An emerging consensus for open evaluation: 18 visions for the future of scientific publishing

A scientific publication system needs to provide two basic services: access and evaluation. The traditional publication system restricts the access to papers by requiring payment, and it restricts the evaluation of papers by relying on just 2–4 pre-publication peer reviews and by keeping the reviews secret. As a result, the current system suffers from a lack of quality and transparency of the peer review process, and the only immediately available indication of a new paper's quality is the prestige of the journal it appeared in. Open access (OA) is now widely accepted as desirable and is beginning to become a reality. However, the second essential element, evaluation, has received less attention. Open evaluation (OE), an ongoing post-publication process of transparent peer review and rating of papers, promises to address the problems of the current system and bring scientific publishing into the twenty-first century. Evaluation steers the attention of the scientific community, and thus the very course of science. For better or worse, the most visible papers determine the direction of each field, and guide funding and public policy decisions. Evaluation, therefore, is at the heart of the entire endeavor of science. As the number of scientific publications explodes, evaluation, and selection will only gain importance. A grand challenge of our time, therefore, is to design the future system, by which we evaluate papers and decide which ones deserve broad attention and deep reading. However, it is unclear how exactly OE and the future system for scientific publishing should work. This motivated us to edit the Research Topic “Beyond open access: visions for open evaluation of scientific papers by post-publication peer review” in Frontiers in Computational Neuroscience. The Research Topic includes 18 papers, each going beyond mere criticism of the status quo and laying out a detailed vision for the ideal future system. The authors are from a wide variety of disciplines, including neuroscience, psychology, computer science, artificial intelligence, medicine, molecular biology, chemistry, and economics. The proposals could easily have turned out to contradict each other, with some authors favoring solutions that others advise against. However, our contributors' visions are largely compatible. While each paper elaborates on particular challenges, the solutions proposed have much overlap, and where distinct solutions are proposed, these are generally compatible. This puts us in a position to present our synopsis here as a coherent blueprint for the future system that reflects the consensus among the contributors.1 Each section heading below refers to a design feature of the future system that was a prevalent theme in the collection. If the feature was overwhelmingly endorsed, the section heading below is phrased as a statement. If at least two papers strongly advised against the feature, the section heading is phrased as a question. Figure ​Figure11 visualizes to what extent each paper encourages or discourages the inclusion of each design feature in the future system. The ratings used in Figure ​Figure11 have been agreed upon with the authors of the original papers.2 Figure 1 Overview of key design features across the 18 visions. The design features on the left capture major recurrent themes that were addressed (positively or negatively) in the Research Topic on OE. The columns indicate to what extent each design feature is .