| 数据期刊同行评议视角下科学数据质量评价指标识别 |
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| 引用本文： | 撒旭,王健,范智萱,刘建平,张贵兰,徐波.数据期刊同行评议视角下科学数据质量评价指标识别[J].图书情报工作,2020,64(17):123-130. |
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| 作者姓名： | 撒旭 王健 范智萱 刘建平 张贵兰 徐波 |
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| 作者单位： | 1.中国农业科学院农业信息研究所 北京 100081;2.中国科学技术信息研究所 北京 100038;3.国家科技基础条件平台中心 北京 100038 |
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| 基金项目： | 本文系中国农业科学院农业信息研究所基本科研业务费重点项目"科学数据出版能力建设研究"（项目编号：JBYW-AII-2020-35）和国家科技基础条件平台专项课题"科学数据质量评价研究"（项目编号：2018DDJ1ZZ16）研究成果之一。 |
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| 摘 要： | 目的/意义] 在数据期刊同行评议的视角下识别并构建科学数据质量评价指标,增强对科学数据质量评价的理解,为数据论文同行评议实践提供参考。方法/过程] 利用扎根理论的研究方法,选取20个数据期刊的数据同行评议指南作为质性研究的原始资料,并使用NVivo质性分析软件对资料进行开放式编码、关联式编码和选择性编码,通过理论饱和度检验对编码结果进行检验。结果/结论] 最终构建数据论文同行评议情境下的科学数据质量评价指标体系,识别出数据内在质量、数据表达质量、数据访问质量和数据效用质量4个主范畴和14个评价指标,并具体分析指标的含义及分类,帮助数据论文作者和评审者深入了解科学数据质量的内在结构。
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| 关 键 词： | 数据期刊 科学数据质量 同行评议 评价指标 扎根理论 |
| 收稿时间： | 2020-03-13 |
| 修稿时间： | 2020-04-18 |
Identification of Scientific Data Quality Evaluation Indicators from the Perspective of Data Journals Peer Review |
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| Sa Xu,Wang Jian,Fan Zhixuan,Liu Jianpin,Zhang Guilan,Xu Bo.Identification of Scientific Data Quality Evaluation Indicators from the Perspective of Data Journals Peer Review[J].Library and Information Service,2020,64(17):123-130. |
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| Authors: | Sa Xu Wang Jian Fan Zhixuan Liu Jianpin Zhang Guilan Xu Bo |
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| Institution: | 1.Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081;2.Institute of Scientific and Technical Information of China, Beijing 100038;3.National Science&Technology Infrastructure Center, Beijing 100038 |
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| Abstract: | Purpose/significance] From the perspective of peer review on data journals, this paper identifies and puts forward scientific data quality evaluation indicators to improve the understanding of scientific data quality evaluation and provide a reference for the practice of peer review of data papers.Method/process] Data review guidelines for 20 data journals were selected as source material for qualitative research. This paper used grounded theory and qualitative analysis software NVivo to openly encode, correlate and selectively encode the data, and finally tested the encoding results through the theoretical saturation test.Result/conclusion] Finally, in the context of peer review of data papers, a scientific data quality evaluation index system was established, including four categories of data internal quality, data expression quality, data access quality, and data utility quality and 14 evaluation indicators. Then this article analyzed the specific meaning and classification of the indicators in detail to help the authors and reviewers of data papers understand the internal structure of scientific data quality. |
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| Keywords: | data journal scientific data quality peer review evaluation indicator grounded theory |
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