Social simulation studies are complex. They typically combine various sources of data and hypotheses, that are integrated by intertwined processes of model building, simulation experiment execution, and analysis. Various documentation approaches exist to support the transparency and traceability of complex social simulation studies. In particular, provenance patterns can be used to capture central activities and entities of a simulation study. Entities can include, simulation models, experiments, or research questions, and activities -- model building, calibration, validation, and analysis. The exploitation of provenance standards enables information on sources and activities, which contribute to the generation of an entity, to be queryable and computationally accessible. In this study, we refine the provenance pattern-based approach to address specific challenges of social agent-based simulation studies. Specifically, we focus on the activities and entities involved in collecting and analyzing primary data about human decisions, and the collection and quality assessment of secondary data. We illustrate the potential of this approach by applying it to central activities and results of the Bayesian Agent-Based Population Studies project and by presenting its implementation in a web-based tool.

With the increasing complexity of simulation studies, and thus increasing complexity of simulation experiments, there is a high demand for better support for their conduction. Recently, model-driven approaches have been explored for facilitating the specification, execution, and reproducibility of simulation experiments. However, a more general approach that is suited for a variety of modeling and simulation areas, experiment types, and tools, which also allows for further automation, is still missing. Therefore, we present a novel model-driven engineering (MDE) framework for simulation studies that extends the state-of-the-art by means for knowledge sharing across domains, increased productivity and quality of complex simulation experiments, as well as reusability and automation. We demonstrate the practicality of our approach using case studies from three different fields of simulation (stochastic discrete-event simulation of a cell signaling pathway, virtual prototyping of a neurostimulator, and finite element analysis of electric fields), and various experiment types (global sensitivity analysis, time course analysis, and convergence testing). The proposed framework can be the starting point for further automation of simulation experiments, and therefore can assist in conducting simulation studies in a more systematic and effective manner. For example, based on this MDE framework, approaches for automatically selecting and parametrizing experimentation methods, or for planning following activities depending on the context of the simulation study, could be developed.