In August 2022, the White House Office of Science and Technology Policy (OSTP) issued a memorandum on “Ensuring Free, Immediate, and Equitable Access to Federally Funded Research” [1], representing a transformational step toward making all United States federally funded research both immediately accessible and fully reusable. The OSTP’s memo will be especially impactful in the realm of data sharing, as it asks federal agencies to develop plans to require data underlying published studies to be shared immediately upon publication and explore strategies for sharing all data, even if not tied to a published study. This policy advance takes place within the larger context of international efforts to enable open data sharing at scale, including the national open science plans in Ireland [2], Colombia [3], Spain [4], and France [5]. As contributors to the Higher Education Leadership Initiative for Open Scholarship and the Open Research Funders Group, we believe this moment in time represents an unparalleled opportunity to elevate research software as a core component of the scientific endeavor and to take specific steps to ensure its open and equitable availability.

The OSTP guidance and its accompanying press release [6] outline the importance of applying an equity lens to US federal policy, stating, “Financial means and privileged access must never be the prerequisite to realizing the benefits of federally funded research that the American public deserves” and that “A federal public access policy consistent with our values of equal opportunity must allow for broad and expeditious sharing of federally funded research—and must allow all Americans to benefit from the returns on our research and development investments without delay.” In particular, the OSTP guidance emphasizes the critical role of data sharing to ensure transparency, validation, reproducibility, and integrity of US federally funded research. However, truly meeting these goals will not just require sharing data, but also sharing the research software needed to open and reanalyze data. As explored by the FAIR for Research Software working group (jointly convened by the Research Data Alliance, FORCE11, and the Research Software Alliance), we are using the term “research software” to encompass “source code files, algorithms, scripts, computational workflows and executables that were created during the research process or for a research purpose” [7]. We acknowledge that this definition may continue to evolve through ongoing community discussions, as well as with advances in AI, simulations, algorithms, etc.

Research data, especially in certain specialized fields, is often collected and stored in proprietary file formats that require software with costly licenses to open and analyze. If data are shared under such conditions, without accompanying code, algorithms, and software to allow others to open and analyze the files, this will represent a financial barrier to access that will delay or prohibit reuse of data, especially for underserved populations, citizen scientists, and early career researchers with less means. It might also present technical barriers that could limit the machine readability and reusability of data by assistive devices. All of this would run counter to the guiding principles and overall goals of the 2022 OTSP guidance to make data Findable, Accessible, Interoperable, and Reusable (FAIR). Most importantly, to accurately be able to replicate and reproduce results and build on shared data, we must not only have access to the data themselves, but also understand exactly how they were used and analyzed.

Requiring research software to be shared as part of an integral data sharing policy is not without precedent within the US federal government. The CHIPS and Science Act of 2022 [8] includes language on the need for federal grantees to describe how they will archive and preserve research software as part of their data management plans. The National Institutes for Health’s Strategic Plan for Data Science [9] emphasizes that “Extracting understanding from large-scale or complex biomedical research data requires algorithms, software, models, statistics, visualization tools, and other advanced approaches such as machine learning, deep learning, and artificial intelligence.” In response to the OSTP memo, the Department of Transportation has stated their intention to add “Source Code and Software, among the categories of accessible Research Outputs” to their forthcoming updated public access plan [10]. NASA’s Science Mission Directorate has also released an update to its comprehensive Scientific Information Policy (SPD-41a) [11], which applies to a significant portion of NASA’s research expenditures, and which requires that research data and software are shared openly at the time of publication or by the end of the funding award. There is now an opportunity to expand US federal policies in similar ways and align their research software sharing aspects across agencies.

To do this, we recommend:

While the context of these recommendations is, given the timeline of the OSTP memorandum [1], primarily focused on US federal agencies, the general principles may be adapted and adopted by a range of public and private funders, regardless of geography. In making these recommendations, we recognize that considerations for sharing research software relate to, but do not precisely overlap with, considerations for sharing research papers and data. The ways in which research software is developed, when in the project lifecycle it can be productively shared, and how (and by whom) it is maintained represent just a few of the differences. As a consequence, a uniform approach for “sharing research objects” that treats research software as fundamentally interchangeable with research papers and data will not succeed. A well-developed US federal strategy in this area will require significant consideration of the ways in which overarching principles such as FAIRness, sustainability, open infrastructure, curation good practices, and expansive licensing must be adapted to address the unique parameters of research software.