🟡Newsletter - May 2025
Monthly Newsletter
and isLeveraging Machine-Actionable DMPs for Enhanced Research Workflows
Disclaimer The views expressed here are my own in my professional capacity as a Data Steward and Research Data Manager. They do not necessarily represent the official positions of the Novo Nordisk Foundation Center for Stem Cell Medicine – reNEW.
Introduction
Data Management Plans (DMPs) are a cornerstone of good research practice. They guide how data will be collected, stored, shared, and preserved throughout the research lifecycle. Traditionally, DMPs have been static documents—useful at the planning stage but rarely integrated into the day-to-day workflow.
Today, research environments are increasingly complex, interconnected, and data-driven. To meet these demands, we need to move beyond static text documents toward machine-actionable Data Management Plans (maDMPs)—dynamic, interoperable tools that integrate seamlessly into research systems and workflows.
Key Challenges with Traditional DMPs
Static Format – Cannot adapt easily to evolving project needs.
Manual Processes – Require repeated data entry across multiple systems.
Limited Integration – Information is locked in PDFs or Word documents, not reusable by machines.
Compliance Burden – Updating and reporting require manual effort, risking errors and omissions.
Best Practices and Solutions: Moving to maDMPs
Efficiency
Automate information exchange between systems (e.g., repositories, funding portals).
Reduce administrative work through auto-population of metadata fields.
Accuracy
Machine-readable formats eliminate repetitive manual entry and reduce human error.
Interoperability
Integrate with research infrastructure such as Laboratory Information Management Systems (LIMS), repositories, and publication platforms.
Compliance
Automatically track alignment with funder, institutional, and legal requirements.
Enable real-time updates and reporting without rewriting the DMP from scratch.
Local Context: reNEW and UCPH
At reNEW and across the University of Copenhagen, DMPs are required or strongly encouraged for funded projects. Current practice often involves text-based templates, which can be limiting for collaborative, data-intensive research.
Implementing machine-actionable DMPs could:
Improve alignment between project planning, UCPH-approved storage systems, and FAIR data principles.
Support compliance with Horizon Europe, ERC, and Danish funder mandates through automated reporting.
Enhance collaboration between UCPH research groups and international partners by enabling system-to-system DMP sharing.
Practical Recommendations
Adopt maDMP Tools – Use platforms like the Research Data Alliance’s maDMP specifications or DMPonline with machine-actionable export formats.
Integrate with Existing Systems – Connect DMP tools with institutional repositories, ERDA storage, and publication workflows.
Automate Metadata Population – Allow DMP fields to feed dataset registration and DOI requests directly feed dataset registration and DOI requests.
Embed Compliance Checks – Configure maDMPs to flag non-compliance before reporting deadlines.
Promote Collaborative Access – Ensure collaborators can view and update maDMPs in real time.
Looking Ahead
Machine-actionable DMPs are more than an administrative upgrade—they are a strategic enabler of reproducible, efficient, and collaborative science. By adopting maDMPs, reNEW researchers can reduce manual burden, increase compliance confidence, and focus more on discovery.
Last updated