Data Science Corps

Overview

This solicitation has a primary focus on broadening participation in data science for undergraduates (including students in community college, Minority-Serving Institutions, other emerging research institutions, and institutions in EPSCoR jurisdictions), and teachers and students in grades 6 -12. Equitable access to data science education presents an opportunity to open doors to higher education, higher-paying careers, and support a more engaged citizenry. The DSC solicitation prompts the PI community to envision and implement diverse and creative mechanisms by which to provide all students with age and developmentally appropriate data science training to gain the expertise needed for understanding and interpreting data. The DSC funded projects should contribute to research and practice that supports data science literacy and practices, as well as creating and enhancing the theoretical and empirical foundations for effecting data science learning.

Proposals responsive to this solicitation respond to and implement one or more of the four following mechanisms: 

I.    Learning in the Community: Effective data science education and training happens in the community. PIs are encouraged to engage students with stakeholder communities, so that students can obtain immersive educational and training experiences via hands-on training on real-world problems and data generated by and of importance to communities at all levels, thus expanding the supply of data science talent in support of local, regional, and national economies and society at large. 

II.    Flexible Educational Pathways: Flexible educational pathways with multiple points of entry can be effective mechanisms to integrate and provide data science education and training to students with varied educational backgrounds and experiences, skill level, and technical maturity. Effective pathways provide students with data science expertise in a tiered manner in support of building a diverse workforce trained in data management, data analytics, and data-driven decision-making. 

III.    Across the Data Life Cycle: Foundational data science education and training needs to expose students to a variety of disciplinary approaches that track the full data life cycle, from data collection, processing, storage, to data management, analytics, and decision-making. PIs are encouraged to address the inherent interdisciplinarity of data science and bring multiple perspectives, including but not limited to computer science, statistics, mathematics, and information technology. 

IV.    Data Science in STEM: Today's very large datasets and modern data science tools are revolutionizing scientific inquiry knowledge generation, and advancement across the scientific disciplines. NSF welcomes proposals that provide data science training to students pursuing their primary studies in diverse scientific and engineering disciplines and so drive data-centric inquiry and innovation in the sciences. Competitive proposals additionally establish the central role of ethics in data science training and are expected to instill and cultivate ethics across the proposed student experiences. Meaningful student experiences include exposure to FAIR (Findability, Accessibility, Interoperability, and Reuse of digital assets: https://www.go-fair.org/fair-principles/ ) and CARE principles (Collective Benefit, Authority to Control, Responsibility, and Ethics: https://www.gida-global.org/care ), in alignment with goals for the 2023 Federal Year of Open Science (https://www.usgs.gov/special-topics/year-of-openscience/news/white-house-office-science-technology-policy-open-science). To support the development of a diverse STEM workforce, prospective PIs are encouraged to expand the participation of diverse students in the Data Science Corps program and broaden opportunities to student groups, Institutions of Higher Education, and geographic regions that are not yet fully represented in STEM disciplines. Projects responding to this solicitation should also support diversity among participating units and bring together diverse teams that engage in a tight and meaningful collaboration activity within or among educational institutions. These efforts should be described in the Management and Coordination Plan to be submitted as a Supplementary Document.

An individual may participate in only one proposal as Principal Investigator, co-Principal Investigator, or Senior Personnel in any project. This eligibility constraint will be strictly enforced to treat everyone fairly and consistently. If an individual exceeds this limit, any proposal submitted after the first proposal is received at NSF will be returned without review. No exceptions will be made.

Up to $10,000,000 is expected to be available, subject to availability of funds. Awards will typically be in the range of $800,000 to $1,200,000 for a duration of three years.

Co-Funding Opportunities: The Eric and Wendy Schmidt Fund for Strategic Innovation has committed to providing an unrestricted donation to the NSF for the purpose of funding data science learning opportunities for students in grades 6 through 12. NSF and Schmidt Futures will provide funds toward awards in this age range. Schmidt representatives will have access to all the proposals targeting this age range, be invited to sit in on the NSF review panel's discussion of those proposals, and be able to discuss the reviews with the NSF Data Science Corps Program Directors.