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

Autor/inn/en	Wolcott, Holly N.; Fouch, Matthew J.; Hsu, Elizabeth R.; DiJoseph, Leo G.; Bernaciak, Catherine A.; Corrigan, James G.; Williams, Duane E.
Titel	Modeling time-dependent and -independent indicators to facilitate identification of breakthrough research papers.
Quelle	In: Scientometrics, (2016) 2, S.807-817 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-016-1861-1
Schlagwörter	Transformative research; Breakthrough prediction; Indicators; Co-author network metrics; Citation velocity
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.
Erfasst von	OLC
Update	2023/2/05