Purpose: This study analyzed the peer review systems, criteria, and editorial committee structures of data journals, aiming to determine the current state of data peer review and to offer suggestions. Methods: We analyzed peer review systems and criteria for peer review in nine data journals indexed by Web of Science, as well as the positions of the editorial committee members of the journals. Each data journal's website was initially surveyed, and the editors-in-chief were queried via email about any information not found on the websites. The peer review criteria of the journals were analyzed in terms of data quality, metadata quality, and general quality. Results: Seven of the nine data journals adopted single-blind and open review peer review methods. The remaining two implemented modified models, such as interactive and community review. In the peer review criteria, there was a shared emphasis on the appropriateness of data production methodology and detailed descriptions. The editorial committees of the journals tended to have subject editors or subject advisory boards, while a few journals included positions with the responsibility of evaluating the technical quality of data. Conclusion: Creating a community of subject experts and securing various editorial positions for peer review are necessary for data journals to achieve data quality assurance and to promote reuse. New practices will emerge in terms of data peer review models, criteria, and editorial positions, and further research needs to be conducted.
Bibliographical noteFunding Information:
This study was funded by the Korea Institute of Science and Technology Information (KISTI) (contract number: P19032).
© 2020 Korean Council of Science Editors.
- Data journals
- Data peer review
- Editorial positions
- Peer review criteria
- Peer review system