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Biomedical Research as an Open Digital
Enterprise Philip E. Bourne Ph.D.
Associate Director for Data Science
National Institutes of Health
http://www.slideshare.net/pebourne
A View from the Funding Agencies
“It was the best of times, it was the
worst of times, it was the age of
wisdom, it was the age of foolishness,
it was the epoch of belief, it was the
epoch of incredulity, it was the season
of Light, it was the season of
Darkness, it was the spring of hope, it
was the winter of despair …”
A Tale of Two Numbers
Source Michael Bell http://homepages.cs.ncl.ac.uk/m.j.bell1/blog/?p=830
We (the NIH) Are Working On, But As
Yet Do Not Have Good Answers To:
1. Today, how much are we actually
spending on data and software related
activities?
2. How much should we be spending to
achieve the maximum benefit to
biomedical science relative to what we
spend in other areas?
There are other drivers of change out
there besides economics and an
increasing emphasis on data and
analytics
47/53 “landmark” publications
could not be replicated
[Begley, Ellis Nature,
483, 2012] [Carole Goble]
Reproducibility
 Most of the 27 Institutes and Centers of the NIH are
currently reviewing the ability to reproduce research
they are funding
 The NIH recently convened a meeting with publishers
to discuss the issue – a set of guiding principles
arose
Reproducibility – More is in the Works
 Much of the research life cycle is now digital -
encourage the reliability, accessibility, findability,
usability of data, methods, narrative, publications etc.
 How?
 Data sharing plans
 Standards frameworks
 Data and software catalogs
 PubMedCentral
? The Commons – PMC for the complete lifecycle
? Machine readable data sharing plans
? Small funding to communities
? Support for training and best practices in eScholarship
Growth as Another Driver
 Evidence:
– Google car
– 3D printers
– Waze
– Robotics
From: The Second Machine Age: Work, Progress,
and Prosperity in a Time of Brilliant Technologies
by Erik Brynjolfsson & Andrew McAfee
To Summarize Thus Far …
A time of great (unprecedented?)
scientific development but limited
funding
A time of upheaval in the way we do
science
From a funders perspective…
A time to squeeze every cent/penny to
maximize the amount of research that
can be done
A time when top down approaches
meet bottom up approaches
Top Down vs Bottom Up
 Top Down
– Regulations e.g. US:
Common Rule, FISMA,
HIPPA
– Data sharing policies
• OSTP
• GWAS
• Genome data
• Clinical trials
– Digital enablement
– Moves towards
reproducibility
 Bottom Up
– Communities emerge
and crowd source
• Collaboration
• Data shared
• Open source
software
• Common principles
• Standards
And Considering This Audience…
It was the age when software
developers are in the greatest demand
for science..
It was the age when the rewards
outside academia are greater than the
rewards inside
Optimistically This is a Time of
Opportunity
 The time for software
developers is here
 The time to derive new
business models is here
 The time to foster best
software practices is here
 ….
Okay so what are we doing about it?
To start with we are thinking about the
complete research lifecycle
The Research Life Cycle
IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION
Tools and Resources Will Continue To
Be Developed
IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION
Authoring
Tools
Lab
Notebooks
Data
Capture
Software
Analysis
Tools
Visualization
Scholarly
Communication
Those Elements of the Research Life Cycle Need to
Become More Interconnected Around a Common
Framework
IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION
Authoring
Tools
Lab
Notebooks
Data
Capture
Software
Analysis
Tools
Visualization
Scholarly
Communication
Those Elements of the Research Life Cycle
Need to Become More Interconnected Around a
Common Framework
IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION
Authoring
Tools
Lab
Notebooks
Data
Capture
Software
Analysis
Tools
Visualization
Scholarly
Communication
Commercial &
Public Tools
Git-like
Resources
By Discipline
Data Journals
Discipline-
Based Metadata
Standards
Community Portals
Institutional Repositories
New Reward
Systems
Commercial Repositories
Training
Those Elements of the Research Life Cycle Need to
Become More Interconnected Around a Common
Framework
IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION
Authoring
Tools
Lab
Notebooks
Data
Capture
Software
Analysis
Tools
Visualization
Scholarly
Communication
Commercial &
Public Tools
Git-like
Resources
By Discipline
Data Journals
Discipline-
Based Metadata
Standards
Community Portals
Institutional Repositories
New Reward
Systems
Commercial Repositories
Training
What are we proposing as that
common framework?
