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?
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.
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.
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":
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:
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:
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):
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 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 means that data is preserved for several decades or even centuries. You can categorise your datasets according to the anticipated preservation periods:
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 email@example.com.