Presented by Amrina Ferdous – Data Science Emphasis
Hybrid presentation – Zoom link
225 Biomedical Research Center, Math Building, Boise State University
A social network analysis (SNA) is a graph-based method for visualizing social networks. In our study, we have worked with the SNA based on the co-authorship patterns within the National Institutes of Health (NIH) Center of Biomedical Research Excellence (COBRE) in the Matrix Biology network based on cited journal publications. Note that “COBRE in the Matrix Biology” is the name of the grants that we are interested in. The data set under this grant includes 123 articles published by 474 individual authors from 2014 to February 2020. The purpose of the study is to investigate the growth pattern and success of the Biomedical research program’s efforts utilizing these bibliometric data during 2014 to February 2020. These analyses allow us to better understand factors that determine the success of new programs. We are interested in their relationship strength and predicting their future behaviors using Pearsons’ correlation coefficients and machine learning models respectively. Pearson’s correlation indicates that co-authorship network visualization and analysis is a useful tool to understand the relationship between a center-based thematic research focus with access to shared core facilities and research productivity of young investigators[a]. The predictive models help us to identify which variables are good predictors to predict the future behaviors of Hub. Note that junior investigators, senior researchers and research scientists within a shared core facility act as a central Hub. We are also interested to see the future research growth over time with the funding.