EMBL Australian Bioinformatics Resource AHM - Data Commons
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This document discusses the development of the NIH Data Commons, which aims to create a shared framework and infrastructure for biomedical data. It notes the increasing amounts of data being generated and the need for data sharing and interoperability. The Data Commons framework treats data, tools, and publications as digital objects that are findable, accessible, interoperable and reusable. Current pilots include deploying reference datasets in the cloud, indexing data and tools, and a credits system for cloud resources. Challenges discussed include metrics, costs, standards, incentives and sustainability. The framework's relevance for supporting open data in Australia is also addressed.
EMBL Australian Bioinformatics Resource AHM - Data Commons
- 1. BD2K and why bioinformatics matters relevance to Australia EMBL - Australia AHM 2016 Vivien Bonazzi Senior Advisor for Data Science Technologies ADDs (Assoc. Director for Data Science) Office Office of the Director (OD) National Institutes of Health (NIH)
- 2. The NIH Data Commons Digital Ecosystems for using and sharing FAIR Data EMBL - Australia AHM 2016 Vivien Bonazzi Senior Advisor for Data Science Technologies ADDs (Assoc. Director for Data Science) Office Office of the Director (OD) National Institutes of Health (NIH)
- 3. http://datascience.nih.gov/bd2k A word about BD2K
- 4. What’s driving the need for a Data Commons?
- 5. Convergence of factors Mountains of Data Increasing need and support for Data sharing Availability of digital technologies and infrastructures that support Data at scale
- 8. 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
- 9. 9 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. 9
- 12. 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. Development of the NIH Data Commons
- 15. 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
- 16. 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. Data Commons’s
- 18. 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
- 19. 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
- 20. 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
- 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
- 24. Commons Framework Pilots Software and Services
- 25. Commons Framework • FAIRness Metrics • Data-object registry • Interoperability of APIs • Workflow sharing and docker registry • Commons Framework Publications
- 26. Resource Search & Indexing Discoverability of data and software
- 27. Cloud Credits Model $ denominated NIH credits to use cloud resources (IaaS) and services (PaaS/SaaS)
- 28. 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
- 29. Authorization /authentication layer Digital Ecosystem
- 31. Considerations 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 and versioning Copies of data Interoperability with other Commons (clouds)
- 32. 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
- 34. Relevance to Australia The value of Australian Data * Unique flora and fauna e.g Marsupials Indigenous Australians Understanding of genomic structure – health & disease Medicinal products Making this data (securely) available With high quality annotation and metadata Attributions to original authors On the cloud Via open standard APIs Aggregation of data via an Australian wide Commons?
- 35. Authorization /authentication layer Oz Digital Ecosystem
- 36. 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
- 37. 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)
- 38. Stay in Touch QR Business Card LinkedIn @Vivien.Bonazzi Slideshare Blog (Coming soon!)
Editor's Notes
- #2: Current snapshot of Commons status
- #3: Current snapshot of Commons status
- #8: The mission of the Office of Science and Technology Policy is threefold; provide the President and his senior staff with accurate, relevant, and timely scientific and technical advice on all matters of consequence; to ensure that the policies of the Executive Branch are informed by sound science; 3) to ensure that the scientific and technical work of the Executive Branch is properly coordinated so as to provide the greatest benefit to society.
- #21: Detailed description of the Commons Framework can be found at : https://datascience.nih.gov/commons
- #29: Detailed description of the Commons Framework can be found at : https://datascience.nih.gov/commons