Program Snapshot

The New Models of Data Stewardship (NMDS) program supported research efforts to enhance biomedical discovery by piloting new digital data management strategies. These strategies contributed to NIH efforts to develop a modern biomedical data ecosystem as described in the NIH Strategic Plan for Data Science. They also worked to make data for research findable, accessible, interoperable, and reusable (FAIR) in the cloud.

From FY 2017-2018 the Common Fund supported an integrated set of activities to develop and test new strategies for data management through two initiatives:

• The NIH Data Commons Pilot Phase explored new ways to store, access, and share biomedical data and associated tools in the cloud so they were FAIR.
• The Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) Initiative established partnerships with commercial cloud service providers to reduce economic and technological barriers to accessing and computing on large biomedical datasets to accelerate biomedical advances.

The Office of Data Science Strategy (ODSS) was launched in fall 2018 to catalyze new capabilities in biomedical data science and is coordinating implementation of the NIH Strategic Plan for Data Science. ODSS, in close partnership with the NIH Center for Information Technology (CIT), will continue to support and manage the STRIDES Initiative. STRIDES will provide access to cloud storage, compute, and training services for NIH datasets and users. The deliverables from the Data Commons Pilot Phase will inform a broader trans-NIH data ecosystem strategy to be developed by the ODSS.

The outputs of the NMDS program will also be used by the Office of Strategic Coordination to enhance the utility of Common Fund datasets. The Common Fund will support coordination among program researchers and staff to establish a FAIR and cloud-based data ecosystem for Common Fund datasets .

What were the outcomes of the NMDS program?

The NMDS program ended in 2018. It generated several tools to help researchers work in the digital cloud environment; including advances such as:

• Computational tools and grading rubrics to assess the FAIRness of digital research objects
• Digital tools and infrastructures for searching and indexing digital objects and data sets in the cloud
• A new method for fast, open, and free big data analysis in the cloud environment
• New modes for community engagement, training, outreach, and support across multiple levels of expertise
• Partnerships with Google Cloud and Amazon Web Services via the STRIDES Initiative to develop and test new ways to implement cloud services in support of biomedical research
• A guidebook that captures current best practices in using public cloud service providers for biomedical research
• An Application Programming Interface (API) registry for the definition and discovery of APIs
• Documentation of core metadata required to register data sets in the cloud and assign them each a Globally Unique IDentifier (GUID)
• A service for registering namespaces for Compact Identifiers
• A prototype use case library compiling a collection of user narratives, stories, and science objectives that describe the cross-disciplinary workflow between scientists and engineers
• Guidelines for restricted data access and derived data management • Services to resolve GUIDs (and object contents of GUIDs) and compact identifiers
• Interoperability across multiple robust and sustainable software stacks implementing Commons standards