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Proc Natl Acad Sci U S A. 2014 Aug 26;111(34):12325-30. doi: 10.1073/pnas.1401992111. Epub 2014 Aug 11.

Collective credit allocation in science.

Author information

1
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; Center for Complex Network Research and Departments of Physics, Biology, and Computer Science, Northeastern University, Boston, MA 02115;
2
Center for Complex Network Research and Departments of Physics, Biology, and Computer Science, Northeastern University, Boston, MA 02115; Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115; and Center for Network Science, Central European University, 1051, Budapest, Hungary barabasi@gmail.com.

Abstract

Collaboration among researchers is an essential component of the modern scientific enterprise, playing a particularly important role in multidisciplinary research. However, we continue to wrestle with allocating credit to the coauthors of publications with multiple authors, because the relative contribution of each author is difficult to determine. At the same time, the scientific community runs an informal field-dependent credit allocation process that assigns credit in a collective fashion to each work. Here we develop a credit allocation algorithm that captures the coauthors' contribution to a publication as perceived by the scientific community, reproducing the informal collective credit allocation of science. We validate the method by identifying the authors of Nobel-winning papers that are credited for the discovery, independent of their positions in the author list. The method can also compare the relative impact of researchers working in the same field, even if they did not publish together. The ability to accurately measure the relative credit of researchers could affect many aspects of credit allocation in science, potentially impacting hiring, funding, and promotion decisions.

KEYWORDS:

network science; scientific impact; team science

PMID:
25114238
PMCID:
PMC4151753
DOI:
10.1073/pnas.1401992111
[Indexed for MEDLINE]
Free PMC Article

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