Data Uncertainty Propagation
Data Uncertainty Propagation is a computational framework that characterizes how uncertainties in input data propagate through a system or model to affect the reliability and precision of its outputs, distinguishing it from other uncertainty analysis methods by explicitly tracing the influence of data-level noise, incompleteness, or variability across processing stages.
102
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6.3K
Citations
367
Authors
185
Institutions
Publications per year
2017–2026
31
Parents
102
Matthew Horstwood, Jan Košler, George E. Gehrels et al. · Geostandards and Geoanalytical Research · 2016 · 1K citations · Full text
Andrea Sciacchitano, Bernhard Wieneke · Measurement Science and Technology · 2016 · 500 citations · Full text
On aggregation errors in atmospheric transport inversions
Thomas Kaminski, P. J. Rayner, Martin Heimann et al. · Journal of Geophysical Research Atmospheres · 2001 · 307 citations · Full text
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Nuclear Research and Consultancy Group
Petten, The Netherlands
7 h-index
20 publications
876 citations
Commissariat à l'Énergie Atomique et aux Énergies Alternatives
Paris, France
4 h-index
7 publications
140 citations

Universidad Politécnica de Madrid
Madrid, Spain
3 h-index
9 publications
254 citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
3 h-index
5 publications
103 citations
Paul Scherrer Institute
Villigen, Switzerland
3 h-index
17 publications
741 citations
Istituto Nazionale di Ricerca Metrologica
Turin, Italy
2 h-index
5 publications
61 citations
Belgian Nuclear Research Centre
Mol, Belgium
2 h-index
4 publications
242 citations

Delft University of Technology
Delft, The Netherlands
2 h-index
2 publications
759 citations
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5 publications · 259 citations
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4 publications · 132 citations
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Repository · 2 publications · 23 citations
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