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204 results for “shape model”
Models representative of the AGN spectral shapes (preliminary)
<p>The Table gives the Models obtained that are representative of the AGN spectral shapes.</p>
Datasets and R script to replicate the theoretical modeling from the article entitled Multiple hosts, multiple impacts: the role of vertebrate host diversity in shaping mosquito life history and pathogen transmission
<p>Datasets and R script to replicate the theoretical modeling from the article entitled "<strong> </strong>Multiple hosts, multiple impacts: the role of vertebrate host diversity in shaping mosquito life history and pathogen transmission" by Vantaux A., Moiroux N., Dabire K. R., Cohuet A., Lefevre T. 2023</p>
Modeling of pressure induced magnetic and magnetocaloric effects in dissipative magnetic shape memory alloy systems
<p>This article presents a coupled magneto-thermo-mechanical model of pressure-dependent Magneto-caloric Effect (MCE) and magnetization responses for polycrystalline Magnetic Shape Memory Alloys (MSMA). Coupled constitutive equations are derived from a Helmholtz free energy function in a consistent thermodynamic way. The hysteretic and dissipative characteristics of phase transformations in MSMAs are captured by the internal state variables approach with their evolution equations. The model is calibrated and validated with the existing experimental data. The validated constitutive model is then exploited to predict MCEs at different pressures and magnetic field levels. Some predicted results are compared with the available experimental data.</p>
Modeling of pressure induced magnetic and magnetocaloric effects in dissipative magnetic shape memory alloy systems
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Hybrid dynamic model for shape memory alloy linear and unimorph actuators
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Foot shape-function model data
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Realistic 3D avian vocal tract model demonstrates how shape affects sound filtering (Passer domesticus)
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Data from: How far can I extrapolate my species distribution model? Exploring Shape, a novel method
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MVCNN++: CAD model shape classification and retrieval using multi-view convolutional neural networks
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Stan code from: Simulation modeling reveals the evolutionary role of landscape shape and species dispersal on genetic variation within a metapopulation
Different shapes of landscape boundaries can affect the habitat networks within them and consequently the spatial genetic-patterns of a metapopulation. In this study, we used a mechanistic framework to evaluate the effects of landscape shape, through watershed elongation, on genetic divergence among populations at the metapopulation scale. Empirical genetic data from four, sympatric stream-macroinvertebrates having aerial adults were collected from streams in Japan to determine the roles of species-specific dispersal strategies on metapopulation genetics. Simulation results indicated that watershed elongation allows the formation of river networks with fewer branches and larger topographic constraints. This results in decreased interpopulation connectivity but a lower level of spatial isolation of distal populations (e.g., those found in headwaters) occurring in the landscapes examined. Distal populations had higher genetic divergence when their downstream-biased dispersal (relative to upstream- and/or overland-biased dispersal) was high. This underscores the importance of distal populations influencing genetic divergence at the metapopulation scale for species having downstream-biased dispersal. In turn, lower genetic divergence was observed under watershed elongation when the genetic isolation of distal populations was decreased in such species. This strong association between landscape shape and evolutionary processes highlights the importance of natural, spatial architecture in assessing the effectiveness of conservation and management strategies.
Data from: Can collective memories shape fish distributions? A test, linking space-time occurrence models and population demographics
Social learning can be fundamental to cohesive group living, and schooling fishes have proven ideal test subjects for recent work in this field. For many species, both demographic factors, and inter- (and intra-) generational information exchange are considered vital ingredients in how movement decisions are reached. Yet key information is often missing on the spatial outcomes of such decisions, and questions concerning how migratory traditions are influenced by collective memory, density-dependent and density-independent processes remain open. To explore these issues, we focused on Atlantic herring (Clupea harengus), a long-lived, dense-schooling species of high commercial importance, noted for its unpredictable shifts in winter distribution, and developed a series of Bayesian space-time occurrence models to investigate wintering dynamics over 23 years, using point-referenced fishery and survey records from Icelandic waters. We included covariates reflecting local-scale environmental factors, temporally-lagged prey biomass and recent fishing activity, and through an index capturing distributional persistence over time, derived two proxies for spatial memory of past wintering sites. The previous winter's occurrence pattern was a strong predictor of the present pattern, its influence increasing with adult population size. Although the mechanistic underpinnings of this result remain uncertain, we suggest that a 'wisdom of the crowd' dynamic may be at play, by which navigational accuracy towards traditional wintering sites improves in larger and/or denser, better synchronized schools. Wintering herring also preferred warmer, fresher, moderately stratified waters of lower velocity, close to hotspots of summer zooplankton biomass, our results indicative of heightened environmental sensitivity in younger cohorts. Incorporating spatiotemporal correlation structure and time-varying regression coefficients improved model performance, and validation tests on independent observations one-year ahead illustrate the potential of uniting demographic information and non-stationary models to quantify both the strength of collective memory in animal groups and its relevance for the spatial management of populations.
