The role of information professionals in interpreting the results of journal league lists for research performance evaluation

This paper discusses some of the key content and software features which must be analyzed by skilled librarians/information professionals to deliver reliable, accurate, and appropriately comprehensive set of bibliographic data and bibliometric indicators about the status of the journals listed in the promotion, tenure and grant applications, as well as in journal desiderata for purposes of collection development in academic and special libraries

Citation Analysis with Microsoft Academic

We explore if and how Microsoft Academic (MA) could be used for bibliometric analyses. First, we examine the Academic Knowledge API (AK API), an interface to access MA data, and compare it to Google Scholar (GS). Second, we perform a comparative citation analysis of researchers by normalizing data from MA and Scopus. We find that MA offers structured and rich metadata, which facilitates data retrieval, handling and processing. In addition, the AK API allows retrieving frequency distributions of citations. We consider these features to be a major advantage of MA over GS. However, we identify four main limitations regarding the available metadata. First, MA does not provide the document type of a publication. Second, the 'fields of study' are dynamic, too specific and field hierarchies are incoherent. Third, some publications are assigned to incorrect years. Fourth, the metadata of some publications did not include all authors. Nevertheless, we show that an average-based indicator (i.e. the journal normalized citation score; JNCS) as well as a distribution-based indicator (i.e. percentile rank classes; PR classes) can be calculated with relative ease using MA. Hence, normalization of citation counts is feasible with MA. The citation analyses in MA and Scopus yield uniform results. The JNCS and the PR classes are similar in both databases, and, as a consequence, the evaluation of the researchers' publication impact is congruent in MA and Scopus. Given the fast development in the last year, we postulate that MA has the potential to be used for full-fledged bibliometric analyses.Comment: preprin

Google Scholar, Web of Science, and Scopus: A systematic comparison of citations in 252 subject categories

[EN] Despite citation counts from Google Scholar (GS), Web of Science (WoS), and Scopus being widely consulted by researchers and sometimes used in research evaluations, there is no recent or systematic evidence about the differences between them. In response, this paper investigates 2,448,055 citations to 2299 English-language highly-cited documents from 252 GS subject categories published in 2006, comparing GS, the WoS Core Collection, and Scopus. GS consistently found the largest percentage of citations across all areas (93%¿96%), far ahead of Scopus (35%¿77%) and WoS (27%¿73%). GS found nearly all the WoS (95%) and Scopus (92%) citations. Most citations found only by GS were from non-journal sources (48%¿65%), including theses, books, conference papers, and unpublished materials. Many were non-English (19%¿38%), and they tended to be much less cited than citing sources that were also in Scopus or WoS. Despite the many unique GS citing sources, Spearman correlations between citation counts in GS and WoS or Scopus are high (0.78-0.99). They are lower in the Humanities, and lower between GS and WoS than between GS and Scopus. The results suggest that in all areas GS citation data is essentially a superset of WoS and Scopus, with substantial extra coverage.Alberto Martin-Martin is funded for a four-year doctoral fellowship (FPU2013/05863) granted by the Ministerio de Educacion, Cultura, y Deportes (Spain). An international mobility grant from Universidad de Granada and CEI BioTic Granadafunded a research stay at the University of Wolverhampton.Martín-Martín, A.; Orduña-Malea, E.; Thelwall, M.; Delgado Lopez-Cozar, E. (2018). Google Scholar, Web of Science, and Scopus: A systematic comparison of citations in 252 subject categories. Journal of Informetrics. 12(4):1160-1177. https://doi.org/10.1016/j.joi.2018.09.0021160117712

Aplicaciones métricas de Google Scholar para la evaluación del impacto científico.

La aparición de Google Scholar en 2004 supuso una revolución tanto en los procesos de búsqueda de información académica como en los de evaluación científica (especialmente en las áreas de las Ciencias sociales y humanas) gracias a su ingente base de datos, basada en la recopilación de cualquier documento académico online, a pesar de los errores e imprecisiones existentes en la correcta vinculación de citas. Este trabajo presenta la elaboración de diferentes productos métricos de información elaborados a partir de Google Scholar (H Index Scholar, Publishers Scholar Metrics, Journal Scholar Metrics, La Biblioteconomía Española según Google Scholar Citations) con el propósito de mostrar la potencialidad y utilidad de esta base de datos a la hora de ser utilizada por los diferentes actores involucrados en la creación, difusión y evaluación de la actividad científica