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447 results for “Radiographs”
Image sets used in the development of a connected auto-encoders based approach to separate mixed X-radiographs from double-sided paintings
<p>The following sets of images were used during the development of an algorithm (described in the publication detailed below) designed to separate the mixed X-radiographs from double-sided paintings into two hypothetical X-ray images corresponding to each side of the painting, when visible images of the two sides of the painting are available.</p> <p>The images sets are taken from a painting that is only painted on one side and were used to assess the regularization parameters associated with the separation approach. The details are taken from the visible image and the X-radiograph of Anthony van Dyck’s painting <em>Lady Elizabeth Thimbelby and Dorothy, Viscountess Andover</em> dated to about 1635 and now in the collection of the National Gallery in London (NG6437). See <a href="https://www.nationalgallery.org.uk/paintings/anthony-van-dyck-lady-elizabeth-thimbelby-and-her-sister">https://www.nationalgallery.org.uk/paintings/anthony-van-dyck-lady-elizabeth-thimbelby-and-her-sister</a> for further details of the painting.</p> <p>The code can be downloaded from: <a href="https://github.com/ART-ICT/Xray_Separation_2RGB">https://github.com/ART-ICT/Xray_Separation_2RGB</a> and the algorithm is described in W. Pu, B. Sober, N. Daly, C. Zhou, Z. Sabetsarvestani, C. Higgitt, I. Daubechies and M. Rodrigues, ‘Image Separation with Side Information: A Connected Auto-Encoders Based Approach’, <em>Transactions on Image Processing, </em>2023 </p> <p><strong>All images © The National Gallery, London</strong></p> <p> </p> <p><strong><em>Datasets available: </em></strong></p> <p><strong>NG6437_vis_800pixel_230502.tif</strong>: 800 pixel thumbnail visible image of the entire painting showing the location of the two details used for the algorithm development. This image is derived from a visible image of the whole painting acquired 25 November 2019 (Original file: N-6437-00-000041.tif; 6272 x 5940 pixels).</p> <p><strong>NG6437_xray_800pixel_230502.tif</strong>: 800 pixel thumbnail image of the X-radiograph of the entire painting showing the location of the two details used for the algorithm development. This image is derived from the composite X-radiography of the whole painting created by mosaicking digital scans of the individual sheets of film and then registering the resulting image to the high resolution visible image described above (Original file: N-6437-00-000049.tif; 36847 x 32516 pixels).</p> <p><strong>NG6437_vis_crop_01_230502.tif</strong>: 1543 x 2078 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_01_230502.tif</strong>: 1543 x 2078 pixel detail of the X-radiograph corresponding to NG6437_vis_crop_01_230502.tif. The X-ray images were acquired using sheets of film (27 November 2019) and 16-bit digital scans were then produced (original files: N-6437-00-000047-009 and -014 (each 9539 x 7199 pixels), processed 28 January 2020). This crop is an 8-bit composite image of 2 X-ray plates that had been manually registered to the high resolution visible image described above using Adobe Photoshop.</p> <p><strong>NG6437_vis_crop_02_230502.tif</strong>: 1562 x 2023 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_02_230502.tif</strong>: 1543 x 2078 pixel detail of the X-radiograph corresponding to NG6437_vis_crop_02_230502.tif. The X-ray images were acquired using sheets of film (27 November 2019) and 16-bit digital scans were then produced (original files: N-6437-00-000047-002 and -007 (each 9539 x 7199 pixels), processed 28 January 2020). This crop is an 8-bit composite image of 2 X-ray plates that had been manually registered to the high resolution visible image described above using Adobe Photoshop.</p> <p><strong>NG6437_vis_crop_03_230502.tif</strong>: 2088 x 2088 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_03_230502.tif</strong>: 2088 x 2088 pixel detail taken from the composite X-radiograph described above corresponding to NG6437_vis_crop_03_230502.tif. </p> <p><strong>NG6437_vis_crop_04_230502.tif</strong>: 2088 x 2088 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_04_230502.tif</strong>: 2088 x 2088 pixel detail taken from the composite X-radiograph described above corresponding to NG6437_vis_crop_04_230502.tif. </p> <p> </p>
