# Use cases {#sec-use-cases}

To understand how OSS development practices affect the development of data and
metadata standards, it is informative to demonstrate this cross-fertilization
through a few use cases. As we will see in these examples, some fields, such as
astronomy, high-energy physics and earth sciences have a relatively long
history of shared data resources from organizations such as LSST, CERN, and
NASA, while other fields have only relatively recently become aware of the
value of data sharing and its impact. These disparate histories inform how
standards have evolved and how OSS practices have pervaded their development.
It also demonstrates field-specific limitations on the adoption of OSS tools
and practices that exemplify some of the challenges, which we will explore
subsequently.

## Astronomy

An early prominent example of a community-driven standard is the FITS (Flexible
Image Transport System) file format standard, which was developed in the late
1970s and early 1980s [@wells1979fits], and has been adopted worldwide for
astronomy data preservation and exchange. Essentially every software platform
used in astronomy reads and writes the FITS format. It was developed by
observatories in the 1980s to store image data in the visible and x-ray
spectrum. It has been endorsed by the International Astronomical Union (IAU),
as well as funding agencies. Though the format has evolved over time, “once
FITS, always FITS”. That is, the format cannot be evolved to introduce changes
that break backward compatibility. Among the features that make FITS so durable
is that it was designed originally to have a very restricted metadata schema.
That is, FITS records were designed to be the lowest common denominator of word
lengths in computer systems at the time. However, while FITS is compact, its
ability to encode the coordinate frame and pixels, means that data from
different observational instruments can be stored in this format and
relationships between data from different instruments can be related, rendering
manual and error-prone procedures for conforming images obsolete. Nevertheless,
the stability has also raised some issues as the field continues to adapt to
new measurement methods and the demands of ever-increasing data volumes and
complex data analysis use-case, such as interchange with other data and the use
of complex data bases to store and share data [@Scroggins2020-ut]. Another
prominent example of the use of open-source processes to develop standards in
Astronomy is in the tools and protocols developed by the International Virtual
Observatory Alliance (IVOA) and its national implementations, e.g., in the US
Virtual Astronomical Observatory[@Hanisch2015-cu]. The virtual observatories
facilitate discovery and access across observatories around the world and
underpin data discovery in astronomy. The IVOA took inspiration from the
World-Wide Web Consortium (W3C) and adopted its process for the development of
its standards (i.e., Working drafts $\rightarrow$ Proposed Recommendations
$\rightarrow$ Recommendations), with individual standards developed by
inter-institutional and international working groups. One of the outcomes of
the coordination effort is the development of an ecosystem of software tools
both developed within the observatory teams and within the user community that
interoperate with the standards that were adopted by the observatories.

## High-energy physics (HEP)

Because data collection is centralized, standards to collect and store HEP data
have been established and the adoption of these standards in data analysis has
high penetration [@Basaglia2023-dq]. A top-down approach is taken so that
within every large collaboration, standards are enforced, and this adoption is
centrally managed. Access to raw data is essentially impossible because of its
large volume, and making it publicly available is both technically very hard
and potentially ill-advised. Therefore, analysis tools are tuned specifically
to the standards of the released data. Incentives to use the standards are
provided by funders that require data management plans that specify how the
data is shared (i.e., in a standards-compliant manner).

## Earth sciences

The need for geospatial data exchange between different systems began to be
recognized in the 1970s and 1980s, but proprietary formats still dominated.
Coordinated standardization efforts brought the Open Geospatial Consortium
(OGC) establishment in the 1990s, a critical step towards open standards for
geospatial data. The 1990s have also seen the development of key standards such
as the Network Common Data Form (NetCDF) developed by the University
Corporation for Atmospheric Research (UCAR), and the Hierarchical Data Format
(HDF), a set of file formats (HDF4, HDF5) that are widely used, particularly in
climate research. The GeoTIFF format, which originated at NASA in the late
1990s, is extensively used to share image data. In the 1990s, open web mapping
also began with MapServer (https://mapserver.org) and continued later with
other projects such as OpenStreetMap (https://www.openstreetmap.org). The
following two decades, the 2000s-2020s, brought an expansion of open standards
and integration with web technologies developed by OGC, as well as other
standards such as the Keyhole Markup Language (KML) for displaying geographic
data in Earth browsers. Formats suitable for cloud computing also emerged, such
as the Cloud Optimized GeoTIFF (COG), followed by Zarr and Apache Parquet for
array and tabular data, respectively. In 2006, the Open Source Geospatial
Foundation (OSGeo, https://www.osgeo.org) was established, demonstrating the
community's commitment to the development of open-source geospatial
technologies. While some standards have been developed in the industry (e.g.,
Keyhole Markup Language (KML) by Keyhole Inc., which Google later acquired),
they later became international standards of the OGC, which now encompasses
more than 450 commercial, governmental, nonprofit, and research organizations
working together on the development and implementation of open standards
(https://www.ogc.org).

## Neuroscience

In contrast to the previously-mentioned fields, Neuroscience has traditionally
been a "cottage industry", where individual labs have generated experimental
data designed to answer specific experimental questions. While this model still
exists, the field has also seen the emergence of new modes of data production
that focus on generating large shared datasets designed to answer many
different questions, more akin to the data generated in large astronomy data
collection efforts [@Koch2012-ve]. This change has been brought on through a
combination of technical advances in data acquisition techniques, which now
generate large and very high-dimensional/information-rich datasets, cultural
changes, which have ushered in new norms of transparency and reproducibility,
and funding initiatives that have encouraged this kind of data collection.
However, because these changes are recent relative to the other cases mentioned
above, standards for data and metadata in neuroscience have been prone to adopt
many elements of modern OSS development. Two salient examples in neuroscience
are the Neurodata Without Borders file format for neurophysiology data
[@Rubel2022NWB] and the Brain Imaging Data Structure (BIDS) standard for
neuroimaging data [@Gorgolewski2016BIDS]. BIDS in particular owes some of its
success to the adoption of OSS development mechanisms [@Poldrack2024BIDS]. For
example, small changes to the standard are managed through the GitHub pull
request mechanism; larger changes are managed through a BIDS Enhancement
Proposal (BEP) process that is directly inspired by the Python programming
language community's Python Enhancement Proposal procedure, which is used to
introduce new ideas into the language. Though the BEP mechanism takes a
slightly different technical approach, it tries to emulate the open-ended and
community-driven aspects of Python development to accept contributions from a
wide range of stakeholders and tap a broad base of expertise.

## Community science

Another interesting use case for open-source standards is community/citizen
science. This approach, which has grown in the last 20 years, has many benefits
for both the research field that harnesses the energy of non-scientist members
of the community to engage with scientific data, as well as to the community
members themselves who can draw both knowledge and pride in their participation
in the scientific endeavor. It is also recognized that unique broader benefits
are accrued from this mode of scientific research, through the inclusion of
perspectives and data that would not otherwise be included. To make data
accessible to community scientists, and to make the data collected by community
scientists accessible to professional scientists, it needs to be provided in a
manner that can be created and accessed without specialized instruments or
specialized knowledge. Here, standards are needed to facilitate interactions
between an in-group of expert researchers who generate and curate data and a
broader set of out-group enthusiasts who would like to make meaningful
contributions to the science. This creates a particularly stringent constraint
on transparency and simplicity of standards. Creating these standards in a
manner that addresses these unique constraints can benefit from OSS tools, with
the caveat that some of these tools require additional expertise. For example,
if the standard is developed using git/GitHub for versioning, this would
require learning the complex and obscure technical aspects of these system that
are far from easy to adopt, even for many professional scientists.


