Skip to main content
SpringerLink
Log in

What Research Skills Do Scholars Excel at?—Based on Individual Contribution and External Recognition

  • Conference paper
  • First Online:
Wisdom, Well-Being, Win-Win (iConference 2024)

Part of the book series: Lecture Notes in Computer Science ((volume 14597))

Included in the following conference series:

Abstract

Diverse research skills are used by scholars in their studies, such as methods design, data investigation, and writing, which significantly influence their research quality and subsequent scientific reputation. Existing scholar evaluation programs focus on providing a composite indicator for ranking scholars and lack specific analysis of scholars’ research skill performance. This study proposes a practical framework that characterizes research skill performance from two perspectives: individual contribution and external recognition. Individual contribution is assessed through the author’s contribution statement, while external recognition is evaluated through citations and altmetrics. Based on these two perspectives, the framework measures scholars’ skill performance in three dimensions: proficiency, academic reputation, and social impact. These dimensions provide a detailed and specific evaluation of individual scholar performance and allow for the advancement of successful research collaborations. The application of this framework to a specific scholar, Smith GD, who has published the most articles in the open-access journal PLOS Medicine, demonstrates its effectiveness in portraying scholars’ academic performance. The results show that Smith GD shows strong research skills in methodology, results analysis and validation, and manuscript writing, but could benefit from collaborating with colleagues who excel in practical operations and external support.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Whitley, R.: The intellectual and social organization of the sciences, 2nd ed. Oxford [England]; New York: Oxford University Press (2000)

    Google Scholar 

  2. Wildgaard, L., Schneider, J.W., Larsen, B.: A review of the characteristics of 108 author-level bibliometric indicators. Scientometrics 101(1), 125–158 (2014). https://doi.org/10.1007/s11192-014-1423-3

    Article  Google Scholar 

  3. Cole, S., Cole, J.R.: Scientific output and recognition: a study in the operation of the reward system in science. Am. Sociol. Rev. 32(3), 377 (1967). https://doi.org/10.2307/2091085

    Article  Google Scholar 

  4. Wildgaard, L.: A comparison of 17 author-level bibliometric indicators for researchers in astronomy, environmental science, philosophy and public health in web of science and google scholar. Scientometrics 104(3), 873–906 (2015). https://doi.org/10.1007/s11192-015-1608-4

    Article  Google Scholar 

  5. Harzing, A.-W.: Publish or Perish, Harzing.com, Feb. 06, 2016. https://harzing.com/resources/publish-or-perish (Accessed 01 Mar 2023)

  6. Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. 102(46), 16569–16572 (2005). https://doi.org/10.1073/pnas.0507655102

    Article  Google Scholar 

  7. Egghe, L.: Theory and practise of the g-index. Scientometrics 69(1), 131–152 (2006). https://doi.org/10.1007/s11192-006-0144-7

    Article  Google Scholar 

  8. Kosmulski, M.: A new Hirsch-type index saves time and works equally well as the original h-index. ISSI Newsl. 2(3), 4–6 (2006)

    Google Scholar 

  9. Zhang, C.-T.: The e-index, complementing the h-index for excess citations. PLoS ONE 4(5), e5429 (2009). https://doi.org/10.1371/journal.pone.0005429

    Article  Google Scholar 

  10. Mabrouk, P.A.: Research skills and ethics—a graduate course empowering graduate students for productive research careers in graduate school and beyond. J. Chem. Educ. 78(12), 1628 (2001). https://doi.org/10.1021/ed078p1628

    Article  Google Scholar 

  11. Tractenberg, R.E., Umans, J.G., McCarter, R.J.: A mastery rubric: guiding curriculum design, admissions and development of course objectives. Assess. Eval. High. Educ. 35(1), 15–32 (2010). https://doi.org/10.1080/02602930802474169

    Article  Google Scholar 

  12. Kersnik, J., Ungan, M., Klemenc-Ketis, Z.: Why does teaching research skills to family medicine trainees make sense? Eur. J. Gen. Pract. 21(4), 253–256 (2015). https://doi.org/10.3109/13814788.2015.1057813

    Article  Google Scholar 

  13. Beuning, P.J.: Research skills and ethics: the 20-year evolution of a professional development graduate course. Anal. Bioanal. Chem. 409(4), 859–862 (2017). https://doi.org/10.1007/s00216-016-9989-7

