Honorable Mention

InnovationInsights: A Visual Analytics Approach for Understanding the Dual Frontiers between Science and Technology

Yifang Wang, Yifan Qian, Xiaoyu Qi, Nan Cao, Dashun Wang

DOI: 10.1109/TVCG.2023.3327387

Room: 109

2023-10-25T03:00:00ZGMT-0600Change your timezone on the schedule page
2023-10-25T03:00:00Z
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Science is central to improving the human condition. Not only has science long been recognized as the engine for long-run economic growth and prosperity, but also it has been essential to creating critical solutions to confront emergent threats to humanity, from climate change to the COVID-19 pandemic. While scientific research propels both fundamental understanding and practical applications, there has been a lack of visual analytics approaches to explore the complex linkages (i.e., the dual frontiers) between scientific advances and technical inventions. Here we introduce InnovationInsights, which represents an initial step toward filling this crucial gap.
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Keywords

Science of Science, Innovation, Academic Profiles, Patent Data, Publication Data, Visual Analytics

Abstract

Science has long been viewed as a key driver of economic growth and rising standards of living. Knowledge about how scientific advances support marketplace inventions is therefore essential for understanding the role of science in propelling real-world applications and technological progress. The increasing availability of large-scale datasets tracing scientific publications and patented inventions and the complex interactions among them offers us new opportunities to explore the evolving dual frontiers of science and technology at an unprecedented level of scale and detail. However, we lack suitable visual analytics approaches to analyze such complex interactions effectively. Here we introduce InnovationInsights, an interactive visual analysis system for researchers, research institutions, and policymakers to explore the complex linkages between science and technology, and to identify critical innovations, inventors, and potential partners. The system first identifies important associations between scientific papers and patented inventions through a set of statistical measures introduced by our experts from the field of the Science of Science. A series of visualization views are then used to present these associations in the data context. In particular, we introduce the Interplay Graph to visualize patterns and insights derived from the data, helping users effectively navigate citation relationships between papers and patents. This visualization thereby helps them identify the origins of technical inventions and the impact of scientific research. We evaluate the system through two case studies with experts followed by expert interviews. We further engage a premier research institution to test-run the system, helping its institution leaders to extract new insights for innovation. Through both the case studies and the engagement project, we find that our system not only meets our original goals of design, allowing users to better identify the sources of technical inventions and to understand the broad impact of scientific research; it also goes beyond these purposes to enable an array of new applications for researchers and research institutions, ranging from identifying untapped innovation potential within an institution to forging new collaboration opportunities between science and industry.