COVID-19 Preprints and Their Publishing Rate: An Improved Method | medRxiv

As the COVID-19 pandemic persists around the world, the scientific community continues to produce and circulate knowledge on the deadly disease at an unprecedented rate. During the early stage of the pandemic, preprints represented nearly 40% of all English-language COVID-19 scientific corpus (6,000+ preprints | 16,000+ articles). As of mid-August 2020, that proportion dropped to around 28% (13,000+ preprints | 49,000+ articles). Nevertheless, preprint servers remain a key engine in the efficient dissemination of scientific work on this infectious disease. But, giving the uncertified nature of the scientific manuscripts curated on preprint repositories, their integration to the global ecosystem of scientific communication is not without creating serious tensions. This is especially the case for biomedical knowledge since the dissemination of bad science can have widespread societal consequences. Scope: In this paper, I propose a robust method that allows the repeated monitoring and measuring of COVID-19 preprints' publication rate. I also introduce a new API called Upload-or-Publish. It is a free micro-API service that enables a client to query a specific preprint manuscript's publication status and associated meta-data using a unique ID. The beta-version is currently working and deployed. Data: I use Covid-19 Open Research Dataset (CORD-19) to calculate COVID-19 preprint corpus' conversion rate to peer-reviewed articles. CORD-19 dataset includes 10,454 preprints from arXiv, bioRxiv, and medRxiv. Methods: I utilize conditional fuzzy logic to link preprints with their published counterparts. My approach is an important departure from previous studies that rely exclusively on bio/medRxiv API to ascertain preprints' publication status. Findings: As expected, the findings suggest a positive relationship between the time elapsed since preprints' first server upload and preprints harboring a published status. For instance, as of mid-September, close to 50% of preprints uploaded in January were published in peer-review venues. That figure is at 29% for preprints uploaded in April, and 5% for preprints uploaded in August. As this is an ongoing project, it will continue to track the publication rates of preprints over time.