Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data
Alongside molecular insights into genes and proteins, biological imaging holds great promise for deepening scientific understanding of complex cellular systems and advancing predictive, personalized therapies for human health. To realize this potential, quality-assured image data must be shared globally across laboratories to enable comparison, pooling, and reanalysis-unlocking value far beyond the original purpose of data collection. Two broad sets of requirements are essential to enable image data sharing in the life sciences. The companion article Enabling Global Image Data Sharing in the Life Sciences outlines the need to develop cyberinfrastructure for sharing bioimage data. In this manuscript, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse bioimage data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.