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arXiv 1801.08720cs.DL
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识别研究领域中有影响力的单一出版物:CRExplorer的新分析机会

Identifying single influential publications in a research field: New analysis opportunities of the CRExplorer

Andreas Thor, Lutz Bornmann, Werner Marx, Rüdiger Mutz

摘要:

参考文献年谱(RPYS)是为了识别在给定论文集中(主要是某些主题或领域的论文集)具有最大影响力的引用文献(CR)而开发的。程序CRExplorer(见www.crexplorer.net)由Thor,Marx,Leydesdorff和Bornmann(2016 a,2016 b)专门开发,用于将RPYS应用于从Scopus或Web of Science下载的出版物集。在这项研究中,我们提出了一些先进的方法,已新开发的CRExplorer。这些方法能够识别和表征在较长时期内(许多引用年份)具有影响力的CR。这项研究使用了1978年至2016年期间发表在Scientometrics上的所有论文。指标N_TOP50、N_TOP25和N_TOP10可用于识别在多个引用出版物年份中属于50%、25%或10%最常引用出版物(CR)的CR。例如,在Scientometrics数据集中,洛特卡(1926)关于科学生产力分布的论文在36年的引用中属于前10%的出版物(CR)。此外,新版本的CRExplorer分析了跨引用年份的CR的影响顺序。在引用年份中,CR可能具有低于平均值(-)、平均值(0)或高于平均值(+)的影响(平均值是指预期值)。程序使用序列(例如00++-0--00)来识别具有典型影响力分布的论文。例如,CR可以具有早期影响,但不具有晚期影响(“热门论文”,例如+-),反之亦然(“睡美人”,例如-0000-++)。

英文摘要:

Reference Publication Year Spectroscopy (RPYS) has been developed for identifying the cited references (CRs) with the greatest influence in a given paper set (mostly sets of papers on certain topics or fields). The program CRExplorer (see www.crexplorer.net) was specifically developed by Thor, Marx, Leydesdorff, and Bornmann (2016a, 2016b) for applying RPYS to publication sets downloaded from Scopus or Web of Science. In this study, we present some advanced methods which have been newly developed for CRExplorer. These methods are able to identify and characterize the CRs which have been influential across a longer period (many citing years). The new methods are demonstrated in this study using all the papers published in Scientometrics between 1978 and 2016. The indicators N_TOP50, N_TOP25, and N_TOP10 can be used to identify those CRs which belong to the 50%, 25%, or 10% most frequently cited publications (CRs) over many citing publication years. In the Scientometrics dataset, for example, Lotka's (1926) paper on the distribution of scientific productivity belongs to the top 10% publications (CRs) in 36 citing years. Furthermore, the new version of CRExplorer analyzes the impact sequence of CRs across citing years. CRs can have below average (-), average (0), or above average (+) impact in citing years (whereby average is meant in the sense of expected values). The sequence (e.g. 00++---0--00) is used by the program to identify papers with typical impact distributions. For example, CRs can have early, but not late impact ("hot papers", e.g. +++---) or vice versa ("sleeping beauties", e.g. ---0000---++).

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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