Shoulder kinematics derived from radiographic and optical motion analysis
<p>This dataset contains torso/arm, scapula, and humerus kinematics from subjects performing a variety of static poses and dynamic activities. The humerus and scapula were imaged at 100 Hz using a biplane fluoroscopy/dynamic stereoradiography system. Then, 3D models of the humerus and scapula were constructed from each subject’s CT scan. Model-based markerless tracking ascertained the 3D position and orientation of each bone model by semi-automatically aligning digitally reconstructed radiographs against each frame of the radiographic recordings. The kinematics of the torso and arm were measured using skin marker motion capture and co-calibrated spatially and temporally to the radiography system.</p> <p>This repository contains an expanded release of data found in doi:10.5281/zenodo.7542486 and doi:10.5281/zenodo.10972005. The rationale to provide a new repository is that this release, and forthcoming releases, will follow a new format that provides more granular data for past and ongoing studies from our laboratory. These studies may include motion analysis data from healthy controls, pathologic subjects, and those after surgical intervention.</p> <p>v1.1 now contains transforms from Vicon to biplane fluoro coordinate systems.</p> <p> </p>
DATASET Clinical and Radiographic Evaluation Data of Autotransplantation using 3D Replicas
<p>This dataset repository contains detailed data and analysis scripts related to a research study on autotransplantation using 3D replicas. The study aims to evaluate clinical outcomes and radiographic changes over time in dental autotransplantation cases. The dataset includes CSV files containing clinical data, detailed radiographic evaluation results, and HTML files documenting analysis procedures and findings.</p>
Synthetic proton radiographs for testing direct inversion algorithms
<p>Proton radiographs generated by particle tracing in specified radial force profiles in cylinders and spheres saved in pradformat (github.com/phyzicist/pradformat) in a zipped folder intended as tests for direct inversion algorithms. For details see: J. R. Davies, and P. V. Heuer, https://arxiv.org/abs/2203.00495</p> <p>Version 2 includes 3 additional radiographs for a spherical Gaussian potential with a reduced bin width and more bins (0.02R and 200x200 bins)</p> <p>Version 3 corrects an error in the x values given for the original spherical Gaussian potentials with negative mu values sphGauss_mum0p25 and sphGauss_mum0p5. The bin widths were half that of the spherical Gaussian results with positive mu values. </p> <p>Version 4 corrects an error in the x values given for the mesh run and adds a smoothed version of the source intensity (mu0)</p>
Radiographic outcomes for Biodentine group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Radiographic outcomes for Biodentine group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</p>
Radiographic outcomes for MTA group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Radiographic outcomes for MTA group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</p>
Dataset for Detection and Segmentation of the Radiographic Features of Pulmonary Edema
