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