Data-driven Discovery

Data-Driven Discovery is a scientific methodology in which empirical data serve as the primary basis for generating hypotheses, models, or theories, distinguished from theory-driven approaches by its reliance on observational or experimental data to guide and initiate the discovery process.

340

Publications

26.1K

Citations

1.6K

Authors

612

Institutions

Publications per year

2017–2026

227

Authors

1.6K

  1. JN
    J. Nathan Kutz

    University of Washington Applied Physics Laboratory, United States

    10 publications · 2.2K citations

    10

    h-index

  2. SL
    Steven L. Brunton

    University of Washington, United States

    8 publications · 2K citations

    8

    h-index

  3. VH
    Vasant Honavar

    Iowa State University, United States

    5 publications · 106 citations

    5

    h-index

  4. PP
    Paris Perdikaris

    University of Pennsylvania, United States

    4 publications · 1.6K citations

    4

    h-index

  5. GE
    George Em Karniadakis

    Brown University, United States

    4 publications · 1.6K citations

    4

    h-index

1–5 of 1.6K

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 1.6K citations

    11

    h-index

  2. Nature Communications

    8 publications · 1.1K citations

    8

    h-index

  3. Scientific Reports

    7 publications · 396 citations

    7

    h-index

  4. Chemical Science

    7 publications · 356 citations

    6

    h-index

  5. Proceedings of the VLDB Endowment

    PVLDB · 6 publications · 236 citations

    6

    h-index

1–5