Abstract. Download indicators are of major potential interest because the great quantity of readily available download data means that any statistical inferences drawn from them will be of robust significance. We study the relationship between citation and downloads at the journal and paper levels, and the influence of language on that relationship. The data used were taken from the Scopus (citations) and ScienceDirect (downloads) databases.

Abstract. Subject classification arises as an important topic for bibliometrics and scientometrics, searching to develop reliable and consistent tools and outputs. Such objectives also call for a well delimited underlying subject classification scheme that adequately reflects scientific fields. Within the broad ensemble of classification techniques, clustering analysis is one of the most successful.

Abstract

Background
Models for the production of knowledge and systems of innovation and science are key elements for characterizing a country in view of its scientific thematic profile. With regard to scientific output and publication in journals of international visibility, the countries of the world may be classified into three main groups according to their thematic bias.

Abstract. In a reply to Jerome K. Vanclay’s manuscript “Impact Factor: outdated artefact or stepping-stone to journal certification?” we discuss the value of journal metrics for the assessment of scientific-scholarly journals from a general bibliometric perspective, and from the point of view of creators of new journal metrics, journal editors and publishers. We conclude that citation-based indicators of journal performance are appropriate tools in journal assessment provided that they are accurate, and used with care and competence.

Abstract. An overview is given of research in the major countries of Europe in the area of renewable energies. The analysis used the Scopus (Elsevier) database of scientific literature, calculating bibliometric indices (primary production, average citations per document, percentage variation, SJR, etc.) for the geographical domain of Europe during the period 2002–2007.

Abstract. A study is described of the rank/JIF (Journal Impact Factor) distributions in the high-coverage Scopus database, using recent data and a three-year citation window. It includes a comparison with an older study of the Journal Citation Report categories and indicators, and a determination of the factors most influencing the distributions. While all the specific subject areas fit a negative logarithmic law fairly well, those with a greater External JIF have distributions with a more sharply defined peak and a longer tail—something like an iceberg.

Abstract.In recent years a number of studies have focused on Argentina’s 2001 economic crisis and its political, social, and institutional repercussions. To date, however, no studies have analyzed its effects upon the country’s scientific system from a scientometric perspective, in terms of resources dedicated to scientific activity and the final output and impact.

Abstract. A size-independent indicator of journals’ scientific prestige, the SCImago Journal Rank (SJR) indicator, is proposed that ranks scholarly journals based on citation weighting schemes and eigenvector centrality. It is designed for use with complex and heterogeneous citation networks such as Scopus. Its computation method is described, and the results of its implementation on the Scopus 2007 dataset is compared with those of an ad hoc Journal Impact Factor, JIF(3y), both generally and within specific scientific areas.

Abstract. The limitations of citation-based indicators include a lack of coverage, no normalization with respect to the length of reference lists (with a potential bias in favour of reviews), and different citation habits. As a consequence, the distributions of the indicators are not comparable across different disciplines.

Abstract. The present paper proposes a method for detecting, identifying and visualizing research groups. The data used refer to nine Carlos III University of Madrid departments, while the findings for the Communication Technologies Department illustrate the method. Structural analysis was used to generate co-authorship networks. Research groups were identified on the basis of factorial analysis of the raw data matrix and similarities in the choice of co-authors.