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FIGURE 5. Cassidulus caribaearum Lamarck, 1801 in Synchrotron micro-CT scanning leads to the discovery of a new genus of morphologically conserved echinoid (Echinodermata: Cassiduloida)
FIGURE 5. Cassidulus caribaearum Lamarck, 1801 (CASIZ 222205 [neotype] [A–D, I–L]; CASIZ 112638 [E–G, M]), SEM images: (A–B) juveniles attached to the test; (C) aboral, (D) bourrelet, (E) oral and (F) miliary spines; stalk of (G) large tridentate pedicellariae (detail of projections in H), (I) small tridentate pedicellariae (detail of stereom in J), and of (K) ophicephalous pedicellariae; (L) sphaeridium; and (M) young spine. Scale bars: A–B, M, 100 µm; C–I, 200 µm; K–L, 50 µm.
FIGURE 6. Cassidulus caribaearum Lamarck, 1801 in Synchrotron micro-CT scanning leads to the discovery of a new genus of morphologically conserved echinoid (Echinodermata: Cassiduloida)
FIGURE 6. Cassidulus caribaearum Lamarck, 1801 (CASIZ 222205 [neotype] [B–E, J–L]; CASIZ 112638 [A, F–I]), SEM images: valves of (A–B) triphyllous pedicellariae, (C–D) ophicephalous pedicellariae, (E–G) large tridentate pedicellariae ([H] detail of valve head), and (I–K) small tridentate pedicellariae ([L] detail of valve head). Scale bars: A–B, 15 µm; C–D, H, L, 30 µm; E–G, I–K, 100 µm.
Time-lapse helical X-ray computed tomography (CT) data of tensile fatigue damage in GFRP
<p>The X-ray CT data here is published with the paper - </p> <p>Wang, Y.; Mikkelsen, L.P.; Pyka, G.; Withers, P.J. Time-Lapse Helical X-ray Computed Tomography (CT) Study of Tensile Fatigue Damage Formation in Composites for Wind Turbine Blades. <em>Materials</em> <strong>2018</strong>, <em>11</em>, 2340.</p> <p>https://doi.org/10.3390/ma11112340</p> <p>More information about the data and the material can be found in the paper above.</p> <p> </p> <p>If using the data here, please cite the above paper.<em> </em></p> <p>Contact details for author: Ying Wang, ying.wang-4@manchester.ac.uk</p>
CT Data of a Pen-Spring: Application to Under-Sampled Dynamic X-ray Tomography
<p>This is the documentation of Computed Tomography (CT) data of a pen-spring. It can be freely used for scientific purposes with appropriate references to the data and to this document in https://arxiv.org/abs/1907.01871. The provided data set includes the X-ray sinograms finalSino of a single 2D slice from a different height of the spring. The finalSino was obtained from a measured 10-projection or 100-projection sinogram using fan-beam geometry<br> by down-sampling and taking logarithms. The data set includes also those original measured sinograms and corresponding measurement matrices.</p>
Outpatient CT scan scheduling data
<p>Outpatient Scheduling Data for Computed Tomography (CT) obtained from the consultation scheduling management system of the Radiology Department of Hospital de Clinicas de Porto Alegre (HCPA), a public, 850-bed, tertiary care teaching hospital in southern Brazil. Data collected cover 8,382 appointments made for this type of radiological exam performed in the year 2017.</p>
LoDoPaB-CT Dataset
<p>A Benchmark Dataset for Low-Dose CT Reconstruction Methods.</p> <p>The following Data Descriptor article provides full documentation:</p> <p>Leuschner, J., Schmidt, M., Baguer, D.O. <em>et al.</em> LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction. <em>Sci Data</em> <strong>8, </strong>109 (2021). <a href="https://www.nature.com/articles/s41597-021-00893-z">https://www.nature.com/articles/s41597-021-00893-z</a></p> <p> </p> <p>The python library DIVαℓ (<a href="https://github.com/jleuschn/dival">github.com/jleuschn/dival</a>) can be used to download and access the dataset.</p> <p>Reconstructions from the <a href="http://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX">LIDC/IDRI dataset</a> are used as a basis for this dataset.