Research Data Management: Prepping: Writing a Data Management Plan

Data Management Plans (DMPs) are written documents that detail how data will be handled throughout its lifecycle. They outline how data will be collected, stored, processed, analyzed, shared, protected, and preserved, while addressing compliance with relevant legal, ethical, and institutional requirements.

DMPTool is an online platform designed to help researchers create and manage data management plans. It offers templates from funders like the NIH and NSF, guidance and best practices, and resources tailored to specific funding agencies and research institutions. Additionally, DMPTool allows researchers to collaborate on DMPs, enabling multiple team members to contribute to the plan.

You can create a DMPTool account using your UW NetID. Learn more about DMPTool and how to use it in our DMPTool guide.

Research data management (RDM) is a foundational aspect of responsible research practices. It promotes the integrity, reproducibility, and usability of research data. Planning for data management involves making several ethical, legal, and practical decisions before you begin your research project. Tackling these questions before you begin your research streamlines the work, protects your data, and is often required by common funding institutions and organizations.

Federally funded research generally requires you to write a DMP. The following list contains common federal funders and their DMP policies:

• CDC
• Department of Defense
• Department of Energy
• FDA
• NASA
• NIH
• NEH
• NSF

If your funder is not on this list, make sure you familiarize yourself with their policies around data management plans. SPARC provides an up-to-date listing of data sharing policies by funding organization.

While research data management plan can differ, they typically includes similar key components that cover the entire lifecycle of research data. Subsequent pages in this guide will take a deeper look at some of the following components of data management plans.

• Data Description: A detailed description of the types of data that will be collected or generated during the research project. This can include formats, types (e.g., qualitative, quantitative), volumes, and any associated metadata.
• Roles and Responsibilities: A clear delineation of roles within the research team regarding data management. This should indicate who is responsible for data collection, storage, sharing, backup, and other critical tasks.
• Data Collection Methods: An explanation of how the data will be collected, including instruments or software used, data sources, and any standard protocols followed during data collection.
• Data Documentation and Metadata: Information on how data will be documented to ensure usability and reproducibility. This can include metadata standards, data dictionaries, and other documentation practices to describe and contextualize the data.
• Data Storage and Security: Information about where and how the data will be stored, both during and after the project. Your plan should also include how you plan to keep your data secure.
• Data Organization and Format: Information about file naming conventions, folder structures, file formats, etc.
• Compliance with Legal and Ethical Requirements: Details on compliance with relevant laws, regulations, and ethical guidelines. This can include issues like informed consent, data anonymization, HIPAA, or other applicable rules. Read about ethical considerations in data management here,
• Data Publishing and Sharing: Information on whether and how data will be shared with others, including plans for data repositories, access policies, and any embargo periods before data becomes publicly available. This section might also address data licensing and intellectual property rights. Read about where you can deposit your data in our Data Publishing and Sharing guide.
• Data Preservation and Archiving: Plans for long-term data preservation, including which data will be archived, where it will be stored, and for how long.