- Current scientific research remains siloed, limiting researchers’ ability to discover relevant work, reproduce experiments, and jointly analyze experimental datasets across laboratories and disciplines.
- AI-enabled community scientific discovery requires broader access to relevant, high-quality experimental data, including hidden raw data accompanied by metadata on generation, measurement, and structure.
- The report states that the United States lacks sufficient data-sharing infrastructure, including resources, talent integration capacity, management tools, standardization frameworks, and policies for data uptake, integration, and analysis.
- It recommends a government-managed inventory of scientific datasets, overseen through Commerce, NIST, and the Federal Chief Data Officer Council, with metadata and incentives for researcher participation.
- It recommends dedicated scientific data-sharing public-private partnerships with privacy, security, access, competition, and IP-related controls tailored to priority domains and specific scientific challenges.
Data’s Role in Unlocking Scientific Potential examines how access to high-quality data affects U.S. scientific research and AI-enabled discovery. Building on Dr. Tom Mitchell’s concept of “community scientific discovery” and SCSP’s National Data Action Plan, the paper argues that fragmented research practices, unavailable experimental data, weak incentives, and inadequate data-sharing infrastructure hinder scientific progress. It contrasts U.S. advantages—large public and private data assets, technology firms, universities, data centers, and data production—with China’s strategic policies to collect foreign data, regulate domestic data, incentivize sharing, and explore data as a commodity. The paper recommends two main federal actions: creating comprehensive inventories of scientific datasets and establishing scientific data-sharing public-private partnerships across priority domains. It then outlines infrastructure needed for those partnerships, including interoperability standards, increased access to government and national laboratory datasets, privacy-enhancing technologies, exploration of data IP protections, and selective use of synthetic data. The policy relevance is framed around preserving U.S. scientific and technological leadership by converting siloed datasets into shared resources for faster hypothesis generation, experimentation, validation, and deployment.
Without a strong commitment to leveraging its data assets, the United States risks falling behind in the race for technological and scientific leadership.
SCSP
- Create comprehensive data inventories across scientific domains. Establish a government-managed inventory where organizations catalog information about their datasets, including key metadata.
- Create scientific data sharing public-private partnerships. Establish dedicated repositories for scientific and experimental data across various scientific disciplines.
- Develop clear data standards for interoperability. Establish clear data standards tailored to the partnership’s specific use cases to promote rapid and open data exchange.
- Increase access to U.S. Government and national laboratories’ data. Increase scientists’ access to existing and proposed government programs to make high-quality resources more widely available.
Published by the Special Competitive Studies Project. Co-publishers and external authors, where they exist, are credited in the byline above.