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

TL;DR

This paper reviews early meta-research on COVID-19, highlighting shifts in research practices, data use, communication, and researcher impacts, and discusses potential long-term changes in scientific work post-pandemic.

Contribution

It provides an overview of emerging trends in COVID-19 meta-research, emphasizing the shift from reactive to long-term perspectives and identifying persistent changes in research practices.

Findings

Increased use of pre-prints and open data during COVID-19.

Shift to virtual conferences and online research dissemination.

Growing public engagement with research via social media.

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.