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.