Modeling of Scientific Workflows
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In the age of big data, scientists of all disciplines need to become data scientists to analyze increasing amounts of research data. Often the available standard software is not sufficient for specific research purposes, so that the development of own, purpose-specific software becomes necessary. Hence, computational thinking, programming, and software development skills are not only taught in computer science and closely related study programs anymore but increasingly to students of all disciplines (Marshall 2017).
Many of the software applications that (data) scientists develop for their work are however not written from scratch. Rather, they reuse existing computational components such as programming libraries, web services, and command-line tools and combine them anew for the specific purpose. The applications hence “orchestrate” components, by defining the execution order of the components and the flow of data between them. Over the last years, the scientific...
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