1. Data commons co-locate large biomedical datasets with cloud computing infrastructure and analysis tools to create shared resources for the research community. 2. The NCI Genomic Data Commons is an example of a data commons that makes over 2.5 petabytes of cancer genomics data available through web portals, APIs, and harmonized analysis pipelines. 3. The Gen3 platform is an open source software stack for building data commons that can interoperate through common APIs and data models to support reproducible, collaborative research across projects.

1. How Data Commons Are Changing the Way That Large
Biomedical Datasets are Analyzed and Shared
Robert L. Grossman
Center for Data Intensive Science
University of Chicago
& Open Commons Consortium
February 12, 2018
Molecular Med Tri Conf

2. 1. What is a Data Commons?

3. NCI Genomic Data Commons*
• The GDC makes over 2.5
PB of cancer genomics
data available to the
research community.
• The data is harmonized
by processing with a
common set of
bioinformatics pipelines.
• Each month, the GDC is
used by over 20,000
users and over 2 PB of
data is downloaded.
• The GDC is based upon
an open source
software stack that can
be used to build other
data commons.*See: NCI Genomic Data Commons: Grossman, Robert L., et al. "Toward a shared vision for cancer
genomic data." New England Journal of Medicine 375.12 (2016): 1109-1112.
The GDC consists of: 1) a data exploration & visualization portal (DAVE), 2) a
data submission portal, 3) a data analysis and harmonization system system, 4)
an API so third party can build applications.

4. A
B
C
D

5. Systems 1 & 2: Data Portals to Explore and Submit Data
See: NCI Genomic Data Commons: Grossman, Robert L., et al. "Toward a shared vision for cancer
genomic data." New England Journal of Medicine 375.12 (2016): 1109-1112.

6. • MuSE
(MD Anderson)
• VarScan2 (Washington
Univ.)
• SomaticSniper
(Washington Univ.)
• MuTect2
(Broad Institute)
Source: Zhenyu Zhang, et. al. and the GDC Project Team, Uniform Genomic Data Analysis in
the NCI Genomic Data Commons, to appear.
System 3: Data Harmonization System To Analyze all of the
Submitted Data with a Common Pipelines

7. System 4: An API to Support User Defined Applications and
Notebooks to Create a Data Ecosystem
https://gdc-api.nci.nih.gov/files/5003adf1-1cfd-467d-8234-0d396422a4ee?fields=state
API URL Endpoint Optional Entity ID Query parameters
• Based upon a (graph-based) data model
• Drives all internally developed applications, e.g. data portal
• Allows third parties to develop their own applications
• Can be used by other commons, by workspaces, by other
systems, by user-developed applications and notebooks
For more about the API, see: Shane Wilson, Michael Fitzsimons, Martin Ferguson, Allison Heath, Mark Jensen, Josh Miller, Mark W. Murphy, James
Porter, Himanso Sahni, Louis Staudt, Yajing Tang, Zhining Wang, Christine Yu, Junjun Zhang, Vincent Ferretti and Robert L. Grossman, Developing
Cancer Informatics Applications and Tools Using the NCI Genomic Data Commons API, Cancer Research, volume 77, number 21, 2017, pages e15-e18.

8. What is a Data Commons?
Data commons co-locate data with cloud computing infrastructure and
commonly used software services, tools & apps for managing, analyzing and
sharing data to create an interoperable resource for the research community.*
*Robert L. Grossman, Allison Heath, Mark Murphy, Maria Patterson and Walt Wells, A Case for Data Commons Towards Data Science as a Service, IEEE
Computing in Science and Engineer, 2016. Source of image: The CDIS, GDC, & OCC data commons infrastructure at a University of Chicago data center.

