Presentation about FAIR state of the union for system integrator companies associated with DTL

1. AGENDA
13.00 Welcome & goal of the meeting
13.10 Short introductions
Also outlining what they would want to learn today
•Bluebee
•Centric
•ICT automatisering
•MedVision
•Ordina
•Ortec
13.45 FAIR technology & Tools
14.30 Q & A,
15.00 Wrap up

2. FAIR Data - GO FAIR
Interconnected Data Infrastructure
Towards an Internet of FAIR Data and Services
Luiz Olavo Bonino – luiz.bonino@dtls.nl - June 15, 2017

3. SUMMARY
• FAIR Data overview
• GO FAIR initiative
• DTL’s technology developments

4. Why do we need FAIR Data?

5. The Data Tsunami
THE BIG DATA PROBLEM
5

6. The Data Tsunami
Datarrhoeia
THE BIG DATA PROBLEM
6

7. The Data Tsunami
Datarrhoeia
Standards
THE BIG DATA PROBLEM
7

8. The Data Tsunami
Datarrhoeia
Standards
Needle Transport
THE BIG DATA PROBLEM
8

9. The Data Tsunami
Datarrhoeia
Standards
Needle Transport
Do It Yourself Data
THE BIG DATA PROBLEM
9

10. THE UNDERLYING PROBLEM…
FRAGMENTATION of…
• data
• sample collections
• image collections
• regulations
• software tools
• research initiatives
• funding
• expertise
• etc.

11. THE DATA PROBLEM IN HEALTH RESEARCH &
INNOVATION
Most data do not TALK to each other
Data are lost and/or hard to find
Inhibits scaling of effective knowledge discovery
Inhibits fully effective health care and research &
innovation
Research data malpractice (Life Science example):
Only 12% of NIH funded datasets are deposited in recognized repositories: so over
200,000 ‘invisible’ public datasets can not be re-used effectively.
Approximately 50% of funded research not reproducible

12. DATA LOSS IS SIGNIFICANT, DATA GROWTH IS
STAGGERING
Computer speed and storage
capacity is doubling every 18
months and this rate is steady
DNA sequence data is doubling
every 6 months over the last 3
years and looks to continue for
this decade
Nature news, 19 December 2013

13. What is FAIR Data?

14. FAIR DATA PRINCIPLES: STARTED IN THE
NETHERLANDS
Leiden 2014
Initiated by

15. FAIR DATA PRINCIPLES
Findable:
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 it describes;
F4. (meta)data are registered or indexed in a searchable
resource;
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;
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;
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;
https://www.nature.com/articles/sdata201618

16. FAIR DATA PRINCIPLES - METADATA
Findable:
F1. metadata 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 it describes;
F4. (meta)data are registered or indexed in a searchable
resource;
Accessible:
A1. metadata 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;
Interoperable:
I1. metadata use a formal, accessible, shared, and
broadly applicable language for knowledge
representation;
I2. metadata use vocabularies that follow FAIR
principles;
I3. metadata include qualified references to other
(meta)data;
Reusable:
R1. metadata are richly described with a plurality of
accurate and relevant attributes;
R1.1. metadata are released with a clear and
accessible data usage license;
R1.2. metadata are associated with detailed
provenance;
R1.3. metadata meet domain-relevant community
standards;

17. FAIR DATA PRINCIPLES - DATA
Findable:
F1. 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 it describes;
F4. (meta)data are registered or indexed in a searchable
resource;
Accessible:
A1. 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;
Interoperable:
I1. data use a formal, accessible, shared, and broadly
applicable language for knowledge representation;
I2. data use vocabularies that follow FAIR principles;
I3. data include qualified references to other
(meta)data;
Reusable:
R1. data are richly described with a plurality of
accurate and relevant attributes;
R1.1. data are released with a clear and accessible
data usage license;
R1.2. data are associated with detailed provenance;
R1.3. data meet domain-relevant community
standards;

18. FAIR DATA PRINCIPLES - SUPPORTING
INFRASTRUCTURE
Findable:
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 it describes;
F4. (meta)data are registered or indexed in a searchable
resource;
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;
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;
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;

19. DATA STEWARDSHIP: A TWO WAY STREET (1)
ucs
storage
sustainability
maintenance
license
privacy security
stewardship
access
?
standards
ontologies
?
If data are not interoperable
Ridiculogram
Cross data analytics are not instructive

