Background Data Uncertainty Analysis

Background Data Uncertainty Analysis is a systematic methodological framework within background systems research that characterizes and quantifies the epistemic and aleatoric uncertainties inherent in background data sources, distinguishing it from other analytical approaches by its exclusive focus on the propagation and mitigation of data-derived uncertainties in contextually embedded computational models.

15

Publications

546

Citations

47

Authors

37

Institutions

Publications per year

2017–2026

6

Authors

47

  1. LP
    Lars Peter Riishøjgaard

    Goddard Space Flight Center, United States

    2 publications · 135 citations

    2

    h-index

  2. GJ
    G. J. Davies

    Imperial College London, United Kingdom

    1 publications · 98 citations

    1

    h-index

  3. BR
    Bethany Reilly

    University of Wisconsin–Madison, United States

    1 publications · 54 citations

    1

    h-index

  4. NP
    N. Priel

    Weizmann Institute of Science, Israel

    1 publications · 15 citations

    1

    h-index

  5. CM
    C. M. Macdonald

    University of Oklahoma, United States

    1 publications · 54 citations

    1

    h-index

1–5 of 47

Venues

  1. Journal of Instrumentation

    JINST · 2 publications · 111 citations

    2

    h-index

  2. Tellus A Dynamic Meteorology and Oceanography

    2 publications · 135 citations

    2

    h-index

  3. Journal of Cosmology and Astroparticle Physics

    JCAP · 2 publications · 73 citations

    2

    h-index

  4. Measurement

    1 publications · 19 citations

    1

    h-index

  5. The International Journal of Life Cycle Assessment

    LCA · 1 publications · 23 citations

    1

    h-index

1–5