On 2017-12-07, the NIH Data Commons kicked off its Pilot Phase (stage 1), where a consortium that will collaborate to develop the key capabilities.

The NIH Data Commons will be implemented in a four-year pilot phase to explore the feasibility and best practices for making digital objects available through collaborative platforms. This will be done on public clouds, which are virtual spaces where service providers make resources, such as applications and storage, available over the internet. The goal of the NIH Data Commons Pilot Phase is to accelerate biomedical discoveries by making biomedical research data Findable, Accessible, Interoperable, and Reusable (FAIR) for more researchers.

Quote from press release NIH awards to test ways to store, access, share, and compute on biomedical data in the cloud (2017-11-06), emphasis added.

The NIH Data Commons Pilot Phase Consortium (DCPPC) joins together nine strong teams with expertise in metadata and scalable data management.

University of Chicago’s Ian Foster, Kyle Chard and Ravi Madduri partner with USC’s Carl Kesselman for their proposal Commons Platform for Promoting Continuous FAIRness, building on existing work with Globus data management, Galaxy workflows, MinId identifiers and BDBag Research Objects. Foster leads the Data Commons Indexing and Search work.

UCSD’s Lucila Ohno-Machado joins Oxford’s Susanna-Assunta Sansone with their proposal CALIFORNIA: Cloud-agnostic Architecture to Locate Indexed FAIR Objects and safely Reuse them in New Integrated Analyses. Susana is also behind the ISA Framework and FAIR Sharing. Ohno-Machado leads the Data Commons Research Ethics, Privacy, and Security work.

UCDavis’ Open Science advocate C Titus Brown proposed Tools and Workflows for Mining Genomic Data on Many Clouds and will also organize interoperability workshops. His blog post highlights his take on Data Commons. Brown coordinates the Data Commons Coordination and Training work together with Owen White.

More workflows, as Seven Bridges join forces with Repositive, Elsevier and the Boston Veterans Affair Research Institute to form the FAIR4CURES team with their FAIR data to drive CURES proposal, building on their expertise with Common Workflow Language, Rabix and Mendeley Data Hub.

For more on Data Commons, checkout the Twitter hashtag #CommonsPilot and https://commonfund.nih.gov/bd2k/commons