Scientific researchers, laboratories and organisations can be profiled and compared by analysing their published works, including documents ranging from academic papers to web sites, blog posts and Twitter feeds. This paper describes how the vector space model from information retrieval, more normally associated with full text search, has been employed in the open source SubSift software to support workflows to profile and compare such collections of documents. SubSift was originally designed to match submitted conference or journal papers to potential peer reviewers based on the similarity between the paper's abstract and the reviewer's publications as found in online bibliographic databases. The software is implemented as a family of RESTful web services that, composed into a re-usable workflow, have already been used to support several major data mining conferences. Alternative workflows and service compositions are now enabling other interesting applications.