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464 results for “model structure”
Structural 3D domain reconstruction of the RNA genome from viruses from a secondary structure model
<p>Fragments and final models of reconstructed STMV genome from in virio and in vitro secondary structures reported in Larman et al. (2017).</p> <p>Simulation scripts for simulations of genome and fragments.</p> <p>Full code of SPQR package for performing simulations.</p>
Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters
<p>The datasets archived here include simulation results shown in the paper, “Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters”, published in Hydrology and Earth System Sciences (Maertens et al., 2021). The simulations were conducted over the South-American Dry Chaco and output on the different water budget components was produced with three land surface models (CLM2.0, CLSM-F2.5, and Noah3.6) embedded in the NASA Land Information System (LIS). The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction), and (iii) yearly land cover information. For each experiment described in the manuscript, we provide daily netcdf files (0.125° resolution, period Jan 1992 – Dec 2015). The conducted experiments are simulations with (i) default LIS parameters (BL), (ii) updated soil parameters (REV<sub>s</sub>) and (iii) interannualy varying vegetation and land cover parameters (REV<sub>SV</sub>).<br> For details, please refer to</p> <p>Maertens, M., De Lannoy, G. J. M., Apers, S., Kumar, S. V., and Mahanama, S. P. P.: Land Surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters, Hydrology and Earth System Sciences.</p> <p>Please contact Michiel Maertens (michiel.maertens@kuleuven.be) or Gabriëlle De Lannoy (gabrielle.delannoy@kuleuven.be) for any questions.</p>
Supplementary material 1 from: Grace JB, Steiner M (2021) A protocol for modelling generalised biological responses using latent variables in structural equation models. One Ecosystem 6: e67320. https://doi.org/10.3897/oneeco.6.e67320
A protocol for modelling generalised biological responses using latent variables in structural equation models
Supplementary material 3 from: Grace JB, Steiner M (2021) A protocol for modelling generalised biological responses using latent variables in structural equation models. One Ecosystem 6: e67320. https://doi.org/10.3897/oneeco.6.e67320
A protocol for modelling generalised biological responses using latent variables in structural equation models
Supplementary material 2 from: Grace JB, Steiner M (2021) A protocol for modelling generalised biological responses using latent variables in structural equation models. One Ecosystem 6: e67320. https://doi.org/10.3897/oneeco.6.e67320
A protocol for modelling generalised biological responses using latent variables in structural equation models
Surface Deformations from Glacial Isostatic Adjustment Models with Laterally Homogeneous, Compressible Earth Structure
<p>The zipped file contains global 1-degree grid files of four different surface deformation parameters:</p> <ul> <li>Horizontal velocity, North component (vnorth), in mm/a</li> <li>Horizontal velocity, East component (veast), in mm/a</li> <li>Vertical velocity (vup), in mm/a</li> <li>Geoid change (dgeoid), in mm/a</li> </ul> <p>calculated from a set of 26 different glacial isostatic adjustment models applying 10 different radially varying (=layered) earth structures and 3 different global ice models.</p> <p>The different earth structures and the available combinations with ice models can be found in the readme.pdf.</p> <p>File naming is simply {ice model}_{earth model}_{parameter}.grd. For example, anu-ice_f72_vup.grd is the global vertical velocity grid file calculated with a GIA model with ANU-ICE ice history and 60 km lithospheric thickness, 4E20 Pa s upper mantle viscosity and 2E21 Pa s lower mantle viscosity. Grid files (NetCDF format) were generated with GMT6 (Wessel et al., 2019), thus can be directly used.