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DataScience@NIH: Pivoting to the Future
Michael F. Huerta, Ph.D.Coordinator of Data Science & Open Science
Associate Director for Program Development, NLM, NIH
Food and Drug Administration BioCompute – March 16, 2017
Biomedical Science
NLM’s Science & Approach
Biomedical Science
LiteratureData
Biomedical Science
LiteratureData Software
Reagents Workfllows
Biomedical Science
Information Science
Biomedical Science
Curate
Information Science
Biomedical Science
Acquisition ClassificationMetadata
Selection Preservation
Information Science
Biomedical Science
Information Science
Informatics
Biomedical Science
Compute in context
Information Science
Informatics
Biomedical Science
Computation
Software tools
Ontologies
Algorithms
Information Science
Informatics
Biomedical Science
Data Science
Information Science
Informatics
Biomedical Science
Extract insight from data
Data Science
Information Science
Informatics
Biomedical Science
Statistics
Visualization
Probabilistics Artificial
intelligence
Data Science
Information Science
Informatics
Biomedical Science
Data Science
Information Science
Open Science Informatics
Biomedical Science
Open Science
Findable Accessible
Re-usable Interoperable
Open Science
Findable Accessible
Re-usable Interoperable
Attributable
Sustainable
Unified Medical Language System
BioProject
Data Science at NIH
Data Science at NIHNIH Designates NLM as the Lead
Data Science at NIHNIH Designates NLM as the Lead
n Data Science - NLM should be the intellectual and programmatic epicenter for data science at NIH and stimulate its advancement throughout biomedical research and application.
Data Science at NIHNIH Designates NLM as the Lead
n Data Science - NLM should be the intellectual and programmatic epicenter for data science at NIH and stimulate its advancement throughout biomedical research and application.
n Open Science - NLM should lead efforts to support and catalyze open science, data sharing, and research reproducibility, striving to promote the concept that biomedical information and its transparent analysis are public goods.
NIH Support for Informatics & Data ScienceResearch, Tools & Workforce Development
NIH Support for Informatics & Data ScienceResearch, Tools & Workforce Development
n US Human Brain Project à Neuroinformaticsn Biomedical Informatics Research Network (BIRN)
NIH Support for Informatics & Data ScienceResearch, Tools & Workforce Development
n US Human Brain Projectn Biomedical Informatics Research Network (BIRN)n Biomedical Informatics Science & Tech Initiativen NIH Blueprint for Neurosciencen Big Data to Knowledge Initiativen The BRAIN Initiative
NIH Support for Informatics & Data ScienceResearch, Tools & Workforce Development
n US Human Brain Projectn Biomedical Informatics Research Network (BIRN)n Biomedical Informatics Science & Tech Initiativen NIH Blueprint for Neurosciencen Big Data to Knowledge Initiativen The BRAIN Initiativen Many other IC-specific initiatives & programs
NIH Support for Data-Centric & Open ScienceLarge Scale Data
NIH Support for Data-Centric & Open ScienceLarge Scale Data
TOPMed
The Human Connectome
Project
TOPMed
The Human Connectome
Project
DataScience@NIHBuild on NIH-Wide Opportunities
DataScience@NIHBuild on NIH-Wide Opportunities
n Findable – PubMedu Finding literatureu Finding data via PubMed Central data deposit (10/17)
DataScience@NIHBuild on NIH-Wide Opportunities
n Findable – PubMedu Finding literatureu Finding data via PubMed Central data deposit (10/17)
n Accessible - Holdren Memo to increase accessu NIH plan for publications – PubMed Centralu NIH plan for data – Peer reviewed DMP for all researchu Many repositories open for data deposit and withdrawal
DataScience@NIHBuild on NIH-Wide Opportunities
n Findable – PubMedu Finding literatureu Finding data via PubMed Central data deposit (10/17)
n Accessible - Holdren Memo to increase accessu NIH plan for publications – PubMed Centralu NIH plan for data – Peer reviewed DMP for all researchu Many repositories open for data deposit and withdrawal
n Interoperable - Standardsu NLM – UMLS, SNOMED-CT, LOINC, RxNorm, etc.u Repository & Initiative-related standards across NIHu NIH Clinical Common Data Element Task Force
