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5,635 results for “3D”
Jigsaw Duck - Synthesis of 3D jigsaw puzzles over freeform 2-manifolds.
<p>This 3D puzzle has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (<a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/</a>).</p> <p>This specific duck puzzle model has been created using function composition of puzzle tiles over the shell geometry of a duck.</p> <p>Puzzle model is provided, in parts, in OBJ file format.</p>
LGM-Lateglacial 3D ice surface reconstructions of the Dora Baltea glacier system (western Italian Alps)
<p>3D ice surface configurations of six LGM-Lateglacial ice stages of the Dora Baltea glacier system (western Italian Alps).</p> <p>Ice-configurations were obtained by combining existing and new chronological constraints from glacial and postglacial landforms/deposits from the Dora Baltea catchment into 2D and 3D ice surface reconstructions, similar to the approach of the GlaRe ArcGIS toolbox (Pellitero et al., 2016).</p> <p>Mean position of the study area: 45.7412/7.3978 (°N/°E, WGS84)</p>
Surrogate-based optimization using an artificial neural network for a parameter identification in a 3D marine ecosystem model
<p><strong>Abstract:</strong></p> <p>Parameter identification for marine ecosystem models is important for the assessment and validation of marine ecosystem models against observational data. The surrogate-based optimization (SBO) is a computationally efficient method to optimize complex models. SBO replaces the computationally expensive (high-fidelity) model by a surrogate constructed from a less accurate but computationally cheaper (low-fidelity) model in combination with an appropriate correction approach, which improves the accuracy of the low-fidelity model. To construct a computationally cheap low-fidelity model, we tested three different approaches to compute an approximation of the annually periodic solution (i.e., a steady annual cycle) of a marine ecosystem model: firstly, a reduced number of spin-up iterations (several decades instead of millennia), secondly, an artificial neural network (ANN) approximating the steady annual cycle and, finally, a combination of the both approaches. Except for the low-fidelity model using only the ANN, the SBO yielded a solution close to the target and reduced the computational effort significantly. If an ANN approximating appropriately a marine ecosystem model is available, the SBO using this ANN as low-fidelity model presents a promising and computational efficient method for the validation.</p> <p> </p> <p><strong>Content:</strong></p> <ul> <li>SQLite database including the data of the different optimization runs</li> <li>Structure and weights of the used artificial neural network</li> <li>Tracer concentrations obtain from the high-fidelity model for the different optimization runs</li> </ul>
3D FEM-based inverse model of Nevado del Ruiz - St. Isabel volcanoes (Colombia)
<p><strong>Description of model and data</strong></p> <p>The files include a FEM-based inverse model for the optimization of parameters of a pressure source responsible for surface deformation. The investigated source parameters are the position of the source center, the three semi-axis, the source strike orientation, the source dip orientation, and the source overpressure. The observations used for the inversion are ascending and descending ground velocities. The optimization is based on Least-Squares objectives using the Monte Carlo method. The file of observations needed for the computation of the Least-Squares objectives (to be uploaded in the optimization node) requires four columns (x,y,z, velocities. All in meters, UTM coordinates-UTM zone 18N, and comma-separated).</p> <p>The model takes into consideration the heterogeneous distribution of material elastic properties. The model does not provide the files for the observations and material properties (at the link: https://zenodo.org/record/5575972), but the structure for the optimization model in which new files can be uploaded for a customized model.</p> <p>The model includes the compensation for the stresses induced by the topography (edifices’ load). The file for the construction of the topographic surface is included as a .txt file (the position x,y of the points is in UTM coordinates-UTM zone 18N, the altitude z is in meters). The far-field is modeled as a hemisphere and it is located at 35 km from the center of the model, which is between the Nevado del Ruiz volcano and Santa Isabel volcano.</p> <p>The model is built with Comsol Multiphysics v 5.6 using the modules Optimization and Structural Mechanics modules, and it is provided as a Comsol .mph file.</p> <p> </p> <p>Datasets and model are results of PICVOLC project. PICVOLC has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 79381</p>
Data for: Epicardial slices: an innovative 3D organotypic model to study epicardial cell physiology and activation.
