Towards metrics to assess and encourage FAIRness

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Towards metrics to assess and encourage FAIRness

Michel Dumontier, Ph.D.

Distinguished Professor of Data Science

@micheldumontier::FAIR@Elixir:2017-03-23


Principles that apply to all digital resources and their metadata.

software, images, data, repositories, web services

http://www.nature.com/articles/sdata201618

http://www.nature.com/articles/sdata201618


Horizon 2020: Data Management PlanSection 2. FAIR data 1. Making data findable, including provisions for

metadata (5 questions2. Making data openly accessible (10 questions)3. Making data interoperable (4 questions)4. Increase data re-use (through clarifying licenses - 4

questions)

Additional sections: 5. Data summary (6 questions, 5 of which also cover

aspects of FAIRness) 6. Allocation of resources (4 questions) 7. Data security (2 questions)8. Ethical aspects (2 questions) 9. Other issues (2 questions)

Total of 23 + 16 = 39 questions!! https://goo.gl/Strjua

https://goo.gl/Strjua


Hypothesis

Improving the FAIRness of a digital resource will increase its discovery and reuse.


Fundamental Questions• What do we mean by FAIRness? • In what ways can we assess the FAIRness of a digital resource?• To what degree can we automate this assessment?• Must we treat each type of digital resource differently?• Who will use the metrics? The producers, the funders, or the

users?• Can one resource be more FAIR than another? Will/should this

impact funding decisions?• Should only one organization define these metrics? Or can

anybody make their own metrics? What happens if a digital resources scores well against one set of metrics, but not another?


What is FAIRness?

FAIRness reflects the extent to which a digital resource addresses the FAIR principles as per the expectations defined by a community of stakeholders.


What is a metric?

• A metric is a standard of measurement. • It must provide clear definition of what is being

measured, why one wants to measure it. • It must describe the process by which you

obtain a valid measurement result, so that it can be reproduced by others. It needs to specify what a valid result is.


Example of a FAIRness MetricF1 (meta)data are assigned a globally unique and persistent identifier

Aspect: Identifier PersistenceRationale: An identifier can be used to find, access, and reuse a resource. As such, it must be available to users in the longest term possible otherwise we will not be able to perform those functions with the identifier in hand. Relevant FAIR Principles: F,A,I,RMetric: Availability of data management plan, which includes a section dealing with continuity and contingencies related to the persistence of identifiers. The value of the metric is true or false.Procedure: Check and verify the URL in the resource metadata points to a data management plan with continuity section. Document should follow a community standard, or recommend a basic structure.


NIH Commons Framework Working Group onFAIR Metrics

Aim: To identify and prototype methods to assess the FAIRness of a digital resource.

– Identify and include initial stakeholders– Develop and discuss potential metrics– Explore ways in which to report and assess

metrics.


Current Thinking:FAIRness Index

• A FAIRness Index is a collection of metrics that are aligned to the FAIR principles and can be consistently and transparently evaluated.

• A community, comprised of clearly defined stakeholders (researchers, publishers, users, etc), may define their own FAIRness Index that expresses what makes a digital resource ideally or maximally FAIR.

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Stakeholders

People worried about – Findability– Accessibility– Interoperability– Reuse– Provenance– Licensing– Citation– Value


People who are - Potential users- Resource creators- Academics- Publishers- Industry- The public- Funding agencies


Ways can we gather information to assess FAIRness

A) Self assessmentB) Self-appointed FAIR Assessment TeamC) Automated assessmentD) CrowdsourcingE) All of the above


• Is there structured metadata describing the resource? – Check for embedded metadata as microdata or linked data– Check for hyperlinked documents with standardized formats: HCLS dataset

description/DCAT schema.org annotations, etc• Are entries identified with a persistent identifier?

– Is there a DOI with scholarly publications?– Is there a permanent URL for each item (w/out query parameters) – Is there a resource type specified, does it use a well known vocabulary such as

EDAM, identifiers.org, etc.• Can the resource be found in a recognized repository?

– E.g. a database in Biosharing– E.g. a tool in Elixir bio.tools– E.g. gene expression data in GEO

• Can the resource be found with a web search engine? – What rank does the resource appear at when using the identifier or title in a

web search?

Findable


Example FAIR MetricsAccessible metrics• Are the (meta)data accessible by permanent URL? • Can you obtain the resource as a standardized language (e.g. HTML, XML, JSON, JSON-LD)? • Are the data downloadable in bulk or in part with an application programming interface (API)?

