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.

datasets available to search

ShareScore release 0.9.0

Dataset results

15 results for “microCT scan”

Learn how ShareScore rates datasets ↗

zenodo48/100

MicroCT scans of a hybrid poplar leaf dehydrating, with annotated slices for model training

Dataset of a leaf segment of a hybrid poplar (P. maximowiczii x P. nigra ‘Max3’) leaf scanned using microcomputed tomography (microCT) over time as it dehydrates.   Data acquisition methodology Plants were brought to the TOMCAT tomographic beamline of the Swiss Light Source at the Paul Scherrer Institute (Villigen, Switzerland). Before microCT scanning, a young fully expanded leaf was detached from the plant and a short strip (0.4 x 1.5 cm) was cut between second-order veins. The base of the strip was wrapped in polyimide tape and inserted into a styrofoam block fixed on a sample holder. The strip was immediately scanned by imaging 1801 projections of 100 ms under a beam energy of 21 keV and a magnification of 40x, yielding a final voxel size of 0.1625 µm (field of view: ~416x416x312 µm). The leaf was left to dehydrate in the holder and additional scans were taken 10, 20, 25, and 30 minutes after the initial scan. Scanned projections were reconstructed to a transverse view using both absorption (gridrec; Marone et al. 2012) and phase contrast enhancement (Paganin et al. 2002) reconstruction.   Dataset description On the reconstructed images a region of interest was identified using a paradermal view (i.e. top to bottom of the leaf) and used to manually align the scans of each time step. Thereafter, all images were cropped to that ROI, ensuring that the same region of the leaf was present in all image stacks. For all stacks, files start with: DEHYDRATION_small_Leaf4_time_N_ where N is the time point, with values from 1 to 5 equaling 0, 10, 20, 25, and 30 minutes. Following this prefix is either GRID (gridrec reconstruction), PAGANIN (phase contrast enhancement reconstruction), or LABELLED (hand labelled slices or ground truth). For GRID and PAGANIN, 8-bit grayscale stacks are provided. The AOI suffix indicates the region of interest. Stacks have been hand labelled over three orientations (for visual examples of the orientations see <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time1.png?versionId=26fc15aa-702e-4052-b162-702cc567634c">Labeled_Sections_order_time1.png</a> and <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time2.png">Labeled_Sections_order_time2.png</a>): <ol> <li>CROSS (cross sectional, or transverse, view)</li> <li>LONGI (longitudinal view: similar to cross sectional view but starting normal to it, i.e. along the depth of the stack starting from the left of the cross-sectional view)</li> <li>PARADERMAL (top to bottom view: starting at the upper epidermis)</li> </ol> A general idea of the slice range within one LABELLED stack is presented after the orientation, as: STARTtoENDbyRANGE The exact position of the labelled slices for each time point can be found in the <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_slices_positions.txt?versionId=93d7e22f-9f07-4f98-8c49-d93e9a2e1ce5">Labeled_slices_positions.txt </a>file. Note that one-based indexing is used (as in ImageJ), not zero-based indexing (as in e.g. Python).   References Marone F, Stampanoni M. 2012. Regridding reconstruction algorithm for realtime tomographic imaging. Journal of Synchrotron Radiation 19: 1029–1037. Paganin D, Mayo SC, Gureyev TE, Miller PR, Wilkins SW. 2002. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. Journal of Microscopy 206: 33–40.

opencc-by-4.0Sep 2022View details →

zenodo44/100

Stacks of microCT Scans, Cell size, weight, volume and thallus size data supporting the paper 'Mechanical regulation of tissue flatness in Marchantia'

<div> <div> <div> These data are the supporting elements to the following paper: 'Mechanical regulation of tissue flatness in Marchantia' </div> </div> </div>  .tif files contain MicroCT (MCT) scans of 16-day-old Marchantia polymorpha thalli. Three genotypes were analysed here: fer-2 mutant (from Mecchia et al., 2022), FER-OE #9 (proMpEF1::MpFERONIA-mCitrine trangenic line 9) from Mecchia et al., 2022), and Tak-1 (WT line). These plants were grown in 3 different media: Gamborgh B5 + vitamins and 0.6, 1.2 and 2.5% agar, and one stress condition consisting of the adjunction of a thin PDMS film at 4, to mimich external mechanical stimulus (only performed on thalli grown on 1.2% agar). MicroCT scans were performed at the faculity of odontology of Université Paris-Cité (Plateform imagerie du vivant) with the technical support of Lotfi Slimani and Baptiste Casel. https://piv.u-paris.fr/micro-ct-haute-resolution/  All files already have embeded scales. Each file name consists of a unique ID number in the following form: P+<LETTER>+<NUMBER>-<CONDITION> -LETTER: One letter = one imaging session -NUMBER: Individual and Genotype: 33-40 -> Tak1; 200-207->fer-2; 41-49 -> FER-OE -CONDITION : AGAR0.6/AGAR2.5/PDMS. Absence of condition indicates growth on standard medium (1.2% agar). PDMS indicated growth on standard medium and supplementation of a topping PDMS film at day 4)   -Volume data were calculated from MicroCT scans -thallus projected surfaces were calculated from MicroCT scans <a href="https://zenodo.org/api/records/13981438/draft/files/Lambda%20curvature%20calculation.ipynb/content" target="_blank" rel="noopener noreferrer">-Lambda curvature calculation.ipynb</a> is suited for MorphographX mesh exported .txt files.