<p>Raw data set for Npj Regenerative Medicine article: Epicardial slices: an innovative 3D organotypic model to study epicardial cell physiology and activation.</p>
A Thermogelling Organic-Inorganic Hybrid Hydrogel with Excellent Printability, Shape Fidelity and Cytocompatibility for 3D Bioprintingg
<p>Dataset for manuscript submitted for peer review</p>
Multi-material 3D Printing of Thermoplastic Elastomers for Development of Soft Robotic Structures with Integrated Sensor Elements
<p>Embedded sensing can benefit soft robots with the ability to interact with their environment but producing embedded soft sensors can be challenging. Multi-material Fused Deposition Modeling (FDM) additive manufacturing allows producing complex structures, by combining more than one kind of polymeric material. For multi-material FDM, conductive thermoplastic elastomer filaments have been developed. This allows the printing of flexible functional structures, based on thermoplastic elastomer structures with conductive paths that are of great interest for stretchable electronics and soft robotic applications. In this study, stretchable piezoresistive elastomer strain sensor composites were successfully produced by using multi-material FDM. A piezoresistive thermoplastic elastomer was printed on the top of a nonconductive, flexible thermoplastic elastomer strip using FDM multi-material 3D printer. FDM elastomer filaments with different shore hardness as substrate materials for the gripper structure were used. The hardness of the elastomer affected the printability and the adhesion to the conductive elastomer material, which was used as a strain sensor material. The hardness affected the strain sensor properties too. The piezoresistive response, dynamic behavior, drift, relaxation and sensitivity of the printed multi-material strips were investigated by tensile tests. Soft robotic grippers with integrated sensing elements to detect deformation while touching the objective were selected as a case study. The soft grippers with the integrated sensors exhibited intelligent response by recognizing when they were griping a small or big object and when an obstacle was inhibiting their function.</p>
3D-Scere static files and result table
<p>Static files, result table and graphical representations associated with the 3D-Scere project (dashboard and publication).</p> <p> </p> <p>3D_distances.parquet.gzip: three-column parquet file with 3D distances between <em>Saccharomyces cerevisiae </em>chromosomal features.</p> <p>SCERE.db: SQLite database of <em>Saccharomyces cerevisiae </em>features build from the <a href="https://www.yeastgenome.org/">SGD</a>.</p> <p>Table1.tsv: tab-separated values file with Kolmogorov Smirnov test results for <em>Saccharomyces cerevisiae </em>transcription factors.</p> <p>supplementary-data-file-S4.zip: ZIP file with graphical representations associated to each transcriptional module.</p>
Fatiando a Terra Data: Alps - 3D GPS velocities
<p>This is a compilation of 3D GPS velocities for the Alps. The horizontal velocities are reference to the Eurasian frame. All velocity components and even the position have error estimates, which is very useful and rare to find in a lot of datasets.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's mean for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made:</strong> Combined the data from 3 different files, keeping the 3-component velocities in the Eurasion frame, coordinates, uncertainties, and station ID; exported to a compressed CSV file.</p> <p><strong>Source:</strong> Sánchez, Laura; Völksen, Christof; Sokolov, Alexandr; Arenz, Herbert; Seitz, Florian (2018): Present-day surface deformation of the Alpine Region inferred from geodetic techniques (data). PANGAEA, <a href="https://doi.org/10.1594/PANGAEA.886889">https://doi.org/10.1594/PANGAEA.886889</a></p> <p><strong>Source license:</strong> <a href="https://doi.org/10.1594/PANGAEA.886889">CC-BY-3.0</a></p> <p><strong>Repository:</strong> <a href="https://github.com/fatiando-data/alps-gps-velocity">https://github.com/fatiando-data/alps-gps-velocity</a></p>
3D Adjoint tomography model of the Italian lithosphere
