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MIFA: Metadata, Incentives, Formats and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis

Nature Methods (2025)Cite this article

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Abstract

Artificial intelligence (AI) methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new algorithms, but access to such data is often hindered by the lack of standards for sharing datasets. We discuss the barriers to sharing annotated image datasets and suggest specific guidelines to improve the reuse of bioimages and annotations for AI applications. These include standards on data formats, metadata, data presentation and sharing, and incentives to generate new datasets. We are sure that the Metadata, Incentives, Formats and Accessibility (MIFA) recommendations will accelerate the development of AI tools for bioimage analysis by facilitating access to high-quality training and benchmarking data.

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Fig. 1: Diverse annotation types belonging to AI-ready datasets stored at the BioImage Archive and EMPIAR.
Fig. 2: Metadata modules for AI image datasets.
Fig. 3: MIFA recommendations for FAIR AI data sharing.

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Data availability

Data availability is not applicable to this article as no new data were created or analyzed for this work. All used images are already publicly accessible, permissibly licensed and referenced by identifier.

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Acknowledgements

To improve support for image annotations of AI-related datasets and to develop annotation standards for the community, a workshop was held with 45 community experts from various backgrounds, including data generators, annotators, curators, AI researchers, bioimage analysts and software developers. The workshop sessions resulted in a series of recommendations on four main topics: Metadata, Incentives, Formats and Accessibility (MIFA), which are described above. We are grateful to the FAIR AI workshop participants F. Ballllosera, A. Bhardwaj, J. -M. Burel, A. French, M. Hammer, D. Hensen, K. Ho, S. Jasek, I. Kemmer, J. Kriel, A. Iudin, W. Ouyang, A. Papaleo, A. Rupaningal, C. Strambio De Castillia, B. Wester, S. Weyand and G. Zaki for their insightful inputs and valuable contributions to the discussion.

The workshop was organized in the framework of the AI4Life project, which has received funding from the European Union’s Horizon Europe Research and Innovation Programme under grant agreement no. 101057970. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under contract no. 75N91019D00024. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government.

J.M. is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; 501864659 as part of NFDI4BIOIMAGE). M.L.J. is supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (CC1076), the UK Medical Research Council (CC1076) and the Wellcome Trust (CC1076). G.J.K. and P.K. were supported by EMBL-EBI and the Wellcome Trust (221371/Z/20/Z). N.N. acknowledges support from the Swedish Research Council (2023-05450), Sigurd and Elsa Goljes Memorial Foundation, IngaBritt och Arne Lundberg foundation, Magnus Bergvall Foundation, and Greta and Johan Kock’s foundations. V. Ulman was supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (90254). C.T. and V. Ulman are supported by grant nos. 2020-225265 and 2024-342803, respectively, from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. P.P.-G. is a member of the national infrastructure France-Bioimaging supported by the French national research agency (ANR-24-INBS-0005 FBI BIOGEN). A.M.-B. is supported by Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación (MCIN/AEI/10.13039/501100011033/), under grant PID2023-152631OB-I00.

Author information

Authors and Affiliations

  1. European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK

    Teresa Zulueta-Coarasa, Liviu Anita, Kolawole Babalola, Gerard J. Kleywegt, Paul Korir, Aybüke Küpcü Yoldaş, Craig Russell, Ugis Sarkans, Virginie Uhlmann & Matthew Hartley

  2. Fondazione Human Technopole, V.le Rita Levi-Montalcini, Milan, Italy

    Florian Jug

  3. Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Heidelberg, Germany

    Aastha Mathur, Bugra Oezdemir & Beatriz Serrano-Solano

  4. German BioImaging – Gesellschaft für Mikroskopie und Bildanalyse e.V., Konstanz, Germany

    Josh Moore

  5. Universidad Carlos III de Madrid, Madrid, Spain

    Arrate Muñoz-Barrutia

  6. Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain

    Arrate Muñoz-Barrutia

  7. Edinburgh Pathology, Centre for Genomic and Experimental Medicine, and CRUK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK

    Peter Bankhead

  8. Nantes Université, CHU Nantes, CNRS, Inserm, BioCore, US16, SFR Bonamy, Nantes, France

    Perrine Gilloteaux

  9. Imaging Platform, Broad Institute, Cambridge, MA, USA

    Nodar Gogoberidze

  10. Electron Microscopy Science Technology Platform, Francis Crick Institute, London, UK

    Martin L. Jones

  11. Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    Anna Kreshuk

  12. European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany

    Luca Marconato

  13. Division of Computational Genomics and System Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Luca Marconato

  14. Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany

    Luca Marconato

  15. Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA

    Kedar Narayan

  16. Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

    Kedar Narayan

  17. Department of Experimental Medical Science, Lund University Bioimaging Centre (LBIC), & Nanolund, Lund University, Lund, Sweden

    Nils Norlin

  18. Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA

    Jessica L. Riesterer

  19. Scalable Minds GmbH, Potsdam, Germany

    Norman Rzepka

  20. European Molecular Biology Laboratory, Data Science, Heidelberg, Germany

    Christian Tischer

  21. BioVisionCenter, Universität Zürich, Zürich, Switzerland

    Virginie Uhlmann

  22. IT4Innovations, VSB – Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Vladimír Ulman

  23. CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic

    Vladimír Ulman

Authors
  1. Teresa Zulueta-Coarasa
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  2. Florian Jug
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  3. Aastha Mathur
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  4. Josh Moore
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  5. Arrate Muñoz-Barrutia
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  6. Liviu Anita
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  7. Kolawole Babalola
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  8. Peter Bankhead
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  9. Perrine Gilloteaux
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  10. Nodar Gogoberidze
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  11. Martin L. Jones
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  12. Gerard J. Kleywegt
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  13. Paul Korir
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  14. Anna Kreshuk
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  15. Aybüke Küpcü Yoldaş
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  16. Luca Marconato
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  17. Kedar Narayan
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  18. Nils Norlin
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  19. Bugra Oezdemir
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  20. Jessica L. Riesterer
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  21. Craig Russell
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  22. Norman Rzepka
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  23. Ugis Sarkans
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  24. Beatriz Serrano-Solano
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  25. Christian Tischer
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  26. Virginie Uhlmann
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  27. Vladimír Ulman
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  28. Matthew Hartley
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Contributions

All authors attended the workshop and participated in the plenary and breakout room discussions. T.Z.-C. and M.H. led the writing process. F.J., A.M., J.M. and A.M.-B. (in alphabetical order) acted as chairs of the breakout rooms and summarized discussions. A.M., J.M., A.M.-B., L.A., K.B., P.B., P.G., N.G., M.L.J., G.J.K., P.K., A.K., A.K.Y., L.M., K.N., N.N., B.O., J.L.R., C.R., N.R., U.S., B.S.-S., C.T., V. Uhlmann and V. Ulman (in alphabetical order) provided comments and edited the manuscript.

Corresponding author

Correspondence to Matthew Hartley.

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Competing interests

N.R. is an employee of and owns equity in scalable minds GmbH, which is a company that sells image analysis software and services. The remaining authors declare no competing interests.

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Nature Methods thanks Yingke Xu and Michele Darrow for their contribution to the peer review of this work. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team.

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Zulueta-Coarasa, T., Jug, F., Mathur, A. et al. MIFA: Metadata, Incentives, Formats and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis. Nat Methods (2025). https://doi.org/10.1038/s41592-025-02835-8

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