1. The Data Commons An introduction & Overview
BD2K AHM, November 29, 2016
Vivien Bonazzi (ADDS)
Current snapshot of Commons status

2. Outline What’s driving the need for a Data Commons?
Development of the Data Commons at NIH
Current Data Commons Pilots
Next steps
Considerations & Concluding Thoughts

3. What’s driving the need for a Data Commons?

4. Convergence of factors
Mountains of Data
Increasing need and support for Data sharing
Availability of digital technologies and infrastructures that support Data at scale

7. Went into effect January 25, NCI guidance: management/nci-policies/genomic-data Requires public sharing of genomic data sets

8. Recommendation #4: A national cancer data ecosystem for sharing and analysis.
Create a National Cancer Data Ecosystem to collect, share, and interconnect a broad array of large datasets so that researchers, clinicians, and patients will be able to both contribute and analyze data, facilitating discovery that will ultimately improve patient care and outcomes. 8

11. Challenges with 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 support FAIR data

12. What’s Changing?
Digital ecosystems

13. Development of the
NIH Data Commons

14. Changing the conversation around Data sharing and access
NIH Data Commons
How do we find data, software, standards?
How can we make (large) data, annotations, software, metadata accessible?
How do we reuse data, tools and standards?
How do we make more data machine readable?
How do we leverage existing digital technologies systems, infrastructures?
How do we collaborate?
How do we enable digital ecosystem?

15. Data Commons enabling data driven science
Enable investigators to leverage all possible data and tools in the effort to accelerate biomedical discoveries, therapies and cures by
driving the development of data infrastructure and data science capabilities through collaborative research and robust engineering
Matthew Trunnel, FHC

16. Data Commons’s

17. Developing a Data Commons
Treats products of research – data, methods, papers etc. as digital objects
These digital objects exist in a shared virtual space
Find, Deposit, Manage, Share, and Reuse data, software, metadata and workflows
Digital object compliance through FAIR principles:
Findable
Accessible (and usable)
Interoperable
Reusable

18. The Data Commons. is a framework
The Data Commons is a framework that supports FAIR data access and sharing and fosters the development of a digital ecosystem

19. The Data Commons Framework
Compute Platform: Cloud
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/workflows
Data
“Reference” Data Sets
User defined data
Digital Object Compliance
App store/User Interface
SaaS
PaaS
Detailed description of the Commons Framework can be found at :
IaaS

20. Mapping BD2K Activities and Commons Pilots to the Commons Framework
BD2K Centers, MODS, HMP & Interoperability
Supplements
Compute Platform: Cloud or HPC
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/workflows
Data
“Reference” Data Sets
User defined data
Digital Object Compliance
App store/User Interface
NCI & NIAID Cloud Pilots
+ GDC
BioCADDIE/Other
Indexing
NIH + Community
defined data sets
Cloud credits model (CCM)

21. Current Data Commons Pilots

22. Current Data Commons Pilots
Making large and/or high impact NIH funded data sets and tools accessible in the cloud
Reference Data Sets
Explore feasibility of the Commons Framework Facilitate collaboration and interoperability
Commons
Framework Pilots
Developing Data and Software indexing methods
Leveraging BD2K Efforts: bioCADDIE and others.
Collaborating with external groups
Resource Search & Index
Cloud Credit Model
Provide access to cloud (IaaS) and PaaS/SaaS via credits
Connecting credits to the grants system

23. Reference Data Sets Pilot Large, High-Impact Datasets in the Cloud
Reference Data Sets Pilot Large, High-Impact Datasets in the Cloud Vivien Bonazzi

24. Software: Services & Tools
Mapping to the Commons Framework Large, High-Impact Datasets in the Cloud - Populating the Commons
Compute Platform: Cloud or HPC
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/workflows
Data
“Reference” Data Sets
User defined data
Digital Object Compliance
App store/User Interface
Large, High-Impact Data Sets in the Cloud

25. Kick-start the Commons with Commons-compliant data and tools
Overview: Large, High-Impact Datasets in the Cloud - Populating the Commons
Make large, high impact, NIH funded data sets available in the cloud/commons
Co-locate large datasets and compute power, to improve access, use, re-use, and sharing of data and tools
Kick-start the Commons with Commons-compliant data and tools
Data must adhere to Common compliance /FAIR principles
Provide an indexable test data sets for bioCADDIE (and other indexing efforts)

26. What will we learn: Large, High-Impact Datasets in the Cloud - Populating the Commons
This pilot project will inform NIH on:
Which Clouds are most functional, practical, and cost effective?
What is involved in moving data resources to the Cloud?
What will it cost?
How to manage challenges associated with both open access and controlled access data?
How do we find data and resources across clouds?
How do we compute across clouds?

