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Research Data Management

Publishing (meta)data

The openness of research data increases the visibility and impact of your research, speeds up the adoption of your research findings and the creation of innovations, and facilitates disciplinary and interdisciplinary collaboration, both within the scientific community and in the wider social circle. Open data improves the transparency and reliability of science, empowering and democratizing science.

Research data and related published research results produced at Hanken are open and available for shared use. The discoverability and citability of research data are to be ensured.

When opening your data, consider the following questions:

1. How to describe and publish the metadata of your data? Metadata are data about data and describe the context, content, structure, compilation, and management of research data (See the section on Metadata and data documentation on this page). It is through the metadata that the datasets become visible, findable and first assessed for downloads and reuse. Creating appropriate and rich metadata is the key to making data open, understandable, and reusable. It is strongly recommended to use Fairdata Qvain metadata tool to describe and publish your (meta)data. 

2. What part of the data will be opened and published? 

  • Research data are archived and published in national or international repositories when possible. If you cannot publish your research data, open and publish the metadata of your research data. Note that the metadata of the data holding personal information can still be able to be opened, although the actual data cannot be. 
  • Data with personal information can only be published anonymised. Pseudonymised data are still personal data, and therefore cannot be opened without explicit consent for that purpose. See Anonymisation and Personal Data by the Finnish Social Science Data Archive (FSD).
  • The data subjects need to be informed of your (anonymised) data archival plan. The consent of the data subject is required for the opening of the material, from which the research participants are directly identifiable. If you plan to publish personal data, contact Hanken’s Data Protection Officer (DPO,

3. Where will the data be opened? Choose suitable repositories for publishing and archiving your data already at the beginning of the project.

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

5. Which license will you use to open and share your data? Licensing is necessary for publishing data. It is recommended to use Creative Commons (CC) license CC BY 4.0 for published datasets when possible. See Legal compliance

6. Will some part of the data be destroyed? See 8. Data erasure and data publishing. See also Data disposal by FSD and Five steps to decide what data to keep by the Digital Curation Centre (DCC).


Remember to register your datasets in Hanken's research database - Haris. You can register standalone datasets or datasets that are connected to a publication. If a publication has a relating dataset, you shall create two separate records in Haris – one for the publication and one for the dataset. The records can then be connected under the heading Relations to other content in the template. 

Remember to add the persistent identifiers (PIDs, e.g., DOI and URN) for your metadata (for example, from Qvain) and for your datasets in the repository where you have stored or published your datasets. It is not possible to upload files in the record for datasets in Haris. 

The information you have registered in Haris about your datasets will be displayed  on Haris public portal.

More information, please see Register your datasets in the LibGuide on Haris. E-mail if your have questions about publishing your research (meta)data or reporting datasets in Haris.

FAIR data principles

The FAIR data principles, formulated by Force11, are the guiding principles on how to make data truly open. FAIR is an acronym for "findable, accessible, inter-operable, and re-useable": 

Fair data principles

The FAIR data principles can be formularized as “Findable+Accessible+Interoperatable=Reusable.” Making data reusable, and reusing and benefiting from existing datasets, are the fundamental motives of open data. A FAIR+R (FAIR and reproducible) solution is also argued for (See Christophe Bontemps and Valérie Orozco. 2021. “Toward a FAIR Reproducible Research”, in Abdelaati Daouia and Anne Ruiz-Gazen (eds.) Advances in Contemporary Statistics and Econometrics. Springer International Publishing.)

FAIR is not equal to open or free. Data can be closed and paid for yet perfectly FAIR, while data that are open and free are often not FAIR, and thus regarded cost-inefficient and re-useless.

