Bioimaging workflow based on OMERO, eLabFTW, and Adamant for integrating images with multimodal metadata

This research data management workflow for bioimaging is designed to bridge the gap between image metadata and experimental / process metadata. By linking images and microscopy-related metadata with broader experimental records, the workflow particularly supports the adoption of the FAIR (Findable, Accessible, Interoperable, Reusable) data principles in interdisciplinary fields where bioimaging is used to analyse treated samples requiring multimodal metadata. A Jupyter Notebook guides the user through the metadata level, data handling level, and data processing level and connects various software components in a modular manner. On the metadata level, microscope-specific metadata are captured using the Micro-Meta App and stored as reusable digital assets. Adamant provides a user interface to collect and process schema-based metadata related to the experiment / treatment procedure. Structured imaging and process metadata are attached to the complete experiment description in eLabFTW. On the data handling level, OMERO serves as the central platform for storing and managing microscopy images together with their metadata retrieved from eLabFTW (workflow with ELN) or directly from JSON files (workflow without ELN). On the data processing level, OMERO supports both automated and manual image analysis either directly via the Jupyter Notebook or FIJI. Due to the modularity of the workflow, the integrated tools can be substituted with equivalent systems based on institutional / user requirements. Whether in teaching or research settings, this workflow supports high-throughput, reproducible imaging workflows, ensuring that data, metadata, and analysis steps remain transparent, interoperable, and reusable across diverse bioimage analysis platforms.