Chinese Journal of Scientific and Technical Periodicals ›› 2022, Vol. 33 ›› Issue (5): 596-601. doi: 10.11946/cjstp.202108290693

Previous Articles     Next Articles

Findings and solutions in quality control for dataset publishing review: Taking Digital Journal of Global Change Data Repository as an example

SHI Ruixiang()(), LIU Chuang   

  1. Editorial Office of Journal of Global Change Data & Discovery, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China
  • Received:2021-08-29 Revised:2022-04-08 Online:2022-05-15 Published:2022-06-22


石瑞香()(), 刘闯   

  1. 中国科学院地理科学与资源研究所《全球变化数据学报(中英文)》编辑部,北京市朝阳区大屯路甲11号 100101
  • 作者简介:石瑞香(ORCID:0000-0002-9851-715X),博士,副编审,E-mail:;
    刘 闯,博士,研究员。
  • 基金资助:


[Purposes] This paper analyzes the main findings and solutions in quality control for dataset publishing review, which is expected to improve the quality of research data and serve as a reference for establishing standards and norms for quality review of data journals. [Methods] Taking Digital Journal of Global Change Data Repository as an example, we reviewed the metadata and datasets and compared the metadata, datasets, and data papers, thereby summarizing the data problems and analyzing the reasons. [Findings] The main data problems are as follows: incomplete and nonstandard metadata, inconsistent data description in datasets, metadata, and data papers, unsuitable development methodology for datasets or lack of scientific basis, dataset inconsistent with actual situation, incomplete dataset, inconsistent dataset content, and dataset used failing to be cited or cited incorrectly. [Conclusions] A lot of data problems are found in research data review, which is closely related to the current evaluation mechanism and the authors' negligence on data quality.

Key words: Data journal, Data publishing, Data review, Data repository


【目的】 分析数据期刊科学数据质量评审中发现的主要问题,为促进科学数据质量提升,建立数据期刊质量评审行业标准和规范提供参考与借鉴。【方法】 以《全球变化数据仓储电子杂志(中英文)》的数据质量评审实践为例,对元数据质量评审、实体数据质量评审、元数据-实体数据-数据论文初稿对照评审3个方面发现的主要数据质量问题进行归纳和总结,并对其原因进行分析。【结果】 数据质量的主要问题有:元数据内容不完整,表达不规范;实体数据和元数据、数据论文中的数据描述不一致;实体数据的研发方法有疏漏,或缺少科学依据;实体数据与实际情况不符;实体数据内容不完整;实体数据内容不一致;引用实体数据不标注出处,或者标注不规范。【结论】 目前数据质量评审发现的问题比较多,这与当前的科研评价机制、部分作者对待数据缺乏严谨态度等有密切关系。

关键词: 数据期刊, 数据出版, 数据质量评审, 数据仓储