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Just as new forms of high-quality scientific data lead to new scientific discoveries, new forms of high-quality metadata lead to new methods of scholarly research. JSTOR Labs builds experimental tools for research and teaching on top of the JSTOR digital library of academic journals and books. In doing so, they leverage the scale of JSTOR’s corpus, JSTOR’s strong and consistent metadata, and natural language processing and other machine learning methods to extend this metadata in new directions. In this talk, I’ll showcase some of the award-winning research tools JSTOR Labs has built and describe the metadata foundation that enables these new forms of academic research.