Belinda Seto, Ph.D.Deputy Director

National Institute of Biomedical Imaging and Bioengineering

Belinda Seto, Ph.D.Deputy Director

National Institute of Biomedical Imaging and Bioengineering

NIH and Biomedical ‘Big Data’

Myriad Data Types

Other ‘Omic

Imaging Phenotypic

Clinical

Genomic

Exposure

Data and Informatics Working Group

acd.od.nih.gov/diwg.htm

At a pivotal point: Risk failing to capitalize on technology

advances Bordering on “institutional malpractice”

Cultural changes at NIH are essential

Aim to develop new opportunities for: Data sharing

Data analysis Data integration

Long-term NIH commitment is required

Overarching Themes

NIH is Tackling the ‘Big Data’ Problem

1. New NIH Leadership Position:

Associate Director for Data Science (ADDS)

2. New Internal NIH Governing/Oversight Body:

Scientific Data Council (SDC)

3. New Trans-NIH Initiative:

Big Data to Knowledge (BD2K)

What’s in a Name?

Computational Biology

Big Data

Information Science

Bioinformatics

Biomedical Informatics

Quantitative Biology

Data Science

Biostatistics

  NIH Data Science ‘Programmatic Czar’

(aka, Point Person, Strategic Leader, etc.)

 

Reports to NIH Director

Eric Green, Acting 

Search underway (Eric Green & Jim Anderson, Co-Chairs of Search Committee)

Associate Director for Data Science: Overview

Principal advisor to NIH Director and NIH leadership

Provides vision and leadership in data science

Chair, Scientific Data Council (and thus chief steward of Scientific Data Council responsibilities)

Program lead for Big Data to Knowledge (BD2K)

Coordinates data science activities, both within and outside of NIH

Leads long-term NIH strategic planning in data science

NIH leader responsible for promoting trans-NIH, national, and global policies for data sharing

Coordination with NIH Chief Information Officer

  

Associate Director for Data Science: Responsibilities

  High-level internal NIH group 

Chaired by Associate Director for Data Science

Reports to NIH Steering Committee

Trans-NIH representation

 

Scientific Data Council: Overview

Acting Chair: Eric Green (Acting ADDS & NHGRI) 

Members:  James Anderson (DPCPSI)Sally Rockey (OER)Michael Gottesman (OIR)Kathy Hudson (OD)Andrea Norris (CIT)Judith Greenberg (NIGMS)Betsy Humphreys (NLM)Douglas Lowy (NCI)John J. McGowan (NIAID)Alan Koretsky (NINDS)Michael Lauer (NHLBI)Belinda Seto (NIBIB)

 

Acting Executive Secretary: Allison Mandich (NHGRI)

 

Scientific Data Council: Membership

 

Trans-NIH programmatic leadership and coordination of data science activities

Oversight of BD2K

Trans-NIH intellectual and programmatic ‘Hub’ for data science (coordination and convening functions)

Coordination with data science activities beyond NIH (e.g., other government agencies, other funding agencies, and private sector)

Long-term NIH strategic planning in data science

Major role in data sharing policy development and oversight

Coordination with ‘parallel’ Administrative Data Council

Scientific Data Council: Responsibilities

Big Data to Knowledge (BD2K): Overview

 

Major trans-NIH initiative addressing an NIH imperative and key roadblock

Aims to be catalytic and synergistic Overarching goal:

By the end of this decade, enable a quantum leap in the ability of the biomedical research enterprise to maximize the value of the growing volume and complexity of biomedical data

 

http://bd2k.nih.gov

 I. Facilitating Broad Use of Biomedical

Big Data

II. Developing and Disseminating Analysis Methods and Software for

Biomedical Big Data

III. Enhancing Training for Biomedical Big Data

IV. Establishing Centers of Excellence for Biomedical Big Data

BD2K: Four Programmatic Areas

 IA. Facilitating Broad Use of Biomedical Big Big

Data -- Data Catalog

• RFI responses received – June 25• 62 responses received

• Data Catalog Workshop held Aug 21, 22• Fran Berman, chair• Jenny Larkin (NHLBI), Ron Margolis (NIDDK),

co-organizers

 

BD2K: Four Programmatic Areas

 IB. Facilitating Broad Use of Biomedical Big Big

Data – Data/Metadata Standards

• Frameworks for Community-based Standards Efforts Workshop• September 25,26• Susanna Santone & David Kennedy, co-chairs• Mike Huerta (NLM), Leslie Derr (OD) co-org

BD2K: Four Programmatic Areas

 IC. Facilitating Broad Use of Biomedical Big Data -Enabling research use of clinical data

• Workshop September 11, 12• Robert Cardiff & Dan Masys, co-chairs• Leslie Derr (OD), Jerry Sheehan (NLM) co-org• Webcast w/ real-time, online discussion forum