20. DATA STEWARDSHIP: A TWO WAY STREET (2)
If data are interoperable Data analytics provide new knowledge

21. What is the current status in
Europe and the USA?

22. EVOLVED RAPIDLY INTO A GLOBAL MOVEMENT
Rapid acceptance and endorsement process
 The Lorentz conference
 The FAIR website
 Research Data Alliance endorsement
 DTL flagship project
 FORCE11 international partner
 Articles accepted in NATURE
 NIH accepts FAIR compliance in Life Sciences Commons
 DTL director Prof. Barend Mons Chair High Level Expert Group EC
 The Personal Health Train Initiative started
 EC announces European Open Science Cloud with FAIR as leading principle
World 2016

23. AND RECENTLY EVEN THE G20 WANTS FAIR!
“We support appropriate efforts to promote open science and facilitate appropriate access to
publicly funded research results on findable, accessible, interoperable and reusable (FAIR)
principles.” (Statement 12)
http://europa.eu/rapid/press-release_STATEMENT-16-2967_en.htm
Let’s GO FAIR

24. EC TAKES ACTION: THE EUROPEAN OPEN SCIENCE CLOUD
Europe acknowledged the
problem
Moved for a solution:
EOSC
Data Stewardship (DS) for
better discovery
Internet of Data of FAIR
Data & Services
Training of 500.000 data
experts
Financing
€2B for initial phase EOSC
DS market $85B annually

25. THE MANDATED FUTURE OF FAIR DATA STEWARDSHIP
April 2016

26. Paradigm shift in research funding!
FAIR Data Stewardship mandatory
5-10% of each grant earmarked
THE EUROPEAN OPEN SCIENCE CLOUD

27. FAIR ADOPTED BY USA (NIH) AS WELL
THE NIH
The NIH Commons initiative
The Commons Data
Vouchers for mandatory data
stewardship

28. How can this all be
implemented?

29. GO FAIR: A PROCESS FOR IMPLEMENTATION

30. THE IMPLEMENTATION NETWORK APPROACH
Implementation Networks can be
content based, FAIR data & services
based or Analytics based, but the
strongest nodes combine these
aspects. A A topical or domain GO FAIR
Network has technology, domain
expertise and content
B A FAIR data modeling and publishing
GO FAIR Network has Linked Data
or other FAIR experts
C A Data analytics Network has FAIR
compliant analytics and learning
tools and visualization expertise
B
FAIR Data
Services
A
Domain
content
C
Analytics

31. NL-DE CALL FOR ACTION – 30-05-2017
https://www.dtls.nl/germany-netherlands-call-action-european-open-science-cloud/
Joint position paper on implementing EOSC
through GO FAIR from Dutch and German
Secretaries of State
”As science becomes increasingly data-
driven, making data FAIR will create
real added value…”
”Funding providers have to
acknowledge the effort related to data
management as a prerequisite for FAIR
data.”
”Germany and the Netherlands propose
to support the GO FAIR initiative as a
promising approach towards
establishing the EOSC.”

32. DTL’s FAIR Data Technology
Development

33. FAIR DATA ECOSYSTEM (DTL)
Create PublishPlan Find
0110011
1100101
1001100
BYOD FAIR Hackathon
Training

34. FAIR DATA ECOSYSTEM (DTL)
Create Publish AnnotateFind
0110011
1100101
1001100
DataFAIRport
DTL

35. THE FAIRPORT ECOSYSTEM

36. FAIR transformation FAIR transformation
Analysis transformation Analysis transformation

37. BRING YOUR OWN DATA - BYOD
• Goals:
– Learn how to make data linkable “hands-on” with experts
– Create a “telling story” to demonstrate its use
– Make FAIR Data at the source
• Composition:
– Data owners – specialists on given datasets
– Data interoperability experts
– Domain experts

38. Domain Expert
Data Owner FAIR Data Expert

39. BYOD

40. BYOD PLANNING
Preparation Execution Follow Up

41. BYOD PLANNING
Preparation
Identify Plan
Datasets
Attendees' profile
Output data access
Tentative dates
Tentative venue
Costs
Funds
Coordination
Set date
Invite attendees
Set venue
Catering
Lodging
Financial planning
Publicity
Working document
Preparatory calls
Data hosting
Software hosting
Documentation hosting

