6. Data management plan (DMP)

All the information dealt with so far should be summarised into a data management plan, typically referred to with the acronym DMP. A DMP is an important part of your research plan, usually as an attachment to it.

With the help of a data management plan, you can perceive the entire life cycle of your research data, i.e. where the data comes from and what happens to the data in different stages of the research. The DMP guides you in planning the use of the data throughout the research project.

Data management of each research project should be planned before the project starts, which is also when data management plan should be created. It is important to update your DMP as things become more specific or change as the research progresses.

Why a data management plan (DMP)?

Research data management and the drafting of a DMP are an integral part of good research practices. Planning helps save time and money.

Many research funders (e.g. Research Council of Finland, Horizon Europe) require a data management plan. Remember to check the guidelines of your funder and organisation first (see UEF Open Science and Research Policy), so that you are aware of what kind of requirements they have.

Creating a data management plan before you start to collect and handle the data

reduces the risk of losing research data
helps in predicting and controlling the complex details related to ownership and rights of use
supports in making your data FAIR: Findable, Accessible, Interoperable and Reusable
increases the reuse of your data and the visibility of your research through FAIR-related measures
helps in meeting the funders’ requirements
reflects the principal investigator’s managerial skills as a project leader.

The content and structure of the DMP may vary, for example, according to the requirements and instructions of the funder.

It often consists of the following sections (DMP 1 – DMP 6)

DMP 1: General description of the data

In the general description of data in the DMP, you need to describe what types of data are produced, collected, processed, and reused during the research project. Creating a table may be helpful for the drafting of the DMP if various types of data are used in the research, and having the table as reference for the later sections has its advantages.

If several types of data are generated during the research, it is advisable to distinguish between them, for example in the following fashion: A) data to be collected (e.g. interviews), B) data created in the research project (e.g. analyses) and C) previously produced data used in the research (e.g. archival data). Research data typically consist of text, images, audio recordings, video recordings, numerical data, source codes or software. It is recommended to use standard formats that are as open as possible and known to the scientific community, so that the data can be opened for other users to read in the future.

A size estimation of the data is needed to evaluate the required storage space. In the DMP, the size of the data does not mean number of interviewees or variables. These are described in the research plan.

The general description explains how the coherence and quality of data will be ensured. The quality of data does not mean the quality of research methods, which is a matter addressed in the research plan. Instead, the quality of data management described in the DMP ensures that the data remains flawless throughout the life cycle, so that the information content does not change when the data is copied, converted from one file format to other, analysed, or processed in some way.

Vinkkejä, tips

Collect the technical descriptions of your research data into a concise table.

DMP 2: Ethical and legal compliance

Ethical and legal issues related to research data management are described in the DMP. These issues hold significance for the entire life cycle of the data, i.e. how the data can be collected, preserved and processed, as well as who may use the data and for what purposes.

The data management plan should explain whether confidential data such as personal data, sensitive information, business secrets, or military data will be processed during the research. The project may also require a research permit, which should be clarified at the beginning of the study.

If personal data is being processed in the research, at least the following issues should be addressed (see also chapter 2 of this learning material):

what kind of personal data is collected
does the research include special personal data groups aka sensitive data
is there need for an ethical review
how will the research participants’ privacy be ensured
is the data pseudonymized or anonymized
how will the anonymisation or the pseudonymization be carried out
what procedures are used to inform research participants about data processing?

In addition, the DMP should include information on who owns the data and what kind of agreements should be drawn concerning ownership and rights of use, or if the research participants will have copyright over the things they produce, such as drawings or images. You need to check the guidelines of your organisation and the funder of the research and be sure that the permissions for storing and sharing the data are in order. These things should be confirmed as early as possible.

DMP 3: Documentation and metadata

Data documentation means the description of the research data in a way that informs where, when, why and how the research data was collected. Description helps other users understand, find, and utilise the data. It also enables them to cite your data properly. During the research, information relevant to the content of the study is described, whereas after the research, descriptive information is compiled for the data’s publishing stage, such as information on the location of the data and the rights of use. The description may include information on folder structure, file naming conventions or versioning, for example. It should also reveal what glossaries or standardised expressions were used (e.g. “Written dates follow the ISO standard 8601”).

Metadata standards and README files, i.e. text files containing descriptive information, can be used for description.

Metadata play a key role in the implementation of the FAIR principles. Metadata plays a key role in the implementation of FAIR principles. This explains why many DMP models bundle metadata and FAIR in the same section.

Vinkkejä, tips

Describe and explain all the types of documentation (README files, metadata) that will help secondary users to find, understand and reuse your data.
Consider how the data will be organised during the project.
Describe, for example, the naming conventions, version control and the folder structure for the files.

