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Quantifying long-term scientific impact.

Dashun Wang1, Chaoming Song, Albert-László Barabási

  • 1Center for Complex Network Research, Department of Physics, Northeastern University, Boston, MA 02115, USA.

Science (New York, N.Y.)
|October 5, 2013
PubMed
Summary
This summary is machine-generated.

Citation patterns are predictable. A new model reveals a universal temporal pattern in scientific paper citations, offering reliable measures of influence beyond traditional metrics.

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Area of Science:

  • Bibliometrics
  • Scientometrics
  • Information Science

Background:

  • Citation-based metrics like impact factors lack predictability.
  • Assessing the long-term influence of scientific papers is challenging.

Purpose of the Study:

  • To investigate the long-term predictability of citation patterns.
  • To develop a mechanistic model for citation dynamics.
  • To establish reliable measures of scientific influence.

Main Methods:

  • Derivation of a mechanistic model for citation dynamics.
  • Analysis of citation histories across diverse papers, journals, and disciplines.
  • Collapsing citation data into a universal temporal curve.

Main Results:

  • All scientific papers exhibit a common, universal temporal citation pattern.
  • The derived model successfully collapses diverse citation histories into a single curve.
  • Identified underlying mechanisms governing scientific impact and influence.

Conclusions:

  • Long-term predictability exists in scientific citation patterns.
  • The universal temporal pattern offers a more reliable measure of influence.
  • Findings have potential implications for science policy and evaluation.