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

by Marconato, Luca , Rzepka, Norman , Korir, Paul , Kreshuk, Anna , Ulman, Vladimír , Muñoz-Barrutia, Arrate , Yoldaş, Aybüke Küpcü , Oezdemir, Bugra , Babalola, Kola , Gogoberidze, Nodar , Zulueta-Coarasa, Teresa , Hartley, Matthew , Narayan, Kedar , Sarkans, Ugis , Bankhead, Pete , Tischer, Christian , Serrano, Beatriz , Uhlmann, Virginie , Liviu Anita , Jones, Martin , Moore, Josh , Mathur, Aastha , Kleywegt, Gerard J , Norlin, Nils , Riesterer, Jessica , Jug, Florian , Perrine Gilloteaux

in Accessibility / Annotations / Artificial intelligence / Datasets / Guidelines / Image analysis / Image quality / Incentives / Metadata

2025

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

in Accessibility / Annotations / Artificial intelligence / Datasets / Guidelines / Image analysis / Image quality / Incentives / Metadata

2025

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

in Accessibility / Annotations / Artificial intelligence / Datasets / Guidelines / Image analysis / Image quality / Incentives / Metadata

2025

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Paper

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

Marconato, Luca,

Rzepka, Norman,

Korir, Paul,

Kreshuk, Anna,

Ulman, Vladimír,

Muñoz-Barrutia, Arrate,

Yoldaş, Aybüke Küpcü,

Oezdemir, Bugra,

Babalola, Kola,

Gogoberidze, Nodar,

Zulueta-Coarasa, Teresa,

Hartley, Matthew,

Narayan, Kedar,

Sarkans, Ugis,

Bankhead, Pete,

Tischer, Christian,

Serrano, Beatriz,

Uhlmann, Virginie,

Liviu Anita,

Jones, Martin,

Moore, Josh,

Mathur, Aastha,

Kleywegt, Gerard J,

Norlin, Nils,

Riesterer, Jessica,

Jug, Florian,

Perrine Gilloteaux

2025

Overview

Artificial Intelligence methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new methods, but access to such data is often hindered by the lack of standards for sharing datasets. We brought together community experts in a workshop to develop 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 positive that the MIFA (Metadata, Incentives, Formats, and Accessibility) recommendations will accelerate the development of AI tools for bioimage analysis by facilitating access to high quality training data.

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Publisher

Cornell University Library, arXiv.org

Subject

Accessibility

/ Annotations

/ Artificial intelligence