(377h) Scientific Data Management with Signac

Dodd, P., University of Michigan
Adorf, C. S., University of Michigan
Glotzer, S. C., University of Michigan
Researchers in the field of computational physics, chemistry, and material science are regularly posed with the challenge of managing large and heterogeneous data spaces. The amount of data increases in lockstep with computational efficiency multiplied by the amount of available computational resources, which shifts the bottleneck within the scientific process from data acquisition to data post-processing and analysis. We present a framework designed to aid in the integration of various specialized formats, tools and workflows. The signac framework provides all basic components required to create a well-defined and thus collectively accessible data space, simplifying data access and modification through a homogeneous data interface. The framework's data model is designed not to require absolute commitment to the presented implementation, simplifying adaption into existing data sets and workflows. This approach not only increases the efficiency of the production of scientific results, but also significantly lowers barriers for collaborations requiring shared data access.