I will use Natural Language Processing and Machine Learning techniques to analyze issue backlogs in applications from institutions throughout the community. I will apply a variety of techniques in an attempt to answer questions like, What kinds of open issues do we have in general as a community? Can I extract an interesting set of widely-desired features or widely-held use cases? Can I identify connections that might lead to collaboration across institutions? What solutions already exist that might advance open issues? Can I link open issues in one backlog to merged PRs in another repository? What have people been working on recently? Can we characterize the full set of issues that have been closed over the past year? What patterns of development do repositories follow? Can we describe the life cycle of repository development by aligning issues based on their creation / completion dates relative to the initial commit? These may or may not be the exact questions my talk will address, depending on the direction the project naturally takes. I will focus on applications in use or under development at institutions, as opposed to community-maintained engines and core gems. This talk will describe my process, results, and evaluate the success of the endeavor., and A presentation at Samvera Connect 2019 described thus