</p> <p>Ice thickness histories of ICE-6G_C (Argus et al., 20214; Peltier et al., 2015) and ICE-7G_NA (Roy & Peltier, 2017) were downloaded from W. R. Peltier’s data website at the University of Toronto, Canada: <a href="https://www.atmosp.physics.utoronto.ca/~peltier/data.php">https://www.atmosp.physics.utoronto.ca/~peltier/data.php</a></p> <p>Note that the velocity field of ICE-6G_C(VM5a) can be compared to the one available from W. R. Peltier’s data website. The files provided here are not a substitute for the ones by W. R. Peltier and colleagues! Analyzing the difference between the files from this work and the ones available on the data website can help getting an error estimate from the two GIA model implementations (see further below). The user will find minor differences in the uplift component but larger ones in far field areas of the horizontal components.</p> <p>ICE-7G_NA(VM7) results are added as complement for interested users. However, note that ICE-7G_NA contains ice thickness history modifications in North America only and a fully global re-optimization of the ice thickness is warranted. Hence, excessive use and interpretation of these grid files, especially on global scale, should be avoided.</p> <p>ANU-ICE is a global 1-degree ice thickness model merged from several regional models (Lambeck, 1995; Fleming & Lambeck, 2004; Lambeck et al., 2010; 2014; 2017) kindly provided by Anthony Lambert and Kurt Lambeck, ANU, Canberra, Australia. The regional models contain differing spatial and temporal resolutions that were unified to fit the global 1-degree spatial resolution at mainly common time steps (500–1000 years). The Antarctic Ice Sheet part contains changes in the last time steps, thus some larger changes in the velocities can be found there.</p> <p>The software ICEAGE (Kaufmann, 2004) is used for calculating the grids, which applies the viscoelastic normal-mode method (Peltier, 1974; Wu, 1978). The sea-level equation is solved in a pseudo-spectral approach (Mitrovica et al., 1994; Mitrovica & Milne, 1998) in an iterative procedure in the spectral domain. See further details in Kaufmann and Lambeck (2000; 2002). The spherical harmonic expansion in the spectral domain is truncated at degree 192, which corresponds to ~1° spatial resolution. The models are spherically symmetric (1D), compressible, with Maxwell-viscoelasticity, rotational feedback, and time-dependent coastlines. The Earth’s core is, as assumed to be inviscid, incorporated as lower boundary condition. Rheological parameters such as depth-dependent density, Young’s modulus, etc., are taken from PREM (Preliminary Reference Earth Model; Dziewonski & Anderson, 1981).</p> <p><strong>Acknowledgments</strong></p> <p>HS would like to thank Jeff Freymueller for discussions on model selection.</p> <p><strong>References</strong></p> <p>Argus, D. F., Peltier, W., Drummond, R., Moore, A.W. 2014. The Antarctica component of postglacial rebound model ICE-6G_C (VM5a) based on GPS positioning, exposure age dating of ice thicknesses, and relative sea level histories. Geophysical Journal International 198, 537–563, doi:10.1093/gji/ggu140.</p> <p>Dziewonski, A. M., Anderson, D. L. 1981. Preliminary reference Earth model. Physics of the Earth and Planetary Interiors 25, 297–356, doi:10.1016/0031-9201(81)90046-7.</p> <p>Fleming, K., Lambeck, K. 2004. Constraints on the Greenland ice sheet since the Last Glacial Maximum from sea-level observations and glacial-rebound models. Quaternary Science Reviews 23, 1053–1077, doi:10.1016/j.quascirev.2003.11.001.</p> <p>Kaufmann, G. 2004. Program Package ICEAGE, Version 2004. Manuscript. Institut für Geophysik der Universität Göttingen.</p> <p>Kaufmann, G., Lambeck, K., 2000. Mantle dynamics, postglacial rebound and the radial viscosity profile. Physics of the Earth and Planetary Interiors 121, 301–324, doi:10.1016/S0031-9201(00)00174-6.</p> <p>Kaufmann, G., Lambeck, K., 2002. Glacial isostatic adjustment and the radial viscosity profile from inverse modeling. Journal of Geophysical Research Solid Earth 107, ETG 5-1-ETG 5-15, doi:10.1029/2001JB000941.</p> <p>Lambeck, K. 1995. Late Devensian and Holocene shorelines of the British Isles and North Sea from models of glacio-hydro-isostatic rebound. Journal of the Geological Society London 152, 437–448, doi:10.1144/gsjgs.152.3.0437.