Quantitative evaluation of large maps of science

Richard Klavans and Kevin W. Boyack
Additional contact information
Richard Klavans: SciTech Strategies, Inc.
Kevin W. Boyack: Sandia National Laboratories

Scientometrics, 2006, vol. 68, issue 3, No 11, 475-499

Abstract: Summary This article describes recent improvements in mapping the world-wide scientific literature. Existing research is extended in three ways. First, a method for generating maps directly from the data on the relationships between hundreds of thousands of documents is presented. Second, quantitative techniques for evaluating these large maps of science are introduced. Third, these techniques are applied to data in order to evaluate eight different maps. The analyses suggest that accuracy can be increased by using a modified cosine measure of relatedness. Disciplinary bias can be significantly reduced and accuracy can be further increased by using much lower threshold levels. In short, much larger samples of papers can and should be used to generate more accurate maps of science.

Date: 2006
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DOI: 10.1007/s11192-006-0125-x

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