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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. 

Covid-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood has surged, researchers have been disadvantaged by not having access to a platform that can quickly synthesize rapidly emerging information and link the expertise it contains to established knowledge foundations. Aiming to fill this gap, in this paper we propose a research framework that can assist scientists in identifying, retrieving, and understanding Covid-19 knowledge from the ocean of scholarly articles. Incorporating Principal Component Decomposition (PDC), a knowledge model based on text analytics, and hierarchical topic tree analysis, the proposed framework profiles the research landscape, retrieves topic-specific knowledge and visualizes knowledge structures. Addressing 127,971 Covid-19 research papers from PubMed, our PCD topic analysis identifies 35 research hotspots, along with their correlations and trends. The hierarchical topic tree analysis further segments the knowledge landscape of the whole dataset into clinical and public health branches at a macro level. To supplement this analysis, we also built a knowledge model from research papers on vaccinations and fetched 92,286 pre-Covid publications as the established knowledge foundation for reference. The hierarchical topic tree analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization. 

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. 

Efforts to involve data science in policy analysis can be traced back decades but transforming analytic findings into decisions is still a far from straightforward task. Data-driven decision-making requires understanding approaches, practices, and research results from many disciplines, which makes it interesting to investigate whether data science and policy analysis are moving in parallel or whether their pathways have intersected. Our investigation, from a bibliometric perspective, is driven by a comprehensive set of research questions, and we have designed an intelligent bibliometric framework that includes a series of traditional bibliometric approaches and a novel method of charting the evolutionary pathways of scientific innovation, which is used to identify predecessor-descendant relationships in technological topics. Our investigation reveals that data science and policy analysis have intersecting lines, and it can foresee that a cross-disciplinary direction in which policy analysis interacting with data science has become an emergent area in both communities. However, equipped with advanced data analytic techniques, data scientists are moving faster and further than policy analysts. The empirical insights derived from our research should be beneficial to academic researchers and journal editors in related research communities, as well as policy-makers in research institutions and funding agencies.