FAIR bioimaging data management workflow for plasma medicine: Integrating OMERO, eLabFTW, and Adamant via Jupyter notebook

Plasma medicine increasingly relies on advanced bioimaging to quantify cellular, molecular, and tissue-level responses to reactive species generated by cold atmospheric plasmas. These imaging datasets are typically heterogeneous, high-dimensional, and generated across multiple experimental conditions, instruments, and laboratories. Without systematic data management, such complexity limits reproducibility, cross-study comparability, and long-term reuse. The FAIR principles including Findability, Accessibility, Interoperability, and Reusability, provide a guideline to address these challenges, but their practical implementation in plasma medicine remains fragmented. Here, we introduce a FAIR-aligned bioimaging data management workflow, published on GitHub (https://github.com/INP-SDT/Bioimaging-Data-Management-Workflow-for-Plasma-Medicine), which integrates OMERO for image data storage and metadata management, eLabFTW for capturing experimental context via an electronic laboratory notebook (ELN), and Adamant for structured metadata curation and schema-driven annotation. These tools are programmatically connected via APIs and Jupyter notebooks, enabling an automated workflow for image data annotation and downstream analysis. This integrated approach bridges experimental documentation, image data, and machine-readable metadata, providing a scalable foundation for reproducible bioimaging studies and data sharing in plasma medicine.