BD2K and the Commons : ELIXR All Hands

BD2K & the Commons @ NIH
Vivien Bonazzi, Ph.D.
Senior Advisor for Data Science Technologies
Office of Data Science (ADDS)
National Institutes of Health

US Government Memo - Increasing Access to
Results of Federally Funded Scientific Research
In Feb 2013 the US OSTP issued a memo calling for
all US Federal Agencies to make digital assets
from federally funded research available
OSTP - Office of Science Technology Policy at the White House
Public Access to Data
Memohttp://www.whitehouse.gov/sites/default/files/microsites/ostp/
ostp_public_access_memo_2013.pdf

US Government Memo - Increasing Access to
Results of Federally Funded Scientific Research
Each agency’s public access plan shall:
Maximize access, by the general public and without
charge, to digitally formatted scientific data
created with Federal funds while:
i) protecting confidentiality and personal privacy
ii) recognizing proprietary interests, business confidential information, and intellectual
property rights and avoiding significant negative impact on intellectual property
rights, innovation, and U.S. competitiveness, and
iii) preserving the balance between the relative value of long-term preservation and
access and the associated cost and administrative burden.

NIH Response
 In response to the
incredible growth of large
biomedical (digital)
datasets, the Director of
NIH established a special
Data and Informatics
Working Group (DIWG)
http://acd.od.nih.gov/diwg.htm

NIH Response
Establish new data science research and training programs
Fulfilling the recommendation of the ACD WG report
Big Data to Knowledge (BD2K) - 2013
http://datascience.nih.gov/bd2k
Establish a new position:
NIH Associate Director of Data Science
(ADDS)
Phil Bourne – 2014

BD2K – Big Data to Knowledge
 Expanding training programs in data science
 Find and Sharing Data & Software though Indexes
 Targeted Software tools and methods
 Data wrangling
 Privacy security of data
 Data repurposing
 Applications of metadata
 Advance Big methods, tools and applications
 BD2K Centers of Excellence)
https://datascience.nih.gov/bd2k/funded-programs

To enable biomedical research as
a digital enterprise through which
new discoveries are made and
knowledge generated by
maximizing community
engagement and productivity.

NIH ADDS Mission Statement
To use data science
to foster an
Open Digital Ecosystem
that will accelerate
efficient, cost-effective
biomedical research
to enhance health, lengthen
life, and reduce illness and
disability

Enabling digital Ecosystems
via a Commons & BD2K
Leveraging BD2K efforts
Harnessing e-infrastructures
- Public-private partnerships & Interagency collaborations
Collaborating with external communities

Commons : Achieving a Balance
Biomedical Use Cases + Data Science + e-infrastructures
Supporting open biomedical science using robust, scalable
and flexible digital technologies
In collaboration with global communities

What are the PRINCIPLES of a Commons?
 Supports a digital biomedical ecosystem
 Treats products of research – data, software, methods,
papers etc. as digital objects
 Digital objects exist in a shared virtual space
Find, Deposit, Manage, Share and Reuse data,
software, metadata and workflows
 Digital objects need to conform to FAIR principles:
 Findable
 Accessible (and usable)
 Interoperable
 Reusable

Developing a Commons Framework
 Exploits new scalable computing technologies - Cloud
 Making digital objects : FAIR
 Indexable/Findable, Accessible & Usable, Interoperable,
Reproducible
 Simplifies access, sharing and interoperability of digital objects
such as data, software, metadata and workflows
 Provides physical or logical access to digital objects
 Provides understanding and accounting of usage patterns
 Is potentially more cost effective given digital growth
 Gives currency to digital objects and the people who develop and
support them

Commons Framework
Compute Platform: Cloud or SC Facilities
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/workflows
Data
“Reference” Data Sets
User defined data
DigitalObjectCompliance
App store/User Interface
https://datascience.nih.gov/commons

Commons Framework
Compute Platform: Cloud or SC Facilities
Data
User defined data
IaaS
PaaS
SaaS
https://datascience.nih.gov/commons

Commons: Digital Object Compliance
 Attributes of digital research objects in the Commons
Initial Phase
 Unique digital object identifiers of resolvable to original authoritative
source
 Machine readable
 A minimal set of searchable metadata
 Physically available in a cloud based Commons provider
 Clear access rules (especially important for human subjects data)
 An entry (with metadata) in one or more indices
Future Phases
 Standard, community based unique digital object identifiers
 Conform to community approved standard metadata and ontologies for
enhanced searching
 Digital objects accessible via open standard APIs
 Are physically and logical available to the commons

Towards Data Commons’
co-locate data, storage and computing
infrastructure with commonly used tools for
accessing, analyzing, sharing data to create an
open interoperable resource for the research
community.

