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250 results for “3D analysis”
3D scans of two types of railway ballast including shape analysis information
<p>This data set contains 3D scanner data of two types of railway ballast “Calcite” (stems from Croatia) and “Kieselkalk”, also known as Helvetic Siliceous Limestone, (stems from Switzerland).<br> From each type of ballast 25 stones are scanned. The files are provided in .ply format.<br> For the scanned meshes several shape descriptors are provided: elongation, flatness, sphericity, convexity index.<br> Additional to the 3D scans, both simplified and rounded versions of the meshes are included.<br> For these meshes information on three different angularity indices are available.<br> The scanned ballast types are the same, as previously investigated in uniaxial compression tests and direct shear tests:<br> Suhr, Bettina, & Six, Klaus. (2018).<br> "Compression tests and direct shear test of two types of railway ballast [Data set]"<br> Zenodo. http://doi.org/10.5281/zenodo.1423742</p> <p> </p> <p>A detailed shape analysis of the results is conducted in:<br> Bettina Suhr, William A. Skipper, Roger Lewis, and Klaus Six<br> "Shape analysis of railway ballast stones: curvature-based calculation of particle angularity"<br> <em>Scientific Reports, </em><strong>2020</strong><em>, 10</em>, 6045<br> DOI: https://doi.org/10.1038/s41598-020-62827-w</p> <p>A summary of several shape descriptors can be found in:<br> B. Suhr and K. Six:<br> "Simple particle shapes for DEM simulations of railway ballast -- influence of shape descriptors on packing behaviour"<br> Granular Matter, <strong>2020</strong><em>, 22</em><br> DOI: https://doi.org/10.1007/s10035-020-1009-0</p> <p><br> This data set is organised as follows:<br> 1_ScanMeshesCleaned<br> scanned meshes:<br> K_1.ply - K_25.ply Calcite (German: Kalzit)<br> KK_1.ply - KK_25.ply Kieselkalk<br> 2_CSE1 <br> simplifications of the scanned meshes, little simplifications, used in the detailed shape analysis<br> CSE1_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 3_CSE2 <br> simplifications of the scanned meshes, more simplified, used in the detailed shape analysis<br> CSE2_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 4_CSE3 <br> simplifications of the scanned meshes, even more simplified, used in the detailed shape analysis<br> CSE3_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 5_CSE4 <br> simplifications of the scanned meshes, most simplified, used in the detailed shape analysis<br> CSE4_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 6_RoundedMeshes<br> artificially rounded versions of the scanned ballast meshes, used in the detailed shape analysis<br> RoundedMeshes_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 7_TestBodies <br> meshes of artificial test bodies, constructed for testing different angularity indices in the detailed shape analysis<br> TestBodies_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> scanMeshesInfo.csv: summary of several shape descriptors of the scanned meshes<br> README.txt </p> <p><br> Check the README.txt file for more information on the technical aspects of scanning.</p> <p> </p>
Advancing Vanadium Redox Flow Battery Analysis: A Deep Learning Framework for High-Throughput 3D Visualization and Bubble Quantification via Synchrotron X-ray Tomography
<p>Dataset and model of UTILE-Redox - Deep Learning based Tool for Autonomous 3D Bubble Analysis of Vanadium Flow Batteries from Synchrotron X-ray Imaging. This project focuses on the deep learning-based automatic analysis of Vanadium Redox Flow Batteries (VRFB) Synchrotron X-ray tomographies. This repository contains the Python implementation of the UTILE-Redox software for automatic volume analysis, feature extraction, and visualization of the results.</p>
Neural Network and objective analysis reconstruction of 3D Mediterranean physical fields from surface satellite and in situ observations at 1/24 deg
