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.

Two partly conflicting academic pressures from the seriousness of the Covid-19 pandemic are the need for faster peer review of Covid-19 health-related research and greater scrutiny of its findings. This paper investigates whether decreases in peer review durations for Covid-19 articles were universal across 97 major medical journals, as well as Nature , Science , and Cell . The results suggest that on average, Covid-19 articles submitted during 2020 were reviewed 1.7–2.1 times faster than non-Covid-19 articles submitted during 2017–2020. Nevertheless, while the review speed of Covid-19 research was particularly fast during the first 5 months (1.9–3.4 times faster) of the pandemic (January–May 2020), this speed advantage was no longer evident for articles submitted in November–December 2020. Faster peer review was also associated with higher citation impact for Covid-19 articles in the same journals, suggesting it did not usually compromise the scholarly impact of important Covid-19 research. Overall, then, it seems that core medical and general journals responded quickly but carefully to the pandemic, although the situation returned closer to normal within a year.

While funders increasingly request evidence of the societal benefits of research, all academics in the UK must periodically provide this information to gain part of their block funding within the Research Excellence Framework (REF). The impact case studies produced in the UK are public and can therefore be used to gain insights into the types of sources used to justify societal impact claims. This study focuses on the URLs cited as evidence in the last public REF to help researchers and resource providers to understand what types can be used and the disciplinary differences in their uptake. Based on a new semiautomatic method to classify the URLs cited in impact case studies, the results show that there are a few key online types of source for most broad fields, but these sources differ substantially between subject areas. For example, news websites are more important in some fields than others, and YouTube is sometimes used for multimedia evidence in the arts and humanities. Knowledge of the common sources selected independently by thousands of researchers may help others to identify suitable sources for the complex task of evidencing societal impacts.

The COVID-19 pandemic requires a fast response from researchers to help address biological, medical, and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals, and the public may need to identify important new studies quickly. In response, this paper assesses the coverage of scholarly databases and impact indicators during March 21, 2020 to April 18, 2020. The rapidly increasing volume of research is particularly accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed. Google Scholar’s results included many false matches. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited 3 weeks later. Researchers needing wide scope literature searches (rather than health-focused PubMed or medRxiv searches) should start with Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as indicators of likely importance.

A research doctorate normally culminates in publishing a dissertation reporting a substantial body of novel work. In the absence of a suitable citation index, this article explores the relative merits of alternative methods for the large-scale assessment of dissertation impact, using 150,740 UK doctoral dissertations from 2009–2018. Systematic methods for this were designed for Google Books, Scopus, Microsoft Academic, and Mendeley. Fewer than 1 in 8 UK doctoral dissertations had at least one Scopus (12%), Microsoft Academic (11%), or Google Books citation (9%), or at least one Mendeley reader (5%). These percentages varied substantially by subject area and publication year. Google Books citations were more common in the Arts and Humanities (18%), whereas Scopus and Microsoft Academic citations were more numerous in Engineering (24%). In the Social Sciences, Google Books (13%) and Scopus (12%) citations were important and in Medical Sciences, Scopus and Microsoft Academic citations to dissertations were rare (6%). Few dissertations had Mendeley readers (from 3% in Science to 8% in the Social Sciences) and further analysis suggests that Google Scholar finds more citations, but does not report information about all dissertations within a repository and is not a practical tool for large-scale impact assessment.