Executive Order 14303, “Restoring Gold Standard Science,” was signed by President Trump on May 23, 2025, and published in the Federal Register on May 29, 2025. It directs federal agencies to adopt a defined set of criteria for what counts as trustworthy scientific evidence, and requires agencies to make the underlying data, analyses, code, and models behind policy-relevant science publicly available. For research administrators and data stewards, its significance is less in the order’s own text, which binds federal agencies rather than universities or grantees directly, and more in how it is already reshaping agency data-management, transparency, and scientific-integrity policy that flows down into grant terms and reporting requirements. This guide explains what the order actually says, what it requires and from whom, and how it intersects with existing research data management standards such as the NIH’s Rigor and Reproducibility policy and the FAIR principles.
What Executive Order 14303 does
The order responds to what it characterizes as a decline in public confidence in federal science, citing a documented reproducibility crisis in parts of the research community and specific instances the administration frames as politicization of scientific findings by federal agencies. Rather than creating new legislation, it directs the Office of Science and Technology Policy (OSTP) and the heads of federal agencies that fund or produce science to build a common definition of rigorous, trustworthy science into their own policies, guidance, and internal procedures.
The order is binding on federal agencies and their employees. It does not itself impose new legal obligations directly on universities, grantees, or individual researchers, but agency implementation is expected to surface in updated funding-opportunity language, data management plan requirements, scientific integrity policies, and reporting expectations, which is exactly where it becomes operationally relevant for institutional research administration.
The nine “Gold Standard Science” tenets
Section 3 of the order defines “Gold Standard Science” as science that is:
- Reproducible
- Transparent
- Communicative of error and uncertainty
- Collaborative and interdisciplinary
- Skeptical of findings and assumptions
- Structured for falsifiability of hypotheses
- Subject to unbiased peer review
- Accepting of negative results as a valid, publishable outcome
- Conducted free from conflicts of interest
These read as a compressed statement of principles that already exist, in more developed form, across research-integrity and open-science literature: reproducibility and replicability as distinct, well-studied concepts; open science practices around transparency and error reporting; and unbiased peer review terminology already standardized by ANSI/NISO Z39.106-2023. The order does not cite or formally adopt any of these existing standards; it states the nine tenets as agency policy criteria in their own right.
Data, transparency, and code-sharing requirements
The provisions most relevant to research data management sit in the order’s transparency requirements. Agencies are directed to make publicly available the data, analyses, and conclusions behind scientific or technological information that will substantially affect public policy or private-sector decisions, including the source code behind models used to produce that information. Agency employees are required to transparently document known and potential uncertainties, disclose model assumptions, and apply a “weight of scientific evidence” approach that factors in study design, replicability, peer review status, and data transparency when characterizing how strong a given finding is.
This overlaps substantially with, but is not identical to, the data management plan requirements research administrators already track under funder policy, such as the NIH’s 2023 Data Management and Sharing Policy. Where an NIH or NSF DMP requirement is a condition attached to a specific award, EO 14303’s data-transparency provisions are directed at the agency itself, covering data and models the agency relies on to make or justify policy, not primarily data an individual grantee produces. The practical connection for institutions is upstream: as agencies revise their own scientific-integrity and data policies to implement the order, funding announcements and post-award reporting expectations are a likely channel through which these expectations reach grantees.
Implementation timeline
The order set an aggressive schedule for agency action:
- Within 30 days of the order (by late June 2025): the OSTP Director was directed to issue implementation guidance for agencies.
- Within 30 days of that OSTP guidance being published: agency heads were directed to begin updating their own policies to align with it.
- Within 60 days of OSTP guidance: agencies were directed to report on their implementation actions.
OSTP issued its implementation guidance on June 23, 2025, setting an August 22, 2025 deadline for agencies to report their intended implementation actions. Individual agencies have published or are publishing their own implementation pages and secretarial orders describing how they intend to apply the order within their own programs; these agency-level documents, not the executive order text itself, are where discipline-specific and program-specific detail will actually appear, and they are the right place for an institution tracking a specific funder to look next.
What this means for research administrators and data stewards
Three practical implications follow from the order for institutions that receive federal research funding:
- Watch agency-level implementation, not just the order text. EO 14303 sets principles; the operative detail for any given funder shows up in that agency’s own implementation guidance, scientific integrity policy revisions, and funding opportunity announcements, which will differ by agency and are still being published on a rolling basis.
- Expect reinforced emphasis on existing DMP and reproducibility expectations, not a wholesale replacement of them. The order’s tenets largely restate goals that funder rigor and reproducibility policies and open data requirements already pursue; institutions with mature data management planning practices are better positioned to absorb whatever specific changes individual agencies make.
- Distinguish this from unrelated uses of “gold standard.” “Gold Standard Science” as used in this executive order has no relationship to “gold” open access (the publishing model funded by article processing charges) or to “gold standard” as a general methodological term for a reference-standard method in a given field. The overlapping terminology is coincidental.
How it relates to existing reproducibility and data standards
EO 14303 does not create a new certification, badge, or compliance framework of its own. Existing mechanisms that already operationalize several of its nine tenets include the NIH Rigor and Reproducibility policy, journal- and publisher-level reproducibility and artifact-badging schemes, preregistration of study protocols, and the FAIR principles for making research data findable, accessible, interoperable, and reusable. None of these are named or mandated by the order itself; the order operates at the level of federal agency policy and public communication of agency-relied-upon science, and research administrators should treat funder-specific implementation guidance, once published, as the authoritative source for any new institutional obligation, rather than inferring specific new requirements from the order’s text alone.
Frequently asked questions
Is “Restoring Gold Standard Science” a real executive order?
Yes. It is Executive Order 14303, signed May 23, 2025, and published in the Federal Register on May 29, 2025 (90 FR 22601).
Does EO 14303 apply directly to universities and grant recipients?
No, not directly. The order binds federal agencies and their employees. Its effect on universities and other grantees comes indirectly, through the funding-opportunity language, data policies, and reporting requirements that individual agencies adopt as they implement it.
Does the order require researchers to share their raw data publicly?
The order’s public-availability requirement targets data, analyses, and code that a federal agency itself relies on for policy-relevant scientific or technological information, not, on its own text, every dataset produced under a federal grant. Any new obligation on grantee data sharing would come through an agency’s own subsequent policy, not directly from the order.
How does this differ from the NIH Data Management and Sharing Policy?
The NIH’s 2023 DMSP is a funder condition attached to individual awards, requiring a data management and sharing plan for most NIH-funded research generating scientific data. EO 14303 operates one level up, directing federal agencies, including NIH, to align their own scientific practices and public data disclosures with a common set of rigor criteria. The two can reinforce each other but are separate instruments.
