Guidelines for data management plan

As open as possible, as closed as necessary

A separate, obligatory appendix to the application is the data management plan, in which you describe how you manage your research data during the project and make them available after the research project has been completed. Even if your project will not produce data to be stored or if you will use existing, openly available data, you should still append a data management plan to your application. In the appendix, describe your data management as briefly as possible. Answer the questions concisely and where applicable.

Draft the plan according to the structure below. If you want, you can use the data management planning tool DMPTuuli. The structure and contents of the appendix are the same regardless of which method you choose.

More guidelines on the details to be described in the plan are available in DMPTuuli and on the Academy of Finland’s website. Both sources include the same additional guidelines. DMPTuuli may also contain university-specific guidelines. The Finnish Social Science Data Archive (FSD) has also drafted its own guidelines as well as templates for data management plans.

In the September 2017 call, the application is drafted following current legislation. When the funding period starts in the autumn of 2018, however, funding recipients must follow the EU’s new General Data Protection Regulation, which is currently being drafted.

Read more:

Structure of data management plan

On the first page of the plan, write the applicant’s (the PI’s) name, research topic and application number. Also, remember to date the plan. The plan should not be longer than 1–2 pages. Plans drafted with DMPTuuli are slightly longer than this, because of differences in text layout.

1.       General description of data

What types of data (e.g. qualitative, quantitative, measurements) will the project collect or use? The data may be either new data or openly available data. What are the file formats of the data?

The data content is described in more detail in the research plan.

2.       Documentation and quality

How will the data be documented? For example, what identifiers and metadata standard will be used? How will the quality of the data and the documentation be ensured?

3.       Storage and backup

How will the data be stored and backed up during the whole life cycle of the research project (e.g. access to data)?

4.       Ethics and legal compliance

How will ethical issues concerning data storage (e.g. sensitive personal information, third-party access to data) be considered? How will copyright and IPR issues be managed?

Please note that the ethical issues that concern data collection and research implementation are described in the research plan.

5.       Data sharing and long-term preservation

How and when will the data be made available for reuse by other researchers? With what party (data archive, storage service, etc.) will you collaborate to manage the data and make them available? What resources will enable data sharing and long-term preservation?

For each of these sections, we have posted the detailed Academy’s guidelines and tips for best practices in DMPTuuli on our website: detailed Academy data management plan guidelines and best practices in DMPTuuli

Updated for Sebtember 2017 call.

Last modified 2 Aug 2017
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