Open access to research data
Openly accessible research data can be more easily discovered, examined, shared, cited, and reused. This allows more researchers to validate and build upon previously collected data. Data produced within, or funded by, the public sector is a strategic national resource for the development of society, business, and the public sector. The use and reuse of data can promote transparency, innovation, efficiency, and provide a solid basis for decision-making and research.
Re-use of research data
Open access to research data means that data from research is made available for others to find, review, and reuse, to the extent possible. This work is guided by the principle of “as open as possible, as closed as necessary” and is based on managing data in accordance with the FAIR principles.
Open access to research data means that – to the extent possible – research data are made available for others to find, examine, and reuse. This includes, for example, data files, code, documentation, and metadata related to the research process.
Research data cannot always be made fully open without restrictions. In Sweden, the principle applies that research data generated with public funding should be “as open as possible, as closed as necessary.” This means taking legal, ethical, and security considerations into account when providing access.
To be usable by others, data often need to comply with the so-called FAIR principles: they should be Findable, Accessible, Interoperable, and Reusable. However, these are general principles, not universal criteria. Given the varying requirements and needs of different research areas, the FAIR principles must be adapted to the scientific disciplines, fields, and practices in which they are applied.
Open access to research data requires technical infrastructure, clear guidelines, and resources for management, storage, and, where necessary, anonymisation. Many research funders in Sweden and internationally now require that data management be planned and documented, often through Data Management Plans (DMPs).
The FAIR Principles
The idea that data and metadata should be Findable, Accessible, Interoperable, and Reusable (FAIR) constitutes guiding principles in research data management. The purpose is to make data and services as useful as possible, both for humans and for machines. The FAIR principles are not a technical standard, but rather describe qualities that enhance reusability over time and allow for different approaches to implementation.
FAIR applies across all research domains and addresses barriers such as unclear terminology, insufficient metadata, and challenges in combining data from different sources. Among other things, the principles state that data should be assigned persistent identifiers, have rich and machine-readable metadata, and come with clear licenses and access conditions. They also state that data should be possible to integrate with other resources through shared formats and standards.
FAIR does not necessarily mean open access—legitimate restrictions, such as those related to privacy or security, may restrict access—but even such data should be properly described and findable. Implementation should be adapted to the traditions of each research field, with the aim of improving data quality, reducing duplication of effort, and supporting open science.
Source: Mons, B. et at. (2017). "Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud." Information Services and Use, 37(1), 49–56. DOI: 10.3233/ISU-170824