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The Vision for Data @ the NIH
Philip E. Bourne, PhD, FACMI
Associate Director for Data Science
National Institutes of Heal...
Office of Biomedical
Data Science
Mission Statement
To foster an open ecosystem that
enables biomedical research to be
con...
Let Me Give You 4 Examples of What
Drives Us …
1. We are at a Point of Deception …
 Evidence:
– Google car
– 3D printers
– Waze
– Robotics
– Sensors
From: The Second Ma...
1. We Are At a Point of Deception
The 6D Exponential Framework
Digitization of Basic &
Clinical Research & EHR’s
Deception...
2. Democratization Will Follow
The Story of Meredith
http://fora.tv/2012/04/20/Congress_Unplugged_
Phil_Bourne
Stephen Fri...
47/53 “landmark” publications
could not be replicated
[Begley, Ellis Nature,
483, 2012] [Carole Goble]
3. Disruption Can O...
4. Demonetization, Democratization?
“And that’s why we’re here today. Because something
called precision medicine … gives us one of the greatest
opportunities...
Precision Medicine Initiative
Vision: Build a broad research program to encourage
creative approaches to precision medicin...
Precision Medicine Initiative
 National Research Cohort
– >1 million U.S. volunteers
– Numerous existing cohorts (many fu...
National Research Cohort:
What Early Success Might Look Like
 A real test of pharmacogenomics—right drug at the right
dos...
Precision Medicine:
What Success Might Look Like
50-year-old woman with type 2 diabetes visits her
doctor
Now
– Though wo...
Precision Medicine:
What Success Might Look Like
50-year-old woman with type 2 diabetes visits her
doctor
Future: + 2 yea...
Other Diseases:
What Success Might Look Like
50-year-old woman with type 2 diabetes visits her
doctor
Future: + 5 years
–...
Other Diseases:
What Success Might Look Like
50-year-old woman with type 2 diabetes visits her
doctor
Future: + 10 years
...
EHRsPatient Partnerships
Data Science
GenomicsTechnologies
The BD2K Program is Central
to the Mission
Planned – Black; Available- Green
Elements of The Digital Enterprise
Communities Policies
Infrastructure
• Intersection:
• Sustainability
• Efficiency
• Col...
Elements of The Digital Enterprise
Communities Policies
Infrastructure
• Intersection:
• Sustainability
• Efficiency
• Col...
Consider an example…
 Big Data: The study involved
MRI images & GWAS data
from over 30,000 people
 Collaboration: Data came
from many differe...
 Community – Enigma, BD2K
 Policy
– Improved consent methods
– Cloud accessibility for human subjects data
– Trusted par...
Communities: Thus Far
 Visioning workshop convened 9/3/14
 Launched BD2K ($32M)
– 12 Centers of data excellence
– Data D...
Communities: 2015 Activities
 New FOAs with outreach to new
communities – math, stats, comp science etc.
 Work with e.g ...
Communities: Questions?
 Societies of the modern age?
 How to enable these groups?
 How to marry the funding of individ...
Policies: Now & Forthcoming
 Data Sharing
– Genomic data sharing announced
– Data sharing plans on all research awards
– ...
Policies - Forthcoming
 Data Citation
– Goal: legitimize data as a form of scholarship
– Process:
• Machine readable stan...
BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
DDICC
Software
Standard
s
Infrastructure - The
Com...
The Commons
Digital Objects
(with UIDs)
Search
(indexed metadata)
Computing
Platform
TheCommons
Vivien Bonazzi
George Koma...
The Commons: Compute Platforms
The Commons
Conceptual Framework
Public Cloud
Platforms
Super Computing
(HPC) Platforms
Oth...
The Commons:
Business Model
[George Komatsoulis]
Infrastructure: Standards
 2013 Workshop on Frameworks for Community-
Based Standards
 August 2014 Input on Information ...
Elements of The Digital Enterprise
Communities Policies
Infrastructure
• Intersection:
• Sustainability
• Efficiency
• Col...
Elements of The Digital Enterprise
Communities Policies
Infrastructure
• Intersection:
• Sustainability
• Efficiency
• Col...
Workforce Training
Problem: Lack of
Biomedical Data Science Specialists
BD2K T32/T15
BD2K K01
Career path workshops – eg AAU
Challenge
model ...
Problem: Limited Access to
Data Science Training
BD2K R25
Metadata
for training
materials
Community-sourced
cataloging and...
I not only use all the brains
I have, but all I can borrow.
– Woodrow Wilson
Associate Director for Data Science
Commons BD2K Efficiency
Sustainability Education Innovation Process
• Cloud – Data &
C...
NIHNIH……
Turning Discovery Into HealthTurning Discovery Into Health
philip.bourne@nih.gov
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The Vision for Data @ the NIH

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Keynote given at the Bio-IT World Conference and Expo, April 21, 2015 in Boston, USA.

