Recently,Shiyun Wang, a doctoral student under the instruction of ProfessorGang Liand Associate ProfessorJin Mao, published online an article entitled withQuantifying scientific breakthroughs by a novel disruption indicator based on knowledge entitiesin theJournal of the Association for Information Science & Technology (JASIST), a top journal in the field of Library and Information Science.

Shiyun Wangis the first author,Yaxue Ma, a postdoctoral fellow of Nanjing University,Yun Bai, a doctoral student of Wuhan University, andZhentao Liang, a doctoral student of Wuhan University, participated in the related work of the article.

Measuring scientific breakthroughs enables tracking and forecasting of emerging areas in science and assists stakeholders in making more informed decisions on funding allocation, employment, and scientific awards. However, it is difficult to precisely define a scientific breakthrough. Therefore, according to the different characteristics of scientific breakthroughs, predecessors tried to identify the scientific breakthroughs identification channels and put forward the identification methods.

Previous studies that generally detect scientific breakthroughs based on citation patterns, but paid less attention to knowledge content. In comparison to citations, the knowledge content of an article demonstrates its contribution to the knowledge system more directly. Based on this, this article comprehensively considers citation patterns and knowledge content characteristics, and proposes a novel disruption indicator based on knowledge entities to quantify scientific breakthroughs.

Disruption indicator calculation method

The disruption indicator based on knowledge entities proposed in this article contains two main components. One is to measure the extent to which focal paper departs from the existing research streams, and the other is to measure the degree of focus shifts inspired by focal paper along focal paper's diffusion path. In order to objectively evaluate this novel disruption indicator, based on PubMed dataset, this research compiled four test datasets for scientific breakthroughs according to scientific breakthroughs identification channels such as prize/honor, editorial highlights, citations from scholarly documents and expert opinions. Through empirical analysis, it is verified that this disruption indicator is significantly better than the disruption indicator based on citation proposed by predecessors.

This article not only offers empirical insights into conceptual understanding of scientific breakthroughs but also provides practical disruption indicator for scientists and science management agencies searching for valuable research.

Links to the full article:https://doi.org/10.1002/asi.24719