Abstract

Journals are ranked in the subject categories of the Journal Citation Reports (JCR) according to their impact factors (JIF). The calculation of this indicator includes both citations received from other journals and self-citations from the journal itself. In this paper the current model of the JCR is compared with an alternate model using the JIF without self-citations as a variable. In order to do this, simulations were carried out using JIF without self-citations as an alternative variable. We studied the relationship between the two variables (JIF with self-citations and JIF without self-citations), the gain obtained by the journals with the usual JIF with self-citations, the changes in the ranking in the whole JCR, and changes in the internal ranking in the JCR’s subject categories. A small negative correlation was found between the gain that the journals obtained and their JIF. The results obtained suggest that if JIF without self-citations were used as the variable, the ranking of journals in the JCR and within the subject categories would be, in general, very different from the current one. The quantification of these differences is one of the more interesting results of this work.

Abstract

Bibliometrics is facing an intricate universe of data that is constantly changing and expanding, with a wide ecosystem of data sources in which it is easy to get lost. In this complex setting, APIs are positioned as the perfect solution to deal with this heterogeneity and to obtain adapted data. Although APIs have not come as a surprise, it is now time to highlight their value and promote their use, which will make them easier to utilize. In this paper we therefore take a practical approach to APIs in the bibliometrics field. To this end, we provide an introduction to APIs and the new work processes that their use entails. We also offer a corpus in which we compile 49 APIs of interest to bibliometricians, organized by type and access, as well as programming packages in R and Python that facilitate their use.