Author
Jin Bihui
Bio: Jin Bihui is an academic researcher. The author has an hindex of 2, co-authored 2 publications receiving 545 citations.
Papers
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TL;DR: The R- and AR-indices are introduced and it is proposed the pair (h, AR) as a meaningful indicator for research evaluation and a relation characterizing the h-index in the power law model is proved.
Abstract: Based on the foundation laid by the h -index we introduce and study the R - and AR -indices. These new indices eliminate some of the disadvantages of the h -index, especially when they are used in combination with the h -index. The R -index measures the h -core’s citation intensity, while AR goes one step further and takes the age of publications into account. This allows for an index that can actually increase and decrease over time. We propose the pair ( h , AR ) as a meaningful indicator for research evaluation. We further prove a relation characterizing the h -index in the power law model
588 citations
01 Jan 2005
TL;DR: In this paper, it is shown that if China succeeds in developing a well-structured technology transfer system from research to economic development, the increasing technological and scientific outputs will be a main source of future economic growth.
Abstract: It is shown that for all essential scientific and technological indicators China shows an exponential increase. Only for the number of R&D personnel the increase is linear. Special attention goes to the contribution of Chinese firms in the output of scientific articles. It is shown that, although this output increases exponentially in absolute numbers, its share in the total scientific output of China is lower than that of European countries. It is concluded that if China succeeds in developing a well-structured technology transfer system from research to economic development, the increasing technological and scientific outputs, illustrated in this article, will be a main source of future economic growth.
3 citations
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TL;DR: The findings indicate that the h index is better than other indicators considered (total citation count, citations per paper, and total paper count) in predicting future scientific achievement.
Abstract: Bibliometric measures of individual scientific achievement are of particular interest if they can be used to predict future achievement. Here we report results of an empirical study of the predictive power of the h index compared with other indicators. Our findings indicate that the h index is better than other indicators considered (total citation count, citations per paper, and total paper count) in predicting future scientific achievement. We discuss reasons for the superiority of the h index.
899 citations
TL;DR: This contribution presents a comprehensive review on the h-index and related indicators field, studying their main advantages, drawbacks and the main applications that can be found in the literature.
748 citations
01 Aug 2020
TL;DR: SciSci has revealed choices and trade-offs that scientists face as they advance both their own careers and the scientific horizon, and offers a deep quantitative understanding of the relational structure between scientists, institutions, and ideas, which facilitates the identification of fundamental mechanisms responsible for scientific discovery.
Abstract: The rapid development of digital libraries and the proliferation of scholarly big data have created an unprecedented opportunity to explore scientific production and reward at scale. Fueled by the data exploration and computational advances in digital libraries, the science of science is an emerging multidisciplinary field that aims to quantify patterns for scientific relationships and dependencies, and how scientific progress emerges from the scholarly big data. In this tutorial, we will provide an overview of the science of science, including major topics on scientific careers, scientific collaborations and scientific ideas. We will also discuss its historical context, the state-of-art models and exciting discoveries, and promising future directions for participants interested in mining scholarly big data.
579 citations
TL;DR: The historical development of scientometrics, sources of citation data, citation metrics and the “laws” of scientometry, normalisation, journal impact factors and other journal metrics, visualising and mapping science, evaluation and policy, and future developments are considered.
560 citations
TL;DR: The results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others.
Abstract: Background: The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. Methodology: We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. Conclusions: Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.
544 citations