PurposeThis article aims to determine the percentage of “Sparking” articles among the work of this year’s Nobel Prize winners in medicine, physics, and chemistry.Design/methodology/approachWe focus on under-cited influential research among the key publications as mentioned by the Nobel Prize Committee for the 2020 Noble Prize laureates. Specifically, we extracted data from the Web of Science, and calculated the Sparking Indices using the formulas as proposed by Hu and Rousseau in 2016 and 2017. In addition, we identified another type of igniting articles based on the notion in 2017.FindingsIn the fields of medicine and physics, the proportions of articles with sparking characteristics share 78.571% and 68.75% respectively, yet, in chemistry 90% articles characterized by “igniting”. Moreover, the two types of articles share more than 93% in the work of the Nobel Prize included in this study.Research limitationsOur research did not cover the impact of topic, socio-political, and author’s reputation on the Sparking Indices.Practical implicationsOur study shows that the Sparking Indices truly reflect influence of the best research work, so it can be used to detect under-cited influential articles, as well as identifying fundamental work.Originality/valueOur findings suggest that the Sparking Indices have good applicability for research evaluation.

In this contribution, we conduct a multi-angular analysis of the interdisciplinarity of Nobel Prize winning research compared to non-Nobel Prize winning articles, based on a large data set. Here interdisciplinarity is measured by the diversity of references, using two true diversity indicators.Articles mentioned by the Nobel Prize committee in Physiology or Medicine (in short: NP articles) awarded during the period from 1900 to 2016 are the focus of our research. These articles are compared with those in a dataset of articles that do not include a Nobel Prize winner among their authors.Moreover, these non-NPs articles were not only published in the same year and in the same research field as the NP ones but were also dealing with the same research topic (such articles are referred to as non-NP articles).The results suggest that the topic-related knowledge included in Nobel Prize winning work is higher than that in non-NPs, hence with lower interdisciplinarity than the latter. Our findings provide useful clues to better understand the characteristics of transformative research, here represented 2 by key publications by Nobel Prize laureates in Physiology or Medicine, and their pattern of knowledge integration.

Purpose This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years. Design/methodology/approach We collected publications on CRISPR between 2011 and 2020 from the Web of Science, and traced all the patents citing them from lens.org. 15,904 articles and 18,985 patents in total are downloaded and analyzed. The LDA model was applied to identify underlying research topics in related research. In addition, some indicators were introduced to measure the knowledge transfer from research topics of scientific publications to IPC-4 classes of patents. Findings The emerging research topics on CRISPR were identified and their evolution over time displayed. Furthermore, a big picture of knowledge transition from research topics to technological classes of patents was presented. We found that for all topics on CRISPR, the average first transition year, the ratio of articles cited by patents, the NPR transition rate are respectively 1.08, 15.57%, and 1.19, extremely shorter and more intensive than those of general fields. Moreover, the transition patterns are different among research topics. Research limitations Our research is limited to publications retrieved from the Web of Science and their citing patents indexed in lens.org. A limitation inherent with LDA analysis is in the manual interpretation and labeling of “topics”. Practical implications Our study provides good references for policy-makers on allocating scientific resources and regulating financial budgets to face challenges related to the transformative technology of CRISPR. Originality/value The LDA model here is applied to topic identification in the area of transformative researches for the first time, as exemplified on CRISPR. Additionally, the dataset of all citing patents in this area helps to provide a full picture to detect the knowledge transition between S&T.

The purpose of this study is to explore if elite scientists play a key role in the genesis of transformative research. As there exist different types of transformative research, this paper focuses on one type of work, i.e. under-cited influential work,referred to as “sparking” articles. A comparative study between the h-indices of authors citing Nobel Prize-winning papers of sparking type and those of authors citing ordinary ones is conducted, focusing on the first author and the corresponding author of each paper. The results show that the citers of the Top 1% or Top 10% Citations Sets in the sparking group have much higher h-indices than those in the ordinary group. These findings imply that elite scientists, operationalized as those with a high h-index in the corresponding fields, are more sensitive to sparking work and, as such play a pivotal role in the genesis of transformative research. This investigation provides new insight into the study of detecting transformative research, and hence, contributes to the science of science.