Interactive Overlays of Journals and the Measurement of Interdisciplinarity on the basis of Aggregated Journal-Journal Citations

TL;DR

This paper presents a method for creating interactive journal overlays on global science maps to measure interdisciplinarity using citation data and community detection algorithms, enabling detailed analysis of scientific diversity.

Contribution

It introduces a novel approach combining journal overlays, Rao-Stirling's interdisciplinarity measure, and community detection to analyze scientific diversity and relationships across disciplines.

Findings

Citing citation matrices offer a more comprehensive interdisciplinarity measure.

Overlay maps can be exported with various visualization options.

Local and global maps serve different analytical purposes.

Abstract

Using "Analyze Results" at the Web of Science, one can directly generate overlays onto global journal maps of science. The maps are based on the 10,000+ journals contained in the Journal Citation Reports (JCR) of the Science and Social Science Citation Indices (2011). The disciplinary diversity of the retrieval is measured in terms of Rao-Stirling's "quadratic entropy." Since this indicator of interdisciplinarity is normalized between zero and one, the interdisciplinarity can be compared among document sets and across years, cited or citing. The colors used for the overlays are based on Blondel et al.'s (2008) community-finding algorithms operating on the relations journals included in JCRs. The results can be exported from VOSViewer with different options such as proportional labels, heat maps, or cluster density maps. The maps can also be web-started and/or animated (e.g., using PowerPoint). The "citing" dimension of the aggregated journal-journal citation matrix was found to provide a more comprehensive description than the matrix based on the cited archive. The relations between local and global maps and their different functions in studying the sciences in terms of journal litteratures are further discussed: local and global maps are based on different assumptions and can be expected to serve different purposes for the explanation.