DMP 4: Storage and backup

The data management plan describes how the collection, storage and backing up of the data are implemented during the research project.

This section specifies

where the data is stored
how the files are backed up
who will be responsible for controlling access to the data, and
how secure access to the data is monitored and controlled during the research.

If there are several parties involved, it is important to plan how the secure transfer of data is ensured within the research group or between collaborators.

Taking care of data security is extremely important, in particular if the data is sensitive and contains, e.g. special personal data, politically sensitive information, or trade secrets

Vinkkejä, tips

It is advisable to use a safe and secure storage provided and maintained by your organisation’s IT support or other reliable IT provider such as CSC.
Do not use external hard drives as your main storage option.
Follow your institution’s requirements on secure data storage.

DMP 5: Opening, publishing and archiving data after the research project

In the early stages of the project, it is important to plan further use of the data, i.e. how the data can be made openly available to the scientific community. The DMP describes which part of the data can be made openly available or published.

If only the metadata is published, this section of the DMP can provide reasons for doing so. For example, the data or parts of the data cannot be opened, as they include confidential information such as sensitive personal data.

It is also important to describe where and when the data and/or the metadata are published or made available. In the initial phase of the research, the publishing or opening channel may not yet be decided, which can be clearly stated in the DMP.

Following the conclusion of the research project, parts of the research data can be disposed, whereas valuable data may be archived. DMP can be used to describe which parts of the data will be preserved and which will be disposed of, as well as in which stage the disposal or archival is carried out. Although it can be difficult to estimate where valuable data will be archived and for how long the data will be preserved, it is advisable to plan ahead in the early stages of the project. Long-term preservation is the process of preserving digital information for as long as necessary, spanning several decades or even centuries.

Use data repositories and archives that are committed to long-term curation.
Not all research data need to be archived: long-term preservation is meant only for unique or hard-to-repeat data with lasting value.
There are numerous domestic and international data repositories and archives for long-term preservation.

DMP 6: Responsibilites, roles and resources of research data management

The DMP briefly describes who (e.g. name, position, institution) is responsible for data management during the research project and the data itself after the project has concluded. Responsibility over data management may lie with an individual researcher, a research team or collaborators. This section is also where the resources needed for data management (e.g. expenses, workload and time) are assessed, i.e. the resources required to implement the actions described in the data management plan.

The guidelines for planning data management instruct to consider the implementation of open science and FAIR principles. It is therefore important to describe what resources are needed to store and open the data according to FAIR principles. The individual measures that support the FAIR principles are not listed in this section.

Estimate your data management costs, for example, with the help of the following guidelines and tools:

Costs of data management (Utrecht University)
Data management costing tool (TU Delft)

Tools, templates and guidelines for writing a DMP

DMPTuuli is a tool created for helping researchers of Finnish research organisations write data management plans. It instructs you to consider all the relevant questions about data management. In order to use DMPTuuli, you need to create a DMPTuuli account or sign in with your institutional credentials (HAKA).

DMPTuuli can be used to work on a DMP with partners, and the completed plan can be attached to the funding application in the required format (e.g., docx, pdf).

DMPTuuli has DMP examples as well as general and funder or organization-specific guidelines (e.g. Research Council of Finland, European Commission, UEF). You can choose the guidelines you need before you begin writing your data management plan in DMPTuuli.

General Finnish DMP Guidance and additional instructions for planning the management of sensitive and confidential data are also found in DMPTuuli.

In addition to DMPTuuli, there are several other internationally used DMP tools as well, such as

DMPOnline (Digital Curation Center, DCC, Iso-Britannia)
Research Data Management support (Utrecht University)
Data Stewardship Wizard
Argos (OpenAIRE, EUDAT).

Note that in addition to this learning material, you can find UEF instructions and guidelines in UEF Data Support and UEF Library websites.
Familiarise yourself with General Finnish DMP Guidance.
If your research has a funder, find out if it has requirements or guidelines for data management.
When writing your DMP, try to give a comprehensive description about how you will manage your research data from the technical point of view.
Answer all questions that are relevant particularly for your research data.
Avoid overlap with the research plan, where you describe the research methods and analysis. In the DMP, you can refer to the research plan and vice versa.
A data management plan should not be thought as a simple administrative task for which standardised text can be pasted in from model templates, with little intention to implement the planned data management measures. So, describe only the measures that are really needed, e.g. to enable the data to be opened.
If something is still unresolved or only at the planning stage, it is still worth writing in the DMP. This shows that you are aware of key data management measures and reminds yourself and the entire research group of the questions still open.
Careful planning of data management measures during the starting and development stages of your research project will prevent later panic and frustration.

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