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  <doc>
    <id>6949</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>24</pageNumber>
    <edition>EPJ Data Science</edition>
    <issue>1</issue>
    <volume>7</volume>
    <type>article</type>
    <publisherName>EPJ Data Science</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2018-07-13</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Human mobility and innovation spreading in ancient times: a stochastic agent-based simulation approach</title>
    <abstract language="eng">Human mobility always had a great influence on the spreading of cultural, social and technological ideas. Developing realistic models that allow for a better understanding, prediction and control of such coupled processes has gained a lot of attention in recent years. However, the modeling of spreading processes that happened in ancient times faces the additional challenge that available knowledge and data is often limited and sparse. In this paper, we present a new agent-based model for the spreading of innovations in the ancient world that is governed by human movements. Our model considers the diffusion of innovations on a spatial network that is changing in time, as the agents are changing their positions. Additionally, we propose a novel stochastic simulation approach to produce spatio-temporal realizations of the spreading process that are instructive for studying its dynamical properties and exploring how different influences affect its speed and spatial evolution.</abstract>
    <parentTitle language="eng">EPJ Data Science</parentTitle>
    <identifier type="doi">10.1140/epjds/s13688-018-0153-9</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2018-07-02</enrichment>
    <enrichment key="SourceTitle">EPJ Data Science</enrichment>
    <author>Natasa Djurdjevac Conrad</author>
    <submitter>Natasa Djurdjevac Conrad</submitter>
    <author>Luzie Helfmann</author>
    <author>Johannes Zonker</author>
    <author>Stefanie Winkelmann</author>
    <author>Christof Schütte</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="persons" number="schuette">Schütte, Christof</collection>
    <collection role="persons" number="natasa.conrad">Conrad, Natasa</collection>
    <collection role="persons" number="winkelmann">Winkelmann, Stefanie</collection>
    <collection role="projects" number="INNOSPREAD">INNOSPREAD</collection>
  </doc>
  <doc>
    <id>7596</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>37</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>15</volume>
    <type>article</type>
    <publisherName>Public Library of Science</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-11-12</completedDate>
    <publishedDate>2020-11-12</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Furnace and the Goat—A spatio-temporal model of the fuelwood requirement for iron metallurgy on Elba Island, 4th century BCE to 2nd century CE</title>
    <parentTitle language="eng">PLOS ONE</parentTitle>
    <identifier type="doi">10.1371/journal.pone.0241133</identifier>
    <identifier type="url">https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241133</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="SourceTitle">PLOS ONE</enrichment>
    <author>Fabian Becker</author>
    <submitter>Erlinda Körnig</submitter>
    <author>Natasa Djurdjevac Conrad</author>
    <author>Raphael A. Eser</author>
    <author>Luzie Helfmann</author>
    <author>Brigitta Schütt</author>
    <author>Christof Schütte</author>
    <author>Johannes Zonker</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="persons" number="schuette">Schütte, Christof</collection>
    <collection role="persons" number="natasa.conrad">Conrad, Natasa</collection>
    <collection role="projects" number="INNOSPREAD">INNOSPREAD</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
    <collection role="projects" number="MathPlusEF5-1">MathPlusEF5-1</collection>
  </doc>
  <doc>
    <id>6709</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>7</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mathematical modeling of the spreading of innovations in the ancient world</title>
    <parentTitle language="eng">eTopoi. Journal for Ancient Studies</parentTitle>
    <identifier type="issn">ISSN 2192-2608</identifier>
    <identifier type="doi">10.17171/4-7-1</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="FulltextUrl">http://journal.topoi.org/index.php/etopoi/index</enrichment>
    <author>Natasa Djurdjevac Conrad</author>
    <submitter>Erlinda Koernig</submitter>
    <author>Daniel Fuerstenau</author>
    <author>Ana Grabundzija</author>
    <author>Luzie Helfmann</author>
    <author>Martin Park</author>
    <author>Wolfram Schier</author>
    <author>Brigitta Schütt</author>
    <author>Christof Schütte</author>
    <author>Marcus Weber</author>
    <author>Niklas Wulkow</author>
    <author>Johannes Zonker</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="persons" number="schuette">Schütte, Christof</collection>
    <collection role="persons" number="weber">Weber, Marcus</collection>
