Falsified Data Detection

Falsified Data Detection is a systematic methodological framework within research integrity science that identifies deliberate manipulations or fabrications in empirical data by distinguishing anomalous patterns indicative of intentional misconduct from legitimate variability in authentic research outputs.

89

Publications

6.2K

Citations

243

Authors

148

Institutions

Publications per year

2017–2026

32

Authors

243

  1. EM
    Elisabeth M. Bik

    Stanford University, United States

    5 publications · 389 citations

    5

    h-index

  2. AC
    Arturo Casadevall

    Albert Einstein College of Medicine, United States

    5 publications · 389 citations

    5

    h-index

  3. FC
    Ferric C. Fang

    University of Washington, United States

    5 publications · 389 citations

    5

    h-index

  4. PW
    Peter Winker

    Justus-Liebig-Universität Gießen, Germany

    4 publications · 44 citations

    4

    h-index

  5. MB
    Marc Buyse

    International Drug Development Institute (Belgium), Belgium

    3 publications · 437 citations

    3

    h-index

1–5 of 243

Venues

  1. Statistical Journal of the IAOS

    5 publications · 56 citations

    5

    h-index

  2. Science

    4 publications · 93 citations

    4

    h-index

  3. CHANCE

    2 publications · 68 citations

    2

    h-index

  4. Science and Engineering Ethics

    2 publications · 110 citations

    2

    h-index

  5. IEEE Transactions on Industrial Informatics

    2 publications · 300 citations

    2

    h-index

1–5