</p> <p>Lambeck, K., Purcell, A., Zhao, J., Svensson, N.-O. 2010. The Scandinavian ice sheet: from MIS 4 to the end of the last glacial maximum. Boreas 39 (2), 410–435, doi:10.1111/j.1502-3885.2010.00140.x.</p> <p>Lambeck, K., Rouby, H., Purcell, A., Sun, Y., Sambridge, M. 2014. Sea level and global ice volumes from the Last Glacial Maximum to the Holocene. Proceedings of the National Academy of Sciences of the United States of America 111 (43), 15296–15303, doi:10.1073/pnas.1411762111.</p> <p>Lambeck, K., Purcell, A., Zhao, J. 2017. The North American Late Wisconsin ice sheet and mantle viscosity from glacial rebound analyses. Quaternary Science Reviews 158, 172–210, doi:10.1016/j.quascirev.2016.11.033.</p> <p>Mitrovica, J. X., Davis, J. L., Shapiro, I. I. 1994. A spectral formalism for computing three–dimensional deformations due to surface loads: 1. Theory. Journal of Geophysical Research Solid Earth 99(B4), 7057–7073, doi:10.1029/93JB03128.</p> <p>Mitrovica, J. X., Milne, G. A. 1998. Glaciation-induced perturbations in the Earth’s rotation: a new appraisal. Journal of Geophysical Research Solid Earth 103, 985–1005, doi:10.1029/97JB02121.</p> <p>Peltier, W. R. 1974. The impulse response of a Maxwell Earth. Reviews of Geophysics and Space Physics 12(4), 649–669, doi:10.1029/RG012i004p00649.</p> <p>Peltier, W., Argus, D., Drummond, R. 2015. Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_C (VM5a) model. Journal of Geophysical Research Solid Earth 120, 450–487, doi:10.1002/2014JB011176.</p> <p>Roy, K., Peltier, W. R. 2017. Space-geodetic and water level gauge constraints on continental uplift and tilting over North America: regional convergence of the ICE-6G_C (VM5a/VM6) models. Geophysical Journal International 210(2), 1115-1142, doi:10.1093/gji/ggx156.</p> <p>Wessel, P., Luis, J. F., Uieda, L., Scharroo, R., Wobbe, F., Smith, W. H. F., Tian, D. 2019. The Generic Mapping Tools version 6. Geochemistry, Geophysics, Geosystems 20, 5556–5564, doi:10.1029/2019GC008515.</p> <p>Wu P. 1978. The response of a Maxwell earth to applied surface mass loads: glacial isostatic adjustment. MSc thesis, University of Toronto, Toronto, Ontario, Canada.</p>
Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years
<p>Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years</p>
Datasets related to paper "Crustal structure and fault geometries of the Garhwal Himalaya, India: Insight from new high-resolution gravity data modeling and PSO inversion"
<p>The files include Satellite gravity data, topography and earthquake data used in the paper "Crustal structure and fault geometries of the Garhwal Himalaya, India: Insight from new high-resolution gravity data modeling and PSO inversion" by Chamoli A., Rana S., Dwivedi D., Pandey A.K.. This has been submitted to Journal of Geophysical Research: Solid Earth. Restrictions applied to the availability of the land gravity data set.</p>
Case studies from doubleHelix: nucleic acid sequence identification, assignment and validation tool for cryo-EM and crystal structure models
<p>Case studies from "doubleHelix: nucleic acid sequence identification, assignment and validation tool for cryo-EM and crystal structure models"</p>
Case studies from: Sequence assignment validation in protein crystal structure models with checkMySequence
<p>Case studies from "Sequence assignment validation in protein crystal structure models with checkMySequence"</p>
set of SQS models of 128 atomic structure
<p>These are the SQS POSCARS of binary BCC models with diversified concentration of alloying element - appropriate for elastic constants calculations</p>
Structure Databases: Analysis and Augmentation of Guest-Host Interaction Energy Models as CHA and AEI Zeolite Crystallization Phase Predictors
<p>Structures and energies associated with the paper: Analysis and Augmentation of Guest-Host Interaction Energy Models as CHA and AEI Zeolite Crystallization Phase Predictors</p>
Datasets associated with the publication: "The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models".
