Scientific workflows have to deal with highly heterogeneous data environments. In particular, they have to carry out complex data provisioning tasks that filter and transform heterogeneous input data in such a way that underlying tools or services can ingest them. This results in a high complexity of workflow design. Scientists often want to design their workflows on their own, but usually do not have the necessary skills to cope with this complexity. Therefore, we have developed a pattern-based approach to workflow design, thereby mainly focusing on workflows that realize numeric simulations. This approach removes the burden from scientists to specify low-level details of data provisioning. In this demonstration, we apply a prototype implementation of our approach to various use cases and show how it makes simulation workflow design tailor-made for scientists.