The vast majority of environmental microbiology data are collected by individual researchers funded to answer discrete, well-defined science questions. Experiments are rigorously designed, relevant data are collected to address the proposed questions, and samples are processed and analyzed by students and postdocs for targeted publications. Recent advancements in data science, computation, and data management best practices have enabled our community to start asking more of these data. The goal of this session is to kick-off a community discussion around how to improve meta-analyses of diverse data types, which are often limited by data discovery and access, interoperability of data models, data re-usability, and data compatibility. We welcome any transdisciplinary work that highlights how data, especially projects that inform our understanding of the Earth Microbiome, have been used to reach beyond their originally intended purposes. Presentations on applying FAIR data principles, effective use of ontologies, and metadata curation processes are encouraged.