</p> <p> </p> <p>The ZIP files included in the LoDoPaB dataset contain multiple <a href="https://www.hdfgroup.org/HDF5/">HDF5</a> files. Each HDF5 file contains one HDF5 dataset named <code>"data"</code>, that provides a number of samples (128 except for the last file in each ZIP file). For example, the <code>n</code>-th training sample pair is stored in the files <code>"observation_train_%03d.hdf5"</code> and <code>"ground_truth_train_%03d.hdf5"</code> where <code>"%03d"</code> is <code>floor(n / 128)</code>, at row <code>(n mod 128)</code> of <code>"data"</code>.</p> <p><em>Note:</em> each last ground truth file (i.e. <code>ground_truth_train_279.hdf5</code>, <code>ground_truth_validation_027.hdf5</code> and <code>ground_truth_test_027.hdf5</code>) still contains a HDF5 dataset of shape (128, 362, 362), although it contains less than 128 valid samples. Thus, the number of valid samples needs to be determined from the total samples numbers in the part (i.e. "train": 35820, "validation": 3522, "test": 3553), or from the corresponding observation file, for which the first dimension of the HDF5 dataset matches the number of valid samples in the file.</p> <p>The randomized patient IDs of the samples are provided as CSV files. The patient IDs of the train, validation and test parts are integers in the range of 0–631, 632–691 and 692–751, respectively. The ID of each sample is stored in a single row.</p> <p><em>Acknowledgements</em></p> <p>Johannes Leuschner, Maximilian Schmidt and Daniel Otero Baguer acknowledge the support by the Deutsche<br> Forschungsgemeinschaft (DFG) within the framework of GRK 2224/1 “π3: Parameter Identification – Analysis,<br> Algorithms, Applications”. We thank Simon Arridge, Ozan Öktem, Carola-Bibiane Schönlieb and Christian<br> Etmann for the fruitful discussion about the procedure, and Felix Lucka and Jonas Adler for their ideas and<br> helpful feedback on the simulation setup. The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.</p>
Fig. 6. 3D in An XXL-CT-scan of an XXL Tyrannosaurus rex skull
Fig. 6. 3D-rendering of the CT dataset with crate sideways removed (left). Cutting through the crate in lateral directions reveals the bone structures inside the sandstone matrix (right).
Figure 6 in An XXL-CT-scan of an XXL Tyrannosaurus rex skull
Figure 6 (left) shows a 3D-rendering of the same dataset. To distinguish the different materials a false-color representation based on the different absorption values of the materials were used. The virtually removed sideways uncovers the wrapping around the skull and sandstone block as well as the additional wooden supporting structures. Figure 6 (right) cuts through the crate in lateral direction and shows the interior of the skull and sandstone block. The white colored structure marks the bone fragments. The next step was to virtually excavate the skull. The good material contrast made it possible to mask out the bulk of the sandstone matrix and the supporting structures by setting appropriate thresholds. High absorption parts like the screws as well as noise particles had to be removed manually. The result of this segmentation can be seen in figure 7. At this stage the segmented skull is still represented by three dimensional pixels (Voxels) with a specific absorption value. To allow for further processing in CAD software e.g. for 3D-printing preparation the segmented skull was converted to a triangular surface mesh in the stl Format.
Fig. 5 in An XXL-CT-scan of an XXL Tyrannosaurus rex skull
Fig. 5. CT slice through the skull. The skull bones clearly stand out from the sandstone matrix and the supporting structures.
Fig. 4 in An XXL-CT-scan of an XXL Tyrannosaurus rex skull
Fig. 4. Top view of crate with skull included when scanning in natural orientation (left) and upright orientation (right). The arrow marks the estimated maximum penetration length for both setups.