<p><strong>Objectives:</strong> This comprehensive dataset is well suited for training, evaluating, and using machine learning models to detect, segment, and analyze radiological features associated with pulmonary edema in chest X-ray images.</p> <p><strong>Description:</strong> This dataset consists of a collection of chest X-rays extracted from the <a href="https://physionet.org/content/mimic-cxr-jpg/2.0.0/" target="_blank" rel="noopener">MIMIC database</a>, carefully collected at the Beth Israel Deaconess Medical Center. In total, it comprises 1000 chest X-rays obtained from 741 patients with features suggestive of edema. These X-rays were carefully selected for manual annotation. The annotations are rich and detailed, covering specific radiological features commonly associated with pulmonary edema, including cephalization, Kerley lines, pleural effusions, bat wings, and infiltrates. The dataset includes a wide variety of radiological features, with a total of 4263 annotations (<em>Table 1</em>). Furthermore, each chest radiograph is thoughtfully assigned a severity category, categorizing it as "no edema", "vascular congestion", "interstitial edema", or "alveolar edema".</p> <p><strong>Annotation Method:</strong> The annotation process was meticulously performed by a highly qualified clinician with over 10 years of radiology experience, utilizing both frontal and lateral views for each chest X-ray study. Cephalization and Kerley lines were delineated using polylines, while other features were delineated using binary masks. This methodological approach was carefully chosen to provide a comprehensive data set that would ensure accuracy in subsequent analyses and label assignments. </p> <p>Notably, all features are represented as bounding boxes, meticulously defined by their respective upper-left (x1; y1) and lower-right (x2; y2) corners. In addition, selected features are provided with masks encoded in base 64 format. To facilitate seamless decoding, we provide a conversion script called "mask_converter.py" that allows the transformation of encoded masks into a versatile numpy array format. This feature improves the usability of the dataset for precise analysis and deep learning applications.</p> <p><strong>Datasets:</strong></p> <ol> <li><strong>SLY dataset:</strong> The dataset contains chest X-ray images labeled by clinicians, including both stacked frontal and lateral images. We obtained this dataset by annotating it on the <a href="https://supervisely.com/" target="_blank" rel="noopener">Supervisely platform</a>, and it is stored in JSON and PNG formats.</li> <li><strong>Source dataset:</strong> The dataset is a transformed version of the SLY dataset. In this dataset, all annotations are consolidated into a single spreadsheet, and only frontal view images are represented.</li> <li><strong>Processed dataset: </strong>The dataset focuses exclusively on the lung area for analysis, as other areas surrounding the lung typically contain extraneous information that clinicians do not use in their decision-making process.</li> <li><strong>COCO dataset:</strong> A collection of subsets prepared in the <a href="https://towardsdatascience.com/how-to-work-with-object-detection-datasets-in-coco-format-9bf4fb5848a4" target="_blank" rel="noopener">COCO format</a> and suitable for training and testing. It includes subsets for each feature and for all features evaluated in this study.</li> </ol> <div> <p><strong>Access to the Study:</strong> Further information about this study, including curated source code, dataset details, and trained models, can be accessed through the following repositories:</p> <ul> <li><strong>Source code:</strong> <a href="https://github.com/ViacheslavDanilov/edema_quantification" target="_blank" rel="noopener">https://github.com/ViacheslavDanilov/edema_quantification</a></li> <li><strong>Dataset:</strong> <a href="https://doi.org/10.5281/zenodo.8383776" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8383776</a></li> <li><strong>Lung segmentation models:</strong> <a href="https://doi.org/10.5281/zenodo.8393555" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8393555</a></li> <li><strong>Radiographic feature detection models:</strong> <a href="https://doi.org/10.5281/zenodo.8393565" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8393565</a></li> </ul> <p> </p> <p><em><strong>Table 1.