Until very recently, most NLP tasks (e.g., parsing, tagging, etc.) have been conﬁned to a very limited number of languages, the so-called majority languages. Now, as the ﬁeld moves into the era of developing tools for Resource Poor Languages (RPLs)—a vast majority of the world’s 7,000 languages are resource poor—the discipline is confronted not only with the algorithmic challenges of limited data, but also the sheer difﬁculty of locating data in the ﬁrst place. In this demo, we present a resource which taps the large body of linguistically annotated data on the Web, data which can be repurposed for NLP tasks. Because the ﬁeld of linguistics has as its mandate the study of human language—in fact, the study of all human languages—and has wholeheartedly embraced the Web as a means for dissementating linguistic knowledge, the consequence is that a large quantity of analyzed language data can be found on the Web. In many cases, the data is richly annotated and exists for many languages for which there would otherwise be very limited annotated data. The resource, the Online Database of INterlinear text (ODIN), makes this data available and provides additional annotation and structure, making the resource useful to the Computational Linguistic audience. In this paper, after a brief discussion of the previous work on ODIN, we report our recent work on extending ODIN by applying machine learning methods to the task of data extraction and language identiﬁcation, and on using ODIN to “discover” linguistic knowledge. Then we outline a plan for the demo presentation.