NIH Data Commons Pilot Phase 2017 Awardees

NIH Data Commons Pilot Phase Consortium (DCPPC)
Along with NIH staff, contracting staff, and external program consultants, the following awardees will form the NIH Data Commons Pilot Phase Consortium (DCPPC). After a kick-off meeting in December, the DCPPC will have 180 days to develop a roadmap for a NIH Data Commons Pilot Phase.

Awards Made Under Research Opportunity Announcement (ROA) RM-17-026
The following awards were made using a special type of funding mechanism called Other Transactions (OT). The OT mechanism gives NIH considerable flexibility in making and managing awards. This will be particularly important for the DCPPC to stay nimble as it approaches the complex task involved in the development of the NIH Data Commons Pilot Phase under the ever-changing conditions of data science and biomedical science.

Project Title Awardees Institution
University of Maryland NIH Data Commons Facilitation Center Crabtree, Jonathan University of Maryland
Felix, Victor University of Maryland
Giglio, Michelle Gwinn University of Maryland
O'Connor, Timothy University of Maryland
Sansone, Susanna-Assunta University of Oxford, Oxford e-Research Centre
Schriml, Lynn University of Maryland
White, Owen University of Maryland
Development and Implementation Plan for Community Supported FAIR Guidelines and Metrics Dumontier, Michel Maastricht University
Ma'ayan, Avi Icahn School of Medicine at Mount Sinai
Klenk, Juergen Deloitte Consulting LLP
Sansone, Susanna-Assunta University of Oxford, Oxford e-Research Centre
Schurer, Stephan University of Miami
Patient-centric information commons under FAIR principals (PIC-FAIR) Avillach, Paul Harvard Medical School
Kohane, Isaac Harvard Medical School
Kreda, David Harvard Medical School
Manrai, Arjun K. Harvard Medical School
Patel, Chirag J. Harvard Medical School
Susan, Churchill Harvard Medical School
Versmee, Laura Harvard Medical School
The Commons Alliance: A partnership to catalyze the creation of an NIH Data Commons Grossman, Robert L. University of Chicago
Haussler, David University of California, Santa Cruz
O’Connor, Brian University of California, Santa Cruz 
Paten, Benedict University of California, Santa Cruz
Philippakis, Anthony Broad Institute
Tang, Yajing (Phillis) University of Chicago
Yung, Christina University of Chicago
A Commons Platform for Promoting Continuous FAIRness Chard, Kyle University of Chicago: Globus
Foster, Ian University of Chicago: Globus
Kesselman, Carl University of Southern California
Madduri, Ravi University of Chicago: Globus
Tools and workflows for mining genomic data on many clouds Brown, C. Titus University of California, Davis
Zaranek, Alexander Wait Curoverse Innovations, Inc.
A collaboration for the NIH Data Commons Ahalt, Stanley Renaissance Computing Institute: RENCI
Boyles, Rebecca RTI International
Castillo, Claris Renaissance Computing Institute: RENCI
Conway, Michael Renaissance Computing Institute: RENCI
Cox, Steven Renaissance Computing Institute: RENCI
Dumontier, Michel Maastricht University
Haendel, Melissa Oregon Health Science University
Mungall, Christopher Lawrence Berkeley National Laboratory
Oprea, Tudor University of New Mexico Health Sciences Center
Peter, Robinson The Jackson Laboratory
FAIR data to drive CURES Davis-Dusenbery, Brandi Seven Bridges Genomics Inc
Nielsen, Fiona Repositive
Pyarajan, Saiju US Department of Veterans Affairs
de Waard, Anita Elsevier
CALIFORNIA: Cloud-agnostic Architecture to Locate Indexed FAIR objects and safely reuse them in new integrated analyses Ohno-Machado, Lucila

University of California, San Diego

Jiang, Xiaoqian University of California, San Diego
Sansone, Susanna-Assunta University of Oxford, Oxford e-Research Centre
Xu, Hua University of Texas Health Science Center at Houston

Key Capabilities
As part of the DCPPC, the awardees will design innovative solutions that meet the needs of the computational, data, and scientific key capabilities of the NIH Data Commons Pilot Phase. These key capabilities include:

  1. Guidelines and metrics for making data Findable, Accessible, Interoperable, and Reusable (FAIR)
  2. An approach to Global Unique Identifiers (GUIDs)
  3. Application Program Interfaces (APIs) based on open standards 
  4. Architecture independent of a specific cloud platform or provider
  5. Workspaces to find and interact with data and associated tools
  6. Research ethics, privacy, and security (including authentication and authorization)
  7. Indexing and search functionality
  8. Use cases that demonstrate how the NIH Data Commons Pilot could advance biomedical research
  9. Coordination, training, and outreach

DCPPC Test Case Data Sets
The stewards for each of the following data sets received supplemental funding to existing grants to participate as members of the DCPPC. These data sets will serve as test cases to develop the capabilities of the NIH Data Commons Pilot Phase.

Data Set Principal Investigator(s) Institution(s)
Genotype Tissue Expression (GTEx)

Ardlie, Kristin

Broad Institute Inc.
Getz, Gad Broad Institute Inc.
Alliance of Genome Resources (AGR) Michael Cherry     Stanford University
Trans-Omics for Precision Medicine (TOPMed) Abecasis, Goncalo University of Michigan
Blackwell, Tom University of Michigan
Borecki, Ingrid University of Washington
Laurie, Catherine University of Washington
Psaty, Bruce University of Washington
Weir, Bruce University of Washington

Learn more about each of the Test Case Data Sets

In addition to the 12 awards issued to support the NIH Data Commons Pilot Phase, The MITRE Corporation, operator of the HHS federally funded research and development center, will provide a broad range of support services for the NIH Data Commons Pilot Phase including the following:

  • Management and business best practices in cloud-based computing and storage
  • Assessment services to analyze usage, costs, business models, and long-term data viability
  • Communications, coordination, training, and other logistical support 

This contract is administered by the Center for Information Technology, and is a trans-NIH initiative funded by multiple Institutes, Centers, and Offices. 

This page last reviewed on November 7, 2017