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are shared by chemistry and geology, how federal funding is distributed across the
landscape of disciplines. Drawing a boundary line for a disciplinary is challenging;
drawing a boundary line for a constantly evolving disciplinary is even more so. We
will highlight some recent examples of how researchers deal with such challenges.
8.4.1
Mapping Scientific Disciplines
Derek de Solla Price is probably the first person to anticipate that the Science
Citation Index (SCI) may contain the information for revealing the structure of
science. Price suggested that the appropriate units of analysis would be journals and
aggregations of journals by journal-journal citations would reveal the disciplinary
structure of science. An estimation mentioned in (Leydesdorff and Rafols
2009
)
sheds light on the density of a science map at the journal level. Among the 6,164
unique journals in the 2006 SCI, there were only 1,201,562 pairs of journal citation
relations out of the possible 37,994,896 connections. In other words, the density
of the global science structure is 3.16 %.
4
How stable is such a structure at the
level of journal? How volatile is the structure of science at the document level or a
topic level? Where are the activities concentrated or distributed with reference to a
discipline, an institution, or an individual?
A widely seen global map of science is the USCD map, depicting 554 clusters
of journals and how they are interconnected as sub-disciplines of science (See
Fig.
8.17
). The history of the UCSD map is described in (Borner et al.
2012
).
The map was first created by Richard Klavans and Kevin Boyack in 2007 for the
University of California San Diego (UCSD). The source data for the map was
a combination of Thomson Reuters Web of Science (2001-2004) and Elsevier's
Scopus (2001-2005). Similarities between journals were computed in 18 different
ways to form matrices of journal-journal connections. These matrices were then
combined to form a single network of 554 sub-disciplines in terms of clusters of
journals. The layout of the map was generated using the 3D Fruchterman-Reingold
layout function in Pajek. The spherical map was then unfolded to a 2D map on a
flat surface with a Mercator projection. Each cluster was manually labeled based on
journal titles in the cluster. The 2D version of the map was further simplified to a
1D circular map - the circle map. The 13 labeled regions were ordered using factor
analysis. The circle map is used in Elsevier's SciVal Spotlight.
The goal of the UCSD map was to provide a base map for research evaluation.
With 554 clusters, it provides more categories than the subject categories of the Web
of Science. While the original goal was for research evaluation, the map is being
used as a base map to superimpose overlays of additional information in systems
such as Sci2 and VIVO.
5
Soon after the creation of the UCSD map, Richard Klavans
4
Assume this is a directed graph of 6,146 journals.
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