20190527_Brecht Wyns & Christophe Bahim _ FAIR data maturity model

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FAIR Data Maturity Model
Research policy monitoring in the era of
Open Science and Big Data
27th May 2019
2019-05-27 www.rd-alliance.org - @resdatall 1

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www.rd-alliance.org - @resdatall 2
Who knows the FAIR principles?
2019-05-27
Raise your hand

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Who applies the FAIR principles?
2019-05-27
Raise your hand

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Who measures the FAIR principles?
2019-05-27
Raise your hand

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The FAIR Principles
2019-05-27
FINDABLE ACCESSIBLE INTEROPERABLE REUSABLE
discoverable with machine
readable metadata, identifiable
and locatable by means of a
standard identification
mechanism
available and
obtainable to both
human and machine
sufficiently described and
shared with the least
restrictive licences, allowing
the widest reuse possible
across scientific disciplines
and borders, and the least
cumbersome integration with
other data sources
both syntactically parseable
and semantically
understandable, allowing
data exchange and reuse
among scientific disciplines,
researches, institutions,
organisations and countries

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FAIR data as a measure for Open Science
FAIR research data management as a way to
Improve scientific research;
Contribute to growth and accelerate innovation;
Increase the reproducibility of research; and
Better inform citizens and society about the results and value of research.
Relies on a set of common principles across multiple scientific
disciplines
2019-05-27
SOUNDS AMAZING!
BUT…

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FAIR data maturity model
www.rd-alliance.org - @resdatall 72019-05-27
FAIR
The principles are not strict
➔ Ambiguity
➔ Wide range of interpretations of FAIRness
Different FAIR Assessment Frameworks
➔ Different metrics
➔ No comparison of results
➔ No benchmark
Solution
• Set of core assessment criteria for FAIRness
• FAIR data maturity model & toolset
• RDA recommendation
• FAIR data checklist
Join the RDA Working Group: RDA WG web page | GitHub

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Stakeholders
2019-05-27
FAIR data maturity
model WG
Members
Chairs
GO
FAIR
FAIR
Metrics
Support
TARGET AUDIENCE
• Researchers, data stewards, other data professionals
• Data service owners, e.g. infrastructure, repositories
• Organisations that manage research data
• Policymakers
…

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FAIR data maturity model in the context of Open Science
1 I Solution for research policy monitoring
Clear set of indicators and levels associated with them
Interoperability of existing/emerging FAIR assessment frameworks
Pushing data owners to the next level of FAIRness
2 I Foster innovation and societal impact
Better data quality
More data can be processed
Clear context and provenance of data
Accelerate innovation in a global digital economy
Savings in money and in time
2019-05-27

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2019-04-03
WG
methodology,
timeline &
scope

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Development methodology
Bottom-up approach comprising 4 phases
1. Definition
2. Development
▪ Assessment of the four FAIR principles in four ‘strands’
▪ Fifth ‘strand’: beyond the FAIR principles
3. Testing
4. Delivery
2019-05-27

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Overview of the methodology

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Timeline
Q2Q1 Q3 Q4 Q5 Q6
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13 M14 M15 M16 M17 M18
Today
Workshop #3 [June]
▪ Presentation of results
▪ Discussion on
indicators & levels
Workshop #4 [September]
▪ Proposals
▪ Proposed approach towards
guidelines, checklist and
testing
Workshop #2 [April]
▪ Approval of methodology &
scope
▪ Hands-on exercise
Workshop #1 [February]
▪ Introduction to the WG
▪ Existing approaches
▪ Landscaping exercise
2019-05-27
… and more to come!
RDA 13th Plenary - US RDA 14th Plenary - FI

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Scope
Entity | Dataset and data-related aspects (e.g. algorithms, tools and
workflows)
Nature | Generic assessment (i.e. cross-disciplines)
Time | Periodically throughout the lifecycle of the data
Respondent | People with data literacy (e.g. researchers, data librarians,
data stewards)
Audience | Researchers, data stewards, data professionals, data service
owners, organisations involved in research data and policy makers