<p>Daily Mediterranean 3D fields of temperature, salinity and geostrophic current at 1/24° of resolution, up to 150m-depth and from 2016 to mid 2022, obtained through a 3 steps approach: (1) Temperature and salinity 3D fields have been first estimated by a machine learning approach by using mediterranean reanalysis outputs (https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R) together with satellite observations, (2) a combination of this first step with in situ observations through an Optimal interpolation to remove part of large scale biases, (3) the computation of geostrophic currents using the thermal wind equation. This work has been funded by the European Space Agency through the 4DMED-SEA project [ESA contract No. 4000141547/23/I-DT].</p>
A quantitative analysis of the interplay of environment, neighborhood and cell state in 3D spheroids - Dataset
<p>This is the umbrella archive that contains all the datasets and code associated with:</p> <p><strong>A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids</strong></p> <p> Vito RT Zanotelli<br> Matthias Leutenegger<br> Xiao‐Kang Lun<br> Fanny Georgi<br> Natalie de Souza<br> Bernd Bodenmiller</p> <p><em>Mol Syst Biol. (2020) 16: e9798</em><br> <a href="https://doi.org/10.15252/msb.20209798">https://doi.org/10.15252/msb.20209798</a></p> <p><em>Please cite this article if you re-use any of the data or code.</em></p> <p>Datasets:</p> <ul> <li>Brightfield plate images: Contains all plate acquisitions for the individual sphere plates before pooling <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991929">10.5281/zenodo.3991929</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991931">10.5281/zenodo.3991931</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.3991925">10.5281/zenodo.3991925</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991927">10.5281/zenodo.3991927</a><br> </li> </ul> </li> </ul> </li> <li>Slidescan images: Contains all fluorescent slidescan images of the cuts derived from the pooled spheroid sample blocks <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991921">10.5281/zenodo.3991921</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991923">10.5281/zenodo.3991923</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.4066430">10.5281/zenodo.4066430</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991919">10.5281/zenodo.3991919</a><br> </li> </ul> </li> </ul> </li> <li>Spillover acquisitions: Contains all spillover acquisitions associated with the two experiments: <ul> <li>4 cellline experiment: <ul> <li>p173/p176: <a href="http://doi.org/10.5281/zenodo.3991945">10.5281/zenodo.3991945</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161/p165: <a href="http://doi.org/10.5281/zenodo.3991947">10.5281/zenodo.3991947</a><br> </li> </ul> </li> </ul> </li> <li>Imaging mass cytometry acquisitions: <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991937">10.5281/zenodo.3991937</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991939">10.5281/zenodo.3991939</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.3991933">10.5281/zenodo.3991933</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991935">10.5281/zenodo.3991935</a><br> </li> </ul> </li> </ul> </li> <li>Processed data set: <ul> <li>4 cellline experiment: <a href="https://doi.org/10.5281/zenodo.3991942">10.5281/zenodo.3991942</a></li> <li>Overexpression experiment: <a href="http://doi.org/10.5281/zenodo.4271917">10.5281/zenodo.4271917</a></li> </ul> </li> </ul> <p>Code:</p> <p>A snakemake pipeline to reproduce the analyses from this raw data can be found at: <a href="http://github.com/BodenmillerGroup/SpheroidPublication">http://github.com/BodenmillerGroup/SpheroidPublication</a></p> <p>A version already containing all the containers can be found at: <a href="http://doi.org/10.5281/zenodo.4071861">10.5281/zenodo.4071861</a></p> <p> </p>
PanDDA analysis of BRD1 screened against 3D-Fragment-Consortium Fragment Library (HTML Summary)
<p>Interactive summary page for "PanDDA analysis of BRD1 screened against 3D-Fragment-Consortium Fragment Library".</p> <p><strong>Please click on "0_index.html" in the "Files" section to open the interactive summary.</strong></p> <p>All datasets are also available as combined zip files from https://zenodo.org/record/48769 .</p> <p> </p>
Dataset of the scientific paper "A Comparative Analysis of 2D and 3D Tasks for Virtual Reality Therapies Based on Robotic-Assisted Neurorehabilitation for Post-stroke Patients" (Front. Aging Neurosci.)
