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478 results for “3D Data”
Data from: Computational 3D histological phenotyping of whole zebrafish by X-ray histotomography
Organismal phenotypes frequently involve multiple organ systems. Histology is a powerful way to detect cellular and tissue phenotypes, but is largely descriptive and subjective. To determine how synchrotron-based X-ray micro-tomography (micro-CT) can yield 3-dimensional whole-organism images suitable for quantitative histological phenotyping, we scanned whole zebrafish, a small vertebrate model with diverse tissues, at ~1 micron voxel resolutions. Using micro-CT optimized for cellular characterization (histo-tomography), brain nuclei can be computationally segmented and assigned to brain regions. Shape and volume can be computed for populations of nuclei, motor neurons and red blood cells. Computed cell density revealed striking individual phenotypic variation. Unlike histology, histo-tomography allows the detection of phenotypes that require millimeter scale context in multiple planes. We expect the computational and visual insights into 3D tissue architecture provided by histo-tomography to be useful for reference atlases, hypothesis generation, comprehensive organismal screens, and diagnostics.
Data from: Comparative analysis of 2D and 3D distance measurements to study spatial genome organization
The spatial organization of genomes is non-random, cell-type specific, and has been linked to cellular function. The investigation of spatial organization has traditionally relied extensively on fluorescence microscopy. The validity of the imaging methods used to probe spatial genome organization often depends on the accuracy and precision of distance measurements. Imaging-based measurements may either use 2 dimensional datasets or 3D datasets which include the z-axis information in image stacks. Here we compare the suitability of 2D vs 3D distance measurements in the analysis of various features of spatial genome organization. We find in general good agreement between 2D and 3D analysis with higher convergence of measurements as the interrogated distance increases, especially in flat cells. Overall, 3D distance measurements are more accurate than 2D distances, but are also more susceptible to noise. In particular, z-stacks are prone to error due to imaging properties such as limited resolution along the z-axis and optical aberrations, and we also find significant deviations from unimodal distance distributions caused by low sampling frequency in z. These deviations are ameliorated by significantly higher sampling frequency in the z-direction. We conclude that 2D distances are preferred for comparative analyses between cells, but 3D distances are preferred when comparing to theoretical models in large samples of cells. In general and for practical purposes, 2D distance measurements are preferable for many applications of analysis of spatial genome organization.
The raw data of the first replicate 3d primary and lipid metabolite profiling.
<p>The raw data of the first replicate 3d primary and lipid metabolite profiling.</p>
The raw data of the second replicate 3d primary and lipid metabolite profiling.
<p>The raw data of the second replicate 3d primary and lipid metabolite profiling.</p>
Data from: A 3D Analysis of Dendritic Solidification and Mosaicity in Ni-Based Single Crystal Superalloys
<p>Author: F. Scholz, M. Cevik, P. Hallensleben, P. Thome, G. Eggeler, J. Frenzel</p> <p>Affiliation: Ruhr University Bochum</p> <p>Date: 08/2021</p> <p>Material: Nickel-base superalloy ERBO/1 (more details: Parsa, A. B., et al. Advanced scale bridging microstructure analysis of single crystal Ni-base superalloys. Adv. Eng. Mater. 2015, 17 (2), 216-230, <a href="https://doi.org/10.1002/adem.201400136">https://doi.org/10.1002/adem.201400136</a>)</p> <p>Casting: Bridgman seed technique; Withdrawal rate: 180 mm/h, Thermal gradient 13.3 K/mm (more details: Hallensleben, P., et al. On the evolution of cast microstructures during processing of single crystal Ni-base superalloys using a Bridgman seed technique, Mat. Des. 2017, 128, 98–111, <a href="https://doi.org/10.1016/j.matdes.2017.05.001">https://doi.org/10.1016/j.matdes.2017.05.001</a>)</p> <p>Sample: Cross sectional slices extracted perpendicular to the growth direction of a single crystal superalloy cylinder (diameter 12mm, length 120 mm).</p> <p>Image acquisition: Optical microscope of type Axio (Carl Zeiss GmbH) equipped with a high-resolution CCD-camera of type Leica DFC320 and stepper-motor driven sample stage of type Tango Desktop (Märzhäuser)</p> <p>Image pre-processing: Preparation of wide-field image collages using the stitching procedures implemented in software package Imagic ims (<a href="https://imagic.ch/en/imagic-ims">https://imagic.ch/en/imagic-ims</a>, 07/2021)</p> <p>Image post-processing: Image registration with CorelDraw X7 (: <a href="https://www.coreldraw.com/en/">https://www.coreldraw.com/en/</a>, 07/2021) using a contour reference mask</p> <p>Quantitate analysis: Dendrite positions were extracted using the software package ImageJ (<a href="https://imagej.de.softonic.com/">https://imagej.de.softonic.com/</a>, 07/2021).