42. BYOD PLANNING
Execution
Day One
Introduction
SW, LD, Ontology intro
Use case intro
Workgroups division
Working sessions
WWW/TTTALA
Day Two
Progress report
Working sessions
Groups reports
WWW/TTTALA
Day Three
Data integration
Answer driving question
Explore data
Demo improvement
Final report
WWW/TTTALA

43. BYOD PLANNING
Follow-Up
D+15
Report difficulties
Clarifications
Next steps
D+45
Report difficulties
Clarifications
Next steps
Implementation
Expand FAIRification
Review models
Implement solution
Scale-up solution
Deploy

44. BYOD SUBJECTS
• Rare diseases 3x
– Patient registry
– Biobanks
– Pathways (RETT
syndrome)
• Molecular data
– Genotype
– Peptides
• Plant breeding 2x
– Germplasm
– Phenotype
– Passport data
• Protein expression
– Transcription start sites
– Tissue expression
• Library
– Book data
• Finances
– Mobile financial data

45. Based on OpenRefine

46. FAIRIFICATION PROCESS
• Retrieve original data
• Dataset identification and analysis
• Definition of the semantic model
• Data transformation
• License assignment
• Metadata definition
• FAIR Data resource (data, metadata, license) deployment

47. FAIRIFICATION
FAIR Data Resource
submit generate
Generic
semantic
model

49. FAIRIFIER
• Transform non-FAIR datasets into FAIR Data
Resources (dataset in FAIR format, license
and metadata)
• Data munging
• Semantic modeling
• License definition
• Metadata definition and extraction
• Data publication

50. FAIRIFIER

51. FAIRIFICATION PROCESS
• Retrieve original data
• Dataset identification and analysis
• Definition of the semantic model
• Data transformation
• License assignment
• Metadata definition
• FAIR Data resource (data, metadata, license)
deployment

52. FAIRIFICATION
FAIR Data Resource
submit generate
Semantic
model

53. FAIRIFICATION - NEW DATASET TYPE
FAIR Data Resource
submit generate
FAIR Data Model
Registry
store
Semantic
Model &
Non-FAIR -
FAIR
mapping

54. FAIRIFICATION - RECURRING DATASET TYPE
FAIR Data Resource
submit generate
FAIR Data Model
Registry
query
Semantic
Model &
Non-FAIR -
FAIR
mapping
retrieve

56. FAIR DATA POINT
A particular class of FAIR Data System that provides access to datasets in a FAIR
manner. The datasets can be external or internal to the FAIR Data Point. Also,
the source data can be a non-FAIR dataset or a FAIR Data Resource. If the
source data is non-FAIR, the FAIR Data Point needs to made the necessary FAIR
transformations on the fly.

58. FAIR Data Point metadata
Title
Responsible institution(s)
Contact
FAIR API version
License
…

59. FAIR Data Point metadata
Catalog metadata
Title
Theme taxonomy
Issued date
… DCAT

60. FAIR Data Point metadata
Catalog 1 metadata
Dataset metadataTitle
Publisher
License
Theme(s)
Version
…
DCAT/HCLS

61. FAIR Data Point metadata
Catalog 1 metadata
Dataset 1 metadata
Distribution metadata
Title
Media type
Download/access URL
License
… DCAT

62. FAIR Data Point metadata
Catalog metadata
Dataset metadata
Distribution
metadata
Data record metadata
Type
Domain
Range
…
RML

63. FAIR Data Point metadata
Catalog 2 metadataCatalog 1 metadata
Dataset 1 metadata
Distribution 1.a
metadata
Data record metadata
Distribution 1.b
metadata
Dataset 2 metadata
Distribution 2.a
metadata
Data record metadata
Distribution 2.b
metadata
Dataset 3 metadata
Distribution 3.a
metadata
Data record metadata

64. METADATA LAYERS
Data Repository (FDP)
(Dataset) Catalog(s)
Dataset
Distribution
Data Record

65. FAIR DATA POINT - ARCHITECTURE

66. FAIR DATA POINT - GUI - FOR TECHIES

67. FAIR DATA POINT - GUI - FOR “NORMAL" PEOPLE
}
}
Repository
metadata
Catalog
metadata
summary

68. FAIR DATA POINT - GUI
}
}
Repository
metadata
Catalog
metadata
summary
}
Dataset/
distribution
metadata
summary
} Catalog
metadata

