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Abstract

We introduce a novel methodology for mapping academic institutions based on their journal publication profiles. We believe that journals in which researchers from academic institutions publish their works can be considered as useful identifiers for representing the relationships between these institutions and establishing comparisons. However, when academic journals are used for research output representation, distinctions must be introduced between them, based on their value as institution descriptors. This leads us to the use of journal weights attached to the institution identifiers. Since a journal in which researchers from a large proportion of institutions published their papers may be a bad indicator of similarity between two academic institutions, it seems reasonable to weight it in accordance with how frequently researchers from different institutions published their papers in this journal. Cluster analysis can then be applied to group the academic institutions, and dendrograms can be provided to illustrate groups of institutions following agglomerative hierarchical clustering. In order to test this methodology, we use a sample of Spanish universities as a case study. We first map the study sample according to an institution's overall research output, then we use it for two scientific fields (Information and Communication Technologies, as well as Medicine and Pharmacology) as a means to demonstrate how our methodology can be applied, not only for analyzing institutions as a whole, but also in different disciplinary contexts.

Type

a

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Abstract

Bibliometric mapping and visualization techniques represent one of the main pillars in the field of scientometrics. Traditionally, the main methodologies employed for representing data are multidimensional scaling, principal component analysis, or correspondence analysis. In this paper we aim to present a visualization methodology known as biplot analysis for representing bibliometric and science and technology indicators. A biplot is a graphical representation of multivariate data, where the elements of a data matrix are represented according to dots and vectors associated with the rows and columns of the matrix. In this paper, we explore the possibilities of applying biplot analysis in the research policy area. More specifically, we first describe and introduce the reader to this methodology and secondly, we analyze its strengths and weaknesses through 3 different case studies: countries, universities, and scientific fields. For this, we use a biplot analysis known as JK-biplot. Finally, we compare the biplot representation with other multivariate analysis techniques. We conclude that biplot analysis could be a useful technique in scientometrics when studying multivariate data, as well as an easy-to-read tool for research decision makers.

Type

a

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Abstract

We present an analysis of data citation practices based on the Data Citation Index (DCI) (Thomson Reuters). This database launched in 2012 links data sets and data studies with citations received from the other citation indexes. The DCI harvests citations to research data from papers indexed in the Web of Science. It relies on the information provided by the data repository. The findings of this study show that data citation practices are far from common in most research fields. Some differences have been reported on the way researchers cite data: Although in the areas of science and engineering & technology data sets were the most cited, in the social sciences and arts & humanities data studies play a greater role. A total of 88.1% of the records have received no citation, but some repositories show very low uncitedness rates. Although data citation practices are rare in most fields, they have expanded in disciplines such as crystallography and genomics. We conclude by emphasizing the role that the DCI could play in encouraging the consistent, standardized citation of research data-a role that would enhance their value as a means of following the research process from data collection to publication.

Type

a

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Type

a