Is the API documented using Swagger, smartAPI, or follow the Hydra protocol?

Interoperable metrics• Are the (meta)data described with a community vocabulary?• Are the data and metadata linked to other datasets, vocabularies and ontologies?• Are the data and metadata expressed in universal languages (e.g. XML, JSON, JSON-LD,

RDF/XML)

Reusable metrics• Is there a license specified? Is it a standardized license? Is it linked to in the resource metadata?• Is it clear how the work should be cited? See the FORCE11 Data Citation Implementation Pilot

and bioCADDIE Working Group 5. • Is there any indication of reuse beyond its original context and original creators?• Is there any indication of access through published statistics?

https://www.force11.org/group/dcip

https://biocaddie.org/group/working-group/working-group-5-dataset-citation-metrics

https://biocaddie.org/group/working-group/working-group-5-dataset-citation-metrics


A first attempt!

• IDCC17 Practice Paper “Are the FAIR Data Principles fair?” by Alastair Dunning, Madelein de Smael, Jasmin Böhmer

• web-interfaces, help-pages and metadata-records of over 40 data repositories were examined to score the individual data repository against the FAIR principles

• 2 months

Data: http://dx.doi.org/10.4121/uuid:5146dd06-98e4-426c-9ae5-dc8fa65c549f Paper: https://zenodo.org/record/321423#.WNFNrTvytm8

http://dx.doi.org/10.4121/uuid:5146dd06-98e4-426c-9ae5-dc8fa65c549f

https://zenodo.org/record/321423#.WNFNrTvytm8


37 repositories


Scoring the resources


Overall Evaluation




Summary of Study

• Offers an initial larger scale assessment• Issues

– confusion about what is meant by each principle, clarified after the study through discussion

– Fully manual effort, but AFAIK inter-annotator agreement not established

– not easy to scale, can we automate it?


Metrics for Digital Repositories

• Data Seal of Approval– 6 core requirements– 16 criteria

• DIN31644: Information and documentation - Criteria for trustworthy digital archives– 10 core requirements– 34 criteria

• ISO16363: : Audit and certification of trustworthy digital repositories– 100+ criteria


DSA

The data can be found on the Internet

The data are accessible (clear rights and licences)

The data are in a usable format

The data are reliable

The data are identified in a unique and persistent way so that they can be referred to


DSA 16 requirements1. mission to provide access to and preserve data2. licenses covering data access and use and monitors compliance.3. continuity plan 4. ensures that data created/used in compliance with norms.5. adequate funding and qualified staff through clear governance6. mechanism(s) for expert guidance and feedback 7. guarantees the integrity and authenticity of the data8. accepts data and metadata to ensure relevance and understandability9. applies documented processes in archival10. responsibility for preservation that is documented.11. expertise to address data and metadata quality12. Archiving according to defined workflows.13. enables discovery and citation.14. enables reuse with appropriate metadata.15. infrastructure16. infrastructure https://www.datasealofapproval.org


Data Seal of Approval

• self-assessment in the DSA online tool. The online tool takes you through the 16 requirements and provides you with support.

• Once you have completed your self-assessment you can submit it for peer review.

https://assessment.datasealofapproval.org/accounts/login/

https://www.datasealofapproval.org/en/information/requirements/


• Score data on each FAIR dimension (e.g. from 1 to 5)

• Total score of FAIRness as an indicator of data quality

• Scoring can only be partly automatic, not all principles can be established objectively: – scoring at ingest by data archivists of TDR– after reuse by data users (community review)

From: https://dans.knaw.nl/nl/actueel/PresentationP.D..pdf

https://dans.knaw.nl/nl/actueel/PresentationP.D..pdf







DANS FAIR metrics proposal




@micheldumontier::BD2K Metadata WG:16-10-201531

http://www.w3.org/TR/hcls-dataset/

http://www.w3.org/TR/hcls-dataset/

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http://hw-swel.github.io/Validata/ VALIDATA DEMO

@gray_alasdair www.macs.hw.ac.uk/~ajg33

RDF constraint validation toolConfigurable to any profile

Declarative reusable schema description

Shape Expression (ShEx) constraints

Open source javascript implementation

http://hw-swel.github.io/Validata/


[email protected]: http://dumontierlab.com

Presentations: http://slideshare.com/micheldumontier

• Early stages of thinking about FAIR metrics and FAIR indexes

• Lots of opportunities to explore different models• Send me an email if you’re interested in collaborating

or participating in the working group

METRICS

http://dumontierlab.com/

http://slideshare.com/micheldumontier