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464 results for “model structure”
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
Examining factors affecting sustainable performance of building projects using structural equation modeling
Open the record for dataset details and reuse information.
Convergence in simulating global soil organic carbon by structurally different models after data assimilation
<p>This is the data for results shown in the article accepted by Global Change Biology: Convergence in simulating global soil organic carbon by structurally different models after data assimilation</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>
Including population and environmental dynamic heterogeneities in continuum models of collective behaviour with applications to locust foraging and group structure Data and Code
<p>This dataset includes all data used for the creation of "Including dynamic population and environmental heterogeneity in continuum models of collective behaviour with applications to locust foraging and group structure" as well as a snapshot of the code used.<br><br>Each zip should be unzippable and the code should operate with only the contents of the zip file.</p>
Structural insights into distinct mechanisms of RNA polymerase II and III recruitment to snRNA promoters - segmented EM density used in integrative modeling
<p>segmented cryoEM map and derived gaussian mixture model used in the integrative modeling of the human SNAPc complex </p>
The success of alien plants in an arid ecosystem: Structural equation modeling reveals hidden effects of soil resources
<p> data</p>
FIGURE. Variable positions in the ITS2 secondary structure of some Coelastrella sensu lato species. The ITS2 model of Coelastrella striolata strain CAUP H 3602 (JX513881) was used to map sequence differences. Variable positions of analyzed strains (GenBank numbers can be found in Table 3, 4 are given next to the main structure and are marked in bold. Hemi- Compensatory Base Changes in conservative regions are circled and Compensatory Base Change is contoured. Sequences of strains with GenBank numbers JX513879 (C. aeroterrestrica), JX513882 (C. terrestris), JX513884 (C. rubescens), MH176120 (C. rubescens var. oocystiformis), JX513880 (C. multistriata), JX513887 (C. oocystiformis) were used as representatives of Coelastrella species. The strains analyzed in this study are underlined. in Morphological and phylogenetic relations of members of the genus Coelastrella (Scenedesmaceae, Chlorophyta) from the Ural and Khentii Mountains (Russia, Mongolia)
FIGURE. Variable positions in the ITS2 secondary structure of some Coelastrella sensu lato species. The ITS2 model of Coelastrella striolata strain CAUP H 3602 (JX513881) was used to map sequence differences. Variable positions of analyzed strains (GenBank numbers can be found in Table 3, 4 are given next to the main structure and are marked in bold. Hemi- Compensatory Base Changes in conservative regions are circled and Compensatory Base Change is contoured. Sequences of strains with GenBank numbers JX513879 (C. aeroterrestrica), JX513882 (C. terrestris), JX513884 (C. rubescens), MH176120 (C. rubescens var. oocystiformis), JX513880 (C. multistriata), JX513887 (C. oocystiformis) were used as representatives of Coelastrella species. The strains analyzed in this study are underlined.
Influence of number of individuals and observations per individual on a model of community structure
<p>Social network analysis is increasingly applied to understand animal groups. However, it is rarely feasible to observe every interaction among all individuals in natural populations. Studies have assessed how missing information affects estimates of individual network positions, but less attention has been paid to metrics that characterize overall network structure such as modularity, clustering coefficient, and density. In cases such as groups displaying fission-fusion dynamics, where subgroups break apart and rejoin in changing conformations, missing information may affect estimates of global network structure differently than in groups with distinctly separated communities due to the influence single individuals can have on the connectivity of the network. Using a bat maternity group showing fission-fusion dynamics, we quantify the effect of missing data on global network measures including community detection. In our system, estimating the number of communities was less reliable than detecting community structure. Further, reliably assorting individual bats into communities required fewer individuals and fewer observations per individual than to estimate the number of communities. Specifically, our metrics of global network structure (i.e., graph density, clustering coefficient, R<sub>com</sub>) approached the 'real' values with increasing numbers of observations