DataScience@NIHBuild on NIH-Wide Opportunities
n Findable – PubMedu Finding literatureu Finding data via PubMed Central data deposit (10/17)
n Accessible - Holdren Memo to increase accessu NIH plan for publications – PubMed Centralu NIH plan for data – Peer reviewed DMP for all researchu Many repositories open for data deposit and withdrawal
n Interoperable - Standardsu NLM – UMLS, SNOMED-CT, LOINC, RxNorm, etc.u Repository & Initiative-related standards across NIHu NIH Clinical Common Data Element Task Force
n Big Data to Knowledge Initiative u Data science research & toolsu Commons & cloud pilotsu Workforce developmentu Open science prize
DataScience@NIH: Pivot to the Future
DataScience@NIH: Pivot to the FutureStrategic Engagement Across & Beyond NIH
DataScience@NIH: Pivot to the FutureStrategic Engagement Across & Beyond NIH
n Sustainability solutions – urgent to address
Sustainable
Sustainable
Sustainable
More expensive are the opportunity costsof not addressing sustainability
Sustainable
Perhaps we can even bend the cost-curve
DataScience@NIH: Pivot to the FutureStrategic Engagement Across & Beyond NIH
n Sustainability solutions
DataScience@NIH: Pivot to the FutureStrategic Engagement Across & Beyond NIH
n Sustainability solutions u Enterprise-wide approaches (balance w IC needs)
t Solve common problems oncet Converge on data-related standardst Lessons learned & best practices
DataScience@NIH: Pivot to the FutureStrategic Engagement Across & Beyond NIH
n Sustainability solutions u Enterprise-wide approaches (balance w IC needs)
t Solve common problems oncet Converge on data-related standardst Lessons learned & best practices
u Value assessment & criteria for investment in policy changes, infrastructure, data acquisition, preservation, etc.
t Cost vs benefitt Develop and use evidence base & models
• Econometric & other studies• Innovate in curation (e.g., RFA-LM-17-001)
DataScience@NIH: Pivot to the FutureStrategic Engagement Across & Beyond NIH
n Sustainability solutions u Enterprise-wide approaches (balance w IC needs)
t Solve common problems oncet Converge on data-related standardst Lessons learned & best practices
u Value assessment & criteria for investment in policy changes, infrastructure, data acquisition, preservation, etc.
t Cost vs benefitt Develop and use evidence base & models
• Econometric & other studies• Innovate in curation (e.g., RFA-LM-17-001)
u Range of storage optionst Minimal function repository à full guided services
DataScience@NIH: Pivot to the FutureStrategic Engagement Across & Beyond NIH
DataScience@NIH: Pivot to the FutureStrategic Engagement Across & Beyond NIH
n Engage and partner across sectors & around the world
DataScience@NIH: Pivot to the FutureStrategic Engagement Across & Beyond NIH
n Engage and partner across sectors & around the worldn Grow a talented workforce intra- & extramural
u Data science expertsu Train across bio, info, & data scienceu NIH staff – research, technical, program, review & policy
DataScience@NIH: Pivot to the FutureStrategic Engagement Across & Beyond NIH
n Engage and partner across sectors & around the worldn Grow a talented workforce intra- & extramural
u Data science expertsu Train across bio, info, & data scienceu NIH staff – research, technical, program, review & policy
n Promote open science & citizen scienceu Evidence-based changes in policies & practicesu Strategic incentive structure across full research enterpriseu Empower research participants, patients, & citizens
DataScience@NIH: Pivot to the FutureStrategic Engagement Across & Beyond NIH
n Engage and partner across sectors & around the worldn Grow a talented workforce intra- & extramural
u Data science expertsu Train across bio, info, & data scienceu NIH staff – research, technical, program, review & policy
n Promote open science & citizen scienceu Evidence-based changes in policies & practicesu Strategic incentive structure across full research enterpriseu Empower research participants, patients, & citizens
n Continue research & innovation in data science u Analytics, statistics, artificial intelligenceu Innovations in storage and accessu Use of provenance, PUIDs, & block chain to form imputed generate
metadata in a more machine-driven & adaptive ecosystem