Research synthesis methods such as meta-analysis rely on either individual participant data or appropriate summary statistics (e.g., measurement of precision such as standard deviation and standard error) of trial data for implementation. A barrier to study inclusion in research synthesis occurs when no precision metrics are explicitly included in the primary report. Typically, such otherwise credible studies are omitted in the research synthesis leading to potential publication bias and loss of statistical power. We developed a user-friendly R shiny web app to estimate the mean squared error (MSE) from published reports (e.g., Plant Disease Management Reports) considering information on treatment means, significance level, number of replications, and post-hoc test results from balanced and randomized trials analyzed via ANOVA. To achieve this, users upload a csv file containing trial information into the app, then specify the experimental design and the post-hoc test that was applied in the trial analysis. Multiple trials can be processed by specifying a trial identifier column in the csv file. Validation of the procedure using 1,000 individual participant simulated datasets with variable number of treatments and effect sizes demonstrated a strong Lin’s concordance correlation (0.503 ≤ ρ_c ≤ 0.994) between values of MSE obtained with ANOVA and MSE FindR. Additionally, values of the bias correction factor ranged from 0.875 to 0.999 across all simulation scenarios, indicating an excellent agreement between the best fitted and identity lines. With MSE FindR, researchers can conveniently obtain an estimate of variability from published reports lacking measurement of precision, ultimately enabling the inclusion of such studies in a quantitative review of research data evaluating product efficacy or genotype performance in coordinated Fusarium head scab wheat and barley trials in the US. Inclusion of summary statistics (e.g., means, estimates of precision, significance level, and the number of replications) should still be considered as a standard approach in summary trial reports for the US wheat and barley scab initiative community.