per individual and, as the number of individuals included increased, the variance in these estimates decreased. Similar to previous studies, we recommend that more observations per individual should be prioritized over including more individuals when resources are limited. We recommend caution when making conclusions about animal social networks when a substantial number of individuals or observations are missing, and when possible, suggest subsampling large datasets to observe how estimates are influenced by sampling intensity. Our study serves as an example of the reliability, or lack thereof, of global network measures with missing information, but further work is needed to determine how estimates will vary with different data collection methods, network structures, and sampling periods.</p>
PrISM: Precision for Integrative Structural Models
<p>Datasets with input, output and scripts to reproduce results and figures from "PrISM: Precision for Integrative Structural Models". Manuscript at https://www.biorxiv.org/content/10.1101/2021.06.22.449385v4.</p>
EMRNA: Accurate RNA structure determination from cryo-EM maps by deep learning and integrated modeling
<p>EMRNA: Accurate RNA structure determination from cryo-EM maps by deep learning and integrated modeling.</p><p>Here stores the input files and output structures of EMRNA and the reproduction result of auto-DRRAFTER.</p>
Fig. 4 in The ant fauna of Castelporziano Presidential Reserve (Rome, Italy) as a model for the analysis of ant community structure in relation to environmental variation in Mediterranean ecosystems
Fig. 4 Multidimentional scaling performed on abundance ranks of ant species during May and July 2006. Symbols represent the 28 transects analysed (see text for details) and they are sorted according to trapping period
Fig. 3 in The ant fauna of Castelporziano Presidential Reserve (Rome, Italy) as a model for the analysis of ant community structure in relation to environmental variation in Mediterranean ecosystems
Fig. 3 Overall ant abundance (mean) from pitfall trapping along the 7 sampled sites during May and July
Fig. 2 in The ant fauna of Castelporziano Presidential Reserve (Rome, Italy) as a model for the analysis of ant community structure in relation to environmental variation in Mediterranean ecosystems
Fig. 2 Ant species richness (mean) from pitfall trapping along the 7 sampled sites during May and July
Fig. 1 in The ant fauna of Castelporziano Presidential Reserve (Rome, Italy) as a model for the analysis of ant community structure in relation to environmental variation in Mediterranean ecosystems
Fig. 1 Species accumulation curves of low vegetation sites (A) and of high vegetation sites (B). The values on y-axis correspond to Sobs (Mao Tau) values as described in Colwell (2005). Each line represents a different site type
Tree species recognition with quantitative structure models
<p>A quantitative structure model (QSM) contains the geometric and topological structure of a reconstructed tree. As such, QSMs enable computation of detailed tree properties that have been laborious or impossible to measure before. The computed tree properties can be used as classification features for tree species recognition.</p> <p>The first half of this video illustrates how we define the 15 classification features our research group has used for a species recognition study. An example QSM is used to visualize the relevant tree parts and key steps in the feature computations.</p> <p>The second half shows how the feature values of over a thousand Finnish trees of three different species, Silver birch, Scots pine and Norway Spruce, are distributed, and how well the species separate in the defined feature dimensions. One example QSM of each tree species shown on the right-hand-side with the only the tree parts visible that are related to the current feature.</p> <p>Table of contents:<br> 0:01 Feature illustration<br> 3:46 Viewer guide on screen elements<br> 5:20 Feature value distributions<br> 9:07 Credits</p> <p>The contents of this video link directly to the paper titled "Automatic tree species recognition with quantitative structure models" published in Remote Sensing of Environment (http://dx.doi.org/10.1016/j.rse.2016.12.002).</p> <p>For more information about QSMs, please visit the groups homepage, or watch the other videos on the topic: "3D Forest Information" (https://www.youtube.com/watch?v=wANRdliE1zQ) and "Cylinder reconstruction" (https://www.youtube.com/watch?v=j0Emjwp-fmU).</p> <p>This animation was produced by the Inverse Problems research group in the Department of Mathematics at Tampere University of Technology (http://math.tut.fi/inversegroup).</p> <p>Animation created using Blender 2.77a (http://www.blender.org).</p> <p>Music:<br> "Life of Riley"<br> "Thinking of you"<br> "Jarvic 8"<br> by Kevin MacLeod (http://incompetech.com)<br> Licensed under Creative Commons: By Attribution 3.0<br> http://creativecommons.org/licenses/by/3.0/</p>
