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Data! Philip E. Bourne Ph.D. Associate Director for Data Science National Institutes of Health

Data! Philip E. Bourne Ph.D. Associate Director for Data Science National Institutes of Health. Some Context: NIH Data Science History. 6/12. 3/14. 2/14. Findings: Sharing data & software through catalogs Support methods and applications development Need more training

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Data! Philip E. Bourne Ph.D. Associate Director for Data Science National Institutes of Health

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  1. Data! Philip E. Bourne Ph.D. Associate Director for Data Science National Institutes of Health

  2. Some Context: NIH Data Science History 6/12 3/14 2/14 • Findings: • Sharing data & software through catalogs • Support methods and applications development • Need more training • Need campus-wide IT strategy • Hire CSIO • Continued support throughout the lifecycle

  3. My Bias • Still a scientist • A funder who still thinks like a PI • Not yet attuned to the federal system • Big supporter of OA via PLOS and others

  4. Data – A Few Observations … • We talk about the promise of big data, but we don’t even know the value of little data (aka could “Big Data” be the new “AI”) • Good data is expensive in terms of time and money • Looking at data retroactively is really expensive • Good data begats trust; trust begats community; community is God • The way we support scientific data currently is not sustainable • There is no workable business model currently for scientific data

  5. Data – A Few NIH Observations … • We have little idea how much we spend on data – estimated over $1bn per year • We have even less idea how much we should be spending • Point 2 is part of a culture clash between the more observational history of biomedicine and the new analytical approach to discovery

  6. ADDS Mission Statement To foster an ecosystem that enables biomedical research to be conducted as a digital enterprise that enhances health, lengthens life and reduces illness and disability

  7. What Problems Are We Trying to Solve?Possible Solutions • Sustainability – 50% business model • Efficiency – sharing best practices in longitudinal clinical studies • Collaboration - identification of collaborators at the point of data collection not publication • Reproducibility – data accessible with publication • Integration – phenotype homogenization • Accessibility – clinical trials registration • Quality – sharing CDEs across institutes • Training – keeping trainees in the ecosystem

  8. The Data Ecosystem Community Policy • Sustainable business model • Collaboration • Training Infrastructure

  9. Raw Materials to Seed the Ecosystem • NIH mandate & support • ADDS team of 8 people • Intramural participation of over 100 team members across ICs • Funding through BD2K: • ~$30M in FY14 • ~$80M in FY15 • ....

  10. Example Communities • NIH • 20/27 ICs • Agencies • NSF • DOE • DARPA • NIST • Government • OSTP • HHS HDI • ONC • CDC • FDA • Private sector • Phrma • Google • Amazon • Organizations • PCORI • RDA, ELIXIR • CCC • CATS • FASEB, ISCB • Biophysical Society • Sloan Foundation • Moore Foundation

  11. Example Policies • Clinical data harmonization • Data citation • Machine readable data sharing plans on all grants • New review models, audiences etc. • Open review • Micro funding • Standing data committees to explore best practices • Crowd sourcing

  12. Example Infrastructure: The Commons The Why: Data Sharing Plans The How: The End Game: Data Scientific Discovery Knowledge NIH Awardees Government The Long Tail Usability Software Standards Index The Commons Quality Security/ Privacy Data Discovery Index BD2K Centers Private Sector Rest of Academia Core Facilities/HS Centers Metrics/ Standards Sustainable Storage Clinical /Patient Cloud, Research Objects, Business Models

  13. What Does the Commons Enable? • Dropbox like storage • The opportunity to apply quality metrics • Bring compute to the data • A place to collaborate • A place to discover http://100plus.com/wp-content/uploads/Data-Commons-3-1024x825.png

  14. One Possible Commons Business Model HPC, Institution … [Adapted from George Komatsoulis]

  15. Pilots Around A Virtuous CycleExpect a Funding Call

  16. Training & Diversity • Training & Diversity Goals: • Develop a sufficient cadre of diverse researchers skilled in the science of Big Data • Elevate general competencies in data usage and analysis across the biomedical research workforce • Combat the Google bus • How: • Traditional training grants • Work with IC’s on a needs assessment • Standards for course descriptions with EU • Work with institutions on raising awareness • Partner with minority institutions • Virtual/physical training center(s)?

  17. What Can Open Access Publishers Do? • Work with NIH on supporting data citation • Experiment with the idea of micropublication • Other?

  18. philip.bourne@nih.gov NIH… Turning Discovery Into Health

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