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250 results for “3D analysis”
Research Data supporting "3D Tomographic Analysis of the Order-Disorder Interplay in the Pachyrhynchus congestus mirabilis Weevil"
<p>This data and the descriptions below should be read in conjunction with the manuscript and “Supporting Info”, both of which may be found at the following DOI: https://doi.org/10.1002/advs.202202145.</p>
Doctoral School 2022: Data for 3D analysis lesson
<p>Data for 3D analysis lesson, see the notebook here: https://github.com/alert-geomaterials/2022-doctoral-school</p>
Data from: A 3D geometric morphometric analysis of the bovid distal humerus, with special reference to Rusingoryx atopocranion (Pleistocene, Eastern Africa)
<p>The family Bovidae [Mammalia: Artiodactyla] is speciose and has extant representatives on every continent, forming key components of mammal communities. For these reasons, bovids are ideal candidates for studies of ecomorphology. In particular, the morphology of the bovid humerus has been identified as highly related to functional variables such as body mass and habitat. This study investigates the functional morphology of the bovid distal humerus in isolation due to its increased likelihood of preservation in the fossil record, and the resulting opportunity for better understanding the ecomorphology of extinct bovids. A landmark scheme of 30 landmarks was used to capture the 3D distal humerus morphology in 111 extant bovid specimens. We find that the distal humerus has identifiable morphologies associated with body mass, habitat preference, and tribe affiliation, and that some characteristics are shared between high body mass bovids and those living on hard, flat terrain which is likely due to the high stress on the bone in both cases. We directly apply our findings regarding extant bovids to the extinct alcelaphine bovid, <em>Rusingoryx</em> <em>atopocranion</em> from the mid to late Pleistocene (>33-45 ka) Lake Victoria region of Kenya. This species is known for some peculiar morphologies including a domed cranium with hollow nasal crests, and having small hooves for a bovid of its size. Another interesting aspect of <em>Rusingoryx</em>'s skeletal morphology which has not been addressed is an unusual protrusion on the lateral epicondyle of the distal humerus. Despite considerable individual variation in the <em>Rusingoryx</em> specimens, we find evidence to support its historical assignment to the tribe Alcelaphini, and that it likely preferred open grassland habitats, which is consistent with independent reconstructions of the paleoenvironment. We also provide the most accurate body mass estimate for <em>Rusingoryx</em> to date, based on distal humerus centroid size. Overall, we are able to conclude that the distal humerus in extant bovids is highly informative regarding body mass, habitat preference and tribe, and that this can be applied directly to a fossil taxon with promising results.</p>
Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations
<p>The supporting data and utilities from the publication "Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations" are recorded in this dataset. </p> <p>Non-isothermal phase-field simulations of SLS process on SS316L material and subsequent elasto-plastic calculations were performed to analyze the development of plastic deformation and residual stress in SLS produced components during the processing. The dependence of the fusion zone, residual stress and plastic strain on the processing parameters namely, Beam power (Unit: Watts) and Scan speed (Unit: mm/s) were investigated. </p> <p>To promote FAIR research data principles, the processed simulation data from the thermo-elasto-plastic calculations for all the process parameter sets (hereby refered as P-v sets) are curated in this dataset. The raw temporal data obtained from the processing simulations and the elasto-plastic could not be included in this dataset due to its high volume. However, the corresponding raw data can be requested by contacting the creators of this dataset (Yangyiwei Yang: <a href="mailto:yangyiwei.yang@mfm.tu-darmstadt.de">yangyiwei.yang@mfm.tu-darmstadt.de</a> and Somnath Bharech: <a href="mailto:somnath.bharech@tu-darmstadt.de">somnath.bharech@tu-darmstadt.de</a>).