High-impact and transformative science (HITS) metrics: Definition, exemplification, and comparison

Countries, research institutions, and scholars are interested in identifying and promoting high-impact and transformative scientific research. This paper presents a novel set of text- and citation-based metrics that can be used to identify high-impact and transformative works. The 11 metrics can be grouped into seven types: Radical-Generative, Radical-Destructive, Risky, Multidisciplinary, Wide Impact, Growing Impact, and Impact (overall). The metrics are exemplified, validated, and compared using a set of 10,778,696 MEDLINE articles matched to the Science Citation Index ExpandedTM. Articles are grouped into six 5-year periods (spanning 1983–2012) using publication year and into 6,159 fields constructed using comparable MeSH terms, with which each article is tagged. The analysis is conducted at the level of a field-period pair, of which 15,051 have articles and are used in this study. A factor analysis shows that transformativeness and impact are positively related (ρ = .402), but represent distinct phenomena. Looking at the subcomponents of transformativeness, there is no evidence that transformative work is adopted slowly or that the generation of important new concepts coincides with the obsolescence of existing concepts. We also find that the generation of important new concepts and highly cited work is more risky. Finally, supporting the validity of our metrics, we show that work that draws on a wider range of research fields is used more widely.

[1]  T. Kuhn,et al.  The Structure of Scientific Revolutions. , 1964 .

[2]  S. Younkin,et al.  Correlative Memory Deficits, Aβ Elevation, and Amyloid Plaques in Transgenic Mice , 1996, Science.

[3]  Thane Chambers Scholarly Metrics Under the Microscope: From Citation Analysis to Academic Auditing , 2015 .

[4]  Pumin Zhang,et al.  Transgenic RNA interference in mice. , 2007, Physiology.

[5]  Michael Horowitz,et al.  The Sanford Lorraine Cross Award for medical innovation: Advancing a rigorous and repeatable method for recognizing translational research leaders who today are bringing emerging transformative innovations to patients , 2020, Journal of clinical and translational research.

[6]  Robert L. Goldstone,et al.  Science map metaphors: a comparison of network versus hexmap-based visualizations , 2017, Scientometrics.

[7]  Luís M. A. Bettencourt,et al.  Scientific discovery and topological transitions in collaboration networks , 2009, J. Informetrics.

[8]  David Stuart,et al.  Beyond Bibliometrics: Harnessing Multidimensional Indicators of Scholarly Impact , 2015, Online information review (Print).

[9]  Benjamin F. Jones,et al.  Age dynamics in scientific creativity , 2011, Proceedings of the National Academy of Sciences.

[10]  Jian Wang,et al.  Bias Against Novelty in Science: A Cautionary Tale for Users of Bibliometric Indicators , 2015 .

[11]  Johan Bollen,et al.  A Principal Component Analysis of 39 Scientific Impact Measures , 2009, PloS one.

[12]  George M. Santangelo,et al.  Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level , 2015, bioRxiv.

[13]  David F. Channell Pasteur's Quadrant: Basic Science and Technological Innovation , 1999 .

[14]  Albert-László Barabási,et al.  Quantifying Long-Term Scientific Impact , 2013, Science.

[15]  Pierre Azoulay,et al.  The Mobility of Elite Life Scientists: Professional and Personal Determinants , 2016, Research policy.

[16]  J. Marx New 'Alzheimer's Mouse' Produced , 1996, Science.

[17]  Ufuk Akcigit,et al.  Young, Restless and Creative: Openness to Disruption and Creative Innovations , 2014 .

[18]  C. J. Chen,et al.  Introduction to Scanning Tunneling Microscopy , 1993 .

[19]  Katy Börner,et al.  Mixed-indicators model for identifying emerging research areas , 2011, Scientometrics.

[20]  Russell J. Funk,et al.  A Dynamic Network Measure of Technological Change , 2017, Manag. Sci..