IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v106y2016i1d10.1007_s11192-015-1782-4.html
   My bibliography  Save this article

A graphical article-level metric for intuitive comparison of large-scale literatures

Author

Listed:
  • Xiaoxi Ling

    (Dalian University of Technology)

  • Yu Liu

    (Dalian University of Technology)

  • Zhen Huang

    (Dalian University of Technology)

  • Parantu K. Shah

    (Harvard School of Public Health and Dana-Farber Cancer Institute)

  • Cheng Li

    (Peking University)

Abstract

With the advances of all research fields, the volume of scientific literature has grown exponentially over the past decades, and the management and exploration of scientific literature is becoming an increasingly complicated task. It calls for a tool that combines scientific impacts and social focuses to visualize relevant papers from a specific research area and time period, and to find important and interesting papers. Therefore, we propose a graphical article-level metric (gALM), which captures the impact and popularity of papers from scientific and social aspects. These two dimensions are combined and visualized graphically as a circular map. The map is divided into sectors of papers belonging to a publication year, and each block represents a paper’s journal citations by block size and readerships in Mendeley by block color. In this graphical way, gALM provides a more intuitive comparison of large-scale literatures. In addition, we also design an online Web server, Science Navigation Map (SNM), which not only visualizes the gALM but provides it with interactive features. Through an interactive visualization map of article-level metrics on scientific impact and social popularity in Mendeley, users can intuitively make a comparison of papers as well as explore and filter important and relevant papers by these metrics. We take the journal PLoS Biology as an example and visualize all the papers published in PLoS Biology during 2003 and 2014 by SNM. From this map, one can easily and intuitively find basic statistics of papers, such as the most cited papers and the most popular papers in Mendeley during a time period. SNM on the journal PLoS Biology is publicly available at http://www.linkscholar.org/plosbiology/ .

Suggested Citation

  • Xiaoxi Ling & Yu Liu & Zhen Huang & Parantu K. Shah & Cheng Li, 2016. "A graphical article-level metric for intuitive comparison of large-scale literatures," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 41-50, January.
  • Handle: RePEc:spr:scient:v:106:y:2016:i:1:d:10.1007_s11192-015-1782-4
    DOI: 10.1007/s11192-015-1782-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-015-1782-4
    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/s11192-015-1782-4?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. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Cameron Neylon & Shirley Wu, 2009. "Article-Level Metrics and the Evolution of Scientific Impact," PLOS Biology, Public Library of Science, vol. 7(11), pages 1-6, November.
    3. Felice Frankel & Rosalind Reid, 2008. "Big data: Distilling meaning from data," Nature, Nature, vol. 455(7209), pages 30-30, September.
    4. Xuemei Li & Mike Thelwall & Dean Giustini, 2012. "Validating online reference managers for scholarly impact measurement," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 461-471, May.
    5. Stefanie Haustein & Isabella Peters & Cassidy R. Sugimoto & Mike Thelwall & Vincent Larivière, 2014. "Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 656-669, April.
    6. Haustein, Stefanie & Siebenlist, Tobias, 2011. "Applying social bookmarking data to evaluate journal usage," Journal of Informetrics, Elsevier, vol. 5(3), pages 446-457.
    7. Ludo Waltman & Rodrigo Costas, 2014. "F1000 Recommendations as a Potential New Data Source for Research Evaluation: A Comparison With Citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(3), pages 433-445, March.
    8. Koon-Kiu Yan & Mark Gerstein, 2011. "The Spread of Scientific Information: Insights from the Web Usage Statistics in PLoS Article-Level Metrics," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-7, May.
    Full references (including those not matched with items on IDEAS)

