Scientific workflow (SWF) system is a specific workflow management system applied to science arena. For years, SWF systems are widely applied to many applications, namely in physics, climate modeling, drug discovery process, etc. However, current SWF systems face the challenge to adapt the flexibility and lack of decision support for scientist. We believe the major reason for the failure is due to do not make context explicit. We propose a solution to introduce contextual graphs (CxG) in the four phases of the SWF lifecycle, each of which is expressed in a standard format, including a case study in virtual screening. Contextual graph allows to model scientists’ decision making processes as a uniform representation of knowledge, reasoning, and of contexts, so that scientists are closely involved in each phase of SWF lifecycle to maximize the decision support. Finally, we conclude and highlight that using CxG is the key human-centered process for SWF systems.