# Cross-sector interactions {#sec-cross-sector}

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" [@nstc2022desirable]. 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..." [@NIST2019]. 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"
[@Nosek2019CultureChange]. 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 [@Larobina2023-vq, @Mustra2008-xk]. 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.





