yt is a Python package designed for domain-specific inquiry of volumetric data, licensed under the BSD license and available at yt-project.org. Utilizing numerous components of the scientific Python ecosystem, it is able to ingest data from numerous different sources from domains such as astrophysics, nuclear engineering, weather and climate, oceanography, and seismology. Building on top of a parallelized framework for data selection, analysis, processing and visualization, inquiry can be driven based on relevant, physical quantities rather than those specific to data formats. I will describe recent advances in the yt 3.0 series, including support for particle, octree, patch and unstructured mesh datasets; interactive and batch volume rendering using both software and OpenGL backends; semantically-rich ontologies of fields, derived quantities and affiliated units (powered by sympy); user-defined kernel estimates for density; support for visualization in non-Cartesian domains; and a flexible chunking system for data IO. I will describe some of the non-astrophysics domains that yt has been applied to, and the infrastructure implemented to support that. Finally, I will describe the community-driven approach taken to designing, developing and implementing new features, and describe some of the challenges this has presented in the context of scientific software developers.