Data Management Plans (DMPs) have become an integral component of modern research. While the funding agencies and institutions increasingly require them, their true value lies in providing a structured framework for handling research data throughout its lifecycle, thereby contributing to the success and sustainability of research projects.
Without the right support, writing a DMP can be time-consuming and overwhelming. Funders and institutions often expect specific formats, and it’s easy to overlook details like data preservation and licensing. The Research Data Management Organiser (RDMO) tool offers a practical solution for drafting, maintaining, and aligning DMPs with both funders’ expectations and open science principles. By guiding researchers through the relevant questions, ensuring compliance with international standards, and supporting collaborative planning, RDMO reduces administrative burden while enhancing the openness and integrity of scientific research.
What is a DMP?
A Data Management Plan (DMP) is a “living” document that describes the structure of research data before, during, and after the project.
In practice, a DMP acts as the “life story” of your data, from birth (collection) to legacy (reuse), ensuring that decisions made early in the project support its long-term usability.
Instead of beginning with a blank page, RDMO provides you with structured questionnaires that guide you step-by-step (e.g., How do you assign metadata for your software?) The beauty of it is its flexibility: none of the questions are mandatory, so you can add the information at hand and return later to complete or refine your answers as your project develops.
Customized templates
Different funders, institutions, or journals often require DMPs in slightly different formats. Inside RDMO, you can choose from 14 different templates that are specific to either funding agencies (Horizon Europe, ERC, DFG, VW Foundation), institution-specific templates (data management in climate modelling projects, data management plans with mathematical research data, etc.), or beginner-friendly, brief questionnaire templates.
Collaboration features
Another useful feature is the possibility to invite a new member to a certain project and assign one of the following roles: Guest (who can only read), Author (who can answer questions), Manager (who can additionally create snapshots, export the project, import values, and update the project settings), or Owner (like you).
Flexible export options
Lastly, but not least, are the various exporting options to generate documents in formats required by funders, journals, or institutions (e.g., XML, CSV (comma-separated), CSV (semicolon-separated), JSON).
Did you know?
Software Management Plans (SMPs) are conceptually similar to DMPs. Within RDMO, you can find a template specifically designed for SMP, providing seamless support for the development of your project.
FAIR Principles
A well-structured DMP naturally aligns with the FAIR principles (Findable, Accessible, Interoperable, and Reusable) by ensuring that data is managed with long-term usability in mind.
Why are FAIR Principles important?
The implementation of FAIR not only strengthens data management practices but also promotes data to be understandable and interoperable by both humans and machines. In turn, this provides opportunities to enhance reproducibility, drive innovation, and advance the knowledge of the world as we know it.
For Max Planck researchers, RDMO is readily available via the Max Planck Digital Library (MPDL).
For a clear and structured record that you can easily refer back to later, we recommend starting with the “Brief questionnaire” template. With just six key questions, it can help you quickly outline the foundation of your project.
7. FDM Workshop (Deutsch) | October 28-30, 2025 (hybrid)
Research Data Management at the Max Planck Society in collaboration with the MPG-NFDI Workshop. Registration is available via the official workshop website.
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