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FAIR principles and data management planning Hugo Besemer AIMS Webinar 2017-05-25

Webinar@AIMS_FAIR Principles and Data Management Planning

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Page 1: Webinar@AIMS_FAIR Principles and Data Management Planning

FAIR principles anddata management

planning Hugo Besemer

AIMS Webinar

2017-05-25

Page 2: Webinar@AIMS_FAIR Principles and Data Management Planning

FAIR principles anddata management

realities Hugo Besemer

AIMS Webinar

2017-05-25

Page 3: Webinar@AIMS_FAIR Principles and Data Management Planning

A FAIRLY short timeline

• January 2014 Workshop in Leiden (the Netherlands)• 2014 Results on Force11 site• 15 March 2016 Article in ‘Scientific data’• 26 July 2016 H2020 Programme Guidelines • December 2016 Webinar FAIR / repositories

Guiding Principles for Findable, Accessible, Interoperable and Re-usable Data Publishing version b1.0

Discussion about indicators of ‘FAIRness’

Page 4: Webinar@AIMS_FAIR Principles and Data Management Planning

A bit longer timeline

Page 5: Webinar@AIMS_FAIR Principles and Data Management Planning

What ‘FAIR’ does NOT want to be and what it wants to achieve

• It is NOT a specification• It is NOT a syntax (it aims to be syntax agnostic)• It is meant to precede technology and other implementation choices

• In my own words : these guidelines aim to create a research data environment that is FAIR to machines and humans

Page 6: Webinar@AIMS_FAIR Principles and Data Management Planning

FFto be findableto be findable

• F1. (meta)data are assigned a globally unique and persistent identifier • F2. data are described with rich metadata (defined by

R1 below) • F3. metadata clearly and explicitly include the

identifier of the data it describes • F4. (meta)data are registered or indexed in a

searchable resource

Page 7: Webinar@AIMS_FAIR Principles and Data Management Planning

Proposed indicators F(indable)

• 1.No PID and no metadata/documentation• 2.PID without or with insufficient* metadata• 3.Sufficient* metadata without PID• 4.PID with sufficient* metadata–Information on data provenance• 5.PID, rich metadata and additional documentation–Additional

explanation of how data can be used

* Sufficient = enough metadata to understand what the data is about

Page 8: Webinar@AIMS_FAIR Principles and Data Management Planning

F(indable) @ Wageningen

• Presently departments decide what data is published• At best data that is underlying publications (pressure from journals

helps at lot….)• There are ongoing (series of) datasets that are only known to insiders

Page 9: Webinar@AIMS_FAIR Principles and Data Management Planning

AAto be accessibleto be accessible

•A1. (meta)data are retrievable by their identifier using a standardized communications protocol •A1.1 the protocol is open, free, and universally

implementable •A1.2 the protocol allows for an authentication and

authorization procedure, where necessary •A2. metadata are accessible, even when the data are

no longer available

Page 10: Webinar@AIMS_FAIR Principles and Data Management Planning

Proposed indicators A(ccessible)

1.No user license / unclear conditions of reuse / metadata nor data are accessible

2.Metadata are accessible (even when the data are not or no longer available)

3.User restrictions apply (of any kind, including privacy, commercial interests, embargo period, etc.)

4.Public Access (after registration)

5.Open Access (unrestricted, CC0 –perhaps also CCby?)

Page 11: Webinar@AIMS_FAIR Principles and Data Management Planning

Accessible @ Wageningen

• Probably the most important problem: who decides who can get access (and who will grant the permission technically)• We have been awaiting guidelines on ownership / usage rights for

three years.

Page 12: Webinar@AIMS_FAIR Principles and Data Management Planning

IIto be interoperableto be interoperable

•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

Page 13: Webinar@AIMS_FAIR Principles and Data Management Planning

Proposed indicators I(nteroperable)

1. Proprietary, non-open format data

2.Proprietary format, accepted by DSA Certified Trusted Data Repository

3.Non-proprietary, open format (= “preferred” or “archival” format)

4.Data is additionally harmonized/ standardized, using standard vocabularies

5.Data is additionally linked to other data to provide context

Page 14: Webinar@AIMS_FAIR Principles and Data Management Planning

I(nteroperable) @ Wageningen

• In response to a blog about this the people working with ontologies met for the first time• Their main concerns• How to find the relevant ontologies• Can we rely on them to justify investments (consistency, process of

maintenance

• H2020 coordinators have no clue what all this is about

Page 15: Webinar@AIMS_FAIR Principles and Data Management Planning

R R to be Reusable: to be Reusable:

•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

Also in F4

Also in F2, I1

Also in I1

Page 16: Webinar@AIMS_FAIR Principles and Data Management Planning

Proposed indicators R(e-usable)

“First we attempted to operationalise R – Re-usable as well ... but we changed our mind

Reusable – is it a separate dimension? Partly subjective: it

depends on what you want to use the data for!”

Page 17: Webinar@AIMS_FAIR Principles and Data Management Planning

ReferencesGuiding principles for findable, accessible, interoperable and re-usable data publishing version B1.0

https://www.force11.org/fairprinciples

The FAIR Guiding Principles for scientific data management and stewardship

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

Guidelines on FAIR Data Management in Horizon 2020 http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf

FAIR Data in Trustworthy Data Repositories Webinar

https://eudat.eu/events/webinar/fair-data-in-trustworthy-data-repositories-webinar

Two blogs about FAIR @ Wageningen

•https://weblog.wur.eu/openscience/can-wageningen-fair/

•https://weblog.wur.eu/openscience/vocabularies-and-the-i-in-fair-data-principles/