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129 results for “model reconstruction”
200-years ice core bromine reconstruction at Dome C (Antarctica): observational and modelling results
<p>Bromine enrichment (Br<sub>enr</sub>) has been proposed as an ice core proxy for past sea-ice reconstruction. Understanding the processes that influence bromine preservation in the ice is crucial to achieve a reliable interpretation of ice core signals and to potentially relate them to past sea ice variability. Here, we present a 210 years bromine record that sheds light on the main processes controlling bromine preservation in the snow and ice at Dome C, East Antarctic plateau. Using observations alongside a modelling approach, we demonstrate that the bromine signal is preserved at Dome C and it is not affected by the strong variations in ultraviolet radiation reaching the Antarctic plateau due to the stratospheric ozone hole. Based on this, we investigate whether the Dome C Br<sub>enr</sub> record can be used as an effective tracer of past Antarctic sea ice. Due to the limited time window covered by satellite measurements and the low sea-ice variability observed during the last 30 years in East Antarctica, we cannot fully validate Br<sub>enr </sub>as an effective proxy for past sea-ice reconstructions at Dome C.</p> <p>In this database, annual averaged data for Br, Na and Br<sub>enr</sub> for the shallow ice core are provided.</p> <p> </p>
(Dataset) Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America
<p>Dataset for the paper:</p> <p>Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America</p> <p>For more infomation, please look into the README file or contact ljliu@illinois.edu, thank you!</p>
Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
<p><span>Machine learning can be as good as maximum likelihood when reconstructing phylogenetic topologies and determining the best evolutionary model on four taxon alignments.</span></p> <p><span>Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the Maximum Likelihood tree reconstruction method. Here we show that for quartet trees, Machine Learning using neural networks can be as good as the Maximum Likelihood method to infer the best tree topology and the best model of sequence evolution for nucleotide as well as amino acid sequences. For this purpose we simulated data sets for a wide range of branch lengths, evolutionary models and model parameters and compared the topologies and inferred models obtained with Machine learning with those obtained with the Maximum Likelihood and the Neighbour Joining method. Our results show that neural networks are a promising avenue for determining relatedness between taxa, which is likely to accelerate the construction of phylogenetic trees in the future, while maintaining a high accuracy.</span></p>
Gamma-hemolysin components: computational strategies for LukF-Hlg2 dimer reconstruction on a model membrane
<p>Project files provided as supporting information to the manuscript “Gamma-Hemolysin Components: Computational Strategies for LukF-Hlg2 Dimer Reconstruction on a Model Membrane”. The data set contains the following folders:</p> <ul> <li>LukF_Hlg2_distance: files with the minimum distance between LukF and Hlg2 as a function of time for the simulated replica (Fig. S1); files with the minimum distance between each LukF residue and the Hlg2 monomer and vice versa, averaged over the last 600 ns of simulation in the replica where the spontaneous dimerization is observed (fig. S5).</li> <li>HADDOCK_dimer_crystal_pore_displacement: files with the displacement between Hlg2 residues in the HADDOCK model dimer and the same dimer in the crystal pore, after alignment on the LukF monomer (for both the HADDOCK model in presence and in absence of the LukF N-ter) (Fig. S13)</li> <li>interface_area: files with the interface area between the LukF and the Hlg2 monomers in the replica where the spontaneous dimerization on the membrane is observed, as a function of time (total interface and contribution of LukF and Hlg2 rim domains) (Fig. S4)</li> <li>angles: files with the histograms of the angle between LukF and the axis perpendicular to the membrane, for the simulation of the single LukF monomer and for that capturing the spontaneous dimerization on the membrane (Fig. 4); file with the angle between the LukF and the Hlg2 axis as a function of time in the replica where the spontaneous dimerization is observed (Fig. S6)</li> <li>HADDOCK_scores: files with the HADDOCK scores of the predicted LukF-Hlg2 dimers and their RMSD values computed with respect to the same dimer in the crystal pore. The data are reported for the four top-scored models of each cluster ( for both the HADDOCK models in the presence and in the absence of the LukF N-ter) (Fig. 5).</li> <li>RMSD: files with the RMSD as a function of time for the LukF and the Hlg2 monomers in the replica where the spontaneous dimerization on the membrane is observed (Fig. 2, Fig. S2, Fig. S3)</li> <li>RMSF: files with the RMSF of the LukF and the Hlg2 residues in the replica where the spontaneous dimerization on the membrane is observed and in the simulations of the single monomers (Fig. 2)</li> <li>interaction_persistences: files with H-bond (side chain + backbone and backbone only atoms) salt-bridge, and hydrophobic contact persistence matrices for the single LukF monomer simulated alone (299 x 299) and for the LukF-Hlg2 dimer (299+280 x 299+280) (Fig. 4, Fig. S7 + interactions reported in the manuscript)</li> <li>distance_protein_membrane: files with the minimum distance between each monomer and the membrane, in the last 200ns of the simulation of spontaneous dimerization on the membrane (Figure S9).</li> <li>distance_residues_interface: files with the distance between functionally relevant residues measured along the simulation of the HADDOCK dimer in the absence of LukF N-terminus (Figure S14).</li> </ul>
