基于大数据技术优化科技期刊同行评议模式研究

Optimization of peer review mode of scientific journals based on big data technology

【目的】基于大数据技术,优化科技期刊同行评议模式,以期提升同行评议的效率和质量,促进同行评议客观、高效和良性发展。【方法】首先,从期刊的作者、编辑和专家的角度分析同行评议面临的一些困难和问题;其次,结合同行评议的目标和需求,应用大数据技术,提出进一步优化科技期刊同行评议模式的举措。【结果】利用大数据大、多、快的优势,提出构建“广、快、全”的评审专家数据库并实行阶梯式动态管理、建立同行评议专家信用评价体系、健全互动反馈评审机制、完善多元化的奖励机制、加强编辑与专家的协同与沟通等优化措施。【结论】通过采取大数据技术,可进一步优化科技期刊同行评议模式,加强同行评议的科学性、高效性和公正性。

[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.