Scalable Metadata Environments (MDEs) are an artistic approach for designing immersive environments for large scale data exploration in which users interact with data by forming multi-scale patterns that they alternatively disrupt and reform. Developed and prototyped as part of an art-science research collaboration‚ we define an MDE as a 4D virtual environment structured by quantitative and qualitative metadata describing multidimensional data collections. Entire data sets (e.g.10s of millions of records) can be visualized and sonified at multiple scales and at different levels of detail so they can be explored interactively in real-time within MDEs. They are designed to reflect similarities and differences in the underlying data or metadata such that patterns can be visually/aurally sorted in an exploratory fashion by an observer who is not familiar with the details of the mapping from data to visual‚ auditory or dynamic attributes. While many approaches for visual and auditory data mining exist‚ MDEs are distinct in that they utilize qualitative and quantitative data and metadata to construct multiple interrelated conceptual coordinate systems. These “regions” function as conceptual lattices for scalable auditory and visual representations within virtual environments.