Skip to main content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.

Research Data Management

Reusing data

Reusing and benefiting from existing datasets is a fundamental motive of data opening and sharing. Research data are valuable resources that often require a lot of time and money to create. It is thence worthwhile to consider reusing existing datasets that previous studies have generated and publicly archived. Yet reusing data is not only about saving time and resources. It also improves data repeatability and verifiability, and thus the reliability of scientific outputs.

At the same time, optimal use and reuse of archived data become possible only when the accessibility and reusability of research data have been ensured. Properly managed and openly published research data with appropriate licenses enable and facilitate shared use. FAIR data principles in the section below give guidance on how to make your data truly open and reusable. See also Sharing data below about how to open and publish your data.

Services for searching datasets include:

Some new initiatives that aim to collect and mediate open data include:

  • Mendeley Data portal from Elsevier, announced in late 2018, imports data from different data depositories, journals and archives, and also allows registered users to archive their own data. 
  • Google Dataset Search (beta) utilises the Google search engine to identify datasets across the web and different existing data depositories. 

When reusing data, good practices for the attribution of authorship and data citation must be followed. See the citing instructions Citing archival data by the Finnish Social Science Data Archive. See also how to reuse research data by OpenAIRE.

Sharing 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, all 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 ought to be open and available for shared use. The discoverability and citability of research data ought to be ensured.

When opening your data, consider the following questions:

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

  • Data with personal information can only be published anonymised. Pseudonymised data is 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).
  • Personal information can be shared subject to a license, if the original processing purpose allows it. If you plan to share data which includes personal information, contact Hanken’s Data Protection Officer
  • Note that the metadata of the data holding personal information can still be able to be opened, although the actual data cannot be. 

2. Where will the data be opened?

  • Choose suitable repositories for sharing and opening your data already at the beginning of the project. Check that your data fulfill the repository requirements. 
  • Choose repositories which use persistent identifiers (DOI, URN). Read The use of Persistent Identifiers for Research Datasets: Recommendation by the Finnish Scientific Community for Open Research.
  • Check the recommendations of the publishers, learned societies, and funders in your own field. Where have you or your colleagues published data?
  • Specific repositories for one data type can be found in, a registry of research data repositories covering over 2,000 repositories.
  • General repositories include: 
  • If you cannot open the data, you can open your metadata about your project data, for example, at the national Etsin or at Zenodo.
  • Register your dataset 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, our recommendation is to 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. Availability to the data is made by adding a link or a DOI to the file location. It is not possible to upload files in the record for datasets. E-mail if your have questions about reporting datasets.

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

4. Will some part of the data be destroyed? More information, see Data disposal by the Finnish Social Science Data Archive (FSD).

5. Which license will you use to open and share your data? Agreements on data ownership and other intellectual property rights must be concluded before commencing any actual research activities.

More information, see Five steps to decide what data to keep by the Digital Curation Centre (DCC).

Long-term pre­ser­va­tion of data

Long-term preservation means that 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. A data archiving plan is part of research quality and transparency. If your data has long-term value, consider:

  • What part of the data is archived?
    Special categories of personal data are advised to be destroyed when the project ends. GDPR, however, does not require the destruction of data. It requires that participants need to be informed about data preservation and the basis of the duration of preservation. If you are preserving personal information, contact Hanken's Data Protection Officer
  • Where will the data be archived?
    • Finnish Ministry of Education and Culture has established Fairdata-PAS service (Digital Preservation Service for Research Data) for Finnish research organizations for long-term preservation of the nationally most significant research data. Digital Preservation Service for Research Data is meant for digital preservation of research data for several decades, or even centuries. See the guidelines by the University of Helsinki for assessing the value of research data. If you wish to sign up for the queue for Fairdata-PAS, please contact
    • You can also contact if you need other kinds of archiving services.
  • How long will the data be preserved?
  • Are there some costs related to archiving? Who takes care of them?
  • Will some part of the data be destroyed? See Data disposal by the Finnish Social Science Data Archive (FSD).

More information, see Five steps to decide what data to keep by the Digital Curation Centre (DCC).

FAIR data principles

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

To be Findable:

  • F1. (meta)data are assigned a globally unique and persistent identifier
  • F2. data are described with rich metadata (defined by R1 below)
  • F3. metadata clearly and explicitly include the identifier of the data it describes
  • F4. (meta)data are registered or indexed in a searchable resource

To be Accessible:

  • A1. (meta)data are retrievable by their identifier using a standardised communications protocol
  • A1.1. the protocol is open, free, and universally implementable
  • A1.2. the protocol allows for an authentication and authorisation procedure, where necessary
  • A2. metadata are accessible, even when the data are no longer available

To be Interoperable:

  • I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation
  • I2. (meta)data use vocabularies that follow FAIR principles
  • I3. (meta)data include qualified references to other (meta)data

To be Reusable:

  • R1. meta(data) are richly described with a plurality of accurate and relevant attributes
  • R1.1. (meta)data are released with a clear and accessible data usage license
  • R1.2. (meta)data are associated with detailed provenance
  • R1.3. (meta)data meet domain-relevant community standards

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.

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.

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.

More information, see:

Fairdata services

The Fairdata services are 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.
  • 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.

Data documentation and metadata

Documentation (describing the data, human readable) and metadata (data about data, the who, what, when, where, why, how of your data, computer readable) both provide information about the data. When making data FAIR, metadata plays a crucial role. Systematically described research data is the key to making your data understandable, findable and reusable. Data quality improves with clear and detailed documentation and metadata. 

Use Fairdata Qvain, a metadata tool, to describe and publish your datasets. Qvain is part of the Fairdata services to support your research data to go FAIR. 


Remember that you shall register both your publications and datasets in Haris - Hanken's research database. The information you have registered in Haris about your datasets will be transferred to Etsin and available at the Ministry's research database portal/tutkimustietovarannon

More information, see: