The Future of Research Strategy: Integrating Data Analytics for Enhanced University Outcomes

Telling a Story with Research Networks

Introduction

In this section, we explore Social Network Analytics (SNA) and its application to sponsored research data. This data reveals key insights about faculty interactions within research teams and collaborations across different teams. Central to these networks are two pivotal elements: the source and the target, usually representing the Principal Investigator (PI) and the Co-Investigator (CO-I) in a research context. These networks enable us to visually articulate vast datasets, uncover latent insights, and translate complex information into comprehensible network diagrams.

What is a Research Network?

To comprehend a research network, envision a hypothetical scenario where a year's worth of sponsored research data is organized in a spreadsheet. Imagine one tab listing all research faculty, and another detailing awards, including PI and COI names. Now, picture affixing post-it notes, each with a faculty member's name, on a wall. With 600 researchers, there would be 600 notes. For each award, locate the PI and COI, bring them closer on your wall, and connect them with a line. This process creates clusters of notes, symbolizing your research community. The density of lines between individuals indicates the closeness of their collaboration.

The Role of Sponsored Research Data

Sponsored research data is one of the most dynamic databases in a university, continually updated with new awards and proposals. It's particularly suited for tracking changes and recording PI-COI interactions, making it a rich source for networking data in an academic setting.

The dynamic nature of research networks, continually fed by the ERA system, makes them ideal for analyzing the impact of various events and initiatives in an academic institution.

A diagram of a research network, where each node is a researcher, each color is a research cluster, and each line is a collaboration. This method shows large datasets in one image, where the data points (nodes) are always less than or equal to the number of researchers.

Strategic Initiatives and Research Networks

The effects of strategic initiatives on research networks offer critical insights into the evolving landscape of collaborative research. By analyzing the narratives around these initiatives, we aim to present a detailed view of their influence on the research community.

Example: Impact of Cluster Hirings

Two years after several strategic faculty cluster hirings, we wish to evaluate their impact on our research networks. This analysis involves:

• Story Start: Examining the initial state of the research networks before cluster hirings, setting the context for this strategy.

• Story Middle: Investigating the changes in collaborative efforts and interdisciplinary connections in the two-year post-recruitment phase.

• Story End: Assessing the long-term effects of these hirings on research collaborations.

More Than Just Visuals

Tools like NodeXL, R, Python, Vos Viewer, and Gephi facilitate network creation. Additional metrics, such as Graph density, Degree, and modularity, are used to define connections. Modularity, for instance, determines the research group each individual belongs to, based on their connections and relative node positioning.

Conclusion

The analysis of strategic initiatives' impact on research networks offers a comprehensive understanding of collaborative research dynamics in academic institutions. By dissecting the start, middle, and end of each initiative, we gain insights into their influence on research networks. This informs decision-making and promotes continuous improvement towards academic excellence. Network analysis, applied to various contexts like diversity initiatives, start-up Funding for new faculty, and new faculty hire mentoring programs, further aids in understanding research dynamics.

You already have the data, put it to use!