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This paper maps research outcomes and identifies spillover effects at a US University Research Center (URC) that offers user facilities for nanotechnology research. We use scientometric and network science approaches to analyze measures of topical orientation, productivity, impact, and collaboration applied to URC-related Web of Science abstract publications records. A focus is on the analysis of spillover effects on external organizations (i.e., non-affiliated users). Our findings suggest the URC’s network relies on external organizations acting as brokers, to provide access to the facilities to other external organizations. Analysis of heterophily indicates that collaboration among internal and external organizations is enhanced by the facilities, while articles written by a mix of co-authors affiliated with internal and external organizations are likely to be more cited. These results provide insights on how URCs with user facilities can create conditions for diverse collaboration and greater research impact.

 

While the COVID-19 pandemic morphs into less malignant forms, the virus has spawned a series of poorly understood, post-infection symptoms with staggering ramifications, i. e., long COVID (LC). This bibliometric study profiles the rapidly growing LC research domain [5,243 articles from PubMed and Web of Science (WoS)] to make its knowledge content more accessible. The article addresses What? Where? Who? and When? questions. A 13-topic Concept Grid presents bottom-up topic clusters. We break out those topics with other data fields, including disciplinary concentrations, topical details, and information on research “players” (countries, institutions, and authors) engaging in those topics. We provide access to results via a Dashboard website. We find a strongly growing, multidisciplinary LC research domain. That domain appears tightly connected based on shared research knowledge. However, we also observe notable concentrations of research activity in different disciplines. Data trends over 3 years of LC research suggest heightened attention to psychological and neurodegenerative symptoms, fatigue, and pulmonary involvement. 

Presentation showing the calculation of tech emergence indicators by state for nanotechnology research 

We examine to what extent two variables – institutional groups and emerging terms – contribute to explain the network dynamics of research on microneedles by estimating an Exponential Random Graph Model (ERGM. 

This study investigates the effect of regulatory uncertainty on the translation of scientific discovery on emerging research topics to technical applications in science-driven industry. Our empirical analysis using the case of the US Federal Drug and Food Administration’s release of the report on the regulatory approach to nanomedicine in 2007 shows that; (1) the regulatory uncertainty decelerated the translation of nanomedicine research to technical applications, (2) this effect was particular for the nanomedicine research on emerging topics in the field. Our further analysis suggests that the effect of the regulatory uncertainty originated from the suppressed business activities in the field where the regulatory uncertainty presents. Our study elaborates on how regulatory authority actions shape the innovation process by shedding light on the impact of regulatory uncertainty on the development of technical applications of an emerging scientific area. 

Technological convergence network (TCN) is an effective method to identify the advancement of technology convergence. However, the previous TCN investigations are limited to a single level of IPC (abbreviation of International Patent Classification) rather than different IPC hierarchies, which can only provide decision support for policy-makers with one dimension instead of various ones. In this study, we propose a new approach to construct TCNs across different IPC hierarchies based on technology co-classification analysis, and further identify key technology fields by employing the indicator of betweenness centrality (BC) in the TCNs from any IPC hierarchy. This study makes two important contributions. First, theoretically, our study is to contribute to understanding the advancement of technological convergence from various IPC hierarchies, rather than a single IPC level. Second, methodologically, the new approach we propose can benefit decision-makers serving at various levels of technology management agencies. We conclude possible implications and future directions.