Nowadays, complex technical systems are frequently developed by composing discipline-specific tools into an automated high-level workflow. Constructing, executing, and maintaining this workflow together with the infrastructure supporting it involves several distinct roles. We argue that awareness of these roles and providing explicit software support for them accelerates these processes and enables fast iteration on the design of the workflow itself.

Andreas Schreiber is head of the Department for Intelligent and Distributed Systems and at the German Aerospace Center (DLR), located in Berlin, Braunschweig, Bremen, Cologne, and Oberpfaffenhofen. His research fields include reproducible science, provenance, machine learning, explainable AI, software visualization, and software analytics. His mission is to combine these fields to creative cutting-edge software systems to solve challenging tasks in aeronautics and space research. He is also an Open Source and Open Science advocate at DLR. Andreas organizes events for Python, such as the workshop series Python for High-Performance and Scientific Computing (PyHPC) at the Supercomputing conferences. He co-founded Medando, a startup that develops mobile health apps for Android that support personalized medicine, independent living, and self-tracking (Quantified Self).