Bayesian hypothesis testing reveals that reproducible models in Systems Biology get more citations

Abstract Many measures have been taken to develop data and modeling standards towards a F.A.I.R. data and model management by the Systems Biology community. Still, there is an ongoing debate about incentives and merits for the individual researcher to make their research results reproducible. Here, we pose the specific question whether reproducible models have higher impact in terms of citations. Therefore, we statistically analyze 328 published models which were recently classified by Tiwari et al. based on their reproducibility. For hypotheses testing, we use a flexible Bayesian approach that provides full distributional information for all quantities of interest and can handle outliers. The results show that in the period from 2013, i.e. 10 years after the introduction of SBML, to 2020, the group of reproducible models is significantly more cited than the non-reproducible group. We show that this effect is not explained by differences in journal impact factors and that it increases with additional standardization of data and error model integration via PEtab. Overall, our statistical analysis demonstrates long-term merits of reproducible modeling for the individual researcher in terms of citations. Moreover, it provides evidences for an increased use of reproducible models in the scientific community.