The pursuit of truth in research should be both an ideal in aspiration and also a reality in practice. The PORTAL‐DOORS Project (PDP) strives to promote creative authenticity, fair citation, and adherence to integrity and ethics in scholarly research publishing using the FAIR family of quantitative metrics with acronym FAIR for the phrases Fair Attribution to Indexed Reports and Fair Acknowledgment of Information Records, and the DREAM principles with acronym DREAM for the phrase Discoverable Data with Reproducible Results for Equivalent Entities with Accessible Attributes and Manageable Metadata. This report presents formalized definitions for idea‐laundering plagiarism by authors, idea‐bleaching censorship by editors, and proposed assertion claims for authors, reviewers, editors, and publishers in ethical peer‐reviewed publishing to support integrity in research. All of these principles have been implemented in version 2 of the PDP‐DREAM ontology written in OWL 2. This PDP‐DREAM ontology will serve as the model foundation for development of a software‐guided workflow process intended to manage the ethical peer‐reviewed publishing of web‐enabled open access journals operated online with PDP software.

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In prior work, we proposed a family of metrics as a tool to quantify adherence to or deviation from good citation practices in scholarly research and publishing. We called this family of metrics FAIR as an acronym for Fair Attribution to Indexed Reports and Fair Acknowledgment of Information Records, and introduced definitions for these metrics with counts of instances of correct or incorrect attribution or nonattribution in primary research articles with citations for previously published references. In the present work, we extend our FAIR family of metrics by introducing a collection of ratio‐based metrics to accompany the count‐based metrics described previously. We illustrate the mathematical properties of the ratio‐based metrics with various simulated examples in order to assess their suitability as a means of identifying papers under peer review as more or less likely to be suspicious for plagiarism. These FAIR metrics would alert peer reviewers to prioritize low‐scoring manuscripts for closer scrutiny. Finally, we outline our planned strategy for future validation of the FAIR metrics with an approach using both expert human analysts and automated algorithms for computerized analysis.

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