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. In conclusion, this statistical analysis demonstrates long-term benefits of reproducible modeling for the individual researcher and the scientific community.
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Created at: 3rd Oct 2022 at 18:29
Statistical analysis and BEST method of Kruschke for python applied on citation data in Systems Biology
The statistical analysis was performed in a jupyter notebook.
This notebook contains the commands for all performed analyses (Statistical_analysis_of_FAIR_citations.ipynb)
The Bayesian Estimation Superseeds the t Test (BEST) method of Kruschke 2013 was used for the Bayesian significance testing.
The method was implemented in a python class together with visualization and distributional analysis methods (BEST_method_python_Kruschke2012.py).
The results of the statistical analysis, including
Full posterior traces for all analysis are avalable.
Furthermore all visualizations of the paper are included.
The folder contains the jupyter notebook for the execution of all analyses of the study.
The BEST method is used in the notebook and is added in a separate python skript.
- Statistical analysis and python BEST method.zip
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Created: 3rd Oct 2022 at 18:29
Last updated: 3rd Oct 2022 at 18:32