69. FAIR DATA POINT - GUI - DATASET

70. FAIR DATA POINT

71. EXISTING DATA REPOSITORIES

72. EXTENDING EXISTING DATA REPOSITORIES
+

73. FAIR HACKATHON - GOALS
• Align solutions with FAIR Data Point
specifications.
– Metadata content
– API
– Data

74. FAIR HACKATHON OUTCOME
• FAIR data model for solutions content;
• Architecture of the required
adjustments/extensions;
• Technical specification of the
adjustments/extensions;
• Proof-of-concept of the adjusted solution;

75. FDP-COMPLIANT (BETA) SOLUTIONS
RDRF

76. 0110011
1100101
1001100

77. 0110011
1100101
1001100
metadata
index
retrieves
metadata
search
interfaces
(GUI and API)

79. • Allow third-party annotation on existing
knowledge bases
• Capture the provenance of the annotator and
the original statement
Open RDF
Knowledge AnnotatorORKA

80. DEMO: HTTP://DEV-VM.FAIR-DTLS.SURF-HOSTED.NL:8080/#/

81. DEMO: HTTP://DEV-VM.FAIR-DTLS.SURF-HOSTED.NL:8080/#/

82. DEMO: HTTP://DEV-VM.FAIR-DTLS.SURF-HOSTED.NL:8080/#/
ANNOTATIONS GO TO NANOPUB STORE

83. • A particular class of FAIR Data System to provide support for data
interoperability;
• Supports publication and access to FAIR data.
• Fosters an ecosystems of applications and services;
• Federated architecture: different FAIRports (and other FAIR Data
Systems) are interconnectable;
• Supports citations of datasets and data items;
• Provides metrics for data usage and citation;

84. DATA FAIRPORT
F A I
R

85. What’s in for me?

86. BENEFITS OF FAIR DATA STEWARDSHIP (1)
If you are a government/funder/policy maker:
Better organized stakeholder community
Less resources lost on slack and overhead
Increased ‘Return on Investment’ of public funding
Opportunity to harmonize fragmented legislation in a way that facilitates
personalized medicine & health research
Participate in international developments, e.g. European Open Science
Cloud
Improved societal impact:
increased involvement of citizen/patient (digital control of own data)
Increased economic benefits in health care sector including prevention

87. BENEFITS OF FAIR DATA STEWARDSHIP (2)
If you are an institution:
No more short term point solutions over and over again
Compliance by design to technical, ELSI and scientific standards
Easier & safer (inter)national exchange
More efficient business operations
Less risks
More successes

88. BENEFITS OF FAIR DATA STEWARDSHIP (3)
If you are a citizen:
Better opportunities for active participation
With better prevention lower insurance premiums
Better privacy: you are in control of your own data!
More benefit from tax and charity money spent
Faster development of new preventive, diagnostic and
therapeutic solutions

89. BENEFITS OF FAIR DATA STEWARDSHIP (4)
If you are a company/entrepreneur:
Your proprietary data
compliant with FAIR public domain data
Improved data analytics and knowledge predictions
More effective discovery process
Offer current customers better analytics, applications and services
Develop new FAIR applications and services
Provide training/certification services
Save cost and increase revenue
Access to the ‘5% market’: 100B+!

90. JUST AN IMPRESSION OF HOW BIG THIS MARKET IS
€2B for initial phase EOSC
Total EU (28) plus USA 86,5 B for Data Stewardship (DS)
annually
EU (28)
GDP 52,000 B
2.4 % of GDP to R&D = 1,248 B
@ 5% = 62 B for DS
USA
GDP 18,000 B
2.73 % of GDP to R&D = 491 B
@ 5% = 24,5 B for DS
The Netherlands
GDP 818 B
1,973% of GDP to R&D = 16B
@ 5% = 800M for DS
(Source OECD)
€62 billion
€800 million

91. FAIR DATA SERVICE PROVIDER BUSINESS
ARCHITECTUREGO FAIR Support Office
(+ 7 MS)
GO FAIR
expertise broker
Certification
Certified
FAIR Data Service
provider
Analytics
Certified
FAIR Data Service
provider
Software development
Certified
FAIR Data Service
provider
Training/Staffing
Academic (5%) and private sector customers
Etc.

92. Q&A/CONTACT INFO
Luiz Bonino
CTO FAIR Data
E-mail: luiz.bonino@dtls.nl
Tel NL: +31 624619131
Skype: luizolavobonino