Although funding is essential for some types of research and beneficial for others, it may constrain academic choice and creativity. Thus, it is important to check whether it ever seems unnecessary. Here we investigate whether funded U.K. research tends to be higher quality in all fields and for all major research funders. Based on peer review quality scores for 113,877 articles from all fields in the U.K.’s Research Excellence Framework (REF) 2021, we estimate that there are substantial disciplinary differences in the proportion of funded journal articles, from Theology and Religious Studies (16%+) to Biological Sciences (91%+). The results suggest that funded research is likely to be of higher quality overall, for all the largest research funders, and for 30 out of 34 REF Units of Assessment (disciplines or sets of disciplines), even after factoring out research team size. There are differences between funders in the average quality of the research supported, however. Funding seems particularly associated with higher research quality in health-related fields. The results do not show cause and effect and do not take into account the amount of funding received but are consistent with funding either improving research quality or being won by high-quality researchers or projects.

National research evaluation initiatives and incentive schemes choose between simplistic quantitative indicators and time-consuming peer/expert review, sometimes supported by bibliometrics. Here we assess whether machine learning could provide a third alternative, estimating article quality using more multiple bibliometric and metadata inputs. We investigated this using provisional three-level REF2021 peer review scores for 84,966 articles submitted to the U.K. Research Excellence Framework 2021, matching a Scopus record 2014–18 and with a substantial abstract. We found that accuracy is highest in the medical and physical sciences Units of Assessment (UoAs) and economics, reaching 42% above the baseline (72% overall) in the best case. This is based on 1,000 bibliometric inputs and half of the articles used for training in each UoA. Prediction accuracies above the baseline for the social science, mathematics, engineering, arts, and humanities UoAs were much lower or close to zero. The Random Forest Classifier (standard or ordinal) and Extreme Gradient Boosting Classifier algorithms performed best from the 32 tested. Accuracy was lower if UoAs were merged or replaced by Scopus broad categories. We increased accuracy with an active learning strategy and by selecting articles with higher prediction probabilities, but this substantially reduced the number of scores predicted.