The document provides an overview of the development of the NIH Data Commons. It discusses factors driving the need for a data commons, including large amounts of data being generated and increased support for data sharing. It outlines the goals of making data findable, accessible, interoperable and reusable. Several pilots are exploring the feasibility of the commons framework, including placing large datasets in the cloud and developing indexing methods. Considerations in fully realizing the commons are also discussed, such as standards, discoverability, policies and incentives.
1. The Data Commons
An introduction & Overview
BD2K AHM, November 29, 2016
Vivien Bonazzi (ADDS)
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
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
5.
6.
7. https://gds.nih.gov/
Went into effect January 25, 2015
NCI guidance:
http://www.cancer.gov/grants-training/grants-management/nci-
policies/genomic-data
Requires public sharing of genomic data sets
8. 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
9.
10.
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
14. 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?
Changing the conversation around
Data sharing and access
NIH Data Commons
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
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
that supports
FAIR data access and sharing
and
fosters the development
of a digital ecosystem
https://datascience.nih.gov/commons
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
DigitalObjectCompliance
App store/User Interface
PaaS
SaaS
IaaS
https://datascience.nih.gov/commons
20. NIH + Community
defined data sets
BD2K Centers,
MODS, HMP &
Interoperability
Supplements
Cloud credits
model (CCM)
BioCADDIE/Other
Indexing
NCI &
NIAID
Cloud
Pilots
+ GDC
Compute Platform: Cloud or HPC
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/workflows
Data
“Reference” Data Sets
User defined data
DigitalObjectCompliance
App store/User Interface
Mapping BD2K Activities and Commons Pilots
to the Commons Framework
22. Current Data Commons Pilots
Explore feasibility of the Commons Framework
Facilitate collaboration and interoperability
Making large and/or high impact NIH funded data sets and tools
accessible in the cloud
Developing Data and Software indexing methods
Leveraging BD2K Efforts: bioCADDIE and others.
Collaborating with external groups
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
Vivien Bonazzi
24. Compute Platform: Cloud or HPC
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/workflows
Data
“Reference” Data Sets
User defined data
DigitalObjectCompliance
App store/User Interface
Mapping to the Commons Framework
Large, High-Impact Datasets in the Cloud - Populating the
Commons
Large, High-Impact
Data Sets in the
Cloud
25. 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)
Overview:
Large, High-Impact Datasets in the Cloud - Populating the Commons
26. 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?
What will we learn:
Large, High-Impact Datasets in the Cloud - Populating the Commons
27. 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
Proposed Components:
Large, High-Impact Datasets in the Cloud
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
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
PI 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/KOHA
NE
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 NHGRI • 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 NHGRI • The entire HMP1 data set made accessible on AWS
• Analysis tools for microbiome data in AWS
Ma’ayan NHLBI • A Cloud-Based Microscopy Imaging Commons Portal with microscopy data
and metadata
Sternberg NHGRI • 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 NHGRI • Development of a common data model for the MODs
• Development of APIs accessing data across the MODs
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
34. Commons Framework Pilots : Updates
Sept. 2015 – First set of CFPs awarded
Nov. 2015 - CFPs participated in the AHM and the
Commons breakout session
Feb. 2016 - 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
• Planned Fall 2017
38. Resource Search & Indexing
Discoverability of data and software
Ian Fore, Ron Margolis, Alison Yao, Claire Schulkey Dawei Lin
39. Compute Platform: Cloud or HPC
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/workflows
Data
“Reference” Data Sets
User defined data
DigitalObjectCompliance
App store/User Interface
Mapping to the Commons Framework
Large, High-Impact Datasets in the Cloud - Populating the
Commons
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. 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
Current Activities
43. Compute Platform: Cloud or HPC
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/workflows
Data
“Reference” Data Sets
User defined data
DigitalObjectCompliance
App store/User Interface
Mapping to the Commons Framework
Large, High-Impact Datasets in the Cloud - Populating the
Commons
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. 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
Commons Credits Model Pilot
47. 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
Commons Credits Model Pilot
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
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
Detailed description of the Commons Framework can be found at : https://datascience.nih.gov/commons
You may want to remove the ICs column – not relevant
This slide maps the FY15 funded CFPs to the framework.
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.