</strong> Summary of annotated radiological features and severity labels</em></p> <table> <tbody> <tr> <td> <p><strong>Radiological feature</strong></p> </td> <td> <p><strong>Number </strong><strong>of objects</strong></p> </td> <td> <p><strong>Severity </strong><strong>l</strong><strong>abel</strong></p> </td> <td> <p><strong>Number of cases</strong></p> </td> </tr> <tr> <td> <p>Cephalization</p> </td> <td> <p>1656</p> </td> <td> <p>No edema</p> </td> <td> <p>21</p> </td> </tr> <tr> <td> <p>Kerley line</p> </td> <td> <p>609</p> </td> <td> <p>Vascular congestion</p> </td> <td> <p>74</p> </td> </tr> <tr> <td> <p>Pleural effusion</p> </td> <td> <p>317</p> </td> <td> <p>Interstitial edema</p> </td> <td> <p>51</p> </td> </tr> <tr> <td> <p>Bat wing</p> </td> <td> <p>1604</p> </td> <td> <p>Alveolar edema</p> </td> <td> <p>595</p> </td> </tr> <tr> <td> <p>Infiltrate</p> </td> <td> <p>77</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>TOTAL</p> </td> <td> <p>4263</p> </td> <td> <p>TOTAL</p> </td> <td> <p>741</p> </td> </tr> </tbody> </table> <p> </p> </div>
Supplementary Materials for "Simultaneous single-shot radiographic imaging using a laser-driven x-ray and proton micro-source"
<p>Simulation Data Repository, please read the contained README file in the contained simulation/ directory.</p> <p>This directory contains a copy of the used PIConGPU source code, version 0.5.0-dev-60ad9eb85 and analysis scripts.</p> <p>The PIConGPU source code is archived including its complete git history (git version 2.17.1) in source/picongpu.tar.gz with the input parameter template inside in share/picongpu/examples/Wneedle .</p> <p>Generally, PIConGPU source code is available via <a href="https://doi.org/10.5281/zenodo.591746">https://doi.org/10.5281/zenodo.591746</a> with its public git repository being maintained on <a href="https://github.com/ComputationalRadiationPhysics/picongpu">https://github.com/ComputationalRadiationPhysics/picongpu</a> .</p> <p>The two simulations’ exact input is modified accordingly in the directory input/ inside: 2D_a0-45_Z-10_ppc-20_002_light.tar.gz (p-polarized; along X) 2D_a0-45_Z-10_ppc-20_003_light.tar.gz (s-polarized; along Z).</p> <p>“Heavy” simulation data (checkpoints in simOutput/checkpoints/, full-resolution field and particle output in simOutput/bp/ ) has been stripped from this archive and are archived on NERSC’s HPSS tape archive.</p> <p>Analysis scripts are provided as Jupyter notebooks (DensityPlot_polX.ipynb and DensityPlot_polZ.ipynb) and depend on the following software:</p> <p>- adios 1.13.1 python bindings with enabled c-blosc transformations<br> - numpy 1.17.1<br> - matplotlib 3.1.1<br> - PIConGPU post-processing helper modules located in each simulation root directory under “input/lib/python/”<br> <br> The detailed conda environment can be found in the README.</p>
Text-fig. 5. Skull from Moča (Komárno district, southern Slovakia), frontal radiograph of skull showing hypoplasia of the right frontal sinus. in A Late Upper Palaeolithic Skull From Moča (The Slovak Republic) In The Context Of Central Europe
Text-fig. 5. Skull from Moča (Komárno district, southern Slovakia), frontal radiograph of skull showing hypoplasia of the right frontal sinus.
Text-fig. 6. Hyainailouros sulzeri BIEDERMANN, 1863 from Arrisdrift, Sperrgebiet, Namibia. GSN AD 106'99, left mandible with m1. a: radiograph showing unerupted m3 and m2, b: occlusal view, c: buccal view. pp = undetermined premolars, pa = paraconid, pr = protoconid. in New Hyaenodonts (Ferae, Mammalia) From The Early Miocene Of Napak (Uganda), Koru (Kenya) And Grillental (Namibia)
Text-fig. 6. Hyainailouros sulzeri BIEDERMANN, 1863 from Arrisdrift, Sperrgebiet, Namibia. GSN AD 106'99, left mandible with m1. a: radiograph showing unerupted m3 and m2, b: occlusal view, c: buccal view. pp = undetermined premolars, pa = paraconid, pr = protoconid.
Effect of Secukinumab on Radiographic Progression in Ankylosing Spondylitis as Compared to GP2017 (Adalimumab Biosimilar)
ClinicalTrials.gov study NCT03259074. IPD Sharing: YES. Countries: 29. Publications: 2.
Study of Efficacy and Safety of Secukinumab in Patients With Non-radiographic Axial Spondyloarthritis
ClinicalTrials.gov study NCT02696031. IPD Sharing: YES. Countries: 24. Publications: 5.