In order to ensure that your data and/or their metadata are FAIR, follow the following steps:

  • Save your data in a open file format such as Rich Text Format (.rtf) or .csv. These are more interoperable and less subject to loss and obsolescence than proprietary formats.
  • Archive your data in an established digital repository at the end of the project. Remember to choose a repository that provides a persistent identifier (PID), such as DOI or URN.
  • Create descriptive metadata for the data. Most of the FAIR data principles concerns metadata. It is crucial to describe and document your research data to make them truly open and reusable. See Data documentation and metadata in the following section.
  • License your data with a license that clearly state the conditions and restrictions for reuse.

More information, see:

It is recommended to use the Fairdata services offered by the Ministry of Education and Culture and produced by CSC – IT Center for Science Ltd for data management, data storage, metadata creation, dataset dissemination and distribution as well as digital preservation of research data. The services include:

  • IDA, Research Data Storage – Safe storage for research data.
  • Qvain, Research Metadata Tool – A metadata tool for describing and publishing datasets.
  • Etsin, Research Dataset Finder – Discover, access and download research data from all fields of science.
  • PAS, Digital Preservation Service for Research Data – Reliable preservation of digital information for decades or even centuries.

Read How to make the research dataset FAIR? and learn more about the Fairdata services.

Metadata and data documentation

Data documentation means describing the data, is data about data, and provides information about the who, what, when, where, why, how of the data. Investing time in documenting the data makes it easy to understand them for both others and yourself, and decrease the risk of false interpretation of the data. Data documentation can be a readme file (human readable) and metadata (computer readable): 

  • Readme files are text documents (e.g., in the format .txt) providing information about data files to ensure they are interpreted correctly. A readme file explains what data a research project has, how the data were created, where the data originate from, how to interpret them, what the abbreviations mean, what software is needed to use the data, how the data have been modified, and can include information about the title, creator, funder, relevant dates of data collection and publication, location, methodology, subject, file formats, file naming system and folder structure, data version, licence, and repository.

Write a readme file about your data and data files. Put the readme file in the most obvious place in the data file folders to ensure that it can be noticed and seen immediately.

  • Metadata are technical data that describe a research dataset. When making data FAIR, metadata plays the key role. Systematically described research data is the key to making your data understandable, findable and reusable.

Metadata should be machine-readable and machine-actionable; that is, data need to be richly and systematically described in the way that machine can interpret and navigate all the metadata and linked data across different websites, and retrieve and transmit the right ones for a person conducting semantic queries. There are standard methods available for data documentation called metadata standards, which should be used if suitable for the data. The Fairdata Qvain metadata tool makes describing and publishing research data smooth and effortless for researchers without requiring technical skills.

It is strongly recommended to use Fairdata Qvain metadata tool to describe and publish your (meta)data. Qvain is part of the Fairdata services to support your research data to go FAIR. Data described and published by Qvain metadata tool are transferred automatically to Finnish metadata warehouse Metax, which is integrated with both Etsin (research dataset finder) and the Finnish National Research Information Hub (in Finnish: Tutkimustietovaranto, a service also commissioned by the Ministry of Education and CSC).

See Qvain User Guide.


Other important issues include data formats, file naming conventions, version control, and directory structure. See Data formats and organizing.

Remember to register both your publications and datasets in Haris - Hanken's research database. The information you have registered in Haris about your datasets will be displayed on Hairs public portal.

More information, see:

Long-term pre­ser­va­tion of data

Long-term preservation means that data is preserved for several decades or even centuries. You can categorise your datasets according to the anticipated preservation 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 fourth category. That is, data is preserved for more than 25 years. When creating your data, you need to consider how long it will be preserved. Also remember to check discipline-specific, funder-related, and publishers' data preservation time length requirements. 

Finnish Ministry of Education and Culture has established Fairdata-PAS service (Digital Preservation Service for Research Data, DPS for Research Data) for Finnish research organizations for long-term preservation of the nationally most significant research data. The service is meant for digital preservation of research datasets that have significant value to the organization or on a national level currently and especially also in the future.

See Digital Preservation (Fairdata-PAS): Guidelines for UH Evaluators by the University of Helsinki.

If you wish to sign up for the queue for DPS for Research Data, please contact