Current Commons Pilots
 Explore feasibility of the Commons framework
 Provide data objects to populate the Commons
 Facilitate collaboration and interoperability
 Provide access to cloud (IaaS) and PaaS/SaaS via credits
 Connecting credits to NIH Grants
 Making large and/or high value NIH funded data sets and tool
accessible in the cloud
 Developing Data & Software Indexing methods
 Leveraging BD2K efforts bioCADDIE et al
 Collaborating with external groups

Other Commons Activities
 Testing cloud environments to enable access, sharing. use and
reuse of large data sets and accompanying tools
 The Cancer Genome Atlas (TCGA) - NCI
 Human Microbiome Project (HMP) - NIAID
 Providing a portals to view representation and analysis of large
data sets (Genomic Data Commons – NCI)

 Exploring feasibility of the Commons framework using
the BD2K Centers, MODs, and HMP groups
 Facilitating connectivity, interoperability and access to
digital objects
 Providing digital research objects to populate the
Commons
 Enable biomedical science to happen more easily and
robustly
 Connecting biology use cases with data science
Commons Framework Pilots
BD2K Centers, MODs, HMP

BD2K Centers,
MODS and HMP
Compute Platform: Cloud or HPC
Data
User defined data
Mapping to the Commons framework:
PaaS
SaaS

Does your work map to the Commons framework?
Good
Bad
Ugly
How does it enable science?
Using robust computational methods
Enable biomedical use cases

PI Parent grant’s
IC
Project description
TOGA NIBIB • Cloud-hosted data publication system
• Allows the automatic creation and publication of data a personalized data
repository
MUSEN NIAID • Smart APIs – improved handling for metadata within APIs
• Ontological support for metadata within an API
• Improving smart API discoverability: a registry of APIs
HAN NIGMS • Docker container hub for BD2K community
• Docker containers for genomic analysis applications and pipelines
• Benchmark, Evaluation & best practices
COOPER/KOHANE NHGRI • Cloud based authenticated API access and exchange of causal modeling data
, tools + genomic and phenomic data (PICI)
• Docker containers for CCD tools available in AWS
HAUSSLER NHGRI • Secure sharing of germline genetic variations for a targeted panel of breast
cancer susceptibility genes and variations
• (GA4GH) API : being able to query this data and metadata
Ohno-Machado NHLBI • Development of an ecosystem for repeatable science
• easy reuse of data AND software; tracking of provenance.
• Use of container technologies for software and data reuse.
Sternberg NHGRI • Development of a cloud-based literature curation system for specific curation
tasks of the collaborating sites.
• An API to provide programmatic access to the relevant papers in PMC
White NHGRI • The entire HMP1 data set made accessible on AWS
• Analysis tools for microbiome data in AWS
Westerfield NHGRI • Development of a common data model for the MODs
• Development of APIs accessing data across the MODs

 More specifically from a Data Science perspective
 Open standards for APIs and Docker containers
 Docker registry and best practices
 Improved metadata handing in APIs
 Data Object registry and indexing
 Reusing what is currently available
 bioCADDIE, schema.org and schema.org
 Publication
 Preprint server with Links to all digital objects

 Example of a biomedical Use Case:
 Develop a common gene model for all the MODs
 Develop a open well structured, resuable and documented
API that can be used across the MOD data
 Why?
• To be able to query a human gene against all MOD orthologs
• Improved understanding of health and disease states
• Improved understanding of genome structure & organization

The purpose of the Commons Framework is to support
BOTH
Biological use cases + Data Science methods
To allow biological research to happen at scale

The Cloud Credits Model
The Commons
Cloud Provider
A
Cloud Provider
B
Investigator
NIH
Provides credits
HPC Provider
Uses credits in
Commons
Enabling search: Index
Commons Compliance
Commons Conformance

Drivers of the Cloud Credits Model
 Scalability
 Exploiting new computing models
 Potentially Cost Effectiveness
 Simplified sharing of digital objects
Cloud computing supports many of these
objectives

Cloud credits
model (CCM)
Data
User defined data
Mapping pilots to the Commons framework: Cloud
Credits Model:
IaaS
PaaS
SaaS

 Supports simplified data sharing by driving science into publicly
accessible computing environments that still provide for
investigator level access control
 Scalable for the needs of the scientific community for the next 5
years
 Democratize access to data and computational tools
 Cost effective
 Competitive marketplace for biomedical computing services
 Reduces redundancy
 Uses resources efficiently
Advantages of this Model

