NSF 24-587: Research on the Science and Technology Enterprise

Research Focus

This program supports research that advances understanding of the U.S. science and technology (S&T) enterprise and improves the statistical methods underlying it. The National Center for Science and Engineering Statistics (NCSES) seeks projects that analyze characteristics of scientists and engineers, track S&T workforce demand and supply, measure research and development (R&D) outcomes across sectors and fields, and develop indicators of U.S. competitiveness. Research may use NCSES surveys—including the Survey of Doctorate Recipients (SDR), Survey of Earned Doctorates (SED), Survey of Graduate Students and Postdoctorates in Science and Engineering (GSS), and Higher Education Research and Development Survey (HERD)—or combine NCSES data with administrative records, social media, or other novel data sources. Priority areas include improved analytical techniques for workforce and R&D indicators; new methods for measuring S&T inputs, outputs, and socioeconomic impacts; globalization of science and engineering; survey design and data collection methodologies; privacy protection and data linkage; and innovative dissemination of S&T statistics through interactive visualizations and user-centered reporting.

At a Glance

• Who can apply: U.S. institutions of higher education (including community colleges), nonprofit research organizations, and federally recognized tribal nations; doctoral dissertation improvement awards require dissertation advisor as PI and student as co-PI.
• Funding & project length: $1,500,000 total anticipated; 5–10 awards expected annually; project duration not stated.
• Award mechanism: Standard Grant, Continuing Grant, or Doctoral Dissertation Research Improvement Grant.
• Key dates: Full proposals due January 21, 2025 and June 17, 2025 (then third Tuesday of January and June annually, 5 p.m. local time).
• Best fit for: Researchers in science policy, higher education, workforce studies, survey methodology, and data science using large-scale nationally representative datasets to study S&T personnel, R&D investment, or STEM education outcomes.