9. Research ethics
committees (RECs) review
the ethical acceptability of
research involving human
participants. Historically,
the principal emphases of
RECs have been to protect
participants from physical
harms and to provide
assurance as to
participants’ interests and
welfare.*
[The Framework] is
guided by, Article 27 of
the 1948 Universal
Declaration of Human
Rights. Article 27
guarantees the rights
of every individual in
the world "to share in
scientific advancement
and its benefits"
(including to freely
engage in responsible
scientific inquiry)…*
Protect patients
The right of
patients to benefit
from research.
*GA4GH Framework for Responsible Sharing of Genomic and Health-Related Data, see goo.gl/CTavQR
Data sharing with protections provides the evidence
so patients can benefit from advances in research.
Data commons balance protecting patient data with open
research that benefits patients:

10. • Supports big data & data
intensive computing with
cloud computing
• Researchers can analyze data
with collaborative tools
(workspaces) – i. e. data does
not have to be downloaded)
• Data repository
• Researchers
download data.
Databases
Data Clouds
Data Commons
• Supports big data
• Workspaces
• Common data models
• Core data services
• Data & Commons
Governance
• Harmonized data
• Data sharing
• Reproducible research
1982 - present
2010 - 2020
2014 - 2024

11. 2. The Gen3 Data Commons Platform

12. OCC Open Science Data Cloud (2010)
OCC – NASA Project Matsu (2009)
NCI Genomic Data Commons* (2016)
OCC-NOAA Environmental Data
Commons (2016)
OCC BloodPAC (2017)
Bionimbus Protected Data Cloud* (2013)
*Operated under a subcontract from NCI / Leidos Biomedical
to the University of Chicago with support from the OCC.
** CHOP is the lead, with the University of Chicago developing
a Gen3 Data Commons for the project.
Kids First (2017)**
Gen3
Gen2
Gen1
OCC is the Open
Commons Consortium
NCI CRDC (2017)
Brain Commons (2017)

13. cdis.uchicago.edu
• Open source
• Designed to support
project specific data
commons
• Designed to support
an ecosystem of
commons, workspaces,
notebooks &
applications.
• We are starting to
build an open source
Gen3 community.
• Cloud agnostic,
including your own
private cloud.

14. The Gen3 is Based on Widely Used Open Source Tech

15. Gen3 is Cloud Agnostic
• The Gen3 service for Digital IDs (indexd) enables Gen3 managed
digital objects to be on 1, 2, 3 or more private or public clouds.
• Gen3 client applications can then retrieve the digital object from a
local or remote cloud as required.

16. The Gen3 Data Model
is customizable &
extensible. The Gen3 Data
Model extends the GDC
data model.

17. Object-based
storage with access
control lists
Scalable workflows
Community
data
products
Data Commons Framework Services (Digital ID, Metadata, Authentication, Auth.,
etc.) that support multiple data commons.
Apps
Database
services
Data Commons 1
Data Commons 2
Portals for
accessing &
submitting
data
Workspaces
APIs
Data Commons Framework Services
Workspaces
Workspaces
Notebooks
Apps
Apps & Notebooks
Gen3 Framework
Services are designed to
support multiple Gen3
Data Commons

18. Core Gen3 Data Commons Framework Services
• Digital ID services
• Metadata services
• Authentication services
• Authorization services
• Data model driven APIs for submitting, searching & accessing data
• Designed to span multiple data commons
• Designed to support multiple private and commercial clouds
• In the future, we will support portable workspaces

19. NCI Clouds
Resources
Compliant
appsBionimbus,
Cancer Collab.,
etc.
FAIR Principles
Your Data Commons
Other data commons
Commons
Services
Operations
Center
Commons
services
Commons Services
Framework
appapp
app

20. Data Commons (bloodpac.org)
BloodPAC is a public-private
consortium for liquid biopsy data
developed using Gen3 technology by
the Open Commons Consortium
(OCC) containing:
1. Datasets from circulating tumor
cells, circulating tumor DNA, and
exosome assays
2. Relevant clinical data (e.g. clinical
diagnosis, treatment history and
outcomes)
3. Sample preparation and handling
protocols from different studies.

