# Introduction {#sec-intro}

Data-intensive discovery has become an important mode of knowledge production
across many research fields and it is having a significant and broad impact
across all of society. This is becoming increasingly salient as recent
developments in machine learning and artificial intelligence (AI) promise to
increase the value of large, multi-dimensional, heterogeneous data sources.
Coupled with these new machine learning techniques, these datasets can help us
understand everything from the cellular operations of the human body, through
business transactions on the internet, to the structure and history of the
universe. However, the development of new machine learning methods and
data-intensive discovery more generally depends on Findability, Accessibility,
Interoperability and Reusability (FAIR) of data [@Wilkinson2016FAIR] as well as
metadata [@Musen2022metadata]. One of the main mechanisms through which the
FAIR principles are promoted is the development of *standards* for data and
metadata. Standards can vary in the level of detail and scope, and encompass
such things as *file formats* for the storage of certain data types, *schemas*
for databases that organize data, *ontologies* to describe and organize
metadata in a manner that connects it to field-specific meaning, as well as
mechanisms to describe *provenance* of analysis products.

Community-driven development of robust, adaptable and useful standards draws
significant inspiration from the development of open-source software (OSS) and
has many parallels and overlaps with OSS development. OSS has a long history
going back to the development of the Unix operating system in the late 1960s.
Over the time since its inception, the large community of developers and users
of OSS have developed a host of socio-technical mechanisms that support the
development and use of OSS. For example, the Open Source Initiative (OSI), a
non-profit organization that was founded in the 1990s developed a set of
guidelines for licensing of OSS that is designed to protect the rights of
developers and users. On the technical side, tools such as the Git Source-code
management system support complex and distributed open-source workflows that
accelerate, streamline, and robustify OSS development. Governance approaches
have been honed to address the challenges of managing a range of stakeholder
interests and to mediate between large numbers of weakly-connected individuals
that contribute to OSS. When these social and technical innovations are put
together they enable a host of positive defining features of OSS, such as
transparency, collaboration, and decentralization. These features allow OSS to
have a remarkable level of dynamism and productivity, while also retaining the
ability of a variety of stakeholders to guide the evolution of the software to
take their needs and interests into account.

Data and metadata standards that use tools and practices of OSS ("open-source
standards" henceforth) reap many of the benefits that the OSS model has
provided in the development of other technologies. The present report explores
how OSS processes and tools have affected the development of data and metadata
standards. The report will triangulate common features of a variety of use
cases; it will identify some of the challenges and pitfalls of this mode of
standards development, with a particular focus on cross-sector interactions;
and it will make recommendations for future developments and policies that can
help this mode of standards development thrive and reach its full potential.

