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

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 usi...

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Bibliographic Details
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.v1
https://tandf.figshare.com/articles/journal_contribution/Machine_learning_and_the_identification_of_Smart_Specialisation_thematic_networks_in_Arctic_Scandinavia/14790380/1
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Summary: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.

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