{"references": ["R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/", "Bates, D., M\u00e4chler, M., Bolker, B., and Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1\u201348. https://doi.org/10.18637/jss.v067.i01", "Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org", "Hothorn, T., Bretz, F., and Westfall, P. (2008). Simultaneous Inference in General Parametric Models. Biometrical journal, 50(3), 346\u2013363. https://doi.org/10.1002/bimj.200810425"]}

Below, R lines used for conducting statistical analysis, i.e., introducing some descriptive statistic and performing statistical tests. Data were visualized by boxplots. Then, linear mixed models were fitted (R function "lmer"). Specifically, all the outcomes of interest ("a") were modeled depending on treatment groups as fixed effect ("Groups") and a random intercept effect ("Number"). As explorative analysis, Dunnett's tests for comparisons of the treatment groups versus a control group were conducted and visualized by confidence intervals. Specifically, two approaches were employed to assess the significance of pairwise comparisons between the treatment and two control groups.