Vivien Bonazzi Ph.D. Program Director: Computational Biology (NHGRI) Co Chair Software Methods & Systems (BD2K) Biomedical Big Data Initiative (BD2K)

Myriad Data Types Other ‘Omic ImagingPhenotypic Clinical Genomic Exposure

Data and Informatics Working Group acd.od.nih.gov/diwg.htm

What Are the Big Problems to Solve? 1. Locating the data 2. Getting access to the data 3. Extending policies and practices for data sharing 4. Organizing, managing, and processing biomedical Big Data 5. Developing new methods for analyzing biomedical Big Data 6. Training researchers who can use biomedical Big Data effectively

Overarching Strategy and Goals Two initiatives being proposed to overcome roadblocks Big Data to Knowledge (BD2K) – enable the biomedical research enterprise to maximize the value of biomedical data InfrastructurePlus – create an adaptive environment at NIH to sustain world-class biomedical research

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

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

Area 1: Data Sharing & Access A. Policies to Facilitate Data Sharing. B. Data Catalog: Data Discovery, Citation, Links to Literature. C. Frameworks for Community-Based Solutions to Developing Data Standards. D. Enabling Research Use of Clinical Data. Facilitating usage and sharing of biomedical big data  New Policies to Encourage Data & Software Sharing  Index of Research Datasets to Facilitate Data Location & Citation  Community-based Development of Data & Metadata Standards

Area 2: Software and Systems Development A. Grants for software development B. Software Registry: Making biomedical software findable and citable C. Cloud computing: Facilitating Data Analysis D. Dynamic Social Engagement via social media Development of analysis methods and software  Software to Meet Needs of the Biomedical Research Community  Facilitating Data Analysis: Access to Large-scale Computing  Dynamic Community Engagement of Users and Developers

Software Grants Current and emerging needs for using, managing, and analyzing the larger and more complex data sets inherent to biomedical Big Data  Compression/Reduction  Visualization  Provenance  Data Wrangling Area 2: Software and Systems Development

Big Data needs Big Computing Cloud Computing  Leveraging the cloud  Storing and analyzing huge data sets  Collaborative environment  Developing appropriate policies for use of controlled access data in the cloud (dbGaP)  Developing working relationships with major cloud providers  AWS, Google, Microsoft (Azure) HPC  More exploration with Supercomputing facilities Area 2: Software and Systems Development

Area 3: Training Enhancing computational training  Increase Number of Computationally Skilled Trainees  Strengthen the Quantitative Skills of All Researchers  Enhance NIH Review and Program Oversight

Area 4: Centers A. Investigator-initiated Centers B. NIH-specified Centers Establishing centers of excellence  Collaborative environments & technologies  Data integration  Analysis & modeling methods  Computer science & statistical approaches

Big Data to Knowledge (BD2K) bd2k.nih.gov

Biomedical Research as Part of the Digital Enterprise Philip E. Bourne Ph.D. Associate Director for Data Science National Institutes of Health

Myriad Data Types Other ‘Omic ImagingPhenotypic Clinical Genomic Exposure

Myriad Data Types Other ‘Omic ImagingPhenotypic Clinical Genomic Exposure

Components of The Academic Digital Enterprise  Consists of digital assets  E.g. datasets, papers, software, lab notes  Each asset is uniquely identified and has provenance, including access control  E.g. publishing simply involves changing the access control  Digital assets are interoperable across the enterprise

Let’s Break Down the Silos  New policies, regulations e.g. data sharing  Economic drivers  The promise of shared data

The NIH is Starting to Think About the Digital Enterprise Big Data to Knowledge (BD2K) bd2k.nih.gov

This is great, but BD2K is just a start, what will the end product look like?

To get to that end point we have to consider the complete research lifecycle

The Research Life Cycle will Persist IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION

Tools and Resources Will Continue To Be Developed IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Authoring Tools Lab Notebooks Data Capture Software Analysis Tools Visualization Scholarly Communication

Those Elements of the Research Life Cycle will Become More Interconnected Around a Common Framework IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Authoring Tools Lab Notebooks Data Capture Software Analysis Tools Visualization Scholarly Communication

New/Extended Support Structures Will Emerge IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Authoring Tools Lab Notebooks Data Capture Software Analysis Tools Visualization Scholarly Communication Commercial & Public Tools Git-like Resources By Discipline Data Journals Discipline- Based Metadata Standards Community Portals Institutional Repositories New Reward Systems Commercial Repositories Training

Thank You Questions?