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Central administration at Rutgers University requested help from the Office for Research to develop a proof-of-concept tool to count collaborations between faculty, departments, schools and chancellor units across the university. This presentation will discuss how the Data, Analytics and Business Intelligence team focused on research administration and research output data to define a “collaboration,” structure available data, create interactive visualizations and allow end users to customize the level of detail displayed. This presentation will begin by discussing the importance of generating reliable collaboration data in a university setting including use cases for the data. It will then describe the dataset that was used for the proof-of-concept project and discuss why these sources were important. Finally, the presentation will discuss future development goals and collaborators for the collaboration project.
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Bringing Artificial Intelligence to Research Analytics Research Highlighter-MatchMaker at SUNY UAlbany
Research analytics has become an indispensable part in today’s higher education institutions’ organizational structure. It not only provides a mechanism or platform to present research awards and expenditures data to internal and external data users, but also serves a superior tool that enables data-driven strategic decision makings with more reliable, valid, and in-depth analysis. The development of Artificial Intelligence techniques could push the boundary of Research Analytics to an even higher level by incorporating the abundance of data scattered across the university. We propose to present one attempt adopted by University at Albany, State University of New York (UAlbany) to incorporate AI techniques to Research Analytics: a project called Research Highlighter-MatchMaker. The Research Highlighter-MatchMaker project aims to identify research clusters at UAlbany, recommend funding opportunities for faculty/researcher, and suggest potential collaborations. Based on AI and Big Language Models, the project will identify core research clusters by UAlbany faculty and researchers and suggest potential collaborations for multidisciplinary projects across the campus. Moreover, the project aims to recommend more suitable and relevant funding opportunities to different faculty and researchers based on their research track records including their grant proposal abstracts and publication abstracts, with a potential to incorporate more research data associated with researchers such as technology transfer agreements and compliance documents in the future. One major strength of this self-established project is that it takes advantage of AI techniques to provide more personalized and tailored matches for faculty and researchers by considering their research track records on file, instead of providing limited recommendations based on only keyword match that is adopted by many current products on the market. Our purpose is to offer personalized suggestions to faculty and researchers with well-matched funding opportunities, save their time and effort on searching and screening a long list of funding opportunities from databases on their own, and avoid the occurrence of missing good funding opportunities that they could have successfully obtained.
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- Award ID(s):
- 2324388
- PAR ID:
- 10566919
- Publisher / Repository:
- University of Kentucky Libraries
- Date Published:
- Subject(s) / Keyword(s):
- FOS: Computer and information sciences
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Proceeding:
- https://doi.org/10.13023/SS21-PE15
