[目的/意义]揭示北京大学开放研究数据平台3个代表性数据集的利用现状，为科学数据开放共享相关研究提供参考。[方法/过程]选择CNKI中利用CFPS、CHARLS和CLHLS3个数据集的论文作为研究对象，采用网络分析方法并借助相关工具，分别从作者、机构、主题维度对合作研究进行分析。[结果/结论]2011年后各研究主体的年度合作度趋于稳定，随研究主体范围扩大，合作率逐渐降低。3种中心度指标相互补充可发现重要机构。一级机构呈现以北京大学为核心，部分机构间保持合作，众多机构积极参与合作的情形；合作机构数目和总合作次数排名靠前的二级机构较稳定，相互间保持稳固合作关系，大致可将其分为3类机构。核心作者群表现出部分作者团体合作频繁且合作关系稳固，并存在中介作者可连通几个合作团体。合作研究论文涉及主题丰富，重点研究主题包括老年人身心健康、家庭消费及资产情况以及老龄化人口的社会保障。为促进科学数据开放共享的发展，未来可从建设权威性科学数据开放共享集成平台、融合学术数据库、组织学术竞赛等方面改进。
[Purpose/significance] This paper aims to reveal the utilization status of the three representative datasets in Peking University Open Research Data Platform and provide references for the related research of scientific data open sharing.[Method/process] This paper selected CNKI papers using CFPS, CHARLS and CLHLS as the research objects, and adopted network analysis method and relevant tools to analyze their collaborative research from the dimensions of authors, institutions and themes.[Result/conclusion] After 2011, the annual collaboration degree of the research subjects has tended to be stable, and the collaboration rate gradually decreased with the expansion of the research subjects. The three centrality indicators can complement each other to find important institutions. The first-level institutions show that Peking University is the core, some institutions maintain collaboration, and many institutions actively participate in collaboration. The top-ranked second-level institutions in terms of the number of collaborative institutions and the total number of collaboration times are relatively stable, and maintain a solid collaborative relationship mutually, which can be roughly classified into three categories. The core author groups show that some author groups collaborate frequently and stably, and there are intermediary authors connecting to several collaborative groups. Moreover, collaborative research papers cover a variety of topics, focusing on physical and mental health of elderly people, family consumption and assets, as well as social security issues for the aging population. It can be improved in terms of building an authoritative integration platform of scientific data open sharing, integrating academic databases, and organizing academic competitions to promote the development of scientific data open sharing in the future.
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