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The Vision for Data @ the NIH

  1. 1. The Vision for Data @ the NIH Philip E. Bourne, PhD, FACMI Associate Director for Data Science National Institutes of Health Bio-IT World, Boston April 21, 2015
  2. 2. Office of Biomedical Data Science Mission Statement To foster an open ecosystem that enables biomedical research to be conducted as a digital enterprise that enhances health, lengthens life and reduces illness and disability & to train the next generation of data scientists Goals expanded from recommendations in the June 2012 DIWG and BRWWG reports.
  3. 3. Let Me Give You 4 Examples of What Drives Us …
  4. 4. 1. 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
  5. 5. 1. We Are At a Point of Deception The 6D Exponential Framework Digitization of Basic & Clinical Research & EHR’s Deception We Are Here Disruption Demonetization Dematerialization Democratization Open science Patient centered health care
  6. 6. 2. Democratization Will Follow The Story of Meredith http://fora.tv/2012/04/20/Congress_Unplugged_ Phil_Bourne Stephen Friend
  7. 7. 47/53 “landmark” publications could not be replicated [Begley, Ellis Nature, 483, 2012] [Carole Goble] 3. Disruption Can Occur
  8. 8. 4. Demonetization, Democratization?
  9. 9. “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
  10. 10. Precision Medicine Initiative Vision: Build a broad research program to encourage creative approaches to precision medicine, test them rigorously, and, ultimately, use them to build the evidence base needed to guide clinical practice. Near Term: apply the tenets of precision medicine to a major health threat – cancer Longer Term: generate the knowledge base necessary to move precision medicine into virtually all areas of health and disease
  11. 11. 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
  12. 12. National Research Cohort: What Early Success Might Look Like  A real test of pharmacogenomics—right drug at the right dose for the right patient  New therapeutic targets by identifying loss-of-function mutations protective against common diseases – PCSK9 for cardiovascular disease – SLC30A8 for type 2 diabetes  Resilience – finding individuals who should be ill but aren’t  New ways to evaluate mHealth technologies for prevention/management of chronic diseases
  13. 13. Precision Medicine: What Success Might Look Like 50-year-old woman with type 2 diabetes visits her doctor Now – Though woman’s glucose control has been suboptimal, doctor renews her prescription for drug often used for type 2 diabetes – Continues to monitor blood glucose with fingersticks and glucometer, despite dissatisfaction with these methods
  14. 14. Precision Medicine: What Success Might Look Like 50-year-old woman with type 2 diabetes visits her doctor Future: + 2 years – Volunteers for new national research network • Sample of her DNA, along with her health information, sent to researchers for sequencing/analysis • Can view her health/research data via smartphone – Agrees to researchers’ request to track her glucose levels via tiny implantable chip that sends wireless signals to her watch, researchers’ computers • Using these data, she changes diet, medicine dose schedule
  15. 15. Other Diseases: What Success Might Look Like 50-year-old woman with type 2 diabetes visits her doctor Future: + 5 years – Receives word from her doctor about a new drug based upon improved molecular understanding of type 2 diabetes – When she enters drug’s name into her smartphone’s Rx app, her genomic data show she’ll metabolize the drug slowly • Her doctor alters the dose accordingly
  16. 16. Other Diseases: What Success Might Look Like 50-year-old woman with type 2 diabetes visits her doctor Future: + 10 years – Celebrates her 60th birthday and reflects with her family about how proud she is to be part of cohort study – Her glucose levels remain well controlled; she’s suffered no diabetes-related complications – Her children decide to volunteer for cohort study
  17. 17. EHRsPatient Partnerships Data Science GenomicsTechnologies
  18. 18. The BD2K Program is Central to the Mission Planned – Black; Available- Green
  19. 19. Elements of The Digital Enterprise Communities Policies Infrastructure • Intersection: • Sustainability • Efficiency • Collaboration • Training
