Abstract: We introduce a structured dataset and accompanying processing pipeline for nearly 200 years of decision making, derived from the Proceedings of the Board of Regents of the University of Michigan. This English-language collection holds official decision making records of one of the oldest public research universities in the United States, covering the period of 1837-2023. These records span 55,177 pages across 71 volumes, containing roughly 36 million o200k_base text tokens. We describe how our pipeline makes use of open-source reasoning Large Language Models (LLMs) to extract insights from these records, at meeting, volume and collection level. We separate each volume into individual Board meetings—1,755 in total—and then extract meeting-level metadata records (date, attendees, location, presiding officer) and governance events in several categories: degree programs, leadership transitions, organizational units, fundraising, buildings, and strategic plans. The event categories we extract were defined together with University of Michigan librarians, reflecting the kinds of requests for hard-to-reach information they have received about these records over the years. We also include a derivative reasoning dataset that pairs each meeting with a model-generated summary, chain-of-though traces, and decade-level synthesis. Finally, we include EPUB exports for each meeting to increase the accessibility of the information for human readers. These datasets are of particular interest for research on historical governance and decision-making and, as a narrow-domain corpus paired with reasoning traces, may also contribute to AI training efforts. None of this data processing would be possible without clearly established ground truths. To that end, we evaluate each stage of the pipeline against hand-annotated reference data.