Cost-based measurement of data FAIRness of public data sources in life sciences

Antoon Bronselaer, PhD antoon.bronselaer@ugent.be

Filip Pattyn, PhD Filip.pattyn@ontoforce.com

Assessing FAIRness

?

Counting the principles?

F1. F2. F3. F4. A1. A1.1. A1.2. A2. I1. I2. I3. R1. R1.1. R1.2. R1.3.

F1. F2. F3. F4. A1. A1.1. A1.2. A2. I1. I2. I3. R1. R1.1. R1.2. R1.3.

Total count Total count

Data source 1 Data source 2

Counting the principles?

F1. F2. F3. F4. A1. A1.1. A1.2. A2. I1. I2. I3. R1. R1.1. R1.2. R1.3.

F1. F2. F3. F4. A1. A1.1. A1.2. A2. I1. I2. I3. R1. R1.1. R1.2. R1.3.

Total count Total count

Data source version 1 Data source version 2

Difficult to compare

FAIRness

?

0% FAIR ? 100% FAIR ?

• F1. (meta)data are assigned a globally unique and persistent identifier

• F1. (meta)data are assigned a globally unique and persistent identifier

– Article ID

– Journal ID

• F1. (meta)data are assigned a globally unique and persistent identifier

– Article ID

– Journal ID

– Author URIs

• F1. (meta)data are assigned a globally unique and persistent identifier

– Article ID

– Journal ID

– Author URIs

– Reference IDs

– Classification IDs

• F1. (meta)data are assigned a globally unique and persistent identifier

– Article ID

– Journal ID

– Author URIs

– Reference IDs

– Classification IDs

– Affiliation?

• F1. (meta)data are assigned a globally unique and persistent identifier

– Article ID

– Journal ID

– Author URIs

– Reference IDs

– Classification IDs

– Affiliation?

• F1. (meta)data are assigned a globally unique and persistent identifier

– Article ID

– Journal ID

– Reference IDs

– Classification IDs

– Affiliation?

– Author URIs?

When is a dataset FAIR or FAIR enough?

• Propagation of FAIRness

– I2. (meta)data use vocabularies that follow FAIR principles

> >

How to measure FAIRness?

• Measuring FAIRness

–Clear definition of what is being measured and why one wants to measure it.

–Describe what’s a valid result and how one obtains it, thus reproducible

• Qualities of a good measurement

–Clear: easy to understand

–Realistic: no over-engineering

–Objective: quantitative, machine-interpretable, scalable and reproducible

–Discriminating: able to distinguish differences

Thanks Michel Dumontier

What’s the rationale behind FAIR?

• (Re-)use data for multiple purposes

• What’s the impact for the end-user? Who’s the audience?

• More FAIRness should mean less hurdles to solve a use case

More FAIR means less effort

• What’s the effort needed to make a data source more FAIR so one can solve a use case?

• Effort quantified as a cost

–Time

–Human and Machine resources

• Unit of measure

–Price

• Potential to calculate the Return On Investment (ROI) on FAIR data

More FAIR means less effort

transformations

…

…

…

more FAIR

application

graphical UI

API

Different types of effort

transformations

…

…

…

more FAIR

application

graphical UI

API

FAIR enough means less effort

application

graphical UI

API

DISQOVER

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User friendliness

Usability / Transparency / Traceability

Food for thought

• Cost vs. Time of data transformations

– Faster by more expensive skilled data scientist

– Slower by less expensive junior data scientist

– Manual vs. automated

• Track evolution data source FAIRness

–More FAIR data generation

–Make legacy data more FAIR

–Technological advancements

FAIRness as a cost-based measurement

Calculate ROI of FAIRness

Consensus units of cost

May the

ONTOFORCE …

Hans Constandt Bérénice Wulbrecht Ali Adiby Dries Schaumont Paulo Van Huffel Niels Vanneste Peter Verrykt Kenny Knecht, PhD Paul Vauterin, PhD

Faculty of Engineering and Architecture Department of Telecommunications and

information processing

Prof. Antoon Bronselaer

Yoram Timmerman

Filip Pattyn, PhD

filip@ontoforce.com +32 486 739 129 www.disqover.com www.ontoforce.com