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Register a new userQuantifying Success in Science: An Overview
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Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions.
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In the recent article Janosov, Battiston, & Sinatra report that they separated the inputs of talent and luck in creative careers. They build on the previous work of Sinatra et al which introduced the Q-model. Under the model the popularity of different elements of culture is a product of two factors: a random factor and a Qfactor, or talent. The latter is fixed for an individual but randomly distributed among different people. This way they explain how some individuals can consistently produce high-impact work. They extract the Q-factors for different scientists, writers, and movie makers from statistical data on popularity of their work. However, in their article they reluctantly state that there is little correlation between popularity and quality ratings of of books and movies (correlation coefficients 0.022 and 0.15). I analyzed the data of the original Q-factor article and obtained a correlation between the citation-based Q-factor and Nobel Prize winning of merely 0.19. I also briefly review few other experiments that found a meager, sometimes even negative, correlation between popularity and quality of cultural products. I conclude that, if there is an ability associated with a high Q-factor it should be more of a marketing ability than an ability to produce a higher quality product. Janosov,
One of the features of modern science is the formation of stable large collaborations of researchers working together within the projects that require the concentration of huge financial and human resources. Results of such common work are published in scientific papers by large co-authorship teams that include sometimes thousands of names. The goal of this work is to study the influence of such publications on the values of scientometric indicators calculated for individuals, research groups and science of Ukraine in general. Bibliometric data related to Ukraine, some academic institutions and selected individual researchers were collected from Scopus database and used for our study. It is demonstrated that while the relative share of publications by collective authors is comparatively small, their presence in a general pool can lead to statistically significant effects. The obtained results clearly show that traditional quantitative approaches for research assessment should be changed in order to take into account this phenomenon. Keywords: collective authorship, scientometrics, group science, Ukraine.
Tracing the evolution of specific topics is a subject area which belongs to the general problem of mapping the structure of scientific knowledge. Often bibliometric data bases are used to study the history of scientific topic evolution from its appearance to its extinction or merger with other topics. In this chapter the authors present an analysis of the academic response to the disaster that occurred in 1986 in Chornobyl (Chernobyl), Ukraine, considered as one of the most devastating nuclear power plant accidents in history. Using a bibliographic database the distributions of Chornobyl-related papers in different scientific fields are analysed, as are their growth rates and properties of co-authorship networks. Elements of descriptive statistics and tools of complex-network theory are used to highlight interdisciplinary as well as international effects. In particular, tools of complex-network science enable information visualization complemented by further quantitative analysis. A further goal of the chapter is to provide a simple pedagogical introduction to the application of complex-network analysis for visual data representation and interdisciplinary communication.
We analyze the reaction of academic communities to a particular urgent topic which abruptly arises as a scientific problem. To this end, we have chosen the disaster that occurred in 1986 in Chornobyl (Chernobyl), Ukraine, considered as one of the most devastating nuclear power plant accidents in history. The academic response is evaluated using scientific-publication data concerning the disaster using the Scopus database to present the picture on an international scale and the bibliographic database Ukrainika naukova to consider it on a national level. We measured distributions of papers in different scientific fields, their growth rates and properties of co-authorship networks. {The elements of descriptive statistics and the tools of the complex network theory are used to highlight the interdisciplinary as well as international effects.} Our analysis allows to compare contributions of the international community to Chornobyl-related research as well as integration of Ukraine in the international research on this subject. Furthermore, the content analysis of titles and abstracts of the publications allowed to detect the most important terms used for description of Chornobyl-related problems.
Todays scientific research is an expensive enterprise funded largely by taxpayers and corporate groups monies. It is a critical part in the competition between nations, and all nations want to discover fields of research that promise to create future industries, and dominate these by building up scientific and technological expertise early. However, our understanding of the value chain going from science to technology is still in a relatively infant stage, and the conversion of scientific leadership into market dominance remains very much an alchemy rather than a science. In this paper, we analyze bibliometric records of scientific journal publications and patents related to graphene, at the aggregate level as well as on the temporal and spatial dimensions. We find the present leaders of graphene science and technology emerged rather late in the race, after the initial scientific leaders lost their footings. More importantly, notwithstanding the amount of funding already committed, we find evidences that suggest the Golden Eras of graphene science and technology were in 2010 and 2012 respectively, in spite of the continued growth of journal and patent publications in this area.
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