Publication Date
12-2023
Date of Final Oral Examination (Defense)
9-12-2023
Type of Culminating Activity
Dissertation
Degree Title
Doctor of Philosophy in Computing: Data Science
Department Filter
Computer Science
Department
Computer Science
Supervisory Committee Chair
Julia Oxford, Ph.D.
Supervisory Committee Member
Ken Cornell, Ph.D.
Supervisory Committee Member
Eric Hayden, Ph.D.
Abstract
A social network analysis (SNA) is a graph-based method for visualizing social networks. Our study uses the SNA to analyze co-authorship patterns in National Institutes of Health (NIH) - Centers of Biomedical Research Excellence (COBRE), Idaho Institutional Development Award (IDeA), Network of Biomedical Research Excellence (INBRE), and Biomedical Research Infrastructure Network (BRIN) grants between 2001 and 2022. It is of interest to us to analyze the growth and expansion of research over time to look at the social networks of research Hubs, the social networks of potential research Hubs in the near future, and the social networks of potential leaders of research Hubs. Junior investigators, senior researchers, and research scientists within a shared core facility act as a central Hub. Based on cited journal publications, we have analyzed the “COBRE” grant-related network in the Matrix Biology network. Note that the grant is entitled, “COBRE in the Matrix Biology”. The study aims to investigate the growth pattern and success of the biomedical research program’s efforts utilizing these bibliometric data. Network analysis allows us to understand better factors that determine the success of new and existing programs. We are also interested in their relationship strength and predicting their future behaviors using Pearson’s correlation coefficients and machine learning models, respectively. Pearson’s correlation indicates that co-authorship network visualization and analysis is a valuable tool to understand the relationship between a center-based thematic research focus with access to shared core facilities and the research productivity of young investigators. The predictive models help us identify which variables are good predictors to predict the future behaviors of Hub. This bibliographic data analysis helps us understand the research growth and future research drivers with their social network patterns for the Idaho State researchers and the Boise State University researchers.
DOI
https://doi.org/10.18122/td.2163.boisestate
Recommended Citation
Ferdous, Amrina, "Social Network Analysis of a Biomedical Research Co-Authorship Network" (2023). Boise State University Theses and Dissertations. 2163.
https://doi.org/10.18122/td.2163.boisestate
