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Identifying potential “breakthrough” publications using refined citation analyses: Three related explorative approaches

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Identifying potential “breakthrough” publications using refined citation analyses : Three related explorative approaches. / Schneider, Jesper W.; Costas, Rodrigo.

I: Journal of the Association for Information Science and Technology, Bind 68, Nr. 3, 01.03.2017, s. 709-723.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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Schneider, JW & Costas, R 2017, 'Identifying potential “breakthrough” publications using refined citation analyses: Three related explorative approaches', Journal of the Association for Information Science and Technology, bind 68, nr. 3, s. 709-723. https://doi.org/10.1002/asi.23695

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Schneider, Jesper W. ; Costas, Rodrigo. / Identifying potential “breakthrough” publications using refined citation analyses : Three related explorative approaches. I: Journal of the Association for Information Science and Technology. 2017 ; Bind 68, Nr. 3. s. 709-723.

Bibtex

@article{24ac8dde259142efaa079d8e2bd352b6,
title = "Identifying potential “breakthrough” publications using refined citation analyses: Three related explorative approaches",
abstract = "The article presents three advanced citation-based methods used to detect potential breakthrough articles among very highly cited articles. We approach the detection of such articles from three different perspectives in order to provide different typologies of breakthrough articles. In all three cases we use the hierarchical classification of scientific publications developed at CWTS based on direct citation relationships. We assume that such contextualized articles focus on similar research interests. We utilize the characteristics scores and scales (CSS) approach to partition citation distributions and implement a specific filtering algorithm to sort out potential highly-cited “followers,” articles not considered breakthroughs. After invoking thresholds and filtering, three methods are explored: A very exclusive one where only the highest cited article in a micro-cluster is considered as a potential breakthrough article (M1); as well as two conceptually different methods, one that detects potential breakthrough articles among the 2% highest cited articles according to CSS (M2a), and finally a more restrictive version where, in addition to the CSS 2% filter, knowledge diffusion is also considered (M2b). The advance citation-based methods are explored and evaluated using validated publication sets linked to different Danish funding instruments including centers of excellence.",
author = "Schneider, {Jesper W.} and Rodrigo Costas",
year = "2017",
month = mar,
day = "1",
doi = "10.1002/asi.23695",
language = "English",
volume = "68",
pages = "709--723",
journal = "American Society for Information Science and Technology. Journal",
issn = "2330-1635",
publisher = "Wiley-Blackwell",
number = "3",

}

RIS

TY - JOUR

T1 - Identifying potential “breakthrough” publications using refined citation analyses

T2 - Three related explorative approaches

AU - Schneider, Jesper W.

AU - Costas, Rodrigo

PY - 2017/3/1

Y1 - 2017/3/1

N2 - The article presents three advanced citation-based methods used to detect potential breakthrough articles among very highly cited articles. We approach the detection of such articles from three different perspectives in order to provide different typologies of breakthrough articles. In all three cases we use the hierarchical classification of scientific publications developed at CWTS based on direct citation relationships. We assume that such contextualized articles focus on similar research interests. We utilize the characteristics scores and scales (CSS) approach to partition citation distributions and implement a specific filtering algorithm to sort out potential highly-cited “followers,” articles not considered breakthroughs. After invoking thresholds and filtering, three methods are explored: A very exclusive one where only the highest cited article in a micro-cluster is considered as a potential breakthrough article (M1); as well as two conceptually different methods, one that detects potential breakthrough articles among the 2% highest cited articles according to CSS (M2a), and finally a more restrictive version where, in addition to the CSS 2% filter, knowledge diffusion is also considered (M2b). The advance citation-based methods are explored and evaluated using validated publication sets linked to different Danish funding instruments including centers of excellence.

AB - The article presents three advanced citation-based methods used to detect potential breakthrough articles among very highly cited articles. We approach the detection of such articles from three different perspectives in order to provide different typologies of breakthrough articles. In all three cases we use the hierarchical classification of scientific publications developed at CWTS based on direct citation relationships. We assume that such contextualized articles focus on similar research interests. We utilize the characteristics scores and scales (CSS) approach to partition citation distributions and implement a specific filtering algorithm to sort out potential highly-cited “followers,” articles not considered breakthroughs. After invoking thresholds and filtering, three methods are explored: A very exclusive one where only the highest cited article in a micro-cluster is considered as a potential breakthrough article (M1); as well as two conceptually different methods, one that detects potential breakthrough articles among the 2% highest cited articles according to CSS (M2a), and finally a more restrictive version where, in addition to the CSS 2% filter, knowledge diffusion is also considered (M2b). The advance citation-based methods are explored and evaluated using validated publication sets linked to different Danish funding instruments including centers of excellence.

UR - http://www.scopus.com/inward/record.url?scp=84994651258&partnerID=8YFLogxK

U2 - 10.1002/asi.23695

DO - 10.1002/asi.23695

M3 - Journal article

AN - SCOPUS:84994651258

VL - 68

SP - 709

EP - 723

JO - American Society for Information Science and Technology. Journal

JF - American Society for Information Science and Technology. Journal

SN - 2330-1635

IS - 3

ER -