Scientific workflows play an important role in computational research, as the essential artifacts for communicating the methods used to produce the research findings. We are witnessing a growing number of efforts of treating workflows as first-class artifacts for sharing and exchanging actual scientific knowledge, either as part of scholarly articles or as stand-alone objects. However, workflows are not born to be reliable, which can seriously damage their reusability and trustworthiness as knowledge exchange instruments. Scientific workflows are commonly subject to decaying, which consequently undermines their reliability. In this paper, we propose the hypothesis that reliability of workflows can be notably improved by advocating scientists to preserve a minimal set of information that is essential to assist the interpretations of these workflows and hence improve their reproducibility and reusability. By measuring and monitoring the completeness and stability of this information over time, we are then able to indicate the reliability of scientific workflows, which is critical for establishing trustworthy reuse of these important scientific artifacts and supporting the claims in related publications.