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Scientific workflows have become an archetype to model in silico experiments in the Cloud by scientists. There is a class of workflows that are used to by "data valets" to prepare raw data from scientific instruments into a science-ready form for use by scientists. These share data-intensive traits with traditional scientific workflows, yet differ significantly, for example, in the required degree of reliability and the type of provenance collected. We compare and contrast science application and data valet workflows through exemplar eScience projects to drive shared and unique requirements for scientific workflows across diverse users in a Science Cloud.