中国科学数据（中英文网络版）2024，Vol.9Issue(1)：366-388,23.DOI:10.11922/11-6035.csd.2023.0164.zh
数据论文引用计量规律初探——基于中国数据期刊的案例研究
Exploring citation metrics of data papers:case studies based on two Chinese data journals
摘要
Abstract
Data publishing provides an innovative model for data sharing.To gain better insights into data publishing,this paper uses a literature review and case studies to build up a citation metrics framework and conducts case studies on two Chinese data journals,Chinese Science Data and the Journal of Global Change Data and Discovery.This paper reveals the status of data publishing and reusability of the linked datasets in the two journals.And over thirty indicators have been analyzed,covering paper indicators(e.g.subjects,team size,and funding sources),data indicators(e.g.data sources,data scale,types,visits,and downloads),citation indicators(e.g.cited,citing,and time metrics),etc.The results unveil that,at the macro level,data publishing has become an important way for data sharing.It effectively promotes open cooperation,together with funding support,high data quality control,and facilitates the sharing of special collections of datasets across domains.Besides,national data centers play a vital role in supporting data publishing.At the micro level,datasets typically have strong associations with subjects but may still share commonalities across domains.Moreover,several indicators may contribute to decoding data reusability,such as combining data visits and downloads,and further links to citations.In addition,independent data publishing is on the rise in China,and we suggest nourishing data culture,diversifying business models,reinforcing data governance,developing robust platforms and technologies,and better incentives and metrics for data sharing in the long run.关键词
开放数据/数据论文/数据出版/数据重用/数据引用/引用计量Key words
open data/data paper/data publishing/data reuse/data citation/citation metrics引用本文复制引用
张丽丽,惠嘉怡,刘瑞霖..数据论文引用计量规律初探——基于中国数据期刊的案例研究[J].中国科学数据（中英文网络版）,2024,9(1):366-388,23.基金项目
本研究获得国家自然科学基金(72104229),中国科学院国际合作局国际伙伴计划(241711KYSB20200023)和中国科学院计算机网络信息中心所级课题(CNIC20220101)资助. （72104229）