DenPAR: Annotated Intra-Oral Periapical Radiographs Dataset
<p>This dataset, named "DenPAR: Annotated Intra-Oral Periapical Radiographs Dataset," comprises a collection of intraoral periapical (IOPA) radiographs specifically curated for the detection of alveolar bone loss. The dataset includes the following:</p> <ul> <li><strong>IOPA Radiographs</strong>: Radiographs images suitable for dental analysis.</li> <li><strong>Alveolar Crestal Bone Level Annotations</strong>: Precise markings indicating bone levels for detection and evaluation of bone loss.</li> <li><strong>Keypoint Annotations</strong>: Annotations include critical points such as the Cemento-Enamel Junction (CEJ), APEX, and bounding box for each tooth.</li> <li><strong>Teeth Annotations</strong>: Data identifying individual teeth in each radiograph.</li> <li><strong>Tooth Segmentation</strong>: Segmented regions of each tooth, aiding in targeted analysis.</li> </ul> <p>This dataset aims to support research and development in dental imaging, particularly in automated bone loss detection and related dental diagnostic applications.</p> <p> </p> <blockquote> <h2><strong>Recommended Citation:</strong></h2> <h3>If you use this dataset, please cite the original article in Nature Scientific Data:</h3> <p>Rasnayaka, S., Leuke Bandara, D., Jayasundara, A. <em>et al.</em> DenPAR: Annotated Intra-Oral Periapical Radiographs Dataset for Machine Learning. <em>Sci Data</em> <strong>12</strong>, 1615 (2025). <a href="https://doi.org/10.1038/s41597-025-05906-9">https://doi.org/10.1038/s41597-025-05906-9</a></p> </blockquote> <p> </p> <p> </p>
Radiographic data of a wooden block with metal markers at the J. Paul Getty Museum X-ray facility
<p><strong>Summary</strong></p> <p>This submission contains radiographic data containing metal markers of a small wooden block. </p> <p>The data is made available as part of [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the X-ray facility located at the J. Paul Getty Museum, Los Angeles. Full details can be found in [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>List of Contents</strong></p> <p>The content of the submission is given below. All raw data (i.e. no corrections) is made available in .tif format.</p> <p>The folder contains:</p> <ul> <li>darks: folder containing darkfield images, IMG<em>*.dicom</em></li> <li>flats: folder containing flatfield images, IMG<em>*.dicom</em></li> <li>data1: raw (unprocessed or uncorrected) projection data, IMG<em>*.dicom</em></li> <li>data2: raw (unprocessed or uncorrected) projection data, IMG<em>*.dicom</em></li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>Accompanying code can be found here.</p> <p><em> </em></p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><strong>References</strong></p> <p><a href="https://www.nature.com/articles/s41467-024-48102-w">Bossema, F.G., Palenstijn, W.J., Heginbotham, A. <em>et al.</em> Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums. <em>Nat Commun</em> <strong>15</strong>, 3939 (2024). https://doi.org/10.1038/s41467-024-48102-w</a></p> <p><em> </em></p> <p> </p>
Radiographic data of a wooden block with metal markers at the Rijksmuseum X-ray facility
<p><strong>Summary</strong></p> <p>This submission contains radiographic data containing metal markers of a small wooden block. </p> <p>The data is made available as part of [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the X-ray facility located at the Rijksmuseum, Amsterdam. Full details can be found in [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>List of Contents</strong></p> <p>The content of the submission is given below. All raw data (i.e. no corrections) is made available in .tif format.</p> <p>The folder contains:</p> <ul> <li>darks_flats: folder containing darkfield and flatfield images, <em>frame*.tif</em></li> <li>data: raw (unprocessed or uncorrected) projection data, <em>frame*.tif</em></li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a>Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). Accompanying code can be found here.</p> <p><em> </em></p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><strong>References</strong></p> <p><a href="https://www.nature.com/articles/s41467-024-48102-w">Bossema, F.G., Palenstijn, W.J., Heginbotham, A. <em>et al.</em> Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums. <em>Nat Commun</em> <strong>15</strong>, 3939 (2024). https://doi.org/10.1038/s41467-024-48102-w</a></p>
Radiographic data of 'Python killing a Gnu' by Antoine-Louis Barye, The J. Paul Getty Museum collection
<p><strong>Summary</strong></p> <p>This submission contains radiographic data including small metal markers of the sculpture <a href="https://www.getty.edu/art/collection/object/103RQ1"><em>Python Killing a Gnu </em>(1840s–1860s), Antoine-Louis Barye (French, 1796 - 1875), the J. Paul Getty Museum collection</a> number 85.SE.48. (h 27.9 cm, w 39.1 cm, d 20.5 cm.)</p> <p>The data is made available as part of [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the X-ray facility located at the J. Paul Getty Museum, Los Angeles. Full details can be found in [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>List of Contents</strong></p> <p>The content of the submission is given below. All raw data (i.e. no corrections) is made available in .tif format.</p> <p>The folder contains:</p> <ul> <li>darks_flat: folder containing darkfield and flatfield images, <em>frame*.tif</em></li> <li>data: raw (unprocessed or uncorrected) projection data, <em>frame*.tif</em></li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a>Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). Accompanying code can be found here.</p> <p><em> </em></p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><strong>References</strong></p> <p><a href="https://www.nature.com/articles/s41467-024-48102-w">Bossema, F.G., Palenstijn, W.J., Heginbotham, A. <em>et al.</em> Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums. <em>Nat Commun</em> <strong>15</strong>, 3939 (2024). https://doi.org/10.1038/s41467-024-48102-w</a></p> <p><em> </em></p> <p> </p>
