Data management

In a research project, a transparent and systematic approach to data management has great benefits. Here you can read about how to handle your research data during research projects, how to preserve your research data for the future and how you can make the data accessible to others.

Create a data management plan

A data management plan (DMP) is a plan for how to handle research data and describes how the data has been collected, is stored and made accessible.

Creating and actively using a data management plan will simplify several aspects of data management, for example by providing an overview of your research data and associated legal requirements. A data management plan will become even more useful if it is used as a living document during the course of a project.

The University Library has launched a new tool for creating data management plans, LiU DMP Create. If you are a LiU researcher and want to test LiU DMP Create, please contact us at

In the future, LiU researchers will be able to access LiU DMP Create with their LiU-ID, and by filling in a form with questions create a data management plan from scratch.

Key components in a data management plan

A data management plan should include six key components:

  1. A description of your research data
  2. Information about the documentation and quality control of your research data
  3. Information about the storage and back-up copying of your research data
  4. Information about legal and ethical aspects of your research data
  5. Information about the accessibility and long-term preservation of your research data
  6. Information about responsibilities and resources related to your research data

Research funders

Research funders have various requirements regarding data management plans. In some cases, they have specific recommendations, in others, a set of demands. The Sherpa Juliet database can be used to search individual research funders’ requirements. See the linked page below for further information.

> Data management requirements – Grants Office LiU

Working with research data

Organizing and structuring research data

If research data is consistently structured and organized, the handling of research data will be less dependent on individuals. The data will also be easier to retrieve in the future. This is especially important in collaborative research projects involving several partners. It should be possible to understand the content and how it has been processed. Organize your data by:

  • Creating a systematic and transparent file structure
  • Being consistent in naming your files
  • Being consistent in versioning your data files, so that it is clearly indicated which file is the latest, which is the master, and which file constitutes the basis for which analysis etc.

Storage of research data

Safe storage of research data is crucial. Never store research data on your personal computer without making a back-up. Instructions and recommendations for how to store and back-up research data is available at Insidan.

> Organising and storing files - LiU intranet

Legal aspects

There are ethical as well as legal aspects that have bearing on research data management. In LiU DMP Create, there are questions dealing with such aspects that impact the storage, preservation and accessibility of research data. LiU’s Legal Office can offer advice to LiU researchers and other employees about legal aspects of data management:

> Legal Advice, LiU

Information security

Sensitive research data will need to be protected. By classifying your data, you can make sure that it receives the right level of protection. LiU has guidelines regarding information security that need to be followed when processing personal data.

> LiU Guidelines for information security (in Swedish)

Processing of personal data

When handling personal data at LiU, the General Data Protection Regulation (GDPR) applies, as well as additional legislation. When collecting, processing and storing research data that contains personal data, the GDPR must be followed.

> More about data Protection


Research data can be searched online, for example with Google Dataset Search. If research data has been published in an online repository together with relevant metadata, the data is searchable, findable and reusable by others. The data will thereby also be citable.

Preservation of research data

Archiving and selective disposal

It is important to preserve information that provides context for how the project was carried out and that facilitates interpretation of the data. Today, archiving of research data for long-term preservation is done by the researcher or research group that carried out the project. As a researcher, you have a responsibility to make sure the research data from your completed projects is preserved.

Research data needed for verification of research results shall be preserved for a minimum of 10 years. Data that is considered of future scientific significance in its research field or data considered of relevance for another field of research, shall be preserved indefinitely. The same applies to data considered of historical (either scientific, cultural or personal) value, and data considered of great public interest. Usually, it is researchers involved in the project that knows best which data is important to preserve. It is therefore highly recommended that the principal investigator prepares the project’s research data for archiving and provides documentation about which data that should be preserved and which data that can be disposed after a period of time.

In order to make your data comprehensible in the future, it is important to organize it and remove any irrelevant information. Drafts and working material can be deleted without any specific legal foundation. Research data that is a public record but no longer needed for verification purposes or is estimated as of no use to future research or of no public interest does not need to be saved, according to RA-FS 1999:1.

> Regler för bevarande och gallring av forskningshandlingar vid Linköpings universitet (in Swedish)

If you need advice regarding filing, contact your local archive coordinator (at where xxx is substituted for your department’s acronym) or the University Archive at

Making research data accessible


To make research data accessible means making it open access or making it into open data online. This is also referred to as publishing research data.

Journals and research funders may require you to make your data accessible. Research data can be made available via:

  • Journals/at the request of a journal
  • Repositories such as DiVA.

When research data is made available, it is given metadata (making it easier to find) and documentation (providing context). File formats are reviewed (see the FAIR-principles for further information). In a repository, datasets are actively managed to be accessible and usable. Research data files up to 16MB in size can be published in DiVA.

> Publish data in DiVA

Documentation and metadata

When publishing your research data, you should also provide the relevant documentation.

Documentation is what is required for you or someone else to be able to understand or reuse research data (datasets) sometime in the future. Be sure to include methodological considerations, research purpose and the questions asked. Good documentation also helps when you need to recreate research results for verification purposes.

The documentation provided may vary between research fields. Documentation regarding measurements could be information about which standard that was used, and regarding an interview information about the circumstances under which the interview was conducted. Sometimes a research diary or a lab book can be used as support for documentation.

Metadata is often referred to as “data about data”. Metadata allows the information to be machine readable. Metadata is structured information with the purpose to define, explain, describe and localize data.

Citing research data

Just as you cite a publication, you can cite research data. Research data has often been given permanent identifiers (usually a DOI or a URN:NBN), which will need to be included in the reference.

> Citation technique and styles
> How to Cite Datasets and Link to Publications (Digital Curation Centre)