Optimization of peer review mode of scientific journals based on big data technology
[Purposes]Based on big data technology,the peer review mode of scientific journals is optimized,so as to improve the efficiency and quality of peer review and promote objective,efficient,and high-quality development.[Methods]Firstly,the paper analyzed some difficulties and problems faced by peer review from three perspectives:authors,editors,and experts.Then,according to the objectives and needs of peer review,it put forward the measures to optimize the peer review mode of scientific journals by using big data technology.[Findings]By using the advantages of big data technology,namely large scale,sufficiency,and fast speed,the paper proposes to build a"wide,fast,and complete" review expert database and implement the stepped dynamic management,establish a credit evaluation system of peer review experts,promote the review mechanism of interactive feedback,perfect diversified reward mechanisms,and strengthen the collaboration and communication between editors and experts.[Conclusions]The peer review mode of scientific journals can be further optimized by big data technology,and the scientific,efficient,and fair development of peer review can be strengthened.
Big data technologyScientific journalPeer reviewReview expertEvaluation system
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