Bayesian hypothesis testing reveals that reproducible models in systems biology get more citations

This study investigates the citations of reproducible vs. not reproducible papers and is based on 328 published models, classified by Tiwari et al. based on their reproducibility are analyzed in this study. Hypothese testing is performed using a flexible Bayesian approach for a complete assessment of posteriors. The approach handels outliers via a non-central t distribution. Results show that reproducible papers are significantly more citet between 2013 and 2020, i.e. 10 years after the introduction of SBML. This trend persists also for later periods with more than 95% credibility. In conclusion, this statistical analysis demonstrates long-term benefits of reproducible modeling for the individual researcher and the scientific community.

SEEK ID: https://fairdomhub.org/studies/1103

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Projects: Working Group Nicole Radde

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Höpfl, S. (2023). Bayesian hypothesis testing reveals that reproducible models in systems biology get more citations. FAIRDOMHub. https://doi.org/10.15490/FAIRDOMHUB.1.STUDY.1103.2
Note: This is a citation for Snapshot 2 of this Study, the contents of which may vary from what is shown on this page.
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Snapshot 2 (11th Jan 2023) DOI
Snapshot 1 (3rd Oct 2022) DOI
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Created: 29th Sep 2022 at 12:57

Last updated: 11th Jan 2023 at 13:06

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