    Article  Google Scholar 

  14. Uebel, K., Iqbal, M.P., Barbara‐Ann Adelstein: A pragmatic approach to promoting research skills in all medical students. Med. Educ. 54(5), 445–446 (2020). https://doi.org/10.1111/medu.14097

    Article  Google Scholar 

  15. Kong, X., Liu, L., Yu, S., Yang, A., Bai, X., Xu, B.: Skill ranking of researchers via hypergraph. PeerJ Comput. Sci. 5, e182 (2019). https://doi.org/10.7717/peerj-cs.182

    Article  Google Scholar 

  16. Dalmeet Singh Chawla: Digital badges aim to clear up politics of authorship. Nature 526(7571), 145–146 (2015). https://doi.org/10.1038/526145a

    Article  Google Scholar 

  17. Farhadi, F., Sorkhi, M., Hashemi, S., Hamzeh, A.: An effective expert team formation in social networks based on skill grading. In: 2011 IEEE 11th International Conference on Data Mining Workshops, Vancouver, BC, Canada: IEEE, Dec. 2011, pp. 366–372. https://doi.org/10.1109/ICDMW.2011.28

  18. Kong, X., Jiang, H., Yang, Z., Xu, Z., Xia, F., Tolba, A.: Exploiting publication contents and collaboration networks for collaborator recommendation. PLoS ONE 11(2), e0148492 (2016). https://doi.org/10.1371/journal.pone.0148492

    Article  Google Scholar 

  19. Li, L., Tong, H., Cao, N., Ehrlich, K., Lin, Y.-R., Buchler, N.: Enhancing team composition in professional networks: problem definitions and fast solutions. IEEE Trans. Knowl. Data Eng. 29(3), 613–626 (2017). https://doi.org/10.1109/TKDE.2016.2633464

    Article  Google Scholar 

  20. Allen, L., Scott, J., Brand, A., Hlava, M., Altman, M.: Publishing: credit where credit is due. Nat. News 508(7496), 312 (2014). https://doi.org/10.1038/508312a

    Article  Google Scholar 

  21. Ding, J., Liu, C., Zheng, Q., Cai, W.: A new method of co-author credit allocation based on contributor roles taxonomy: proof of concept and evaluation using papers published in PLOS ONE. Scientometrics 126(9), 7561–7581 (2021). https://doi.org/10.1007/s11192-021-04075-x

    Article  Google Scholar 

  22. Rahman, M.T., Regenstein, J.M., Kassim, N.L.A., Haque, N.: The need to quantify authors’ relative intellectual contributions in a multi-author paper. J. Informetr. 11(1), 275–281 (2017). https://doi.org/10.1016/j.joi.2017.01.002

    Article  Google Scholar 

  23. Yang, S., Xiao, A., Nie, Y., Dong, J.: Measuring coauthors’ credit in medicine field — Based on author contribution statement and citation context analysis. Inf. Process. Manag. 59(3), 102924 (2022). https://doi.org/10.1016/j.ipm.2022.102924

    Article  Google Scholar 

  24. Hagen, N.T.: Harmonic allocation of authorship credit: source-level correction of bibliometric bias assures accurate publication and citation analysis. PLoS ONE 3(12), e4021 (2008). https://doi.org/10.1371/journal.pone.0004021

    Article  Google Scholar 

  25. Hagen, N.T.: Harmonic publication and citation counting sharing authorship credit equitably – not equally, geometrically or arithmetically. Scientometrics 84(3), 785–793 (2010). https://doi.org/10.1007/s11192-009-0129-4

    Article  Google Scholar 

  26. Hagen, N.T.: Harmonic coauthor credit: a parsimonious quantification of the byline hierarchy. J. Informetr. 7(4), 784–791 (2013). https://doi.org/10.1016/j.joi.2013.06.005

    Article  Google Scholar 

  27. Holcombe, A.O.: Contributorship, not authorship: use credit to indicate who did what. Publications 7(3), 48 (2019). https://doi.org/10.3390/publications7030048

    Article  Google Scholar 

  28. Allen, L., O’Connell, A., Kiermer, V.: How can we ensure visibility and diversity in research contributions? hHow the contributor role taxonomy (CRediT) is helping the shift from authorship to contributorship. Learn. Publ. 32(1), 71–74 (2019). https://doi.org/10.1002/leap.1210

