine in Spanish
<p>A collection of 1200 texts (292 173 tokens) about clinical trials studies and clinical trials announcements in Spanish:</p> <p>- 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO).<br> - 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos.</p> <p>Texts were annotated with entities from the Unified Medical Language System semantic groups: anatomy (ANAT), pharmacological and chemical substances (CHEM), pathologies (DISO), and lab tests, diagnostic or therapeutic procedures (PROC). 46 699 entities were annotated (13.98% are nested entities). 10% of the corpus was doubly annotated, and inter-annotator agreement (IAA) achieved a mean F-measure of 85.65% (±4.79, strict match) and a mean F-measure of 93.94% (±3.31, relaxed match). </p> <p>The corpus is freely distributed for research and educational purposes under a Creative Commons Non-Commercial Attribution (CC-BY-NC-A) License.</p>
A micro-CT of a human skull
<p>This is a micro-CT data set of a human skull - including raw data, a 3D reconstruction and a bone segmentation. See "README.pdf" for a detailed description.</p>
Confocal and micro-CT data of Nothotytthonyx serratus, holotype, NIGP179427
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Nothotytthonyx serratus</em> (NIGP179427) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "<em>Nothotytthonyx</em>, a new genus of Malthininae (Coleoptera: Cantharidae) from mid-Cretaceous amber of northern Myanmar" (DOI:10.3897/zookeys.1092.81701).</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm (Argon) or 561 nm (DPSS 561-10) laser excitation lines. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 6.1511 μm; power, 4 W; acceleration voltage, 50 kV; exposure time, 1.5 s; projections, 2401. The TIFF stack is provided.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.