27. Proposed Components: Large, High-Impact Datasets in the Cloud
Biomedical data resources and tools
Support to migrate large, high-impact datasets and associated tools into multiple cloud providers
Data an tools sets must be FAIR
Cloud Infrastructure
Support for cloud storage and architectural engineering to support data and tools
Coordination
Facilitate activities across the biomedical data resources and cloud providers
Development of market place/app store approaches
Auth: Authorization & Access controls
Tracking metrics (cost, usage etc.) and impact of the overall project

28. Reference Data Sets – Next Steps
NIH Data Task Force
Chaired by Francis Collins
Involves many NIH ICs
Developing some shorter term preliminary pilots for larger NIH funded data sets in the cloud
Expect to see some announcements in Jan/Feb 2017
RFI – engage in dialoged with the community
Planned Winter 2017
FOAs – Supporting large high impact data sets in the cloud
Spring 2017

29. Commons Framework Pilots Exploring feasibility of the Commons Framework : Software and Services layer Valentina Di Francesco

30. Commons Framework Pilots (CFPs)
Exploring feasibility of the Commons Framework
Facilitating connectivity, interoperability and access to digital objects
Providing digital research objects to populate the Commons

31. Commons Framework Pilots
Parent grant’s IC
Project description
TOGA
NIBIB
Cloud-hosted data publication system
Allows the automatic creation and publication of data a personalized data repository
MUSEN
NIAID
Smart APIs – improved handling for metadata within APIs
Ontological support for metadata within an API
Improving smart API discoverability: a registry of APIs
HAN
NIGMS
Docker container hub for BD2K community
Docker containers for genomic analysis applications and pipelines
Benchmark, Evaluation & best practices
COOPER/KOHANE
NHGRI
Cloud based authenticated API access and exchange of causal modeling data , tools + genomic and phenomic data (PIC)
Docker containers for CCD tools available in AWS
HAUSSLER
Secure sharing of germline genetic variations for a targeted panel of breast cancer susceptibility genes and variations
(GA4GH) API : being able to query this data and metadata
Ohno-Machado
NHLBI
Development of an ecosystem for repeatable science
easy reuse of data AND software; tracking of provenance.
Use of container technologies for software and data reuse.
White
The entire HMP1 data set made accessible on AWS
Analysis tools for microbiome data in AWS
Ma’ayan
A Cloud-Based Microscopy Imaging Commons Portal with microscopy data and metadata
Sternberg
Development of a cloud-based literature curation system for specific curation tasks of the collaborating sites.
An API to provide programmatic access to the relevant papers in PMC
MODs PIs
Development of a common data model for the MODs
Development of APIs accessing data across the MODs
You may want to remove the ICs column – not relevant

32. Commons Framework Pilots
APIs
Containerization:
Docker containers, guidelines, registry store
Workbenches, Connectors
Indexing
Market Place/App Store

33. Mapping the Commons Framework PILOTS to the Commons Framework
White - HMP
Compute Platform: Cloud or HPC
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/workflows
Data
“Reference” Data Sets
User defined data
App store/User Interface
Musen
Ma’ayan
Cooper
Han
Haussler
MODs
Sternberg
Ohno-Machado
Toga
This slide maps the FY15 funded CFPs to the framework.

34. Commons Framework Pilots : Updates
Sept – First set of CFPs awarded
Nov CFPs participated in the AHM and the Commons breakout session
Feb Established Common Framework Working Group (CFWG)
CFWG members: Pilots’ PIs and/or technical leads; few PIs of the BD2K interoperability projects
Meeting in person on March 1, 2016

35. Commons Framework Pilots : Updates
March 2016 – CFPs meeting in person
To develop an initial plan for the implementation of Commons Framework
Meeting presentations here
A manuscript describing the outcomes of the meeting was submitted
Established the Commons Framework Working Group (CFWG) and sub-WGs on the following topics:
FAIRness Metrics (Neil McKenna & Michel Dumontier)
Data-object registry (Lucila Ohno-Machado, Michel Dumontier, Wei Wang)
Interoperability of APIs (Michel Dumontier)
Workflow sharing and docker registry (Umberto Ravaioli & Brian O’Connor)
Commons Framework Publications (Owen White)
Nov 28, 2016 – Held a CFWG meeting in person
These groups will present a report of their activities at the Commons Session tomorrow at 10:30am

