Abstract
In recent years there has been a great deal of interest in "scientific workflows". These allow scientists to specify large computational experiments involving a range of different activities, such as data integration, modelling and analysis, and visualization, to name a few. Activities can be composed, often using a graphical programming environment, so that the output of one stage can be passed as input to the next, forming a pipeline of arbitrary complexity. Scientific workflows have been used to great effect in a number of different disciplines including computational chemistry, ecology and bioinformatics.