opencc-by-4.0Oct 2024View details →

zenodo40/100

MicroCT scans of sun and shade grown leaves of Cabernet Sauvignon and Blaufränkisch grapevine (Vitis vinifera L.) cultivars

Image data set of sun and shade-grown leaves of Cabernet Sauvignon (CS) and Blaufränkisch (BF) grapevine (Vitis vinifera L.) cultivars. When using this dataset, please cite: <blockquote> Théroux-Rancourt G, Herrera C, Voggeneder K, Luijken N, Nocker L, Savi T, Scheffknecht S, Schneck M, Tholen D. (accepted) Analyzing anatomy over three dimensions unpacks the differences in mesophyll diffusive area between sun and shade Vitis vinifera leaves. AoB Plants </blockquote>   Data acquisition methodology Plants were brought to the TOMCAT tomographic beamline of the Swiss Light Source at the Paul Scherrer Institute (Villigen, Switzerland) in pots (Cabernet Sauvignon (CS)) or as cut shoots with the cut end placed in water (Blaufränkisch (BF)). Before scanning, a leaf was cut from the stem and a thin strip of ~1.5 mm width and 1.5 cm length was cut in between apparent higher-order veins, immediately wrapped in polyimide tape and inserted into a styrofoam block glued with wax onto a holder. Three (CS) or two (BF) strips were cut at different locations on the leaf surface to ensure within-leaf replications and to get better leaf-level averages. The strip was immediately scanned by imaging 1801 projections of 100 ms under a beam energy of 21 keV and magnified using a 40x (CS) or 20x (BF) objective, yielding respective final voxel sizes of 0.1625 µm (field of view: ~416x416x312 µm) and 0.325 µm (field of view: ~832x832x624 µm). Scanned projections were reconstructed to cross-sectional view using both absorption (gridrec; Marone et al. 2012) and phase contrast enhancement (Paganin et al. 2002) reconstructions. Dataset description After aligning the stacks to be parallel to the image edges using ImageJ (Schneider et al. 2012), at least nine slices were hand labeled using a graphics pen display tablet to precisely segment the background, the epidermis, and the vasculature. The mesophyll cells and the intercellular airspace were segmented by thresholding each absorption and phase contrast scan to maximize airspace volume (background) and taking care to avoid false segmentation within the cells  (i.e. false segmentation of airspace). The hand-labeled slices were then used to automatically segment the whole stack using a Python-based random-forest machine learning approach (Théroux-Rancourt, Jenkins, et al. 2020).   File naming convention For all stacks, files start with: Cultivar_Treatment_Plantn_leaf_N_ <ul> <li>Cultivar: CS for Cabernet Sauvignon, BF for Blaufränkisch</li> <li>Treatment: Sun for plants grown under high light, Shade for plants grown under low light</li> <li>Plantn: Plant number; 1-6 for CS, 1-5 for BF</li> <li>leaf: Leaf number; 1-4</li> </ul> N specifies the type of stack: <ul> <li>GRID (gridrec reconstruction)</li> <li>PAGANIN (phase contrast enhancement reconstruction)</li> <li>labelled-stack (hand labeled slices / ground truth; stacks have been hand labeled in cross-sectional view.)</li> <li>SEGMENTED (automatically segmented stack using random-forest machine learning approach)</li> <li>STOMATAL_REGIONS_BBOX_CROPPED (Stack with individually segmented stomatal vaporsheds, i.e. airspace closest to a stoma. The stack has been cropped in paradermal view around the stomata closest to the stack's edges, i.e. a bounding box (BBOX) with stomatal vaporsheds fully enclosed).</li> </ul> All stacks are provided as 8-bit grayscale TIF files. Stacks have been hand labeled in cross-sectional view.   