  Data science models, although successful in a number of commercial domains,
have had limited applicability in scientific problems involving complex
physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm
that aims to leverage the wealth of scientific knowledge for improving the
effectiveness of data science models in enabling scientific discovery. The
overarching vision of TGDS is to introduce scientific consistency as an
essential component for learning generalizable models. Further, by producing
scientifically interpretable models, TGDS aims to advance our scientific
understanding by discovering novel domain insights. Indeed, the paradigm of
TGDS has started to gain prominence in a number of scientific disciplines such
as turbulence modeling, material discovery, quantum chemistry, bio-medical
science, bio-marker discovery, climate science, and hydrology. In this paper,
we formally conceptualize the paradigm of TGDS and present a taxonomy of
research themes in TGDS. We describe several approaches for integrating domain
knowledge in different research themes using illustrative examples from
different disciplines. We also highlight some of the promising avenues of novel
research for realizing the full potential of theory-guided data science.