36. Commons Framework WG - Next Steps
GET INVOLVED: See Valentina Di Francesco or WG leads for details
A broad announcement to the BD2K research community went out in late summer – we are seeking more participants
Contribute to the implementation of the Commons Framework
Suggest other scientific areas of interest that need coordination
Generate guidelines that all of our peers will use as we begin to jumpstart the NIH Commons
Participate in meetings of the CFWG and hear the latest news

37. Commons Framework – Next Steps
FOA: Support investigator-initiated projects to further develop the Data Commons Framework
Could leverage and expand upon resources developed with the Reference data sets
Planned Fall 2017
FOA: Making existing data and tools Commons Compliant/FAIR
Competitive Supplements to existing NIH Awards.
Provide support to existing projects to make current digital resources FAIR & Commons Compliant
Digital resources could include: data, analytical software, or workflows

38. Resource Search & Indexing
Resource Search & Indexing Discoverability of data and software Ian Fore, Ron Margolis, Alison Yao, Claire Schulkey Dawei Lin

39. Software: Services & Tools
Mapping to the Commons Framework Large, High-Impact Datasets in the Cloud - Populating the Commons
Compute Platform: Cloud or HPC
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/workflows
Data
“Reference” Data Sets
User defined data
Digital Object Compliance
App store/User Interface
Indexing

40. An Indexing Ecosystem for the Commons: a virtual environment for ‘FIND’
Enable biomedical research by providing scientists with the ability to FIND digital resources
Establish a mature resource discovery tool(s) that can be sustained as long as the need for it exists
Focus on characteristics of the tool as infrastructure
Maintains a defined level of service
Contribute to a Commons that is reliable, available, easy to use, and adaptable

41. Current Activities Compare ongoing activities and identify needs
Identify indexing activities in and outside NIH
BD2K: bioCADDIE, Centers of Excellence
ICs: NLM, NCI, NHGRI, other
Non-BD2K: Elixir (EBI), Publishers (Elsevier), Repositories, schema.org
Compare ongoing activities and identify needs
Benchmarking
Identify gaps in strategy
Dimensions to consider
Content, Metadata, Platform/ Technology
Coordinate with other BD2K PMWGs
Standards
Specific Center WGs

42. Cloud Credits Model George Komatsoulis

43. Software: Services & Tools
Mapping to the Commons Framework Large, High-Impact Datasets in the Cloud - Populating the Commons
Compute Platform: Cloud or HPC
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/workflows
Data
“Reference” Data Sets
User defined data
Digital Object Compliance
App store/User Interface
Cloud Credits Pilot

45. How do credits work from the point of view of an investigator?
Investigators receive credits worth a certain amount (in dollars) that can be used at the conformant provider(s) of their choice
Credits are pre-purchased and applied to the account of the investigator with the relevant provider(s)
As the investigator uses services with a conformant provider, the provider debits the value of the investigators usage against the pre-loaded credits
INVESTIGATORS ARE NOT BILLED BY PROVIDERS AS LONG AS THEY DO NOT EXCEED THEIR CREDIT ALLOCATION.

46. Commons Credits Model Pilot
3 year pilot to test this business model to facilitate researcher use of cloud resources (enhance data sharing and potentially reduce costs).
Contract with the CMS Alliance to Modernize Healthcare (CAMH) Federally Funded Research and Development Center (FFRDC) managed by the MITRE corporation
FFRDCs are special purpose, government-owned but contractor-managed entities that meet R&D needs that can’t be well managed by traditional grants and contracts
Examples: National Labs and organizations like RAND
Pilot will not directly interact with the existing grant system.
Instead is modeled on the mechanisms being used to gain access to NSF and DOE national resources (HPC, light sources, etc.)
The only required qualification for applying for credits will be that the investigator must have an existing NIH grant

47. Commons Credits Model Pilot
Current List of Approved Vendors
DLT = Amazon Web Services Reseller
IBM
Onix = Google Reseller
Broad and ISB NCI Cloud Pilots accessible via Google
Two more approved but negotiating participation agreement
First batch of credits issued Sep 29, 2016
8 Investigators (cohort 1) that are part of an ‘alpha test’
Only IBM/AWS at the time
93% AWS, 7% IBM
First credits have been used, usage information coming
First “production” credit request period opening this month

