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Ten principles for machine-actionable data management plans.

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Data management plans (DMPs) are evolving into machine-actionable DMPs (maDMPs) to enhance research practices. This shift aims to reduce administrative burdens and improve data management across all stakeholders.

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Area of Science:

  • Data Science
  • Research Administration
  • Information Management

Background:

  • Data management plans (DMPs) are often treated as administrative tasks, not integral to research.
  • There's a growing recognition of the value in making DMPs more machine-actionable.
  • Current practices limit the potential of DMPs for researchers, funders, and administrators.

Purpose of the Study:

  • To advocate for and guide the implementation of machine-actionable Data Management Plans (maDMPs).
  • To highlight the benefits of maDMPs for various stakeholders in the research ecosystem.
  • To present a framework for integrating maDMPs into existing research workflows.

Main Methods:

  • The paper outlines 10 principles for putting machine-actionable DMPs into practice.
  • It details specific actions for diverse stakeholders to collaborate on maDMP goals.
  • Existing initiatives are reviewed to demonstrate progress and identify areas for action.

Main Results:

  • Widespread recognition of the need for machine-actionable DMPs (maDMPs) across the research community.
  • Identification of benefits including reduced administrative burden and improved data quality.
  • Demonstration of progress through existing initiatives and a call to action for further involvement.

Conclusions:

  • Adopting machine-actionable DMPs (maDMPs) is crucial for modernizing research practices.
  • Collaboration among researchers, funders, and institutions is key to realizing maDMP benefits.
  • The presented principles offer a roadmap for achieving greater efficiency and data integrity in research.