Radiographic data of a wooden block with metal markers at the FleX-ray laboratory (high resolution)
<p><strong>Summary</strong></p> <p>This submission contains radiographic data containing metal markers of a small wooden block. </p> <p>The data is made available as part of [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].</p> <p><strong>List of Contents</strong></p> <p>The content of the submission is given below. All raw data (i.e. no corrections) is made available in .tif format.</p> <p>The data folder contains:</p> <ul> <li>darkfield image, <em>di000000.tif</em>,</li> <li>flatfield image before acquisition, <em>io000000.tif</em>, and after acquisition, <em>io000001.tif</em>,</li> <li>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em>,</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata,</li> <li><em>scan settings.txt</em>, a text file with scanner metadata in more human readable format, and</li> <li><em>data settings XRE.ini</em>, a snapshot text file of basic geometry information at the start of a scan.</li> <li><em>script_executed.txt</em>, the text file containing the list of commands the apparatus has executed.</li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a>Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a>GitHub page</a>.</p> <p><em> </em></p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><em> </em></p> <p><strong>Acknowledgments</strong></p> <p>The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project numbers 341-60-001, 639.073.506 and 628.007.033 and Netherlands Institute for Conservation, Art and Science (NICAS).</p> <p><strong>References</strong></p> <p>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,” <em>J. Imaging</em>, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p><a href="https://www.nature.com/articles/s41467-024-48102-w">Bossema, F.G., Palenstijn, W.J., Heginbotham, A. <em>et al.</em> Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums. <em>Nat Commun</em> <strong>15</strong>, 3939 (2024). https://doi.org/10.1038/s41467-024-48102-w</a></p>
Figure S1 Surgical extraction of supernumerary teeth under sedation and analgesia, and a periapical radiograph 2 weeks later.
<p>Figure S1 Surgical extraction of supernumerary teeth under sedation and analgesia, and a periapical radiograph 2 weeks later.</p>
Dataset from: Limb development in skeletally-immature large-sized dogs: a radiographic study
<p>Dataset from the publication: "Limb development in skeletally-immature large-sized dogs: a radiographic study", published in PLoS ONE, 2021.</p> <p>Despite the extreme morphological variability of the canine species, data on limb development are limited and the time windows for the appearance of the limb ossification centres (OCs) reported in veterinary textbooks, considered universally valid for all dogs, are based on dated studies. The aim of this study was to acquire up-to-date information regarding the arm, forearm and leg bone development in skeletally-immature large-sized dogs from 6 weeks to 16 weeks of age. Nine litters of 5 large-sized breeds (Boxer, German Shepherd, Labrador Retriever, Saarloos Wolfdog, White Swiss Shepherd Dog) were included, for a total of 54 dogs, which were subject to clinical and radiographic examination on a bi-weekly basis. The appearance of 18 limb OCs was recorded and 14 radiographic measurements were performed; their relationship with age and body weight was investigated and any breed differences were analysed.</p>
Radiographers' Perspectives on Clinical Supervision of Students in Ireland
<p>The authors developed and executed an online national survey of clinical radiographers in Ireland involved in supervision of radiography students. The aim of the study was to explore radiographers’ attitudes and perceptions of confidence in undertaking clinical supervision, along with perceived barriers in a ‘real-life’ clinical department. The survey instrument was designed to gather information across the following domains: (1) Demographic data of participants; (2) Attitude and perceived level of confidence towards clinical supervision; (3) Perceived potential barriers to the efficacy of clinical supervision; (4) Potential supports to improve radiographer motivation and quality of supervision. There is an opportunity for this dataset to add to the evidence base on radiography clinical education. Such information has the potential to inform future practices and interventions tailored to the needs of the context by contributing towards engaging the full spectrum of those involved in all aspects of the clinical education of student radiographers. Thus far, this is, to our knowledge, the first national survey conducted in Europe, of radiographers to identify perceptions, barriers and potential enablers of effective student supervision in the clinical setting.</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
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.