Micro-CT scans of unloaded and loaded glenoid bone used for DVC
<p>Micro-CT (mCT) scans to estimate glenoid bone strain with Digital Volume Correlation (DVC). There are 8 mCT sets of the same sample. Sets mCT1 to mCT6 are unloaded glenoid for error estimation. Sets mCT7 and mCT8 are used for strain measurement: set mCT7 is unloaded and set mCT8 corresponds to 1500 N axial loading. Scans were performed consecutively pairwise: after each scan pair, the specimen was removed from the micro-CT and repositioned. Rigid registration of the fixed side and bone masking have been performed. Scans format is MHD.</p>
Head Neck CBCT CT datasets
<p><span>Planning CT images were acquired using the Brilliance CT Big Bore Scanner (Phillips Medical Systems, Andover, MA). Each CT image data includes 90-176 slices with a thickness of 3mm, and each slice has 512×512 voxels with a resolution of 1.1719 mm. Multiple CBCT images were acquired using an onboard Imager (OBI, Varian Medical Systems, Palo Alto, CA) in full-fan mode by Varian Edge linear accelerator during treatment, but only the images obtained on the first treatment day were used in this study. Each CBCT data contains 88 slices with a thickness of 3mm. Each slice has 270×270 voxels with a resolution of 1mm. All the scans have ground truth segmentations, each of which contains 5 anatomical structures.</span></p> <p><span>To keep the same spatial resolution between CT images and CBCT images, we resampled the CT image to a resolution of 1 mm×1 mm×3 mm. Both CT and CBCT images contain extraneous information, such as background regions, shoulder regions, and treatment beds. Thus, we cropped all CT and CBCT images to a standardized size and removed any irrelevant information within the field of view (FOV) to ensure that the images shared a consistent receptive field that covered only the head-neck region of interest. The final CT and CBCT images contain 240×240×64 voxels with a resolution of 1 mm×1 mm×3 mm.<br></span></p> <p> </p>
BRAIN CT PERFUSION IN ACUTE ISCHEMIC STROKE PATIENTS IN THE EARLY TIME WINDOW
Open the record for dataset details and reuse information.
Paired X-ray Micro CT Scanning and individual foraminifera isotopic analysis reveal (de)coupled changes in carbonate preservation and temperature
<p>To assess the coupling between drivers of isotopic variability and carbonate dissolution in foraminifera, we combine individual foraminiferal carbon and oxygen isotopic analyses (IFA) and X-ray MicroCT Scanning (XMCT)-based measurements of test densities (a proxy for the extent of post-depositional dissolution). As a proof-of-concept application of this approach, we analyze Globigerina bulloides tests from both coretop and downcore (latest Miocene/earliest Pliocene-aged) sediment from Ocean Drilling Project (ODP) Site 1088 (Agulhas Ridge).<br>Sediments from both the core intervals were wet-sieved over a 150 μm sieve and oven-dried at 55°C overnight. Individual tests of G. bulloides were picked from the >150 μm size fraction and gently brushed with the wet tip of a paintbrush to remove any adhering clay. Broken or partially fragmented tests were avoided. 25-50 individuals were mounted on a 1.2cm x 1 cm cardboard sample holder using double-sided carbon and imaged at a time on a Bruker Skyscan 1272 Micro-CT Scanner. In total, 123 individuals from the coretop sample and 88 individuals from the downcore sample were scanned.<br>Stable carbon (δ¹³C) and oxygen (δ¹⁸O) isotopic compositions of individual foraminifera were measured using a Kiel Carbonate Device IV coupled to a Thermo Fisher MAT 253 Plus Isotope Ratio Mass Spectrometer (IRMS). All individuals were weighed on a Sartorius ultramicrobalance before analysis, and only samples with mass >10 µg were analyzed as per the long-term precision setup of analyzing small quantities of carbonates on this machine. The XMCT-IFA dataset in this study consists of δ¹³C and δ¹⁸O values of 90 foraminifera (67 coretop and 23 downcore individuals). We compare population averages of XMCT parameters and isotopic compositions from both the coretop and downcore sediments.</p>