Quantitative Structure Models
<p>Summary of how Quantitative Structure Models are reconstructed, and how they are being utilized. The process starts with the laser scanning of a forest. The produced point cloud contains millions of points, and numerous trees. The point cloud is segmented automatically into trees and then into branches. Each branch is reconstructed by fitting several cylinders to the data. The branching topology is stored together with the geometric structure to a single tree model, which is called a Quantitative Structure Model (QSM).</p> <p>QSMs can be used in many applications, as they allow easy access to tree properties, such as, volume, area, branch count, crown shape, taper curve, and size distribution. These properties can be used by forest scientists, forest industry, and forest owners to, e.g., evaluate the current or future state of a forest, or its value. The model properties can also be used for automatic species recognition. Furthermore, QSMs can be visualized in various ways to produce realistic virtual representations of locations, such as, national or city parks, for the travel industry. The models can be textured and augmented with leaves to achieve either a realistic or a fantasy look, to suite the needs of game developers.</p> <p>For more information about QSMs, please visit the groups homepage, or watch the other videos on the topic: "3D Forest Information" (https://www.youtube.com/watch?v=wANRdliE1zQ) and "Cylinder reconstruction" (https://www.youtube.com/watch?v=j0Emjwp-fmU).</p> <p>This animation was produced by the Inverse Problems research group in the Department of Mathematics at Tampere University of Technology (http://math.tut.fi/inversegroup).</p> <p>Animation created using Blender (http://www.blender.org).</p> <p>Music:<br> "Delay Rock" by Kevin MacLeod (http://incompetech.com)<br> Licensed under Creative Commons: By Attribution 3.0<br> http://creativecommons.org/licenses/by/3.0/</p> <p>Textures:<br> "Bark 0007"<br> xoio (xoio.de)</p> <p>"Cherry Leaf"<br> BrianHanson2nd (deviantart.com)<br> Licensed under Creative Commons: By Attribution 3.0<br> http://creativecommons.org/licenses/by/3.0/</p> <p>The animation builds upon but does not directly feature the "Prunus avium - Cherry Tree" point cloud data by Jan Hackenberg (http://www.simpletree.uni-freiburg.de/openData.html) shared under the Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International license<br> http://creativecommons.org/licenses/by-nc-sa/4.0/</p>
35 Years of the Technology Acceptance Model: Insights from Meta-analytic Structural Equation Modelling
<p>The data files are scripts for R software.</p>
Dataset S4-6: Leveraging co-evolutionary insights and AI-based structural modeling to unravel receptor-peptide ligand-binding mechanisms
<h3>Significance statement:</h3> <p>This study presents proof-of-concept for a rapid and inexpensive alternative to classical structure-based approaches for resolving ligand-receptor binding mechanisms. It relies on a multilayered bioinformatic approach that leverages genomic data across diverse species in combination with AI-based structural modeling to identify true ligand and receptor homologues, and subsequently predict their binding mechanisms. <em>In silico </em>findings were validated by multiple experimental approaches, which investigated the effect of amino acid changes in the proposed binding pockets on ligand-binding, complex formation with a co-receptor essential for downstream signaling, and activation of downstream signaling. Our analysis combining evolutionary insights, <em>in silico</em> modeling and functional validation provides a framework for structure-function analysis of other peptide-receptor pairs, which could be easily implemented by most laboratories.</p> <h3><span>Zip file contains:</span></h3> <p><span>Dataset S4:</span><span> </span><strong><span>Plasmid maps of constructs used in this study.</span></strong></p> <p><span>Dataset S5:</span><span> </span><strong><span>AFM and AF3 predicted structures (.pdb) and AFM confidence metrics (.pae)</span></strong></p> <p><span>Dataset S6:</span><span> </span><strong><span>Unedited files (.tiff) of co-IP and western blotting.</span></strong></p>
Model-based Investigation of Electron Precipitation-driven Density Structures and their Effects on Auroral Scintillation
<p>This folder contains the precipitation modeling results and the camera dataset used for this study. It also includes the details of the supplementary material cited in the text in sections 3 (table params), 3.1 (minimum total energy flux), 4.1 (minimum total energy flux), and 4.2 (density profiles for different characteristic energies). </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.