Workflow patterns for scientific collaborations

Project description

Computational workflow systems ? such as Taverna ( are becoming a popular mechanism for linking online resources in the scientific community. myExperiment ( provides a common interface for depositing, finding, and sharing scientific workflows. It is a joint development by the University of Manchester and the University of Southampton, and currently has over 1100 workflows deposited by a world wide community of computational scientists.

Authoring workflows is a time-consuming and challenging task. Finding workflows in myExperiment that are potentially useful is difficult. Figuring out how to adapt one workflow to make a new one is non-trivial. myExperiment as a wealth of content that could reveal emergent workflow patterns; analysis of the content could reveal useful adaption recommendations for workflow authors.
This PhD focuses on exploiting the pooled experiences of workflow authors and users pooled in myExperiment and the Taverna user base to uncover workflow patterns, rank workflows and recommend workflows. The approach will use a mix of techniques, from text mining and profile analysis to social collaboration.

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