The ultimate goal of this research work is to realize the vision of a self-organizing linked process for scientific data. This could be an alternative to the current approach to model complex scientific experiments as workflow of dependent tasks. In this vision processes are used as a method to link scientific data at different stages. This goes beyond the idea of the Web Linked Data approach simply connecting pieces of data and information using Semantic Web techniques. Workflow management systems will remain as execution engine but will not be forced onto scientists to design experiment. This ecosystem can be thought of as a semantically annotated network where data and processes are interlinked to describe meaningful scientific data transformations. While this network expands as a result of adding new data and processes, multiple alternative paths for data exploration are created. The ecosystem will be able to assist scientists to identify the data transformation required for his/her application and map them to appropriate resources ranging for a simple desktop computer to large federation of computing resources like clouds and grids.
To realise this vision, we have initiated three parallel research tracks addressing various challenges