• To identify actionable steps that NIH can take to accelerate the use of clinical data in research

• Near and long-term needs for research, infrastructure, standards and policies

• Organizers are collecting information about relevant initiatives

 

BD2K: Four Programmatic Areas

 II. Developing and Disseminating Analysis Methods and Software for Biomedical Big Data

• FOAs for BD2K-specific software needs in FY15• RFI issued August 8, responses due Sept 6• 4 topic areas: data visualization,

compression/reduction, provenance, wrangling

• Software Catalogue Workshop: • Feb 18-19, 2014• Chairs: Asif Dhar and Owen White

BD2K: Four Programmatic Areas

 II. Developing and Disseminating Analysis Methods and Software for Biomedical Big Data

• Updated broad-based software development FOAs (“BISTI”), notice of intent to publish

• Cloud computing: • joint BD2K-Infrastructure Plus working

group initiated• on-going discussion with NCI, joint survey

results being written up • on-going discussion with commercial

providers.

 

BD2K: Four Programmatic Areas

 II. Developing and Disseminating Analysis Methods and Software for Biomedical Big Data

• Dynamic Community Engagement: micro-blog and twitter developed for BD2K workshops

 

BD2K: Four Programmatic Areas

 III. Enhancing Training for Biomedical

Big Data

• RFI, >100 responses received• Workshop held July 29, 30• Karen Bandeen-Roche, Zak Kohane, co-chairs• Michelle Dunn (NCI), Bettie Graham (NHGRI),

organizers• Webcast, archived

 

BD2K: Four Programmatic Areas

 III. Enhancing Training for Biomedical

Big Data – Workshop recommendations

• Opportunity for extraction of knowledge from Big Data is often highest at the interface of at least two disciplines; training programs should be designed to work at interfaces

• Training programs should be designed to provide skills to work effectively in Team Science

• Dual mentoring should be encouraged• Flexibility needed to encourage innovation and to take

best advantage of local expertise and talent• Trainees need access to large data sets

 

BD2K: Four Programmatic Areas

 III. Enhancing Training for Biomedical Big Data – Workshop recommendations

• Training in quantitative science and experimental design will be increasingly important to clinical researchers and even clinicians

• Principles of reproducible research must be stressed• There are training needs across the full spectrum of

scientists, in terms of both experience and activities• The jobs that need to be done in effective Big Data

science may not correspond to traditional academic jobs

• A diverse workforce should be a major goal of data science training activities

 

BD2K: Four Programmatic Areas

 IV. Establishing Centers of Excellence for Biomedical Big Data

• Investigator-initiated centers• FOA released July 22• Applications due November 20• Technical Information Webinar Sept 12

• NIH-Initiated centers• LINCS-BD2K Data Coordination and

Integration Center (+ $2.5M from Common Fund)

• Principles being developed

BD2K: Four Programmatic Areas

Nature | News & Views:

Alzheimer's disease: From big data to mechanismVivek Swarup & Daniel H. Geschwind

This work is also exemplary in demonstrating the extraordinary value of publicly available data resources. Published data on human gene expression, Alzheimer's disease GWAS and neuroimaging provide the pillars of Rhinn and collaborators' paper. Integrative analyses of these data by the authors, and previously by others, weaken the view that substantive biological experimentation only takes place at the wet bench, and highlight the value of innovative re-analyses of existing data.

Nature | News & Views:

Alzheimer's disease: From big data to mechanismVivek Swarup & Daniel H. Geschwind

This work is also exemplary in demonstrating the extraordinary value of publicly available data resources. Published data on human gene expression, Alzheimer's disease GWAS and neuroimaging provide the pillars of Rhinn and collaborators' paper. Integrative analyses of these data by the authors, and previously by others,

weaken the view that substantive biological experimentation only takes place at the wet bench, and highlight the value of innovative re-analyses of existing data.

Nature | News & Views:

Alzheimer's disease: From big data to mechanismVivek Swarup & Daniel H. Geschwind

This work is also exemplary in demonstrating the extraordinary value of publicly available data resources. Published data on human gene expression, Alzheimer's disease GWAS and neuroimaging provide the pillars of Rhinn and collaborators' paper. Integrative analyses of these data by the authors, and previously by others,

weaken the view that substantive biological experimentation only takes place at the wet bench, and highlight the value of innovative re-analyses of existing data.

The biomedical research enterprise is undergoing a major ‘phase change’ with respect to Big Data and data science

Trans-NIH problem needing trans-NIH solutions Solutions include multifaceted cultural changes New NIH plans are:

Mission criticalTransformationalTransitional-- en route to longer-term commitment

Closing Thoughts

Questions?