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119 results for “computer simulation”
All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
<p>We present two different methods to estimate parameters within a partial differential equation (PDE) model of cancer invasion. The model describes the spatio-temporal evolution of three variables -- tumour cell density, extracellular matrix density and matrix degrading enzyme concentration -- in a one-dimensional tissue domain. The first method is a likelihood-free approach associated with Approximate Bayesian Computation (ABC); the second is a two-stage gradient matching method based on smoothing the data with a Generalized Additive Model (GAM) and matching gradients from the GAM to those from the model. Both methods performed well on simulated data. To increase realism, additionally we tested the gradient matching scheme with simulated measurement error and found that the ability to estimate some model parameters deteriorated rapidly as measurement error increased.</p>
Data from: Bayesian quantification of ecological determinants of outcrossing in natural plant populations: computer simulations and the case study of biparental inbreeding in English yew
The mating system is a central parameter of plant biology because it shapes their ecological and evolutionary properties. Therefore, determining ecological variables that influence the mating system is important for a deeper understanding of the functioning of plant populations. Here, using old concepts and recent statistical developments, we propose a new statistical tool to make inferences about ecological determinants of outcrossing in natural plant populations. The method requires co-dominant genotypes of seeds collected from maternal plants within different locations. Using extensive computer simulations, we demonstrated that the method is robust to the issues expected for real-world data, including the Wahlund effect, inbreeding and genotyping errors such as allele dropout and allele misclassification. Furthermore, we showed that the estimates of ecological effects and outcrossing rates can be severely biased if genotyping errors and genetic differentiation are not treated explicitly. Application of the new method to the case study of a dioecious tree (Taxus baccata) allowed revealing that female trees that grow in lower local densities have a greater tendency towards mating with relatives. Moreover, we also demonstrated that biparental inbreeding is higher in populations that are characterised by a longer mean distance between trees and a smaller mean trunk perimeter. We found these results to agree with both the theoretical predictions and the history of English yew.
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 "Mo-Si alloys studied by atomistic computer simulations using a novel machine-learning interatomic potential: Thermodynamics and interface phenomena"
<p>This dataset was used to fit a general purpose machine-learning interatomic potential for Mo-Si alloys based on the Atomic Cluster Expansion (ACE) formalism. It supports the paper "Mo-Si alloys studied by atomistic computer simulations using a novel machine-learning interatomic potential: Thermodynamics and interface phenomena".</p>
Towards quantum utility for NMR quantum simulation on a NISQ computer
<p>Included are CSV files to reproduce the plots from the publication "Towards quantum utility for NMR quantum simulation on a NISQ computer" and a jupyter notebook script for plotting. If all files are saved in the same folder it should work immediately. Plotly library is used for plotting.</p>
Supplementary materials: "Synthesizing Particle-in-Cell Simulations Through Learning and GPU Computing for Hybrid Particle Accelerator Beamlines"
<p>Data archive for stage 3 of manuscript for PASC24. See Readme stage 3.txt for more details.</p> <p> </p> <p>This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231 and by LLNL under Contract DE-AC52-07NA27344. This material is based upon work supported by the U.S. Department of Energy, OFfice of Science, Office of High Energy Physics, General Accelerator R&D (GARD), under contract number DE-AC02-05CH11231. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative.This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.</p>
Data for: A linear response framework for simulating bosonic and fermionic correlation functions on quantum computers
<p>Response functions are a fundamental aspect of physics; they represent the link between experimental observations and the underlying quantum many-body state. However, this link is often under-appreciated, as the Lehmann formalism for obtaining response functions in linear response has no direct link to experiments. Within the context of quantum computing, and by using a linear response framework, we restore this link by making the experiment an inextricable part of the quantum simulation. This method can be frequency- and momentum-selective, avoids limitations on operators that can be directly measured, and is ancilla-free. As prototypical examples of response functions, we demonstrate that both bosonic and fermionic Green's functions can be obtained, and apply these ideas to the study of a charge-density-wave material on {\emph{ibm\_auckland}}. The linear response method provides a robust framework for using quantum computers to study systems in physics and chemistry.</p>
