Authors: Beaumont, Christopher, U. Hawaii; Robitaille, Thomas, MPIA; Borkin, Michelle, Harvard; Goodman, Alys
Modern research projects incorporate data from several sources, and new insights are increasingly driven by the ability to interpret data in the context of other data. Glue (http://glueviz.org) is a graphical environment built on top of the standard Python science stack to visualize relationships within and between data sets. With Glue, users can load and visualize multiple related data sets simultaneously. Users specify the logical connections that exist between data, and Glue transparently uses this information as needed to enable visualization across files. This functionality makes it trivial, for example, to interactively overplot catalogs on top of images.
The central philosophy behind Glue is that the structure of research data is highly customized and problem-specific. Glue aims to accomodate and to simplify the "data munging" process, so that researchers can more naturally explore what their data has to say. The result is a cleaner scientific workflow, and more rapid interaction with data.