FAIR principles
The FAIR Data Principles are a set of guiding principles for the management and stewardship of research data. FAIR stands for Findable, Accessible, Interoperable, and Reusable. These principles are designed to facilitate the sharing and reuse of research data (FAIRification process), particularly in the context of scientific research and data-driven disciplines. By adhering to these principles, researchers can maximize the value of their data, as well as increase visibility, and enhance reproducibility of their work.
To be findable: the first step in (re)using data is to find them. Data and Metadata should be easy to find for both humans and computers. To achieve this, data should be assigned a unique identifier, and metadata describing the data should be rich, clear, standardized and indexed in a searchable resource.
To be accessible: data should be easily accessible to both humans and computers, possibly including authentication and authorization, where necessary. This means that data should be stored in a repository or data centre that provides secure and reliable access under specific conditions or restrictions where appropriate. FAIR does not mean that data need to be open! Metadata must be accessible, even when data is not available. Data should be made accessible using a standardized communications protocol. The protocol is open, free, and universally implementable.
To be interoperable: data should be structured and described in a way that allow them to be integrated, combined and exchanged with other data and systems. This principle emphasizes the importance of using a formal, accessible, shared and broadly applicable language for knowledge representation.
To be reusable: data should be designed for reuse and should be well-documented. This includes providing comprehensive metadata, clear and accessible licensing information, and guidance on how to properly cite the data. Additionally, data should be in a format that can be easily used and analysed by other researchers. Data should conform to community norms when possible.
Expand All
-
How fair are your data?
This practical checklist contains measures that should be taken to improve data FAIRness.
Findable:
- a persistent identifier is assigned to your data
- there are rich metadata, describing your data
- the metadata are online in a searchable resource e.g. a catalogue or data repository
- the metadata record specifies the persistent identifier
Accessible:
- following the persistent ID will take you to the data or associated metadata
- the protocol by which data can be retrieved follows recognised standards e.g. http
- the access procedure includes authentication and authorisation steps, if necessary
- metadata are accessible, wherever possible, even if the data aren’t
Interoperable:
- data is provided in commonly understood and preferably open formats
- the metadata provided follows relevant standards
- controlled vocabularies, keywords, thesauri or ontologies are used where possible
- qualified references and links are provided to other related data
Reusable:
- the data are accurate and well described with many relevant attributes
- the data have a clear and accessible data usage license
- it is clear how, why and by whom the data have been created and processed
- the data and metadata meet relevant domain standards
(Jones, S., & Grootveld, M. (2017). How FAIR are your data? https://doi.org/10.5281/zenodo.5111307)
-
SNSF checklist for FAIR repositories
The SNSF expects researchers to share their data according to the FAIR Data Principles on publicly accessible, digital repositories. It is important to note that the FAIR Data Principles do not require researchers to share all their data without any restrictions.
To make the transition towards FAIR research data easier, the SNSF decided to define a set of minimum criteria that repositories have to fulfil to conform with the FAIR Data Principles. The answer to each of the questions below must be "yes":
- Are datasets (or ideally single files in a dataset) given globally unique and persistent identifiers (e.g. DOI)?
- Does the repository allow the upload of intrinsic (e.g. author's name, content of dataset, associated publication, etc.) and submitter-defined (e.g. definition of variable names, etc.) metadata?
- Is it clear under which licence (e.g. CC0, CC BY, etc.) the data will be available, or can the user upload/choose a licence?
- Are the citation information and metadata always (even in the case of datasets with restricted access) publicly accessible?
- Does the repository provide a submission form requesting intrinsic metadata in a specific format (to ensure machine readability/interoperability)?
- Does the repository have a long-term preservation plan for the archived data?
