![]() ![]() We show the utility of MIHCSME for HCS data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing Findable, Accessible, Interoperable and Reusable (FAIR) bioimaging data throughout the Netherlands Bioimaging network. In addition, ISA compliance enables broader integration with other types of experimental data, paving the way for visual omics and multi-Omics integration. The tabular template provides an easy-to-use practical implementation of REMBI, specifically for High Content Screening (HCS) data. It has been developed by combining the ISA (Investigations, Studies, Assays) metadata standard with a semantically enriched instantiation of REMBI (Recommended Metadata for Biological Images). The Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) is a metadata model and reusable tabular template for sharing and integrating high content imaging data. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. FAIR principles are now being adapted in the context of AI models and datasets. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. ![]()
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