Why Manage Data?

Data management enables researchers to properly organize, document, and store research data, resulting in more easily discoverable and reusable data while addressing funding agency requirements for transparency and reproducibility of research methods. 

Think of data as having a life cycle. Often, it's tempting to skip the planning phase and dive straight into data collection, making decisions as you go along. However, careful planning will ultimately save you time in the long run and ensure a smoother process. During the planning stage, you will map out the processes and identify the required and available resources to support your research.

Data Management Plans

A Data Management Plan (DMP) is a document that describes how you will obtain and use your data during a research project and what you will do with your data long after the project ends. Think of it as a formal and concise document articulating how data will be handled during and after completing your research. Often, a DMP encompasses all phases of the Data Life Cycle - from planning to collecting, analyzing, and ultimately preserving and storing the data.

Most funding agencies require researchers to submit a DMP and their research proposals outlining how scientific data from their research will be managed and shared. While compliance is critical for those seeking grants, we like to think of DMPs as a tool that applies to any research project because a well-thought-out plan means you are more likely to:

  • stay organized
  • work more efficiently with your team and set specific responsibilities
  • better share and safeguard data while increasing your research impact
  • prevent unauthorized use or the breach of sensitive information, when applicable
  • better engage your team
  • avoid potential issues related to data and code licensing
  • better budget for storage, preservation, and long-term archiving of the data

A DMP is a straightforward blueprint for managing your data and provides guidelines for you and your team on policies, access, roles, etc. It is an accountability and productivity tool that helps you anticipate the required resources, explore alternatives, and identify available support and personnel to help you achieve your research goals while conforming with reproducible standards and open science principles.

While planning is essential, it is equally important to recognize that no plan is perfect, as change is inevitable. To make your DMP as robust as possible, treat it as a "living document" that you periodically review with your team and adjust as the needs of the project change. 

How to Plan?

  • Plan early: research shows that over time, information is lost, and this is inevitable, so it's essential to think about long-term plans for your research at the beginning before you're deep in your project. And ultimately, you'll save more time.
  • Plan in collaboration: high engagement of your team and stakeholders is not only a benefit to your project, but it also makes your DMP more resilient. When you include diverse expertise and perspectives in the planning stages, you're more likely to overcome obstacles in the future.
  • Use existing resources: don't reinvent the wheel! There are many great DMP resources out there. Consider the article Ten Simple Rules for Creating a Good Data Management Plan, which has concise guidelines on what to include in a DMP. Or use an online tool like DMPTool, as we will see in a bit, which provides official DMP templates from funders like NSF, example answers, and allows for collaboration.
  • Make revising part of the process: Don't let your DMP collect dust. Revise the DMP part of your research project and use it as a guide to ensure you're keeping on track.
  • Include FAIR and CARE principles: Think of the resources discussed in the previous lesson. Including FAIR and CARE in your DMP's planning process will make it easier to include and maintain throughout the project. Operationalize these acronyms in your answers and incorporate the terminology (and their underlying meaning) into your workflow.

What to include in a DMP?

Most funding agencies require a DMP as part of a funding application, but the specific requirements differ across and even within agencies, depending on the discipline/division and program. Suppose you are writing a data management plan for a solicitation proposal. In that case, the funding agency will have guidelines for the information they want to be provided in the plan. Always check which template applies to the specific call you are submitting your proposal and use the most updated version available. You may also consider consulting SPARC Open Data and the DMPTool for a list of federal mandates and funder policy documents, including specific guidelines and templates.

After you have identified which template or specific guidelines you should follow, ask yourself:

  • Do you thoroughly understand all the requirements? Or do you need any clarification before you start?
  • Is there a page limit to what you can submit in your proposal? Would having an appendix or a more extended version of your DMP for internal use (and not for submission) be beneficial?

These plans are typically two pages long and are reviewed as an integral part of the proposal. Depending on the relevant scientific community, they are considered under intellectual merit, broader impacts, or both. A good plan should include information about the study design, the data to be collected, metadata, policies for data access, sharing, and reuse, as well as long-term archival and preservation.


Fortunately, you do not need to create a DMP from scratch or use a text processor. A great tool is available to assist you in crafting your DMP, which provides web-based templates (based on funder requirements) that enable you to input the data management plan for your project: the DMPTool. Some other advantages of using this tool to craft your DMPs include the ability for users to:

  • Add project details: You can continually update your project's status and provide more descriptive information.
  • Add project contributors: Include contributors and DMP collaborators with specific permissions (co-owner, editor, or read-only access).
  • Specify expected research outputs: You can specify expected and completed research outputs. This includes options for flagging sensitive data or personally identifiable information (PII), selecting intended repositories and metadata schema, providing access information, indicating data size, anticipating the release date, and specifying the license.
  • Request feedback: You can request an expert review from our local team directly through the tool.
  • Set plan visibility: You can restrict who can access your plan.
  • Register your plan: To obtain a DMP ID, a DOI explicitly designed for data management plans. This registration allows you to link your plan to your ORCID and project outputs, such as datasets and journal articles, making it easier to demonstrate compliance with your funder's requirements by the end of the project.
  • Download in preferred format: you can download the plan in your selected style.

Schedule a consultation with our team to learn more:

Recommended Resources


UC Research Data Policy

The University of California Research Data Policy was issued on August 9, 2022, and became effective on July 15, 2022. The intent of this Policy is to clarify the ownership of and responsibility for research data generated during the course of university research, encourage active data management and sharing practices, and provide guidance with respect to procedures when a researcher leaves the University. This Policy applies to all research data generated or collected during the course of university research. 

University of California Research Data Policy [PDF]

EFFECTIVE JULY 15, 2022. This UC Policy clarifies ownership of and responsibility for Research Data generated during the course of University Research.

Frequently Asked Questions about UC's Research Data Policy

FAQs and additional information on the University of California's Research Data Policy.

Why the UC Research Data Policy is Important 

AUGUST 10, 2022 - This Office of Scholarly Communication blog post describes the importance of establishing a systemwide research data policy as part of UC's broader efforts to demonstrate commitment to research data.