FAIR Image Analysis Across Sciences

FAIR Image Analysis Across Sciences

Duration
2025-2026

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 helps researchers make data more accessible, work together across fields, and drives innovations in bioimaging, astrophysics, and environmental sciences.

Challenge

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.

About the project

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.

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.

Funding

This is an open science project funded under OSCARS. "OSCARS is a four-year Horizon Europe project that will foster the uptake of Open Science in Europe by consolidating the achievements of world-class European RIs in the ESFRI roadmap and beyond into lasting interdisciplinary FAIR data services and working practices across scientific disciplines and communities." (https://www.oscars-project.eu/about-oscars)

The OSCARS project has received funding from the European Commission’s Horizon Europe Research and Innovation programme under grant agreement No. 101129751

Disclaimer: Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor the Granting authority can be held responsible for them.

Project partners

  • Euro-BioImaging ERIC Bio-Hub, coordinator (Finland)
  • École Polytechnique Fédérale de Lausanne (EPFL) (France)
  • Simula Research Laboratory (Norway)
  • University of Bergen (Norway)