Researchers of all disciplines, from Life Sciences and Astronomy to Computational Chemistry, create and use ever-increasing amounts of complex data, and rely more and more on compute-intensive modelling, simulation and analysis. Scientific workflows have become a key paradigm for managing complex tasks and have emerged as a unifying mechanism for handling scientific data. Workflows capture the essence of the scientific process, providing a means to describe it via logical data- or work-flows. Workflows are mapped onto concrete Distributed Computing Infrastructures (DCIs) to perform large-scale experiments. The learning curve for reusing workflows, however, is still steep because workflows typically have their own user interfaces/APIs, description languages, provenance strategies, and enactment engines, which are not standard and do not interoperable. Workflow integration or reuse therefore is currently impractical, thereby inhibiting the growth in uptake and proliferation of workflows in scientific practice.The SHIWA project aims to leverage existing solutions and enable cross-workflow and inter-workflow exploitation of DCIs by applying both coarse- and fine-grained strategies. The coarse-grained approach treats workflow engines as distributed black box systems, where complete sub-workflows are sent to pre-existing enactment engines. The fine-grained approach addresses language interoperability by defining an intermediate representation to be used for translation of workflows across systems (currently selected: ASKALON, Pegasus, P-Grade, MOTEUR, Triana). SHIWA will develop, deploy and operate the SHIWA Simulation Platform to offer users production-level services supporting workflow interoperability following both approaches. A Repository will facilitate publishing and sharing of workflows, and a Portal will enable their actual enactment. Three use cases based on medical imaging applications will serve to drive and evaluate this platform from a user's perspective