Preprint: “What We Should Learn From Pandemic Publishing”

The preprint linked below was recently shared on arXiv.

Title

What We Should Learn From Pandemic Publishing

Authors

Satyaki Sikdar
Indiana University
Loyola University Chicago

Sara Venturini
MIT
University of Padova

Marie-Laure Charpignon
MIT

Sagar Kumar
Northeastern University

Francesco Rinaldi
University of Padova

Francesco Tudisco
The University of Edinburgh
Gran Sasso Science Institute

Santo Fortunato
Indiana University

Maimuna S. Majumder
Harvard Medical School
Boston Children’s Hospital

Source

via arXiv

DOI: 10.48550/arXiv.2410.01838

Abstract

Authors of COVID-19 papers produced during the pandemic were overwhelmingly not subject matter experts. Such a massive inflow of scholars from different expertise areas is both an asset and a potential problem. Domain-informed scientific collaboration is the key to preparing for future crises.

From the Paper

The scientific community’s response to the COVID-19 pandemic was a highly collaborative effort. This reality prompted us to investigate the allocation of human capital within and between outbreak scientists, bellwethers, and newcomers over time. We envision the ideal scenario as one where bellwethers can easily interact with outbreak scientists and engage in domain-informed collaboration. Therefore, we were particularly interested in quantifying the propensity for bellwethers to work with outbreak scientists during the COVID-19 pandemic.

The first two years of the pandemic were characterized by a rapid growth in the number of publications, followed by sustained scientific production at approximately 13,000 COVID-19- related papers per month. We used publication data from the OpenAlex database5 to determine the composition of each paper’s authoring team according to our taxonomy (i.e., outbreak scientist, bellwether, newcomer). Outbreak scientists predominantly emanated from Medicine (48%), whereas bellwethers had more diverse backgrounds like Computer Science (12%), Psychology (8%), and Business (3.4%).

Direct to Article Abstract, Link to Full Text

Filed under: Data Files, Journal Articles, News, Publishing