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Norris, M.; Oppenheim, C.: ¬The h-index : a broad review of a new bibliometric indicator (2010)
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- Abstract
- Purpose - This review aims to show, broadly, how the h-index has become a subject of widespread debate, how it has spawned many variants and diverse applications since first introduced in 2005 and some of the issues in its use. Design/methodology/approach - The review drew on a range of material published in 1990 or so sources published since 2005. From these sources, a number of themes were identified and discussed ranging from the h-index's advantages to which citation database might be selected for its calculation. Findings - The analysis shows how the h-index has quickly established itself as a major subject of interest in the field of bibliometrics. Study of the index ranges from its mathematical underpinning to a range of variants perceived to address the indexes' shortcomings. The review illustrates how widely the index has been applied but also how care must be taken in its application. Originality/value - The use of bibliometric indicators to measure research performance continues, with the h-index as its latest addition. The use of the h-index, its variants and many applications to which it has been put are still at the exploratory stage. The review shows the breadth and diversity of this research and the need to verify the veracity of the h-index by more studies.
- Date
- 8. 1.2011 19:22:13
- Object
- h-index
-
Rostaing, H.; Barts, N.; Léveillé, V.: Bibliometrics: representation instrument of the multidisciplinary positioning of a scientific area : Implementation for an Advisory Scientific Committee (2007)
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- Date
- 30.12.2007 11:22:39
-
Meho, L.I.; Rogers, Y.: Citation counting, citation ranking, and h-index of human-computer interaction researchers : a comparison of Scopus and Web of Science (2008)
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- Abstract
- This study examines the differences between Scopus and Web of Science in the citation counting, citation ranking, and h-index of 22 top human-computer interaction (HCI) researchers from EQUATOR - a large British Interdisciplinary Research Collaboration project. Results indicate that Scopus provides significantly more coverage of HCI literature than Web of Science, primarily due to coverage of relevant ACM and IEEE peer-reviewed conference proceedings. No significant differences exist between the two databases if citations in journals only are compared. Although broader coverage of the literature does not significantly alter the relative citation ranking of individual researchers, Scopus helps distinguish between the researchers in a more nuanced fashion than Web of Science in both citation counting and h-index. Scopus also generates significantly different maps of citation networks of individual scholars than those generated by Web of Science. The study also presents a comparison of h-index scores based on Google Scholar with those based on the union of Scopus and Web of Science. The study concludes that Scopus can be used as a sole data source for citation-based research and evaluation in HCI, especially when citations in conference proceedings are sought, and that researchers should manually calculate h scores instead of relying on system calculations.
- Object
- h-index
-
Chan, H.C.; Kim, H.-W.; Tan, W.C.: Information systems citation patterns from International Conference on Information Systems articles (2006)
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- Date
- 3. 1.2007 17:22:03
-
Wan, X.; Liu, F.: Are all literature citations equally important? : automatic citation strength estimation and its applications (2014)
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- Abstract
- Literature citation analysis plays a very important role in bibliometrics and scientometrics, such as the Science Citation Index (SCI) impact factor, h-index. Existing citation analysis methods assume that all citations in a paper are equally important, and they simply count the number of citations. Here we argue that the citations in a paper are not equally important and some citations are more important than the others. We use a strength value to assess the importance of each citation and propose to use the regression method with a few useful features for automatically estimating the strength value of each citation. Evaluation results on a manually labeled data set in the computer science field show that the estimated values can achieve good correlation with human-labeled values. We further apply the estimated citation strength values for evaluating paper influence and author influence, and the preliminary evaluation results demonstrate the usefulness of the citation strength values.
- Date
- 22. 8.2014 17:12:35
-
Huang, M.-H.; Huang, W.-T.; Chang, C.-C.; Chen, D. Z.; Lin, C.-P.: The greater scattering phenomenon beyond Bradford's law in patent citation (2014)
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- Date
- 22. 8.2014 17:11:29
-
Ntuli, H.; Inglesi-Lotz, R.; Chang, T.; Pouris, A.: Does research output cause economic growth or vice versa? : evidence from 34 OECD countries (2015)
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- Date
- 8. 7.2015 22:00:42
-
Nicholls, P.T.: Empirical validation of Lotka's law (1986)
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- Source
- Information processing and management. 22(1986), S.417-419
-
Nicolaisen, J.: Citation analysis (2007)
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- Date
- 13. 7.2008 19:53:22
-
Fiala, J.: Information flood : fiction and reality (1987)
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- Source
- Thermochimica acta. 110(1987), S.11-22
-
Chang, Y.-W.; Huang, M.-H.: ¬A study of the evolution of interdisciplinarity in library and information science : using three bibliometric methods (2012)
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- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.22-33
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Kuan, C.-H.; Liu, J.S.: ¬A new approach for main path analysis : decay in knowledge diffusion (2016)
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- Date
- 22. 1.2016 14:23:00
-
Cerda-Cosme, R.; Méndez, E.: Analysis of shared research data in Spanish scientific papers about COVID-19 : a first approach (2023)
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- Abstract
- During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta-research to help understand science communication in crises and improve its effectiveness. The objective is to study how many Spanish scientific papers on COVID-19 published during 2020 share their research data. Qualitative and descriptive study applying nine attributes: (a) availability, (b) accessibility, (c) format, (d) licensing, (e) linkage, (f) funding, (g) editorial policy, (h) content, and (i) statistics. We analyzed 1,340 papers, 1,173 (87.5%) did not have research data. A total of 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data, and 5.2% share their data as supplementary material. There is a small percentage that shares their research data; however, it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.
- Date
- 21. 3.2023 19:22:02
-
Su, Y.; Han, L.-F.: ¬A new literature growth model : variable exponential growth law of literature (1998)
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- Date
- 22. 5.1999 19:22:35
-
Van der Veer Martens, B.: Do citation systems represent theories of truth? (2001)
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- Date
- 22. 7.2006 15:22:28
-
Diodato, V.: Dictionary of bibliometrics (1994)
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- Footnote
- Rez. in: Journal of library and information science 22(1996) no.2, S.116-117 (L.C. Smith)
-
Bookstein, A.: Informetric distributions : I. Unified overview (1990)
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- Date
- 22. 7.2006 18:55:29
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Bookstein, A.: Informetric distributions : II. Resilience to ambiguity (1990)
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Lewison, G.: ¬The work of the Bibliometrics Research Group (City University) and associates (2005)
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Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014)
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