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The rivalry between Bernini and Borromini from a scientometric perspective
Scientometrics ( IF 3.5 ) Pub Date : 2020-05-28 , DOI: 10.1007/s11192-020-03514-5
Martin Wieland , Juan Gorraiz

From a historical point of view, Rome and especially the University of La Sapienza, are closely linked to two geniuses of Baroque art: Bernini and Borromini. In this study, we analyze the rivalry between them from a scientometric perspective. This study also serves as a basis for exploring which data sources may be appropriate for broad impact assessment of individuals and/or celebrities. We pay special attention to encyclopaedias, library catalogues and other databases or types of publications that are not normally used for this purpose. The results show that some sources such as Wikipedia are not exploited according to the possibilities they offer, especially those related to different languages and cultures. Moreover, analyses are often reduced to a minimum number of data sources, which can distort the relevance of the outcome. Our results show that other sources normally not considered for this purpose, like JSTOR, PQDT, Google Scholar, Catalogue Holdings, etc. can provide more relevant or abundant information than the typically used Web of Science Core Collection and Scopus. Finally, we also contrast opportunities and limitation of old and new (YouTube, Twitter) data sources (particularly the aspects quality and accuracy of the search methods). Much room for improvement has been identified in order to use data sources more efficiently and with higher accuracy.

中文翻译:

从科学计量学的角度看贝尔尼尼和博罗米尼之间的竞争

从历史的角度来看,罗马,尤其是拉萨皮恩扎大学,与巴洛克艺术的两位天才贝尔尼尼和博罗米尼有着密切的联系。在这项研究中,我们从科学计量学的角度分析了它们之间的竞争。这项研究还可以作为探索哪些数据源可能适合对个人和/或名人进行广泛影响评估的基础。我们特别关注通常不用于此目的的百科全书、图书馆目录和其他数据库或出版物类型。结果表明,维基百科等某些来源并没有根据它们提供的可能性而被利用,尤其是与不同语言和文化相关的可能性。此外,分析通常会减少到最少数量的数据源,这可能会扭曲结果的相关性。我们的结果表明,通常不会出于此目的考虑的其他来源,如 JSTOR、PQDT、Google Scholar、Catalog Holdings 等,可以提供比通常使用的 Web of Science 核心合集和 Scopus 更相关或更丰富的信息。最后,我们还对比了新旧(YouTube、Twitter)数据源的机会和局限性(特别是搜索方法的质量和准确性)。为了更有效和更准确地使用数据源,已经确定了很大的改进空间。我们还对比了新旧(YouTube、Twitter)数据源的机会和局限性(特别是搜索方法的质量和准确性)。为了更有效和更准确地使用数据源,已经确定了很大的改进空间。我们还对比了新旧(YouTube、Twitter)数据源的机会和局限性(特别是搜索方法的质量和准确性)。为了更有效和更准确地使用数据源,已经确定了很大的改进空间。
更新日期:2020-05-28
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