  Similar to Open Data initiatives, data science as a community has launched
initiatives for sharing not only data but entire pipelines, derivatives,
artifacts, etc. (Open Data Science). However, the few efforts that exist focus
on the technical part on how to facilitate sharing, conversion, etc. This
vision paper goes a step further and proposes KEK, an open federated data
science platform that does not only allow for sharing data science pipelines
and their (meta)data but also provides methods for efficient search and, in the
ideal case, even allows for combining and defining pipelines across platforms
in a federated manner. In doing so, KEK addresses the so far neglected
challenge of actually finding artifacts that are semantically related and that
can be combined to achieve a certain goal.
