Bioimaging Data Management Workflow for Plasma Medicine

Abstract Bioimaging experiments in plasma medicine generate complex datasets that go beyond conventional imaging studies by combining microscopy data with heterogeneous metadata from biological experiments and highly parameterized gas plasma treatments. Plasma exposure conditions, such as device configuration, gas composition, and treatment conditions, are critical determinants of biological outcome, yet they are rarely captured in a standardized, machine-readable, and reusable manner. To address this gap, we present a research data management (RDM) workflow that operationalizes the Findable, Accessible, Interoperable, and Reusable (FAIR) principles across the bioimaging data lifecycle in plasma medicine. The workflow is implemented as a structured pipeline integrating open-source tools, including OMERO for image data management, eLabFTW as an electronic laboratory notebook, Adamant for schema-driven metadata collection, and Micro-Meta App for standardized documentation of microscopy acquisition settings that are connected via programming interfaces to enable persistent linkage of metadata to image datasets using standardized annotations. The workflow is documented in a reproducible tutorial with an open-source Python Jupyter notebook hosted on GitHub. By integrating plasma treatment metadata with imaging data, this approach improves reproducibility, cross-study comparability, and data reuse in plasma medicine research. Competing Interest Statement The authors have declared no competing interest.