Scientists around the world have been racing to respond to the deadly coronavirus pandemic. Seemingly overnight, many of the world’s top researchers shifted their focus to the study of COVID-19, leading to a constant stream of new pre-print articles.
More than 2,000 COVID-19-referencing pre-prints – completed studies awaiting peer review and journal publication – have appeared since China announced the outbreak in January, with 375 articles published last week alone.
Now, researchers at the University of Toronto have developed a tool, called CiteNet, to help their colleagues find, survey and review this new literature in a faster more efficient way.
The trio co-developed CiteNet with fellow graduate student John Giorgi in collaboration with Bo Wang, an assistant professor in the department of medical biophysics in the Faculty of Medicine and a faculty member at the Vector Institute for Artificial Intelligence.
“If you find a paper that you think is interesting, CiteNet can help you find others like it,” says Forster.
CiteNet indexes papers from the pre-print servers BioRxiv and MedRxiv (pronounced as “bio-archive” and “med-archive”), where most COVID-19 papers appear before publication. But instead of the needle-in-a-haystack approach of keyword searches employed by most academic search engines, CiteNet uses algorithms to intelligently gather literature related to COVID-19 and sort it based on defined search criteria.
CiteNet is still in the development phase, but due to the COVID-19 pandemic, Forster and Giorgi have made the demonstration version of the tool available to the public. They have also created and posted a CiteNet video tutorial to illustrate how to use the app.
“We hope CiteNet will be a useful, up-to-date tool for all members of the scientific community looking to find answers in the global fight against COVID-19,” Wang says.