Open peer review (OPR), where review reports and reviewers’ identities are published alongside the articles, represents one of the last aspects of the open science movement to be widely embraced, although its adoption has been growing since the turn of the century. This study provides the first comprehensive investigation of OPR adoption, its early adopters and the implementation approaches used. Current bibliographic databases do not systematically index OPR journals, nor do the OPR journals clearly state their policies on open identities and open reports. Using various methods, we identified 617 OPR journals that published at least one article with open identities or open reports as of 2019 and analyzed their wide-ranging implementations to derive emerging OPR practices. The findings suggest that: (1) there has been a steady growth in OPR adoption since 2001, when 38 journals initially adopted OPR, with more rapid growth since 2017; (2) OPR adoption is most prevalent in medical and scientific disciplines (79.9%); (3) five publishers are responsible for 81% of the identified OPR journals; (4) early adopter publishers have implemented OPR in different ways, resulting in different levels of transparency. Across the variations in OPR implementations, two important factors define the degree of transparency: open identities and open reports. Open identities may include reviewer names and affiliation as well as credentials; open reports may include timestamped review histories consisting of referee reports and author rebuttals or a letter from the editor integrating reviewers’ comments. When and where open reports can be accessed are also important factors indicating the OPR transparency level. Publishers of optional OPR journals should add metric data in their annual status reports.

Data citation, where products of research such as data sets, software, and tissue cultures are shared and acknowledged, is becoming more common in the era of Open Science. Currently, the practice of formal data citation—where data references are included alongside bibliographic references in the reference section of a publication—is uncommon. We examine the prevalence of data citation, documenting data sharing and reuse, in a sample of full text articles from the biological/biomedical sciences, the fields with the most public data sets available documented by the Data Citation Index (DCI). We develop a method that combines automated text extraction with human assessment for revealing candidate occurrences of data sharing and reuse by using terms that are most likely to indicate their occurrence. The analysis reveals that informal data citation in the main text of articles is far more common than formal data citations in the references of articles. As a result, data sharers do not receive documented credit for their data contributions in a similar way as authors do for their research articles because informal data citations are not recorded in sources such as the DCI. Ongoing challenges for the study of data citation are also outlined.

This study examines characteristics of data sharing and data re-use in Genetics and Heredity, where data citation is most common. This study applies an exploratory method because data citation is a relatively new area. The Data Citation Index (DCI) on the Web of Science was selected because DCI provides a single access point to over 500 data repositories worldwide and to over two million data studies and datasets across multiple disciplines and monitors quality research data through a peer review process. We explore data citations for Genetics and Heredity, as a case study by examining formal citations recorded in the DCI and informally by sampling a selection of papers for implicit data citations within publications. Citer-based analysis is conducted in order to remedy self-citation in the data citation phenomena. We explore 148 sampled citing articles in order to identify factors that influence data sharing and data re-use, including references, main text, supplementary data/information, acknowledgments, funding information, author information, and web/author resources. This study is unique in that it relies on a citer-based analysis approach and by analyzing peer-reviewed and published data, data repositories, and citing articles of highly productive authors where data sharing is most prevalent. This research is intended to provide a methodological and practical contribution to the study of data citation.

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