Data-driven Scientific Discovery

Data-Driven Scientific Discovery is a scientific inquiry paradigm that distinguishes itself by prioritizing empirical data as the primary source of insight, enabling the formulation and validation of hypotheses through systematic analysis, pattern recognition, and computational modeling, rather than relying predominantly on theoretical or intuition-based approaches.

565

Publications

45.8K

Citations

2.7K

Authors

829

Institutions

Publications per year

2017–2026

295

Authors

2.7K

  1. SK
    Scott Klasky

    Oak Ridge National Laboratory, United States

    7 publications · 148 citations

    7

    h-index

  2. PL
    Pat Langley

    Stanford University, United States

    7 publications · 483 citations

    7

    h-index

  3. AK
    Anuj Karpatne

    Virginia Tech, United States

    6 publications · 2.2K citations

    6

    h-index

  4. JM
    Jan M. Żytkow

    Wichita State University, United States

    6 publications · 340 citations

    6

    h-index

  5. VK
    Vipin Kumar

    University of Minnesota, United States

    6 publications · 2.3K citations

    6

    h-index

1–5 of 2.7K

Venues

  1. arXiv (Cornell University)

    Repository · 26 publications · 1.6K citations

    17

    h-index

  2. Proceedings of the National Academy of Sciences

    PNAS · 9 publications · 1.7K citations

    9

    h-index

  3. npj Computational Materials

    8 publications · 501 citations

    8

    h-index

  4. Science

    8 publications · 3.8K citations

    8

    h-index

  5. Nature Communications

    7 publications · 624 citations

    7

    h-index

1–5