Publish-Review-Curate Modelling for Data Paper and Dataset: A Collaborative Approach

Abstract

Research datasets—capturing natural, societal, or artificial phenomena—are critical in generating new scientific insights, validating research models, and supporting data-intensive discovery. Data papers that describe and contextualise these datasets aim to ensure their findability, accessibility, interoperability, and reusability (FAIR) while providing academic credit to data creators. However, the peer review of data papers and associated datasets presents considerable challenges, requiring reviewers to assess both the syntactic and semantic integrity of the data, metadata quality, and domain-specific scientific relevance. Furthermore, the coordination between journal editors, reviewers, and curators demands substantial effort, often leading to publication delays in the conventional review and then publishing framework. This study proposes a novel Publish-Review-Curate (PRC) model tailored to the synchronised publication and review of data papers and their underlying datasets. Building on preprint and open science practices, the model defines a collaborative, multi-stakeholder workflow involving authors, peer reviewers, data experts, and journal editors. The PRC model integrates open feedback, transparent peer review, and structured curation to improve research data's quality, discoverability, and impact. By articulating conceptual and operational workflows, this study contributes a practical framework for modernising data publishing infrastructures and supporting the co-evaluation of narrative and data artefacts.