“Common Workflow Language” explained in 64 seconds

In many scientific fields such as
bioinformatics, medical imaging, and astronomy
large quantities of data need to be analyzed. This can involve large-scale
and repetitive processes in long pipelines of different tools — referred to
as workflows. It can be very time-consuming to run data for all these
different tools by hand and convert outputs to various formats to make them
compatible with the next step. Workflow management systems are designed
to alleviate this problem by allowing these workflows to be expressed formally
and providing infrastructure to set up. execute, and monitor them. This formal
expression of workflows allows for scientists to easily share and reuse
them. Crucially they can also be used to verify results of computation for
published work. However there are many competing [ways] for describing
workflows which is a barrier to this aim. Currently there are over a hundred
different data analysis workflow systems with no interoperability between them.
The need has arisen to have a single common standard and so the “Common
Workflow Language” project was created: an open standard designed to express
workflows and their tooling in groups of YAML structured text files.

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