
# Recommendations for open-source data and metadata standards {#sec-recommendations}

In conclusion of this report, we would like to propose a set of recommendations
that distill the lessons learned from an examination of data and metadata
standards through the lense of open-source software development practices. We
divide this section into two parts: one aimed at the science and technology
communities that develop and maintain open-source standards, and the other
aimed at policy-making and funding agencies, who have an interest in fostering
more efficient, more robust, and more transparent open-source standards.

## Science and technology communities:

### Establish standards governance based on OSS best practices

While best-practice governance principles are also relatively new in OSS
communities, there is already a substantial set of prior art in this domain, on
which the developers and maintainers of open-source data and metadata standards
can rely. For example, it is now clear that governance principles and rules can
mitigate some of the risks and challenges mentioned in @sec-challenges,
especially for communities beyond a certain size that need to converge toward a
new standard or rely on an existing standard. Developers and maintainers should
review existing governance practices such as [The Open Source
Way](https://www.theopensourceway.org/the_open_source_way-guidebook-2.0.html#_project_and_community_governance).


### Foster meta-standards development

One of the main conclusions that arise from our survey of the landscape of
existing standards is that there is significant knowledge that exists across
fields and domains and that informs the development of standards within each
field, but that could be surfaced to the level where it may be adopted more
widely in different domains and be more broadly useful. One approach to this is
a comparative approach: In this approach, a readiness and/or maturity model can
be developed that assesses the challenges and opportunities that a specific
standard faces at its current phase of development. Developing such a maturity
model, while it goes beyond the scope of the current report, could lead to the
eventual development of a meta-standard or a standard-of-standards. This would
encompass a succinct description of cross-cutting best-practices that can be
used as a basis for the analysis or assessment of an existing standard, or as
guidelines to develop new standards. For instance, specific barriers to
adopting a data standard that take into account the size of the community and
its specific technological capabilities should be considered.

More generally, meta-standards could include formalization for versioning of
standards and interactions with specific related software. This includes
amplifying formalization/guidelines on how to create standards (for example,
metadata schema specifications using LinkML (https://linkml.io)). However,
aspects of communication with potential user audiences (e.g., researchers in
particular domains) should be taken into account as well. For example, in the
quality of onboarding documentation and tools for ingestion or conversion into
standards-compliant datasets.

An ontology for the standards-development process -- for example top-down vs
bottom-up, minimum number of datasets, target community size and technical
expertise typical of this community, and so forth -- could help guide the
standards-development process towards more effective adoption and use. A set of
meta-standards and high-level descriptions of the standards-development process
-- some of which is laid out in this report -- could help standard developers
avoid known pitfalls, such as the dreaded proliferation of standards, or such
as complexity-impeded adoption. Surveying and documenting the success and
failures of current standards for a specific dataset / domain can help
disseminate knowledge about the standardization process. Resources such as
[Fairsharing](https://fairsharing.org/) or [Digital Curation
Center](https://www.dcc.ac.uk/guidance/standards) can help guide this process.

### Develop standards in tandem with standards-associated software

Development of standards should be coupled and tightly linked with development
of associated software. This produces a virtuous cycle where the use-cases and
technical issues that arise in software development informs the development of
the standard and vice versa. One of the lessons learned across a variety of
different standards is the importance of automated validation of the standard.
Automated validation is broadly seen as a requirement for the adoption of a
standard and a factor in managing change of the standard over time. To advance
this virtuous cycle, we recommend to make data standards machine readable, and
make software creation an integral part of establishing a standard's schema.
Additionally, standards evolution should maintain software compatibility, and
ability to translate and migrate between standards.


## Policy-making and funding entities:

### Fund the development of open-source standards

While some funding agencies already support standards development as part of
the development of informatics infrastructures, data standards development
should be seen as integral to science innovation and earmarked for funding in
research grants, not only in specialized contexts. Funding models should
encourage the development and adoption of standards, and fund associated
community efforts and tools for this. The OSS model is seen as a particularly
promising avenue for an investment of resources, because it builds on
previously-developed procedures and technical infrastructure and because it
provides avenues for the democratization of development processes and for
community input along the way. At the same time, there is significant
challenges associated with incentives to engage, ranging from the dilution of
credit to individual contributors, and ranging through the burnout of
maintainers and developers. The clarity offered by procedures for enhancement
proposals and semantic versioning schemes adopted in standards development
offers avenues for a range of stakeholders to propose well-defined
contributions to large and field-wide standards efforts (e.g.,
[@pestilli2021community]), and potentially helps alleviate some of these
concerns by providing avenues for individual contributions to surface, as well
as clarity of process, which can alleviate the risks of maintainer burnout.

### Invest in data stewards

Advancing the development and adoption of open-source standards requires the
dissemination of knowledge to researchers in a variety of fields, but this
dissemination itself may not be enough without the fostering of specialized
expertise. Therefore, it is important to recognize the distinct role that *data
stewards* play in contemporary research. As policy demands for openness become
increasingly high, it is crucial to truly support experts whose role will be to
develop, maintain, and facilitate the adoption and use of open-source
standards. This support needs to manifest in all stages of the career of these
individuals: it will be necessary to set up programs for training for data
stewards, and to invest in the career paths of individuals that receive such
training so that this crucial role is encouraged. Initial proposals for the
curriculum and scope of the role have already been proposed (e.g., in
[@Mons2018DataStewardshipBook]), but we identify here also a need to connect
these individuals directly to the practices that exemplify open-source
standards. Thus, it will be important for these individuals to be facile in the
methodology of OSS. This does not mean that they need to become software
engineers -- though for some of them there may be some overlap with the role of
research software engineers [@Connolly2023Software] -- but rather that they
need to become familiar with those parts of the OSS development life-cycle that
are specifically useful for the development of open-source standards. For
example, tools for version control, tools for versioning, and tools for
creation and validation of compliant data and metadata.

Stakeholder organizations should invest in training grants to establish
curriculum for data and metadata standards education. </ol>

### Review open-source standards pathways

Invest in programs that examine retrospective pathways for establishing data
standards. Encourage publication of lifecycles for successful data standards.
These lifecycles should include the process, creators, affiliations, grants,
and adoption journeys of open-source standards. To encourage sustainable
development of open-source standards, and to build on prior experience, the
documentation and dissemination of lifecycles should be seen as an integral
step of the work of standards creators and granting agencies. In the meanwhile,
it would be good to also retroactively document the lifecycle of existing
standards that are seen as success stories. Research on the principles that
underlie successful open-source standards development can be used to formulate
new standards and iterate on existing ones. Data management plans should
promote the sharing of not only data, but also metadata and descriptions of how
to use it.

### Manage Cross Sector alliances

Encourage cross-sector and cross-domain alliances that can impact successful
standards creation. Invest in robust program management of these alliances to
align pace and create incentives (for instance via Open Source Program Offices
at Universities or other research organizations). Similar to program officers
at funding agencies, standards evolution need sustained PM efforts.
Multi-company partnerships should include strategic initiatives for standard
establishment such as the Pistoia Alliance (https://www.pistoiaalliance.org/).
