Machine learning and the identification of Smart Specialisation thematic networks in Arctic Scandinavia

• Main Authors: Mikko, Moilanen, Stein, Østbye, Jaakko, Simonen
• Format: Text
• Language: unknown
• Published: Taylor & Francis 2021
• Subjects: Biochemistry; Cell Biology; Evolutionary Biology; FOS Biological sciences; Sociology; FOS Sociology; Immunology; FOS Clinical medicine; 80699 Information Systems not elsewhere classified; FOS Computer and information sciences; 19999 Mathematical Sciences not elsewhere classified; FOS Mathematics; Cancer; Science Policy; 110309 Infectious Diseases; FOS Health sciences; Arctic
• Online Access: https://dx.doi.org/10.6084/m9.figshare.14790380; https://tandf.figshare.com/articles/journal_contribution/Machine_learning_and_the_identification_of_Smart_Specialisation_thematic_networks_in_Arctic_Scandinavia/14790380

The European Union (EU) has recognized that universities and research institutes play a critical role in regional Smart Specialisation processes. Our research aims to identify thematic cross-border research domains across space and disciplines in Arctic Scandinavia. We identify potential domains using an unsupervised machine-learning technique (topic modelling). We uncover latent topics based on similarities in the vocabulary of research papers. The proposed methodology can be utilized to identify common research domains across regions and disciplines in almost real time, thereby acting as a decision support system to facilitate cooperation among knowledge producers.