Supported Projects

A Network Science Approach to Conflicts of Interest: Metrics, Policies, and Communication Design

Abstract: The primary purpose of this project is to develop new metrics and mechanisms for the evaluation of conflicts of interest (COI) risks in the biomedical research enterprise. The innovation in COI risk evaluation and communication will draw on a shift away from the conceptualization of COI as a problem of individual researchers toward an understanding of COI as network phenomena. The pressing problems of COI and the bias it inculcates stem not from individuals but from the aggregation of COI across networks of researchers and funders. The research team will leverage machine-learning and high performance computing to (1) track the circulation of COI within biomedical decision networks, and (2) evaluate the extent to which certain conflict network profiles predict increased risks of patient harm. The team will test candidate COI metrics with U.S. Food and Drug Administration's Adverse Events Reporting System drug safety data. The results of this research will underwrite novel evidence-based recommendations for COI policies in biomedical research as well as recommendations for more effective disclosure practices. To achieve these aims, this project will leverage machine-learning to identify systemic COI networks within the biomedical research enterprises for specific drug products. The research team will then evaluate to what extent network metrics predict relative increases in adverse event rates and severity for identified products.Support Provided by:

Transparency to Visibility (T2V): Network Visualization in Humanities Research

Abstract: Humanities researchers have long studied how power and influence circulate through cultural systems. Advances in network visualization tools support this work, allowing scholars to create graphical representations of complex systems. However, extracting, preparing, and visualizing relational data using the textual artifacts commonly studied by humanists can present significant technological challenges. This project will develop and test an innovative approach for efficiently curating and visualizing relationships in ways that align with humanities research. Using sample datasets from medical research, a team of scholars in digital and medical humanities will develop, test, and enhance a new toolkit for automatically extracting and visualizing relationships in large textual corpora. The project team will create both a graphical user interface for the new toolkit and an open-source code repository to support use by humanities scholars with a broad range of technical capabilities. Support Provided by: