Data Infrastructure Building Blocks | Apply Online

Opportunity Summary:

The National Science Foundation (NSF) funding opportunity called Data Infrastructure Building Blocks (DIBBs) is designed to strengthen the shared data foundations that modern science and engineering increasingly depend on. NSF is responding to the reality that research and education are now deeply data-driven: data are not just end products of research, but also fuel new analyses, generate new hypotheses, and enable faster discovery. The central challenge DIBBs targets is how to build practical, scalable, and sustainable ways to store, manage, preserve, access, and reuse an expanding variety of data sets and data streams that differ widely in size, complexity, and format. DIBBs sits within NSF's broader Cyberinfrastructure Framework for 21st Century Science and Engineering (CIF21), which emphasizes coordinated cyberinfrastructure as a key ingredient for innovation across disciplines.

DIBBs specifically aims to fund reusable "building blocks" of data infrastructure that can serve multiple research communities, rather than separate, duplicative systems built in isolation. The program is structured to encourage cross-community solutions to common problems in data stewardship and data use, with an emphasis on reducing wasted effort and improving the chances that resulting tools, standards, and services can be adopted beyond a single domain. NSF's Office of Cyberinfrastructure (OCI) leads the effort in partnership with NSF Directorates and Offices, acknowledging that different fields have very different levels of maturity when it comes to data practices and infrastructure, but still share many underlying needs.

The opportunity is organized into three tracks, each with a distinct purpose and typical project duration. The Conceptualization track supports planning and community-building efforts that help a disciplinary or interdisciplinary group clarify its data storage and data management requirements and move toward an initial prototype concept. NSF is looking for activities that bring stakeholders together to converge on shared problems, refine requirements, and prevent unnecessary reinvention of similar resources. Examples include focused workshops, special sessions at professional meetings, and structured focus groups. These awards are limited to up to one year, and they are expected to produce concrete design specifications for a sustainable data infrastructure that the broader research and education community can discover, search, access, and use.

The Implementation track supports the actual development and deployment of technologies that address parts of the data preservation and access lifecycle. NSF lists lifecycle elements such as data acquisition, documentation, security and integrity, storage, access, analysis and dissemination, migration, and deaccession. A key expectation is that real science and engineering challenges serve as the use cases that guide technology development, so the work is grounded in practical research needs rather than abstract tooling. Implementation awards can run for up to five years, reflecting the larger scope and the effort required to deliver robust infrastructure components.

The Interoperability track focuses on frameworks that make it easier for data systems and communities to work together consistently. This includes developing common approaches for data and metadata formats and content conventions, standardized constructs or protocols, and shared taxonomies or ontologies. NSF emphasizes that interoperability frameworks should be developed through community-based processes so that existing conventions are respected where appropriate, proposed solutions are realistic to implement, and the final outcomes genuinely reflect what communities need. These awards can last up to three years, positioning them between short planning activities and longer implementation efforts.

DIBBs is also tied to priorities in specific NSF Directorates. For example, NSF Geosciences highlights DIBBs as a way to support EarthCube, with an interest in building data infrastructure components that are broadly useful within and beyond the geosciences. The Directorate for Mathematical and Physical Sciences indicates interest in efforts that make disparate data more interoperable, complement existing infrastructure, and improve the availability, accessibility, and usability of heterogeneous data sets. The Directorate for Social, Behavioral and Economic Sciences encourages investigators to use DIBBs to extend work initiated under other CIF21-related efforts, including META-SSS and, together with Education and Human Resources, BCC SBE/EHR. NSF notes that other Directorates and Offices may have their own priorities and points applicants to CIF21 contacts for coordination.

From the administrative side, this is a discretionary NSF program using the cooperative agreement funding instrument. NSF anticipated about 19 awards under this solicitation, with an estimated total funding level of $41.5 million. Individual awards were expected to range from $100,000 to $8,000,000, and there was no cost sharing or matching requirement. Eligibility included US-accredited universities and colleges (including community colleges) and certain non-profit, non-academic organizations such as independent museums, observatories, research laboratories, and professional societies engaged in research or educational activities. A key participation constraint is that a PI or co-PI may submit to only one of the three tracks, although they may serve as senior personnel on more than one proposal.

The solicitation was originally posted May 7, 2012, with proposal deadlines that differed by track: Conceptualization proposals were due July 26, 2012, while Implementation and Interoperability proposals were due August 30, 2012. The opportunity was archived September 30, 2012. Overall, DIBBs can be summarized as NSF's push to help research communities plan, build, and standardize the shared data infrastructure pieces that make large-scale, data-intensive, cross-disciplinary science more efficient, more reproducible, and more broadly usable over time.

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