To build on existing federal efforts supporting scientific rigor and integrity, funding agencies should study and pilot new programs to incentivize researchers’ engagement in credibility-enhancing practices that are presently undervalued in the scientific enterprise.
In scientific work in the service of agency missions, the federal government should use and contribute to open source hardware.
To enhance transparency, encourage collaboration, and optimize public-good impacts, funding agencies should allow researchers to make grant proposals publicly available.
Federal agencies should form Data Collaboratives in which staff and members of the public engage in mutual learning about available datasets and their affordances for clarifying policy problems.
The federal government should take action to support preprinting, preprint review, and “no-pay” publishing models in order to make scholarly publishing of federal outputs more rapid, rigorous, and cost-efficient.
To support these teams and allow for timely resolution to security problems, science funders should offer security-focused grant supplements to funded OSI projects.
The EPA should better integrate community data into environmental research and governance by building internal capacity for recognizing and applying such data, facilitating connections between data communities, and addressing misalignments with data standards.
Public trust is the key to unlocking the full potential of the bioeconomy. Without it, the U.S. may fall short of long-term economic goals and biotech leadership.
Despite significant advances in scientific tools and methods, the traditional, labor-intensive model of scientific research in materials discovery has seen little innovation.
We’ve created a living table to track progress on the Bioeconomy EO, enhance accountability, and follow the state of the U.S. bio economy as it evolves.
Read FAS Senior Policy Fellow Jennifer Pahlka’s testimony on Harnessing AI to Improve Government Services and Customer Experience here.
Five federal policy recommendations to maximize opportunity and minimize risk at the intersection of biology and artificial intelligence