Analytical ultracentrifugation datasets of NT-2Rep-CT
<p>AUC datasets for "Metastable liquid-liquid phase separation and aging lead to strong processing path dependence in mini-spidroin solutions" publication.</p> <p>Dataset RunId 861 corresponds to Fig. 2a and Fig. 4a and contains information about NT-2Rep-CT sample 20 g/l concentration in 20 mM Tris pH 8.0 and 110 mM Tris pH 7.2 buffers.</p> <p>Datasets RunId 989 – 1032 correspond to Fig. 3.</p> <p>RunId 989, 991, 1000, 1018 and 1026 were measured using different protein concentrations: 989: 0.675, 1.25 and 2.5 g/l; 991: 10, 5 and 20 g/l; 1000: 0.1 and 2.5 g/l.</p> <p>Datasets RunId 1018 – 1024 contain information about time dependence of NT-2Rep-CT sample at 0.05, 0.1 and 1 g/l concentration in 20 mM Tris pH 8.0 buffer. RunId 1018 – first experiment (0 hour), RunId 1020 – after 24 hours, RunId 1022 – after 48 hours and RunId 1024 – after 120 hours.</p> <p>Datasets RunId 1026 – 1032 contain information about time dependence of NT-2Rep-CT sample at 1, 5 and 10 g/l concentration in 20 mM Tris pH 8.0 buffer. RunId 1026 – first experiment (0 hour), RunId 1028 – after 24 hours, RunId 1030 – after 48 hours and RunId 1032 – after 120 hours.</p>
Generated Metastatic CT 3D Femurs with lesions segmentation 3/3
<p>This file regroups the 5675 synthetic femurs used and described in the paper: "Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models" Saillard et al. </p> <p><strong>Three repositories are needed </strong>to form the full archive. When all are downloaded, they can be deflated with the following bash command : </p> <pre><code>cat Generated_3DCT_Metastatic_Femurs.tar.gz* | tar faxv -</code></pre> <p>see the Generated Metastatic CT 3D Femurs with lesions segmentation 1/3 for information.</p>
Generated Metastatic CT 3D Femurs with lesions segmentation 2/3
<p>This file regroups the 5675 synthetic femurs used and described in the paper: "Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models" Saillard et al. </p> <p><strong>Three repositories are needed </strong>to form the full archive. When all are downloaded, they can be deflated with the following bash command : </p> <pre><code>cat Generated_3DCT_Metastatic_Femurs.tar.gz* | tar faxv -</code></pre> <p> </p> <p>Generated CT Scans are organized in two directories: one for files generated with DDPM this one) and one without DDPM.</p> <p>├── Generated_3DCT_Metastatic_Femurs_with_DDPM <br>│ ├── img<br>│ ├── lbl<br>│ └── msk</p> <p>└── Generated_3DCT_Metastatic_Femurs_NO_DDPM<br> ├── img<br> ├── lbl<br> └── msk</p> <p>The content of each directory is as follows: </p> <ul> <li>img : the generated 3D CT scan (nii.gz format) from the 26 healthy femurs</li> <li>lbl : the lesions segmentation (nii.gz format) of the generated 3D CT scan</li> <li>msk : the segmentations of the 26 healthy femurs (nii.gz)</li> </ul> <p>A given filename corresponds to in img and lbl directories :</p> <ul> <li>./lbl/MEK03les0MEK14.nii.gz is the lesions segmentation of ./img/MEK03les0MEK14.nii.gz.</li> <li>./lbl/MEK03les197MEK28.nii.gz is the lesions segmentation of ./img/MEK03les197MEK28.nii.gz</li> </ul> <p>But in msk, the corresponding femur mask for these examples is ./msk/MEK03.nii.gz</p> <p> </p>
NasalSeg Dataset for Nasal Cavity and Paranasal Sinuses Segmentation from CT Images
<p>NasalSeg is the first large-scale, open-access dataset for nasal cavity and paranasal sinus segmentation from 3D CT images. The dataset comprises 130 CT scans with pixel-wise annotation of five anatomical structures including the left nasal cavity, right nasal cavity, nasal pharynx, left maxillary sinus, and right maxillary sinu.</p>
Data from: Revision of Icacinaceae from the Early Eocene London Clay flora based on X-ray micro-CT