FAIRmat Tutorial 14: Developing schemas and parsers for FAIR computational data storage using NOMAD-Simulations
<p><a href="https://nomad-lab.eu"><u>NOMAD</u></a> is an open-source, community-driven data infrastructure, focusing on materials science data. Originally built as a repository for data from DFT calculations, the NOMAD software can automatically extract data from the output of a large variety of simulation codes. Our previous computation-focused tutorials (<a href="https://fairmat-nfdi.github.io/AreaC-Tutorial-CECAM-2023/"><u>CECAM workshop</u></a>, <a href="https://fairmat-nfdi.github.io/AreaC-Tutorial10_2023/"><u>Tutorial 10</u></a>, and <a href="https://www.fairmat-nfdi.eu/events/fairmat-tutorial-7/tutorial-7-materials"><u>Tutorial 7</u></a>) have highlighted the extension of NOMAD’s functionalities to support advanced many-body calculations, classical molecular dynamics simulations, and complex simulation workflows. <br>But how can you utilize this infrastructure and associated suite of tools if your simulation code or method is not yet supported? <strong>This tutorial will provide foundational knowledge for customizing NOMAD to fit the specific needs of your computational research project</strong>. The following provides an outline of the major topics that will be covered:</p> <ul> <li>Introduction to the NOMAD software and repository</li> <li>Working with the NOMAD-Simulations schema plugin</li> <li>Extending NOMAD-Simulations to support custom methods and outputs</li> <li>Creating parser plugins from scratch</li> <li>Extra: Interfacing complex simulation and analysis workflows with NOMAD</li> </ul> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
Simulation dataset for "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters"
<p>Simulated Illumina reads for SNP distance method evaluation and comparison used in the article "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters".</p> <p>Details for simulation can be found at https://gitlab.com/rki_bioinformatics/panpasco/tree/master/simulation_dataset.</p>
Datasets, figures and simulation scripts for "Quantum circuit compilation with quantum computers"
<p>The files contain the datasets and figures with the results of the manuscript "<a title="Quantum circuit compilation with quantum computers" href="https://doi.org/10.48550/arXiv.2408.00077" target="_blank" rel="noopener">Quantum circuit compilation with quantum computers</a>".</p> <p>The repository URL links to the repository with the simulation scripts used to produce the datasets and figures.</p>
Computer Simulation of Viking Voyages: Roar_Ege_September_2015
<p>Contains computer simulated routes of Viking voyages for the Roar Ege, with wind data from September 2015. Divided by Route_Month_Year_RouteNumber_Date_Time.</p>
Computer Simulation of Viking Voyages: Roar_Ege_March_2015
<p>Contains computer simulated routes of Viking voyages for the Roar Ege, with wind data from Marcg 2015. Divided by Route_Month_Year_RouteNumber_Date_Time.</p>
Computer Simulation of Viking Voyages: Roar_Ege_June_2015
<p>Contains computer simulated routes of Viking voyages for the Roar Ege, with wind data from June 2015. Divided by Route_Month_Year_RouteNumber_Date_Time.</p>
Computer Simulation of Viking Voyages: Roar_Ege_December_2015
<p>Contains computer simulated routes of Viking voyages for the Roar Ege, with wind data from December 2015. Divided by Route_Month_Year_RouteNumber_Date_Time.</p>
Computer Simulation of Viking Voyages: Oseberg_September_2021
<p>Contains computer simulated routes of Viking voyages for the Oseberg, with wind data from September 2021. Divided by Route_Month_Year_RouteNumber_Date_Time.</p>
Computer Simulation of Viking Voyages: Oseberg_June_2021
<p>Contains computer simulated routes of Viking voyages for the Oseberg, with wind data from June 2021. Divided by Route_Month_Year_RouteNumber_Date_Time.</p>
Computer Simulation of Viking Voyages: Oseberg_December_2021
<p>Contains computer simulated routes of Viking voyages for the Oseberg, with wind data from December 2021. Divided by Route_Month_Year_RouteNumber_Date_Time.</p>
Computer Simulation of Viking Voyages: Oseberg_September_2015
<p>Contains computer simulated routes of Viking voyages for the Oseberg, with wind data from September 2015. Divided by Route_Month_Year_RouteNumber_Date_Time.</p>
Computer Simulation of Viking Voyages: Oseberg_March_2015
<p>Contains computer simulated routes of Viking voyages for the Oseberg, with wind data from March 2015. Divided by Route_Month_Year_RouteNumber_Date_Time.</p>
Computer Simulation of Viking Voyages: Oseberg_June_2015
<p>Contains computer simulated routes of Viking voyages for the Oseberg, with wind data from June 2015. Divided by Route_Month_Year_RouteNumber_Date_Time. (Correction 'Routeing938' are the Dublin_Preston routes, dates are still correct)</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.