KCSE
Search
- Page Path
- HOME > Search
Original Article
- Data journals: types of peer review, review criteria, and editorial committee members’ positions
- Sunkyung Seo, Jihyun Kim
- Sci Ed. 2020;7(2):130-135. Published online August 20, 2020
- DOI: https://doi.org/10.6087/kcse.207
- 8,207 View
- 172 Download
- 4 Web of Science
- 4 Crossref
-
Abstract
PDF
Supplementary Material - 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. -
Citations
Citations to this article as recorded by
- Unleashing the power of AI in science-key considerations for materials data preparation
Yongchao Lu, Hong Wang, Lanting Zhang, Ning Yu, Siqi Shi, Hang Su
Scientific Data.2024;[Epub] CrossRef - Dissemination effect of data papers on scientific datasets
Hong Jiao, Yuhong Qiu, Xiaowei Ma, Bo Yang
Journal of the Association for Information Science and Technology.2024; 75(2): 115. CrossRef - The data paper as a sociolinguistic epistemic object: A content analysis on the rhetorical moves used in data paper abstracts
Kai Li, Chenyue Jiao
Journal of the Association for Information Science and Technology.2022; 73(6): 834. CrossRef - Korean researchers’ motivations for publishing in data journals and the usefulness of their data: a qualitative study
Jungyeoun Lee, Jihyun Kim
Science Editing.2021; 8(2): 145. CrossRef
- Unleashing the power of AI in science-key considerations for materials data preparation
First
Prev
