The academic evaluation of the publication record of researchers is relevant for identifying talented candidates for promotion and funding. A key tool for this is the use of the indexes provided by Web of Science and SCOPUS, costly databases that sometimes exceed the possibilities of academic institutions in many parts of the world. We show here how the data in one of the bases can be used to infer the main index of the other one. Methods of data analysis used in Machine Learning allow us to select just a few of the hundreds of variables in a database, which later are used in a panel regression, yielding a good approximation to the main index in the other database. Since the information of SCOPUS can be freely scraped from the Web, this approach allows to infer for free the Impact Factor of publications, the main index used in research assessments around the globe.
翻译:对研究人员出版记录进行学术评价,对于确定有才华的晋升和供资候选人具有相关性,其中的一个关键工具是使用科学网站和科学与科学卫星系统提供的指数,这些昂贵的数据库有时超出了世界许多地方学术机构的可能性。我们在这里展示如何利用其中一个数据库中的数据推断另一个数据库的主要指数。机器学习所使用的数据分析方法使我们能够在一个数据库中只选择数以百计的变量中的几个变量,这些变量后来被用于小组的回归,从而与其他数据库中的主要指数形成良好的近似。由于科学与科学卫星系统的信息可以自由地从网络中提取,因此这种方法可以推断出版物的免费影响要素,即全球研究评估中使用的主要指数。