Collection
Imaging data management
A community effort to implement FAIR principles
Time to complete:
> 3 hours
This course includes:
Written by:
Last reviewed:
June 2026
All materials are free cultural works licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, except where further licensing details are provided.
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Biological imaging data are a valuable resource for life science research, but their long-term value depends on effective data management. This collection introduces key concepts and best practices for managing imaging data throughout the research lifecycle, including standards such as OME-Zarr, public repositories, REMBI, and ontologies. Participants will explore the principles of FAIR data and discover how community-driven approaches can improve the findability, accessibility, interoperability, and reuse of imaging data.
Who is this course for?
This collection is designed for everyone working with biological imaging data: biologists, ecologists, environmental scientists, imaging specialists, image analysts, data scientists, data curators, data stewards, data managers, IT and computational support, repository managers, project managers, and more.
This collection was developed as part of the STANDFLOW (Towards a Standardised Data Management Workflow) project, supported by EMBL's Planetary Biology Transversal Theme seed grant.
Authors:
Alexandra Zakieva, Ajay Mishra, Isabel Kemmer, Katrina Exter, Jean-Karim Hériché, Aybuke Kupcu Yoldas, Bugra Özdemir, Anna Maria Steyer, Tina Wiegand, Gaëlle Toullec, Christian Schmidt, Charles Girardot, Perrine Paul-Gilloteaux, Flora Vincent, Kim Tamara Gurwitz, Cath Brooksbank, Matthew Hartley, Yannick Schwab (Authors' affiliations)
This collection was peer-reviewed by Alexander Botzki (Vlaams Instituut voor Biotechnologie).
What will I achieve?
By the end of the course you will be able to:
- Describe the role of data management throughout the biological imaging data lifecycle and explain its importance for reproducible research
- Apply key concepts, standards, and resourced for imaging data management, including data management plan, OME-Zarr, public repositories, REMBI, and ontologies
- Identify actions that support FAIR (Findable, Accessible, Interoperable, and Reusable) imaging data and recognise the importance of community collaboration in achieving them
DOI:
10.6019/TOL.imaging-data-management-t.2026.00001.1
Course contents
- Let's begin with sharing our learnings and resources
- Scientific method and data life cycle
- Technical and practical aspects of data management
- Towards a standardised open imaging data format: OME–Zarr
- Data storage: Public Repositories, REMBI, and Ontologies
- Working together
- Identity yourself in the data world
- Summary and acknowledgements
- Authors' affiliations
- Your feedback
- Resources and references
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The average time to read through the main body of the course is 3 hours (not including exercises and external links). The time may vary depending on your prior knowledge and how you choose to work through the course.
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