Towards FAIRer Data: Tailored Guidelines for Enhanced Reproducibility of Research Syntheses in Psychology and Education

As research syntheses gain prominence in behavioral sciences, one of the greatest challenges to reproducibility and data reuse lies in the heterogeneity of data formats and research synthesis types. Despite notable efforts towards standardization, such as the adoption of the FAIR principles and the PRISMA reporting guidelines, some aspects are still overlooked in current data-sharing guidelines. To address this gap, we propose a two-step approach focusing on the standardized documentation and curation of synthesis data. First, we conduct a heuristic analysis to identify different types of syntheses and assess their reproducibility based on criteria derived from established guidelines. To complement existing guidelines, implications of AI tools for documentation and data sharing in research syntheses will also be examined, as well as current practices and requirements of research data centers and repositories. This lays the foundation for the development of comprehensive guidelines. In the second step, we formulate tailored guidelines for different types of syntheses. These guidelines, designed to assist researchers in effectively documenting and sharing their data, complement and expand existing frameworks like PRISMA and FAIR, and ensure alignment with data curation processes. By working with data providers and integrating the guidelines into data management workflows, particularly within research data centers' portfolios, we aim to facilitate the seamless documentation, sharing, and reuse of research synthesis data, thereby contributing to evidence-based advancements in the fields of psychology and education. peerReviewed publishedVersion