  Discovery of new knowledge is increasingly data-driven, predicated on a
team's ability to collaboratively create, find, analyze, retrieve, and share
pertinent datasets over the duration of an investigation. This is especially
true in the domain of scientific discovery where generation, analysis, and
interpretation of data are the fundamental mechanisms by which research teams
collaborate to achieve their shared scientific goal. Data-driven discovery in
general, and scientific discovery in particular, is distinguished by complex
and diverse data models and formats that evolve over the lifetime of an
investigation. While databases and related information systems have the
potential to be valuable tools in the discovery process, developing effective
interfaces for data-driven discovery remains a roadblock to the application of
database technology as an essential tool in scientific investigations. In this
paper, we present a model-adaptive approach to creating interaction
environments for data-driven discovery of scientific data that automatically
generates interactive user interfaces for editing, searching, and viewing
scientific data based entirely on introspection of an extended relational data
model. We have applied model-adaptive interface generation to many active
scientific investigations spanning domains of proteomics, bioinformatics,
neuroscience, occupational therapy, stem cells, genitourinary, craniofacial
development, and others. We present the approach, its implementation, and its
evaluation through analysis of its usage in diverse scientific settings.
