Can data peer review support research integrity?
Peer review is a standard practice in scholarly publishing, upholding the integrity of published research alongside editorial workflows. But with a culture of data sharing growing at pace, can we do more to uphold research integrity through data peer review? Here, Rebecca Grant (Head of Data and Software Publishing, F1000) shares why peer review of underlying research data is central to the F1000 publishing model and what it could mean for the future of peer review.
Peer reviewers consider the contents of a submitted manuscript, including its relationship to current literature in the field, its methodology, and the conclusions drawn by the authors. Although peer review of the research data underlying a paper’s findings is not yet standard across all publishing outlets, at F1000, we believe that it has a crucial role in supporting research integrity, upholding the principles of open science, and enhancing the replicability of research findings.
At F1000, our Open Data Policy is key to our approach to supporting open research and research integrity. Many researchers believe there is a reproducibility crisis in science, with the majority of scientists reporting that they have experienced a failure to reproduce the experiments of another scientist or even their own experiments. Sharing well-formatted, well-described data in public repositories and with open licenses, alongside any software or code necessary to reproduce the experiment, is a fundamental step towards reproducible research. These requirements underpin the F1000 Open Data Policy across all our platforms and are crucial to ensure that others can review, replicate, reinterpret, and reuse research.
F1000 supports an open peer review model, with peer reviewers providing their reports and authors publishing revisions after the initial version of an article has been made public. Unlike many publishers, we also prompt our peer reviewers to assess the study’s research data as part of the peer review process. Openly shared research data supports transparency in methodology and allows validation of a study’s results, improving the reproducibility and quality of published research.
To support our peer reviewers in assessing datasets, we have developed new peer review questions to make the assessment criteria more straightforward. During the peer review process, we ask reviewers to consider the question: “Are all the source data underlying the results available to ensure full reproducibility?” Now, our new guidance also includes prompts to consider the article’s data availability statement and its clarity, as well as the metadata describing the dataset and its usefulness.
