Workshop: Euro-BioImaging’s Guide to FAIR bioimage data


Wondering how to make the most out of your bioimaging data?

Join us for a free, online workshop that will introduce the FAIR (Findable, Accessible, Interoperable and Reusable) principles, focusing on their implementation for bioimaging data.

Speakers will include representatives from Euro-BioImaging, BioImage Archive, the AI4Life project, and more … Our special guest, Katrín Möller, a post-doctoral researcher at Biomedical Center of the University of Iceland, will share her personal story of depositing her microglia dataset on the BioImage Archive.


5th NEUBIAS Conference: Defragmentation Training School

i3S - Instituto de Investigação e Inovação em Saúde da Universidade do Porto

The Defragmentation Training School 2 is addressed to the new generation of bioimage analysts and will take place from the 8th to the 12th of May 2023. The training school is supported by EOSC-Life, Euro-BioImaging, AI4Life and i3S.More information about the Defragmentation Training School can be found here.


5th NEUBIAS Conference: Open Symposium

i3S - Instituto de Investigação e Inovação em Saúde da Universidade do Porto

The Open Symposium will start on Thursday the 11th of May and will focus on recent scientific developments and open tools in bioimage analysis. There will be a special session for AI4Life, covering Deep Learning-related topics. More information about the Open Symposium can be found here.

EUR200 – EUR350

Microscopy data analysis: machine learning and the BioImage Archive


This virtual course will show how public bioimaging data resources, centred around the BioImage Archive, enable and enhance machine learning based image analysis. The content will explore a variety of data types including electron and light microscopy and miscellaneous or multi-modal imaging data at the cell and tissue scale. Participants will cover contemporary biological image […]


DL@MBL: Deep Learning for Microscopy Image Analysis


The goal of this course is to familiarize researchers in the life sciences with state-of-the-art deep learning techniques for microscopy image analysis and to introduce them to tools and frameworks that facilitate independent application of the learned material after the course.