When is a model FAIR – and why should we care?

1. When is a model FAIR – and why should we care? Dagmar Waltemath Basel Computational Biology Conference, Sep 13 2021 | https://www.bc2.ch/ CC BY-NC-ND 3.0 Department of Medical Informatics University Medicine Greifswald (Germany)

2. © Copyright Universitätsmedizin Greifswald 2 • Identifiable data items • Persistent • Searchable • Identifiers use standard protocols • Authentification • Access to meta data, even if data not accessible • Formal, accesssible data representation • Qualified references • Licensing • Provenance • Standards compliance FAIR pripnciples published by Wilkinson et al (2016): https://doi.org/10.1038/sdata.2016.18 The FAIR principles in bio* sciences

3. © Copyright Universitätsmedizin Greifswald FAIR pripnciples published by Wilkinson et al (2016): https://doi.org/10.1038/sdata.2016.18 Finding computational models 3 Data should be uniquely & persistently identifiable; researchers should find your data. F1. (Meta)data are assigned a globally unique and persistent identifier F2. Data are described with rich metadata F3. Metadata clearly and explicitly include the identifier of the data they describe F4. (Meta)data are registered or indexed in a searchable resource

4. © Copyright Universitätsmedizin Greifswald BioModels COVID-19 collection: https://www.ebi.ac.uk/biomodels/search?offset=20&numResults=10&sort=relevance-desc&query=COVID-19&domain=biomodels Finding computational models 4 Data should be uniquely & persistently identifiable; researchers should find your data. F1. (Meta)data are assigned a globally unique and persistent identifier F2. Data are described with rich metadata F3. Metadata clearly and explicitly include the identifier of the data they describe F4. (Meta)data are registered or indexed in a searchable resource Example: Globally unique and persistent model ID in BioModels

5. © Copyright Universitätsmedizin Greifswald PMR2: Metadata for an exposure https://models.physiomeproject.org/exposure/e5cfb42225d4534a1e08979e57cf8bdd/cloutier_2009_a.cellml/cmeta Finding computational models 5 Data should be uniquely & persistently identifiable; researchers should find your data. F1. (Meta)data are assigned a globally unique and persistent identifier F2. Data are described with rich metadata F3. Metadata clearly and explicitly include the identifier of the data they describe F4. (Meta)data are registered or indexed in a searchable resource Example: Metadata about models in PMR2

8. © Copyright Universitätsmedizin Greifswald Identifiers.org as a resolution services for URIs in Computational Biology: http://identifiers.org/ Finding computational models 8 Data should be uniquely & persistently identifiable; researchers should find your data. F1. (Meta)data are assigned a globally unique and persistent identifier F2. Data are described with rich metadata F3. Metadata clearly and explicitly include the identifier of the data they describe F4. (Meta)data are registered or indexed in a searchable resource Example: Model repositories and metadata indexed at identifiers.org

9. © Copyright Universitätsmedizin Greifswald Identifiers.org as a resolution services for URIs in Computational Biology: http://identifiers.org/ Accessing computational models 9 Conditions under which the data can be used should be clear (to machines & humans). A1. (Meta)data are retrievable by their identifier using a standardised communications protocol A2. Metadata are accessible, even when the data are no longer available (and experiments)

10. © Copyright Universitätsmedizin Greifswald Identifiers.org as a resolution services for URIs in Computational Biology: http://identifiers.org/ Accessing computational models 10 Conditions under which the data can be used should be clear (to machines & humans). A1. (Meta)data are retrievable by their identifier using a standardised communications protocol A2. Metadata are accessible, even when the data are no longer available (and experiments) Example: Retrieving COVID-19 models from BioModels HTTPS SPARQL

11. © Copyright Universitätsmedizin Greifswald 11 Interoperable models across systems Machine-readable and using terminologies, vocabularies or ontologies that are commonly used in the field I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation I2. (Meta)data use vocabularies that follow FAIR principles I3. (Meta)data include qualified references to other (meta)data

12. © Copyright Universitätsmedizin Greifswald SBML L3 V1 Core Annotation Scheme, taken from https://resolver.caltech.edu/CaltechAUTHORS:20130108-162112228 12 Interoperable models across systems Machine-readable and using terminologies, vocabularies or ontologies that are commonly used in the field I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation I2. (Meta)data use vocabularies that follow FAIR principles I3. (Meta)data include qualified references to other (meta)data Example: Annotation of models (archives) using bioontologies, RDF & following the metadata specification.

