Preliminary analysis of COVID-19 academic information patterns: a call for open science in the times of closed borders

Abstract

The Pandemic of COVID-19, an infectious disease caused by SARS-CoV-2 motivated the scientific community to work together in order to gather, organize, process and distribute data on the novel biomedical hazard. Here, we analyzed how the scientific community responded to this challenge by quantifying distribution and availability patterns of the academic information related to COVID-19. The aim of this study was to assess the quality of the information flow and scientific collaboration, two factors we believe to be critical for finding new solutions for the ongoing pandemic. The RISmed R package, and a custom Python script were used to fetch metadata on articles indexed in PubMed and published on Rxiv preprint server. Scopus was manually searched and the metadata was exported in BibTex file. Publication rate and publication status, affiliation and author count per article, and submission-to-publication time were analysed in R. Biblioshiny application was used to create a world collaboration map. Preliminary data suggest that COVID-19 pandemic resulted in generation of a large amount of scientific data, and demonstrates potential problems regarding the information velocity, availability, and scientific collaboration in the early stages of the pandemic. More specifically, the results indicate precarious overload of the standard publication systems, significant problems with data availability and apparent deficient collaboration. In conclusion, we believe the scientific community could have used the data more efficiently in order to create proper foundations for finding new solutions for the COVID-19 pandemic. Moreover, we believe we can learn from this on the go and adopt open science principles and a more mindful approach to COVID-19-related data to accelerate the discovery of more efficient solutions. We take this opportunity to invite our colleagues to contribute to this global scientific collaboration by publishing their findings with maximal transparency.

Keywords: Bibliometric; COVID-19; Data; Open science; Pandemic.

Fig. 1

a The number of Rxiv, PubMed and Scopus articles on COVID-19. b Histogram portraying the number of COVID-19 articles per submission date (data only for accepted articles). c Histogram portraying a number of accepted COVID-19 articles per acceptance date in a journal. d Histogram portraying the number of articles published each day in BioRxiv and MedRxiv. Color indicates whether the article was published in a journal. e Publications status for COVID-19 articles and other articles from the same journal during 2020. (Color figure online)

Fig. 2

Comparison of submission-to-publication (SP) time for published papers on COVID-19 (on the left), papers published since December 2019 not related to COVID (middle), and papers published from December 2018 through March 2019 (on the right)

Fig. 3

a The number of articles published depending on the language of the article. b The number of published papers by the country of the publisher, with color indicating the language of the article. (Color figure online)

Fig. 4

a The distribution of affiliation (top graphs) and author (bottom graphs) count per article. The two graphs on the left represent the affiliation and author count distribution on all articles, while graphs on the right represent the affiliation and author count distribution only for original research papers. Based on the data retrieved with search phrases No. 3 (COVID-19), No. 4 (Year 18/19), No. 5 (Not COVID-19) for all publication types and only for original research papers. b Number of articles from each country based on Scopus database, with SCP:MCP ratio indicated by color. c A map displaying number of papers per country (indicated with intensity of blue color) and collaborations (indicated with lines). A graph presented in a is based on data retrieved from PubMed on 11th of April 2020, and b, c are based on the data downloaded from Scopus database on the same date. (Color figure online)