This document discusses making biobank data and samples FAIR (Findable, Accessible, Interoperable, and Reusable). It explains the four FAIR principles and provides examples of how to apply each one. To make resources findable, they need unique and persistent identifiers, rich metadata, and to be findable through other systems. To make them accessible, they need to be retrievable using open standards. To make them interoperable, standardized languages and vocabularies should be used. And to make them reusable, they need to be richly described and released with clear usage terms and provenance. The document recommends three steps to make samples and data FAIR: include sufficient metadata using common

1. FAIR Sample and Data Access
David van Enckevort
david.van.enckevort@umcg.nl
NIH Scientific Data SIG

2. Introduction
• Project Manager
• Genomics Coordination
Centre at Dept. Genetics
• Member of the group lead by
Prof. M.A. Swertz
• My focus: sharing of biobank
data and material

3. Outline
• FAIR Principles
• Making your data and samples FAIR
• Examples of FAIR samples and data

4. FAIR Principles
• Findability
• Accessibility
• Interoperability
• Reusability
Make resources sustainable for reuse
doi: 10.1038/sdata.2016.18

5. Findable
• Resources are assigned a globally unique and
persistent identifier
• Resources are described with rich metadata
• Metadata clearly and explicitly include the identifier
of the resource it describes
• Resource can by found through other systems

6. Accessible
• Retrievable by their identifier using a standardized
communications protocol
• The protocol is open, free, and universally
implementable
• The protocol allows for an authentication and
authorization procedure, where necessary
• Metadata are accessible, even when the data are no
longer available

7. Interoperable
• Use a formal, accessible, shared, and broadly
applicable language for knowledge representation.
• Use vocabularies that follow FAIR principles
• Include qualified references to other (meta)data

8. Reusable
• Richly described with a plurality of accurate and
relevant attributes
• Released with a clear and accessible usage license
• Associated with detailed provenance
• Meet domain-relevant community standards

9. Make your samples & data FAIR
• Make knowledge explicit (do not assume people will
know things that are obvious to you)
• Include sufficient metadata (units, SOPs, access
conditions, consent)
• Provide the raw data (e.g. when you need BMI also
provide length and weight)
Step 1

10. Make your samples & data FAIR
• Use standards information models
• Encode data using ontologies
Step 2

11. Standard information models
• Define the minimal information you should capture
to make your data (re)usable
• Provide structure to the information
Common information models: MIABIS, MIAPE, MIAME
https://biosharing.org/

12. Ontologies
• Provide a well defined and unambiguous meaning to
a term
• Provide relations between terms, e.g. ’Breast Cancer’
is a ‘Cancer’ is a ‘Disease’
Common ontologies include: OBIB, HPO, OMIM
http://bioportal.bioontology.org/

13. • Information model defines what information to
include
• Ontologies define acceptable values
Improve interoperability and reusability

14. Make your samples & data FAIR
• Publish metadata about your samples and data
collections
• Make it available to others
Step 3

15. Sample
Catalogue
ID-Cards

16. RD-Connect Sample Catalogue
• Biobanks from EuroBiobank and RD-Connect
• Integrated with ID-Card and the RD-Connect
platform
• Will list over 50 000 rare disease samples
https://samples.rd-connect.eu/

18. How FAIR helps
• Common structure and protocols allows the
catalogue to aggregate data from the individual
biobanks
• Common terms allows researchers to find the right
data and samples
• Linked data allows integration with the Genomics
platform and ID-Cards system

19. www.umcg.nl
Thank you for your attention!