    <collection role="persons" number="natasa.conrad">Conrad, Natasa</collection>
    <collection role="projects" number="INNOSPREAD">INNOSPREAD</collection>
  </doc>
  <doc>
    <id>9299</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>doctoralthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>2023-11-09</thesisDateAccepted>
    <title language="eng">Coarse Graining of Agent-Based Models and Spatio-Temporal Modeling of Spreading Processes</title>
    <identifier type="url">http://dx.doi.org/10.17169/refubium-41220</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <advisor>Christof Schütte</advisor>
    <author>Johannes Zonker</author>
    <submitter>Johannes Zonker</submitter>
    <collection role="msc" number="60-XX">PROBABILITY THEORY AND STOCHASTIC PROCESSES (For additional applications, see 11Kxx, 62-XX, 90-XX, 91-XX, 92-XX, 93-XX, 94-XX)</collection>
    <collection role="msc" number="70-XX">MECHANICS OF PARTICLES AND SYSTEMS (For relativistic mechanics, see 83A05 and 83C10; for statistical mechanics, see 82-XX)</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
    <collection role="projects" number="MathPlusEF5-1">MathPlusEF5-1</collection>
    <thesisGrantor>Freie Universität Berlin</thesisGrantor>
  </doc>
  <doc>
    <id>9254</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>software</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-01-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Supplementary code and data for Royal Society Open Science Manuscript rsos.230495</title>
    <abstract language="eng">In this repository are all files necessary to run the agent-based model of the paper "Insights into drivers of mobility and cultural dynamics of African hunter–gatherers over the past 120 000 years", Royal Society Open Science, 10(11), 2023.</abstract>
    <identifier type="doi">10.12752/9254</identifier>
    <enrichment key="zib_relatedIdentifier">https://doi.org/10.1098/rsos.230495</enrichment>
    <enrichment key="zib_SoftwareLicence">https://www.gnu.org/licenses/gpl-3.0.html</enrichment>
    <enrichment key="zib_SoftwareType">Matlab software</enrichment>
    <enrichment key="zib_DownloadUrl">https://git.zib.de/bzfzonke/huntergatherermodelpublic</enrichment>
    <enrichment key="zib_ProjectHomepage">https://git.zib.de/bzfzonke/huntergatherermodelpublic</enrichment>
    <enrichment key="zib_requestDoi">1</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Johannes Zonker</author>
    <submitter>Natasa Djurdjevac Conrad</submitter>
    <author>Cecilia Padilla-Iglesias</author>
    <author>Natasa Djurdjevac Conrad</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="persons" number="natasa.conrad">Conrad, Natasa</collection>
    <collection role="projects" number="INNOSPREAD">INNOSPREAD</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
    <collection role="projects" number="MathPlusEF5-1">MathPlusEF5-1</collection>
    <collection role="projects" number="DynOOCompNet">DynOOCompNet</collection>
    <thesisPublisher>Zuse Institute Berlin (ZIB)</thesisPublisher>
  </doc>
  <doc>
    <id>9833</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>10</volume>
    <type>article</type>
    <publisherName>Springer International Publishing</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-01-07</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Detection of dynamic communities in temporal networks with sparse data</title>
    <abstract language="eng">Temporal networks are a powerful tool for studying the dynamic nature of a wide range of real-world complex systems, including social, biological and physical systems. In particular, detection of dynamic communities within these networks can help identify important cohesive structures and fundamental mechanisms driving systems behaviour. However, when working with real-world systems, available data is often limited and sparse, due to missing data on systems entities, their evolution and interactions, as well as uncertainty regarding temporal resolution. This can hinder accurate representation of the system over time and result in incomplete or biased community dynamics. In this paper, we compare established methods for community detection and, using synthetic data experiments and real-world case studies, we evaluate the impact of data sparsity on the quality of identified dynamic communities. Our results give valuable insights on the evolution of systems with sparse data, which are less studied in existing literature, but are frequently encountered in real-world applications.</abstract>
    <parentTitle language="eng">Applied Network Science</parentTitle>
    <identifier type="doi">10.1007/s41109-024-00687-3</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="SourceTitle">Applied Network Science</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Natasa Djurdjevac Conrad</author>
    <submitter>Natasa Conrad</submitter>
    <author>Elisa Tonello</author>
    <author>Johannes Zonker</author>
    <author>Heike Siebert</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="persons" number="natasa.conrad">Conrad, Natasa</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