Plant material and growth conditions The experiment was carried out over two consecutive years, in 2018 and 2019, at the facilities of BOKU UFT (Tulln, Austria). In the first year, rooted grafts of Vitis vinifera ‘Cabernet Sauvignon’ (clone 191E) on 101-14 rootstock (hereafter named CS) were acquired from a local nursery (Reben Iby, Neckenmarkt, Austria). In the second year, rooted grafts of Vitis vinifera ‘Blaufränkisch’ (clone 13-3 GM) on 5 BB rootstock (hereafter named BF) were acquired from the same nursery. Blaufränkisch is known to have originated from Lower Styria (present day Styria, Slovenia, Maul et al. 2016) and to be genetically different from Cabernet Sauvignon (Magris et al. 2021), which originated in the Bordeaux region in France. The rooted grafts were planted in 7-L pots and allowed to grow in a glasshouse without any environmental control. For CS, nutrient-rich, sieved vineyard soil mixed with perlite (3:1 ratio) was used, while for BF pots were filled with commercial pot substrate containing slow release fertilizer (10 g pot-1, 15-5-20 NPK “Entec vino”). Pots were watered to pot capacity automatically every day. Clones were planted June 1 and April 1 in the first and second year, respectively. When all the plants had at least three mature leaves on one shoot, plants were pruned so that only one dominant shoot remained. To ensure that only leaves fully developed under different light conditions were used for further analyses, the last developing leaf below the tip was marked before moving half of the plants to the shaded environment (on June 29, 2018 for CS and on April 18, 2019 for BF). For the shade treatment, a tent of about 2 m (height) x 1.5 m (width) x 1.5 m (depth) was made from black polypropylene cloth (HaGa-Welt GmbH & Co. KG, Elze, Germany), resulting in a 60% reduction in photosynthetic photon flux density (PPFD). A spectrometer (FLAME-S-VIS- ES, Ocean Optics Inc. Largo, USA) was used to confirm that under both light conditions, relative differences in the contribution of red, green blue and far-red light to the total PPFD were below 5% (i.e. spectrally neutral shade). During the week before synchrotron microCT scanning (last week of August in 2018 and first week of September in 2019), one mature leaf per plant was selected at least three leaves above the previously mentioned mark indicating the last developing leaf at the start of the shade treatment. The measured leaves were estimated to be about one month old, resulting in an average daily light integral (DLI) of 30 (CS sun), 12 (CS shade), 24 (BF sun), and 10 (BF shade) mol m-2 day-1. These estimates were computed using solar radiation measured at a weather station a few meters from the glasshouse, and using PPFD values measured inside the glasshouse. Average daily PPFD was below 700 μmol m-2 s-1 under full light, with maximum recorded values at leaf level of ~1200 μmol m-2 s-1 under full light and ~500 μmol m-2 s-1 under shade, i.e. ~60% reduction.   References Marone F, Stampanoni M. 2012. Regridding reconstruction algorithm for real-time tomo- graphic imaging. J. Synchrotron Radiat. 19: 1029–1037. Paganin D, Mayo SC, Gureyev TE, Miller PR, Wilkins SW. 2002. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. J. Microsc. 206: 33–40. Schneider CA, Rasband WS, Eliceiri KW. 2012. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9: 671–675. Théroux-Rancourt G, Jenkins MR, Brodersen CR, McElrone A, Forrestel EJ, Earles JM. 2020. Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning. Appl. Plant Sci. 8: e11380.