low Cerro Gordo; StL 2, Cerro Gordo sandstone; StL 3, between Cerro Gordo and Chunchullo; StL 4, Chunchullo sandstone; StL 5, bed set between Chunchullo and Tatacoa; StL 6, Tatacoa sandstone; StL 7, bed set below Cerbatana conglomerate; StL 8, Cerbatana conglomerate; StL 9, Monkey beds; StL 10, bed set above Monkey beds; StL 12, bed set above Fish bed; StL 14, bed set below La Venta red beds; StL 15, La Venta red beds; StL 16, bed set between La Venta red beds and El Cardón red beds; StL 17, El Cardón red beds; StL 18, San Francisco sandstone; StL 19, Polonia red beds; D, reconstruction of the head of Neodolodus colombianus based on the 3D model of the almost complete skull of the specimen VPPLT 1696. Abbreviations: Fm, Formation; St m, Stratigraphic meter. Reconstruction of N. colombianus made by Tatsuya Shimura. in New remains of Neotropical bunodont litopterns and the systematics of Megadolodinae (Mammalia: Litopterna)
low Cerro Gordo; StL 2, Cerro Gordo sandstone; StL 3, between Cerro Gordo and Chunchullo; StL 4, Chunchullo sandstone; StL 5, bed set between Chunchullo and Tatacoa; StL 6, Tatacoa sandstone; StL 7, bed set below Cerbatana conglomerate; StL 8, Cerbatana conglomerate; StL 9, Monkey beds; StL 10, bed set above Monkey beds; StL 12, bed set above Fish bed; StL 14, bed set below La Venta red beds; StL 15, La Venta red beds; StL 16, bed set between La Venta red beds and El Cardón red beds; StL 17, El Cardón red beds; StL 18, San Francisco sandstone; StL 19, Polonia red beds; D, reconstruction of the head of Neodolodus colombianus based on the 3D model of the almost complete skull of the specimen VPPLT 1696. Abbreviations: Fm, Formation; St m, Stratigraphic meter. Reconstruction of N. colombianus made by Tatsuya Shimura.
Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
Open the record for dataset details and reuse information.
Data for: Free-Breathing Water, Fat, R2∗ and B0 Field Mapping of the Liver Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (MERLOT)
<p>Data for our manuscript with title "Free-Breathing Water, Fat, R2∗ and B0 Field Mapping of the Liver Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (MERLOT)"</p>
Dorsalistachya quadrisegmentorum - Dataset 1 – Three-dimensional reconstruction model (SPIERSView and VAXML format)
<p>Three-dimensional reconstruction model of <em>Dorsalistachya quadrisegmentorum</em> in SPIERSView and VAXML format.</p> <p>Notes:</p> <ol> <li>SPIERSView file (.SPV) models can conveniently be viewed using the SPIERSView software, freely available in both Windows and Mac versions from http://www.spiers‐software.org. However, note that low-performance computers may not possess a sufficiently powerful graphics card to render and rotate the model.</li> <li>VAXML file format models are saved as a ZIP-compressed VAXML datasets. VAXML uses one or more .STL files to define the geometry of objects that comprise the dataset, together with one .VAXML file that provides metadata on the dataset as a whole, and specifies how the .STL files should be put together. We recommend using the free SPIERS software to view this model format (http://spiers-software.org/). However, .STL files can be opened independently in several freely available software programs (e.g. MeshLab, Blender). Additional information on the VAXML format can be found here: http://spiers-software.org/VAXML.htm.</li> </ol>
Dorsalistachya quadrisegmentorum - Video 1 - Three-dimensional reconstruction model (AVI and MOV format)
<p>Three-dimensional reconstruction of the structure and organization of <em>Dorsalistachya quadrisegmentorum</em> rendered with Blender. Available in .AVI (Audio Video Interleaved) and .MOV (Apple Quicktime) format.</p>
Spectral reconstruction using iteratively reweighted-regulated model from two illumination camera responses
<p>This document shows spectral reflectance data and RGB values of 380 samples for spectral estimation, which includes 5 databases: 1) Spectral reflectance of ColorChecker semigloss chart (CCSG, 140 patches), 2) Spectral reflectance of ColorChecker DC matte chart (CCDC, 240 patches including 232 mattes, and 8 glossy patches), 3) RGB values of ColorChecker semigloss chart (CCSG, 140 patches) , 4) RGB values of ColorChecker DC matte chart (CCDC, 240 patches including 232 mattes, and 8 glossy patches), 5) Spectral Power Distribution value of 3500K & 6500K illumination conditions.</p>
single-cell RNAseq data (data set 20) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset20) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from breast cancer samples downloaded from the GEO website (GSE180286)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <p> </p>
single-cell RNAseq data (data set 18) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset18) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from Liver cancer set 1 samples downloaded from the GEO website (GSE125449)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 17) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset17 was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from PBMC metastatic MCC samples downloaded from the GEO website (GSE117988)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 12) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset12) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor10 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 16) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset16) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CD4 T-cells in PACA samples downloaded from the GEO website (GSE156728)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <p> </p>
single-cell RNAseq data (data set 11) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset11) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor9 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 14) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset14) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor12 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 9) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset9) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor7 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 8) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset8) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor6 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 7) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset7) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor5 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </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.