The Commons
Is …
 A public/private partnership
 An agile development starting with the evaluation of a
few pilots
 An example: porting DbGAP to the cloud
 An experiment with new funding strategies
What The Commons Is and Is Not
 Is Not:
– A database
– Confined to one physical
location
– A new large
infrastructure
– Owned by any one group
 Is:
– A conceptual framework
– Analogous to the Internet
– A collaboratory
– A few shared rules
• All research objects
have unique
identifiers
• All research objects
have limited
provenance
Sustainability and Sharing: The Commons
Data
The Long Tail
Core Facilities/HS Centers
Clinical /Patient
The Why:
Data Sharing Plans
The
Commons
Government
The How:
Data
Discovery
Index
Sustainable
Storage
Quality
Scientific
Discovery
Usability
Security/
Privacy
Commons == Extramural NCBI == Research Object Sandbox == Collaborative Environment
The End Game:
KnowledgeNIH
Awardees
Private
Sector
Metrics/
Standards
Rest of
Academia
Software Standards
Index
BD2K
Centers
Cloud, Research Objects,
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
[Adapted from George Komatsoulis]
One Possible Commons Business Model
HPC, Institution …
Commons Pilots
 Define a set of use cases emphasizing:
– Openness of the system
– Support for basic statistical analysis
– Embedding of existing applications
– API support into existing resources
 Evaluate against the use cases
 Review results & business model with NIH leadership
 Design a pilot phase with various groups
 Conduct pilot for 6-12 months
 Evaluate outcomes and determine whether a wider
deployment makes sense
 Report to NIH leadership summer 2015
What Will Software Development Look
Like in the Commons?
 Software identifiers make software:
– Easy to find
– Easy of use
– Easy to cite
 Which means:
– Need a standard citation scheme
– Publishers must be encouraged to use it
– The software index should facilitate the above AND
• Provide metrics for use
• Ability to provide commentary
Minimal Software Specification
 Title
 Version
 License
 Links to source
 Human readable synopsis
 Author names, affiliations
 Ontological terms describing software
 Dependencies
 Acknowledgements
 Publications
Examples of Folks We Want to
Engage
 Other funding agencies – national and international
 Open Science Framework https://osf.io/
 Evernote https://evernote.com/
 Simtk https://simtk.org/xml/index.xml
 MyExperiment http://www.myexperiment.org/
 Galaxy http://galaxyproject.org/
 Lab notebook systems
 Other systems used already by NIH
Putting it all together in a coherent
strategy….
Associate Director for Data Science
Commons
Training
Center
BD2K
Modified
Review
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
Communication
Collaboration
rogrammatic Theme
Deliverable
Example Features • IC’s
• Researchers
• Federal
Agencies
• International
Partners
• Computer
Scientists
Scientific Data Council External Advisory Board
* Hires made
BD2K – Commons Users
 Centers of Excellence in Data Science (Awards 9/14)
 Data Discovery Index Consortium (Award 9/14)
 Training grants awarded (Awards 9/14)
 Software development (Awards 15)
 Standards framework (Awards 15)
 Software index consortium (Award 15)
 Awards next year ~$100M
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
Some Acknowledgements
 Eric Green & Mark Guyer (NHGRI)
 Jennie Larkin (NHLBI)
 Leigh Finnegan (NHGRI)
 Vivien Bonazzi (NHGRI)
 Michelle Dunn (NCI)
 Mike Huerta (NLM)
 David Lipman (NLM)
 Jim Ostell (NLM)
 Andrea Norris (CIT)
 Peter Lyster (NIGMS)
 All the over 100 folks on the BD2K team

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Biomedical Research as an Open Digital Enterprise

  • 1. Biomedical Research as an Open Digital Enterprise Philip E. Bourne Ph.D. Associate Director for Data Science National Institutes of Health http://www.slideshare.net/pebourne
  • 2. A View from the Funding Agencies
  • 3. “It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair …”
  • 4. A Tale of Two Numbers Source Michael Bell http://homepages.cs.ncl.ac.uk/m.j.bell1/blog/?p=830
  • 5. We (the NIH) Are Working On, But As Yet Do Not Have Good Answers To: 1. Today, how much are we actually spending on data and software related activities? 2. How much should we be spending to achieve the maximum benefit to biomedical science relative to what we spend in other areas?
