Abstract:

[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.

Key words: Big data technology, Scientific journal, Peer review, Review expert, Evaluation system

摘要：

【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。

关键词: 大数据, 科技期刊, 同行评议, 评审专家, 评价制度