As research data management (RDM) evolves, institutions are increasingly leveraging artificial intelligence (AI), automation, and interoperable technologies to move beyond compliance-driven models toward strategic, scalable data stewardship. The Machine-Actionable DMPs (MAP) Project, led by the Association of Research Libraries and the California Digital Library, is pioneering technical solutions that integrate AI-driven evaluation of data management plans (DMPs), facilitate cross-campus coordination, and improve research data infrastructure. Through institutional pilots at Arizona State University, the University of California Riverside, the University of Colorado Boulder, and others, the MAP Project is demonstrating how AI and machine-actionable frameworks can enhance institutional decision-making and resource alignment. This session will showcase cutting-edge developments in AI-assisted DMP evaluation, dynamic data stewardship workflows, and cross-institutional interoperability. It will explore how AI can analyze and refine DMPs to improve compliance tracking, identify gaps in institutional support, and optimize data lifecycle planning. Additionally, it will discuss emerging tools that enhance real-time communication between research offices, libraries, and IT, enabling institutions to develop more responsive and sustainable research data ecosystems.
https://www.arl.org/building-a-scalable-data-management-infrastructure-for-strategic-institutional-coordination/https://www.arl.org/blog/insights-from-arizona-state-university-advancing-research-support-through-open-science-data-sovereignty-and-machine-actionable-plans/https://www.arl.org/blog/arl-cdl-machine-actionable-plans-map-pilot-team-visits-university-of-colorado-boulder/https://www.arl.org/blog/from-compliance-to-sustainable-capacity-strategic-insights-on-research-data-stewardship/https://www.arl.org/blog/uc-riverside-machine-actionable-plans-map-team-builds-promising-communication-prototype/
