-
Zhu, Q.; Kong, X.; Hong, S.; Li, J.; He, Z.: Global ontology research progress : a bibliometric analysis (2015)
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- Date
- 20. 1.2015 18:30:22
17. 9.2018 18:22:23
-
Wu, Q.: ¬The w-index : a measure to assess scientific impact by focusing on widely cited papers (2010)
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- Abstract
- Based on the principles of the h-index, I propose a new measure, the w-index, as a particularly simple and more useful way to assess the substantial impact of a researcher's work, especially regarding excellent papers. The w-index can be defined as follows: If w of a researcher's papers have at least 10w citations each and the other papers have fewer than 10(w+1) citations, that researcher's w-index is w. The results demonstrate that there are noticeable differences between the w-index and the h-index, because the w-index plays close attention to the more widely cited papers. These discrepancies can be measured by comparing the ranks of 20 astrophysicists, a few famous physical scientists, and 16 Price medalists. Furthermore, I put forward the w(q)-index to improve the discriminatory power of the w-index and to rank scientists with the same w. The factor q is the least number of citations a researcher with w needed to reach w+1. In terms of both simplicity and accuracy, the w-index or w(q)-index can be widely used for evaluation of scientists, journals, conferences, scientific topics, research institutions, and so on.
-
Ye, F.Y.: ¬A theoretical approach to the unification of informetric models by wave-heat equations (2011)
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- Abstract
- A simple distribution function f(x, t)=p(x+q)**-ße**alpha*t obeys wave and heat equations, that constructs a theoretical approach to the unification of informetric models, with which we can unify all informetric laws. While its space-type distributions deduce naturally Lotka-type laws in size approaches and Zipf-type laws in rank approaches, its time-type distributions introduce the mechanism of Price-type and Brookes-type laws.
-
Zhang, J.; Yu, Q.; Zheng, F.; Long, C.; Lu, Z.; Duan, Z.: Comparing keywords plus of WOS and author keywords : a case study of patient adherence research (2016)
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Yan, E.; Yu, Q.: Using path-based approaches to examine the dynamic structure of discipline-level citation networks (2016)
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Zhou, Q.; Leydesdorff, L.: ¬The normalization of occurrence and co-occurrence matrices in bibliometrics using Cosine similarities and Ochiai coefficients (2016)
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-
Wang, Q.: ¬A bibliometric model for identifying emerging research topics (2018)
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-
Peng, T.-Q.; Zhu, J.J.H.: Where you publish matters most : a multilevel analysis of factors affecting citations of internet studies (2012)
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-
Naidoo, J.; Huber, J.T.; Cupp, P.; Wu, Q.: Modeling the relationship between an emerging infectious disease epidemic and the body of scientific literature associated with it : the case of HIV/AIDS in the United States (2013)
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-
Ping, Q.; He, J.; Chen, C.: How many ways to use CiteSpace? : a study of user interactive events over 14 months (2017)
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-
Zhang, P.; Wang, OP.; Wu, Q.: How are the best JASIST papers cited? (2018)
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-
Min, C.; Ding, Y.; Li, J.; Bu, Y.; Pei, L.; Sun, J.: Innovation or imitation : the diffusion of citations (2018)
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- Abstract
- Citations in scientific literature are important both for tracking the historical development of scientific ideas and for forecasting research trends. However, the diffusion mechanisms underlying the citation process remain poorly understood, despite the frequent and longstanding use of citation counts for assessment purposes within the scientific community. Here, we extend the study of citation dynamics to a more general diffusion process to understand how citation growth associates with different diffusion patterns. Using a classic diffusion model, we quantify and illustrate specific diffusion mechanisms which have been proven to exert a significant impact on the growth and decay of citation counts. Experiments reveal a positive relation between the "low p and low q" pattern and high scientific impact. A sharp citation peak produced by rapid change of citation counts, however, has a negative effect on future impact. In addition, we have suggested a simple indicator, saturation level, to roughly estimate an individual article's current stage in the life cycle and its potential to attract future attention. The proposed approach can also be extended to higher levels of aggregation (e.g., individual scientists, journals, institutions), providing further insights into the practice of scientific evaluation.
-
Zhang, Q.; Xue, H.; Tang, H.: Knowledge domain and emerging trends in vulnerability assessment in the context of climate change : a bibliometric analysis (1991-2017) (2018)
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-
He, J.; Ping, Q.; Lou, W.; Chen, C.: PaperPoles : facilitating adaptive visual exploration of scientific publications by citation links (2019)
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-
Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014)
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- Date
- 18. 3.2014 19:13:22
-
Scholarly metrics under the microscope : from citation analysis to academic auditing (2015)
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- Date
- 22. 1.2017 17:12:50
-
Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
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- Date
- 22. 8.2014 17:05:18
-
Ohly, P.: Dimensions of globality : a bibliometric analysis (2016)
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- Date
- 20. 1.2019 11:22:31
-
Crespo, J.A.; Herranz, N.; Li, Y.; Ruiz-Castillo, J.: ¬The effect on citation inequality of differences in citation practices at the web of science subject category level (2014)
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- Abstract
- This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.
-
Yan, E.: Finding knowledge paths among scientific disciplines (2014)
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- Date
- 26.10.2014 20:22:22
