New & Noteworthy
Communities in Action: Advancing Society Through AI-Ready Data—How the Federal Statistical System is Accelerating Innovation for a National Secure Data Service
The Federal Statistical System (FSS) and their partners are demonstrating how communities in action—government, industry, academia, and the public—advance society through secure, AI-enabled data innovation. This collaborative model supports priorities in the White House’s America’s AI Action Plan, the Evidence Act, adoption of DCAT-US 3.0 metadata standards, and development of a future National Secure Data Service (NSDS).
Federal agencies increasingly need AI-ready, standardized, and securely accessible data for evidence-based policymaking. The Evidence Act requires agencies to strengthen data governance and inventories. DCAT-US 3.0 enhances metadata quality and interoperability. The AI Action Plan calls for world-class datasets, rapid federal AI adoption, and a secure “front door” for restricted federal data access—core functions of the NSDS. NSDS-initiated projects provide innovative environments where these mandates can be translated into practice. By aligning government needs with private-sector expertise, these projects advance the JSM theme—“Communities in Action: Advancing Society”—and helps build an evidence-driven, AI-enabled federal data ecosystem.
The panel will highlight how four FSS projects operationalize federal AI and data goals through collaboration and innovation. The panelists are vendors and federal technical leads partnering to foster innovation in the public sector through rapid research and development. These projects are generating quick wins using AI technology. The panel will discuss how the public/private partnerships fostered through the solicitation and performance of these projects provide public value.
Improving Data Access
We are excited to announce the release of the final report for the Creation of Synthetic Data and Development and Use of Verification Metrics (Survey of Earned Doctorates) Pilot Project. This comprehensive report explores innovative approaches to expand data access while maintaining privacy protections, using the Survey of Earned Doctorates (SED) data as a case study. The report outlines the methodology and assessments used to balance utility and privacy when creating the synthetic data.
The findings from this project contribute valuable insights to inform the future National Secure Data Service (NSDS) and support evidence-based policymaking, research, and education.
Explore the report to learn more about the methodology, results, and considerations for future data dissemination strategies.
The tools and methodologies developed during the SEDSyn-23 Pilot Project are available in a publicly accessible GitHub repository. Researchers and developers can access the codebase to explore, replicate, and build upon the work. Dive into the repository to review the open-source tools and contribute to advancing synthetic data solutions.
Interested in this project and others like it? Explore our full ADC project page here.
