The Integrative Data Science group is dedicated to advancing the quality, reproducibility, and integrity of scientific data and its analysis, with a particular focus on microbiome research.

We investigate how methodological decisions – from laboratory sample processing to statistical analysis and reporting – shape scientific conclusions in microbiome studies. By addressing these sources of bias and developing laboratory control standards in conjunction with computational tools to mitigate bias, we aim to improve the robustness of microbiome analyses. In the long run, our work supports translational microbiome research, ultimately striving to improve patient care through robust and reliable clinical microbiome findings.

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Our work also addresses broader questions in metascience, examining how reproducibility, standardization, and FAIR principles affect knowledge production. We aim to contribute to solution-oriented approaches by critically evaluating existing workflows and exploring ways to improve transparency and sustainability in data-driven research. In doing so, the group supports more interpretable and trustworthy microbiome science.