Research managers benchmarking universities against international peers face the problem of affiliation disambiguation. Different databases have taken separate approaches to this problem and discrepancies exist between them. Bibliometric data sources typically conduct a disambiguation process that unifies variant institutional names and those of its sub-units so that researchers can then search all records from that institution using a single unified name. This study examined affiliation discrepancies between Scopus, Web of Science, Dimensions, and Microsoft Academic for 18 Arab universities over a five-year period. We confirmed that digital object identifiers (DOIs) are suitable for extracting comparable scholarly material across databases and quantified the affiliation discrepancies between them. A substantial share of records assigned to the selected universities in any one database were not assigned to the same university in another. The share of discrepancy was higher in the larger databases, Dimensions and Microsoft Academic. The smaller, more selective databases, Scopus and especially Web of Science tended to agree to a greater degree with affiliations in the other databases. Manual examination of affiliation discrepancies showed they were caused by a mixture of missing affiliations, unification differences, and assignation of records to the wrong institution.
翻译:以国际同行为基准的大学研究管理人员面临联系不一致的问题。不同的数据库对这个问题采取了不同的处理办法,而且它们之间存在差异。生物量数据源通常采用模糊化过程,将不同的机构名称及其子单位的名称统一起来,这样研究人员就可以用单一统一的名称从该机构检索所有记录。这项研究审查了18所阿拉伯大学Scopus、Scopus、Science、Simes和微软学术之间在五年期间的属性差异。我们确认,数字对象识别器(DOIs)适合于在各数据库中提取可比的学术材料,并量化它们之间的关联差异。在任何一个数据库中分配给选定大学的大量记录没有分配给同一大学。在较大的数据库、尺寸和微软学术中,差异的比例较高。较小型、较有选择性的数据库、Scopus、特别是科学网络往往与其他数据库的属性有较大程度的关联。我们确认,数字对象识别器(DOIs)适合于在它们之间提取可比的学术材料,并对它们之间的属性差异进行量化。在任何一个数据库中分配给选定大学的大量记录没有分配给同一大学,而在另一个大学中,差异在较大的数据库、尺寸和向错误机构分配记录方面的比例较高。