The supplementary material provides a search strategy to identify research articles discussing or comparing methods for estimating between-study variance and its uncertainty in meta-analysis. The search terms focus on keywords related to heterogeneity, meta-analysis, random and mixed-effects models, and uncertainty measures. Appendix Table 1 summarizes scenarios and estimation methods used in simulations and empirical studies comparing different estimators for between-study variance. It includes various data types (log odds ratio, log risk ratio, mean difference, standardized mean difference, hazard ratio) and parameters such as the number of studies (k), true between-study variance (τ²), and outcome measures. Different estimators like DL, DL2, REML, SJ, HO, PM, ML, BM, and FB are compared across various scenarios. Appendix Table 2 compares the properties of these estimators, including bias and mean squared error, with references to studies. Appendix Table 3 compares confidence interval (CI) estimation methods for τ² in terms of coverage probability and length. The references list studies that have contributed to the field of meta-analysis, including works by Novianti, Panityakul, Sidik & Jonkman, Chung, Viechtbauer, and others. The material highlights the importance of accurately estimating between-study variance and its uncertainty in meta-analyses to ensure reliable conclusions.The supplementary material provides a search strategy to identify research articles discussing or comparing methods for estimating between-study variance and its uncertainty in meta-analysis. The search terms focus on keywords related to heterogeneity, meta-analysis, random and mixed-effects models, and uncertainty measures. Appendix Table 1 summarizes scenarios and estimation methods used in simulations and empirical studies comparing different estimators for between-study variance. It includes various data types (log odds ratio, log risk ratio, mean difference, standardized mean difference, hazard ratio) and parameters such as the number of studies (k), true between-study variance (τ²), and outcome measures. Different estimators like DL, DL2, REML, SJ, HO, PM, ML, BM, and FB are compared across various scenarios. Appendix Table 2 compares the properties of these estimators, including bias and mean squared error, with references to studies. Appendix Table 3 compares confidence interval (CI) estimation methods for τ² in terms of coverage probability and length. The references list studies that have contributed to the field of meta-analysis, including works by Novianti, Panityakul, Sidik & Jonkman, Chung, Viechtbauer, and others. The material highlights the importance of accurately estimating between-study variance and its uncertainty in meta-analyses to ensure reliable conclusions.