Presented at the Open Session of the Big Data in Biomedicine conference, Barcelona, November 12, 2014.

1. Opportunities and Challenges for
International Collaboration Around
Big Data
Philip E. Bourne, PhD
Associate Director for Data Science
National Institutes of Health
philip.bourne@nih.gov
November 12, 2014

2. A Bottom Up Exemplar

3. Top Down
Protein
sequence and
functional
annotation
Protein
sequence and
functional
annotation
CCeelllululalarr m mooddeelsls
Pathway and reaction
annotation
Pathway and reaction
annotation
Protein interaction
annotation
Protein interaction
annotation
Evidence-based proteomics
annotation
Evidence-based proteomics
annotation
Gene Ontology
annotation
Gene Ontology
annotation
VVaarriaiannttss A Annnnoottaattioionn ClinVar / OMIM
MedThesaurus
[adapted from Ioannis Xenarios

4. What Else Can we Do from the Top
Down?

5. The NIH Data
Science Mission
Statement
To foster an ecosystem that enables
biomedical* research to be
conducted as a digital enterprise that
enhances health, lengthens life and
reduces illness and disability
* Includes biological, biomedical, behavioral, social,
environmental, and clinical studies that relate to understanding
health and disease.

7. Elements of The Ecosystem
Community Policy
Infrastructure
• Sustainability
• Collaboration
• Training

8. Elements of The Ecosystem
Community Policy
Infrastructure
• Sustainability
Collaboration
• Training
Virtuous
Research
Cycle

9. The Virtuous Cycle
September 3, 2014 Workshop
http://goo.gl/fkWjhS

10. Policies – Now & Forthcoming
 Data Sharing
– Genomic data sharing announced
– Data sharing plans on all research awards
– Data sharing plan enforcement
• Machine readable plan
• Repository requirements to include grant numbers
http://www.nih.gov/news/health/aug2014/od-27.htm

11. Policies - Forthcoming
 Data Citation
– Goal: legitimize data as a form of scholarship
– Process:
• Machine readable standard for data citation (done)
• Endorsement of data citation for inclusion in NIH bib
sketch, grants, reports, etc.
• Example formats for human readable data citations
• Slowly work into NLM/NCBI workflow

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

13. What is the Commons?
 A Conceptual Framework for;
 Sharing, finding, integrating, reusing and
attributing digital research objects
– “Each digital object has a UID that must allow it to
be found, shared and attributed” – The Commons
Document
 The Commons is agnostic of computing platform

14. The Commons:
Framework  Implementation
Digital Objects
(with UIDs)
Search
(indexed metadata)
Computing
Platform
The Commons

15. The Commons:
Framework  Implementation
Digital Objects
(with UIDs)
Search
(indexed metadata)
Computing
Platform
The Commons

16. The Commons:
Framework  Draft Implementations
The Commons
Conceptual Framework
Public Cloud
Platforms
Super Computing
(HPC) Platforms
Other
Platforms ?
 Google, AWS (Amazon)
 Microsoft (Azure), IBM,
other?
 Most easily accessed by
NIH PIs
 In house compute
solutions
 Private clouds, HPC
– Pharma
– The Broad
– Bionimbus
 Low access by NIH PIs
 Super Computing 2014
 ADDS coordinating
meeting with SC centers
 NERSC “Commons Pilot”

17. The Commons:
Framework  Implementation
Digital Objects
(with UIDs)
Search
(indexed metadata)
Computing
Platform
The Commons

18. The Commons:
Framework  Draft Implementation
The Commons
Conceptual Framework
 Digital Objects to populate and test the Commons;
– BD2K centers, NCI Cloud pilots (Google & AWS supported)
– Large Public Data Sets, MODs
 Search
– BD2K Data and Software Discovery Indices
– Google Search functions
 Use cases
Public Cloud
Platforms

19. The Commons:
Framework  Draft Implementation
The Commons
Conceptual Framework
 Next Steps
– Determine which BD2K centers are most appropriate for a
cloud Commons pilot
– Develop a plan of action with NCI Cloud pilots
– Working with DDIC/SW Discovery Indices (UIDs, Search)
– Working with Google and AWS (Amazon) to determine what
is needed computationally
• In kind support (short term pilot)
• Conformant clouds (long term sustainable model)
– Developing Use cases!
Public Cloud
Platforms

20. A Business Model for
The Commons
The Commons:
Framework  Draft Implementation

21. Community – BD2K Awards

22. Community: BD2K Awards
Governance
 November 3 Kick-off PI Meeting
– Emphasis on working groups that span centers and begin
the work of building the ecosystem
• Common API development (with GA4GH)
• Mobile
• Metadata
• Grand challenges
– Emphasize sharing from day 1
– Incentivized to work in the Commons

23. Community Short Term Interactions
 NSF Workshops and Dear Colleague letter
 Workshop with NOAA on public – private
partnerships
 ELIXIR Workshop
– Standards
– Training
 Workshop Inspiring the Game Developer
Community to Engage in and Enhance
Biomedical Research, Dec 2014

24. Community: Training
Data Science Training Goals
1) Build a digital framework for data science
training:
NIH Data Science Workforce Development Center
1) Develop short-tem training opportunities:
Courses, educational resources, etc.
1) Develop the discipline of biomedical data
science and support cross-training
Goals expanded from recommendations in the June 2012 DIWG and Aug 2013
Training workshop reports.

25. Heads Up on What is Coming in FY15
 Calls for using the Commons
 Calls for a standards framework development
 Calls for software development
 Calls to stimulate interactions between communities
(diversity, rotations, library)
 Calls for high risk, high return projects
 Your ideas here…..

26. NNIIHH……
philip.bourne@nih.gov
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