Presented at the BioMedBridges Conference at EBI, November 17, 2015

1. Open Data in a Global Ecosystem
Philip E. Bourne Ph.D., FACMI
Associate Director for Data Science
National Institutes of Health
philip.bourne@nih.gov
BioMedBridges, EBI, November 17, 2015
http://www.slideshare.net/pebourne

2. Not a talking head….
An on-going conversation

3. Some context to start that
conversation …

4. Perspective
 Structural bioinformatics researcher
 Former custodian of the RCSB PDB
 Obsessive about open science e.g., PLOS
 NIH-wide responsibility for developments in
data science

5. Consider this change from my own
career experience ….

6. The History of Computational
Biomedicine According to Bourne
1980s 1990s 2000s 2010s 2020
Discipline:
Unknown Expt. Driven Emergent Over-sold A Service A Partner A Driver
The Raw Material:
Non-existent Limited /Poor More/Ontologies Big Data/Siloed Open/Integrated
The People:
No name Technicians Industry recognition data scientists Academics
Searls (ed) The Roots in Bioinformatics Series PLOS Comp Biol

7. It Follows …
We are entering a period of disruption
in biomedical research and we should
all be thinking about what this means
to bioinformatics & biomedicine
http://i1.wp.com/chisconsult.com/wp-
content/uploads/2013/05/disruption-is-a-
process.jpg
http://cdn2.hubspot.net/hubfs/418817/disruption1.jpg

8. Big Data in Biomedicine…
This speaks to something more
fundamental that more data …
It speaks to new methodologies, new
skills, new emphasis, new cultures,
new modes of discovery …

9. We are at a Point of Deception …
 Evidence:
– Google car
– 3D printers
– Waze
– Robotics
– Sensors
From: The Second Machine Age: Work, Progress,
and Prosperity in a Time of Brilliant Technologies
by Erik Brynjolfsson & Andrew McAfee

10. Disruption: Example - Photography
Digitization
Deception
Disruption
Demonetization
Dematerialization
Democratization
Time
Volume,Velocity,Variety
Digital camera invented by
Kodak but shelved
Megapixels & quality improve slowly;
Kodak slow to react
Film market collapses;
Kodak goes bankrupt
Phones replace
cameras
Instagram,
Flickr become the
value proposition
Digital media becomes bona fide
form of communication

11. Disruption: Biomedical Research
Digitization of Basic &
Clinical Research & EHR’s
Deception
We Are Here
Disruption
Demonetization
Dematerialization
Democratization
Open science
Patient centered health care

12. Disruptive Features: Sustainability
Source Michael Bell http://homepages.cs.ncl.ac.uk/m.j.bell1/blog/?p=830

13. Disruptive Features:
Reproducibility
Changing Value of Scholarship (?)

14. “And that’s why we’re here today. Because something
called precision medicine … gives us one of the greatest
opportunities for new medical breakthroughs that we
have ever seen.”
President Barack Obama
January 30, 2015
Disruptive Features – New Science

15. Precision Medicine Initiative
 National Research Cohort
– >1 million U.S. volunteers
– Numerous existing cohorts (many funded by NIH)
– New volunteers
 Participants will be centrally involved in design and
implementation of the cohort
 They will be able to share genomic data, lifestyle
information, biological samples – all linked to their
electronic health records

16. What Are Some General Implications
of Such a Future?
 Open collaborative science becomes of increasing
importance nationally and internationally
 The value of data and associated analytics becomes
of increasing value to scholarship
 Opportunities exist to improve the efficiency of the
research enterprise and hence fund more research
 Global cooperation between funders will be needed
to sustain the emergent digital enterprise
 Current training content and modalities will not match
supply to demand
 Balancing accessibility vs security becomes more
important yet more complex

