NIH Data Commons Overview
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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.
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NIH Data Commons Overview
- 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
- 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. 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
- 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
- 13. Development of the NIH Data Commons
- 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
- 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 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
- 21. Current Data Commons Pilots
- 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
- 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 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
- 42. Cloud Credits Model George Komatsoulis
- 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
- 44. Investigator CMS FFRDC The Commons Cloud Provider C Cloud Provider B Cloud Provider A Investigator Institution [OPTIONAL] Approves Credit Request Requests Credits Directs reseller to distribute credits Distributes Uses credits 1 2 NIH 3 4 5 7 8 Delivers Funding Recommendation Review & Approval CMS FFRDC Review & Selection 6
- 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
- 55. Stay in Touch QR Business Card LinkedIn @Vivien.Bonazzi Slideshare Blog (Coming soon!)
Editor's Notes
- Current snapshot of Commons status
- 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.