FAIR image data
As a result of the digital revolution, scientific data must be created with longevity in mind more than ever before. FAIR data increase the value of scientific data by enabling it to be more easily incorporated into a variety of different research projects. The FAIR principles thus facilitate and accelerate knowledge generation and scientific progress, improve research transparency, and foster collaboration within the scientific community.
The FAIR principles
What does FAIR mean?
According to the FAIR principles(1), scientific data is of the highest value if it is:
Findable: Data and associated Metadata should be easy to find and discover for both humans and computers by a standard identification mechanism
Accessible: (Meta)data are available and obtainable by their identifier using a standardized communication protocol; even if the data itself is restricted, the metadata is visible
Interoperable: Data needs to be integratable with other data and into applications or workflows for analysis, processing and storage by the use of shared and broadly applicable language
Reusable: To optimize the data for reuse, the data and metadata should be richly described by accurate and relevant attributes.
What are the challenges for BioImage data?
Biological imaging methods present special challenges in regards to FAIR, as they likely generate large volumes (up to several TB) of often complex and multidimensional data in various (proprietary) file formats that must be properly handled, processed and stored.
Supporting FAIR image data
The FAIRification process often begins by recognizing the value of FAIR data and subsequently making adjustments in data acquisition, processing and management where appropriate. Euro-BioImaging promotes and facilitates the adoption of FAIR practices relevant to image data which get implemented at our Nodes and the Hub. To this end, we offer resources, training and 1-on-1 guidance to FAIRify your data in all stages of the data lifecycle – from project planning to data deposition and reuse. We also work closely with dedicated image data repositories making important connections between the resources and the users.
Contact: fairdata@eurobioimaging.eu
FAIR training
We have launched the 'Euro-BioImaging's Guide to FAIR BioImage Data' series of events, which aims to introduce the FAIR principles in the context of bioimaging and provide you with simple yet effective steps for a smooth start to your FAIR journey.
FAIR resources
Publication on FAIR image data ecosystem
To capture the current position of the imaging community in its journey towards FAIR data and ongoing initiatives we have written the article ,Building a FAIR image data ecosystem for microscopy communities, in the journal Histochemistry and Cell Biology (HCB).
Research Data Management Plan Template
Euro-BioImaging strongly encourages its users to prepare a Data Management Plan (DMP). To assist with this, we have developed a comprehensive template with tailored questions for bioimaging research projects, available as a fillable PDF.
Catalogue of FAIR image data resources
We have compiled a Fairsharing catalogue of FAIR, public image data resources including repositories, policies and standards that Euro-BioImaging recommends and supports.
Selected BioImage Data repositories
Open BioImage Data (contact: fairdata@eurobioimaging.eu)
The BioImage Archive: an image deposition database for all microscopy data (from organism to molecular scale) associated with a publication. It adopts the recommended metadata for biological images‘ (REMBI) (2)scheme to define metadata, which improves the FAIRness of the data by enhancing interoperability and re-use.
The Image Data Resource: a public repository of well annotated reference image datasets from scientific studies.It includes the cell-IDR and the tissue-IDR that hold high quality image datasets, that can be visualized and readily re-used.
The Electron Microscopy Public Image ARchive: a public resource for raw images underlying 3D cryo-EM maps / tomograms and 3D datasets obtained from volume EM techniques and soft and hard X-ray tomography.
Pre-clinical BioImage Data (contact: preclinicaldata@eurobioimaging.eu)
The Preclinical Image DAtaset Repository (PIDAR) is a public repository of metadata information describing preclinical image datasets from any imaging modality associated to peer-review publications.
Further reading:
- (1) Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016).
- (2) Sarkans, U., Chiu, W., Collinson, L. et al. REMBI: Recommended Metadata for Biological Images—enabling reuse of microscopy data in biology. Nat Methods 18, 1418–1422 (2021).
- GO FAIR Initiative for implementing the FAIR data principles
- The FAIR cookbook: an online and open resource for the Life Sciences with recipes to make your data FAIR
- RDMkit: The Research Data Management toolkit for Life Sciences
- A practical guide to bioimaging research data management in core facilities
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