22. • The BRAIN Commons is a commons for PTSD, TBI, major depressive
disorders, and other neurological diseases.
• The images above show a meta-analysis of MRI structural measures
in PTSD by Dr. R. Morey, Duke University performed in the commons.
(www.braincommons.org)

23. • Clinical data
• Imaging data
• Genomic data
• Biospecimen data
• Wearable data
Data types supported by the BRAIN Commons
Linked to CDISC &
other relevant
standards

24. 3. Developing Your Own Data Commons Using
the OCC Data Commons Framework

25. www.occ-data.org
• U.S based 501(c)(3) not-for-profit corporation founded in 2008.
• The OCC manages data commons to support medical and health care
research, including the BloodPAC Data Commons and the BRAIN
Commons.
• The OCC manages data commons and cloud computing infrastructure
to support more general scientific research, including the OCC NOAA
Environmental Data Commons and the Open Science Data Cloud.
• It is international and includes universities, not-for-profits, companies
and government agencies.

26. Sharing Data with Data Commons – the Main Steps
1. Require data sharing. Put data sharing requirements into your
project or consortium agreements.
2. Build a commons. Set up, work with others to set up, or join an
existing data commons, fund it, and develop an operating plan,
governance structure, and a sustainability plan.
3. Populate the commons. Provide resources to your data
generators to get the data into data commons.
4. Interoperate with other commons. Interoperate with other
commons that can accelerate research discoveries.
5. Support commons use. Support the development of third party
apps that can make discoveries over your commons.

27. Open Source Software for
Data Commons
Third party open
source apps
Third party vendor
apps
Sponsor developed apps
Public Clouds
Data
Commons
Governance &
Standards
On Premise Clouds
Commons
Services
Operations
Center
Data managed by the data commons
Sponsor or Co-Sponsors
OCC Data Commons Framework occ-data.org
1
2
3
45

28. Research groups
submit data
Clean and
process the
data following
the standards
Researchers use
the commons for
data analysis
Adapt the data
model to your
project
Example: Building the Data Commons Using
the OCC Data Commons Framework
Set up &
configure the
data commons
(CSOC)
Put in place the
OCC data
governance model
New research
discoveries

29. Researcher /
Working Group
Embargo
Consortium
Embargo
Broad Research
Community
Counts
only
Analyzed,
higher level
data
Raw data
What data?
To whom & when?
Infrastructure as a Service (virtual machines)
Platform as a Service (containers)
Software as a Service (software applications hosted by the commons)
Query Gateway
Counts
Approved
Queries
Approved Tools
& Services
Approved
Infrastructure
What service
model?
Public
Various Data sharing Models Are Supported by Data Commons

30. • It is important to note that with the Gen3 platform, multiple geographically
distributed data commons can interoperate in different ways:
o through data peering
o through a FAIR-based set of APIs for applications
o through scattering queries/analyses and gathering the results
o through a controlled and monitored query/analysis gateway

31. The Commons Alliance: Three Large Scale Data Commons Working
Towards Common APIs to Create to Create de Facto Standards
1. NCI Cloud CRDC
Framework Services / NCI
GDC (UChicago / Broad)
2. NIH All of Us (Broad /
Verily)
3. CZI HCA Data Platform
(UCSC/Broad)
For more information, see: Josh Denny, David Glazer, Robert L. Grossman, Benedict Paten & Anthony Philippakis, A Data
Biosphere for Biomedical Research, https://medium.com/@benedictpaten/a-data-biosphere-for-biomedical-research-
d212bbfae95d. Also available at: https://goo.gl/9CySeo

32. Standards for Genomic & Health Data
www.ga4gh.org

33. Six Options for Building a Data Commons
1. Add your project data to an existing data commons.
2. Join an existing partnership that serves multiple related diseases based
upon the Gen3 Data Commons platform or other platform designed with
similar goals
3. Build your own data commons and peer with an existing partnership of
data commons developed using the Gen3 Data Commons Platform or
other platform designed with similar goals.
4. Build your own data commons that follow standards for interoperating
data commons, such as the Commons Alliance standards.
5. Build your own data commons that follow standards for research data,
such as the FAIR standards.
6. Build your own data commons with a new architecture and new
standards and get others to adapt your standards.

