A novel approach to predicting exceptional growth in research

Richard Klavans; Kevin W Boyack; Dewey A Murdick

doi:10.1371/journal.pone.0239177

A novel approach to predicting exceptional growth in research

PLoS One. 2020 Sep 15;15(9):e0239177. doi: 10.1371/journal.pone.0239177. eCollection 2020.

Authors

Richard Klavans¹, Kevin W Boyack², Dewey A Murdick³

Affiliations

¹ SciTech Strategies, Inc., Wayne, PA, United States of America.
² SciTech Strategies, Inc., Albuquerque, NM, United States of America.
³ Center for Security and Emerging Technology (CSET), Georgetown University, Washington, DC, United States of America.

Abstract

The prediction of exceptional or surprising growth in research is an issue with deep roots and few practical solutions. In this study, we develop and validate a novel approach to forecasting growth in highly specific research communities. Each research community is represented by a cluster of papers. Multiple indicators were tested, and a composite indicator was created that predicts which research communities will experience exceptional growth over the next three years. The accuracy of this predictor was tested using hundreds of thousands of community-level forecasts and was found to exceed the performance benchmarks established in Intelligence Advanced Research Projects Activity's (IARPA) Foresight Using Scientific Exposition (FUSE) program in six of nine major fields in science. Furthermore, 10 of 11 disciplines within the Computing Technologies field met the benchmarks. Specific detailed forecast examples are given and evaluated, and a critical evaluation of the forecasting approach is also provided.

Publication types

Research Support, Non-U.S. Gov't
Validation Study

MeSH terms

Forecasting / methods*
Models, Theoretical*
Research / statistics & numerical data
Research / trends*

Associated data

figshare/10.6084/m9.figshare.12241727

Grants and funding

This study was conducted by employees of SciTech Strategies, Inc. (RK, KWB), a commercial entity, and Georgetown University (DAM), and was funded by the Center for Security and Emerging Technologies, Georgetown University. The funder provided support in the form of salaries for all authors. Author DAM of Georgetown did contribute to the study design and preparation of the manuscript but had no role in data collection or analysis. Results of the study were data-driven and not prescribed in the design.