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• W2783507655 abstract "Simulation studies are intricate processes that require interweaving model refinement and executing diverse experiments. Simulation models and data are the result of complex and interactive model and data generating processes. Information about these processes are required to assess the quality of simulation products. Capturing provenance, i.e., information about how a product has been generated, is a major concern both for assessing and reproducing scientific experiments. For parts of a simulation study, support for capturing and managing provenance is available. However, still gaps exist, e.g., how simulation models have been generated and to look therefore beyond individual simulation experiments and even simulation studies. To bridge those gaps it will be central to exploit, refine, and combine diverse methods effectively, as we demonstrate on a concrete case study and its provenance model." @default.
• W2783507655 created "2018-01-26" @default.
• W2783507655 creator A5050061278 @default.
• W2783507655 creator A5063515034 @default.
• W2783507655 date "2017-12-03" @default.
• W2783507655 modified "2023-09-24" @default.
• W2783507655 title "Provenance in modeling and simulation studies: bridging gaps" @default.
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• W2783507655 doi "https://doi.org/10.5555/3242181.3242248" @default.
• W2783507655 hasPublicationYear "2017" @default.
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