In recent years, the volumes of data to be analyzed, as well as the complexity of that analysis, across many scientific fields (from genomics through to exoplanet exploration) have increased massively. As a consequence, workflows (abstract representations of data flow between multiple analytic tools) have become a core part of computational scientific analysis. Automated computational workflows multiply the power of researchers, potentially turning “handcranked” data processing by informaticians into robust factories for complex research output.
However, in order for a piece of software to be usable as a workflow-ready tool, it may require alteration from its likely origin as a stand-alone tool. Research software is often created in response to the need to answer a research question with the minimum expenditure of time and money in resource-constrained projects [3, 4]. The level of quality might range from “it works on my computer” to mature and robust projects with support across multiple operating systems.
