The openness of research data increases the reliability and reproducibility of scientific research. The openness of research data does not require that the data is completely open, but the degree of openness can vary from fully open to strictly closed. As minimum, the openness of research data includes only the openness of its descriptive information (metadata).
We encourage to improve the openness of research data according to the national policy for open research data and methods by supporting data management planning, the reuse of existing data and the publishing of research data and its metadata in trusted national or international data repositories. We participate in the development of research and metadata storage services in our framework organizations.
Source: https://avointiede.fi/en/open-science-expert-panels/open-data
Data management refers to the systematic collection, processing, storage and description of data, so that it remains usable and reliable throughout the entire research life cycle. Responsible data management enables opening data for further use. In managing research data, good scientific practices must be followed, and data handling should be planned in compliance with agreements and legislation as well as data protection and security.
The international FAIR principles guide the responsible production of research data. The aim of these principles is to make research data:
The guidelines in this manual are based on the Data Management Guidelines by the Finnish Social Science Data Archive. The aspects of data management are divided into three phases (before, during and after the research) based on the data life cycle model. It is recommended to familiarize yourself with each phase at the beginning of the research as you are preparing the data management plan. This ensures that the principles of data management are clear to you and the implementation of data management is easier as the research progresses.
Image: Finnish Social Science Data Archive, Data Management Guidelines: Research data life cycle.
Before collecting research data, it is important to take preparatory measures appropriate to the research design and data:
Read more about data management planning, agreements and personal data in the Data Management Handbook.
During the research, attention must be paid to ensuring that the data remains usable throughout its life cycle. This requires, among other things, selecting a secure storage solution, ensuring backups and version control, using clear folder structures and naming conventions, and providing comprehensive data description. Data sharing with collaborators must be conducted securely. If necessary, services provided by CSC can be utilized with, for example, high-performance computing, sensitive data or data sharing.
Familiarize yourself with the guidelines provided by your organization's IT services, and read more in the Data Management Handbook and on CSC's services.
When data is no longer actively used, it is important to ensure its preservation until the end of the planned retention period and to take care of its timely disposal. Anonymizing data containing personal information might also come into question, unless it is a longitudinal study where the data must remain identifiable for longer periods. Many publishers require that articles include a Data Availability Statement (DAS). Ideally, the statement can refer to a data archive and the permanent identifier for the data.
The most reliable way to ensure the usability of data is to publish it in a trusted data archive. Access to published data can be restricted for valid reasons. Descriptive information about the data should be published according to your university's publication principles, even if the data itself cannot be published in a data archive. In this case, data preservation is managed by using the institution's storage solutions, and the researcher must ensure its usability.
A trusted data archive can be identified by, for example, certificates, commitment to FAIR principles, use of permanent identifiers and linked data. Data archives can be searched from various catalogs (e.g. re3data.org). Examples of recommended data archives:
Publishing data description (metadata):
Read more about this topic:
Tritonia supports the data management services of its framework organizations, e.g., by offering guidance and training in data management as well as support and reviewing of data management plans.
Tritonia's services:
