摘要
科学数据引用对于实现科学数据的共享具有重要意义。基于引用行为建立针对科学数据的评估机制,有利于科研人员认识到科学数据再利用的价值,并正视数据引用的重要性,从而规范化数据引用。为了量化评估科学数据对于科研活动的价值,本研究构建了基于引用行为的科学数据集/数据仓储影响力和质量评价模型,并结合文献计量与网络计量方法,运用被引、下载、网络述及等指标,从多角度总结数据引用的行为特征,测量其与科学文献质量之间的关系,从而得出以下结论:①中英文文献在数据引用方面存在差异。英文文献数据再利用率相对较高,数据引用规范性更好;②从文献频次、下载频次、被引频次、网络述及等多角度发现数据引用多个指标之间存在一定的分组关系;③生物信息学领域中英文文献中数据集/仓储质量和文献质量之间存在显著的相关关系。
Scientific data citation is a crucial step for scientific data sharing. The establishment of evaluation mechanism for scientific data based on citation behavior is helpful to make scientific researchers to realize the value of scientific data reuse and necessity of data citation, so as to promote the action of standardizing the data citation format. In order to quantitatively measure the value of scientific data in scientific research activities, this study proposes a model to evaluate the influence and quality of scientific dataset/repository based on citation behavior analysis, some bibliometric and webometric indicators such as cited, downloaded, web mention are proposed to summarize the citation behavior of scientific dataset from multiple perspectives and discuss the relationship between the quality of the scientific literature and the quality of scientific dataset. The major findings are as follows： ①There are differences between Chinese literature and English literature in terms of data citation. The data reuse ratio in English literature is higher and the citing format is much formal;②During the influence analysis of dataset, some clusters can be found among the indicators involved such as document frequency, download time, citation number, and web mention; ③There is a significant correlation between the quality of dataset and the quality of literature in the field of Bioinformatics.
出处
《情报学报》
CSSCI
北大核心
2016年第11期1132-1139,共8页
Journal of the China Society for Scientific and Technical Information
基金
国家哲学社会科学基金青年项目<基于社区发现的学术WEB主题显著度研究>(项目编号:13CTQ031)
中央高校基本科研业务费专项南京农业大学创新项目<科学数据集的引用行为及其影响力研究>(项目编号:SKCX2016005)支持
关键词
科学数据
引用规范
数据共享
生物信息学
scientific data, reference specification, data sharing, bioinformatics
