Quantifying Innovation Ecosystems through Multidimensional Scientometrics: A Network Science Approach for Innovation Policy
Martin Ho
(University of Cambridge)
Henry Price
(Imperial College London)
Eoin O'Sullivan
(University of Cambridge)
Tim Evans
(Imperial College London)
Paper Short Abstract
We apply network science and scientometrics to innovation policy. Multilayer-directed acyclic graphs and granular relational data are used to dynamically quantify knowledge flows, feedback loops, and bottlenecks, offering targets for policymakers and R&D managers’ intervention and evaluations.
Paper Abstract
Historically, analytical tools for understanding innovation systems have relied on abstractions, as the complexity and dynamics of R&D could never be fully modelled. With newly available tools, we combine large-scale relational scientometric datasets from Dimensions with network science to inform critical innovation policy decisions.
Building upon recent work, such as Ho et al.'s analysis of mRNA vaccine development published in Nature Biotechnology, we demonstrate how techniques like multilayer-directed acyclic graphs can attribute impact, identify rate-limiting steps, and reveal hidden dynamics in innovation networks. These methods are applied to real-world case studies, highlighting implications for policy issues such as strategic research portfolio management, infrastructure investment timing, and interagency coordination.
This paper bridges cutting-edge methodological developments with the practical needs of policymakers and R&D programme managers. We explore how innovation network analysis can guide more effective resource allocation across the R&D chain as technologies mature. We also identify new signals for tracking technology readiness and anticipating policy intervention points.
Ultimately, this research demonstrates a data-informed perspective on innovation policy. By showcasing novel analytical frameworks and grounding them in concrete use cases, we hope to stimulate further work at the intersection of network science, scientometrics, and innovation studies. The insights generated can inform the design of more effective, evidence-based strategies for nurturing and steering innovation ecosystems. This paper demonstrates the potential for data-driven approaches to innovation policy, enabled by integrating rich, linked datasets and advanced analytical techniques. We believe this approach will become the foundation for innovation policymakers in the future.
Accepted Poster
Paper Short Abstract
Paper Abstract
Historically, analytical tools for understanding innovation systems have relied on abstractions, as the complexity and dynamics of R&D could never be fully modelled. With newly available tools, we combine large-scale relational scientometric datasets from Dimensions with network science to inform critical innovation policy decisions.
Building upon recent work, such as Ho et al.'s analysis of mRNA vaccine development published in Nature Biotechnology, we demonstrate how techniques like multilayer-directed acyclic graphs can attribute impact, identify rate-limiting steps, and reveal hidden dynamics in innovation networks. These methods are applied to real-world case studies, highlighting implications for policy issues such as strategic research portfolio management, infrastructure investment timing, and interagency coordination.
This paper bridges cutting-edge methodological developments with the practical needs of policymakers and R&D programme managers. We explore how innovation network analysis can guide more effective resource allocation across the R&D chain as technologies mature. We also identify new signals for tracking technology readiness and anticipating policy intervention points.
Ultimately, this research demonstrates a data-informed perspective on innovation policy. By showcasing novel analytical frameworks and grounding them in concrete use cases, we hope to stimulate further work at the intersection of network science, scientometrics, and innovation studies. The insights generated can inform the design of more effective, evidence-based strategies for nurturing and steering innovation ecosystems. This paper demonstrates the potential for data-driven approaches to innovation policy, enabled by integrating rich, linked datasets and advanced analytical techniques. We believe this approach will become the foundation for innovation policymakers in the future.
Poster session
Session 1 Tuesday 1 July, 2025, -