13. © Copyright Universitätsmedizin Greifswald SBML L3 V1 Core Annotation Scheme, taken from https://resolver.caltech.edu/CaltechAUTHORS:20130108-162112228 13 Interoperable models across systems Machine-readable and using terminologies, vocabularies or ontologies that are commonly used in the field I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation I2. (Meta)data use vocabularies that follow FAIR principles I3. (Meta)data include qualified references to other (meta)data Example: Annotation of models (archives) using bioontologies, RDF & following the metadata specification.

14. © Copyright Universitätsmedizin Greifswald OMEX standard: https://doi.org/10.1515/jib-2020-0020; Harmonised annotations: https://doi.org/10.1093/bib/bby087 14 Interoperable models across systems Machine-readable and using terminologies, vocabularies or ontologies that are commonly used in the field I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation I2. (Meta)data use vocabularies that follow FAIR principles I3. (Meta)data include qualified references to other (meta)data Example: Annotation of models (archives) using bioontologies, RDF & following the metadata specification.

15. © Copyright Universitätsmedizin Greifswald 15 Reusing other people‘s models Well-described with metadata & provenance information; data sources can be linked or integrated with other data sources. R1. (Meta)data are richly described with a plurality of accurate and relevant attributes R1.1. (Meta)data are released with a clear and accessible data usage license R1.2. (Meta)data are associated with detailed provenance R1.3. (Meta)data meet domain-relevant community standards

16. © Copyright Universitätsmedizin Greifswald BIOINFORMATICS Open Access licences: https://academic.oup.com/journals/pages/open_access/licences; BioModels Licence: https://www.ebi.ac.uk/biomodels/faq#biomodels-licence 16 Reusing other people‘s models Well-described with metadata & provenance information; data sources can be linked or integrated with other data sources. R1. (Meta)data are richly described with a plurality of accurate and relevant attributes R1.1. (Meta)data are released with a clear and accessible data usage license R1.2. (Meta)data are associated with detailed provenance R1.3. (Meta)data meet domain-relevant community standards Example: Models are published with a clear license information, as are the reference publications

17. © Copyright Universitätsmedizin Greifswald BiVeS: https://sems.bio.informatik.uni-rostock.de/projects/bives/; Screenshot: FAIRDOMHub: https://fairdomhub.org/models/196 17 Reusing other people‘s models Well-described with metadata & provenance information; data sources can be linked or integrated with other data sources. R1. (Meta)data are richly described with a plurality of accurate and relevant attributes R1.1. (Meta)data are released with a clear and accessible data usage license R1.2. (Meta)data are associated with detailed provenance R1.3. (Meta)data meet domain-relevant community standards Example: Modification of models incl. version information as provided in FAIRDOMHub.

18. © Copyright Universitätsmedizin Greifswald Fig.: Curation pipeline for COVID Archives, courtesy Rahuman Sheriff (BioModels); funding: EOSC Fast Track COVID-19; grant no 831644 Example: Making COVID-19 models FAIR

19. © Copyright Universitätsmedizin Greifswald Fig.: https://healthecco.org/technology/ Example: Making COVID-19 data FAIR Lea Gütebier https://healthecco.org/team/

20. © Copyright Universitätsmedizin Greifswald EU FAIRplus Fellowship Programme: https://fairplus-project.eu/getinvolved/fellowship; SHIP data: https://www2.medizin.uni-greifswald.de/cm/fv/ship/ Picture Gerd Altmann on Pixabay (right) and Jair Lázaro on Unsplash (right) Example: Making health data FAIR Esther Thea Inau 0000-0002-8950-2239 Observational health data

21. © Copyright Universitätsmedizin Greifswald Photo by Hayley Seibel on Unsplash “A minimal step towards FAIRness is to provide the data set, as an entity in its own right, with a PID that is not only intrinsically persistent, but also persistently linked to the data set (research object) it identifies. However, without machine-readable metadata it will still be difficult to find the data, unless one knows the PID. So a PID is necessary, but not sufficient.” (https://www.health-ri.nl/fair-principles ) How to: Start https://combine-org.github.io/events/ Join us at COMBINE this year!

22. A little FAIRness is easy to achieve. Dagmar Waltemath | Department of Medical Informatics https://twitter.com/waltelab https://orcid.org/0000-0002-5886-5563

When is a model FAIR – and why should we care?

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