    Article  Google Scholar 

  29. Waltman, L.: An empirical analysis of the use of alphabetical authorship in scientific publishing. J. Informetr. 6(4), 700–711 (2012). https://doi.org/10.1016/j.joi.2012.07.008

    Article  MathSciNet  Google Scholar 

  30. Yang, S., Wolfram, D., Wang, F.: The relationship between the author byline and contribution lists: a comparison of three general medical journals. Scientometrics 110(3), 1273–1296 (2017). https://doi.org/10.1007/s11192-016-2239-0

    Article  Google Scholar 

  31. Larivière, V., Desrochers, N., Macaluso, B., Mongeon, P., Paul-Hus, A., Sugimoto, C.R.: Contributorship and division of labor in knowledge production. Soc. Stud. Sci. 46(3), 417–435 (2016). https://doi.org/10.1177/0306312716650046

    Article  Google Scholar 

  32. Corrêa Jr, E.A., Silva, F.N., da Luciano, F., Costa, D.R., Amancio,: Patterns of authors contribution in scientific manuscripts. J. Informet. 11(2), 498–510 (2017). https://doi.org/10.1016/j.joi.2017.03.003

    Article  Google Scholar 

  33. Lu, C., Zhang, C., Xiao, C., Ding, Y.: Contributorship in scientific collaborations: the perspective of contribution-based byline orders. Inf. Process. Manag. 59(3), 102944 (2022). https://doi.org/10.1016/j.ipm.2022.102944

    Article  Google Scholar 

  34. Kim, J., Kim, J.: Rethinking the comparison of coauthorship credit allocation schemes. J. Informetr. 9(3), 667–673 (2015). https://doi.org/10.1016/j.joi.2015.07.005

    Article  Google Scholar 

  35. Donner, P.: A validation of coauthorship credit models with empirical data from the contributions of PhD candidates. Quant. Sci. Stud. 1(2), 551–564 (2020). https://doi.org/10.1162/qss_a_00048

    Article  Google Scholar 

  36. Ding, Y.: Applying weighted PageRank to author citation networks. J. Am. Soc. Inf. Sci. Technol. 62(2), 236–245 (2011). https://doi.org/10.1002/asi.21452

    Article  Google Scholar 

  37. Small, H.: Citation context analysis. Prog. Commun. Sci. 3, 287–310 (1982)

    Google Scholar 

  38. Sombatsompop, N., Kositchaiyong, A., Markpin, T., Inrit, S.: Scientific evaluations of citation quality of international research articles in the SCI database: Thailand case study. Scientometrics 66(3), 521–535 (2006). https://doi.org/10.1007/s11192-006-0038-8

    Article  Google Scholar 

  39. Hu, Z., Lin, G., Sun, T., Hou, H.: Understanding multiply mentioned references. J. Informetr. 11(4), 948–958 (2017). https://doi.org/10.1016/j.joi.2017.08.004

    Article  Google Scholar 

  40. Pak, C., Yu, G., Wang, W.: A study on the citation situation within the citing paper: citation distribution of references according to mention frequency. Scientometrics 114(3), 905–918 (2018). https://doi.org/10.1007/s11192-017-2627-0

    Article  Google Scholar 

  41. Wang, M., Ren, J., Li, S., Chen, G.: Quantifying a paper’s academic impact by distinguishing the unequal intensities and contributions of citations. IEEE Access 7, 96198–96214 (2019). https://doi.org/10.1109/ACCESS.2019.2927016

    Article  Google Scholar 

  42. Priem, J., Taraborelli, D., Groth, P., Neylon, C.: Altmetrics: A manifesto Oct 26, (2010). https://altmetrics.org/manifesto/ (Accessed Apr. 06, 2023)

  43. Galligan, F., Dyas-Correia, S.: Altmetrics: rethinking the way we measure. Ser. Rev. 39(1), 56–61 (2013). https://doi.org/10.1080/00987913.2013.10765486

    Article  Google Scholar 

  44. Eysenbach, G.: Can tweets predict citations? metrics of social impact based on twitter and correlation with traditional metrics of scientific impact. J. Med. Internet Res. 13(4), e2012 (2011). https://doi.org/10.2196/jmir.2012

