IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v126y2021i2d10.1007_s11192-020-03780-3.html
   My bibliography  Save this article

Validation of the Astro dataset clustering solutions with external data

Author

Listed:
  • Paul Donner

    (Deutsches Zentrum für Hochschul- und Wissenschaftsforschung)

Abstract

We conduct an independent cluster validation study on published clustering solutions of a research testbed corpus, the Astro dataset of publication records from astronomy and astrophysics. We extend the dataset by collecting external validation data serving as proxies for the latent structure of the corpus. Specifically, we collect (1) grant funding information related to the publications, (2) data on topical special issues, (3) on specific journals’ internal topic classifications and (4) usage data from the main online bibliographic database of the discipline. The latter three types of data are newly introduced for the purpose of clustering validation and the rationale for using them for this task is set out. We find that one solution based on the global citation network achieves better results than the competitors across three validation data sources but that another solution based on bibliographic coupling performs best on the special issues data.

Suggested Citation

  • Paul Donner, 2021. "Validation of the Astro dataset clustering solutions with external data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1619-1645, February.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:2:d:10.1007_s11192-020-03780-3
    DOI: 10.1007/s11192-020-03780-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-020-03780-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-020-03780-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Johan Bollen & Herbert Van de Sompel & Aric Hagberg & Luis Bettencourt & Ryan Chute & Marko A Rodriguez & Lyudmila Balakireva, 2009. "Clickstream Data Yields High-Resolution Maps of Science," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-11, March.
    2. Lovro Šubelj & Nees Jan van Eck & Ludo Waltman, 2016. "Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-23, April.
    3. Zhang, Yi & Lu, Jie & Liu, Feng & Liu, Qian & Porter, Alan & Chen, Hongshu & Zhang, Guangquan, 2018. "Does deep learning help topic extraction? A kernel k-means clustering method with word embedding," Journal of Informetrics, Elsevier, vol. 12(4), pages 1099-1117.
    4. Sjögårde, Peter & Ahlgren, Per, 2018. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics," Journal of Informetrics, Elsevier, vol. 12(1), pages 133-152.
    5. Kevin W. Boyack, 2017. "Investigating the effect of global data on topic detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 999-1015, May.
    6. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    7. Richard Klavans & Kevin W. Boyack, 2017. "Which Type of Citation Analysis Generates the Most Accurate Taxonomy of Scientific and Technical Knowledge?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(4), pages 984-998, April.
    8. Shenghui Wang & Rob Koopman, 2017. "Clustering articles based on semantic similarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1017-1031, May.
    9. Shu, Fei & Julien, Charles-Antoine & Zhang, Lin & Qiu, Junping & Zhang, Jing & Larivière, Vincent, 2019. "Comparing journal and paper level classifications of science," Journal of Informetrics, Elsevier, vol. 13(1), pages 202-225.
    10. Kevin W Boyack & David Newman & Russell J Duhon & Richard Klavans & Michael Patek & Joseph R Biberstine & Bob Schijvenaars & André Skupin & Nianli Ma & Katy Börner, 2011. "Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    11. Rob Koopman & Shenghui Wang & Andrea Scharnhorst, 2017. "Contextualization of topics: browsing through the universe of bibliographic information," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1119-1139, May.
    12. Jochen Gläser & Wolfgang Glänzel & Andrea Scharnhorst, 2017. "Same data—different results? Towards a comparative approach to the identification of thematic structures in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 981-998, May.
    13. Theresa Velden & Kevin W. Boyack & Jochen Gläser & Rob Koopman & Andrea Scharnhorst & Shenghui Wang, 2017. "Comparison of topic extraction approaches and their results," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1169-1221, May.
    14. Frank Havemann & Jochen Gläser & Michael Heinz, 2017. "Memetic search for overlapping topics based on a local evaluation of link communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1089-1118, May.
    15. Nees Jan Eck & Ludo Waltman, 2017. "Citation-based clustering of publications using CitNetExplorer and VOSviewer," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1053-1070, May.
    16. Shuo Xu & Junwan Liu & Dongsheng Zhai & Xin An & Zheng Wang & Hongshen Pang, 2018. "Overlapping thematic structures extraction with mixed-membership stochastic blockmodel," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 61-84, October.
    17. Theresa Velden & Shiyan Yan & Carl Lagoze, 2017. "Mapping the cognitive structure of astrophysics by infomap clustering of the citation network and topic affinity analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1033-1051, May.
