Barend Mons

Biosemantics Group LUMC and EMC

LS integrator Netherlands eScience Center

Chair of DTL-dataHead of ELIXIR node NL

EC member of Open PHACTS

Chair of High Level Expert Group EOSC

The opinions expressed in this presentation are my personal opinions and do not necessarily reflect the draft report of the High Level Expert Group for the European Open Science Cloud.

F.A.I.R

Cloudhttps://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/2054-infradev-04-2016.html

Press Conference today

https://vimeo.com/162062013

First GO-FAIR lab in DTL (Health-RI) (2016-17)

Stated aim to create GO-FAIR labs in other

domains (2017-20)

First (sister) GO-FAIR lab in SD

(NIH/NHS/NSF) (2016)

Second (sister) FOIL in Sao Paulo (Scielo)(2016)

Third (sister) GO-FAIR lab in Arusha (2017)

Stated aim to create GO-FAIR labs in other MS

(DE, SWE, UK, FI, CZ) 2017

https://vimeo.com/162062013

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 6-8 months over the last 3 years and looks to continue for this decade‘Oops, that link was the laptop of my PhD student’

Science1.0

90% of world's data generated over last two years (http://www.sciencedaily.com/releases/2013/05/130522085217.htm)

US$28B/year (50%) spent on preclinical research is not reproducibleFreedman et al. http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002165

Only 12% of NIH funded datasets are demonstrably deposited in recognized repositories.

So: approximately 200,000 to 235,000 invisible datasets cannot be effectively reused

Read et al. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0132735

What about US? (as in us, MS)?

The Data Stewardship Cycle

9

5%

something to refer tohttp://www.nature.com/articles/sdata201618

European

Open

Science

Cloud

Cloud

European Open Science Cloud

Cloud

o Public consultation and validation workshops on Open Science (July-December 2014);

o Final report on Open Science (February 2015); o HLEG EOSC stakeholder workshop (30 November 2015); o DSM Consultation on platforms, data and cloud (closed

January 2016); o Research funders' workshop (15 March 2016).

+ PC meetings, EAG meetings, e-IRG meetings, concertation meetings, info days, conferences, events, ...

Strong stakeholder support

Community emerging standardsEOSC

Cloud

stop driving on the left

Trusted access to services & systems

Re-use of shared data

Across disciplinary, social and geographical borders

Federated environment, across Member States

EOSC: FramingCloud

Minimal international guidance and governance

Maximum freedom to implement.

Globally interoperable and accessible

Globally embedded in a ‘Commons’

EOSC: ‘Internet approach’Cloud

Human expertise

Core resources

Standards, Best Practices

Underpinning technical infrastructures

A web of Data and Services

EOSC: ScopeCloud

Open Science

Open Innovation

Systematic and professional data management

Long term data stewardship

EOSC: SupportsCloud

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.

EOSC: Challenges and ObservationsCloud

EOSC: Key requirementsCloud

New modes [4] of scholarly communication Modern reward and recognition practices [3] need to support data sharing and re-use Innovative, fit for purpose funding schemes for sustainable underpinning infrastructures Core data experts [7] need to be trained and their career perspective significantly improved

Cross-disciplinary collaboration-specific measures for review, funding and infrastructure

Support for the transition from scientific insights towards societal innovation [8] The EOSC needs to be developed as an eco-system of infrastructures [2]

Key Performance Indicators should be developed for the EOSC [2]

The EOSC should enable automation of data processing: machine actionability [1]is key.

FAIR principles [1, 6] (http://www.nature.com/articles/sdata201618)

Research Integrity [6]

EOSC: Policy RecommendationsCloud

P1: Take immediate, affirmative action in close concert with Member States

P2: Close discussions about the ‘perceived need’

P3: Build on existing capacity and expertise where possible

P4: Frame the EOSC as supporting Internet based protocols & applications

EOSC: Governance RecommendationsCloud

G1: Aim at the lightest possible, internationally effective governance

G2: Guidance only where guidance is due

G3: Define Rules of Engagement for formal participation in the EOSC

G4: Federate the Gems across Member States

EOSC: Implementation RecommendationsCloud

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

EOSC: 7 Immediate actions based on feed backCloud

II1: Publish the report (final draft available)

II2: Develop, Pilot and implement a Rules of Engagement scheme

II3: Detail the transition and sustainability model (and pilot it)

II4: Train the data experts to bridge between ‘e-INFRA’ and ‘ESFRI’

II5: Assist data stewardship planning and exec. tools for all researchers

II6 Develop the plan for what ‘minimal essential governance’ means in practice

II7 Federate Interoperability standards and best practices (key RDA-role)

executive group(s)

deliverables

II2 II2II3II3II3II3II3 II3II4 II4 II4II5

II5II5II5

II6

II7

II7RDA

II7FDG’sII3 Impactstory

ORCID, FORCE 11

Approves DSPlan before

submission of grant

core resource

EOSC

In DS plan

Review panel(DS = Tickbox)

FUNDERCloud Coin Authority

Monthly invoice to Cloud Coin Authority?

endorsed Repository

Data Publisher

HPA service

GO-FAIR-

Applications &

e-Infra providers

Other Peoples’ Data

FAIR exchange

FAIR Research Data

FAIR Workflows

FOILS

Open Science

Open Access

FAIR data