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SimDL: A Model Ontology Driven Digital Library for Simulation Systems

SimDL (Simulation Digital Library) is designed to enhance the capabilities of simulation systems in computational epidemiology. By utilizing model ontologies, it integrates isolated models and manages simulation content, thereby facilitating collaboration within research groups focused on infectious diseases. Supported by institutions such as the NIH and the Gates Foundation, SimDL promotes the study of populations and the simulation of epidemics to evaluate public policies and health interventions. The library aims to create a comprehensive resource for researchers by automating workflows and improving data discovery.

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SimDL: A Model Ontology Driven Digital Library for Simulation Systems

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  1. SimDL: A Model Ontology Driven Digital Library for Simulation Systems Jonathan Leidig - leidig@vt.edu Edward A. Fox Kevin Hall Madhav Marathe Henning Mortveit JCDL: June 13-17, 2011, Ottawa, Canada Network Dynamics andSimulation Science Laboratory Digital LibraryResearch Laboratory (DLRL) @ Virginia Tech

  2. Domain: Computational Epidemiology • NIH: infectious diseases • Gates Foundation: malaria modeling • Model diseases and populations • Study of the health of a population - simulate epidemics • Test public policies, interventions, adaptive behavior, & economics Network Dynamics andSimulation Science Laboratory Digital LibraryResearch Laboratory (DLRL) @ Virginia Tech

  3. Institutional “Collaboration” Map Malaria research groups – 5 groups Infectious disease research – 35 groups Network Dynamics andSimulation Science Laboratory Digital LibraryResearch Laboratory (DLRL) @ Virginia Tech

  4. Simulation Community Goals • Integrate isolated models • Manage and expose simulation content • Link and workflow models and content • Minimize domain-expert contributions Network Dynamics andSimulation Science Laboratory Digital LibraryResearch Laboratory (DLRL) @ Virginia Tech

  5. Simulation Digital Library (SimDL) • Develop model ontologies • Use ontologies to: • Encapsulate domain knowledge • Manage simulation collections • Compose models into scientific workflows • Build metadata description sets • Capture provenance information • Customize domain-specific services Network Dynamics andSimulation Science Laboratory Digital LibraryResearch Laboratory (DLRL) @ Virginia Tech

  6. Ontology Levels Network Dynamics andSimulation Science Laboratory Digital LibraryResearch Laboratory (DLRL) @ Virginia Tech

  7. Ontology-Based Organization Simulation model ontologies: • Collection metadata schemas • Federated services • Model workflows and compositions Domain ontologies: • Linked-models • Multi-systems and institutions Network Dynamics andSimulation Science Laboratory Digital LibraryResearch Laboratory (DLRL) @ Virginia Tech

  8. Ontology-Based User Interactions Support for user tasks: • Automate workflows • User interfaces • Experiment design Network Dynamics andSimulation Science Laboratory Digital LibraryResearch Laboratory (DLRL) @ Virginia Tech

  9. Ontology-Based Discovery ? Functionality: • High-granularity in browsing • Data profiling – integration • Domain-specific searching • Scientific data ranking Network Dynamics andSimulation Science Laboratory Digital LibraryResearch Laboratory (DLRL) @ Virginia Tech

  10. Prototypes • Swiss Tropical Institute • Malaria simulation models • Virginia Bioinformatics Institute • Disease and network models • Network science cyberinfrastructure • Network simulation & analysis Analyses SimDL Interfaces Cyberinfrastructure Communication Simulation Models • Computational • Platforms Network Dynamics andSimulation Science Laboratory Digital LibraryResearch Laboratory (DLRL) @ Virginia Tech

  11. Conclusion, Future Work, and Challenges Contribution: • Multiple simulation model ontologies • Generic service implementations using model ontologies • Semi-automated model ontology generation • Initial work in linking ontologies Current tasks: • Human-intensive collaboration tasks (vocabularies and ontologies) • Meta-ontology for epidemiology Network Dynamics andSimulation Science Laboratory Digital LibraryResearch Laboratory (DLRL) @ Virginia Tech

  12. Jonathan Leidig - leidig@vt.edu - Acknowledgments This work has been partially supported by: • Swiss Tropic and Public Health Institute: OpenMalaria subcontract • National Institutes of Health: MIDAS project 2U01GM070694-7 • Department of Defense: • DTRA CNIMS Grant HDTRA1-07-C-0113 • DTRA R&D Grant HDTRA1-0901-0017 • National Science Foundation • HSD Grant SES-0729441 • PetaApps Grant OCI-0904844 • SDCI Grant OCI-1032677 Special thanks to: • Drs. Fox, Marathe, and Mortviet • NDSSL and DLRL members Network Dynamics andSimulation Science Laboratory Digital LibraryResearch Laboratory (DLRL) @ Virginia Tech

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