Making Open Science work: The EC’s policies 2015-2019

EuraxessWashington10-12-2019

Jean-Claude BURGELMAN, DG RTD

Structure

•1. Why this policy

•2. What where the main priorities

•3. What was realized

•4. What is still missing or incomplete

Opening up the full research

cycle

Rationale: The nature of science (modus operandi) is changing from a closed system to an open and sharing one

Which Rationale for OS policy

• Better ROI of the R&I investments: if all the results of our public research are made reusable, it will follow that better use is made.

Norori: Eco impact Human Gnoom sequencing: 1 billion eco output, 4 million jobs, 30% more genetic testing, innovative new methods, cures etc

• Faster circulation of new ideas in science and innovation: 22 million EU SME’s will have access to top notch research without having to significantly pay for it

• More transparency of the science system: it’s public taxpayer’s money & and it helps self correction of the system

• Fit for 21st century science purpose: all grand societal challenges NEED cross disciplinary research

A general model for open science in early stage drug discovery

Public-PrivatePartnership Public Domain Commercial

Tools & Basic KnowledgeNOVEL Proteins only!

• Structure• Chemistry• Antibodies• Screening• Cell Assays

Discovery and Exploration

• No patent• No restriction on use• Open access to tools and data.• Target identification & validation

Drug Discovery and Development

Facilitated by access to increased amount of information in the public domain

- (re)Screening- Lead Optimisation- Pharmacology- Metabolism- Pharmacokinetics- Toxicology- Chemical development- Clinical development

CREATIVE COMMONS PROPRIETARY

Weigelt J. EMBO Reports 10:941-5 (2009)

Very Promising

Structural gnomics consortium

Very Promisingfor ALL sciences

Recent Noble prize in economics: all data open in the Dataverserepository of Harvard

• Open Data: default FAIR data sharing across Europe in 2020• Science cloud: up and running … in 2018 • Next generation metrics: Bukarest declaration 2019 • Default OA for publications: 2017, 2018: ORE, etc..

• Rewards: Embrace OS, 2019• Research Integrity: ….• Education and skills: new frame produced but still …. • Citizen Science: significant budget foreseen and formalisation

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2016: focus on 8 policy priorities to support the systemic change

Horizon Europe: A series of measures that go beyond open access (publications and data) to embrace & incentivise OS as modus operandi for research

The evolution of the EU funding programmes for R&I

FP7OA PilotDeposit and open access

H2020OA MandatoryDeposit and open access

& ORD/DMP Pilot

H2020OA MandatoryDeposit and open access

& ORD/DMP by default (opt-out)

Horizon EuropeOA MandatoryDeposit and open access

DMP + FAIR data Mandatory

OD by default (opt-out)& Open Science embedded

Framework programmes

Open Data

• Pinowar, Priem & Orr study (2019)

• (global analysis of 70 million scientific articles published between 1950-2019)

• 2019: 31% available in OA52% of all article views are OA articles

• 2025: 44% available78% viewed

•

You can’t change the direction of the wind, but you can adapt the sail

The state of open data - main findings(Digital Science/Figshare, 2019)

• 79% of 2019 respondents (>8500 researchers) were supportive overall of a national mandate for making primary research openly available

• 67% of respondents think that funders should withhold funding from, or penalisein other ways, researchers who do not share their data if the funder has mandated that they do so

• 69% of respondents think that funders should make the sharing of research data part of their requirements for awarding grants

• 36% of respondents expressed the concern that their data may be misused if it was shared

• 42% of researchers would be encouraged to share their data if it resulted in a co-authorship

Open Science in FP9

• FP9 goes beyond OA (publications & data) to embrace & incentivise Open Science as modus operandi for science

• Clarifies and strengthens the OA obligations;• Empowers the authors of scientific publications;• FAIR data sharing while complying with IPR rules and exploitation obligations;• Broadens Open Access (with opting out options) to other research output;• Promotes compliance with 'Open Science principles' through a combination of

obligations and incentives;• Implements sanctions for those beneficiaries that repeatedly and consistently

fail to provide the required open access, requiring institutions to assume responsibility for their intellectual output;

• Introduces the use of 'new generation' metrics for better assessing the impact of research output and the engagement in Open Science.

What does OS mean for the researcher? (1)

• The norm is/will become

• Publications - immediate OA: quicker and cheaper• Data – mandatory OD: public funded research is a reproducible public good. Both

already mandatory in EC. Increasingly other funders (SE) in particular the big private ones (Welcome trust, Gates etc)

• FAIR will be the standard• DMP universally required • Next Generation Metrics (see the Metrics Toolkit)• MORF (massive online open research flows)• All science will be data driven à Data science literacy (and potentially new career

paths e.g. data scientists, start-ups, science diplomacy)

What does this mean for the researcher? (2)

• It will allow

• Faster & better publication: • Richer publication opportunities: the whole research value chain (not only HI pub)• Return to some basics. E.g. open peer review is nothing more than peer review as

it used to be• Richer measurement of scientific activity (see the Metrics Toolkit)• Different career paths• More research potential in and across disciplines• More transparency

The tools to do it are all there, …… for free

Manual Computational

Deductive 2nd paradigm: theoretical (Newton)

3rd paradigm: computational

(Von Neumann)

Inductive 1st paradigm: empirical (Bacon)

4th paradigm: data-intensive (Venter, DNA sequencing)

Universities and Open Science: back to the roots?

Data explosion (“Here’s the evidence, now what is the hypothesis?”)

A 4th model of science in the making?

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Epistemology and critical thinking back at the heart ofevery discipline

By 2030 the science system will be characterised by

• Open research data as a renewable resource for research and innovation (via EOSC)

• Full & immediate open access to the whole life cycle of a research process

• A system of peer reviewed open access research workflows • Multiple ways to measure and reward scientific productivity• ‘’liquid’’ science (like in SW development)• More ethical? Only – if embedded in codes of conduct and (at

least) doctoral training

• Science as a global commons/public good 17

Still a long way to go …impact of AI

I wish I could start my research career now

• 1. Never were the exogenous factors so good for science (budgets, trajectories, support, etc)

• 2. Never were the endogenous factors so favourable to do good science (open science is making your life easier)

• 3. Never was there so much need for good science (see Harari 21 lessons if you don’t know where to start research on)

Just do it!

To wind up…

Thank you!

More information at http://ec.europa.eu/research/openscience

A lot can be found here:Open science, open data and open scholarship: European policies to make science fit for the 21st century. Jean-Claude Burgelman, Corina Pascu, Katarzyna Szkuta, Rene Von Schomberg, Athanasios Karalopoulos, Konstantinos Repanas, Michel Schouppe. Frontiers in big data, 2019https://www.frontiersin.org/articles/10.3389/fdata.2019.00043/abstract

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