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3,688 results for “Computer”
Tassie BRUV: A benchmark data set for computer vision and movement quantification algorithms
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Mining the health disparities and minority health bibliome: A computational scoping review and gap analysis of 200,000+ articles
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Data and scripts for: Bayesian Phylogenetic Analysis on multi-core Compute Architectures: Implementation and evaluation of BEAGLE in RevBayes with MPI
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Computationally-informed point of departure evaluation for proarrhythmic cardiotoxicity assessment using 3D engineered cardiac microtissues from human iPSC-derived cardiomyocytes
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Computational IHC-H&E mapping in mouse models of colitis (IHC WSIs from paired H&E-IHC WSIs, Part 2/2)
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A computational neuroscience framework for quantifying warning signals
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Regression models generated by APRANK (computational prioritization of antigenic proteins and peptides from complete pathogen proteomes)
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Data for: Parameter selection and optimization of a computational network model of blood flow in single-ventricle patients
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2 Datasets of forests for Computer Vision and Deep Learning techniques
<p>Datasets:</p> <p>1) Coastal forest in Shonai area; contains an orthomosaic (processed by Metashape) of the coastal forest and annotated layers (processed in Gimp)</p> <p>Annotated layers: 0 = annotations of black locust; 1 = annotations of soil; 2 = annotations of man-made; 3 = annotations of other trees and the orthomosaic as JPEG file</p> <p>2) Mixed forest images of the Yamagata University Research Forest taken in the winter season; contains 7 orthomosaics (TIFF file), annotated layers (named wM1 to wM7; processed in Gimp)</p> <p>Orthomosaics: 7 orthomosaics of 5 different sites; for one of the sites we provide 3 orthomosaics of different days and illumination conditions</p> <p>Annotated layers: 0 = annotations of river class; 1 = annotations of deciduous class; 2 = annotations of uncovered class; 3 = annotations of evergreen class; 4 = annotations of man-made class and the orthomosaic as JPEG file</p> <p> </p> <p>The dataset was used for our transfer learning study in the field of forest applications. We used in one experiment the winter images to run deep learning algorithms. In a second experiment we used the coastal forest data for a similar approach.</p>
A machine learning framework for computationally expensive transient models
<p>The following dataset contains DEM simulation data on multiple material properties and operational parameters and their impact on uniform mixing time. Unifrom mixing time is calculated when segregation index reaches 1.1. Segregation index is defined based on the paper: Marigo, M., Cairns, D. L., Davies, M., Ingram, A. & Stitt, E. H. A numerical comparison of mixing efficiencies of solids in a cylindrical vessel subject to a range of motions. <em>Powder Technol.</em> <strong>217</strong>, 540–547 (2012). </p> <p>The dataset was used for training ML model as described in paper: <a href="https://arxiv.org/abs/1907.05928">https://arxiv.org/abs/1907.05928</a></p>
Data, plotting scripts, and figures for "Applying the swept rule for explicit partial differential equation solutions on heterogeneous computing systems"
<p>This dataset contains the figures, as well as the necessary plotting scripts and data to reproduce them, for the article "Applying the swept rule for explicit partial differential equation solutions on heterogeneous computing systems" by Daniel J. Magee, Anthony S. Walker, and Kyle E. Niemeyer (2020).</p> <p>The plotting scripts were all run using Python 3.7.6, and should be compatible with >3.6, but this has not been tested.</p> <p>The code included in this dataset is released under the BSD 3-Clause License. The figures are shared under the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).</p>
Inputs for computational electrophysiology of the Glycine Receptor with GROMACS 19
<p>Inputs for computational electrophysiology of the Glycine Receptor (D&B-open model, doi:10.5281/zenodo.3476169) with GROMACS 19 and the CHARMM36 force-field, using:</p> <p>1- a single membrane system with the application of a constant electric field.</p> <p>2- a double membrane system with the application of the charge imbalance protocol.</p> <p>The model of the glycine receptor was reduced to its transmembrane domain and simulated with atomic positional restraints.</p> <p>Related to the published article: "On the functional annotation of open-channel structures in the glycine receptor".</p>
Data for: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation
<p>Collection of data files used in the paper titled: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation.</p> <p>Additional dataset which covers the noise study CT scans and digital volume correlation noise studies can be found in DOI: <a href="http://www.doi.org/10.5281/zenodo.3352268">10.5281/zenodo.3361832</a></p> <p><strong>Data contains the following:</strong></p> <ul> <li>X-ray CT data for the interrupted direct shear tests of a soil sample containing a Willow plant. The first file is the specimen scanned unloaded, followed by seven incremental shear load steps to 20 mm shear displacement. Files are 8-bit unsigned and 1800x1800x1600px. Voxel resolution is 0.04642 mm. <ol> <li><strong>20180430_HUTCH_1839_DJB_willow_C_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_A_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_B_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_C_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_D_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_E_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_F_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_G_8-bit_1800x1800x1600.raw</strong></li> </ol> </li> <li>Direct shear vs. displacement data is presented in an Excel spreadsheet: <ul> <li><strong>All_load_data_with_reducing_area.xlsx</strong></li> </ul> </li> <li>Normal and shear strain DVC data which has been averaged and plotted against specimen depth is presented in an Excel spreadsheet: <ul> <li><strong>Average_slice_vs_depth_data_all_samples_all_loads3.xlsx</strong></li> </ul> </li> <li>CT scan metadata giving information to the scan settings, voxel resolution, etc. is contained in a .zip file which consists of .xtekct and .XML files generated from the Nikon CT scanner. <ul> <li><strong>CT_Scan_Metadata.zip</strong></li> </ul> </li> <li>Drawings and Solidworks CAD files of the direct shear test rig are contained within the .zip file. There are a number of parts to the assembly. The file SSSB1003-5.SLDASM contains the complete assembly of the direct shear rig and will help identify the part names. The folder CAD_Drawings contains pdf documents of the part drawings. <ul> <li><strong>Direct_Shear_Drawings_Solidworks_Files.zip</strong></li> </ul> </li> <li>Tabulated DVC data taken at 32x32x32px subset size is contained inside a .zip file. These consist of tab separated .dat files from unloaded (data_1.dat) through incremental load steps Load A, Load B... Load G, (data_2.dat, data_3.dat... data_8.dat). The structure of the .dat file contains columns of data with each column number corresponding to the following: (1) x, (2) y (3) z, (4) vx, (5) vy, (6) vz, (7) exx, (8) eyy, (9) ezz, (10) exy, (11) exz, (12) eyz, (13) volumetric strain, (14) is valid. Columns 1-3 are subset positions in mm, 4-6 are displacements in mm, 7-9 are normal strains, 10-12 are shear strains, 13 is volumetric strain and 14 is a binary value indicating if the subset is valid. <ul> <li><strong>DVC_Load_Data_Willow_C.zip</strong></li> </ul> </li> <li>Tabulated DVC data applied to the load step CT data at 32, 48, 64, 96, 128 pixels is presented in the .zip files containing .dat tab separated files. The structure of the .dat files is described in the previous bullet point. These files were used to construct the noise study applied to incrementally loaded CT data by sampling regions away from the shear zone where the strain signals are close to zero. <ul> <li><strong>DVC_Noise_Study_Data_Load_Steps_Willow_C.zip</strong></li> </ul> </li> <li>Processed noise study data which compares the effects of DVC subset size is presented in the .xlsx file. This file contains both the controlled noise experiment data (stationary, magnification and rigid body motion) and noise study data applied to incrementally loaded data using sampled regions away from the shear zone where the strain signals are close to zero. <ul> <li><strong>Willow_C_Processed_Noise_study2.xlsx</strong></li> </ul> </li> <li>Tabulated data which compares the local x displacement vs depth profile from DVC and direct measurement of root position is contained in the following Excel spreadsheet file: <ul> <li><strong>X_Displacement_Root_and_DVC_Comparison_Willow C.xlsx</strong></li> </ul> </li> </ul>
Computer Aided Design (CAD) files for capillaric circuit with 8 retention burst valves
<p>AutoCAD design file and STL file for capillaric circuit with 8 retention burst valves.</p>
Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2014 DDEC Database
<p>~2,900 structures CoRE MOF 2014 structures with DDEC partial atomic charges. </p> <p> </p>
On Transferability of Histological Tissue Labels in Computational Pathology
<p>This Zip file contains all Convolutional Neural Network (CNN) models of HistoNet trained on ADP database introduced in</p> <p><em>Hosseini et. al. “On Transferability of Histological Tissue Labels in Computational Pathology.” Accepted in the European Conference on Computer Vision (ECCV), Glasgow, UK, August 2020 </em></p> <p>For more information on the models and how to train them, please refer to <strong>https://github.com/mahdihosseini/HistoLabelTransfer</strong></p> <p> </p>
Targeted Intracellular Degradation of SARS-CoV-2 via Computationally-Optimized Peptide Fusions
<p>The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has elicited a global health crisis of catastrophic proportions. With only a few vaccines approved for early or limited use, there is a critical need for effective antiviral strategies. In this study, we report a unique antiviral platform, through computational design of ACE2-derived peptides which both target the viral spike protein receptor binding domain (RBD) and recruit E3 ubiquitin ligases for subsequent intracellular degradation of SARS-CoV-2 in the proteasome. Our engineered peptide fusions demonstrate robust RBD degradation capabilities in human cells and are capable of inhibiting infection-competent viral production, thus prompting their further experimental characterization and therapeutic development. </p>
Compute and analyze Essential Biodiversity Variables with PAMPA toolsuite
<p>In this tutorial, we'll be working on data extracted from the data portal DATRAS (Database of Trawls Surveys) of the <br> International Council for the Exploration of the Sea (ICES). After pre-processing to fit the input format of the tools,<br> we'll see how to calculate Essential Biodiversity Variables and construct properly, easily and with good practice, <br> several GLMMs to test the effect of ```year``` and ```site``` on the species richness of each survey studied and <br> on the abundance of each species.</p> <p>https://training.galaxyproject.org/training-material/topics/ecology/tutorials/PAMPA-toolsuite-tutorial</p> <p> </p>
Data associated to the manuscript "Computational Screening of the Physical Properties of Water-in-Salt Electrolytes"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Computational Screening of the Physical Properties of Water-in-Salt Electrolytes</p> <p>Trinidad Mendez-Morales, Zhujie Li and Mathieu Salanne,<br> *ChemRxiv*, 13012646v2, 2020</p> <p>[https://doi.org/10.26434/chemrxiv.13012646.v2](https://doi.org/10.26434/chemrxiv.13012646.v2)</p> <p>*WiS-inputs.zip* contains typical [LAMMPS](https://lammps.sandia.gov/) input files for all the systems.</p> <p>The folder *transport_coefficients* contains the computed viscosities, conductivities and diffusion coefficients for all the systems.</p> <p>The folder *radial_distribution_functions* contains all the partial radial distribution functions for all the systems. The nomenclature of the atoms of the anion is provided in the file *figureS1.pdf* and the atoms from the water molecules are labelled Ow and Hw.</p>
A new set of analytical formulae for the computation of the bootstrap current and the neoclassical conductivity in tokamaks
<p>Provided data includes data sets that have been used to develop a new set of analytical formulas for calculating the bootstrap current and the neoclassical conductivity.</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.