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What is research data management?

Research data management is simply sustainable and efficient management of research data. This means that organization, documentation, storage, processing, archiving and preservation as well as sharing of data are part of a research activity. The lifespan of data often outlives the research project under which it was collected. Data can be reanalyzed or reworked in follow-up projects and can be reused by other researchers.


There are many advantages to focusing on data management regarding research data both for you as a researcher and for society at large.

  • Research becomes more efficient. By organizing files and data in a structured way, you save time, facilitate future data reuse and minimize the risk of data loss
  • The researcher's integrity is secured as accurate and complete research data is crucial for validating, evaluating and reproducing research results
  • Research becomes more visible. Making research data available increases the visibility of researchers and also increases the number of citations.
  • Increases the opportunity for collaboration. By facilitating the sharing and reuse of data for future research, opportunities for collaboration with other researchers can be created.
  • Make  the research safer. You can reduce the risk of data loss by protecting research data through appropriate data storage.
  • Makes it easier to comply with current legislation and funder policies. Many funders and a growing number of journals and publishers now require researchers to share the information at the end of a project or when publishing corresponding research results
  • Enables the data to be FAIR.  FAIR are national guiding principles for scientific data management intended to improve the usability and reuse of data. FAIR stands for Findable, Accessible, Interoperable and Reusable.
  • Creates transparency in research. By handling research data according to good practice and making it available to the public, one can demonstrate that the use of funds from public resources takes place in a responsible manner.

Read more in the article Five selfish reasons to work reproducibly (F. Markowetz, 2015)

Five steps to research data management 

It is possible to divide the handling of research data into five steps:

  1. Plan
  2. Organize
  3. Document
  4. Work with data
  5. Prepare and share


You have a lot to gain from early planning how you will collect and manage your data in your research project. If you are out in good time, the risk of something going wrong or being missed later on is reduced. Reviewing how you protect personal data and your data material, what possible agreements are needed with collaboration partners, whether ethics review is needed and what needs to be taken into account when applying for research funds. A good way to get an overview of the various aspects of data management is to draw up a data management plan

At University West, the Data Access Unit (DAU)  can help with the various parts of the planning phase.

The Swedish National Data Service (SND) lists questions that may be good to ask at the start of a research project:

  • How do I plan to protect personal data and other sensitive information?
  • What is needed to ensure that data is stored securely?
  • What security requirements will be placed on the data?
  • What needs to be specified if several organisations/authorities are to collaborate with data in the project?
  • How much data management costs should I include in the budget?
  • Is a data management plan needed to facilitate data management and future archiving of data?

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In order to keep research documentation and research data in order, thought is required about how everything should be organized and stored. This makes it easier for both you and researchers who may want to take part in your research in the future.

At University West, all researchers are offered a secure space on the folder M: for their research project and data. Contact the Data Access Unit (DAU)  for more information.

In order to be able to organize clearly and consistently, it is important to think about file names, file formats, versioning and folder structure. The term metadata is often used to describe information. Metadata means data about data or description about data and is used to give a picture of the content of, for example, a data collection or web page. There are often elaborated standards on how metadata should be used within different disciplines.

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When it comes to documenting your work, the focus should be on recording and documenting your workflow such as activities, structures and decisions that have been made during the research process. This facilitates the communication in a research group, to remember why certain changes were made and for future researchers to understand the research project. A good aid in this process is a data management plan where you gather all your documentation.

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Work with data

There is a lot to think about when working with research data so that it is safely stored, easy to find and accessible to everyone in the project and for the future.

Questions that may be good to ask yourself according to the Swedish National Data Service are:

  • How can I be sure my data is backed up?
  • In what ways can errors occur in the data material?
  • How do I structure my data in the best way?
  • How to solve access problems to well-protected data?
  • How can I read the files from my analysis program also in the future?

For questions about which software is suitable and available at University West, contact the Data Access Unit (DAU).

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Prepare and share

When the research project begins to come to an end, it is important to consider how the research data should be preserved and made available for the future. It is of great importance to review your data documentation so that it is clear and understandable for both you and others after a longer period of time.

An important step is to make your research data possible for others to find is that it gets a so-called persistent identifier (PID). A PID can be described as a unique ID number or code string, which can identify people, organizations or objects. Examples of PIDs are ISBN numbers for books and DOI numbers for articles and research data.

As part of both the Open Science and FAIR concepts, the vision is that even research data can be shared and openly accessible. Of course, there are restrictions on sharing sensitive data freely with the public based on, for example, personal data and corporate collaboration. In those cases, however, it is possible to write a clear description of the content of the research data. However, sensitive research data cannot completely avoid being shared with, for example, other researchers through testing. Prepare  and share data is a big issue and you can read more about this on our page on sharing data below.

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