Open Science for Innovation: A Knowledge and Policy Perspective

The shift of interest in research policy towards innovation has reinforced efforts to accelerate research and innovation processes. This trend is often supported with information and communication technologies and open approaches that facilitate access to knowledge including for broad audiences. In addition to digitally enabled access, new IT also support every step in the scientific process from data collection to scientific discourse. Automated data analysis is no exception and new statistical learning systems are capable of automatically creating models of scientific data. Systems based on statistical learning have been argued to replace those built from analytic science and there are examples in human language translation and many other areas. These new methods of creating models in science have the power to durably change innovation processes. The synergies between science and IT are deeply rooted in characteristics of information and formal knowledge, and new technologies now expand these synergies to tacit knowledge as well. We are facing a new generation of researchers used to building systems from statistical models and potentially developing new attitudes to theoretical explanation. Beyond this, the new tools for science and innovation may deeply change our understanding of fundamental concepts such as knowledge and explanation as well as policy models and approaches to fostering innovation from science.