Exploiting Provenance and Ontologies In Supporting Best Practices For Simulation Experiments: A Case Study On Sensitivity Analysis

Simulation studies are intricate processes and user support for conducting more consistent, systematic, and efficient simulation studies is needed. Simulation experiments as one crucial part of a simulation study can benefit from semi-automatic method selection, parameterization, and execution. However, this largely depends on the context in which the experiment is conducted. Context information about a simulation study can be provided in form of provenance that documents which artifacts contributed in developing a simulation model. We present an approach that exploits provenance to support best practices for simulation experiments. The approach relies on 1) explicitly specified provenance information, 2) an ontology of methods, 3) best practices rules, and 4) integration with a previously developed experiment generation pipeline. We demonstrate our approach by conducting a sensitivity analysis experiment within a cell biological simulation study.