This workshop calls on the research community to discuss the state-of-the-art and research challenges for supporting modeling and decision making under uncertainty in the computational and data sciences. When creating visual tools for simulations, challenges exist in the uncertainty analysis (UA) of ensembles, the sensitivity analysis (SA) of input-output models, and the decision making process that requires the understanding of risk stemming from both UA and SA. Over the last few years we have seen many different attempts to address these issues, and it is now time to review the achievements in the light of real-world applications. We therefore attempt to broaden the focus of uncertainty analysis to a more comprehensive approach to modeling and discuss the current and future requirements from an application-oriented perspective.