</p> <p>This dataset includes: </p> <ul> <li><code>average_value.csv</code>: Contains average values of mechanical properties (such as residual stress, plastic strain) for the powder bed and the fused strut of all the process parameter sets.</li> <li><code>mesostructures_tep_sls.zip</code> : Contains resampled mesostructures obtained at the last time step of the SLS processing simulations with thermo-elasto-plastic calculations for the P-v sets reported in the aforementioned investigation. Nomenclature of the sub-directories indicating the P-v sets follows: <code>tep_<beam power>-<scan speed></code>. Each of these sub-directories contain the mesostructures from last time step of the thermo-elasto-plastic analysis of each of the four layer scans and is named as: <code>TP_layer{1..4}_output_final.e</code>. These files can be opened using Paraview v.5.8.1 or higher. The nodal values are explained as follows:</li> </ul> <table> <tbody> <tr> <td><strong>Nodal value name</strong></td> <td><strong>Symbol</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> </tr> <tr> <td>T</td> <td>\(T\)</td> <td>Temperature field normalized by \(T_M\)</td> <td>-</td> </tr> <tr> <td>c</td> <td>\(\rho\)</td> <td>Substance order parameter</td> <td>-</td> </tr> <tr> <td>eps_ij </td> <td>\(\varepsilon\)</td> <td>Strain</td> <td>-</td> </tr> <tr> <td>epsp_ij</td> <td>\(\varepsilon^\text{pl}\)</td> <td>Plastic strain</td> <td>-</td> </tr> <tr> <td>peeq</td> <td>\(p_\text{e}\)</td> <td>Accumulated plastic strain</td> <td>-</td> </tr> <tr> <td>sigma_ij </td> <td>\(\sigma\)</td> <td>Stress</td> <td>MPa</td> </tr> <tr> <td>vonmises</td> <td>\(\sigma_\text{e}\)</td> <td>von Mises stress </td> <td>MPa</td> </tr> <tr> <td>u</td> <td>\(\mathbf{u}\)</td> <td>Displacement</td> <td>µm</td> </tr> </tbody> </table> <p> </p>
Tailored 3D microphantoms: an essential tool for quantitative phase tomography analysis of organoids
<p>Raw measurement data and processed results for the paper "Tailored 3D microphantoms: An essential tool for quantitative phase tomography analysis of organoids", Biocybern. Biomed. Eng. 45 (2025) 247–257. https://doi.org/10.1016/j.bbe.2025.03.003.</p> <p>Measurement data.zip - Archive contains .bmp files with raw images captured by the cameras in corresponding systems. Included .txt files contain key system parameters used for demodulation and reconstruction.</p> <p>3D refractive index reconstructions.mat - MATLAB structure file containing expected and measured 3D RI distributions for the corresponding systems and organoid samples. </p> <p> </p>
Thermal and percolative analysis of 3D diffuse-interface composite microstructure
<p>This dataset contains supplementary data and utilities of the publication "Data-driven thermal and percolative analysis of 3D diffuse-interface composite microstructure" (<a href="https://doi.org/10.1016/j.matdes.2023.111746">Fathidoost, 2023</a>).</p> <p>This dataset documents homogenized anisotropic thermal conductivity of the corresponding microstructure (identified by volume fraction (Vf) and aspect ratio (Ar)) as a tensor with normalized interface thermal resistance (see Table 1). Voxelized digital microstructures and utilities are also attached for visualizing the overall thermal anisotropy.</p> <p><em>Table 1: The geometrical and thermal parameters employed in the generated microstructures.</em></p> <table> <thead> <tr> <th>Parameters</th> <th>Value (Unit)</th> <th>Type</th> <th>Increment</th> </tr> </thead> <tbody> <tr> <td>Minor principal axes length</td> <td>5 (nm)</td> <td>Constant</td> <td>-</td> </tr> <tr> <td>Aspect ratio, <span class="math-tex">\(A_\mathrm{r}\)</span></td> <td>[1 ,6]</td> <td>Linear</td> <td>1</td> </tr> <tr> <td>Inclusion volume fraction, <span class="math-tex">\(V_\mathrm{f}\)</span></td> <td>[5, 60]</td> <td>Linear</td> <td>5</td> </tr> <tr> <td>Thermal conductivity ratio, <span class="math-tex">\(K_\mathrm{r}\)</span></td> <td>[15, 100]</td> <td>Linear</td> <td>15</td> </tr> <tr> <td>Normalized interface resistance, <span class="math-tex">\(\tilde{R}_\mathrm{s} \)</span></td> <td>[1e-6, 1e10]</td> <td>Logarithmic</td> <td>1e2</td> </tr> </tbody> </table> <p><strong>Notice:</strong> The digital microstructure has been voxelized and stored in the ExodusII format, which can be loaded and visualized by the post-processing software, such as ParaView. In order to perform the homogenization, interface smoothening is required, i.e., to generate diffuse interfaces. In this work, we smoothened the interface by operating transient Allen-Cahn calculation with finite timesteps. Sec. 2.1 of the publication for more information).</p>
