Meta-research on COVID-19: An overview of the early trends

Abstract

COVID-19 is having a dramatic impact on research and researchers. The pandemic has underlined the severity of known challenges in research and surfaced new ones, but also accelerated the adoption of innovations and manifested new opportunities. This review considers early trends emerging from meta-research on COVID-19. In particular, it focuses on the following topics: i) mapping COVID-19 research; ii) data and machine learning; iii) research practices including open access and open data, reviewing, publishing and funding; iv) communicating research to the public; v) the impact of COVID-19 on researchers, in particular with respect to gender and career trajectories. This overview finds that most early meta-research on COVID-19 has been reactive and focused on short-term questions, while more recently a shift to consider the long-term consequences of COVID-19 is taking place. Based on these findings, the author speculates that some aspects of doing research during COVID-19 are more likely to persist than others. These include: the shift to virtual for academic events such as conferences; the use of openly accessible pre-prints; the `datafication' of scholarly literature and consequent broader adoption of machine learning in science communication; the public visibility of research and researchers on social and online media.