</p> <p>--------------------------------------</p> <p>The five optical micrographs cross sections represent image data which were obtained by tomographic characterization of as-cast single crystal nickel-base superalloy prepared by a seeded Bridgman technique. The material has been studied in the frame of the collaborative research center SFB/TR 103. All details on the applied Bridgman technique are described in the literature (Hallensleben, P., et al., Mat. Des. 2017, 128, 98–111, <a href="https://doi.org/10.1016/j.matdes.2017.05.001">https://doi.org/10.1016/j.matdes.2017.05.001</a> and Hallensleben, P., et al., Crystals 2019, 9 (3), 149, <a href="https://doi.org/10.3390/cryst9030149">https://doi.org/10.3390/cryst9030149</a>). The tomographic image slices were prepared by successive electro discharge machining using incremental steps of 1mm. The image series represents the evolution of dendritic microstructures during the early stages of crystal growth from the back melted seed. The five wide-field micrographs were used to retrieve dendrite positions (enclosed as CSV data for each cross section) to evaluate crystal mosaicity on the basis of dendrite growth directions. All information and a detailed interpretation of tomographic are available in (Scholz, F., PhD-thesis, Ruhr University Bochum, <a href="https://doi.org/10.13154/294-8079">https://doi.org/10.13154/294-8079</a>). We hope that our image data will be useful for other types of solidification research. Please provide a notification by personal mail on the re-use of our raw data. Thank you.</p> <p>All images and dendrite position data were evaluated in the following study concerning dendrite growth behavior, low angle misorientation defects, dendrite arrangements and spacings:</p> <p>Scholz, F.; Cevik, M.; Hallensleben, P.; Thome, P.; Eggeler, G.; Frenzel, J. A 3D Analysis of Dendritic Solidification and Mosaicity in Ni-based Single Crystal Superalloys, Materials 2021, 14 (17), 4904 (https://doi.org/10.3390/ma14174904).</p>
3D rotating cylinder eddy data
<p>Competition between chaotic advection and diffusion: stirring and mixing in a 3D eddy model-- data to reproduce paper figures, all in Matlab files.</p> <p>This work was supported on DOD (MURI) Grant No. N000141110087 as well as US National Science Foundation Grant OCE--1558806. G. Brett received additional support from the Woods Hole Oceanographic Institution Academic Programs Office.</p> <p>Larry Pratt and Irina Rypina (WHOI) supervised this work.</p>
3D Vs model and phase velocity dispersion data of the Pearl River Delta onshore-offshore area
<p>3D Vs model and phase velocity dispersion data for PRD.</p>
Data_3D
<p>Data</p>
Data of: 3D Muographic Inversion in the Exploration of Cavities and Low-density Fractured Zones
<p>This is the dataset for "3D Muographic Inversion in the Exploration of Cavities and Low-density Fractured Zones" titled article, submitted to Geophysical Journal International in 2023.</p><p>Abstract: Muography is an imaging tool based on the attenuation of cosmic muons for observing density anomalies associated with large objects, such as underground caves or fractured zones. Tomography based on muography measurements, that is, three dimensional reconstruction of density distribution from two dimensional muon flux maps, brings along special challenges. The detector field of view covering must be as balanced as possible, considering the muon flux drop at high zenith angles and the detector placement possibilities. The inversion from directional muon fluxes to a 3D density map is usually underdetermined (more voxels than measurements). Therefore, the solution of the inversion can be unstable due to partial coverage. The instability can be solved by geologically relevant Bayesian constraints. However, the Bayesian principle results in parameter bias and artifacts. In this work, linearized (density-length based) inversion is applied by formulating the constraints associated with inversion to ensure the stability of parameter fitting. After testing the procedure on synthetic examples, an actual high-quality muography measurement data set from 7 positions is used as input for the inversion. The resulting tomographic imaging provides details on the complicated internal structures of karstic fracture zone. The existence of low density zones in the imaged space was verified by samples from core drills, which consist of altered dolomite powder within the intact high density dolomite. </p>
Data from: Ellipsoid segmentation model for analyzing light-attenuated 3D confocal image stacks of fluorescent multi-cellular spheroids
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Functional and ecomorphological evolution of orbit shape in Mesozoic archosaurs is driven by body size and diet: Geometric morphometric data, 3D models (stl files), FEA models (Hypermesh, Abaqus files)
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Data from: Comparative analysis of 2D and 3D distance measurements to study spatial genome organization
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Data from: Object recognition and localization from 3D point clouds by maximum-likelihood estimation
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Data from: The tendinopathic Achilles tendon does not remain iso-volumetric upon repeated loading: insights from 3D ultrasound
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Data from: Computational 3D histological phenotyping of whole zebrafish by X-ray histotomography
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Data from: Three-dimensional reconstructions come to life – interactive 3D PDF animations in functional morphology
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Data from: Use of anisotropy, 3D segmented atlas, and computational analysis to identify gray matter subcortical lesions common to concussive injury from different sites on the cortex
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Soil images in DICOM format including Python programs for data transformation, 3D analysis, CNN traininig, CNN analysis
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Data and R code for What you see is where you go: visibility influences movement decisions of a forest bird navigating a 3D structured matrix
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Data from: Validation of perfusion quantification with 3D gradient echo dynamic contrast-enhanced magnetic resonance imaging using a blood pool contrast agent in skeletal swine muscle
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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.