<p>The project IMAGINE_IT (PI Dr. Dimitri Komatitsch) received 40 million CPU-hours on the Tier-0 GENCI/TGCC CURIE supercomputer as a winner of the 9<sup>th</sup> PRACE consortium call (2014). </p> <p>The awarded computational resources allowed us to construct a new 3D tomographic model for the Italian lithosphere, <em>Im25</em>,<em> </em>by combining spectral-element three-dimensional wavefield simulations and an adjoint-state method.</p> <p>To obtain the final model <em>Im25, </em>we performed 25 adjoint tomography iterations and two source inversion iterations for the initial wavespeed model and an intermediate one.</p> <p>The 3D model <em>Im25</em> resolves P- and S-wavespeed values in the Italian lithosphere for frequencies up to ~0.1 Hz (i.e., periods down to ~10 s). It provides improved images of the subsurface of the peninsula and its neighborhood, highlighting the complex structure of the Adriatic plate on the east of the Italian coast, the debated plumbing system of Mount Etna volcano, and the distribution of fluids and gas (CO<sub>2</sub>) in correlation with the Italian seismicity. </p> <p>The obtained <em>V<sub>p</sub></em> and <em>V<sub>s</sub></em><sub> </sub>models are in agreement with high resolution models developed by classical tomography studies in Italy at local scale, and the retrieved <em>V<sub>p</sub></em>/<em>V<sub>s</sub></em> values correspond to the interpretations on fluids and gas distribution in the Italian subsurface from studies in various geophysical fields. The <em>V<sub>s</sub></em> <em>Im25</em> model also compares favorably with <em>V<sub>s</sub></em> profiles extracted from other studies that use different techniques and datasets, and focus on specific zones of Italy.</p> <p>The provided dataset contains the values of <em>V</em><sub><em>p</em></sub> and <em>V<sub>s</sub></em> (in m/s) for model <em>Im25</em> at each point of the geometrical grid used to discretize the study volume.</p>
Experimental Seismic Data Obtained Using a 3D-Printed Model of the Los Angeles Basin Structure
<p>These data were obtained and analyzed by Park et al., (2022) "Seismic wave simulation using a 3D printed model of the Los Angeles Basin" (doi:10.1038/s41598-022-08732-w).</p> <p> </p>
Results of the 3D detection of cracks in tested disc-shaped specimens
<p> </p> <p>3D images of fatigue crack obtained by laboratory tomography and synchrotron tomography within bi-disc specimens.</p>
5D-NP-MATER_MDO - Open Dataset for "Novel, High-Resolution, Subtractive Photoresist Formulations for 3D Direct Laser Writing Based on Cyclic Ketene Acetals"
<p>This is the open dataset for the paper: "Marco Carlotti*, Omar Tricinci, Virgilio Mattoli*, Novel, High-Resolution, Subtractive Photoresist Formulations for 3D Direct Laser Writing based on Cyclic Ketene Acetals, Advanced Materials Technologies, On line (2022) [DOI: 10.1002/admt.202101590] "</p> <p>This include the Supplementary Information file ("SI.pdf") , all the source material used for the paper preparation and more. </p> <p>For each folder (sub-dataset) there is a corresponding readme file describing the content and including metadata.</p> <p> </p>
3D printed photonics devices
<p>This data contains the 1-D intensity measurements in xls and pdf file formats along with the device images</p>
Pre-Preg (PP) Manufacturing and Spring-in monitoring through FBGs, DCs and 3D CMM measurements
<p>ELADINE project is aiming to implement a numerical tool that can reduce reoccurring costs of low-volume production in composite manufacturing of primary structural elements and thus reducing overall manufacturing effort and carbon emissions. A<strong> primary goal of this project is to eliminate tolerance non-compliancy in the manufactured structures caused by natural and unavoidable post-manufacturing distortions, typical for composite materials</strong>. These distortions might render otherwise qualitative components unusable due to their final geometry.</p> <p>Objectives of the Numerical model validation are:</p> <ul> <li>To understand the dominant factors which affects the spring-in phenomenon.</li> <li>To provide the simulation tool with the required values of the properties that influence on spring-in.</li> <li>To verify the simulation tool ability to predict spring-in for a variety of conditions.</li> <li>To develop a procedure of adapting and embedding sensors (dielectric and fiber optic) to obtain proper, useful and accurate signals of the manufacturing parameters (T, degree of cure, strain).</li> <li>To develop interpretation procedures of the signal/curves of sensors to obtain on-line process monitoring information.