Modeling time-dependent and -independent indicators to facilitate identification of breakthrough research papers

Holly N. Wolcott, Matthew J. Fouch, Elizabeth R. Hsu, Leo G. DiJoseph, Catherine A. Bernaciak, James G. Corrigan () and Duane E. Williams ()
Additional contact information
Holly N. Wolcott: Thomson Reuters
Matthew J. Fouch: Thomson Reuters
Elizabeth R. Hsu: National Cancer Institute
Leo G. DiJoseph: Thomson Reuters
Catherine A. Bernaciak: Thomson Reuters
James G. Corrigan: National Cancer Institute
Duane E. Williams: ÜberResearch

Scientometrics, 2016, vol. 107, issue 2, No 27, 807-817

Abstract: Abstract Research funding organizations invest substantial resources to monitor mission-relevant research findings to identify and support promising new lines of inquiry. To that end, we have been pursuing the development of tools to identify research publications that have a strong likelihood of driving new avenues of research. This paper describes our work towards incorporating multiple time-dependent and -independent features of publications into a model to identify candidate breakthrough papers as early as possible following publication. We used multiple random forest models to assess the ability of indicators to reliably distinguish a gold standard set of breakthrough publications as identified by subject matter experts from among a comparison group of similar Thomson Reuters Web of Science™ publications. These indicators were then tested for their predictive value in random forest models. Model parameter optimization and variable selection were used to construct a final model based on indicators that can be measured within 6 months post-publication; the final model had an estimated true positive rate of 0.77 and false positive rate of 0.01.

Keywords: Transformative research; Breakthrough prediction; Indicators; Co-author network metrics; Citation velocity (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s11192-016-1861-1

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