 Novelty:
Never been tried, so we don’t have data about likelihood of success
 Cost Models:
Assumes stable or declining prices among providers
True for the last several years, but we can’t guarantee that it will continue,
particularly if there is significant consolidation in industry
 Service Providers:
Assumes that providers are willing to make the investment to become
conformant
Market research suggests 3-5 providers within 2-3 months of launch
 Persistence:
 The model is ‘Pay As You Go’ which means if you stop paying it stops
going
 Giving investigators an unprecedented level of control over what lives (or
dies) in the Commons
Potential Disadvantages of this Model

Cloud Commons
Reference Data Sets

Data Sets in a Cloud Commons
 Making High Value and/or High Volume NIH funded data
sets available in a cloud commons
 Co-location of large datasets and compute power enables
access, use, resuse and sharing of data and tools
 Data must adhere to FAIR/Commons compliance principles
 Helps “seed” the Commons with FAIR/Commons compliant data
 Provides an Indexable test data sets for bioCADDIE (and other
indexing efforts)

Data
User defined data
Mapping pilots to the Commons framework :
Large, high value Data Sets
NIH defined data sets

Data Sets in the Cloud Commons
 Preliminary possible data sets
 GTex (Genotype-Tissue Expression)
 LINCS (Library of Integrated network based cellular signatures)
 Model Organism Databases (MODs)
 UniProt
 Neuroimaging Resource (NITRIC)
 Radiology Image Share
 Epigenomics
 GenPort
 The Cancer Genome Atlas Project (TCGA) this data set is currently housed at the
GDC but there ARE plans to move to AWS and Google
 BTRIS Data – NIH Clinical center
 NIAID AIDs Data
 dbGAP
 GEO

Data
User defined data
Community Defined Data Sets
Community
defined data sets

Data Sets in a Cloud Commons: Opportunities
 Ability to share data more easily
 Ability to access and compute on data more easily
 Reduced costs:
 Costs is paid by NIH not the individual PI
 Stops continues uploads of the same data sets
 FAIR/ Commons Compliance of data sets

Data Sets in a Cloud Commons: Challenges
 Supporting sensitive (human) data in commercial clouds
 Updating, versioning, maintaining
 Consents for data
 Can be very strict and only valid across 1 data set
 Analysis across data sets may constrained by consents
 Optimizing for cloud environments: performance
 Incentivizing data (and tool) generators to move and maintain
their data in the cloud
 Data peering across clouds
 Commercial clouds are resistant : cyclinders of excellence
 Peering and Virtualization of services

Making things Findable
Indexing & Search methods

Commons Pilots: Search & Index
 Indexing and Searching digital objects in a Commons
Leveraging indexing methods within BD2K
BioCADDIE,
Others approach within BD2K
Schema.org
Coexisting efforts

BD2K Indexing
e.g. BioCADDIE,
Other, schema.org
Data
User defined data
Indexing & Searching

What is bioCADDIE?
biomedical and healthCAre
Data Discovery Index Ecosystem
 University of California San Diego
 PI Lucila Ohno-Machado
 Development of a prototype of Data Discovery Index (DDI)
 Aims – “Pubmed” for Data
1. Help users find shared data
2. Build a prototype data discovery index
3. Evaluate requirements for next phase

ecosystem components for finding data
Policies
criteria for inclusion,
sustainability
Standards
metadata
data
Identifiers
reuse of existing
ID issuing
services
Metadata
minimal set
guidelines for mapping,
accessibility information,
provenanceSearch engine
connection to other
engines,
repositories, data
sets

Commons Pilots
 Leveraging Schema.org
 Marking up a biomedical resource using schema.org
 Flexible and scalable
 Developing a bioschema.org approach
 Helps drive a community standard for reuse by other
groups
 Harnesses the power of search engines to find digital objects

Thankyou
 ADDS Office
Phil Bourne, Michelle Dunn, Jennie Larkin, Mark Guyer, Sonynka Ngosso
 NCBI: George Komatsoulis
 NHGRI: Valentina di Francesco, Kevin Lee
 CIT: Debbie Sinmao, Andrea Norris, Stacy Charland
 Trans NIH BD2K Executive Committee & Working groups
 NCI: Warren Kibbe, Tony Kerlavage, Lou Staudt, Tanja Davidsen, Ian Fore
 NIAID: Nick Weber, Darrell Hurt, Maria Giovanni, JJ McGowan
 Many biomedical researchers, cloud providers, IT professionals

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