摘要: In Canonical Workflow Framework for Research (CWFR) “packages” are relevant in two different directions.
In data science, workflows are in general being executed on a set of files which have been aggregated
for specific purposes, such as for training a model in deep learning. We call this type of “package” a data
collection and its aggregation and metadata description is motivated by research interests. The other type of
“packages” relevant for CWFR are supposed to represent workflows in a self-describing and self-contained
way for later execution. In this paper, we will review different packaging technologies and investigate their
usability in the context of CWFR. For this purpose, we draw on an exemplary use case and show how
packaging technologies can support its realization. We conclude that packaging technologies of different
flavors help on providing inputs and outputs for workflow steps in a machine-readable way, as well as on
representing a workflow and all its artifacts in a self-describing and self-contained way