Automated screening of COVID-19 preprints: Can we help authors to improve transparency and reproducibility?
Résumé
The COVID-19 pandemic has thrust preprints into the spotlight, highlighting their advantages and disadvantages. The lack of peer review allows publication to occur with unprecedented speed, but this has raised concerns among biomedical scientists about the quality of the reported research. The study had three primary objectives in regard to COVID-19 preprints: 1. Test the feasibility of automatically and publicly evaluating preprints on a large scale; 2. Assess the prevalence of common quality problems in preprints; and 3. Compare the quality of preprints to published papers. While a substantial fraction (36.2%) of preprints addressed study limitations, the proportion that met other quality criteria was much lower, with only 20% addressing sex as a variable, ~14% sharing open code or data, 7.6% including non-colorblind safe images, and 7.3% showing misleading bar graphs. Both authors and non-authors interacted with the automated Tweets containing reports for preprints. This project shows that it is feasible to conduct large-scale automated screening of preprints for common quality criteria and provide feedback to study authors and readers before publication. These reports can publicly raise awareness about factors that affect study quality and reproducibility, while helping authors to present their research in a more transparent and reproducible manner.
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