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.
7
datasets available to search
ShareScore release 0.9.0
Dataset results
7 results for “fiber orientation”
Text-fig. 2. Salicaceae (a–g), Cannabaceae (h–n), cf. Betulaceae (o–r). a–g: Saxifragispermum, USNM PAL 772341. Scale bar = 5 mm except as indicated. a–b: Lateral, c: apical, and d: basal views of fruit, reflected light, palladium coated; apex at top of (a, b). e: Equatorial transverse section reflected light; arrows indicate presumed seeds, scale bar = 2 mm. f: Detail of locule contents extracted from (e), transmitted light, scale bar = 200 Μm. g: Interwoven trichomes or fibers from locule, transmitted light, scale bar = 5 Μm. h–j: Celtis. h, i: USNM PAL 772342, reflected light, palladium coated, scale bar = 5 mm. h: Lateral view parallel with plane of dehiscence. i: Lateral view perpendicular to plane of dehiscence. j: DMNH EPI.47809, Celtis in lateral view; showing reticulate sculpture and the vertically-oriented, plane of dehiscence (arrow), scale bar = 5 mm. k–m: Aphananthe. USNM PAL 772344, reflected light, palladium coated, scale bar = 5 mm. k: Apical view, note triangular cross section and apical plug (arrow). l: Lateral view, apex up. m: Lateral view at 90° to (l). n: Detail of cellular pattern at surface of endocarp, scale bar = 0.5 mm. o–r: in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 2. Salicaceae (a–g), Cannabaceae (h–n), cf. Betulaceae (o–r). a–g: Saxifragispermum, USNM PAL 772341. Scale bar = 5 mm except as indicated. a–b: Lateral, c: apical, and d: basal views of fruit, reflected light, palladium coated; apex at top of (a, b). e: Equatorial transverse section reflected light; arrows indicate presumed seeds, scale bar = 2 mm. f: Detail of locule contents extracted from (e), transmitted light, scale bar = 200 Μm. g: Interwoven trichomes or fibers from locule, transmitted light, scale bar = 5 Μm. h–j: Celtis. h, i: USNM PAL 772342, reflected light, palladium coated, scale bar = 5 mm. h: Lateral view parallel with plane of dehiscence. i: Lateral view perpendicular to plane of dehiscence. j: DMNH EPI.47809, Celtis in lateral view; showing reticulate sculpture and the vertically-oriented, plane of dehiscence (arrow), scale bar = 5 mm. k–m: Aphananthe. USNM PAL 772344, reflected light, palladium coated, scale bar = 5 mm. k: Apical view, note triangular cross section and apical plug (arrow). l: Lateral view, apex up. m: Lateral view at 90° to (l). n: Detail of cellular pattern at surface of endocarp, scale bar = 0.5 mm. o–r:
2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy
<p><strong>General</strong></p> <p>This dataset consists out of multiple Second Harmonic Generation (SHG) microscopy scans of collagen fibers in mice bones. Some mices are diseased with osteogenesis imperfecta (brittle bone).</p> <p>We used this data to investigate the segmentation of uncertain local collagen fiber orientations. The corresponding paper "2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy" is accepted at GCPR 2019.</p> <p><strong>Abstract</strong></p> <p>Collagen fiber orientations in bones, visible with Second Harmonic Generation (SHG) microscopy, represent the inner structure and its alteration due to influences like cancer. While analyses of these orientations are valuable for medical research, it is not feasible to analyze the needed large amounts of local orientations manually. Since we have uncertain borders for these local orientations only rough regions can be segmented instead of a pixel-wise segmentation. We analyze the effect of these uncertain borders on human performance by a user study. Furthermore, we compare a variety of 2D and 3D methods such as classical approaches like Fourier analysis with state-of-the-art deep neural networks for the classification of local fiber orientations. We present a general way to use pretrained 2D weights in 3D neural networks, such as Inception-ResNet-3D a 3D extension of Inception-ResNet-v2. In a 10 fold cross-validation our two stage segmentation based on Inception-ResNet-3D and transferred 2D ImageNet weights achieves a human comparable accuracy.</p> <p><strong>Links</strong></p> <p>A preprint of the paper is available at <a href="https://arxiv.org/abs/1907.12868">https://arxiv.org/abs/1907.12868</a>.</p> <p>The final publication is available at Springer via <a href="https://doi.org/10.1007/978-3-030-33676-9_26">https://doi.org/10.1007/978-3-030-33676-9_26</a></p> <p>The source code is available at <a href="https://github.com/Emprime/uncertain-fiber-segmentation">https://github.com/Emprime/uncertain-fiber-segmentation</a>.</p> <p><strong>Data description</strong></p> <p>Please read the accompanying paper for more information about the dataset. Please see the source code for more information about the usage of the data.</p> <ul> <li>shg-ce-de: contains the enhanced and denoised scans as image slices, the scans are sorted by mice (wt wildtyp, het ill mice), scan location and individual scan</li> <li>shg-masks: contains the ground truth masks for the three different classes (similar - Green, dissimilar - Red, not of interest - blue)</li> <li>shg-featues: contains the input and gt for the second stage of the proposed two stage segmentation</li> <li>shg-cross-splits: contains the 10 random splits for the 10 fold cross validation</li> <li>logs-prediction: contains the 10 tensorboard logs, weights and predictions for the 10 fold cross validations</li> </ul>