  • 6. There are other drivers of change out there besides economics and an increasing emphasis on data and analytics
  • 7. 47/53 “landmark” publications could not be replicated [Begley, Ellis Nature, 483, 2012] [Carole Goble]
  • 8. Reproducibility  Most of the 27 Institutes and Centers of the NIH are currently reviewing the ability to reproduce research they are funding  The NIH recently convened a meeting with publishers to discuss the issue – a set of guiding principles arose
  • 9. Reproducibility – More is in the Works  Much of the research life cycle is now digital - encourage the reliability, accessibility, findability, usability of data, methods, narrative, publications etc.  How?  Data sharing plans  Standards frameworks  Data and software catalogs  PubMedCentral ? The Commons – PMC for the complete lifecycle ? Machine readable data sharing plans ? Small funding to communities ? Support for training and best practices in eScholarship
  • 10. Growth as Another Driver  Evidence: – Google car – 3D printers – Waze – Robotics From: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson & Andrew McAfee
  • 11. To Summarize Thus Far … A time of great (unprecedented?) scientific development but limited funding A time of upheaval in the way we do science
  • 12. From a funders perspective… A time to squeeze every cent/penny to maximize the amount of research that can be done A time when top down approaches meet bottom up approaches
  • 13. Top Down vs Bottom Up  Top Down – Regulations e.g. US: Common Rule, FISMA, HIPPA – Data sharing policies • OSTP • GWAS • Genome data • Clinical trials – Digital enablement – Moves towards reproducibility  Bottom Up – Communities emerge and crowd source • Collaboration • Data shared • Open source software • Common principles • Standards
  • 14. And Considering This Audience…
  • 15. It was the age when software developers are in the greatest demand for science.. It was the age when the rewards outside academia are greater than the rewards inside
  • 16. Optimistically This is a Time of Opportunity  The time for software developers is here  The time to derive new business models is here  The time to foster best software practices is here  ….
  • 17. Okay so what are we doing about it?
  • 18. To start with we are thinking about the complete research lifecycle
  • 19. The Research Life Cycle IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION
  • 20. Tools and Resources Will Continue To Be Developed IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Authoring Tools Lab Notebooks Data Capture Software Analysis Tools Visualization Scholarly Communication
  • 21. Those Elements of the Research Life Cycle Need to Become More Interconnected Around a Common Framework IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Authoring Tools Lab Notebooks Data Capture Software Analysis Tools Visualization Scholarly Communication
  • 22. Those Elements of the Research Life Cycle Need to Become More Interconnected Around a Common Framework IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Authoring Tools Lab Notebooks Data Capture Software Analysis Tools Visualization Scholarly Communication Commercial & Public Tools Git-like Resources By Discipline Data Journals Discipline- Based Metadata Standards Community Portals Institutional Repositories New Reward Systems Commercial Repositories Training
  • 23. Those Elements of the Research Life Cycle Need to Become More Interconnected Around a Common Framework IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Authoring Tools Lab Notebooks Data Capture Software Analysis Tools Visualization Scholarly Communication Commercial & Public Tools Git-like Resources By Discipline Data Journals Discipline- Based Metadata Standards Community Portals Institutional Repositories New Reward Systems Commercial Repositories Training
  • 24. What are we proposing as that common framework?
