| Abstract |
Up to now, STI (Science, Technology, Innovation) studies are either rich but small scale (qualitative case studies) or large scale and under-complex – because they generally use only a single dataset like Patstat, Scopus, WoS (Web of Science), OECD STI indicators, etc., and therefore deploying only a few variables – determined by the data available. However, progress in the STI research field depends in our view on the ability to do large-scale studies with often many variables specified by relevant theories: There is a need for studies which are at the same time big and rich. To enable that, combining and integration of STI data and beyond is needed – in order to exploit the huge amount of data that are ‘out there’ in an innovative and meaningful way.
The Semantically Mapping Science (SMS) platform available at http://sms.risis.eu as the technical core within the RISIS EU project is an attempt to produce richer data to be used in social research – through the integration of heterogeneous datasets, ranging from tabular statistical data to unstructured data found on the Web. |