Cross-sector interactions
The importance of standards stems not only from discussions within research fields about how research can best be conducted to take advantage of existing and growing datasets, but also arises from interactions with stakeholders in other sectors. Several different kinds of cross-sector interactions can be defined as having an important impact on the development of open-source standards.
Governmental policy-setting
The development of open practices in research has entailed an ongoing interaction and dialogue with various governmental bodies that set policies for research. For example, for research that is funded by the public, this entails an ongoing series of policy discussions that address the interactions between research communities and the general public. One way in which this manifests in the United States specifically is in memos issued by the directors of the White House Office of Science and Technology Policy (OSTP), James Holdren (in 2013) and Alondra Nelson (in 2022). While these memos focused primarily on making peer-reviewed publications funded by the US Federal government available to the general public, they also lay an increasingly detailed path toward the publication and general availability of the data that is collected in research that is funded by the US government. The general guidance and overall spirit of these memos dovetail with more specific policy guidance related to data and metadata standards. For example, the importance of standards was underscored in a recent report by the Subcommittee on Open Science of the National Science and Technology Council on the “Desirable characteristics of data repositories for federally funded research” (The National Science and Technology Council 2022). The report explicitly called out the importance of “allow[ing] datasets and metadata to be accessed, downloaded, or exported from the repository in widely used, preferably non-proprietary, formats consistent with standards used in the disciplines the repository serves.” This highlights the need for data and metadata standards across a variety of different kinds of data. In addition, a report from the National Institute of Standards and Technology on “U.S. Leadership in AI: A Plan for Federal Engagement in Developing Technical Standards and Related Tools” emphasized that – specifically for the case of AI – “U.S. government agencies should prioritize AI standards efforts that are […] Consensus-based, […] Inclusive and accessible, […] Multi-path, […] Open and transparent, […] and [that] result in globally relevant and non-discriminatory standards…” (National Institute of Standards and Technology 2019). The converging characteristics of standards that arise from these reports suggest that considerable thought needs to be given to how standards arise so that these goals are achieved. Importantly, open-source standards seem to well-match at least some of these characteristics.
The other side of policies is the implementation of these policies in practice by developers of open-source standards and by the communities to which the standards pertain. A compelling road map towards implementation and adoption of open science practices in general and open-source standards in particular is offered in a blog post authored by the Center for Open Science’s co-founder and executive director, Brian Nosek, entitled “Strategy for Culture Change” (Nosek, n.d.). The core idea is that affecting a turn toward open science requires an alignment of not only incentives and values, but also technical infrastructure and user experience. A sociotechnical bridge between these pieces, which makes the adoption of standards possible, and maybe even easy, and the policy goals, arises from a community of practice that makes the adoption of standards normative. Once all of these pieces are in place, making adoption of open science standards required through policy becomes more straightforward and less onerous.
Funding
Government-set policy intersects with funding considerations. This is because it is primarily directed towards research that is funded through governmental funding agencies. For example, high-level policy guidance boils to practice in guidance for data management plans that are part of funded research. In response to the policy guidance, these have become increasingly more detailed and, for example, NSF- and NIH-funded researchers are now required to both formulate their plans with more clarity and increasingly also to share data using specified standards as a condition for funding.
However, there are other ways in which funding relates to the development of open-source standards. For example, through the BRAIN Initiative, the National Institutes of Health have provided key funding for the development of the Brain Imaging Data Structure standard in neuroscience. Where large governmental funding agencies may not have the resources or agility required to fund nascent or unconventional ways of formulating standards, funding by non-governmental philanthropies and other organizations can provide alternatives. One example (out of many) is the Chan-Zuckerberg Initiative program for Essential Open Source Software, which funds foundational open-source software projects that have an application in biomedical sciences. Distinct from NIH funding, however, some of this investment focuses on the development of OSS practices. For example, funding to the Arrow project that focuses on developing open-source software maintenance skills and practices, rather than a specific biomedical application.
Industry
Interactions of data and meta-data standards with commercial interests may provide specific sources of friction. This is because proprietary/closed formats of data can create difficulty at various transition points: from one instrument vendor to another, from data producer to downstream recipient/user, etc. On the other hand, in some cases, cross-sector collaborations with commercial entities may pave the way to robust and useful standards. For example, imaging measurements in human subjects (e.g., in brain imaging experiments) significantly interact with standards for medical imaging, and chiefly the Digital Imaging and Communications in Medicine (DICOM) standard, which is widely used in a range of medical imaging applications, including in clinical settings Mustra, Delac, and Grgic (2008). The standard emerged from the demands of the clinical practice in the 1980s, as digital technologies were came into widespread use in medical imaging, through joint work of industry organizations: the American College of Radiology and the National Association of Electronic Manufacturers. One of the defining features of the DICOM standard is that it allows manufacturers of instruments to define “private fields” that are compliant with the standard, but which may include idiosyncratically organized data and/or metadata. This provides significant flexibility, but can also easily lead to the loss of important information. Nevertheless, the human brain imaging case is exemplary of a case in which industry standards and research standards coexist and need to communicate with each other effectively to advance research use-cases, while keeping up with the rapid development of the technologies.