摘要
伴随大数据时代的来临,科学研究数据及其成果再应用的重要性日益显著,而如何量化评价数据的重要作用和价值以及数据贡献者对科学的贡献显得尤为迫切。然而,目前对这方面的研究尚处于起步阶段。本文基于全球变化暨地球科学2014–2018年发表的数据集,开展"数据影响力积分"分区的计量方法研究,提出科学研究数据对科学影响力的不同类别计量评估体系。以规范性引文为基础,以引用数据的发表论文期刊影响因子为权重,利用年份综合累加的统计学方法,给出了全球变化暨地球科学数据集5年的数据影响力积分,针对积分由大到小的排序,提出以5%、15%、30%和50%比例划分Q1、Q2、Q3和Q4区的影响力分区方法。5年的数据集实例应用表明,该方法不仅可以对数据成果、数据作者和数据出版中心等不同类别在科学数据领域的影响力进行定量评价,也可以对科学影响力进行合理的分区评估。本文提出的数据影响力计量和分区评估方法体系,可以推广应用于其他学科的科学数据集,用以量化评价数据的重要价值和对科学的贡献。
With the coming of data driven sciences,the importance of re-using scientific research data is increasingly significant.How to quantitatively measure the impact of the datasets,as well as the data authors,and data centers is particularly urgent.Based on the statistics of the data citation from 2014 to 2018 and the method of Data Impact Score(DIS)models,the DIS of each dataset among the 192 cited datasets published by the Global Change Research Data Publishing&Repository(GCdata PR)and the Geoscientific Data&Discovery Publishing System during 2014–2018 were calculated using the DIS model.Besides,the DIS of each author among the 565 dataset authors and two data publishing centers were also calculated using the DIS models.There are totally 324 citations with the DIS of 134.069,4 during 2014–2018.Taking 5%,15%,30%and 50%as the critical values,all the datasets were classified into Q1,Q2,Q3 and Q4 levels.As the result,10 datasets were ranked into Q1 with the DIS above 6,24 datasets were ranked into Q2 with the DIS between 2 and 6,41 datasets were ranked into Q3 with the DIS between 0.2–2,and 117 were ranked into Q4 with the DIS less than 0.2.There were 28 data authors ranked into Q1,and the GCdata PR was ranked the top one of the data publishing centers.The DIS methodology can be used for more broad datasets from the data repositories or data publishers in national and international levels.
作者
刘闯
张应华
Liu,C.;Zhang,Y.H.(Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China)
出处
《全球变化数据学报（中英文）》
CSCD
2019年第3期207-226,317-336,共40页
Journal of Global Change Data & Discovery