Comparative Analysis of 3D Printed Bridge Construction in Louisiana
<p>A construction 3D printing system could result in automated infrastructure development at reduced cost and time, significantly boosting overall productivity. Although there has been a growing interest in using construction 3D printing for projects such as house construction, implementing this innovative technology for infrastructure development, particularly bridge construction, has not been investigated as extensively. This study aims to compare the environmental impact of precast and 3D concrete printing (3DCP) techniques with a pedestrian bridge case study, located in Louisiana, where the bridge elements were 3D printed off-site and then transported and assembled on the bridge site. A detailed cradle-to-site life cycle assessment has been performed from the standpoint of material, construction, and installation stages, using an open-source software called OpenLCA. The results of this study showed that the mixtures commonly used in 3DCP have a higher negative environmental impact compared to the precast method due to the higher percentage of cement used in these materials. However, since 3DCP used less material than the precast technique, there is no significant difference in the environmental impact of the total concrete used between the 3DCP and precast bridges. In addition, due to the use of reinforcement and formwork in the precast technique, the environmental impact of the total materials used in the precast bridge was more adverse than the 3DCP bridge. Notably, due to use of electricity for printing, the negative environmental impact of the construction process in 3DCP was significantly higher than in the precast technique. Finally, the total carbon dioxide equivalent emitted during the construction of the 3DCP bridge was 80% of the precast bridge.</p>
Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment
<p>These are 3D live-cell imaging datasets of gastric tumor organoids in co-culture with primary human Natural Killer (NK) cells. The datasets were analyzed by a new, deep learning-based 3D image analysis software tool, SiQ-3D, which we developed and presented in the paper titled "Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment". Interested users can download the SiQ-3D software code from GitHub (https://github.com/simonlbd1/SiQ-3D) or Code Ocean (https://codeocean.com/capsule/6676007/tree/v2), analyze the 3D image datasets locally, and cross-check the results with the SiQ-3D quantified results that we provided here.</p>
Supplementary data for "DNATCO v5.0: Integrated Web Platform for 3D Nucleic Acid Structure Analysis"
<p>Supplementary data for "DNATCO: efficient and accurate analysis of nucleic acid structures"</p> <p>The data in "dnatco.datmos.org_1ehz_4qvi_5hix.zip" contains the DNATCO-annotated extended mmCIF files, full validation reports and NtC-specific restraint files for the three example PDB structures (1ehz, 4qvi, and 5hix) is deposited.</p> <p>A snapshot of the core structure processing library source code from the https://github.com/cernylab/libLLKA repository is included in the "libLLKA-main.zip" file.</p> <p>The fully offline multi-platform CLI version of the dnatco.datmos.org using Node.js is provided in the "dnatco.zip" file</p>
BEHAV3D: A 3D live imaging platform for comprehensive analysis of engineered T cell behavior and tumor response
Open the record for dataset details and reuse information.
Data from: A 3D geometric morphometric analysis of the bovid distal humerus, with special reference to Rusingoryx atopocranion (Pleistocene, Eastern Africa)
Open the record for dataset details and reuse information.
3D micro-CT image of cichlid fish samples for genetic analysis
Open the record for dataset details and reuse information.
