The transformation of a country into a knowledge-based economy has a considerable impact on the role of a contemporary university. The question is whether the university should assume the role of an entrepreneur itself or whether it can continue to be a traditional university -i.e. focusing on teaching and research -with some additional functions for supporting innovation. The article analyzes innovation support systems at Uppsala University, the University of Tartu and the Tallinn University of Technology and focuses on the measures how universities can enhance academia-industry collaboration and improve innovation. The authors state that the development of collaborative arenas for actors from academia and industry is just as important as the efficient and sustainable management of knowledge transfer. Successful interaction between academy and industry requires the knowledge how to act in a proper manner but also financial resources. This may vary between different universities and different settings.
The strategies of the European Union and its Member States suppose that intellectual property (IP) created as a result of R&D is the engine of economic growth and welfare in society. Studies based on the European Regional Innovation Scoreboard (RIS) have demonstrated that R&D investments, support to the high technology industry and patenting intensity of the public sector differ in high-and low-income European countries. This fact refers to the need for adequate IP strategic indicators' system facilitating innovation in less developed countries. The paper aims to conceptualize and suggest a strategic indicators' system of IP for a small efficiency-driven economy. In contrast to the rather modest level of patenting by industry, universities of the Baltic States file approximately 50 % of PCT patent applications. Therefore, it is crucial to overcome barriers, hindering universities' IP commercialization. Academia-Industry collaboration includes two types of IP strategies: Active non-linear and Passive linear behavioural models of universities and public sector. An essential part of the active approach focuses on the "soft measures" for networking with firms in collaborative platforms such as AIMday® at the Uppsala University in Sweden. The proposed IP strategy system involves qualitative and quantitative indicators at the state as well as university and company level. The comparison of academic publishing and patenting by the staff of Tartu and Uppsala universities testifies to their rather same levels of productivity. Three times wider patent families of the inventions of Uppsala origin characterize actors' market ambition as well as the strength of the University-Industry linkages that are more developed in Uppsala than in Tartu.
The authors explore different models of transfer of industrial property on a comparative basis. The article demonstrates that these models differ on a country level and several models may be in use in one legal system. The authors analyze strengths and weaknesses and legal implications of these models in the three Baltic States both at the regulatory level and at the practical level through case studies. The authors conclude that would be preferable to use the model under which the register is vested with negative publicity and the transfer of ownership of industrial property is not made dependent on its recordation.
The authors address the legal issues relating to the creation and use of language models. The article begins with an explanation of the development of language technologies. The authors analyse the technological process within the framework copyright, related rights and personal data protection law. The authors also cover commercial use of language models. The authors' main argument is that legal restrictions applicable to language data containing copyrighted material and personal data usually do not apply to language models. Language models are generally not considered derivative works. Due to a wide range of language models, this position is not absolute.
The authors address the transformation of research data into open data. The article draws on the experience in four countries: Sweden, Finland, Estonia and Lithuania. The transformation process presents several challenges where legal, organizational and individual aspects influence the process. Research data often contain personal data. Research data could also covered with intellectual property (IP) rights. This means that personal data and IP regulations should be integrated into the dissemination model. While there is a potential conflict between the policies for open data that aim to make data freely available and those of an entrepreneurial university that emphasize commercialization of research results, these policies need to be made compatible. Researchers producing data are vital for reconciling the two, but they currently lack the motivation to contribute towards the implementation of the open data policy due to missing career incentives. Keywords open data research data entrepreneurial university personal data protection intellectual property academic career incentives
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