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For years, Data Management Plans (DMPs) have been championed as a cornerstone of responsible research. Is this reputation based on evidence or primarily on professional consensus? Are their benefits real or simply assumed? Originally designed as bespoke tools for complex projects, DMPs have evolved into mandatory requirements by funders and institutions, promoted as essential for managing data throughout the research lifecycle, demonstrating integrity and enabling sharing and reuse. But do these claims hold up under scrutiny? This presentation examines the origins of DMPs, the rationale behind mandates, and what research (or its absence) reveals about their actual impact. It explores criticisms such as administrative burden, poor quality and “tick-box” solutions and asks whether DMPs genuinely improve practice. Finally, it looks ahead to alternative approaches and emerging technologies, such as AI, that could be employed by existing and new digital research infrastructures to streamline data management and free researchers to focus on what matters most: their research.
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