34. 4. Summary and Conclusion

35. Benefits of Data Commons and Data Sharing
1. The data is available to other researchers for discovery, which moves
the research field faster.
2. Data commons support repeatable, reproducible and open research.
3. Some diseases are dependent upon having a critical mass of data to
provide the required statistical power for the scientific evidence (e.g.
to study combinations of rare mutations in cancer)
4. Data commons can interoperate with each other so that over time
data sharing can benefit from a “network effect”
5. With more data, smaller effects can be studied (e.g. to understand
the effect of environmental factors on disease) and machine learning
techniques that require large data can developed.

36. Data Commons
2014 - 2024
Data Clouds
2010 - 2020
Data Ecosystems
2018 - 2028
Databases
1982 - present

37. Summary
1. Data commons co-locate data with cloud computing infrastructure and
commonly used software services, tools & apps for managing,
analyzing and sharing data to create an interoperable resource for the
research community.
2. Data commons provide a platform for open data, open science and
reproducible research that provide the data sharing infrastructure to
support precision medicine.
3. The Gen3 platform is an open source platform that is creating an
ecosystem of data commons, applications and resources.
4. The independent not-for-profit 501(c)(3) Open Commons Consortium
can help you set up your own data commons to support data sharing
for precision medicine.

38. Questions?

39. To get involved:
• Open Commons Consortium to help you build a data commons
o occ-data.org
• Gen3 Data Commons software stack
o cdis.uchicago.edu
• NCI Genomic Data Commons
o gdc.cancer.gov
• BRAIN Commons
o braincommons.org
To learn more about some of the data commons:
• BloodPAC
o bloodpac.org
• OCC-NOAA Environmental Data Commons
o edc.occ-data.org

40. For more information:
• To learn more about data commons: Robert L. Grossman, et. al. A Case for Data Commons: Toward Data Science
as a Service, Computing in Science & Engineering 18.5 (2016): 10-20. Also https://arxiv.org/abs/1604.02608
• To large more about large scale, secure compliant cloud based computing environments for biomedical data, see:
Heath, Allison P., et al. "Bionimbus: a cloud for managing, analyzing and sharing large genomics datasets." Journal
of the American Medical Informatics Association 21.6 (2014): 969-975. This article describes Bionimbus Gen1.
• To learn more about the NCI Genomic Data Commons: Grossman, Robert L., et al. "Toward a shared vision for
cancer genomic data." New England Journal of Medicine 375.12 (2016): 1109-1112. The GDC was developed
using Bionimbus Gen2.
• To learn more about BloodPAC: Grossman, R. L., et al. "Collaborating to compete: Blood Profiling Atlas in Cancer
(BloodPAC) Consortium." Clinical Pharmacology & Therapeutics (2017). BloodPAC was developed using the GDC
Community Edition (CE) aka Bionimbus Gen3
• To learn about the GDC / Gen3 API: Shane Wilson, Michael Fitzsimons, Martin Ferguson, Allison Heath, Mark
Jensen, Josh Miller, Mark W. Murphy, James Porter, Himanso Sahni, Louis Staudt, Yajing Tang, Zhining Wang,
Christine Yu, Junjun Zhang, Vincent Ferretti and Robert L. Grossman, Developing Cancer Informatics Applications
and Tools Using the NCI Genomic Data Commons API, Cancer Research, volume 77, number 21, 2017, pages e15-
e18.
• To learn more about the de facto standards being developed by the Commons Alliance: Josh Denny, David Glazer,
Robert L. Grossman, Benedict Paten, Anthony Philippakis, A Data Biosphere for Biomedical Research,
https://medium.com/@benedictpaten/a-data-biosphere-for-biomedical-research-d212bbfae95d

41. Abstract
Biomedical data has grown too large for most research groups to host
and analyze the data from large projects themselves. Data commons
provide an alternative by co-locating data, storage and computing
resources with commonly used software services, applications and
tools for analyzing, harmonizing and sharing data to create an
interoperable resource for the research community. We give an
overview of data commons and describe some lessons learned from
the NCI Genomic Data Commons, the BloodPAC Data Commons and
the BRAIN Commons. We also give an overview of how an organization
can set up a commons themselves.

42. cdis.uchicago.edu
Robert L. Grossman
rgrossman.com
@BobGrossman
robert.grossman@uchicago.edu
Contact Information
occ-data.org