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    <id>8347</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>16</volume>
    <type>researchdata</type>
    <publisherName/>
    <publisherPlace/>
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    <title language="eng">Simulation data: Data-driven model reduction of agent-based systems using the Koopman generator</title>
    <parentTitle language="eng">PLOS ONE</parentTitle>
    <identifier type="doi">http://doi.org/10.5281/zenodo.4522119</identifier>
    <note>This repository contains the simulation data for the article "Data-driven model reduction of agent-based systems using the Koopman generator" by Jan-Hendrik Niemann, Stefan Klus and Christof Schütte. The archive complete_voter_model.zip contains the simulation results for the extended voter model on a complete graph for the parameters given in the corresponding txt-files to learn a reduced SDE model. The files are of the form [types, time steps, samples, training points].The archive dependency.zip contains additional simulation results of the form [types, time steps, samples, training points] to learn a reduced SDE model. The parameters used are given in the corresponding txt-files.The archive random_voter_model.zip contains the simulation results to learn a reduced SDE model for the given adjacency matrix within the archive. The file aggregate_state is of the form [training points, types, time steps, samples]. The file full_state is of the form [training points, agents, time steps, samples].The archive predator_prey_model.zip contains the simulation results to learn a reduced SDE model and calculation of the mean value of the agent-based model. The data is of the form [types, time steps, samples, training points] and [samples, time steps, types].The archive two_clustered_voter_model.zip contains the simulation results for the extended voter model on a graph with two clusters for the given adjacency matrices to learn a reduced SDE model. The file aggregate_state is of the form [training points, types, time steps, samples]. The file full_state is of the form [training points, agents, time steps, samples].</note>
    <enrichment key="PeerReviewed">yes</enrichment>
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    <enrichment key="zib_DownloadUrl">https://zenodo.org/record/4522119#.YUB1gS223mE</enrichment>
    <enrichment key="zib_relatedIdentifier">https://doi.org/10.1371/journal.pone.0250970</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Jan-Hendrik Niemann</author>
    <submitter>Jan-Hendrik Niemann</submitter>
    <author>Christof Schütte</author>
    <author>Stefan Klus</author>
    <collection role="persons" number="schuette">Schütte, Christof</collection>
    <collection role="projects" number="MathPlus-EF4-3">MathPlus-EF4-3</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
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