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2019-04-03
In practice

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Landscaping exercise
Landscaping exercise as a starting point
Analysis of existing approaches
Publicly available documentation and a survey
Clustering questions and options
FAIR facets [e.g. F1, A2] per principle
Beyond the FAIR principles [e.g. data storage]
Identification of potential overlaps
WG to compare questions and derive common aspects

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Approaches analysed
So far, 11 approaches are on the radar
Approaches considered
ANDS-NECTAR-RDS-FAIR data assessment tool
DANS-Fairdat
DANS-FAIR enough?
The CSIRO 5-star Data Rating Tool
FAIR Metrics questionnaire
Checklist for Evaluation of Dataset Fitness for Use
RDA-SHARC Evaluation
FAIR evaluator
Approach partially considered*
Data Stewardship Wizard
Approaches not considered*
Big Data Readiness
Support Your data: A Research Data Management Guide for Researchers
*Methodologies analysed but partially/not included in the results because of questions that could not be classified
2019-05-27

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Results of the landscaping exercise
Five slide decks classifying questions
FAIR – Findable [Link]
FAIR – Accessible [Link]
FAIR – Interoperable [Link]
FAIR – Reusable [Link]
Beyond the FAIRprinciples (X) [Link]
Questions, options and potential overlaps
A2 metadata is accessible, even when the data are no longer available
1 Will the metadata record be available even if the data is no longer available?
No
Unsure
Yes
2 Are the metadata accessible? F4
No
Yes
5 Please provide the URL to a metadata longevity plan Overlap
7 The existence of metadata even in the absence/removal of data
Example
2019-05-27

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Towards core assessment criteria
* an indicator can be seen as a component of a Principle (e.g. F1, R1)
QUESTIONS
• Overlaps
• Principles overused /
underused
• Beyond the principles
OPTIONS
• Binary (Y/N)
• Multiple choice
• Free text
SCORING MECHANISM
• Stars
• Grade
• Loading bar
• None
INDICATORS*
• Not standardising
questions
• Defining indicators
based on questions
METRICS
Definition of metrics to
measure the indicators
MATURITY
Identification of the
maturity levels
Existing
work
Scope
2019-05-27

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Collaborative document
TAB 1 – Introduction
2019-05-27
TAB 2 – Landscaping Exercise
TAB 3 – Development
TAB 4 – Outstanding issues

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2019-04-03
Next steps

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Next steps
Development of the core assessment criteria on
Google Sheet
Analysis of all the FAIR principles
FAIR – Findable [Link]
FAIR – Accessible [Link]
FAIR – Interoperable [Link]
FAIR – Reusable [Link]
Online workshop #3 on indicators and maturity levels
at 09:00 CEST on the 18 June 2019
at 17:00 CEST on the 18 June 2019

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Resources
RDA FAIR data maturity model WG
https://www.rd-alliance.org/groups/fair-data-maturity-model-wg
RDA FAIR data maturity model WG – Case Statement
https://www.rd-alliance.org/group/fair-data-maturity-model-wg/case-statement/fair-
data-maturity-model-wg-case-statement
RDA FAIR data maturity model WG – GitHub
https://github.com/RDA-FAIR/FAIR-data-maturity-model-WG
RDA FAIR data maturity model WG – Collaborative document
https://docs.google.com/spreadsheets/d/1gvMfbw46oV1idztsr586aG6-
teSn2cPWe_RJZG0U4Hg/edit#gid=0
RDA FAIR data maturity model WG – Mailing list
fair_maturity@rda-groups.org
2019-05-27

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2019-04-03
Thank you!

20190527_Brecht Wyns & Christophe Bahim _ FAIR data maturity model

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20190527_Brecht Wyns & Christophe Bahim _ FAIR data maturity model