Heidelberg/Germany, 22 December 2020 – Early Evidence Base (EEB) prioritizes preprints that are linked with expert peer reviews obtained from a variety of sources, including Review Commons, EMBO Press, eLife, Peerage of Science, Peer Community In and Rapid Reviews: COVID-19. In-depth analyses from experts and responses from the authors are made directly accessible to readers who can use them to form an informed opinion on the reported findings.
Early Evidence Base used an artificial intelligence engine to analyze the scientific content of 40,000 preprints and organize them around coherent scientific topics in a fully automated way. Without prior knowledge or human intervention, the engine automatically identifies emerging topics such as COVID-19 and potentially important molecular and cellular components related to these areas of scientific research.
For non-experts, navigating preprints can be challenging: in absence of peer review, it is often difficult to interpret the data and to evaluate the quality of the results and the strength of the conclusions. “We built Early Evidence Base as a follow-up project to Review Commons, a platform recently launched by EMBO and ASAPbio that organizes the transparent peer review of preprints. We wanted to make refereed preprints produced by a variety of such peer review services more visible and useful to readers by aggregating them and organizing them,” says Thomas Lemberger, Deputy Head of Scientific Publication at EMBO and initiator of the project. “The idea behind Early Evidence Base is to combine human expertise through the peer review process with artificial intelligence to highlight scientific evidence released in preprints and to reinforce trust in this mode of rapid scientific dissemination.”
The urgency to understand and combat SARS-CoV-2 infection has stimulated an unprecedented rate of preprint posting. “For experts, this communication channel is an efficient way to access the latest research without delay and thus to accelerate scientific progress,” says Bernd Pulverer, Head of Scientific Publications at EMBO and Chief Editor of The EMBO Journal. “However, one of the lessons we learned from the pandemic is that there are also serious risks resulting from misinterpretation of preliminary results shared in preprints and from amplifying or perpetuating premature claims by the media and non-experts. With Early Evidence Base, readers will easily access peer reviews that provide expert in-depth analyses of preprints and help contextualize the reported findings,” he further notes.
Early Evidence Base is free for all users and is developed as an open source project. It uses deep learning and graph data analytics to analyse the scientific content of preprints. Aggregation of preprints and peer-reviews is based on the integration of data from multiple platforms including bioRxiv (https://biorxiv.org), hypothes.is (https://hypothes.is), CrossRef (https://crossref.org) and SourceData (https://sourcedata.io). The current pilot platform is under continuous development and will be modified based on feedback from the scientific community.
“EMBO actively promotes transparency and openness in science and publishing. We see preprints as an important development. We contribute to open science by developing new policies and technologies that support the use of preprints in reproducible evidence-based research,” concludes Maria Leptin, EMBO Director.
