Presentation Open Access

Boosting complex Systems Research through RSE Collaboration

Kelling, Jeffrey; Tripathi, Richa; Calabrese, Justin M.


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.7719360</identifier>
  <creators>
    <creator>
      <creatorName>Kelling, Jeffrey</creatorName>
      <givenName>Jeffrey</givenName>
      <familyName>Kelling</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1761-2591</nameIdentifier>
      <affiliation>Helmholtz-Zentrum Dresden - Rossendorf and Chemnitz University of Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Tripathi, Richa</creatorName>
      <givenName>Richa</givenName>
      <familyName>Tripathi</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5349-6271</nameIdentifier>
      <affiliation>Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf</affiliation>
    </creator>
    <creator>
      <creatorName>Calabrese, Justin M.</creatorName>
      <givenName>Justin M.</givenName>
      <familyName>Calabrese</familyName>
      <affiliation>Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Boosting complex Systems Research through RSE Collaboration</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2023</publicationYear>
  <subjects>
    <subject>performance</subject>
    <subject>GPU</subject>
    <subject>complex systems</subject>
    <subject>computational science</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2023-03-10</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Presentation</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/7719360</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.7719359</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/derse23</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Stochastic simulations of complex systems from domains including physics, biology, ecology or economics often require large system sizes, long time scales, and numerous replications to fully explore model behavior. The simple rules defining many models can lead researchers to prefer familiar but inefficient programming techniques, which severely hinder progress&lt;br&gt;
by creating computational bottlenecks. While such studies often benefit from combined domain-specific, statistical, and programming knowledge, few individual researchers span the full range of necessary skills. Here, we present a collaboration on the neutral model of biodiversity in dendritic river networks, where the goal is to analyze biodiversity data across the world&amp;rsquo;s major river systems. We show how we achieved large performance gains by engaging the problem at its foundations and thereby enabled research at a new scale.&lt;/p&gt;</description>
  </descriptions>
</resource>
155
45
views
downloads
All versions This version
Views 155155
Downloads 4545
Data volume 106.5 MB106.5 MB
Unique views 132132
Unique downloads 4141

Share

Cite as