<p>ECMWF HRES and ERA5 datasets for the paper "The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models".</p> <p>Content: </p> <p>========================================================================================</p> <p>Storm Friederike:</p> <p>ECMWF HRES forecast, 18.01.2018 00:00 UTC - 23:00 UTC, hourly, GRIB-format.</p> <p>ECMWF ERA5 reanalysis, 16.01.2018 12:00 UTC - 19.01.2018 00:00 UTC, twelve-hourly data, GRIB-format.</p> <p>========================================================================================</p> <p>Storm Vladiana:</p> <p>ECMWF HRES analysis, 23.09.2016 00:00 UTC - 23.09.2016 00:00 UTC, six-hourly data, rotated North Pole (latitude: 51˚, longitude: 160˚), NetCDF-format.</p> <p>========================================================================================</p> <p>Storm Egon:</p> <p>ECMWF ERA5 reanalysis, 12.01.2017 00:00 UTC - 13.01.2017 06:00 UTC, six-hourly data, GRIB-format.</p>
Dataset: Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture
<p>Dataset for Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture manuscript submitted to Plant Phenomics. The dataset contains raw and processed root architecture images, RhizoVision trait outputs, and the associated R scripts for statistical analysis and model parameterization.</p>
Structures and data for minimalistic models of cage-like molecules in "Systematic exploration of accessible topologies of cage molecules via minimalistic models"
<p>Archives of .csv files containing data of lowest energy conformers for all cage configurations tested and associated .mol files. </p><p> </p><p>For paper: Systematic exploration of accessible topologies of cage molecules via minimalistic models with DOI: <a href="https://doi.org/10.1039/D3SC03991A">10.1039/D3SC03991A</a> </p>
Dataset for the publication: Factors associated with risky drinking decisions in a virtual reality alcohol prevention simulation: A structural equation model
<p>This dataset includes the data from the publication <em>Factors associated with risky drinking decisions in a virtual reality alcohol prevention simulation: A structural equation model. </em>The first table sheet lists the data of the variables; the second table sheet describes the coding of the variables. </p>
Fig. 4. The stability parameters for modelled D10 in In silico characterization of the impact of mutation (LEU112PRO) on the structure and function of carotenoid cleavage dioxygenase 8 in Oryza sativa
Fig. 4. The stability parameters for modelled D10 protein: wild-type (Blue), mutant (Green), Complex I (wild-type & auxin complex) (orange), Complex II (wild-type & cytokinin complex) (purple), Complex III (mutant-auxin complex) (red) and (D) Complex IV (mutant-cytokinin complex) (black) throughout 100 ns simulation: (A) RMSD, (B) RMSF, (C) PCA of C-α movement, and (D) Radius of gyration. The trajectory projected to the two-dimensional space. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Components related to ethical decision making in medical science students: A structural equation model
Open the record for dataset details and reuse information.
Virus Structure Model [interactive]
PICORNAVIRIDAE – Designing Model and Visualization Concepts in Virology Educational T=3 [and T=pseudo3] Virus Structure Model The project was developed during my master's studies in integrated design at the University of the Arts Bremen in cooperation with the Laboratory of Virus Research at the University Bremen. This model combines eight different approaches on the morphology of picornaviruses. Each element [capsomer] can be used as an independent structural model. 12 assembled elements result in a closed virus shell [capsid]. The composition of different designs within one structure enable the possibility to compare different morphological aspects side by side. This digital and interactive model can be used for classroom teaching, for digital lectures or for self-paced learning formats. The modular model concept was originally designed for 3D printing. If you are interested in 3D printing a model on your own, feel free to contact me. https://www.researchgate.net/profile/Joosten_Mueller Source: Objaverse 1.0 / Sketchfab
Virus Capsomer Model [quaternary structure]
PICORNAVIRIDAE – Designing Model and Visualization Concepts in Virology Educational T=3 [and T=pseudo3] Virus Structure Model [assembly unit 08/08] Schematic interpretation of virus protein tertiary structures – showing the main primary structures [ β-jelly-roll and α-helices]. Arranged according to the wedge-shape structure [1]. Based on early schematic protein diagrams [Richardson: 1977] and protein ribbon diagrams [Richardson: 1985]. Source: Objaverse 1.0 / Sketchfab
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.