Harmonizing the Generation and Pre-publication Stewardship of FAIR bioimage data

Biological imaging, combined with molecular insights into genes and proteins, holds immense promise for deepening our understanding of complex cellular systems and accelerating the development of predictive, personalized therapies for human health. To fully realize this potential at scale-and harness the power of AI/ML to extract novel biological insights and therapeutic interventions-it is necessary to transition from siloed datasets to globally shared, rigorously annotated, and computationally ready image data. This demands systematic harmonization of multidimensional bioimaging data, where interoperable formats, and standardized context-rich annotation, quality controls, and analytical pipelines transform scattered observations into a coherent knowledge base ripe for computational mining. Only this machine-actionable aggregation can provide the substrate for AI/ML to extract mechanistic insights into fundamental biological mechanisms, novel diagnostic biomarkers and intervention targets. Enabling seamless image data sharing in the life sciences requires addressing two key areas. The first is outlined in an accompanying publication, Enabling Global Image Data Sharing in the Life Sciences, which focuses on the publicly available repositories needed to share digital array data ¹. This White Paper details a comprehensive set of requirements for integrated image data and metadata management - from acquisition through dissemination - ensuring the contextual information necessary for assessing quality, interpreting scientific validity, and enabling meaningful reuse remains intrinsically linked to the data throughout its lifecycle. Critically, it recognizes that generating harmonized, well-annotated publicly available corpora of FAIR bioimage data requires these datasets to be "FAIR-from-the-start" - an objective that can only be achieved by enabling experimental scientists to manage, organize, and analyze their data according to community standards from the very first experiment. Building on recent progress made by the bioimaging field towards establishing shared practices for bioimaging Quality Control (QC) and metadata capture, we present actionable recommendations to advance these efforts through embedding researcher-friendly integrated software infrastructure directly into pre-publication workflows, thus transforming disorganized data capture into structured, shareable resources ready for aggregation and reuse. Our ultimate goal is to expand the use of streamlined tools and practices thus transforming how researchers capture, annotate, analyze and eventually publish bioimaging data thus laying the foundation for a new era of data-driven discovery.