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Scientific workflows are efficient tools for specifying and automating compute and data intensive in-silico experiments. An important challenge related to their usage is their reproducibility. In order to make it reproducible, many factors have to be investigated which can influence and even prevent this process: the missing descriptions and samples; the missing provenance data about the environmental parameters and the data dependencies; the dependencies of executions which are based ondoi:10.12700/aph.14.2.2017.2.11 fatcat:r5ffdhjcnrbk3m7dwyccxdyh2i