Plan to share

Data management and sharing must be planned before starting the research project

A data management plan, DMP (Fig. 12), is an important document to create at the beginning of your research project. DMP describes and documents the collection, processing, storage, archiving and possible sharing or disposal of research data. DMP outlines all aspects of managing your research data, both during and after your project.

Research data management is all about making your work efficient. With appropriate data management planning even sensitive and proprietary data may be shared, reused and FAIR, at least to some extent. All researchers should be familiar with the basics of research data management, regardless of whether they are planning to share the data or not.

Watch the video about what data management planning is, how you do it and why it is important (5:30).

If you haven’t yet participated in any research data management training, find out what kind of training your organisation provides. UEF Library offers the Basics of Research Data Management course (1-2 ECTS) course for UEF postgraduate students. The study material is openly available for everyone.

Data policies and requirements

Research organisations, funders and journals have data policies that often require or recommend, in addition to creating DMPs, sharing research data for further use when possible. Acquaint yourself with the requirements of your organization and potential funder before creating your DMP. Then, you can consider their demands in the DMP.

Here are some examples of data policies from different organisations:

Find out if your research organisation or funder has a data policy. What does it state about data sharing?

Scientific journals have different practices related to sharing the data. Examples: Elsevier journals, Springer Nature journals.

Some publishers require a data availability statement which describes how the data is available, or in case when the data cannot be shared the reasons need to be specified, see, for example, Springer Nature and Taylor & Francis data availability statements.

Find out what kinds of requirements the journal, where you could plan to publish, has about research data availability.

Tools for creating DMPs

A data management plan is a document which describes the actions needed to manage the data. With the help of a data management plan, a researcher can perceive the entire life cycle of the research data, diminish the risks related to it, as well as ensure its ethical, secure and efficient use during and after the research.

There are many useful online tools that can help you create a DMP. Most tools provide guidance to meet the institutional and funder requirements. Find out if your organisation or country recommends a certain service. Here are some examples about free tools:

DMPTuuli (DMP tool for researchers of Finnish research organisations and collaborators)
DMPOnline (Digital Curation Center, UK)
DMPTool (University of California Curation Center)

Questions to consider when planning data sharing:

Which data and associated metadata, documentation or code will be deposited openly available?
Does the data contain personal, sensitive or other information that restricts sharing?
Is sharing the data ethically acceptable, and have the necessary consents been obtained?
Are there any agreements related to data collection that restrict its sharing?
Do funders or collaborators have specific requirements regarding data sharing?
Has the data been properly anonymized if needed?
Is the data stored and documented in a way that makes it accessible and understandable to others?
Is the data stored in a format that allows sharing?
Are there any costs associated with data sharing, and who will cover them?
Where will the data be made available? Which data repository will be used?
Does the chosen repository/service meet the requirements of your research and of your funder?
How long does the data need to be preserved?

Plan data sharing already in the DMP. It can be updated whenever needed.

Other practical considerations

Responsible data storing during the research

It is important to ensure responsible data management throughout the data’s life cycle. Storing your data properly during the research can save you a lot of time (in finding and interpreting) and frustration (in not losing it). Moreover, when properly structured and annotated during research, you’ll have your data preserved and/or shared with minimal effort at the end of your research.

As you are planning your research, consider which research data infrastructure (i.e. storing solutions) you will use during the research to make the data accessible and who will need access to it. Data protection, data security or contracts may require restricting access to the data, both during and after the research. In most cases, the managed infrastructure provided by your organisation will provide adequate security and back-up and can often be made accessible to collaborators from other organisations. Also, consult with your organisational IT support in cases of large amounts of data. Always follow the guidelines of your organisation about data storing and information security (see e.g. UEF instructions on information processing, requires UEF login). When handling sensitive data, choose a storage service that is secure and suits all the requirements of sensitive data.

Informing research participants

If your research involves human subjects, you need to plan carefully how you will protect the participants, does the data include personal data, how you will manage the data, can you share the data and what procedures it requires. Notice that all data related to an identified or identifiable person is personal data. Even though your data does not include any direct identifiers (name, personal identity number, email address or biometric identifiers), the data subject can be identified by a small number of indirect identifiers (e.g. the professional title and job can be sufficient to identify the person). Read more to check whether you are processing personal data or not from the Finnish Social Science Data Archive’s Data management guidelines.

It is important to inform research participants about plans for data sharing (within your research team or more widely), as well as about preserving of the data to support reuse.

Study more about informing research participants:

Consent for data sharing (UK Data Services guidelines
Informed consent (The Data Management Expert Guide by CESSDA ERIC)
Informing research participants about archiving (Finnish Social Science Archive)

Data anonymisation

Consider whether the data needs anonymisation and plan how you will take care of it. Anonymisation means altering the data in a manner that permanently prevents the identification of individuals. Explain to research participants how you will deidentify the data in practice, for example, by removing all personal information that could identify an individual. If you handle data that needs anonymisation, read anonymisation principles from the Finnish Social Science Archive.

Agreeing about data sharing

If you are involved in a collaborative research project – either with other academic institutions, industry partners, or citizen science – you will need to make sure that your research collaborators agree to data sharing. This should be clarified during the planning stage of your project and built into consortium agreements that are developed to govern your project. Bear in mind that your partners may only agree to sharing particular datasets and request that others are kept confidential. You should also agree at which point during the life of the project the selected datasets will be shared and with whom, and document these decisions so that all partners have a clear idea of what will happen and when.

Remember:

Create a DMP before starting the research project. Do this regardless of whether you are planning to share the data or not.
DMP outlines all aspects of managing research data, both during and after the project.
Use online tools for creating DMPs. Most tools provide guidance to meet the organisational and funder requirements.
In Finland, DMPTuuli is commonly used for creating DMPs.
Responsible data storing and documenting is a part of good scientific practice and enables data sharing for reuse.
Research participants must be informed about data sharing. Data sharing must also be agreed within the research group.
Describe the possible data sharing in your DMP.

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