Data from: High quality statistical shape modelling of the human nasal cavity and applications
The human nose is a complex organ that shows large morphological variations and has many important functions. However, the relation between shape and function is not yet fully understood. In this work, we present a high quality statistical shape model of the human nose based on clinical CT data of 46 patients. A technique based on cylindrical parametrization was used to create a correspondence between the nasal shapes of the population. Applying principal component analysis on these corresponded nasal cavities resulted in an average nasal geometry and geometrical variations, known as principal components, present in the population with a high precision. The analysis led to 46 principal components, which account for 95 percent of the total geometrical variation captured. These variations are first discussed qualitatively, and the effect on the average nasal shape of the first five principal components is visualized. Hereafter, by using this statistical shape model, two application examples that lead to quantitative data are shown: nasal shape in function of age and gender, and a morphometric analysis of different anatomical regions. Shape models, as the one presented here, can help to get a better understanding of nasal shape and variation, and their relationship with demographic data.
Virus Capsomer Model [wedge-shaped structure 1]
PICORNAVIRIDAE – Designing Model and Visualization Concepts in Virology Educational T=3 [and T=pseudo3] Virus Structure Model [assembly unit 02/08] Common schematic representation for visualizing "wedge-shaped" asymmetric units. These are formed by virus protein subunits, five asymmetric units [structural units] combined form a capsomere [advancement: visual subdivision by gaps]. Source: Objaverse 1.0 / Sketchfab
Modelling Cell Shape in 3D Structured Environments: A Quantitative Comparison with Experiments
<p>This repository contains experimental data and computer scripts for the following publication: Link R, Jaggy M, Bastmeyer M, Schwarz US (2024) Modelling cell shape in 3D structured environments: A quantitative comparison with experiments. PLoS Comput Biol 20(4): e1011412. https://doi.org/10.1371/journal.pcbi.1011412</p> <p>There are two directories, “data” and “scripts”.</p> <p> <strong>1) </strong><strong>Directory data</strong></p> <p> WRL-files for experimental data generated with Imaris from Zeiss image files.</p> <p>The WRL-files can be converted to STL-files with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>).</p> <p>The STL-files can be converted to FE-files for the SurfaceEvolver with our script CreateFeFile.py.</p> <p> The WRL-files are named according to the scaffolds:</p> <p>L*.wrl cells in L-shaped scaffolds (n=6).</p> <p>V*.wrl cells in V-shaped scaffolds (n=7).</p> <p>TRight*.wrl cells in right-triangle scaffolds (n=3).</p> <p>TEqui*.wrl cells in equilateral-triangle scaffolds (n=4).</p> <p><strong>2) </strong><strong>Directory scripts</strong></p> <p>ClusterSurfaceLinearPlugin: New CompuCell3D plugin needed to calculate linear surface energy functional for cells with nucleus (using the cluster concept).</p> <p>CompuCell3DScript: Hamiltonian_Comparison.cc3d is the main script for our simulations, uses the directory “Simulation”.</p> <p>CreateFeFile.py: generates surface evolver FE-file from STL-file. A STL-file can be generated from a WRL-file e.g. with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>). </p> <p>SphericalHarmonicsAnalysis.ipynb: Python notebook that calculates the Fourier spectrum and Delta_30, needs WRL-file as input.</p> <p> </p>
Sampled Structures from Diffusion Models for Protein SHAPES
