Data Management Workflow for FAIR Sharing of Bioimaging Datasets in Plasma Medicine

Bioimaging experiments in plasma medicine generate datasets that extend beyond conventional imaging studies by combining microscopy data with heterogeneous metadata from biological experiments and gas plasma treatments. These data are often distributed across multiple systems, making it difficult to maintain links between imaging data, plasma treatment conditions, biological metadata, and microscopy acquisition settings. This fragmentation limits data sharing and repository deposits. To address this challenge, we present a data management workflow for FAIR sharing of bioimaging datasets in plasma medicine. The workflow is implemented as an open-source Jupyter Notebook and connects the established research data management tools Open Microscopy Environment Remote Objects (OMERO) for image data management, eLabFTW as an electronic laboratory notebook for experimental documentation, Adamant as a schema-based metadata acquisition tool, and Micro-Meta App for the standardized capture of microscopy settings. The workflow guides researchers through the data management process, supporting the adoption of the FAIR data principles for the sharing and reuse of imaging datasets. We demonstrate how biological metadata, plasma-treatment parameters, microscopy settings, and image data are associated across the Screen, Plate, and Well hierarchy in OMERO through structured JSON metadata records generated in Adamant, stored in eLabFTW, and linked to imaging datasets. By means of a structured FAIR assessment, the contribution of the workflow components to the achievement of FAIR imaging datasets in plasma medicine is demonstrated, resulting in a FAIR compliance level of 50-60%. Thus, the Jupyter Notebook workflow supports researchers in plasma medicine and other domains with similar requirements by linking image data and metadata to experiment descriptions, enabling FAIR sharing and simplified reuse of bioimaging datasets.