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

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  • Jesper W. Schneider
  • Rodrigo Costas

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  • Jesper W. Schneider & Rodrigo Costas, 2017. "Identifying potential “breakthrough” publications using refined citation analyses: Three related explorative approaches," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(3), pages 709-723, March.
    • Handle: RePEc:bla:jinfst:v:68:y:2017:i:3:p:709-723
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    File URL: http://hdl.handle.net/10.1002/asi.23695
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    Citations

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    Cited by:

    1. Bornmann, Lutz & Tekles, Alexander & Zhang, Helena H. & Ye, Fred Y., 2019. "Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data," Journal of Informetrics, Elsevier, vol. 13(4).
    2. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    3. Rodríguez-Navarro, Alonso & Brito, Ricardo, 2024. "Rank analysis of most cited publications, a new approach for research assessments," Journal of Informetrics, Elsevier, vol. 18(2).
    4. Winnink, J.J. & Tijssen, Robert J.W. & van Raan, A.F.J., 2019. "Searching for new breakthroughs in science: How effective are computerised detection algorithms?," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 673-686.
    5. Liu, Yunmei & Yang, Liu & Chen, Min, 2021. "A new citation concept: Triangular citation in the literature," Journal of Informetrics, Elsevier, vol. 15(2).
    6. Haobai Xue, 2024. "Temporal Evolution of Bradford Curves in Academic Library Contexts," Publications, MDPI, vol. 12(4), pages 1-17, October.
    7. Min, Chao & Bu, Yi & Sun, Jianjun, 2021. "Predicting scientific breakthroughs based on knowledge structure variations," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    8. Li, Xin & Ma, Xiaodi & Feng, Ye, 2024. "Early identification of breakthrough research from sleeping beauties using machine learning," Journal of Informetrics, Elsevier, vol. 18(2).
    9. Shiyun Wang & Yaxue Ma & Jin Mao & Yun Bai & Zhentao Liang & Gang Li, 2023. "Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(2), pages 150-167, February.
    10. Brito, Ricardo & Rodríguez-Navarro, Alonso, 2018. "Research assessment by percentile-based double rank analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 315-329.
    11. Carter Bloch & Mads P. Sørensen & Mitchell Young, 2020. "Tales of Serendipity in Highly Cited Research: an Explorative Study," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 11(4), pages 1596-1613, December.

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