Presented at Yale Day of Data, Yale University, Sept 26 2014.

1. NIH’s Day, Months, Four Years of
Data
Philip E. Bourne Ph.D.
Associate Director for Data Science
National Institutes of Health

2. What is NIH’s Overall Approach to
Data and What Does It Mean to You?

3. Some Context: NIH Data Science History
6/12 2/14 3/14
• Findings:
• Sharing data & software through catalogs
• Support methods and applications development
• Need more training
• Need campus-wide IT strategy
• Hire CSIO
• Continued support throughout the lifecycle

4. My Bias
 Still a scientist
 A funder who still thinks like a PI
 Not yet attuned to the federal system
 Big supporter of open science through prior work with
the Public Library of Science, FORCE11 etc.

5. Data – A Few Observations …
 We talk about the promise of big data, but we don’t
even know the value of little data (aka could “Big
Data” be the new “AI”)
 Good data is expensive in terms of time and money
 Looking at data retroactively is really expensive
 Good data begats trust; trust begats community;
community is God
 The way we support scientific data currently is not
sustainable
 That is, is no business model currently for scientific
data

6. Data – A Few NIH Observations …
1. We have little idea how much we spend on data –
estimated over $1bn per year
2. We have even less idea how much we should be
spending
 Point 2 is part of a culture clash between the more
observational history of biomedicine and the new
analytical approach to discovery

7. Data – An Academic Medical Center
Observation
 A digital enterprise exists when data connections are
made across different areas of the organization such
that productivity and competitiveness are improved
 For example, between education and research
 I am not aware that any such academic institutions
exist?
 Many are starting to wake up to the idea of getting
there
JAMIA 2014, 21(2), 194

8. ADDS 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

9. What Problems Are We Trying to Solve?
One Possible Solution
 Sustainability – 50% business model
 Efficiency – sharing best practices in longitudinal
clinical studies; “trusted investigator”
 Collaboration - identification of collaborators at the
point of data collection not publication
 Reproducibility – data accessible with publication
 Integration – phenotype homogenization
 Accessibility – clinical trials registration
 Quality – sharing CDEs across institutes
 Training – keeping trainees in the ecosystem

10. The Data Ecosystem
Community Policy
Infrastructure
• Sustainable
business
model
• Collaboration
• Training

11. The Data Ecosystem
Community Policy
Infrastructure
• Sustainable
business
model
• Collaboration
• Training
Virtuous
Research
Cycle

12. The Virtuous Cycle
http://goo.gl/fkWjhS

13. Raw Materials to Seed the Ecosystem
 NIH mandate & support
 ADDS team of 8 people
 Intramural participation of over 100 team members
across ICs
 Funding through BD2K:
– ~$30M in FY14
– ~$80M in FY15
– ....

14. Organization to Seed the Ecosystem…

15. Associate Director for Data Science
Scientific Data Council External Advisory Board
Programmatic Theme
Sustainability Education Innovation Process
Deliverable
Commons Training
BD2K Efficiency
Example Features • IC’s
• Cloud – Data &
Compute
• Search
• Security
• Reproducibility
Standards
• App Store
• Coordinate
• Hands-on
• Syllabus
• MOOCs
• Community
• Centers
• Training Grants
• Catalogs
• Standards
• Analysis
• Data
Resource
Support
• Metrics
• Best
Practices
• Evaluation
• Portfolio
Analysis
Collaboration
Partnerships
• Researchers
• Federal
Agencies
• International
Partners
• Computer
Scientists
The Biomedical Research Digital Enterprise

16. Associate Director for Data Science
Scientific Data Council External Advisory Board
Programmatic Theme
Sustainability Education Innovation Process
Deliverable
Commons Training
BD2K Efficiency
Example Features • IC’s
• Cloud – Data &
Compute
• Search
• Security
• Reproducibility
Standards
• App Store
• Coordinate
• Hands-on
• Syllabus
• MOOCs
• Community
• Centers
• Training Grants
• Catalogs
• Standards
• Analysis
• Data
Resource
Support
• Metrics
• Best
Practices
• Evaluation
• Portfolio
Analysis
Collaboration
Partnerships
• Researchers
• Federal
Agencies
• International
Partners
• Computer
Scientists
The Biomedical Research Digital Enterprise

17. Example Communities
– NIH
• 27 ICs
– Agencies
• NSF
• DOE
• DARPA
• NIST
– Government
• OSTP
• HHS HDI
• ONC
• CDC
• FDA
– Private sector
• Phrma
• Google
• Amazon
– Organizations
• PCORI, GA4GH
• RDA, ELIXIR
• CCC
• CATS
• FASEB, ISCB
• Biophysical Society
• Sloan Foundation
• Moore Foundation

18. Example Policies
– Clinical data harmonization
– DbGaP in the cloud
– Data citation
– Machine readable data sharing plans on all grants
– New review models, audiences etc.
• Open review
• Micro funding
• Standing data committees to explore best
practices
• Crowd sourcing

19. Example Infrastructure: The Commons
Data
The Long Tail
Core Facilities/HS Centers
Clinical /Patient
The Why:
Data Sharing Plans
The How:
NIH Knowledge
The
Government
Commons
Data
Discovery
Index
The End Game:
Scientific
Discovery
Usability
Quality
Security/
Privacy
Sustainable
Storage
Awardees
Private
Sector Metrics/
Standards
Rest of
Academia
Software Standards
Index
BD2K
Centers
Cloud, Research Objects,
Business Models

20. What Does the Commons Enable?
 Dropbox like storage
 The opportunity to apply quality metrics
 Bring compute to the data
 A place to collaborate
 A place to discover
http://100plus.com/wp-content/uploads/Data-Commons-3-
1024x825.png

21. One Possible Commons Business Model
[Adapted from George Komatsoulis]
HPC, Institution …

22. Pilots Around A Virtuous Cycle
Expect a FY15 Funding Call to Work in
the Commons

23. TTrraaiinniinngg && DDiivveerrssiittyy
 Training & Diversity Goals:
– Develop a sufficient cadre of diverse researchers skilled in
the science of Big Data
– Elevate general competencies in data usage and analysis
across the biomedical research workforce
– Combat the Google bus
 How:
– Traditional training grants
– Work with IC’s on a needs assessment
– Standards for course descriptions with EU
– Work with institutions on raising awareness
– Partner with minority institutions
– Virtual/physical training center(s)?

24. Closing Question
Calls for increased NIH funding has so
far gone unheeded, what can the
ADDS do (that you have not heard
about) to improve data science
activities?

25. NNIIHH……
philip.bourne@nih.gov
TTuurrnniinngg DDiissccoovveerryy IInnttoo HHeeaalltthh