Pitfalls of Computed Tomography 3D Reconstruction Models in Cranial Nonmetric Analysis
<p>Many studies in the literature have highlighted the utility of virtual 3D databanks as a substitute for real skeletal collections and the important application of radiological records in personal identification. However, none have investigated the accuracy of virtual material compared to skeletal remains in nonmetric variant analysis using 3D models. The present study investigates the accuracy of 20 computed tomography (CT) 3D reconstruction models compared to the real crania, focusing on the quality of the reproduction of the real crania and the possibility to detect 29 dental/cranial morphological variations in 3D images. An interobserver analysis was performed to evaluate trait identification, number, position, and shape. Results demonstrate a false bone loss in 3D models in some cranial regions, specifically the maxillary and occipital bones in 85% and 20% of the samples. Additional analyses revealed several difficulties in the detection of cranial nonmetric traits in 3D models, resulting in incorrect identification in circa 70% of the traits. In particular, pitfalls included the detection of erroneous position, error in presence/absence rates, in number, and in shape. The lowest percentages of correct evaluations were found in traits localized in the lateral side of the cranium and for the infraorbital suture, mastoid foramen, and crenulation. The present study highlights important pitfalls in CT scan when compared with the real crania for nonmetric analysis. This may have crucial consequences in cases where 3D databanks are used as a source of reference population data for nonmetric traits and pathologies and during bone-CT comparisons for identification purposes.</p>
Data from: The effects of aging on neuropil structure in mouse somatosensory cortex—A 3D electron microscopy analysis of layer 1
This study has used dense reconstructions from serial EM images to compare the neuropil ultrastructure and connectivity of aged and adult mice. The analysis used models of axons, dendrites, and their synaptic connections, reconstructed from volumes of neuropil imaged in layer 1 of the somatosensory cortex. This shows the changes to neuropil structure that accompany a general loss of synapses in a well-defined brain region. The loss of excitatory synapses was balanced by an increase in their size such that the total amount of synaptic surface, per unit length of axon, and per unit volume of neuropil, stayed the same. There was also a greater reduction of inhibitory synapses than excitatory, particularly those found on dendritic spines, resulting in an increase in the excitatory/inhibitory balance. The close correlations, that exist in young and adult neurons, between spine volume, bouton volume, synaptic size, and docked vesicle numbers are all preserved during aging. These comparisons display features that indicate a reduced plasticity of cortical circuits, with fewer, more transient, connections, but nevertheless an enhancement of the remaining connectivity that compensates for a generalized synapse loss.
Scripts and raw data for comparing 2D vs 3D image analysis of zebrafish embryo microscopic data
<p>Raw data and MatLab scripts used for image analysis of RNA polymerase II with serine 5 phosphorylation in the C-terminal domain (CTD) of the subunit 1 (Pol II Ser5P) in a fixed zebrafish embryo, comparing a 2D vs 3D approach to segment out the Pol II Ser5P clusters. Pol II Ser5P was labeled by immunofluorescence, microscopy images were acquired by instant-SIM microscopy and analyzed using MatLab scripts and the bioformats importer.</p>
SUPPORTING INFORMATION FOR: Stochastic dynamic mass spectrometric 3D structural analysis of caffeine metabolites
<p>Supporting information for the entitled contribution.</p><p>It contains:</p><p>Static quantum chemical and high accuracy molecular dynamics computational data on protomers, tautomers, zwitterions, and isotopomers of caffeine (CAFF), paraxanthine (PARAXAN), theobromine (THEOBR), theophylline (THEOPH), and guanine (GUA), uric acid (UA), and xantine (XAN), as well as their derivatives.</p><p>The content includes data on characteristic parent and product ions of the analytes in ion mobility spectrometric and mass spectrometric experimental conditions. Tautomers, charge transfer processes, and intramolecular rearrangement; if any, are accounted for considering. </p><p>Molecular mechanics/molecular dynamics data are shown as *.txt files. </p><p>High accuracy molecular dynamics includes adiabatic computations using Born-Oppenheimer approach.</p><p>High accuracy static ground state and transition state computations use M062X/SDD level of theory. </p><p>Figures in color, illustrating the entitled contribution shown as *.pdf files.</p><p>The experimental ion mobility spectrometry and mass spectrometry data are according to reference [1].</p><p>[1] H. Sepman, A. Kruve, S. Tshepelevitsh, H. Hupatz, Experimental IMS and MS/MS data of caffeine metabolites (2022). Zenodo, [https://doi.org/10.5281/zenodo.6637393][ https://zenodo.org/record/6637393] (Accessible for 04.04.2022.)</p><p>They have been used and processed via the following software:</p><p>[2] ProteoWizard 3.0.11565.0 (2017) [https://proteowizard.sourceforge.io/download.html];</p><p>[3] mMass 5.0.0 [http://www.mmass.org/download/old.php];</p><p>[4] AMDIS 2.71 (2012) software [https://chemdata.nist.gov/mass-spc/amdis/downloads/AMDIS_Installer-17.zip]; and</p><p>[5] NIST Search Software 2.0 [https://chemdata.nist.gov/dokuwiki/lib/exe/fetch.php?media=chemdata:nist17:nist17demo.zip], respectively.</p><p> </p><p> </p>