    18. Ruiz-Castillo, Javier & Waltman, Ludo, 2015. "Field-normalized citation impact indicators using algorithmically constructed classification systems of science," Journal of Informetrics, Elsevier, vol. 9(1), pages 102-117.
    19. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    20. Michael J. Kurtz & Guenther Eichhorn & Alberto Accomazzi & Carolyn Grant & Markus Demleitner & Stephen S. Murray, 2005. "Worldwide use and impact of the NASA Astrophysics Data System digital library," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 56(1), pages 36-45, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Calof, Jonathan & Søilen, Klaus Solberg & Klavans, Richard & Abdulkader, Bisan & Moudni, Ismail El, 2022. "Understanding the structure, characteristics, and future of collective intelligence using local and global bibliometric analyses," Technological Forecasting and Social Change, Elsevier, vol. 178(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jochen Gläser & Wolfgang Glänzel & Andrea Scharnhorst, 2017. "Same data—different results? Towards a comparative approach to the identification of thematic structures in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 981-998, May.
    2. Matthias Held & Grit Laudel & Jochen Gläser, 2021. "Challenges to the validity of topic reconstruction," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4511-4536, May.
    3. Theresa Velden & Kevin W. Boyack & Jochen Gläser & Rob Koopman & Andrea Scharnhorst & Shenghui Wang, 2017. "Comparison of topic extraction approaches and their results," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1169-1221, May.
    4. Sjögårde, Peter & Ahlgren, Per, 2018. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics," Journal of Informetrics, Elsevier, vol. 12(1), pages 133-152.
    5. Sitaram Devarakonda & Dmitriy Korobskiy & Tandy Warnow & George Chacko, 2020. "Viewing computer science through citation analysis: Salton and Bergmark Redux," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 271-287, October.
    6. Peter Sjögårde & Fereshteh Didegah, 2022. "The association between topic growth and citation impact of research publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1903-1921, April.
    7. Carlos Olmeda-Gómez & Carlos Romá-Mateo & Maria-Antonia Ovalle-Perandones, 2019. "Overview of trends in global epigenetic research (2009–2017)," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1545-1574, June.
    8. Theresa Velden & Shiyan Yan & Carl Lagoze, 2017. "Mapping the cognitive structure of astrophysics by infomap clustering of the citation network and topic affinity analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1033-1051, May.
    9. Frank Havemann & Jochen Gläser & Michael Heinz, 2017. "Memetic search for overlapping topics based on a local evaluation of link communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1089-1118, May.
    10. Fei Shu & Yue Ma & Junping Qiu & Vincent Larivière, 2020. "Classifications of science and their effects on bibliometric evaluations," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2727-2744, December.
    11. Lin Zhang & Beibei Sun & Fei Shu & Ying Huang, 2022. "Comparing paper level classifications across different methods and systems: an investigation of Nature publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7633-7651, December.
    12. Li, Menghui & Yang, Liying & Zhang, Huina & Shen, Zhesi & Wu, Chensheng & Wu, Jinshan, 2017. "Do mathematicians, economists and biomedical scientists trace large topics more strongly than physicists?," Journal of Informetrics, Elsevier, vol. 11(2), pages 598-607.
    13. Carusi, Chiara & Bianchi, Giuseppe, 2019. "Scientific community detection via bipartite scholar/journal graph co-clustering," Journal of Informetrics, Elsevier, vol. 13(1), pages 354-386.
    14. Yun, Jinhyuk & Ahn, Sejung & Lee, June Young, 2020. "Return to basics: Clustering of scientific literature using structural information," Journal of Informetrics, Elsevier, vol. 14(4).
    15. Rob Koopman & Shenghui Wang & Andrea Scharnhorst, 2017. "Contextualization of topics: browsing through the universe of bibliographic information," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1119-1139, May.
    16. Ballester, Omar & Penner, Orion, 2022. "Robustness, replicability and scalability in topic modelling," Journal of Informetrics, Elsevier, vol. 16(1).
    17. Baccini, Federica & Barabesi, Lucio & Baccini, Alberto & Khelfaoui, Mahdi & Gingras, Yves, 2022. "Similarity network fusion for scholarly journals," Journal of Informetrics, Elsevier, vol. 16(1).
    18. Gerson Pech & Catarina Delgado & Silvio Paolo Sorella, 2022. "Classifying papers into subfields using Abstracts, Titles, Keywords and KeyWords Plus through pattern detection and optimization procedures: An application in Physics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(11), pages 1513-1528, November.
    19. Yun, Jinhyuk, 2022. "Generalization of bibliographic coupling and co-citation using the node split network," Journal of Informetrics, Elsevier, vol. 16(2).
    20. Shome, Samik & Hassan, M. Kabir & Verma, Sushma & Panigrahi, Tushar Ranjan, 2023. "Impact investment for sustainable development: A bibliometric analysis," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 770-800.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:126:y:2021:i:2:d:10.1007_s11192-020-03780-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.