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Trends in sensitivity analysis practice in the last decade.

Federico Ferretti1, Andrea Saltelli2, Stefano Tarantola3

  • 1European Commission, Joint Research Centre (JRC), Unit of Econometrics and Applied Statistics, via Enrico Fermi 2749 TP 361, Ispra, 21027 VA, Italy.

The Science of the Total Environment
|March 4, 2016
PubMed
Summary
This summary is machine-generated.

Researchers are increasingly using global sensitivity analyses (GSA) over traditional one factor-at-a-time (OAT) methods. This shift indicates a move towards more robust model validation in scientific publications.

Keywords:
Bibliometric analysisChemical modellingGlobal sensitivity analysisSensitivity analysis

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Area of Science:

  • Scientific modeling and simulation
  • Computational science
  • Data analysis

Background:

  • Sensitivity analysis (SA) is crucial for understanding model behavior.
  • Traditional methods like local and one factor-at-a-time (OAT) SA rely on linearity assumptions.
  • Global sensitivity analysis (GSA) offers a more comprehensive approach but is less frequently adopted.

Purpose of the Study:

  • To analyze trends in sensitivity analysis practices over the last decade.
  • To compare the adoption rates of OAT versus GSA methods.
  • To identify shifts in SA methodology across scientific disciplines and regions.

Main Methods:

  • Bibliometric analysis of scientific literature.
  • Review of publications in top-ranking journals (Nature, Science).
  • Extended analysis using Elsevier's Scopus database.

Main Results:

  • A notable increase in the application of GSA methods over the past decade, even after adjusting for overall publication growth.
  • While OAT remains prevalent, GSA usage, particularly regression and variance-based techniques, is rising.
  • Trends are consistent across general scientific publications and specifically within chemical modeling.

Conclusions:

  • The scientific community is progressively adopting more rigorous GSA methods.
  • This trend suggests an evolving standard towards more comprehensive model evaluation.
  • Future research should continue to monitor the uptake of advanced SA techniques.