, Volume 106, Issue 1, pp 41–50 | Cite as

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

  • Xiaoxi Ling
  • Yu Liu
  • Zhen Huang
  • Parantu K. Shah
  • Cheng Li


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


Graphical article-level metrics Visualization Science navigation map 



This work was supported by grants from the Natural Science Foundation of China (No.U1233110) and the Fundamental Research Funds for the Central Universities (No. DUT13JR01).


  1. Adie, E., & Roe, W. (2013). Altmetric: Enriching scholarly content with article-level discussion and metrics. Learned Publishing, 26(1), 11–17.CrossRefGoogle Scholar
  2. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. the. Journal of machine Learning research, 3, 993–1022.MATHGoogle Scholar
  3. Bollen, J., Van de Sompel, H., Smith, J. A., & Luce, R. (2005). Toward alternative metrics of journal impact: A comparison of download and citation data. Information Processing & Management, 41(6), 1419–1440.CrossRefGoogle Scholar
  4. Borgman, C. L., & Furner, J. (2002). Scholarly communication and bibliometrics. Annual Review of Information Science and Technology, 36, 3–72.Google Scholar
  5. Costas, R., Zahedi, Z., Wouters, P. (2014). Do altmetrics correlate with citations? extensive comparison of altmetric indicators with citations from a multidisciplinary perspective. Journal of the Association for Information Science and Technology.Google Scholar
  6. Eysenbach, G. (2011). Can tweets predict citations? metrics of social impact based on twitter and correlation with traditional metrics of scientific impact. Journal of Medical Internet Research, 13(4),Google Scholar
  7. Frankel, F., & Reid, R. (2008). Big data: Distilling meaning from data. Nature, 455(7209), 30–30.CrossRefGoogle Scholar
  8. Gunn, W. (2013). Social signals reflect academic impact: What it means when a scholar adds a paper to mendeley. Information Standards Quarterly, 25(2), 33–39.CrossRefGoogle Scholar
  9. Haendel, M. A., Vasilevsky, N. A., & Wirz, J. A. (2012). Dealing with data: A case study on information and data management literacy. PLoS Biology, 10(5), e1001,339.CrossRefGoogle Scholar
  10. Haustein, S., Larivière, V., Thelwall, M., Amyot, D., & Peters, I. (2014a). Tweets versus mendeley readers: How do these two social media metrics differ? IT-Information Technology, 56(5), 207–215.CrossRefGoogle Scholar
  11. Haustein, S., Peters, I., Sugimoto, C. R., Thelwall, M., & Larivière, V. (2014b). Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature. Journal of the Association for Information Science and Technology, 65(4), 656–669.CrossRefGoogle Scholar
  12. Haustein, S., & Siebenlist, T. (2011). Applying social bookmarking data to evaluate journal usage. Journal of Informetrics, 5(3), 446–457.Google Scholar
  13. Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.CrossRefGoogle Scholar
  14. Li, X., Thelwall, M., & Giustini, D. (2012). Validating online reference managers for scholarly impact measurement. Scientometrics, 91(2), 461–471.CrossRefGoogle Scholar
  15. Liu, Y., Huang, Z., Fang, J., Yan, Y. (2014). An article level metric in the context of research community. In: Proceedings of the companion publication of the 23rd international conference on World wide web companion, International World Wide Web Conferences Steering Committee, pp 1197–1202.Google Scholar
  16. Lu, Z. (2011). Pubmed and beyond: a survey of web tools for searching biomedical literature. Database 2011:baq036.Google Scholar
  17. Neylon, C., & Wu, S. (2009). Article-level metrics and the evolution of scientific impact. PLoS Biology, 7(11), e1000,242.CrossRefGoogle Scholar
  18. Priem, J., Hemminger, B.H. (2010). Scientometrics 2.0: New metrics of scholarly impact on the social web. First Monday 15(7).Google Scholar
  19. Taraborelli, D. (2008). Soft peer review: Social software and distributed scientific evaluation. Proceedings of the Eighth International Conference on the Design of Cooperative Systems.Google Scholar
  20. Thelwall, M., Tsou, A., Weingart, S., Holmberg, K., & Haustein, S. (2013). Tweeting links to academic articles. Cybermetrics: International Journal of Scientometrics Informetrics and Bibliometrics, 17, 1–8.Google Scholar
  21. Waltman, L., & Costas, R. (2014). F1000 recommendations as a potential new data source for research evaluation: A comparison with citations. Journal of the Association for Information Science and Technology, 65(3), 433–445.CrossRefGoogle Scholar
  22. Weller, K., & Puschmann, C. (2011). Twitter for scientific communication: How can citations/references be identified and measured. Proceedings of the ACM WebScience, 11, 1–4.Google Scholar
  23. Wouters, P., Costas, R. (2012). Users, narcissism and control: tracking the impact of scholarly publications in the 21st century. SURFfoundation.Google Scholar
  24. Xu, W., Liu, X., Gong, Y. (2003). Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, ACM, pp 267–273.Google Scholar
  25. Yan, K. K., & Gerstein, M. (2011). The spread of scientific information: Insights from the web usage statistics in plos article-level metrics. PloS One, 6(5), e19,917.CrossRefGoogle Scholar
  26. Zahedi, Z., Costas, R., Wouters, P., et al. (2013). What is the impact of the publications read by the different mendeley users? could they help to identify alternative types of impact? plos alm workshop, san francisco. PLoS ALM Workshop.Google Scholar
  27. Zahedi, Z., Costas, R., & Wouters, P. (2014a). How well developed are altmetrics? A cross-disciplinary analysis of the presence of alternative metrics in scientific publications. Scientometrics, 101(2), 1491–1513.CrossRefGoogle Scholar
  28. Zahedi, Z., Fenner, M., Costas, R. (2014b). How consistent are altmetrics providers? study of 1000 plos one publications using the plos alm, mendeley and altmetric. com apis. In: altmetrics 14. Workshop at the Web Science Conference, Bloomington, USA.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2015

Authors and Affiliations

  • Xiaoxi Ling
    • 1
  • Yu Liu
    • 1
  • Zhen Huang
    • 1
  • Parantu K. Shah
    • 2
  • Cheng Li
    • 3
  1. 1.School of SoftwareDalian University of TechnologyDalianChina
  2. 2.Department of BiostatisticsHarvard School of Public Health and Dana-Farber Cancer InstituteBostonUSA
  3. 3.School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Center for Statistical SciencePeking UniversityBeijingChina

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