🔴DMP Guide Sheet
GUIDE SHEET: Data Management Plans
Data Management Planning
The Research Data Lifecycle The research data lifecycle is a framework that illustrates the different stages of managing data throughout a research project, highlighting its progression from initial planning to final reuse. Effective data management requires thoughtful planning and thorough documentation of how data will be collected, organized, stored, and shared.
This lifecycle model breaks down each project phase concerning data, from planning and design, collection and analysis, storage, sharing, and eventual reuse. At its core lies the "Store & Manage" stage, which ensures data integrity, security, and accessibility throughout the project. While the lifecycle is often presented as a linear sequence—from "Plan & Design" to "Access & Reuse"—in practice, researchers frequently move between these stages in a non-linear, iterative manner as their project evolves and new needs arise.
Plan and Design Stage
A Data Management Plan (DMP) is vital for research projects. It is a comprehensive document outlining data handling, security, and sharing throughout and beyond the project. As funding entities and research institutions increasingly demand DMPs, their formality varies from informal internal documents to formal submissions for funding agencies.
DMPs ensure all team members understand data location, organization, documentation, and access procedures. They can be updated as living documents throughout the project. Creating a DMP promotes strategic thinking, collaboration, and consistency among team members while identifying potential weaknesses in the research process.
A well-crafted DMP conserves time, money, and energy by organizing data and planning its management throughout its lifecycle. With many funders now requiring DMPs in their application process, prioritizing their creation is crucial for meeting requirements and ensuring long-term data accessibility, ultimately benefiting the broader research community.
When thinking about your project:
Determine if you need a Data Use Agreement to acquire or share data.
Develop Documentation and metadata standards for better data discovery.
Your institution's and funding agency's expectations and policies
Whether you collect new data or reuse existing data
The kind of data collected and its format.
The quantity of data collected.
Whether versions of the data need to be tracked
Storage of active data, backup policy, and implementation
Storage and archiving options and requirements.
Organizing and describing or labeling the data
Data access and sharing
Privacy, consent, intellectual property, and security issues
Roles and responsibilities for data management on your research team
Budgeting for data management
For more insight into the questions you should ask and answer, check out the Data Management Checklist (UK Data Archive)
Data Management Plans Resources
Research Data Management Training Resources
The University of Copenhagen Guide for writing DMP
European Commission Guidance on DMP
Tools for writing DMP
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