Among the classification or profiling of universities, some institutions may claim to be “comprehensive”, namely to be transversal by nature in terms of education and research, and to rely on this pluridisciplinarity as an asset.
Such a profile may have its justifications, for instance in terms of social and territorial missions; yet, in order to be really competitive on the H&R market, universities strongly benefit from a strengths and weaknesses analysis regarding their specialization:
- Which are the fields in which the researchers publish? Which are the gaps? How does this compare with national and international averages?
- Who are the main collaborators of the institution in its specialization fields? How does this organic network match the formal partnerships and agreements of the university?
- How does this match the research identity of the institutions as it is perceived internally and externally?
- How is the university positioned with respect to its competitor in its specialization fields?
- Who are the institutions that publish in similar fields, and which type of cooperation opportunities should be considered?
Through UNiCS, it is possible to proceed to fine-tuned specialization analyses for universities and H&R institutions.
Our approach goes beyond the fixed categories and nomenclatures used by databases: we have developed a text-mining method based on an exploration of keywords used by researchers in their publications, that allows us to identify the institutions publishing exactly on the same topics, and to use these data for a personalized specialization analysis.
In this purpose, we start from the “visible” production of the institutions as it appears in bibliometric databases . We retrieve all the journals in which the institution publishes and collect the keywords associated with each publication. We enrich these keywords by data collected in complementary sources. On this basis, we search who else has published in the same journals and tagged publications with the same keywords, which gives us an analytic screenshot of scientifically similar institutions and of their respective positioning in terms of volume and impact.
This type of analysis is a powerful decision-making tool for institutions, both in terms of self-diagnosis and regarding the targets to be privileged for their future cooperations.
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