Evaluation of Application Possibilities for Packaging Technologies in Canonical Workflows

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 aggregatedfor specific purposes, such as for training a model in deep learning. We call this type of “package” a datacollection 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-containedway for later execution. In this paper, we will review different packaging technologies and investigate theirusability in the context of CWFR. For this purpose, we draw on an exemplary use case and show howpackaging technologies can support its realization. We conclude that packaging technologies of differentflavors help on providing inputs and outputs for workflow steps in a machine-readable way, as well as onrepresenting a workflow and all its artifacts in a self-describing and self-contained way