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Cited by 367

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses

Open access
Iwanaga, Takuya, Usher, William, Herman, Jonathan·2022·Socio-Environmental Systems Modelling

Abstract

Sensitivity analysis is now considered a standard practice in environmental modeling. Several open-source libraries, such as the Sensitivity Analysis Library (SALib), have been published in the recent past aimed at simplifying the application of sensitivity analyses. Still, there remain issues in software usability and accessibility, as well as a lack of guidance in the interpretation of sensitivity analysis results. This paper describes the changes made and planned to SALib to advance the ease with which modelers may conduct sensitivity analysis and interpret results. We further offer our perspectives from the past 7 years of maintaining SALib for the consideration of those aspiring to launch their own software for sensitivity analysis, develop methodology, or those otherwise interested in becoming involved in a project like SALib. These include the value of a community of practice to foster best practices for sensitivity analysis, the potential for collaboration across different software (for sensitivity analysis) platforms, and the need to specifically support the software development that underpins computational science.

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Metadata

Authors
Iwanaga, T., Usher, W., & Herman, J.
Venue
Socio-Environmental Systems Modelling
Published
2022-05-31
Volume
4
Pages
18155
ISSN
2663-3027 (ISSN-L)
OpenAlex
W4312362262
Cited by
367
References
55
FWCI
45.28
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses. https://app.cochat.ai/papers/10.18174/sesmo.18155
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Iwanaga, T., Usher, W., & Herman, J. (2022). Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses. Socio-Environmental Systems Modelling. https://doi.org/10.18174/sesmo.18155

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.