1. partly FAIR, partly Cloudy
Barend Mons
Leiden University Medical Center
Dutch Techcenter for Life Sciences
Netherlands e-Science Centre
Chair: HLEG-EOSC

3. Data loss is real and significant, while data growth is staggering
Science1.0
Data loss is real and significant, while data growth is staggering
Nature news, 19 December 2013
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
‘Oops, that link was the laptop of my PhD student’
80% of data is lost for reuse

4. Cloud

5. : 2 B (kick start phase)

6. The Data Stewardship Cycle5%
(50 B)
The full data cycle and:
Where the boudaries of ELIXIR are (ref: our discussion on the ‘left side’ (data design and planning and analysis/modelling
I put the third ‘flag’ there to emphasize that TRAINING of respected data experts is a remit of ELIXIR and that the NL node in GOBLET will participate. Argument: the better the (data colelction in) big data experiments of the future is planned and designed, the easier it is for ELIXIR later to deal with the data properly and serve them up for analysis at the end of the cycle. I can also emphaisze that the BBMRI etc. are more on the left and could partner with ELIXIR on the right etc.
We can discuss this slide once more before the meeting to align our messages. 6

7. The Future of FAIR Data Stewardship5%
FAIR

8. Cloud
European
Open Science
Cloud
Introduction (extremely brief)

9. partly FAIR, partly Cloudy

10. EOSC: Challenges and Observations
Cloud
EOSC: Challenges and Observations
The majority of the challenges are social rather than technical
Not just the size of data, but in particular complex data and analytics across domains.
Shortage of data experts globally and in the European Union
Archaic system of rewards and funding of science and innovation
‘Valley of death’ between (e-)infrastructure providers and domain specialists.
Short funding cycles of core research infrastructures are not fit for purpose
Fragmentation between domains causes repetitive and isolated solutions
Distributed data sets increasingly do not move (size & privacy reasons)
Centralised HPC is insufficient to support distributed meta-analysis and learning.
However, the major components for a first generation EOSC are largely ‘there’
But ‘lost in fragmentation’ and spread over 28 Member States.
80% social/ 20% technical

11. EOSC: Framing Trusted access to services & systems
Cloud
EOSC: Framing
Trusted access to services & systems
Re-use of shared data
Across disciplinary, social and geographical borders
Federated environment, across Member States

12. EOSC: ‘Internet approach’
Cloud
EOSC: ‘Internet approach’
Minimal international guidance and governance
Maximum freedom to implement.
Globally interoperable and accessible
Globally embedded in a ‘Commons’

13. EOSC: Scope Human expertise Core resources Standards, Best Practices
Cloud
EOSC: Scope
Human expertise
Core resources
Standards, Best Practices
Underpinning technical infrastructures
A web of Data and Services

14. EOSC: Supports Open Science Open Innovation
Cloud
EOSC: Supports
Open Science
Open Innovation
Systematic and professional data management
Long term data stewardship

15. EOSC: Implementation Recommendations
Cloud
EOSC: Implementation Recommendations
I1: Turn this report into an EC approved document to guide EOSC initiative
I2: Develop, Endorse and implement a Rules of Engagement scheme
I3: Fund a concentrated effort to locate and develop Data Expertise in Europe
I4: Install a highly innovative guided funding scheme for the preparatory phase
I5: Make adequate data stewardship mandatory for all research proposals
I6: Install an executive team to deal with international coherence of the EOSC
I7: Install an executive team to deal with the preparatory phase of the EOSC

16. Open Science Conference
Open Access
FAIR data

17. ‘We support appropriate efforts to promote open science
and facilitate appropriate access to publicly funded
research results on findable, accessible, interoperable and reusable
(FAIR)’

18. Services
Data
Exchange layer
minimal protocols

19. inter MS (self) coordination RoE
MoU
RoE
Partner 1 2 3 4
services opting as ‘Certified EOSC provider
MS-stakeholder
Group
Cloud
Coins
H2020
Funder PI