Boosting complex Systems Research through RSE Collaboration

Stochastic simulations of complex systems from domains including physics, biology, ecology or economics often require large system sizes, long time scales, and numerous replications
to fully explore model behavior. The simple rules defining many models can lead researchers
to prefer familiar but inefficient programming techniques, which severely hinder progress
by creating computational bottlenecks. While such studies often benefit from combined
domain-specific, statistical, and programming knowledge, few individual researchers span
the full range of necessary skills. Here, we present a collaboration on the neutral model
of biodiversity in dendritic river networks, where the goal is to analyze biodiversity data
across the world’s major river systems. We show how we achieved large performance gains
by engaging the problem at its foundations and thereby enabled research at a new scale.