HEALTH-RI THE NETHERLANDS

Transcription

1 HEALTH-RI THE NETHERLANDS INTERCONNECTED DATA INFRASTRUCTURE FOR PERSONALIZED MEDICINE THE EUROPEAN OPEN SCIENCE CLOUD CHALLENGES AND POSSIBILITIES IN USING BIG DATA

2 HEALTH RI AN INTRODUCTORY EXAMPLE

3 WHY DO WE NEED FAIR DATA STEWARDSHIP?

4 THE BIG DATA PROBLEM The Data Tsunami Datarrhoeia Standards Needle Transport Do It Yourself Data 4

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

6 THE BIG DATA PROBLEM IN 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

7 WHAT IS FAIR DATA STEWARDSHIP?

8 Holandês Dutch Nederlands MY FAIR GROCERY LIST Malaysian

9 WHAT IS FAIR DATA? Findable - Metadata: both humans and computer systems Accessible - Long term, clear licence, Open Access where possible Interoperable - Semantic linking of data elements: data should talk! Reusable - Future research; processed and combined

10 ACCESSIBLE UNDER WELL DEFINED CONDITIONS

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

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

13 WHAT LED UP TO THE EUROPEAN OPEN SCIENCE CLOUD?

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

15 EVOLVED RAPIDLY INTO A GLOBAL MOVEMENT World 2016 Rapid acceptance and endorsement process The conference The 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

16 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) Let s GO FAIR!

17 WHAT IS THE CURRENT STATUS IN EUROPE AND THE USA?

18 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 data experts Financing 2B for initial phase EOSC DS market $85B annually

19 THE MANDATED FUTURE OF FAIR DATA STEWARDSHIP April 2016

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

21 HEALTH AGEING RESEARCH AND DATA IN THE NETHERLANDS

22 INTEGRATED LIFE DATA SCIENCE OMICS FROM UNHEALTHY AGEING TO LONGEVITY

23 PERSONALISED MEDICIN AND COMPUTATIONAL BIOLOGY CLASSIFY INDIVIDUALS : WHAT WILL WORK IN WHO?

24 BBMRI BIOBANKING INFRASTRUCTURE EUROPE Goal: to make data and materials rapidly and widely available to researchers by making a catalog and providing tools for harmonization and ELSI

25 GENOME OF THE NETHERLANDS ; DETECTING YOUR ORIGIN METABOLOME IN INDIVIDUALS

26 BBMRI-OMICS: 40 COHORTS SHARING DATA, MAKING THIS PUBLICLY AVAILABLE, SHARING METHODOLOGY AND KNOWLEDGE

27 HEALTH RI AND FAIR DATA EXPERTISE IN THE NETHERLANDS

28 HEALTH RI PLATFORM ENABLING CROSS-DISCIPLINARY RESEARCH

29 >100 EXPERT GROUPS INVOLVED SO FAR Technology & data hotels Access to high-tech expertise & facilities Wet lab & data facilities Collections of biosamples/cohorts Public and private labs Training & education High-quality AMC AMOLF CWI NLeSC NKI SURFSARA University of Amsterdam VU University Amsterdam VUMC Generade LUMC Leiden University Naturalis TUDelft Delft ErasmusMC Leiden Rotterdam Amsterda 17m 9 Groningen Hubrecht Institute UMCU Utrecht University Utrecht 13 1 Zeist 15 Wageningen TNO 6 Nijmegen Enschede Hanze University University Groningen UMCG 1 TU Twente Wageningen UR PRI Radboudumc Radboud University experimental design Measuring CTMM TUe 4 Eindhoven FAIR data stewardship 5 Maastricht Maastricht Univ. MUMC+

30 GO FAIR DATA EXPERTISE

31 WHAT S IN FOR ME?

32 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

33 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

34 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

35 BENEFITS OF FAIR DATA STEWARDSHIP (4) If you are a PI/researcher: Eligible for funding for FAIR Data Stewardship (the 5% ) Assistance from dedicated professional FAIR Data Stewards More time for actual research! Your data interoperate with other data Increased scientific impact (citations; also of datasets!) Increased rate of hypotheses testing. Discovery and innovation

36 CALL TO ACTION

37 + +

38 Q&A/CONTACT INFO