    Article  Google Scholar 

  45. Thelwall, M., Haustein, S., Larivière, V., Sugimoto, C.R.: Do altmetrics work? twitter and ten other social web services. PLoS ONE 8(5), e64841 (2013). https://doi.org/10.1371/journal.pone.0064841

    Article  Google Scholar 

  46. Zahedi, Z., Costas, R., Wouters, P.: How well developed are altmetrics? a cross-disciplinary analysis of the presence of ‘alternative metrics’ in scientific publications. Scientometrics 101(2), 1491–1513 (2014). https://doi.org/10.1007/s11192-014-1264-0

    Article  Google Scholar 

  47. Ortega, J.L.: Blogs and news sources coverage in altmetrics data providers: a comparative analysis by country, language, and subject. Scientometrics 122(1), 555–572 (2020). https://doi.org/10.1007/s11192-019-03299-2

    Article  Google Scholar 

  48. Gumpenberger, C., Glänzel, W., Gorraiz, J.: The ecstasy and the agony of the altmetric score. Scientometrics 108(2), 977–982 (2016). https://doi.org/10.1007/s11192-016-1991-5

    Article  Google Scholar 

  49. Thelwall, M., Nevill, T.: Could scientists use Altmetric.com scores to predict longer term citation counts? J. Informetr. 12(1), 237–248 (2018). https://doi.org/10.1016/j.joi.2018.01.008

    Article  Google Scholar 

  50. Yang, S., Zheng, M., Yonghao, Yu., Wolfram, D.: Are altmetric.com scores effective for research impact evaluation in the social sciences and humanities? J. Informet. 15(1), 101120 (2021). https://doi.org/10.1016/j.joi.2020.101120

    Article  Google Scholar 

Download references

Acknowledgments

This research was made possible through the financial support of the Chinese National Social Science Fund for the Post-Funding Project "Research on Knowledge Communication Systems Based on Full-Text Scientometrics Analysis" (Project ID: 22FTQB003). Furthermore, our sincere appreciation goes to Dr. Nicolas Robinson-Garcia from the University of Granada for his valuable insights and suggestions, which significantly contributed to the refinement of this research. We would also like to express our gratitude to all colleagues and expert reviewers who provided valuable feedback and suggestions during the research process. Finally, a special acknowledgment is due to ChatGPT for generating a substantial portion of the data processing code used in this study. This contribution has been instrumental in advancing the progress of our research.

Author information

Authors and Affiliations

  1. School of Information Management, Wuhan University, Wuhan, People’s Republic of China

    Aoxia Xiao, Siluo Yang & Minshu Jin

  2. Wuhan Library, Chinese Academy of Sciences, Wuhan, People’s Republic of China

    Mingliang Yue

Authors
  1. Aoxia Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siluo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingliang Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Minshu Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Aoxia XIAO: Conceptualization, Methodology, Investigation, Data curation, Visualization, Formal analysis, Writing-original draft, Writing-review & editing; Siluo YANG: Conceptualization, Methodology, Funding acquisition, Supervision, Writing-review & editing; Mingliang YUE: Conceptualization, Methodology, Writing-review & editing; Minshu JIN: Investigation, Data curation.

Corresponding author

Correspondence to Aoxia Xiao .

Editor information

Editors and Affiliations

  1. iSchool organization, Berlin, Germany

    Isaac Sserwanga

  2. University of Tsukuba, Tsukuba, Japan

    Hideo Joho

  3. Jilin University, Changchun, China

    Jie Ma

  4. Stockholm University, Kista, Sweden

    Preben Hansen

  5. Wuhan University, Wuhan, China

    Dan Wu

  6. University of Tsukuba, Tsukuba, Japan

    Masanori Koizumi

  7. University of California, Los Angeles, CA, USA

    Anne J. Gilliland

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xiao, A., Yang, S., Yue, M., Jin, M. (2024). What Research Skills Do Scholars Excel at?—Based on Individual Contribution and External Recognition. In: Sserwanga, I., et al. Wisdom, Well-Being, Win-Win. iConference 2024. Lecture Notes in Computer Science, vol 14597. Springer, Cham. https://doi.org/10.1007/978-3-031-57860-1_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-57860-1_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57859-5

  • Online ISBN: 978-3-031-57860-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation