Research Data Management (RDM)

When opening and publishing your research (meta)data, consider the following questions:

1. How to describe and publish the metadata of your research data?

• It is strongly recommended to use Fairdata Qvain metadata tool to describe and publish the (meta)data. Qvain is part of the Fairdata services offered by the Ministry of Education and CSC. Data described and published by Qvain are transferred automatically to research.fi and Etsin (research dataset finder, also part of the Fairdata services).

• You can log in Qvain with your HAKA account, click "Describe a dataset," and fill in the form. See Qvain User Guide.

• It is through the metadata that your research data become findable and first assessed for downloads and reuse. Creating appropriate and rich metadata is the key to making your research data truly open, understandable, and reusable.

• Note that even if you cannot publish and archive your research data, because, e.g., your data contain personal information, sensitive personal data or confidential data, you can still publish the metadata of your research data..

2. Research data are archived and published in a national or international repository when possible. 

• Recommended general repositories include: 
  • Zenodo by the OpenAIRE project and CERN.
  • IDA, Data storage and archival service, also part of the Fairdata services. See Apply for IDA storage space. 
  • Aila by the Finnish Social Science Data Archive (FSD). Contact asiakaspalvelu.fsd@uta.fi for further assistance.
• Check specific repositories for one data type in re3data.org, a registry of research data repositories covering over 2,000 repositories.
• If your data has long-term value, consider preserving your data in Digital Preservation Service for Research Data. See Long-term pre­ser­va­tion of data below.

3. What part of the data will be opened and published? Will some part of the data be and erased and destroyed?

4. When will the data be available? Do you need to set any embargo period?

5. Which license will you use to publish the (meta)data? Licensing is necessary for publishing data. It is recommended to use Creative Commons license CC BY when possible. 

6. Organize your datasets with standard and non-proprietary data formats, sensible and consistent file naming conventions, and version control. See Data formats and organizing.

7. Remember to Register (meta)data in Haris. 

The FAIR data principles are the guiding principles on how to make data truly open. FAIR can be formularized as “Findable + Accessible + Interoperable = Reusable.” 

The FAIR data principles are mainly about metadata which appears in almost all the FAIR principles. It is recommended to use the Fairdata services offered by the Ministry of Education and Culture and CSC. The services include:

• IDA, research data storage – secure storage for research data.
• Qvain, research metadata tool – a tool for describing and publishing datasets.
• Etsin, research dataset finder – discover and access research data from all fields of science.
• DPS, Digital Preservation Service for Research Data – reliable preservation of digital information for decades or even centuries.

More information, see: 

• FAIR Principles, Go FAIR
• How to make the research dataset FAIR

Data documentation means describing the data, is data about data, and provides information about the who, what, when, where, why, how of the data. Data documentation can be a readme file (human-readable) and metadata (machine-readable): 

• Readme files are text documents (e.g., in the format .txt) providing information about data files to ensure they are interpreted correctly. Put the readme file in the most obvious place in the data file folders so that it can be seen immediately.

• Metadata are technical data that describe a research dataset. The Fairdata Qvain metadata tool makes describing and publishing research data smooth and effortless for researchers without requiring technical skills. 

More information, see:

• Data documentation by CSC 
• Data description and metadata by the Finnish Social Science Data Archive (FSD)

Datasets can be categorized according to their anticipated retention periods:

• 1) Data to be destroyed upon the ending of the project.
• 2) Data to be archived for a verification period, which varies across disciplines, e.g., 5–15 years.
• 3) Data to be archived for potential reuse, e.g., for 25 years.
• 4) Data with long-term value to be preserved by a curated facility for future generations for tens or hundreds of years.

Long-term preservation refers to the 4th category. That is, data are preserved for more than 25 years. 

The Ministry of Education and Culture has established DPS, Digital Preservation Service for Research Data for long-term preservation of the nationally most significant research data. 

If you wish to sign up for the queue for DPS for Research Data, please contact openresearch@hanken.fi.

Making research data open and reusable, and reusing and benefiting from existing datasets, are the fundamental motives of open data. The openness and reuse of research data can:

• increase the visibility and impact of your research,
• are recognised as part of a researcher’s academic merits,
• speed up the adoption of your research findings and creation of innovations,
• facilitate disciplinary and interdisciplinary collaboration, both within the scientific community and in the wider social circle,
• improve knowledge sharing, and increase the transparency and reliability of science, both empowering and democratizing science,
• contribute to attaining several SDGs,
• save time and resources in data production, and
• improve data repeatability and verifiability, research reproducibility, and the reliability of research outputs.

More information about the benefits of open data, see:

• UNESCO Recommendation on Open Science (2021)
• Open science and the SDGs in the LibGuide on Open science