48. Considerations and Concluding Thoughts

49. Considerations Communication
Metrics – Understanding and accounting of data usage patterns
Cost
Cloud Storage
Pay for use cloud compute (NIH credits pilot)
Indirect costs for cloud
Hybrid Clouds – Institution (private) and commercial (public) clouds
Managing Open vs Controlled access data
Auth: single sign on - dreams/nightmares?
Archive vs Working Copies of data
Interoperability with other Commons (clouds)

50. Standards – Metadata, UIDs, APIs
Discoverability – Finding digital objects across clouds
Interfaces – For users with different needs and capabilities
Consent – Reconsenting data, Dynamic consents?
Policies
Data sharing policies that are useful and effective
Keep pace with use of technology (e.g. dbGAP data in the Cloud)
Incentives
Access to, and shareability of FAIR Data as part of NIH grant review criteria
Governance – Community involvement in governance models
Sustainability – Long term support

51. Summary
We need an unprecedented level of convergence and collaboration to drive biomedical science to the next level.
Supporting this model of data-intensive collaborative science requires a shift in academic research culture and new investments in data infrastructure and capabilities.
Matthew Trunnel, FHC

52. Acknowledgments
ADDS Office: Jennie Larkin, Phil Bourne, Michelle Dunn,Mark Guyer, Allen Dearry, Sonynka Ngosso, Tonya Scott, Lisa Dunneback, Vivek Navale (CIT/ADDS)
NCBI: George Komatsoulis
NHGRI: Valentina di Francesco
NIGMS: Susan Gregurick
CIT: Andrea Norris, Debbie Sinmao
NIH Common Fund: Jim Anderson , Betsy Wilder, Leslie Derr
NCI Cloud Pilots/ GDC: Warren Kibbe, Tony Kerlavage, Tanja Davidsen
Commons Reference Data Set Working Group: Weiniu Gan (HL), Ajay Pillai (HG), Elaine Ayres, (BITRIS), Sean Davis (NCI), Vinay Pai (NIBIB), Maria Giovanni (AI), Leslie Derr (CF), Claire Schulkey (AI)
RIWG Core Team: Ron Margolis (DK), Ian Fore, (NCI), Alison Yao (AI), Claire Schulkey (AI), Eric Choi (AI)
OSP: Dina Paltoo, Kris Langlais, Erin Luetkemeier, Agnes Rooke,
Research and Industry: Mathew Trunnell (FHC), Bob Grossman (Chicago), Toby Bloom (NYGC)

53. Acknowledgements- CFPs
NIH CFPs WG
Valentina Di Francesco
Sam Moore
Vivien Bonazzi
Allen Dearry
Maria Giovanni
Susan Gregurick
Weiniu Gan
James Luo
Stacia Friedman-Hill
Ajay Pillai
Leslie Derr
Debbie Sinmao
Eric Choi
Claire Schulkey
George Komatsoulis
CFWG
Owen White
Neil McKenna
Michel Dumontier
Umberto Ravaioli
Brian O’Connor
Lucila Ohno-Machado
Wei Wang
All the other members
It is up to you what to do with the acknowlegmenets.
I would reduce considerably the list of NIH staff and keep the CFWG – the names listed are those of the leaders of the subgroups.

54. Acknowledgements - Credits Model
ADDS Office
Vivien Bonazzi
Phil Bourne
Jennie Larkin
Mark Guyer
MITRE
Ari Abrams-Kudan
Wenling (Eileen) Chang
Peter Gutgarts
Lynette Hirschman
William Kim
Eldred Rubeiro
Bruce Shirk
David Tanenbaum
Lisa Tutterow
Grant Thornton
Katie Beringer
Mike Clifford
Tamara Reynolds
NIH
Tanja Davidsen (NCI)
Valentina di Franceso (NHGRI)
Susan Gregurick (NIGMS)
David Lipman (NCBI)
Vivek Navale (CIT)
Jim Ostell (NCBI)
Debbie Sinmao (CIT)
Nick Weber (NIAID)
NITRD
Peter Lyster

55. Vivien Bonazzi bonazziv@mail.nih.gov Stay in Touch QR Business Card
LinkedIn
@Vivien.Bonazzi
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