In microbiome research, quality control is essential to the repeatability and reproducibility of results. This is especially true for samples with a low bacterial load such as breast milk, where samples can easily become contaminated by reagents. In a new study, researchers propose a framework for an approach to address this challenge. The framework consists of three independent stages: 1) Verification of sequencing accuracy by assessing technical repeatability and reproducibility, 2) Contaminant removal and batch variability correction, and 3) Corroborating the repeatability and reproducibility of the microbiome composition and downstream analysis. The approach was validated using milk microbiota data from the CHILD Cohort, generated in two batches in 2016 and 2019. The framework helped to identify potential contaminant reagents that were missed with standard algorithms, substantially reducing contaminant-induced batch variability. This represents an important advance in quality control efforts in low-biomass microbiome research, demonstrating that within-study quality control that takes advantage of the data structure will enhance the reliability and reproducibility of research in the field.