    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. Bornmann, Lutz, 2014. "Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics," Journal of Informetrics, Elsevier, vol. 8(4), pages 895-903.
    2. Mojisola Erdt & Aarthy Nagarajan & Sei-Ching Joanna Sin & Yin-Leng Theng, 2016. "Altmetrics: an analysis of the state-of-the-art in measuring research impact on social media," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1117-1166, November.
    3. Zoller, Daniel & Doerfel, Stephan & Jäschke, Robert & Stumme, Gerd & Hotho, Andreas, 2016. "Posted, visited, exported: Altmetrics in the social tagging system BibSonomy," Journal of Informetrics, Elsevier, vol. 10(3), pages 732-749.
    4. Ehsan Mohammadi & Mike Thelwall & Stefanie Haustein & Vincent Larivière, 2015. "Who reads research articles? An altmetrics analysis of Mendeley user categories," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(9), pages 1832-1846, September.
    5. Lutz Bornmann, 2015. "Alternative metrics in scientometrics: a meta-analysis of research into three altmetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(3), pages 1123-1144, June.
    6. Sergio Copiello, 2020. "Other than detecting impact in advance, alternative metrics could act as early warning signs of retractions: tentative findings of a study into the papers retracted by PLoS ONE," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2449-2469, December.
    7. Mingyang Wang & Zhenyu Wang & Guangsheng Chen, 2019. "Which can better predict the future success of articles? Bibliometric indices or alternative metrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1575-1595, June.
    8. Hou, Jianhua & Yang, Xiucai, 2020. "Social media-based sleeping beauties: Defining, identifying and features," Journal of Informetrics, Elsevier, vol. 14(2).
    9. Amalia Mas-Bleda & Mike Thelwall & Kayvan Kousha & Isidro F. Aguillo, 2014. "Do highly cited researchers successfully use the social web?," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 337-356, October.
    10. Kuang-hua Chen & Muh-chyun Tang & Chun-mei Wang & Jieh Hsiang, 2015. "Exploring alternative metrics of scholarly performance in the social sciences and humanities in Taiwan," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 97-112, January.
    11. Thomy Tonia & Herman Van Oyen & Anke Berger & Christian Schindler & Nino Künzli, 2016. "If I tweet will you cite? The effect of social media exposure of articles on downloads and citations," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 61(4), pages 513-520, May.
    12. Jianhua Hou & Xiucai Yang & Yang Zhang, 2023. "The effect of social media knowledge cascade: an analysis of scientific papers diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5169-5195, September.
    13. Yu Liu & Dan Lin & Xiujuan Xu & Shimin Shan & Quan Z. Sheng, 2018. "Multi-views on Nature Index of Chinese academic institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 823-837, March.
    14. Liwei Zhang & Jue Wang, 2021. "What affects publications’ popularity on Twitter?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9185-9198, November.
    15. Saeideh Ebrahimy & Jafar Mehrad & Fatemeh Setareh & Massoud Hosseinchari, 2016. "Path analysis of the relationship between visibility and citation: the mediating roles of save, discussion, and recommendation metrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1497-1510, December.
    16. Christian Schlögl & Juan Gorraiz & Christian Gumpenberger & Kris Jack & Peter Kraker, 2014. "Comparison of downloads, citations and readership data for two information systems journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1113-1128, November.
    17. Amalia Mas-Bleda & Mike Thelwall, 2016. "Can alternative indicators overcome language biases in citation counts? A comparison of Spanish and UK research," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 2007-2030, December.
    18. Maryam Moshtagh & Tahereh Jowkar & Maryam Yaghtin & Hajar Sotudeh, 2023. "The moderating effect of altmetrics on the correlations between single and multi-faceted university ranking systems: the case of THE and QS vs. Nature Index and Leiden," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 761-781, January.
    19. Saeed-Ul Hassan & Mubashir Imran & Uzair Gillani & Naif Radi Aljohani & Timothy D. Bowman & Fereshteh Didegah, 2017. "Measuring social media activity of scientific literature: an exhaustive comparison of scopus and novel altmetrics big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 1037-1057, November.
    20. Thelwall, Mike & Fairclough, Ruth, 2015. "Geometric journal impact factors correcting for individual highly cited articles," Journal of Informetrics, Elsevier, vol. 9(2), pages 263-272.

    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:106:y:2016:i:1:d:10.1007_s11192-015-1782-4. 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.