Modelling and computing the quality of information in e-science Data-intensive e-science applications often rely on third-party data found in public repositories, whose quality is largely unknown. Although scientists are aware that this uncertainty may lead to incorrect scientific conclusions, in the absence of a quantitative characterization of data quality properties they find it difficult to formulate precise data acceptability criteria. The definition of such criteria, however, is often a knowledge-intensive experimental process that involves the scientists' expertise. The Qurator project, an EPSRC-funded collaboration between the School of Computer Science at Manchester and the School of Computing in Aberdeen, has developed an information quality management workbench that supports data experts in the specification, rapid deployment and testing of personal quality hypotheses for specific types of data. This is achieved by providing a declarative model and language for the definition of users' hypotheses, called "quality views", and a compilation of views into executable components that can be embedded in a user's data processing application. Quality views are described in terms of an extensible semantic model for Information Quality. This talk presents the model and shows how the Qurator workbench compiles quality views into Taverna Scufl models, effectively creating reusable quality sub-workflows that can be integrated into a host workflow during a deployment step. An application to an in silico proteomics workflow from the Imprint project is presented. Presented by: Mr. Paolo Missier, Research Fellow School of Computer Science University of Manchester