Science clusters
Summary
Unlocking the potential of imaging across scientific fields, the FAIR Image Analysis Across Sciences project breaks down barriers between disciplines by developing robust, reusable, and interoperable workflows. It will utilise public data archives, such as the BioImage Archive and Copernicus data collections, along with advanced AI models, ensuring compliance with FAIR principles throughout the research process, and enhancing knowledge transfer across disciplines. The project empowers researchers to enhance data accessibility, foster interdisciplinary collaboration, and drive innovative solutions for complex challenges in bioimaging, astrophysics, and environmental sciences.
Challenge
Open Science project, Open Science Service, Industry cooperation, Main RI concerned, Crossdomain/Cross-RI
Imaging techniques have become increasingly popular across scientific fields due to their ability to provide rich and comprehensive information. These images, image series, and videos are invaluable resources, offering researchers a wealth of information to explore and analyse. Despite the widespread use of imaging, the practice of extracting quantitative information from image data remains fragmented across scientific communities. Scientists often work in isolated silos, resulting in limited cross-talk and collaboration. This isolation can lead to redundant work and inefficient resource utilisation, as experts in different domains may duplicate efforts unknowingly. The rise of AI and machine learning further complicates this landscape, with access to the necessary tools and computational resources becoming a significant bottleneck for researchers.
The project aims to tackle these challenges by creating image analysis workflows that can be shared and reused across multiple scientific domains, such as bioimaging, environmental sciences, and astrophysics.
Solution
The "FAIR Image Analysis Across Sciences" project addresses these challenges by developing reusable image analysis workflows that can be shared across disciplines such as bioimaging, environmental sciences, and astrophysics. By leveraging FAIR-enabling resources like Galaxy and WorkflowHub, the project aims to democratise access to advanced tools and AI models, ensuring that both established researchers and citizen scientists can benefit from these resources globally, including in the Global South.
The project will also engage with various RIs and Science Clusters to facilitate the long-term sustainability of its outcomes.
Scientific Impact
By promoting domain-agnostic workflows, the project aims to boost scientific research and innovation, increase the adoption of workflow management systems, and enhance data compatibility across domains. The emphasis on interdisciplinary collaboration will empower researchers to share methodologies and foster partnerships between academia and industry, ultimately driving the commercialization of research outcomes and enhancing societal impact.
Principal investigator
Antje Keppler is Director of the Bio-Hub, Euro-BioImaging ERIC, hosted by the EMBL in Germany. She leads an international team to coordinate and operate the pan-European RI services for biological imaging in Euro-BioImaging. She currently is Chair of the ERIC Forum, where 28 ERICs come together to represent their strategic assets and common voice towards European and international key stakeholders. Antje also is coordinator of the international Global BioImaging network, which promotes open science and open access to imaging services in over 61 countries.
Beatriz Serrano-Solano is a Scientific Project Manager at Euro-BioImaging ERIC, where she leads the Communication, Outreach, and Training efforts within AI4Life, a Horizon Europe-funded project advancing AI-driven bioimage analysis.
With a background in Computer Science, Beatriz earned a PhD in Computational Biology, further refining her expertise during her postdoc in image analysis. She then transitioned into community management, spearheading outreach, training, and engagement for the global Galaxy Community.
