Bioimaging, also referred to as biological imaging, encompasses all methods used to visualize the structures and functional mechanisms of living organisms. Fundamental discoveries—such as the identification of the cell as the basic unit of life—are owed to the core tool of bioimaging: the microscope. Today, it is possible to observe how individual molecules interact within a living cell on nanosecond timescales, or how entire model organisms, such as fish or flies, develop over the course of several days.

Each pixel of a bioimaging dataset contains information across multiple spatial, temporal, and spectral dimensions. Given the resulting data volume and complexity, adequate data management is essential for any form of bioimaging data analysis, yet it has so far been only insufficiently realized. For many life and medical science disciplines that rely on bioimaging, this represents a substantial obstacle. No single discipline alone possesses the expertise required to close this gap.

NFDI4BIOIMAGE addresses this challenge as a method-centered consortium, developing solutions that enable bioimaging data to be shared and reused across disciplinary boundaries in the same way they are acquired. The aim is to fully exploit the informational content of these data and to generate new insights through systematic re-analysis. The research data management (RDM) strategy is based on a robust needs assessment that combines a community-wide survey with more than ten years of experience from the German BioImaging network, now the German Society for Microscopy and Image Analysis. A core element of this strategy is the definition of a shared, cloud-compatible, and interoperable digital object that combines binary image data with the corresponding metadata into a single data unit.

NFDI4BIOIMAGE seeks to provide an infrastructure that meets discipline-specific requirements while remaining compatible with other data types and RDM systems across different scientific domains. The integration of imaging and omics data is a particularly promising example, with growing relevance for cancer medicine. To fully realize the potential of such approaches, powerful image analysis tools and appropriate training datasets are required. NFDI4BIOIMAGE will provide both within a scalable cloud environment, including state-of-the-art AI-based methods. The consortium interfaces in particular with GHGA for the integration of genomic and imaging data and with DataPLANT for the definition of FAIR Data Objects.

NFDI4BIOIMAGE’s strong international networking further offers opportunities for researchers in Germany to participate in international initiatives that recognize the FAIRification of bioimaging data as a key challenge in the life and medical sciences.

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