<p>Data associated with SHAPES (Structural and Hierarchical Assessment of Proteins with Embedding Similarity)</p> <ul> <li><code>rmsd.tar.gz</code> : RMSD and TM scores of ESMFold predicted structures with original designed backbones.</li> <li><code>mpnn.tar.gz</code> : ProteinMPNN designed sequences.</li> <li><code>raster.tar.gz</code> : CATH and sampled structures annotated with the grid square label after rasterization. Also includes the specific structures which are displayed in the raster plots.</li> <li><code>dssp.tar.gz</code> : Secondary structure frequences computed by DSSP.</li> <li><code>samples.tar.gz</code> : All sampled structures.</li> </ul> <p>Due to size, the ESMFold predicted structures for eight ProteinMPNN-designed sequences per sampled backbone are not included.</p> <p> </p> <p>Code: <code>proteinshapes-main.zip</code></p>
Computational dataset, scripts and models for 'Lipid shape as a membrane activity modulator of a model antimicrobial peptide'
<p>Analysis scripts and computational models used in the manuscript 'Lipid shape as a membrane activity modulator of a model antimicrobial peptide', by Marcin Makowski, Octávio L. Franco, Nuno C. Santos and Manuel N. Melo.</p>
Dataset - Low-hysteresis shape-memory ceramics designed by multimode modelling
<p>Accompanying Dataset for the article "Low-hysteresis shape-memory ceramics designed by multimode modelling". The dataset contains two .csv files, one each for tetragonal and monoclinic phase, containing lattice parameter data used to train the ML models. Lattice parameters are in angstroms, compositions are in mole percent (cation site), temperatures are in degrees Celsius.</p>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The model codes, data, and plot scripts used in the paper, "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>7_experiments.zip contains modified model code and output data of each experiment in this study.</li> <li>off-line test.zip contains off-line test code and output data.</li> <li>plot_scripts.zip are the NCL scripts used for figures in the paper.</li> </ul>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The model codes, data, and plot scripts used in the paper, "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>Figs&Table are the NCL scripts used for figures and table in the paper.</li> <li>Model_Results contains output data of each experiment in this study.</li> <li>Mods_Scripts contains modified model code.</li> <li>Offline_Code contains off-line test code.</li> </ul>
MITgcm model setup and output for "What determines the shape of the Pine-Island-like ice shelf?"
<p><strong>(Contents)</strong><br> Here it contains<br> <br> jim_run3 = CTRL (Similar to Jordan et al., 2018 but with smaller ocean)</p> <p>melt/run/ = IOCTRL, M(all)V(dyn)U(dyn)</p> <p>melt2/ = M( changing, see below ) V( dyn ) U( 0 )<br> melt2/run/ = M(all)V(dyn)U(0)<br> melt2/run2/ = M(20)V(dyn)U(0)<br> melt2/run3/ = M(GL10)V(dyn)U(0)<br> melt2/run4/ = M(GL20)V(dyn)U(0)</p> <p><br> melt3/ = M ( changing, see below ) V( changing ) U( 0 )<br> melt3/run5/ = M(all)V(2000)U(0)<br> melt3/run10/ = M(20)V(2000)U(0)</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> Input melt data for running ice only experiments<br> #data.streamice#<br> meltCTRL.bin = M(all) ... melt rate data of CTRL for every time steps <br> meltCTRL2.bin = M(GL10)<br> meltCTRL3.bin = M(20 m/a)<br> meltCTRL4.bin = M(GL20)</p> <p>Input velocity data for running ice only experiments <br> melt3/<br> uvel_ext4.bin = U(0 m/a)<br> vvel_ext5.bin = V(2000 m/a)</p> <p><strong>(How to compile and run)</strong><br> mkdir build<br> cd build/<br> module load intel/2021.2.0<br> module load impi/2021.2.0<br> export LANG=en_US.UTF-8<br> export LC_ALL=en_US.utf8<br> ../../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas_tokyo3 -mpi -mods ../code/<br> make depend<br> export LANG=en_US.UTF-8<br> export LC_ALL=en_US.utf8<br> make -j 16</p> <p> mkdir ../run<br> cd ../run/<br> cp ../build/mitgcmuv .<br> cp ../input/* .<br> pjsub job*.pbs<br> <br> <strong>Other important links</strong><br> https://github.com/hgu784/MITgcm_67s<br> <br> For more info please send an email to Yoshihiro.Nakayama@lowtem.hokudai.ac.jp</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.