High-throughput analysis of large heterogeneous and dynamic data spaces with signac

High-throughput generation and analysis of vast data sets offers enormous opportunities for accelerated scientific discovery, but also requires prudent strategies for the management of computational resources and data spaces. This is especially critical when researchers work with heterogeneous and possibly highly dynamic data. The signac framework enables researchers to maintain well-formed and reusable data spaces from early exploration all the way to production runs on supercomputing scales. This is achieved through a transparent data and workflow model as well as a simple and unobtrusive programmatic interface that scales well between preliminary prototyping and concluding stages of a particular computational investigation. Here, we demonstrate the framework's efficacy and versatility by showcasing examples of how signac is applied across various research projects and disciplines.