Presentation at the Garvan Institute, Sydney Australia - May 2016

1. The Data Commons
Digital Ecosystems for Sharing and
Analyzing biomedical Big Data
Vivien Bonazzi, Ph.D.
Senior Advisor for Data Science
Office of Data Science (ADDS)
National Institutes of Health

2. Lets Talk About Biomedical Big Data

4. What Makes Big Data Big?
VOLUME
VELOCITY
VARIETY
VERACITY

5. It’s a signal of the coming Digital Economy
DATA has VALUE
DATA is CENTRAL to the Digital Economy
But its more than this…..

6. An economy characterized by
using data to gain a business
advantage
(yes, institutions are a business)
Organizations that are not born
digital will be at a disadvantage
in the new economy

7. Organizations will be defined by their digital assets
Scientific digital assets
Data
Software
Workflows

8. The most successful organizations of the future will
be those that can leverage their digital assets and
transform them into a digital enterprise

9. Make data
The currency of an organization
Usable in a digital ecosystems – Data Commons

10. The problem with biomedical data
Digital assets includes Data

11. Challenges Biomedical Data
The Journal Article is the end goal
Data is a means to an ends (low value)
Data is not FAIR
Findable, Accessible, Interoperable,
Reproducible
Limited e-infrastructures to

12. The Problem
With
Biomedical DATA
https://www.youtube.com/watch?v=N2zK3sAtr-4

13. What’s
Changing?

14. FAIR principles drive data to become the currency
Policies that promote data sharing via FAIR help
change the culture

15. We also need a digital ecosystem that allows
transactions to occur on FAIR data
at scale

16. The Data Commons
is a platform
that fosters the development of a digital ecosystem

17. The Data Commons platform that fosters development of a digital
ecosystem
Treats products of research – data, software, methods, papers etc as
digital asset (object)
Digital objects need to conform to FAIR principles
Digital objects exist in a shared virtual space
- Find, Deposit, Manage, Share and Reuse: digital assets
Enables interactions between Producers and Consumers of digital assets
Gives currency to digital assets and the people who develop and support
them

18. The Data Commons
is a platform?
that fosters the development of a digital ecosystem

19. “A platform is a plug and play model
that allows multiple participants (producers and
consumers) to connect to it, interact with each
other and create value”
Sangeet Paul Choudary – Platform Scale

20. A lot of what see today uses a platform approach ”
Sangeet Paul Choudary – Platform Scale

21. The goal of the a Data Commons Platform is to
enable interactions between producers and
consumers
Sangeet Paul Choudary – Platform Scale

22. To understand the
Data Commons Platform
(and how it works for biomedical data)
we need to use a Platform stack
to help visualize the concept

23. Sangeet Paul Choudary – Platform Scale
Platforms have 3 layers

24. NIH Data Commons - Platform Stack
https://datascience.nih.gov/commons
Technology
Technology
Data
Network/
market place

25. https://datascience.nih.gov/commons
NIH Data Commons - Platform Stack

26. Initial Phase
Unique digital object identifiers of resolvable to original authoritative source
Machine readable
A minimal set of searchable metadata
Clear access rules (especially important for human subjects data)
An entry (with metadata) in one or more indices
Future Phases
Standard, community based unique digital object identifiers
Conform to community approved standard metadata and ontologies for
enhanced searching
Digital objects accessible via open standard APIs
NIH Data Commons: Digital Asset Compliance
Making things FAIR

29. Data Commons Platform drives digital ecosystem

30. The NIH Data Commons Pilot

31. The NIH Data Commons Pilot
Co-location of large and/or highly
utilized NIH funded data with
storage and computing infrastructure +
Commonly used tools for analyzing and
sharing digital objects
to create an interoperable resource for
the research community.
Investigators will be able to collaborate
and share digital objects within this
environment and connect with others

32. NIH Nascent Commons Pilots

33. An NIH Wide Data Commons Pilot

35. Indexing

36. Indexing

37. Indexing
Authorization /authentication layer

38. Considerations
Metrics - understanding and accounting of data usage patterns
Cost - Cloud Storage, pay for use cloud compute (NIH credits)
Hybrid Clouds – Mix of research and commercial clouds
Connecting - Interoperability with other Commons, clouds
Consent - Reconsenting data, Dynamic consents
Standards – Metadata, UIDs, APIs

39. An Australian Commons Experiment?

40. A Garvan Data Commons Platform?
Garvan DATA
NCI + Cloud
Analysis tools (Inc 3rd party)
Apps Store
Community – Research, Clinical, Public
API connectivity
with other
Commons

41. An Australian Data Commons?
Australian DATA - Flora and Fauna
Commercial Cloud (NCI)
Analysis tools (Inc 3rd party)
Apps Store
Community – Research, Clinical, Public
API connectivity
with other
Commons

42. “To achieve great things, two things are
needed: a plan and not quite enough
time”
Leonard Bernstein

43. Thank you
• ADDS Office
- Phil Bourne, Michelle Dunn, Jennie Larkin, Mark Guyer, Sonynka Ngosso
• NCBI: Jim Ostell, David Lipman, George Komatsoulis
• NHGRI: Valentina di Francesco, Kevin Lee, Eric Green
• NIGMS: John Lorsch, Susan Gregurik
• CIT: Andrea Norris, Debbie Sinmao, Stacy Charland
• NCI: Warren Kibbe, Tony Kerlavage, Lou Staudt, Tanja Davidsen, Ian Fore
• NIAID: JJ McGowan, Nick Weber, Darrell Hurt, Maria Giovanni
• The NIH Common Fund: Betsy Wilder, Jim Anderson, Leslie Derr
• Trans NIH BD2K Executive Committee & Working groups
• Many biomedical researchers, cloud providers, IT professionals
• John Mattick and the Garvan Institute

44. Stay in Touch
QR Business Card
LinkedIn
@Vivien.Bonazzi
Slideshare
Blog
(Coming soon!)
Vivien Bonazzi
bonazziv@mail.nih.gov