Visualizing Networks of Innovation - Cathy N. Davidson, Duke University
Participants: EVL faculty, staff, and students
October 24, 2007
Electronic Visualization Laboratory
842 W. Taylor Street
Investigators: Tim Lenoir (PI), John Madden, Cathy Davidson
Senior Personnel: Rachael Brady
Knowledge of how to support the growth and diffusion of scientific knowledge and technological innovation is critical for creating effective environments for education, research, and economic development. The ecology of social, knowledge, and technological networks significantly affects who has access to them, both topically and temporally.
A key problem of interest to federal policy makers, university research administrators, and regional planners is the role and impact of federal funding of scientific research in the stimulation of economic growth.
Aggregate input-output scenarios combined with anecdotal evidence are most frequently used in such discussions.
However, we live in an era in which rapid formation of flexible transdisciplinary collaborations involving close interaction among numerous scientific and engineering areas of research in close working relationships with industry have become critical to both scientific and industrial development.
Hence, it becomes imperative to understand the local conditions and optimal configurations favoring successful interdisciplinary work in academic research settings and the transfer of knowledge to industry.
Knowledge and technology diffusion in a dynamically evolving ecology of networks (e.g., social, co-author, paper-citation, patent, and funding networks) are complex, and do not yield their secrets to a single methodology.
The proposed project will address this issue by linking sophisticated analytical tools for identifying and clustering closely related documents with visualization techniques that enable users to understand complex diffusion processes.
The visualizations, called “Knowledge Domain Visualizations (KDVs)” aim to communicate the results of the data analyses and to support the interpretation, discovery, understanding, and management of complex data sets.
Knowledge domain visualizations are a special kind of Information Visualization that exploit powerful human vision and spatial cognition to help humans mentally organize and electronically access and manage large, complex information spaces.
Unlike scientific visualizations, KDVs are created from data that have no spatial reference, such as papers, patents, and grants stored in digital libraries.
KDVs use sophisticated data analysis and visualization techniques to objectively identify major research areas, experts, institutions, grants, papers, journals, etc., in a domain of interest.
They can be used to gain an overview of a knowledge domain; to study its homogeneity, import-export factors, and relative speed; to track the emergence and evolution of topics; or to help identify the most productive as well as new research areas.
Date: October 24, 2007
Document: Visualizing Networks of Innovation Proposal