  The web does not only enable new forms of science, it also creates new
possibilities to study science and new digital scholarship. This paper brings
together multiple perspectives: from individual researchers seeking the best
options to display their activities and market their skills on the academic job
market; to academic institutions, national funding agencies, and countries
needing to monitor the science system and account for public money spending. We
also address the research interests aimed at better understanding the
self-organising and complex nature of the science system through researcher
tracing, the identification of the emergence of new fields, and knowledge
discovery using large-data mining and non-linear dynamics. In particular this
paper draws attention to the need for standardisation and data interoperability
in the area of research information as an indispensable pre-condition for any
science modelling. We discuss which levels of complexity are needed to provide
a globally, interoperable, and expressive data infrastructure for research
information. With possible dynamic science model applications in mind, we
introduce the need for a "middle-range" level of complexity for data
representation and propose a conceptual model for research data based on a core
international ontology with national and local extensions.
