Abstract
From applying for competitive grants to showcasing institutional collaboration and research trends, the need for research institutions to demonstrate and increase visibility of research impact is growing. The authors discuss core competencies needed to support bibliometric research and present active and completed impact measurement and visualization (IMV) projects, providing examples from health sciences and academic collaborations. For those considering development of a similar area of expertise within their library, an overview of necessary skillsets, tools, and recommendations for team building and scalability are described. IMV has the potential to be developed in libraries and integrated across research domains. As library roles continue to shift to be more data-centric, it is ever more important for libraries to identify ways to expand information professionals' data skills so that they can be seen as indispensable partners in the data ecosystem.
TopBackground
In recent years at UNC-CH, there has been an increase in requests, particularly from biomedical and health sciences disciplines, for bibliometric analytics and visualization services. These disciplines have received significant grant funding from federal government or industry sectors and have pressing needs to report their research output and demonstrate research impact. To address this burgeoning need, an Impact Measurement and Visualization (IMV) team was established at the UNC Health Sciences Library (HSL) in 2017. The formulation of the IMV team was envisioned to provide support for (1) curriculum revision for a professional school (Dodd, Hayes, Yu, & Mani, 2018); (2) collaborative grant application proposal support by disclosing research collaboration patterns; (3) systematic investigation on the existing research evidence, trends, and gaps associated with research topics (Liu, Yu, & Song, 2020; Xing, et al., 2020; Yu, 2019; Yu, Manish, & Mostafa, 2019); and (4) research outcome assessment for institutional programs (Yu & Hayes, 2018; Haddock et al., 2018; Yu et al., 2020). Additionally, to meet the needs of graduate students in the UNC-CH Library and Information Science Program, a professional course (1.5 credit) covering how to systematically search and analyze scholarly publications and visualize data was developed by HSL Librarians.
Key Terms in this Chapter
Scientometrics: The application of quantitative methods such as bibliometrics and citation analysis to the analysis of scientific research.
Citation Analysis: A major method of bibliometrics that examines the quantitative data derived from the use of citations to reference and connect documents. Citation metrics are used to assess the scholarly influence or impact of publications and researchers.
Artificial Intelligence (AI): Using computer systems to perform tasks typically done by humans. AI can also refer to computers that learn from training data or experience (e.g., from new inputs).
Machine Learning: A type of artificial intelligence (AI) that uses algorithms to predict relevance of items in an unlabeled / unclassified corpus. Unsupervised machine learning (e.g., clustering) does not use training data to make predictions. Supervised machine learning uses a training dataset to make predictions of unlabeled data.
Responsible Metrics: The use of quantitative, citation-based measures or indicators (e.g., citation counts, H-Index, Journal Impact Factor) in the evaluation of education and research in ways that are accurate, appropriate, transparent, and ethical.
Data Visualization: The discovery of data insights and communication of findings through techniques that employ the innate ability to distinguish visual patterns.
Data Management: The practice of describing and organizing research data to increase understanding and enable further analysis of the data. Well-managed data enables data to be discovered, accessed, and reused to replicate results or repurpose for new research.