From Code to Figure: A FAIR-Aligned Data Provenance Chain for Reproducible Simulation Research in Numerical Physics

Abstract

Computational physics increasingly depends on large simulation datasets generated by software that remains under active development for many years. In such settings, reproducibility requires not only well documented data but also explicit links between code versions, simulation inputs, generated outputs, analysis steps, and published figures. Here, we present an integrated workflow for reproducible and FAIR-aligned simulation research in numerical physics. We describe how version control, code review, automated testing, structured logging, metadata-rich output, and standardized post-processing can be combined to support traceability from software development to publication. The presented concepts demonstrated for one particular simulation framework are broadly applicable to computational physics and other data-intensive areas of scientific computing.