IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v101y2014i1d10.1007_s11192-014-1359-7.html
   My bibliography  Save this article

Technical report: the trend of author compound names and its implications for authorship identity identification

Author

Listed:
  • Omar Hernando Avila-Poveda

    (Universidad del Mar (UMAR))

Abstract

Citation analysis has become an essential tool for research and academic effectiveness evaluation of universities. However, authorship identity has long been difficult to resolve in bibliometric analyses for many scientific fields, where performance of algorithms against human judgment is far from universal. Now with the boom of authors with compound names (mainly, Latino researchers and from Portuguese language countries) in scientific publications, clustering methods continue lowering their performance, due to completely forgetting the context and order of names (first name“s” and last name“s”) of each author in the publication (authorship identity). These kinds of mistakes affect visualization of publications, decreasing the likelihood of finding a given article by a specific author and generating bad quotations in the online systems. This has led to an unsuitable registration and unsuitable grouping of author names “ambiguous authorship identity” of each scientific publication. This process requires more work, time, attention, and accountability on the part of authors, reviewers, journal editors, and providers of bibliographic databases. These errors can be corrected by cross-referencing with each full original article, using manual checks and without ignoring the names issue at the moment of drafting and/or reviewing a manuscript. This paper seeks to raise awareness on how to write author names, highlighting the way in which they are being cited and self-citing the name of authors and co-authors in the publications.

Suggested Citation

  • Omar Hernando Avila-Poveda, 2014. "Technical report: the trend of author compound names and its implications for authorship identity identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 833-846, October.
  • Handle: RePEc:spr:scient:v:101:y:2014:i:1:d:10.1007_s11192-014-1359-7
    DOI: 10.1007/s11192-014-1359-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-014-1359-7
    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/s11192-014-1359-7?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. Jian Wang & Kaspars Berzins & Diana Hicks & Julia Melkers & Fang Xiao & Diogo Pinheiro, 2012. "A boosted-trees method for name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 391-411, November.
    2. Li Tang & John P. Walsh, 2010. "Bibliometric fingerprints: name disambiguation based on approximate structure equivalence of cognitive maps," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 763-784, September.
    3. Thomas Gurney & Edwin Horlings & Peter van den Besselaar, 2012. "Author disambiguation using multi-aspect similarity indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 435-449, May.
    4. Zehra Taşkın & Umut Al, 2014. "Standardization problem of author affiliations in citation indexes," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 347-368, January.
    5. Jiang Wu & Xiu-Hao Ding, 2013. "Author name disambiguation in scientific collaboration and mobility cases," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 683-697, September.
    6. Carmen Galvez & Félix Moya-Anegón, 2006. "The unification of institutional addresses applying parametrized finite-state graphs (P-FSG)," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(2), pages 323-345, November.
    7. Timothy D. Fry & Joan M. Donohue, 2014. "Exploring the Author Affiliation Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1647-1667, March.
    8. José M. Soler, 2007. "Separating the articles of authors with the same name," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(2), pages 281-290, August.
    9. F. Collazo-Reyes & M. E. Luna-Morales & J. M. Russell & M. A. Pérez-Angón, 2008. "Publication and citation patterns of Latin American & Caribbean journals in the SCI and SSCI from 1995 to 2004," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(1), pages 145-161, April.
    10. Howard D. White, 2001. "Author-centered bibliometrics through CAMEOs: Characterizations automatically made and edited online," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(3), pages 607-637, January.
    11. Howard D. White, 2001. "Author-centered bibliometrics through CAMEOs: Characterizations automatically made and edited online," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(3), pages 607-637, July.
    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. Jaime A. Teixeira da Silva & Judit Dobránszki, 2018. "Rejoinder to “Multiple versions of the h-index: cautionary use for formal academic purposes”," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 1131-1137, May.

    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. Maxim Kotsemir & Sergey Shashnov, 2017. "Measuring, analysis and visualization of research capacity of university at the level of departments and staff members," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1659-1689, September.
    2. Rehs, Andreas, 2021. "A supervised machine learning approach to author disambiguation in the Web of Science," Journal of Informetrics, Elsevier, vol. 15(3).
    3. Jiang Wu & Xiu-Hao Ding, 2013. "Author name disambiguation in scientific collaboration and mobility cases," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 683-697, September.
    4. Christopher McCarty & James W. Jawitz & Allison Hopkins & Alex Goldman, 2013. "Predicting author h-index using characteristics of the co-author network," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 467-483, August.
    5. Deyun Yin & Kazuyuki Motohashi & Jianwei Dang, 2020. "Large-scale name disambiguation of Chinese patent inventors (1985–2016)," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 765-790, February.
    6. Andrea Ancona & Roy Cerqueti & Gianluca Vagnani, 2023. "A novel methodology to disambiguate organization names: an application to EU Framework Programmes data," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4447-4474, August.
    7. Félix Moya-Anegón & Benjamín Vargas-Quesada & Victor Herrero-Solana & Zaida Chinchilla-Rodríguez & Elena Corera-Álvarez & Francisco J. Munoz-Fernández, 2004. "A new technique for building maps of large scientific domains based on the cocitation of classes and categories," Scientometrics, Springer;Akadémiai Kiadó, vol. 61(1), pages 129-145, September.
    8. Koski, Timo & Sandström, Erik & Sandström, Ulf, 2016. "Towards field-adjusted production: Estimating research productivity from a zero-truncated distribution," Journal of Informetrics, Elsevier, vol. 10(4), pages 1143-1152.
    9. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    10. Jinseok Kim & Jenna Kim, 2018. "The impact of imbalanced training data on machine learning for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 511-526, October.
    11. Fernanda Morillo & Ignacio Santabárbara & Javier Aparicio, 2013. "The automatic normalisation challenge: detailed addresses identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 953-966, June.
    12. Jinseok Kim & Jenna Kim & Jason Owen‐Smith, 2021. "Ethnicity‐based name partitioning for author name disambiguation using supervised machine learning," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 979-994, August.
    13. Hao Wu & Bo Li & Yijian Pei & Jun He, 2014. "Unsupervised author disambiguation using Dempster–Shafer theory," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1955-1972, December.
    14. Byungun Yoon & Sungjoo Lee & Gwanghee Lee, 2010. "Development and application of a keyword-based knowledge map for effective R&D planning," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 803-820, December.
    15. Jinseok Kim & Jenna Kim, 2020. "Effect of forename string on author name disambiguation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(7), pages 839-855, July.
    16. Wu, Jiang, 2013. "Investigating the universal distributions of normalized indicators and developing field-independent index," Journal of Informetrics, Elsevier, vol. 7(1), pages 63-71.
    17. Wang, Peiling & Su, Jing, 2021. "Post-publication expert recommendations in faculty opinions (F1000Prime): Recommended articles and citations," Journal of Informetrics, Elsevier, vol. 15(3).
    18. Pascal Cuxac & Jean-Charles Lamirel & Valerie Bonvallot, 2013. "Efficient supervised and semi-supervised approaches for affiliations disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(1), pages 47-58, October.
    19. Dongwook Shin & Taehwan Kim & Joongmin Choi & Jungsun Kim, 2014. "Author name disambiguation using a graph model with node splitting and merging based on bibliographic information," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 15-50, July.
    20. Bar-Ilan, Judit & Levene, Mark & Lin, Ayelet, 2007. "Some measures for comparing citation databases," Journal of Informetrics, Elsevier, vol. 1(1), pages 26-34.

    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:101:y:2014:i:1:d:10.1007_s11192-014-1359-7. 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.