Recent publications have shown that the majority of studies cannot be adequately reproduced. The underlying causes seem to be diverse. Usage of the wrong statistical tools can lead to the reporting of dubious correlations as significant results. Missing information from lab protocols or other metadata can make verification impossible. Especially with the advent of Big Data in the life sciences and the hereby-involved measurement of thousands of multi-omics samples, researchers depend more than ever on adequate metadata annotation. In recent years, the scientific community has created multiple experimental design standards, which try to define the minimum information necessary to make experiments reproducible. Tools help with creation or analysis of this abundance of metadata, but are often still based on spreadsheet formats and lack intuitive visualizations. We present an interactive graph visualization tailored to experiments using a factorial experimental design. Our solution summarizes sample sources and extracted samples based on similarity of independent variables, enabling a quick grasp of the scientific question at the core of the experiment even for large studies. We support the ISA-Tab standard, enabling visualization of diverse omics experiments. As part of our platform for data-driven biomedical research, our implementation offers additional features to detect the status of data generation and more.