Data management plan

A data management plan, or DMP, is a tool for planning how to organize and structure a data material, as well a documentation of the decisions you make, and the activities you carry out during the course of a research project.  

By thinking about to how to manage the project data at an early stage of a research project, you ensure that the data management is structured and that you follow legal and ethical guidelines. The data management plan should include technical, organizational, structural, and legal aspects of data management, but also information that is important when the times comes to publish and preserve the data material.  

The data management plan does not need to include every detail, but it should be a reliable starting point for where you can find information about the project’s data, regardless of where they are stored or made accessible.

Start writing your data management plan when you apply for funding or plan a research project. A DMP is best used as a “living” document that you keep up to date throughout the process. Update it when a task is completed, when you have finalized a dataset, or when you or your project group have made a new decision regarding the research project.

Many research funding organizations require data management plans for projects that apply for or receive funding. In Sweden, see for example the DMP recommendations from the Swedish Research CouncilOpens in a new tab. In Europe, projects receiving funding from Horizon 2020 and Horizon Europe should provide a data management plan within 6 months from the start of the project; Horizon Europe also requires a brief DMP as part of the application. Increasingly, universities and other research organizations also have requirements for DMPs as part of their policies on research and research data. Funding organizations and universities may provide specific instructions or references to DMP guides, templates, or checklists.

This text is for you as an individual researcher who needs to write a data management plan. If you are part of a larger research project, your data management plan may instead be written by a project manager or the person responsible for data management. 

Checklists for data management plans

There are many tools you can use when writing and maintaining a data management plan. One example is DMPonline, which is used by many universities, as well as the foundation for Sunet DatahanteringsplanOpens in a new tab (sunet.se). Your university may include a specific tool and template in their guidelines for data management plans. Contact your local research data supportOpens in a new tab for assistance in what DMP resources are recommended in your organization.

SND has developed a checklist for data management plans to support researchers in writing and maintaining a data management plan. The SND checklist is adapted to the conditions and regulations in Sweden and is available in Swedish and English. Look at the checklist when you outline your own data management plan to help you identify which data management issues are relevant to your research project. SND's checklist is comprehensive and can be used throughout an entire project, from planning to project completion. It explains what information needs to be provided and why, so it can also provide support for the data management process.

Keep the data management plan up to date

A data management plan should not be a static document that you complete at one time; it is most useful as a living document that evolves and is updated as the research progresses.  

The work with updating a data management plan can be divided into three phases:

• Planning the project and applying for funding
• Working with the data
• Finalizing and publishing the data

Planning the project and applying for funding

In the planning phase, write an overview of what data you will be using and whether that involves reusing existing data or producing or collecting new data. If you will reuse existing datasets, describe how you will access them. Also explain how new data will be produced and quality assured. 

Investigate what your research data management will need in terms of storage and security, with special reference to legal and ethical aspects of the research. Describe the storage solution you plan to use for the amount of data you will handle and their information classification. Will you handle personal data? Then make a list of the university's rules and routines for processing personal data. 

Do you intend to publish the research data? Planning for this early on will help you prepare for how and where data will be made accessible. Are there requirements for open access to research data from funders or from the journals where you hope to publish your research results? What would be the most appropriate way to make your research data openly accessible? Make a provisional list of suitable data repositories in your data management plan, as well as what requirements the different repositories have, for example, regarding file formats. 

If you will be collecting personal data, decide as early as possible whether you will create an anonymous dataset that can be made openly accessible or make the dataset accessible with restricted access. Write this in the data management plan.

If you are required to submit a data management plan together with a funding application, it is generally a less detailed plan that can be developed during the project. The funder often specifies at the application stage what to include in the data management plan.

Working with the data

When the project work begins, update the data management plan with information on where data are stored, where the documentation can be found, and who is responsible for what. You do not have to include all details, but a data management plan can serve as a good starting point for finding project information you may later need. You should also write how you plan to sort and organize data, for example, different versions of data, and how you will name the files. If you later decide to change how you collect and work with data, update the data management plan with the new information.

Document where you keep important project documents. This can include applications to and approvals from the Ethics Review Authority, information classifications or risk assessments, copies of documents sent to study participants, collected consent forms, collaboration agreements with project partners, or license agreements for how external parties may use data. 

A list of important project documents can be made in a separate document, but there should be information in the data management plan on where this list is located. In such a list, indicate where paper documents are kept in original, if there are electronic copies, and where these are saved.  

Finalizing and publishing the data

As the project nears its end, the data management plan should be updated with information on which data will be made accessible (if that has not already been done) and where the entire data material from the project will be archived. If a dataset is published through a data repository, write a short summary about it and provide the DOI for the dataset. For data that will be made accessible through aggregated services (e.g., online databases), you can also add a description of how to find and retrieve the dataset. 

Regardless of whether you made the data accessible, you need to indicate where the entire data material will be stored for long-term preservation, for example, at your institution or in a university archive. This can apply to raw data, large datasets stored offline, or sensitive and protected data that cannot be made accessible via a data repository. By describing in the data management plan where data are accessible, under what conditions someone can access them, and how they may gain access to them, you facilitate the process of fulfilling the FAIR principles when sharing research data, even if the data cannot be made openly accessible.