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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2021
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ftdatacite:10.6084/m9.figshare.14790380 2023-05-15T14:53:25+02:00 Machine learning and the identification of Smart Specialisation thematic networks in Arctic Scandinavia Mikko, Moilanen Stein, Østbye Jaakko, Simonen 2021 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 unknown Taylor & Francis https://dx.doi.org/10.1080/00343404.2021.1925237 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY 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 Text article-journal Journal contribution ScholarlyArticle 2021 ftdatacite https://doi.org/10.6084/m9.figshare.14790380 https://doi.org/10.1080/00343404.2021.1925237 2021-11-05T12:55:41Z 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. Text Arctic DataCite Metadata Store (German National Library of Science and Technology) Arctic |
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DataCite Metadata Store (German National Library of Science and Technology) |
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topic |
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 |
spellingShingle |
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 Mikko, Moilanen Stein, Østbye Jaakko, Simonen Machine learning and the identification of Smart Specialisation thematic networks in Arctic Scandinavia |
topic_facet |
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 |
description |
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. |
format |
Text |
author |
Mikko, Moilanen Stein, Østbye Jaakko, Simonen |
author_facet |
Mikko, Moilanen Stein, Østbye Jaakko, Simonen |
author_sort |
Mikko, Moilanen |
title |
Machine learning and the identification of Smart Specialisation thematic networks in Arctic Scandinavia |
title_short |
Machine learning and the identification of Smart Specialisation thematic networks in Arctic Scandinavia |
title_full |
Machine learning and the identification of Smart Specialisation thematic networks in Arctic Scandinavia |
title_fullStr |
Machine learning and the identification of Smart Specialisation thematic networks in Arctic Scandinavia |
title_full_unstemmed |
Machine learning and the identification of Smart Specialisation thematic networks in Arctic Scandinavia |
title_sort |
machine learning and the identification of smart specialisation thematic networks in arctic scandinavia |
publisher |
Taylor & Francis |
publishDate |
2021 |
url |
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 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
https://dx.doi.org/10.1080/00343404.2021.1925237 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.14790380 https://doi.org/10.1080/00343404.2021.1925237 |
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1766324955326185472 |