17. What Are Some General Implications
of Such a Future?
 Open collaborative science becomes of increasing
importance nationally and internationally
 The value of data and associated analytics becomes
of increasing value to scholarship
 Opportunities exist to improve the efficiency of the
research enterprise and hence fund more research
 Global cooperation between funders will be needed
to sustain the emergent digital enterprise
 Current training content and modalities will not match
supply to demand
 Balancing accessibility vs security becomes more
important yet more complex

18. How Should We Respond as Funders?
 Community:
– Encourage wherever possible a global cultural shift towards
open science
– Encourage global exchanges
– Encourage global projects
 Policies:
– Understand and map data sharing policies, standards etc.
– Understand ethical, legal and societal differences
 Infrastructure:
– Share the burden and the reward

19. How Should We Respond as Funders?
 Community:
– Encourage wherever possible a global cultural shift towards
open science
– Encourage global exchanges
 Policies:
– Understand and map data sharing policies, standards etc.
– Understand ethical, legal and societal differences
 Infrastructure:
– Share the burden and the reward

20. https://www.openscienceprize.org/

21. A Culture of Sharing
1999 20042003 2007 20142008
Research
Tools
Policy
NIH Data
Sharing Policy
Model
Organism
Policy
Genome-wide
Association
(GWAS) Policy
2012
NIH Public
Access Policy
(Publications)
Big Data to
Knowledge
(BD2K) Initiative
Genomic Data
Sharing (GDS)
Policy
Modernization of
NIH Clinical
Trials
White House
Initiative
(2013 “Holdren
Memo”)

22. The BD2K Program
BD2K Budget

23. BD2K FY14 Awards
supported by all NIH Institutes

24. MD2K Applications – CHF and Smoking

25. How Should We Respond as Funders?
 Community:
– Encourage wherever possible a global cultural shift towards
open science
– Encourage global exchanges
– Encourage global projects
 Policies:
– Understand and map data sharing policies, standards etc.
– Understand ethical, legal and societal differences
 Infrastructure:
– Share the burden and the reward

26.  The Commons is a shared virtual space which is
FAIR:
– Find
– Access (use effectively)
– Interoperate
– Reuse
 An environment to find and catalyze the use of
shared digital research objects
The Commons
Concept

27. The Developer or User Defines the
Environment from the Appropriate
Building Blocks

28. The Commons
Components

29. BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
DDICC
Software
Standard
s
Infrastructure - The
Commons
Labs
Labs
Labs
Labs

30. Public Beacons
Host Content
AMPLab 1000 Genomes Project
Broad Institute ExAC
Curoverse PGP, GA4GH Example Data
EBI
1000 Genomes Project, UK10K, GoNL, EVS,
GEUVADIS, UMCG Cardio GenePanel
Google
1000 Genomes Project, Phase III, Illumina Platinum
Genomes
ISB Known VARiants
NCBI NHLBI Exome Sequence Project
OICR 55 cancer datasets
SolveBio 56 public datasets
UCSC ClinVar, LOVD, UniProt
University of Leicester Cafe CardioKit, Cafe Variome Central
WTSI IBD, Native American, Egyptian, UK10K
Over 120 public datasets beaconized across 21 institutions
10s thousands of individuals

32. Commons - Pilots
 The Cloud Credits - business model
 BD2K Centers
 MODs (Model Organism Databases)
 HMP Data and tools available in the cloud
 NCI Cloud Pilots & Genomic Data
Commons

33. I not only use all the brains
I have, but all I can borrow.
– Woodrow Wilson

34. What Can We Do Now?
 Extend the research pilots
concept
 Have TCC & TeSS work
together
 Global hackathons,
competitions
 Closer ties between NLM and
EBI / Elixir
 Student exchanges
 Engage foundations, charities
in more global initiatives
http://wwwdev.ebi.ac.uk/Tools/ddi/

35. ADDS Team
BD2K Representatives

36. NIHNIH……
Turning Discovery Into HealthTurning Discovery Into Health
philip.bourne@nih.gov
https://datascience.nih.gov/
http://www.ncbi.nlm.nih.gov/research/staff/bourne/