Impact of Intraventricular Septal Fiber Orientation on Cardiac Electromechanical Function
<p>Data and materials regarding the submission of the paper. See Data Availability section and https://github.com/valeryozenne/Cardiac-Structure-Database/tree/master/Article-3</p>
Local fiber volume and orientation coefficient of UHPFRC inside specimens SF-1 to 10
<p>This document contains digital version of Figures 6 and 7 in the journal paper titled "High cycle tensile fatigue behavior of steel rebar reinforced - UHPFRC at high R-ratio".</p>
Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers using Constrained Spherical Deconvolution: Data
<p>The data was simulated in GATEv8.0. The macro's and other files for the simulation can be found in the folders: Sim_50_40, Sim80_60 and Sim90_70. Here, the different files to perform the simulation are given, as well as the output data, found in the folders Flats and Projs (flatfields and projections) in the form of a csv-file. The csv-file contains 5000 rows (one for each image), and each row contains a flattened 450x450 image.</p> <p>The files for visualization of the fiber directions are given in the folder MRtrix. This folder contains the reconstructed scatter magnitudes and their respective directions in a mat-file. On this set of directions the constrained spherical deconvolution is applied using the sf_dirs.mif, sf_mask.mif, sf_response.mif and data.mif files. This returns the orientation density function in each voxel as output (odf_csd_14.mif). An alternative approach is performing a Funk-Radon transform on the coefficients of the scatter function (data_coeffs_10.mif) to obtain odf_frt_10.mif.</p> <p>Using a peak finding algorithm the fiber orientations are extracted from these odf-files and written to peaks_x.mif files, with or without a threshold (10% of largest amplitude).</p>
Characterization of the Fiber Orientations in Non-Crimp Glass Fiber Reinforced Composites using Structure Tensor
<p>This dataset contains data, notebooks and code used in the publication:</p> <p>[1] Jeppesen, N., V.A. Dahl, A.N. Christensen, A.B. Dahl, L.P. Mikkelsen, Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor. <em>IOP Conf. Ser.: Mater. Sci. Eng.</em> <strong>942</strong>, 012037, <a href="https://doi.org/10.1088/1757-899X/942/1/012037">https://doi.org/10.1088/1757-899X/942/1/012037</a>, 2020</p> <p>If you reference this dataset, please also consider referencing the paper above.</p> <p>HF401TT-13_FoV16.5_Stitch.zip contains an X-ray CT scan of a non-crimp glass fiber composite sample saved in the TXM file format.</p> <p>HF401TT-13_FoV16.5_Stitch.txm.nii contains a cut-out of the TXM data, where air around the sample has been removed and the result has been saved in the NIfTI file format.</p> <p>The two notebooks, <em>StructureTensorFiberAnalysisDemo</em> and <em>StructureTensorFiberAnalysisAdvancedDemo</em> rely on the NIfTI scan data, HF401TT-13_FoV16.5_Stitch.txm.nii. They demonstrate how to do structure tensor orientation analysis on the data.</p> <p>The HF401TT-13_FoV16.5_Stitch notebook use the TXM scan data, HF401TT-13_FoV16.5_Stitch.zip. It can be used to recreate the results of the published experimental results.</p> <p>To run the notebooks, the Python file, structure_tensor_workers.py, must be in the same directory as the notebook.</p> <p>By default, the notebooks expect the following folder structure:</p> <p>/notebooks: Folder with the notebooks and Python files.<br> /originals: Folder with the data (TXM/NIfTI files).<br> /tmp: Folder for temporary files and output generated running the notebooks.<br> /notebooks/figures: Folder for exporting figures as files (only needed if you want to save figures as files).</p>
Datasets for 'Fiber orientation in a whole mouse heart reconstructed by laboratory phase-contrast micro-CT'
<p>Tomographic dataset, vectorfield of the myocyte orientation and video for 'Fiber orientation in a whole mouse heart reconstructed by laboratory phase-contrast micro-CT' by M. Reichardt et al.. Reconstructed volume at the laboratory setup is given. The tomographic dataset is saved as .raw in single-precision floating-point format and the size of each dataset is indicated in the filename. The vectorfield is saved as .vtk used for Paraview.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.