Research analytics for reviewer recommendation

Peer review plays an important role in research project selection at funding agencies. Given the practical challenge that even the most experienced researcher may be unable to point out the whole deficiencies in a complex body of research work, peer review addresses this problem by introducing independent experts to critically analyze and assess the quality of research proposals. Recommending appropriate reviewers for proposals presents a great challenge for funding agency especially when the number of proposals and reviewers are large. Reviewer recommendation involves several issues which need to be considered: avoiding the conflict of interests between authors and reviewers; whether and to what extent the reviewer has expertise in corresponding areas of proposals. This research investigates how research analytics can be used for reviewer recommendation by integrating three dimensions: connectivity, relevance and quality.