  Scientific discovery is increasingly dependent on a scientist's ability to
acquire, curate, integrate, analyze, and share large and diverse collections of
data. While the details vary from domain to domain, these data often consist of
diverse digital assets (e.g. image files, sequence data, or simulation outputs)
that are organized with complex relationships and context which may evolve over
the course of an investigation. In addition, discovery is often collaborative,
such that sharing of the data and its organizational context is highly
desirable. Common systems for managing file or asset metadata hide their
inherent relational structures, while traditional relational database systems
do not extend to the distributed collaborative environment often seen in
scientific investigations. To address these issues, we introduce ERMrest, a
collaborative data management service which allows general entity-relationship
modeling of metadata manipulated by RESTful access methods. We present the
design criteria, architecture, and service implementation, as well as describe
an ecosystem of tools and services that we have created to integrate metadata
into an end-to-end scientific data life cycle. ERMrest has been deployed to
hundreds of users across multiple scientific research communities and projects.
We present two representative use cases: an international consortium and an
early-phase, multidisciplinary research project.