  • 25. The Commons Is …  A public/private partnership  An agile development starting with the evaluation of a few pilots  An example: porting DbGAP to the cloud  An experiment with new funding strategies
  • 26. What The Commons Is and Is Not  Is Not: – A database – Confined to one physical location – A new large infrastructure – Owned by any one group  Is: – A conceptual framework – Analogous to the Internet – A collaboratory – A few shared rules • All research objects have unique identifiers • All research objects have limited provenance
  • 27. Sustainability and Sharing: The Commons Data The Long Tail Core Facilities/HS Centers Clinical /Patient The Why: Data Sharing Plans The Commons Government The How: Data Discovery Index Sustainable Storage Quality Scientific Discovery Usability Security/ Privacy Commons == Extramural NCBI == Research Object Sandbox == Collaborative Environment The End Game: KnowledgeNIH Awardees Private Sector Metrics/ Standards Rest of Academia Software Standards Index BD2K Centers Cloud, Research Objects,
  • 28. 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
  • 29. [Adapted from George Komatsoulis] One Possible Commons Business Model HPC, Institution …
  • 30. Commons Pilots  Define a set of use cases emphasizing: – Openness of the system – Support for basic statistical analysis – Embedding of existing applications – API support into existing resources  Evaluate against the use cases  Review results & business model with NIH leadership  Design a pilot phase with various groups  Conduct pilot for 6-12 months  Evaluate outcomes and determine whether a wider deployment makes sense  Report to NIH leadership summer 2015
  • 31. What Will Software Development Look Like in the Commons?  Software identifiers make software: – Easy to find – Easy of use – Easy to cite  Which means: – Need a standard citation scheme – Publishers must be encouraged to use it – The software index should facilitate the above AND • Provide metrics for use • Ability to provide commentary
  • 32. Minimal Software Specification  Title  Version  License  Links to source  Human readable synopsis  Author names, affiliations  Ontological terms describing software  Dependencies  Acknowledgements  Publications
  • 33. Examples of Folks We Want to Engage  Other funding agencies – national and international  Open Science Framework https://osf.io/  Evernote https://evernote.com/  Simtk https://simtk.org/xml/index.xml  MyExperiment http://www.myexperiment.org/  Galaxy http://galaxyproject.org/  Lab notebook systems  Other systems used already by NIH
  • 34. Putting it all together in a coherent strategy….
  • 35. Associate Director for Data Science Commons Training Center BD2K Modified Review 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 Communication Collaboration rogrammatic Theme Deliverable Example Features • IC’s • Researchers • Federal Agencies • International Partners • Computer Scientists Scientific Data Council External Advisory Board * Hires made
  • 36. BD2K – Commons Users  Centers of Excellence in Data Science (Awards 9/14)  Data Discovery Index Consortium (Award 9/14)  Training grants awarded (Awards 9/14)  Software development (Awards 15)  Standards framework (Awards 15)  Software index consortium (Award 15)  Awards next year ~$100M
  • 37. 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
  • 38. Some Acknowledgements  Eric Green & Mark Guyer (NHGRI)  Jennie Larkin (NHLBI)  Leigh Finnegan (NHGRI)  Vivien Bonazzi (NHGRI)  Michelle Dunn (NCI)  Mike Huerta (NLM)  David Lipman (NLM)  Jim Ostell (NLM)  Andrea Norris (CIT)  Peter Lyster (NIGMS)  All the over 100 folks on the BD2K team

Editor's Notes

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  2. Ioannidis JPA (2005) Why Most Published Research Findings Are False. PLoS Med 2(8): e124. doi:10.1371/journal.pmed.0020124 http://www.reuters.com/article/2012/03/28/us-science-cancer-idUSBRE82R12P20120328
  3. Federal Information Security Management Act of 2002 The Health Insurance Portability and Accountability Act of 1996