  20. 20. Elements of The Digital Enterprise Communities Policies Infrastructure • Intersection: • Sustainability • Efficiency • Collaboration • Training Virtuous Research Cycle
  21. 21. Consider an example…
  22. 22.  Big Data: The study involved MRI images & GWAS data from over 30,000 people  Collaboration: Data came from many different sights affiliated with the ENIGMA consortium  Methods: To homogenize data from different sites, the group designed standardized protocols for image analysis, quality assessment, genetic imputation, and association  Found five novel genetic variants  Results provided insight into the variability of brain development, and may be applied to study of neuropsychiatric dysfunction
  23. 23.  Community – Enigma, BD2K  Policy – Improved consent methods – Cloud accessibility for human subjects data – Trusted partners – Data sharing  Infrastructure – Standards, compute resources, software
  24. 24. Communities: Thus Far  Visioning workshop convened 9/3/14  Launched BD2K ($32M) – 12 Centers of data excellence – Data Discovery Index Coordination Consortium (DDICC) – Training awards  First successful consortia meeting 11/3-4  Workshops to inform future funding – Software indexing and discoverability – Gaming
  25. 25. Communities: 2015 Activities  New FOAs with outreach to new communities – math, stats, comp science etc.  Work with e.g GA4GH, RDA, FORCE11, NDS ….  IDEAS lab with NSF  Competition with international funders  Software carpentry, hackathons, Pi Day
  26. 26. Communities: Questions?  Societies of the modern age?  How to enable these groups?  How to marry the funding of individuals with the funding of communities?
  27. 27. 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
  28. 28. 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  dbGaP in the cloud (done!)
  29. 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. 30. The Commons Digital Objects (with UIDs) Search (indexed metadata) Computing Platform TheCommons Vivien Bonazzi George Komatsoulis
  31. 31. The Commons: Compute Platforms The Commons Conceptual Framework Public Cloud Platforms Super Computing (HPC) Platforms Other Platforms ?  Google, AWS (Amazon)  Microsoft (Azure), IBM, other?  In house compute solutions  Private clouds, HPC – Pharma – The Broad – Bionimbus  Traditionally low access by NIH
  32. 32. The Commons: Business Model [George Komatsoulis]
  33. 33. Infrastructure: Standards  2013 Workshop on Frameworks for Community- Based Standards  August 2014 Input on Information Resources for Data-Related Standards Widely Used in Biomedical Science – 30 responses  Feb 2015 Workshop Community-based Data and Metadata Standards  Internal CDE Registry project
  34. 34. Elements of The Digital Enterprise Communities Policies Infrastructure • Intersection: • Sustainability • Efficiency • Collaboration • Training
  35. 35. Elements of The Digital Enterprise Communities Policies Infrastructure • Intersection: • Sustainability • Efficiency • Collaboration • Training
  36. 36. Workforce Training
  37. 37. Problem: Lack of Biomedical Data Science Specialists BD2K T32/T15 BD2K K01 Career path workshops – eg AAU Challenge model of funding
  38. 38. Problem: Limited Access to Data Science Training BD2K R25 Metadata for training materials Community-sourced cataloging and indexing of training opportunities Measure utility NIH Workforce Development Center RFA-ES-15-004
  39. 39. I not only use all the brains I have, but all I can borrow. – Woodrow Wilson
  40. 40. Associate Director for Data Science Commons BD2K Efficiency Sustainability Education Innovation Process • 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 The Biomedical Research Digital Enterprise Partnerships Collaboration rogrammatic Theme Deliverable Example Features • IC’s • Researchers • Federal Agencies • International Partners • Computer Scientists Scientific Data Council External Advisory Board Training
  41. 41. NIHNIH…… Turning Discovery Into HealthTurning Discovery Into Health philip.bourne@nih.gov
  • RohanThompson

    Jan. 18, 2016
  • mdperry

    Oct. 25, 2015
  • apalacheteb

    Apr. 29, 2015
  • johnwcobb

    Apr. 23, 2015

Keynote given at the Bio-IT World Conference and Expo, April 21, 2015 in Boston, USA.

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