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[Adapted from George Komatsoulis]
HPC, Institution …](https://image.slidesharecdn.com/yale092514-140925213232-phpapp02/85/Yale-Day-of-Data-21-320.jpg)
Uploaded byPhilip Bourne
Yale Day of Data
The document summarizes NIH's approach to data science and the ADDS mission. It discusses establishing a data ecosystem through community, policy, and infrastructure. The goals are to foster sustainability, efficiency, collaboration, reproducibility, and accessibility. NIH plans to seed the ecosystem through existing resources and funding. Example initiatives include establishing a data commons, standards, and training programs to develop a diverse data science workforce. The overall aim is to support a "digital enterprise" that enhances biomedical research and health outcomes.
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Yale Day of Data
- 1. NIH’s Day, Months,Four Years of Data Philip E. Bourne Ph.D. Associate Director for Data Science National Institutes of Health
- 2. What is NIH’sOverall Approach to Data and What Does It Mean to You?
- 3. Some Context: NIHData Science History 6/12 2/14 3/14 • 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
- 4. My Bias Still a scientist A funder who still thinks like a PI Not yet attuned to the federal system Big supporter of open science through prior work with the Public Library of Science, FORCE11 etc.
- 5. Data – AFew 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 That is, is no business model currently for scientific data
- 6. Data – AFew NIH Observations … 1. We have little idea how much we spend on data – estimated over $1bn per year 2. 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
- 7. Data – AnAcademic Medical Center Observation A digital enterprise exists when data connections are made across different areas of the organization such that productivity and competitiveness are improved For example, between education and research I am not aware that any such academic institutions exist? Many are starting to wake up to the idea of getting there JAMIA 2014, 21(2), 194
- 8. 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
- 9. What Problems AreWe Trying to Solve? One Possible Solution Sustainability – 50% business model Efficiency – sharing best practices in longitudinal clinical studies; “trusted investigator” 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
- 10. The Data Ecosystem Community Policy Infrastructure • Sustainable business model • Collaboration • Training
- 11. The Data Ecosystem Community Policy Infrastructure • Sustainable business model • Collaboration • Training Virtuous Research Cycle
- 12. The Virtuous Cycle http://goo.gl/fkWjhS
- 13. Raw Materials toSeed 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 – ....
- 14. Organization to Seedthe Ecosystem…
- 15. Associate Director forData Science Scientific Data Council External Advisory Board Programmatic Theme Sustainability Education Innovation Process Deliverable Commons Training BD2K Efficiency Example Features • IC’s • Cloud – Data & Compute • Search • Security • Reproducibility Standards • App Store • Coordinate • Hands-on • Syllabus • MOOCs • Community • Centers • Training Grants • Catalogs • Standards • Analysis • Data Resource Support • Metrics • Best Practices • Evaluation • Portfolio Analysis Collaboration Partnerships • Researchers • Federal Agencies • International Partners • Computer Scientists The Biomedical Research Digital Enterprise
- 16. Associate Director forData Science Scientific Data Council External Advisory Board Programmatic Theme Sustainability Education Innovation Process Deliverable Commons Training BD2K Efficiency Example Features • IC’s • Cloud – Data & Compute • Search • Security • Reproducibility Standards • App Store • Coordinate • Hands-on • Syllabus • MOOCs • Community • Centers • Training Grants • Catalogs • Standards • Analysis • Data Resource Support • Metrics • Best Practices • Evaluation • Portfolio Analysis Collaboration Partnerships • Researchers • Federal Agencies • International Partners • Computer Scientists The Biomedical Research Digital Enterprise
- 17. Example Communities –NIH • 27 ICs – Agencies • NSF • DOE • DARPA • NIST – Government • OSTP • HHS HDI • ONC • CDC • FDA – Private sector • Phrma • Google • Amazon – Organizations • PCORI, GA4GH • RDA, ELIXIR • CCC • CATS • FASEB, ISCB • Biophysical Society • Sloan Foundation • Moore Foundation
- 18. Example Policies –Clinical data harmonization – DbGaP in the cloud – 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
- 19. Example Infrastructure: TheCommons Data The Long Tail Core Facilities/HS Centers Clinical /Patient The Why: Data Sharing Plans The How: NIH Knowledge The Government Commons Data Discovery Index The End Game: Scientific Discovery Usability Quality Security/ Privacy Sustainable Storage Awardees Private Sector Metrics/ Standards Rest of Academia Software Standards Index BD2K Centers Cloud, Research Objects, Business Models
- 20. What Does theCommons 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
- 21. One Possible CommonsBusiness Model [Adapted from George Komatsoulis] HPC, Institution …
- 22. Pilots Around AVirtuous Cycle Expect a FY15 Funding Call to Work in the Commons
- 23. TTrraaiinniinngg && DDiivveerrssiittyy 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)?
- 24. Closing Question Callsfor increased NIH funding has so far gone unheeded, what can the ADDS do (that you have not heard about) to improve data science activities?
- 25. NNIIHH…… philip.bourne@nih.gov TTuurrnniinnggDDiissccoovveerryy IInnttoo HHeeaalltthh
Editor's Notes
- #18 Computing Community Consortium Committee on Applied and Theoretical Statistics Office of the National Coordinator