FAIR principles
The FAIR principles were formulated in 2016 to provide guidance to data developers and publishers. The aim is to ensure that research data can be used as widely as possible – to accelerate scientific discovery and benefit society. The FAIR principles are a set of actions to make data discoverable, accessible, interoperable and reusable.
FAIR places particular emphasis on improving the ability of machines to automatically find and use data, while also increasing the possibilities for reuse for researchers.
Following the FAIR principles in the data management process promotes the impact of research.
- Helps colleagues and yourself to understand the data of relevant research projects in the future (good data documentation, long-term availability).
- Promotes data sharing and collaboration.
- Increases the visibility of research, which increases citations.
- Improves the transparency, credibility and reproducibility of research.
- Prevents data loss.
- Ensures compliance with the requirements of research funders and publishers.
By ensuring that your data complies with the FAIR principles, you will continue to be able to easily find, access and reuse your data. The researcher is perhaps the first and most important beneficiary of making their data FAIR.
- Explanation of the acronym FAIR
This means that data can be discovered by both humans and machines, for example by creating meaningful metadata (data about the data) and keywords that are accessible to search engines and research data catalogues. Data are found by unique and persistent identifiers, such as DOIs or handles, and the metadata includes the identifier of the data being described.
F1. (meta)data are assigned a globally unique and persistent identifier
F2. data are described with rich metadata
F3. clear association between metadata and data
F4. (meta)data are registered or indexed in a search resource
This means that the data is archived for long-term storage and can be made accessible using standard technical procedures. This does not mean that the data must be accessible to everyone, but there must be information available on how the data can (or cannot) be retrieved. For example, the data can be marked with Access only with the express permission of the author and include the author's contact information. However, ideally, information on the availability of the data can also be read by machines, for example using machine-readable standard licenses.
A1. (meta)data can be retrieved by their identifier
A1.1 The format is open, free and universally implementable
A1.2 Access may include an authentication or authorization procedure, if necessary
A2. Metadata is available even if the data is no longer accessible
This means the exchange and use of data in different programs, tools and systems - also in the future, for example by using open file formats. The data can be integrated with other data from the same or from other research fields. This is possible by using metadata standards, standard ontologies and controlled vocabularies, as well as links between the data and other digital research objects.
I1. (meta)data are described using a formal, accessible and generally accepted approach to representing knowledge
I2. (meta)data use vocabularies that comply with the FAIR principles
I3. (meta)data contain qualified references to other (meta)data
This means that the data is well documented and organized, and provides rich information about the context in which the data was created. The data should meet community standards and have clear terms for access and reuse, preferably under machine-readable standard licenses. This allows others to both evaluate and validate the results of the original research, thus ensuring the reproducibility of the data, and to develop new projects based on the original results. Reusable data encourages collaboration and avoids duplication of work.
R1. Meta(data) is richly described with accurate and relevant attributes
R1.1. (meta)data is published with a clear and accessible data usage license
R1.2. (meta)data can be traced back to its origin
R1.3. (meta)data complies with domain-specific community standards
- Useful resources on the FAIR principles
Why FAIR - FAIR data principles
The FAIR Guiding Principles for scientific data management and stewardship | Scientific Data
How to get started – three easy steps
- 1. Create a Data Management Plan (DMP)
A data management plan is a living document that specifies what data will be used in a research project and how it will be processed, stored and archived. Preparing a DPP is an important first step in ensuring that data complies with the FAIR principles. It is also a frequent requirement from funders. RSU researchers have access to the RSU DPP template for this purpose, which can be filled out in the online system Argos.
- 2. Describe and document your data
To make data easily findable, it needs appropriate metadata. This can include keywords, references to related documents, researchers' ORCID identifiers, and research grant numbers. It is also important to prepare documentation that explains how the data was created, structured, and processed. This will allow the data to be not only findable, but also reusable.
For questions about metadata and documentation, contact the data curators at datukuratori
rsu[pnkts]lv.To facilitate the process of depositing data in RSU Dataverse, complete the minimum metadata survey.
- 3. Make data available in a trusted repository
When the data is ready for publication, choose a repository that:
- assigns a permanent identifier to both the data and metadata;
- adds metadata to the data according to standard metadata schemas;
- grants a license to the data;
- provides access to the data and provides metadata using an open and standard communication protocol (e.g. http or XML).
Adhering to these criteria ensures that the data complies with many, if not most, FAIR principles. RSU has the RSU Dataverse repository available for this purpose, which meets all of the above conditions. It can store datasets up to 50 GB free of charge.
