  When studying large research corpora, "distant reading" methods are vital to
understand the topics and trends in the corresponding research space. In
particular, given the recognised benefits of multidisciplinary research, it may
be important to map schools or communities of diverse research topics, and to
understand the multidisciplinary role that topics play within and between these
communities. This work proposes Field of Study (FoS) networks as a novel
network representation for use in scientometric analysis. We describe the
formation of FoS networks, which relate research topics according to the
authors who publish in them, from corpora of articles in which fields of study
can be identified. FoS networks are particularly useful for the distant reading
of large datasets of research papers when analysed through the lens of
exploring multidisciplinary science. In an evolving scientific landscape,
modular communities in FoS networks offer an alternative categorisation
strategy for research topics and sub-disciplines, when compared to traditional
prescribed discipline classification schemes. Furthermore, structural role
analysis of FoS networks can highlight important characteristics of topics in
such communities. To support this, we present two case studies which explore
multidisciplinary research in corpora of varying size and scope; namely, 6,323
articles relating to network science research and 4,184,011 articles relating
to research on the COVID-19-pandemic.