The Early Eocene (Ypresian) London Clay Formation contains one of the most important fruit and seed assemblages from the Paleogene, including a large diversity of taxa (>350 spp.) preserved as pyrite permineralizations retaining 3D structure as well as anatomical detail. Despite the importance of the flora for understanding angiosperm biogeographic and evolutionary history, the majority of the fossil material has not been revisited since the original taxonomic treatments by E.M. Reid and M.E.J. Chandler. Given subsequent advances in our understanding of angiosperm phylogeny and fruit morphology, coupled with technological advances in imaging/visualizing fossil material, many of the taxa represented in the flora deserve further study. Here we present a revision of the pantropical family Icacinaceae using X-ray micro-computed tomography (micro-CT) images of 21 species from the flora. Based on the results, four new combinations are made, a new fossil-genus is established for a distinctive species with affinities to the Phytocreneae, and emended diagnoses are provided for eight taxa. Of the seven genera recognized from the flora, only one, Iodes Blume, is extant. This study offers important insights on the biogeographic and evolutionary history of Icacinaceae, which is one of the most abundant and diverse components of the flora from the London Clay Formation.
Data from: Next-generation morphological character discovery and evaluation: an X-ray micro-CT enhanced revision of the ant genus Zasphinctus Wheeler (Hymenoptera, Formicidae, Dorylinae) in the Afrotropics
New technologies for imaging and analysis of morphological characters offer opportunities to enhance revisionary taxonomy and better integrate it with the rest of biology. In this study, we revise the Afrotropical fauna of the ant genus Zasphinctus Wheeler, and use high-resolution X-ray microtomography (micro-CT) to analyse a number of morphological characters of taxonomic and biological interest. We recognise and describe three new species: Z. obamai sp. n., Z. sarowiwai sp. n., and Z. wilsoni sp. n. The species delimitations are based on the morphological examination of all physical specimens in combination with 3D scans and volume reconstructions. Based on this approach, we present a new taxonomic discrimination system for the regional fauna that consists of a combination of easily observable morphological characters visible at magnifications of around 80–100 ×, less observable characters that require higher magnifications, as well as characters made visible through virtual dissections that would otherwise require destructive treatment. Zasphinctus are rarely collected ants and the material available to us is comparatively scarce. Consequently, we explore the use of micro-CT as a non-invasive tool for the virtual examination, manipulation, and dissection of such rare material. Furthermore, we delineate the treated species by providing a diagnostic character matrix illustrated by numerous images and supplement that with additional evidence in the form of stacked montage images, 3D PDFs and 3D rotation videos of scans of major body parts and full body (in total we provide 16 stacked montage photographs, 116 images of 3D reconstructions, 15 3D rotation videos, and 13 3D PDFs). In addition to the comparative morphology analyses used for species delimitations, we also apply micro-CT data to examine certain traits, such as mouthparts, cuticle thickness, and thoracic and abdominal muscles in order to assess their taxonomic usefulness or gain insights into the natural history of the genus. The complete datasets comprising the raw micro-CT data, 3D PDFs, 3D rotation videos, still images of 3D models, and coloured montage photos have been made available online as cybertypes (Dryad, http://dx.doi.org/10.5061/dryad.4s3v1).
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.