Visualization of Disciplinary Profiles: Enhanced Science Overlay Maps

Purpose
The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and providing more accessible means for analysts to generate their own maps.

Design/methodology/approach
We use the combined set of 2015 Journal Citation Reports for the Science Citation Index (n of journals = 8,778) and the Social Sciences Citation Index (n = 3,212) for a total of 11,365 journals. The set of Web of Science Categories in the Science Citation Index and the Social Sciences Citation Index increased from 224 in 2010 to 227 in 2015. Using dedicated software, a matrix of 227 × 227 cells is generated on the basis of whole-number citation counting. We normalize this matrix using the cosine function. We first develop the citing-side, cosine-normalized map using 2015 data and VOSviewer visualization with default parameter values. A routine for making overlays on the basis of the map (“wc15.exe”) is available at http://www.leydesdorff.net/wc15/index.htm.

Findings
Findings appear in the form of visuals throughout the manuscript. In Figures 1–9 we provide basemaps of science and science overlay maps for a number of companies, universities, and technologies.

Research limitations
As Web of Science Categories change and/or are updated so is the need to update the routine we provide. Also, to apply the routine we provide users need access to the Web of Science.

Practical implications
Visualization of science overlay maps is now more accurate and true to the 2015 Journal Citation Reports than was the case with the previous version of the routine advanced in our paper.

Originality/value
The routine we advance allows users to visualize science overlay maps in VOSviewer using data from more recent Journal Citation Reports.

https://www.degruyter.com/view/j/jdis.2017.2.issue-3/jdis-2017-0015/jdis-2017-0015.xml

Author(s): Stephen Carley, Alan L. Porter, Ismael Rafols, Loet Leydesdorff
Organization(s): Georgia Institute of Technology , Universitat Politècnica de València, University of Amsterdam
Source: Journal of Data and Information Science
Year: 2017

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