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Most Performance-based Research Funding Systems (PRFS) draw on peer reviewand bibliometric indicators, two different methodologies which are sometimescombined. A common argument against the use of indicators in such researchevaluation exercises is their low correlation at the article level with peerreview judgments. In this study, we analyse 191,000 papers from 154 highereducation institutes which were peer reviewed in a national research evaluationexercise. We combine these data with 6.95 million citations to the originalpapers. We show that when citation-based indicators are applied at theinstitutional or departmental level, rather than at the level of individualpapers, surprisingly large correlations with peer review judgments can beobserved, up to r <= 0.802, n = 37, p < 0.001 for some disciplines. In ourevaluation of ranking prediction performance based on citation data, we show wecan reduce the mean rank prediction error by 25% compared to previous work.This suggests that citation-based indicators are sufficiently aligned with peerreview results at the institutional level to be used to lessen the overallburden of peer review on national evaluation exercises leading to considerablecost savings.

Peer review and citation data in predicting university rankings, a large-scale analysis