  Scientific workflows automate the analysis of large-scale scientific data,
fostering the reuse of data processing operators as well as the reproducibility
and traceability of analysis results. In exploratory research, however,
workflows are continuously adapted, utilizing a wide range of tools and
software libraries, to test scientific hypotheses. Script-based workflow
engines cater to the required flexibility through direct integration of
programming primitives but lack abstractions for interactive exploration of the
workflow design by a user during workflow execution. To derive requirements for
such interactive workflows, we conduct an empirical study on the use of
Snakemake, a popular Python-based workflow engine. Based on workflows collected
from 1602 GitHub repositories, we present insights on common structures of
Snakemake workflows, as well as the language features typically adopted in
their specification.
