Responsible data management and sharing

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 it is possible. Here are some examples about data policies from different organisations:

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

Journals have different practices related to opening the data. Examples: Elsevier journals, Springer Nature

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

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

You can also check FAIRsharing to search for data policies, and also standards and databases.

Responsible data storing during the research

Storing your data properly 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.

It is important to ensure responsible data management throughout the data’s life cycle. Data protection, data security or contracts may require restricting access to the data. In the research context there may be reasons to limit access to certain purposes or even groups if the data is sensitive and/or could be misused to create social or individual harm. Also, contracts relating to data may limit access and reuse of data. Note all these when considering whether you can share your data and how.

As you are planning your research, consider which research data infrastructure you will use during the research to make the data accessible and who will need access to it. It can be tempting to make use of commercial cloud services (e.g. Google docs or Dropbox) to easily share data with collaborators during the active stage of the project. However, be sure to consider whether you will be working with sensitive data. If so, these cannot be used for secure data sharing. In most cases, managed infrastructure provided by your organisation will provide better 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 storing services and information security (e.g. UEF’s information protection and processing instructions). When handling sensitive data, choose a storage service that is secure and suits all the requirements of sensitive data.

Informing participants and agreeing about data sharing

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 amount of indirect identifiers (e.g. the professional title and job are 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 sharing the data (i.e. within your research team or more widely), as well as about preserving of the data to support reuse.

Study more about informing research participants:

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 de-identify 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.

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.


  • Many research organisations, funders and journals have data policies that outline the requirements or recommendations for data sharing when possible.
  • Responsible data storing is a part of good scientific practice and enables data sharing for reuse.
  • Consider carefully if your data includes personal data, and manage it according to the requirements set for handling personal data.
  • Research participants must be informed about data sharing.
  • Data sharing must be agreed within the research group.

(7/2021 KH)