<p> There are three files with the following information:<br> - data_2d.bin, binary file with information of the different parameters of the nine subjects during 2d tasks<br> - data_3d.bin, binary file with information of the different parameters of the nine subjects during 3d tasks<br> - survey.bin, binary file with the score of the System Usability Scale (SUS) survey of each subject</p>
Images supporting: Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation
<p>Two image datasets (as zip files) including all images analyzed in the manuscript Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation. Images are of pancreatic adenocarcinoma (PDAC) cystic spheroid samples grown in either BME or Matrigel. Some images have background noise in the form of iron oxide nanoparticles introduced to them.</p>
dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
<p>dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning</p>
ExoCAM: A 3D Climate Model for Exoplanet Atmospheres :: Model data and supplementary figures and analysis
<p>This repository contains 3D GCM model output data from the paper, "ExoCAM: A 3D Climate Model for Exoplanet Atmospheres", which is published in the Planetary Science Journal: Trapppist Habitable Atmospheres Intercomparison Special Issue. The model data includes mean climate states for the standard THAI simulations of TRAPPIST-1e, simulations using an upgraded radiative transfer, along with a large variety sensitivity experiments considering common tuning parameters of sub-grid scale cloud and convection physics. In total 43 simulations are included. Also included here are a variety of multi-panel contour plots showing basic results from all simulations as supplemental figures.</p> <p>https://iopscience.iop.org/article/10.3847/PSJ/ac3f3d</p>
Analysis of the alveolar shape in 3D
<p>Compressed version of the GitLab Repository containing the original data for the research article "Analysis of the alveolar shape in 3D".</p> <p>Link to the repository on GitLab: https://gitlab.com/AReimelt/analysis-of-the-alveolar-shape-in-3d</p>
Research compendium for 'The contribution of integrated 3D model analysis to Protoaurignacian stone tool design'
<p><strong>Abstract:</strong> Protoaurignacian foragers relied heavily on the production and use of bladelets. Techno-typological studies of these implements have provided insights into important aspects of cultural variability. However, new technologies have seldom been used to quantify patterns of stone tool design. Taking advantage of a new scanning protocol and open-source software, we conduct the first 3D analysis of a Protoaurignacian assemblage, focusing on the selection and modification of blades and bladelets. We study a large sample of complete blanks and retouched tools from the early Protoaurignacian assemblage at Fumane Cave in northeastern Italy. Our main goal is to validate and refine previous techno-typological considerations employing a 3D geometric morphometrics approach complemented by 2D analysis of cross-section outlines and computations of retouch angle. The encouraging results show the merits of the proposed integrated approach and confirm that bladelets were the main focus of stone knapping at the site. Among modified bladelets, various retouching techniques were applied to achieve specific shape objectives. We suggest that the variability observed among retouched bladelets relates to the design of multi-part artifacts that need to be further explored via renewed experimental and functional studies.</p> <p><strong>Overview of contents:</strong></p> <p>01. AGMT3-D project of the first dataset used in the study;</p> <p>02. AGMT3-D project of the second dataset used in the study;</p> <p>03. Raw outline data of the middle cross-section. Each specimens has a dedicate .txt file;</p> <p>04. Raw outline data of the upper cross-section. Each specimens has a dedicate .txt file;</p> <p>05. Angles3-D project with all files generated by the software (.mat and .xlsx formats) to quantify the mean retouch angle of retouched bladelets;</p> <p>06. R project and scripts for the 1) 2D shape analysis of the middle and upper cross-sections of retouched bladelets and the 2) design of all bivariate plots and boxplots with jittered points used in the paper. All related datasets, principal components, and generated figures are included in the folder;</p> <p>07. Folder with all figures published in the paper and in its supplementary materials;</p> <p>08. Dataset of the first study in .csv with all attributes used to run the statistical analysis presented in the paper;</p> <p>09. Dataset of the second study in .csv with all attributes used to run the statistical analysis presented in the paper;</p> <p>10. Dataset for the study of the upper cross-sections in .csv with all attributes used to run the statistical analysis presented in the paper;</p> <p>11. Dataset for the study of the middle cross-sections in .csv with all attributes used to run the statistical analysis presented in the paper;</p> <p>12. Dataset in .csv used to study the mean retouch angles;</p> <p>13. Supplementary information file in .pdf with all supplementary figures and tables.</p> <p><strong>Extra</strong>: All 3D meshes of blades and bladelets are available on Zenodo following this link: https://doi.org/10.5281/zenodo.6362150.</p>
Sample 3D image data from RIMS method for image analysis code demo
<p>Sample 3D image data from RIMS method applied to mechanical test on hydrogel sphere packings, to be used in image analysis code demo as demonstrated in the ALERT Geomechanics doctoral school 2022. The data is a small subset from a larger set of data as found on Dryad via 10.5061/dryad.6djh9w0x8 and is separated here on Zenodo to make the subset more machine-readable.</p>
Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters).
Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters).
Fig. 12 3D in On Roth's "human fossil" from Baradero, Buenos Aires Province, Argentina: morphological and genetic analysis
Fig. 12 3D models produced from the CT-scans of the 19 skull bone fragments. See Table 2 for references on which bones are contained in each fragment. Since fragments 17 and 18 did not present any diagnostic features, it was not possible to identify them and were not employed in the reconstruction
Semi-Supervised Pre-trained Foundation Model for 3D Structural Feature Analysis of Seismic Images
<p>Codes, trained model, and datasets for the paper "Semi-Supervised Pre-trained Foundation Model for 3D Structural Feature Analysis of Seismic Images".</p>
Fig. 2 in A new subdisarticulated machaeridian from the Middle Devonian of China: Insights into taphonomy and taxonomy using X-ray microtomography and 3D-analysis
Fig. 2. Maps of China and Guangxi (A) adapted from "Croquant" on Wikimedia (licensed under CC BY 3.0). B. Map showing the region of machaeridian locality (asterisk); adapted from Google Maps.
Fig. 5 in A new subdisarticulated machaeridian from the Middle Devonian of China: Insights into taphonomy and taxonomy using X-ray microtomography and 3D-analysis
Fig. 5. Overview of the other objects found in the sample. A. Object 3 could not be identified with certainty, but is likely part of a sclerite from the flank. B. Sclerite 9 might be from the dorsal articulation. C. Objects 15 and 16 might belong to the same, incomplete sclerite. D. Sclerite 11 preserves only the dorsal flange. E. Objects 8, 12, 13, and 14 might actually be parts of two sclerites as indicated by the white lines.
Fig. 6 in A new subdisarticulated machaeridian from the Middle Devonian of China: Insights into taphonomy and taxonomy using X-ray microtomography and 3D-analysis
Fig. 6. View of a pair of 3D-prints of articulated right (1) and left (2) sclerites from obliquely posterior (A) and dorsal (B) views. Note the perfect fit of the sclerites. Within the hinge of sclerite 1 in B, the indentation on the right of the hinge flange is an artefact from tresholding (probably, the shell was too thin in that place). Sclerites 1 and 2 were enlarged 25 times (for original dimensions see Fig. 4).
Fig. 1 in A new subdisarticulated machaeridian from the Middle Devonian of China: Insights into taphonomy and taxonomy using X-ray microtomography and 3D-analysis
Fig. 1. Machaeridian annelid Lepidocoleus kuangguoduni sp. nov., Nandan Formation, Eifelian, near Napiao, Guangxi (China). A. The main plate containing most machaeridian sclerites. B. The counterplate of the same specimen (it was glued back onto the slab prior to CT-scanning); note the limonitic filling of the rugae and the chaotic arrangement of the plates.
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.