Sharing of research data continues to grow, as does the requirement of data publication in some form. Over the past decade, the number of journals that accept data has increased, as have the number and scope of repositories collecting and sharing research data. The Scholarly Kitchen brought this important topic to our attention in their article, “What Constitutes Peer Review of Data? A Survey of Peer Review Guidelines.”

A culture of data sharing is developing, and researchers are responding to data sharing requirements, the efficacy of data sharing, and its growing acceptance as a scientific norm in many fields. Prior to 2010, data sharing was quite limited in scholarly publishing. During the intervening years, the pace of data publishing increased rapidly. The number of datasets being shared annually has increased by more than 400% from 2011 to 2015, and this pace will likely continue.

Peer review of data is similar to peer review of an article, but it has its own issues that make the process more complicated. The overall complexity of a research dataset can be the first challenge. To conduct a proper analysis, the methodology of the data collection should be considered, an examination that can go as deep as describing instrument calibration and maintenance.

The peer review process is still the best way known to scientists to advance their professional disciplines. Without the checks and balances of such a process, scientific literature would end up just another news outlet.

Melody K. Smith

Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.