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.
|Publication status||Published - 21 Apr 2013|