As a result of the digital revolution, scientific data needs to be created with longevity in mind more than ever before. FAIR data increase the value of scientific data by enabling them to be more readily incorporated into a variety of different research projects. The FAIR principles thus facilitate and accelerate knowledge generation and scientific progress, improve research transparency and boost collaboration within the scientific community.
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.
Biological imaging methods present special challenges in regards to FAIR, as they 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 and processing 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: firstname.lastname@example.org
Catalogue of FAIR public image data resources
We have compiled a catalogue of FAIR, public image data resources, repositories, policies and standards that we support. This catalogue is available at Fairsharing.org and includes for example the following BioImage Data repositories:
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.
(1) Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
(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). https://doi.org/10.1038/s41592-021-01166-8;
the Euro-BioImaging Web Portal. To find out more, read our
policy. Please note:
For best experience we do not recommend using Internet Explorer.