opencc-by-4.0Feb 2022View details →

dryad36/100

MicroCT surface scans of mice humeri (climbing experiment)

This data consists of 3D surface scans of 43 mice humeri (right anatomical side), which have been extracted from micro-computed tomography (microCT) files. The original analysis of these bones was conducted in an earlier study (Siegel & Jones [1975] American Journal of Physical Anthropology 42:141-144), focusing on the effects of climbing activity on bone dimensions. In the new study by Karakostis and Wallace (DOI after publication: 10.1002/ajpa.24700), the 3D surface scans were used to compare between habitual climbers (21) and controls (22) by applying the "Validated Entheses-based Reconstruction of Activity" (V.E.R.A.) method on four muscle attachment sites of the humerus (greater tubercle, lesser tubercle, supinator crest, and deltoid tuberosity). Significant entheseal differences were found between activity groups in both univariate and multivariate analyses, showing that our approach can be used to identify skeletal evidence of habitual climbing activities in mice.

opencc-zeroJan 2023View details →

zenodo36/100

Unpaired fast- and slow-acquisition microCT scans of carbon-fibre-reinforced composites

data.zip contains tomographic images of carbon-fibre-reinforced composites with <a href="https://www.toraycma.com/wp-content/uploads/T700S-Technical-Data-Sheet-1.pdf.pdf">Toray T700S</a> fibres in a <a href="http://sicomin.com/datasheets/product-pdf1123.pdf">Sicomin SR8500 KTA313</a> epoxy matrix. The datasets accompany the paper <a href="https://doi-org.remotexs.ntu.edu.sg/10.1016/j.compscitech.2023.110278" target="_blank" rel="noopener">Deep-learning image enhancement and fibre segmentation from time-resolved computed tomography of fibre-reinforced composites</a>. They are used in a two-step process: 1) to train a 3DCycleGAN enhancement algorithm available on <a href="https://github.com/pvilla/3DCycleGaN">https://github.com/pvilla/3DCycleGaN</a>. 2) to train a UNetID segmentation algorithm available on <a href="https://github.com/CMG-KULeuven/UnetID-FibreSeg">https://github.com/CMG-KULeuven/UnetID-FibreSeg</a>. The tomograms were acquired at the TOMCAT Beamline at the Swiss Light Source in October 2021. Acquisition specifications: T700_GF_0p8um_1ms: <ul> <li>detector: GigaFRoST</li> <li>effective voxel size: 800x800x800 nm³</li> <li># projections: 500</li> <li>exposure time: 1 ms</li> <li>total acquisition time: 500 x 1 ms = 0.5 s</li> <li>beam: polychromatic</li> <li>datasets: (same sample, different regions) T700-T-21_GF_0p8um_1ms_1.h5 T700-T-21_GF_0p8um_1ms_2.h5 T700-T-21_GF_0p8um_1ms_3.h5</li> </ul> T700_GF_1p6um_0p5ms: <ul> <li>detector: GigaFRoST</li> <li>effective voxel size: 1600x1600x1600 nm³</li> <li># projections: 500</li> <li>exposure time: 0.5 ms</li> <li>total acquisition time: 500 x 0.5 ms = 0.25 s</li> <li>beam: polychromatic</li> <li>files: (same sample, different regions) T700-T-21_GF_1p6um_0p5ms_1.h5 T700-T-21_GF_1p6um_0p5ms_2.h5</li> </ul> T700_GF_1p6um_3ms: <ul> <li>detector: GigaFRoST</li> <li>effective voxel size: 1600x1600x1600 nm³</li> <li># projections: 500</li> <li>exposure time: 3 ms</li> <li>total acquisition time: 500 x 3 ms = 1.5 s</li> <li>beam: polychromatic</li> <li>files: (same sample, different regions) T700-T-21_GF_1p6um_3ms_1.h5 T700-T-21_GF_1p6um_3ms_2.h5 T700-T-21_GF_1p6um_3ms_3.h5 T700-T-21_GF_1p6um_3ms_4.h5 T700-T-21_GF_1p6um_3ms_5.h5</li> </ul> T700_pco_0p4um: <ul> <li>detector: pco.edge 5.5</li> <li>effective voxel size: 400x400x400 nm³ (scaled from 325x325x325 nm³)</li> <li># projections: 2000</li> <li>exposure time: 250 ms</li> <li>total acquisition time: 2000 x 250 ms = 500 s</li> <li>beam: 15 keV</li> <li>files: (different samples) T700-T-02_pco_0p4um.h5 T700-T-08_pco_0p4um.h5 T700-T-21_pco_0p4um_reference.h5 T700-T-26_pco_0p4um.h5</li> </ul> 3DCycleGAN_trained_models.zip contains pre-trained models for the 3DCycleGAN enhancement algorithm. UnetID_trained_models.zip contains pre-trained models for the UNetID segmentation algorithm.

opencc-by-4.0Feb 2023View details →

dryad36/100

MicroCT scan of the nasal airway of a domestic short hair cat

The peripheral structures of mammalian sensory organs often serve to support their functionality, such as alignment of hair cells to the mechanical properties of the inner ear. Here, we examined the structure-function relationship for mammalian olfaction by creating an anatomically accurate computational nasal model for the domestic cat (Felis catus) based on high resolution microCT and sequential histological sections. Our results showed a distinct separation of respiratory and olfactory flow regimes, featuring a high-speed dorsal medial stream that increases odor delivery speed and efficiency to the ethmoid olfactory region without compromising the filtration and conditioning purpose of the nose. These results corroborated previous findings in other mammalian species, which implicates a common theme to deal with the physical size limitation of the head that confines the nasal airway from increasing in length infinitely as a straight tube. We thus hypothesized that these ethmoid olfactory channels function as parallel coiled chromatograph channels, and further showed that the theoretical plate number, a widely-used indicator of gas chromatograph efficiency, is more than 100 times higher in the cat nose than an "amphibian-like" straight channel fitting the similar skull space, at restful breathing state. The parallel feature also reduces airflow speed within each coil, which is critical to achieving the high plate number, while feeding collectively from the high-speed dorsal medial stream so that total odor sampling speed is not sacrificed. The occurrence of ethmoid turbinates is an important step in the evolution of mammalian species that correlates to their expansive olfactory function and brain development. Our findings reveal novel mechanisms on how such structure may facilitate better olfactory performance, furthering our understanding of the successful adaptation of mammalian species, including F. catus, a popular pet, to diverse environments.

opencc-zeroMay 2023View details →

dryad36/100