Welcome to biOverlay

Welcome to biOverlay

I’m excited to be able to announce the impending arrival of biOverlay, which is similar to an overlay journal for the natural sciences. Like academic journals, we perform peer review of scientific literature. However, our overlay doesn’t publish papers. Authors do not know that papers are selected and sent out for review, and journals should not consider manuscripts that we selected for biOverlay as published. Because our process directly mirrors academic peer review other than the stage of submission, one could imagine that our assessments may be useful to journals when they select which papers to send out for review.

Associate editors identify literature that they consider to be of considerable interest. They then contact a set of scientists who they feel are well-suited to evaluating one or more aspects of the work and request that they serve as referees. Individuals who agree to referee the work are asked to review one or more aspects of the work. These individuals are allowed to share their own name or review anonymously.

Once the reviews have been performed, the associate editor compiles the reviews and summarizes the contribution of the work.

Our reviews will generally follow this style:


Author Name(s)

DOI or permanent link

AE Summary

Reviewer #1 Report

Reviewer #2 Report

Reviewer #3 Report

This should generally be familiar to people who have published in academic journals before. Our goal is to provide valuable context for manuscripts that are of particular import. We expect that most of the items that associate editors will send for review will be preprints, because these often could benefit most from the additional feedback that peer review can provide. However, at times an associate editor may decide that a published manuscript is of sufficient importance that additional review is warranted.

Take a look at our about page to see how we select work to review, how to become an associate editor, or read our conflicts of interest policy.

We’ve been working on putting together an overlay structure for reviews of life sciences manuscripts for a little while. Recently, we’ve been asked how this compares with a number of systems including Appraise, Peer Feedback, preLights and PREReview. We are particularly excited about these efforts. My attempt to quickly compare biOverlay with these services is below.

Comparison with Appraise

Based on the blog post that currently describes Appraise there are a few key differences in the structure of the system. Reviews in Appraise seem to be performed by a set of “Appraise members” that appear to be akin to our associate editors. We maintain the current academic peer review structure in which a set of editors commission and summarize reviews. It also appears that a single review will be sufficient for a commentary in Appraise, while we request three and require at least two reviews to post about an article.

Comparison with Peer Feedback

Based on the blog post that currently describes Peer Feedback, there are a few key differences. Authors select work to be assessed through Peer Feedback, and pay in some form or fashion for reviews and editorial service. Instead, we allow associate editors to select any work that is of particular interest to them. Unlike Peer Feedback, authors cannot submit their work to us and no payments occur in our system.

Comparison with preLights

The preLights service as it exists allows a set of selected individuals to write short highlights of manuscripts. This looks a lot like the old F1000 (now called F1000Prime) except that it focuses on preprints. The highlights tend to be quite short. Unlike this service, we aim to include the rigor of traditional peer review in the process. We also include multiple reviewers’ assessments along with an editor’s summary of the contribution.

Comparison with PREReview

PREReview is described in a blog post and also exists, which allows for a more direct comparison. PREReview provides a user-friendly interface to construct reviews, mint DOIs for those reviews, and makes reviews into a first-class academic output. We have not built and do not offer a specific authoring system for our reviews. Reviewers may post their reviews publicly or share them with us anonymously for posting alongside our associate editors’ comments. We hope that some reviewers will take advantage of PREReview to compose their reviews.

Casey Greene is an Assistant Professor in the Department of Systems Pharmacology and Translational Therapeutics at the University of Pennsylvania's Perelman School of Medicine and the Director of Alex's Lemonade Stand Foundation's Childhood Cancer Data Lab. His lab aims to develop deep learning methods that integrate distinct large-scale datasets to extract the rich and intrinsic information embedded in such integrated data.