</li> </ul> <p>To obtain the data to feed and develop the numerical tool able to estimate the component distortions after its manufacturing, a combination of Fiber Optic Sensors (FOS) based on Fiber Bragg Grating (FBG) technology, Dielectric Curing sensors (DC) and 3D scanning were used to monitor the composite coupon manufacturing and the distortions the days after being demoulded. During the manufacturing process embedded FBGs and DC sensors were used to monitor the coupon temperature and strain distribution and resin curing evolution. After the manufacturing and the demolding, the distortions evolution were monitored by the embedded FBGs and by 3D CMM measurements.</p> <p><strong>In the ELADINE project, the distortion monitoring was made to two Out-of-Autoclave manufacturing technologies: liquid resin infusion and oven cured Pre-Preg (PP)</strong>. For both material systems, slightly curved coupons and C-shaped coupons were the geometries selected as representative for the Skin and spars of the wing box. The Skin coupon was curved panel with a 1475 mm radius (with edge rise of 7,65 mm) that was thought to best replicate the wing profile geometry. The C-spar coupon geometry selected for the study was a non-tapered spar section with two different angle with radius of curvature of 5mm and 12mm. This geometry was chosen to simplify measuring and comparisons with wing demo. Furthermore, three different thickness are studied for the Skin coupons and two for the C-spar coupons which were selected from different zones along the wing. Moreover, a C-spar coupon with variable thickness was studied, as a simulation of the transition between zones with different thickness in the wing.</p> <p><strong>In this dataset, the data obtained from the FBGs, DCs and 3D CMM meassurements during a PP manufacturing process and spring-in distortions monitoring can be found.</strong></p>
3D visualization of bioerosion in archaeological bone
<p>Set of five microCT volume images of archaeological samples. 8-bit TIFF images stacks in zipped folders.</p>
Synthetic 3D PPGIS Data _ Turku - Finland
<p>3D PPGIS data generated synthetically in Turku, Finland. Data is created in an approximately 2 km<sup>2 </sup>area near center. 150X150 m grid cells were used for data generation.</p>
3D Nuclei annotations and StarDist 3D model(s) (rat brain)
<p><strong>Name</strong>: 3D Nuclei annotations and StarDist3D model(s) (rat brain)</p> <p><strong><em>Images: </em></strong>From a large tiling acquisition ( https://doi.org/10.5281/zenodo.6646128 ) individual Tile (xyz : 1024x1024x62) were downsampled and cropped (128x128x62). Four crops, from different tiles (./annotations_BIOP/images/) were manually annotated with ITK-SNAP (./annotations_BIOP/masks/)</p> <p>These four images, and their corresponding masks, were cropped into four quadrants (./crops_BIOP_v1/) in order to get 16 different images (64x64x62).</p> <p><strong><em>Conda environment</em></strong><em>: </em>A conda environment was created using the yml file <em>stardist0.8_TF1.15.yml</em></p> <p><strong><em>Training : </em></strong>Training was performed using the jupyter notebook <em>1-Training_notebook.ipynb</em>.<br> Three different trainings (with the same random seed, same anisotropy, patch size and grid) were performed and produced three different models (./models/)</p> <p>Validation images (from the random seed used) were exported to ease the visual inspection of the results(./val_rdm42/).</p> <p><strong><em>Validation: </em></strong>To save metrics in a csv file and compare predictions to the annotations the jupyter notebook <em>2-QC_notebook.ipynb </em>can be used on the validation folder.</p> <p><strong>Large images</strong>: To test the model on larger images one can use Whole_ds441.tif (or Crop_ds441.tif )<br> These images were obtained using the plugin <a href="https://imagej.net/plugins/bigstitcher/">BigSticher </a>on the raw data ( https://doi.org/10.5281/zenodo.6646128 ), resaved as h5 and exported the downsample by 4 version.</p> <p> </p> <p> </p>
video_03_leg_part_3d
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
Raw Data - 3D Printing Temperature Tailors Electrical and Electrochemical Properties through Changing Inner Distribution of Graphite/Polymer
<p>This Data set contains the raw data of the article:</p> <p>3D Printing Temperature Tailors Electrical and Electrochemical Properties through Changing Inner Distribution of Graphite/Polymer, Small, 2021, 17, 2101233.</p> <p>C. Iffelsberger, C. W. Jellett, and M. Pumera*,</p> <p>https://doi.org/10.1002/smll.202101233</p> <p>Related to the MSCA Project: 888797 LoCatSpot</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.