Superalloys fracture process inference based on overlap analysis of 3D models
<h2>Datasets and code utilized in the paper "Superalloys fracture process inference based on overlap analysis of 3D models"</h2> <h2>Code and data description</h2> <h3>Data for 3D reconstruction</h3> <ul> <li>Original SEM images of Fracture A - Fracture D obtained through the collection method in the paper.</li> </ul> <h3>Data for scale calibration</h3> <ul> <li>Original SEM images sequences of marked points 'dot1' and 'dot2' of Fracture A - Fracture D.</li> </ul> <h3>Sharpness score calculation</h3> <ul> <li>'shapeness.m' : Calculating image sharpness using normalized variance equations.</li> <li>'focus_A_data' - 'focus_D_data' : Sharpness scores for all images in the image sequences and their corresponding sample stage coordinates.</li> </ul> <h3>3D fracture models</h3> <ul> <li>Scale calibrated 3D models of Fracture A - Fracture D.</li> </ul> <h3>Description of internal cracks</h3> <ul> <li>Original images and EDS results for an illustration of the regions of internal crack generation in Fracture A</li> </ul> <p> </p> <p> </p>
3D Models and Code for the Analysis of Later Acheulean Southern Levantine Handaxes
<p>3D models (.wrl / .vrml) of handaxes from four southern Levantine later Acheulean sites (Ma'ayan Barukh, Nahal Zihor, Revadim, Jaljulia).</p> <p>+</p> <p>MATLAB scripts for computing 3D sinuosity, asymmetry, and edge properties. See the CEAM, SINUOSITY, and ASYMMETRY .m files for instructions and citations to dependencies. </p> <p>The SINUOSITY and ASYMMETRY scripts accompany the submitted manuscript "The Skills of Handaxe Making: Quantifying and Explaining Variability in 3D Sinuosity and Bifacial Asymmetry" by Antoine Muller, Gonen Sharon, and Leore Grosman.</p> <p>The CEAM script accompanies Muller, A., Sharon, G., & Grosman, L. (2024). Automatic analysis of the continuous edges of stone tools reveals fundamental handaxe variability. Scientific Reports, 14, 7422. https://doi.org/10.1038/s41598-024-57450-y.</p>
In Situ Volumetric Imaging and Analysis of FRESH 3D Bioprinted Constructs Using Optical Coherence Tomography (Data and 3D models)
<p>These files contain 3D models and reconstructions of the 3D printed models after OCT imaging of the brain stem, circle of willis, kidney, vestibular apparatus, mixing network, and resolution text. These are from the journal article "In Situ Volumetric Imaging and Analysis of FRESH 3D Bioprinted Constructs Using Optical Coherence Tomography" published in <em>Biofabrication </em>(2022).</p>
3D tissue cytometry analysis files for, "An atlas of healthy and injured cell states and niches in the human kidney"
<p>This deposit contains the supporting records of analysis for 3D cytometry presented in, "An atlas of healthy and injured cell states and niches in the human kidney". </p> <p>Contents:</p> <p>1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the preprint doi: 10.1101/2021.07.28.454201. This collection includes the individual tissue specimens, XX-XXXX.zip, further described in detail in "Supplementary Table 2. 3D imaging and spatial transcriptomic experiments." </p> <p>2) neighborhoods by specimen as indicated above and calculated by Volumetric Tissue Exploration and Analysis (VTEA) for a radius of 50 voxels(accounting for anisotropy of voxels) or ~25 um as described in the methods of the preprint, doi: 10.1101/2021.07.28.454201.</p> <p>3) R environment file (.RData as a .zip file) for regenerating analysis from code at: https://github.com/KPMP/Cell-State-Atlas-2022</p> <p>Contents of XX-XXXX.zip files:</p> <p>1) maximum projections as used in the manuscript (all archived at kpmp.org)<br> 2) .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/)<br> 3) .csv file including all the segmented objects and associated measurements<br> 4) .csv file including the neighborhood analysis results also found in the combined .zip file<br> 5) folder of ImageJ/FIJI folder which includes the expert ROIs and the pixel-wise consensus of ROIs drawn by experts used in cell classification strategy outlined in the methods<br> 6) folder of VTEA gate files and images of gating .png files used to label objects and used in cell classification strategy outlined in the methods of doi: 10.1101/2021.07.28.454201</p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: 10.1101/2021.07.28.454201.</p> <p> </p> <p> </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.