By 2025, hundreds of trillions of biomedical digital objects will exist, representing exabytes of multiomics data. 1 Full application of this immense volume of data has the potential to improve health through the development and application of novel approaches and therapies. However, fulfilling this promise requires a) highly scalable computational infrastructure, b) interoperable standards for the integration and analysis of diverse data types and c) workspaces with secure and controlled access protocols. To advance this vision, we have formed a team, referred to as ?FAIR4CURES?, comprised of a complementary group of experienced researchers, software experts, and program directors from government ( Department of Veterans Affairs/Boston VA Research Institute ) as well as small ( Repositive, Seven Bridges ) and large ( Elsevier ) commercial organizations. FAIR4CURES represents five properties we believe are most critical to support development of any successful data analysis ecosystem: C ollaborative, Us able, Re producible, Ex tendable, and Sc alable (CURES), that together enable seamless multidisciplinary and multi-institutional science. By leveraging our existing technologies, methodologies, and expertise, the FAIR4CURES team will rapidly deliver not just a minimum viable product, but also a minimum loveable product as the foundation for NIH?s vision of a Data Commons. Our team has demonstrated expertise and success in building open source and community-endorsed standards, technologies, frameworks, and software for biomedical research. We will reuse resources such as: a) the collaborative workspaces and scalable cloud computing from the Seven Bridges Platform; b) the community-endorsed metrics, data indexing and searching capabilities of the Repositive Platform; c) the long term resolution of cited persistent data objects of the Mendeley Data Platform (Elsevier); all with the d) data privacy, security and compliance expertise from the Department of Veterans Affairs. Our approach will place community and user engagement at its core . A major output of this work will be processes for building, engaging and reusing community-endorsed initiatives and standards via a FAIR Council . This council, composed of diverse stakeholders, including DCPPC members, funders, infrastructure providers, patient advocates, representatives of relevant international communities, standards and technologies ( Figure 1 ), will ensure independent guardianship of community-endorsed guidelines, standards and best practices across all facets of the Data Commons. As the community?s needs evolve, supported and vetted by the FAIR Council, our team will apply well-practiced agile methods to rapidly address these needs. FAIR4CURES members have ongoing relationships and collaborations with leading researchers and tool developers across the world (including other applicants to the DCPPC, see external letter of support ). These relationships will further enhance effective participation in the DCPPC. Therefore, our minimum viable product will not only be technically advanced; our community-driven, collaborative, open access and open source culture will solidly reflect our commitment to NIH?s ambitious goal for a new generation of precise FAIR for CURES.
