Skip to main content
Log in

Dynamic credit allocation in scientific literature

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Collaboration among researchers is an essential component of the scientific process, playing a particularly important role in findings with significant impact. While extensive efforts have been devoted to quantifying and predicting scientific impact, the question of how credit is allocated to coauthors of publications with multiple authors within a complex evolving system remains a long-standing problem in scientometrics. In this paper, we propose a dynamic credit allocation algorithm that captures the coauthors’ contribution to a publication as perceived by the scientific community, incorporating a reinforcement mechanism and a power-law temporal relaxation function. The citation data from American Physical Society are used to validate our method. We find that the proposed method can significantly outperform the state-of-the-art method in identifying the authors of Nobel-winning papers that are credited for the discovery, independent of their positions in the author list. Furthermore, the proposed methodology also allows us to determine the temporal evolution of credit between coauthors. Finally, the predictive power of our method can be further improved by incorporating the author list prior appropriately.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Allen, L., Scott, J., Brand, A., Hlava, M., & Altman, M. (2014). Publishing: Credit where credit is due. Nature, 508(7496), 312–313.

    Article  Google Scholar 

  • Biggs, J. (2008). Allocating the credit in collaborative research. Political Science and Politics, 41(1), 246–247.

    Article  Google Scholar 

  • Campbell, P. (1999). Policy on papers’ contributions. Nature, 399(6735), 393.

    Article  Google Scholar 

  • De Clippel, G., Moulin, H., & Tideman, N. (2008). Impartial division of a dollar. Journal of Economic Theory, 139(1), 176–191.

    Article  MathSciNet  MATH  Google Scholar 

  • Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152.

    Article  Google Scholar 

  • Evans, J. A. (2013). Future science. Science, 342(6154), 44–45.

    Article  Google Scholar 

  • Foulkes, W., & Neylon, N. (1996). Redefining authorship. Relative contribution should be given after each author’s name. British Medical Journal, 312(7043), 1423.

    Article  Google Scholar 

  • Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178(4060), 471–479.

    Article  Google Scholar 

  • Hagen, N. T. (2008). Harmonic allocation of authorship credit: Source-level correction of bibliometric bias assures accurate publication and criterion analysis. PLOS One, 3(12), e4021.

    Article  Google Scholar 

  • Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572.

    Article  MATH  Google Scholar 

  • Hirsch, J. E. (2007). Does the H index have predictive power? Proceedings of the National Academy of Sciences of the United States of America, 104(49), 19193–19198.

    Article  Google Scholar 

  • Hodge, S. E., & Greenberg, D. A. (1981). Publication credit. Science, 213(4511), 950.

    Google Scholar 

  • Kaur, J., Radicchi, F., & Menczer, F. (2013). Universality of scholarly impact metrics. Journal of Informetrics, 7(4), 924–932.

    Article  Google Scholar 

  • Kennedy, D. (2003). Multiple authors, multiple problems. Science, 301(5634), 733.

    Article  Google Scholar 

  • Kim, J., & Diesner, J. (2015). Distortive effects of initial-based name disambiguation on measurements of large-scale coauthorship networks. Journal of the Association for Information Science and Technology. doi:10.1002/asi.23489.

    Google Scholar 

  • Lehmann, S., Jackson, A. D., & Lautrup, B. E. (2006). Measures for measures. Nature, 444(7122), 1003–1004.

    Article  Google Scholar 

  • Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences of the United States of America, 101(suppl 1), 5200–5205.

    Article  Google Scholar 

  • Radicchi, F., Fortunato, S., Markines, B., & Vespignani, A. (2009). Diffusion of scientific credits an dthe ranking of scientist. Physical Review E Statistical, Nonlinear, and Soft Matter Physics, 80(5 Pt 2), 056103.

    Article  Google Scholar 

  • Redner, S. (2005). Citation statistics from 110 years of physical review. Physics Today, 58, 49.

    Article  Google Scholar 

  • Sekercioglu, C. H. (2008). Quantifying coauthor contributions. Science, 322(5900), 371.

    Article  Google Scholar 

  • Shen, H. W., & Barabási, A. L. (2014). Collective credit allocation in science. Proceedings of the National Academy of Sciences of the United States of America, 111(34), 12325–12330.

    Article  Google Scholar 

  • Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A. L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), 596.

    Article  Google Scholar 

  • Stallings, J., Vance, E., Yang, J. S., Vannier, M. W., Liang, J. M., Pang, L. J., et al. (2013). Determining scientific impact using a collaboration index. Proceedings of the National Academy of Sciences of the United States of America, 110(24), 9680–9685.

    Article  MathSciNet  MATH  Google Scholar 

  • Strotmann, A., & Zhao, D. Z. (2012). Author name disambiguation: What difference does it make in authorbased citation analysis? Journal of the American Society for Information Science and Technology, 63(9), 1820–1833.

    Article  Google Scholar 

  • Tscharntke, T., Hochberg, M. E., Rand, T. A., Resh, V. H., & Krauss, J. (2007). Author sequence and credit for contributions in multiauthored publications. PLOS Biology, 5(1), e18.

    Article  Google Scholar 

  • Wang, D., Song, C., & Barabási, A. L. (2013). Quantifying long-term scientific impact. Science, 342(6154), 127–132.

    Article  Google Scholar 

  • Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.

    Article  Google Scholar 

  • Zhang, C. T. (2009). A proposal for calculating weighted citations based on author rank. EMBO Reports, 10(5), 416–417.

    Article  Google Scholar 

Download references

Acknowledgements

This work was funded by the Fundamental Research Funds for the Central Universities under Grant Number 2015RC031 and the State Visiting Scholar Funds from the China Scholarship Council under Grant Number 201607095027. This work was supported in part by the National Science Foundation under Grant Numbers CNS-1513939 and CNS-1408944.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peng Bao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bao, P., Zhai, C. Dynamic credit allocation in scientific literature. Scientometrics 112, 595–606 (2017). https://doi.org/10.1007/s11192-017-2335-9

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-017-2335-9

Keywords

Navigation