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256 results for “Computational models”
Simulation data for paper "Evaluation of Fendiline Treatment in VP40 System with Nucleation-Elongation Process: A Computational Model of Ebola Virus Matrix Protein Assembly"
<p>This is the original simulation data sets for paper "Evaluation of Fendiline Treatment in VP40 System with Nucleation-Elongation Process: A Computational Model of Ebola Virus Matrix Protein Assembly".</p>
Dataset for the publication titlted "A computational mechanics model for producing molecular assembly using molecularly woven pantographs" in the journal Cell Reports Physical Science, authored by Byeonghwa Goh and Joonmyung Choi.
<p>Dataset for the publication titlted "A computational mechanics model for producing molecular assembly using molecularly woven pantographs" in the journal Cell Reports Physical Science, authored by Byeonghwa Goh and Joonmyung Choi.</p>
A multi-omics systems vaccinology resource to develop and test computational models of immunity: 1st challenge dataset and submissions
<p>The goal of the CMI-PB prediction contest is to foster a collaborative research community that can collectively tackle challenges and accelerates scientific progress beyond the capabilities of individual researchers or groups. The CMI-PB consortium has curated multi-source data from multiple individuals, encompassing Ab titers (around four antibodies/features), cell frequency (approximately 20 cell types/features), gene expression (roughly 50,000 RNA transcripts/features), and plasma proteomics (around 50 proteins/features). The challenge requires integrating these diverse data sources to predict different immune responses or tasks. Specifically, you will utilize multi-source data from several individuals on day 0 (baseline) to predict specific immune responses at later time points (1, 3, 7, and 14 days post-booster vaccination).</p> <p>The first CMI-PB challenge, which is an internal challenge, was conducted using datasets from 2020 (train) and 2021 (test). In the following sections, we provide detailed information on the datasets, challenge tasks, submission format, descriptions, and access to the necessary data files for participants to develop their models and make predictions.</p> <p><br><strong>A) Multiomics CMI-PB dataset:</strong></p> <p>We propose a study design that enables a systems-level understanding of the immune responses through computational modeling. Our cohort comprises aP vs. wP infancy-primed subjects boosted with Tdap. We recruit individuals born before 1995 (wP) and after 1996 (aP), collect baseline plasma and blood samples, and then at 1, 3, 7 and 14 days post booster vaccination.</p> <p>With the obtained samples processed, we generated omics data by:</p> <ul> <li> <p>Bulk PBMCs transcriptomics,</p> </li> <li> <p>Plasma proteomics using Olink, which provides a quantitative readout of cytokines, chemokines, and other immune factors,</p> </li> <li> <p>Cell frequency in PBMCs using flow cytometry,</p> </li> <li> <p>Tdap-specific antibodies levels</p> </li> </ul> <p><strong>B) List of tasks can be accessed using the “List of tasks for challenge 1.docx” file, and submissions need to submit in provided format here: “submission template challenge 1.tsv”</strong></p> <p><strong>C) Datasets for model building and making predictions:</strong></p> <p> Data files are divided into two categories: 1) raw dataset and 2) computable matrices.</p> <ol> <li> <p><strong>Raw dataset: </strong>This raw-most dataset is divided into training and test datasets. </p> </li> <li> <p><strong>Computable matrices: </strong>There are three different types of computable matrices. a) Full: These files are generated by dividing raw files into sub-files specific to planned days specific to vaccination. b) harmonized: These are generated by preserving only overlapping features between train and test datasets. b) imputed: MICE imputation is performed to impute missing values in the dataset.</p> </li> </ol> <p><strong>D) Submission evaluation</strong></p> <p>This folder contains all submitted models with ranking files and code for evaluating these models.</p> <p><strong>To learn more about the CMI-PB prediction challenge, visit our website at www.cmi-pb.org.</strong></p>
Dataset for the SFmodel, applied in Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches
<p>Dataset used for the SFmodel, applied in the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches".</p> <p>For units and nomenclature of the variables refer to Units_and_Nomenclature_for_SFmodel_in_Evapotranspiration_dynamics_and_partitioning_in_a_grassed_vineyard.pdf. </p>
Dataset for Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches
<p>Data sets of the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches".</p> <p>You will find all data files needed for this work, organised by the figures of the paper. For the codes, refer to Flavio Bastos Campos. (2024). flaviobastoscampos/ET_dynamics_and_partitioning_vineyard: v2024.1 (v2024.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.10864169" target="_blank" rel="nofollow noopener">https://doi.org/10.5281/zenodo.10864169</a>. </p>
Patient breast MRI images and computational breast phantom data for research in patient-derived realistic breast modelling
<p>The data is comprised of two parts: 1) patient DICOM MRI images and 2) 3D matrix of a computational breast phantom.</p> <ol> <li>The DICOM images are anonymised patient breast MRI images of a female patient diagnosed with invasive ductal carcinoma. The obtaining of the patients’ DICOM images is approved by the Ethics Committee of Medical University of Varna. The acquisition was performed with GE Signa HDxt MRI scanner. The images are from a T1-weigthed Axial multi-phase VIBRANT (3-phase) sequence and with voxel size of 0.7 mm x 0.7 mm x 0.8 mm. Contrast agent is present. The image set can be opened with any standard DICOM reader.</li> <li>The computational breast phantom is derived from the above mentioned dataset. The phantom is in the form of a 3D matrix saved as a MATLAB data file (.mat file). Each voxel has an assigned Hounsfield Unit value depending on its classification: air = 0, adipose tissue = -152, glandular tissue = 42, tumour = 64, skin = 108. The data file can be opened with MATLAB or Octave.</li> </ol>
Processed FDG-PET data from: A computational model of neurodegeneration in Alzheimer's disease
<p>Disruption of mental functions in Alzheimer's disease (AD) and related disorders is accompanied by selective degeneration of brain regions. These regions comprise large-scale ensembles of cells organized into systems for mental functioning, however the relationship between clinical symptoms of dementia, patterns of neurodegeneration, and functional systems is not clear. We developed a model of the association between dementia symptoms and degenerative brain anatomy using F18-fluorodeoxyglucose (FDG) PET and dimensionality reduction techniques patients with AD. This data and code package contains preprocessed FDG-PET images from 423 subjects across the Alzheimer's disease spectrum and the MATLAB code to produce eigenbrains from this data.</p>
Computational models from: Allele-specific activation, enzyme kinetics, and inhibitor sensitivities of EGFR exon 19 deletion mutations in lung cancer
<p>Computational models, compressed molecular dynamics (MD) simulation trajectories, and sample input files for "Allele-specific activation, enzyme kinetics, and inhibitor sensitivities of EGFR exon 19 deletion mutations in lung cancer". An early version of this manuscript is available as a preprint here: https://www.biorxiv.org/content/10.1101/2022.03.16.484661v1</p>
DFT Calculated xyz Files in Support of "Bidentate Rh(I)-Phosphine Complexes for the C-H Activation of Alkanes: Computational Modelling and Mechanistic Insight"
<p>Theoretically calculated xyz files for propane, carbon monoxide, butyraldehyde and multiple Rh-phosphine complexes as well as transition states relevant for the C-H activation and subsequent carbonylation of alkanes.</p> <p>All quantum chemical simulations were performed using the Gaussian 16 software package. Closed-shell equilibrium structures, i.e., minima and transition states (TSs) as well as electronic properties of educts, intermediates, and products involved in the C-H activation of propane (methyl group activation) and subsequent steps mediated by the Rh complexes were obtained at the DFT level of theory. The range-separated B97XD functional was employed. The def2-SVP basis set and the respective effective core potential (ECP) were utilized for all atoms. TSs were fully optimized at the same level of theory using the rational function optimization (RFO) approach as well as nudged elastic band (NEB) method as implemented in the pysisyphus<sup> </sup>software suite. Subsequently, a vibrational analysis was carried out for each stationary point to verify that a minimum or first-order saddle point was obtained on the 3<em>N</em>-6-dimensional potential energy (hyper)surface (PES).</p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
Dataset, Model Statistics, and 3D designs for "From Eyes to Cameras: Computer Vision for High-Throughput Liquid-Liquid Separation"
<p>Dataset, model statistics, and 3D design of high throughput platform associated with HeinSight3.0. </p> <p> </p> <p>Pre-print: https://chemrxiv.org/engage/chemrxiv/article-details/65e5481f9138d231619c1879</p> <p> </p> <p>The code and model of HeinSight3.0 can be found at (https://doi.org/10.5281/zenodo.11053915)</p>
Trained Potentials for Article "Computationally Efficient Machine-Learned Model for GST Phase Change Materials via Direct and Indirect Learning"
<p>We provide 8 files here to get started using our trained potentials:</p> <p>1) *.yaml files for each trained potential. These are the outputs of the PACE training process.</p> <p>2) *.yace files for each trained potential. These are read by LAMMPS to use the trained potential. They can be obtained from the *.yaml files using the command line command: "pace_yaml2yace *.yaml".</p> <p>3) GST_config.data -- a starting configuration of GST to be read by LAMMPS. This configuration contains 504 atoms at density 5.85 g/cm^3.</p> <p>4) sample.inp -- a sample LAMMPS input file using the trained potentials. This currently uses "ACE-Indir2.yace" to run the starting configuration "GST_config.data" for 10 ps at 1200 K. When run, it outputs a log file "test.log" and a dump file "test.dump". The choice of trained potential can be changed in the "pair_coeff" section.</p>
The dataset of the manuscript "GPU-HADVPPM4HIP V1.0: higher model accuracy on China's domestically GPU-like accelerator using heterogeneous compute interface for portability (HIP) technology to accelerate the piecewise parabolic method (PPM) in an air quality model (CAMx V6.10)"
<p><strong>bcfile.zip:</strong> the clean boundary condition files.</p> <p><strong>CAMxv6x_cpp.zip: </strong>the source code of CAMx-HIP version which coupled with HIP-HADVPPM scheme.</p> <p><strong>data.zip:</strong> final data tables used to plot figures.</p> <p><strong>emisfile.zip: </strong>the emission files.</p> <p><strong>icfile.zip:</strong> the clean initial condition files.</p> <p><strong>tuvfile.zip </strong>and <strong>o3mapfile.zip:</strong> the photolysis files.</p> <p><strong>outputfile.zip:</strong> the computation results outputted by CAMx model for Fortran version on the Intel Xeon E5-2682 v4 CPU, CUDA version on the NVIDIA K40m and V100 clusters, and HIP version on the China' s domestically heterogeneous cluster A.</p> <p><strong>wrfcamx.zip:</strong> the meteorological files.</p> <p><strong>offline_test_cuda.zip: </strong>the advection module code written in CUDA C language</p> <p><strong>offline_test_fortran.zip:</strong> the advection module code written in Fortran language</p> <p><strong>offline_test_hip.zip: </strong>the advection module code written in HIP C language</p>
Dataset: Methods for computing the maximum performance of computational models of fMRI responses.