    <collection role="projects" number="MathPlusEF5-5">MathPlusEF5-5</collection>
    <collection role="projects" number="DynOOCompNet">DynOOCompNet</collection>
  </doc>
  <doc>
    <id>9167</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>11</issue>
    <volume>10</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Insights into drivers of mobility and cultural dynamics of African hunter-gatherers over the past 120 000 years</title>
    <abstract language="eng">Humans have a unique capacity to innovate, transmit and rely on complex, cumulative culture for survival. While an important body of work has attempted to explore the role of changes in the size and interconnectedness of populations in determining the persistence, diversity and complexity of material culture, results have achieved limited success in explaining the emergence and spatial distribution of cumulative culture over our evolutionary trajectory. Here, we develop a spatio-temporally explicit agent-based model to explore the role of environmentally driven changes in the population dynamics of hunter–gatherer communities in allowing the development, transmission and accumulation of complex culture. By modelling separately demography- and mobility-driven changes in interaction networks, we can assess the extent to which cultural change is driven by different types of population dynamics. We create and validate our model using empirical data from Central Africa spanning 120 000 years. We find that populations would have been able to maintain diverse and elaborate cultural repertoires despite abrupt environmental changes and demographic collapses by preventing isolation through mobility. However, we also reveal that the function of cultural features was also an essential determinant of the effects of environmental or demographic changes on their dynamics. Our work can therefore offer important insights into the role of a foraging lifestyle on the evolution of cumulative culture.</abstract>
    <parentTitle language="eng">Royal Society Open Science</parentTitle>
    <identifier type="doi">10.1098/rsos.230495</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Johannes Zonker</author>
    <submitter>Natasa Djurdjevac Conrad</submitter>
    <author>Cecilia Padilla-Iglesias</author>
    <author>Natasa Djurdjevac Conrad</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="persons" number="natasa.conrad">Conrad, Natasa</collection>
    <collection role="projects" number="INNOSPREAD">INNOSPREAD</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
    <collection role="projects" number="MathPlusEF5-1">MathPlusEF5-1</collection>
    <collection role="projects" number="DynOOCompNet">DynOOCompNet</collection>
  </doc>
  <doc>
    <id>8155</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>336</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-04-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mathematical modeling of spatio-temporal population dynamics and application to epidemic spreading</title>
    <abstract language="eng">Agent based models (ABMs) are a useful tool for modeling spatio-temporal population dynamics, where many details can be included in the model description. Their computational cost though is very high and for stochastic ABMs a lot of individual simulations are required to sample quantities of interest. Especially, large numbers of agents render the sampling infeasible. Model reduction to a metapopulation model leads to a significant gain in computational efficiency, while preserving important dynamical properties. Based on a precise mathematical description of spatio-temporal ABMs, we present two different metapopulation approaches (stochastic and piecewise deterministic) and discuss the approximation steps between the different models within this framework. Especially, we show how the stochastic metapopulation model results from a Galerkin projection of the underlying ABM onto a finite-dimensional ansatz space. Finally, we utilize our modeling framework to provide a conceptual model for the spreading of COVID-19 that can be scaled to real-world scenarios.</abstract>
    <parentTitle language="eng">Mathematical Biosciences</parentTitle>
    <identifier type="doi">10.1016/j.mbs.2021.108619</identifier>
    <identifier type="arxiv">2205.05000</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2021-04-07</enrichment>
    <author>Stefanie Winkelmann</author>
    <submitter>Stefanie Winkelmann</submitter>
    <author>Johannes Zonker</author>
    <author>Christof Schütte</author>
    <author>Natasa Djurdjevac Conrad</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="persons" number="schuette">Schütte, Christof</collection>
    <collection role="projects" number="SFB1114-C3">SFB1114-C3</collection>
    <collection role="persons" number="natasa.conrad">Conrad, Natasa</collection>
    <collection role="persons" number="winkelmann">Winkelmann, Stefanie</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
    <collection role="projects" number="MathPlusEF5-1">MathPlusEF5-1</collection>
  </doc>
</export-example>