<p>Accompanying data for manuscript: Methods for computing the maximum performance of computational models of fMRI responses. written by Agustin Lage-Castellanos, Giancarlo Valente, Elia Formisano, Federico De Martino, submitted for publication in Plos Computational Biology, July 2018.</p> <p>This dataset provide the Betas for subcortical and a subset of the cortical voxels for three subjects in matlab format.</p> <p>The field bTest refers to the Beta coefficients for every voxel in the test data. The fields beta1 and beta2 refer to the split-half partitions of the bTest coefficients. The field varBparam refers to the parametric variances of the Beta coefficients and the field varBBootstrap refers to the variances of the Betas computed with bootstrap. </p>
The OpenEar library of 3D models of the human temporal bone based on computed tomography and micro-slicing
<p>The OpenEar Dataset provides a library consisting of eight three-dimensional models of the human temporal bone to enable surgical training including color data. Each dataset is based on a combination of multimodal imaging including Cone Beam Computed Tomography (CBCT) and micro-slicing. 3D reconstruction of micro-slicing images and subsequent registration to CBCT images allowed for relatively efficient multimodal segmentation of inner ear compartments, middle ear bones, tympanic membrane, relevant nerve structures, blood vessels and the temporal bone. Raw data from the experiment as well as voxel data and triangulated models from the segmentation are provided in full for use in surgical simulators or any other application which relies on high quality models of the human temporal bone.</p>
Data: Computer modelling of connectivity change suggests epileptogenesis mechanisms in idiopathic generalised epilepsy
<p>We provide the generalised fractional anisotropy connectometry database used in our study titled: <em>Computer modelling of connectivity change suggests epileptogenesis mechanisms in idiopathic generalised epilepsy.</em></p>
Supplementary materials for the paper "Computing with liquid crystal fingers: Models of geometric and logical computation." Physical Review E 84.6 (2011): 061702.
<p>When a voltage is applied across a thin layer of cholesteric liquid crystal, fingers of cholesteric alignment can form and propagate in the layer. In computer simulation, based on experimental laboratory results, we demonstrate that these cholesteric fingers can solve selected problems of computational geometry, logic, and arithmetics. We show that branching fingers approximate a planar Voronoi diagram, and nonbranching fingers produce a convex subdivision of concave polygons. We also provide a detailed blueprint and simulation of a one-bit half-adder functioning on the principles of collision-based computing, where the implementation is via collision of liquid crystal fingers with obstacles and other fingers.</p>
Dataset for the article "MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry"
<p>This dataset contains additional material related to the article: "MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry". The preprint is available at <a href="https://doi.org/10.26434/chemrxiv.7635986">https://doi.org/10.26434/chemrxiv.7635986</a>. Final article is available at <a href="https://doi.org/10.1021/acs.jctc.9b00093">https://doi.org/10.1021/acs.jctc.9b00093</a>.</p>
Computational model results for "Uncertainties of Glacial Isostatic Adjustment model predictions in North America associated with 3D structure"
<p>The mean GIA signals of RSL, u-dot and g-dot with 1σ, 2σ and 3σ uncertainties in North America. </p>
Processed model of a Computed Tomography scan of Roman window glass from Ephesos
<h1>Origin</h1> <p>The model is based on a tomography scan on an antique window glass fragment from Ephesos (today Efes, Turkey). The sample was contributed by the <em>Austrian Archaeological Institute</em>, Vienna, and has the inventory ID EVH12/1017/1322.</p> <p>The original scan data is published as</p> <p>Grobe and Schuetz (2021). Computed tomography scan of Roman window glass from Ephesos. <a href="https://doi.org/10.5281/zenodo.5651890">doi:10.5281/zenodo.5651890</a></p> <p>and is described in:</p> <p>Grobe, Noback, and Schuetz (2021). A model chain to simulate daylight in historic built environments. Presented at: <em>Widening Horizons - 27 Annual Meeting of the European Association of Archaeologists</em>, Kiel, Germany. <a href="https://doi.org/10.5281/zenodo.5495764">doi:10.5281/zenodo.5495764</a></p> <h1>Processing</h1> <p>The scan was processed to prepare its conversion into a simulation model, i.e.</p> <ul> <li>small isolated mesh components were deleted,</li> <li>disconnected and duplicate vertices and faces were removed,</li> <li>the outer surfaces were re-constructed by meshlab's implementation of the _Screened Poisson_ algorithm, and</li> <li>the resulting mesh was intersected with an exctruded rectangle.</li> </ul>
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.