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Use these FAIR data self assessment tool to assess the FAIRness of your data and how to enhance it, if necessary.
FAIR stands for: Findable, Accessible, Interoperable, and Reusable. Data that meet these principles are more optimal for reuse and discoverability and in turn increase your research's exposure.
The FAIR principles were published in 2016 in a Scientific Data article titled FAIR Guiding Principles for Scientific Data Management and Stewardship. The principles were developed to aid in the discovery and reuse of research data.
The increasing availability of online resources means that data need to be created with longevity in mind. By providing other researchers with access to your data, you are facilitating knowledge discovery and improving research transparency.
These FAIR principles describe how research outputs should be organised so they can be easily accessed, understood, exchanged and reused. Many funding bodies promote FAIR data to maximise the integrity and impact of their research investments.
FAIR data principles is a good framework to follow when creating a data management plan (DMP). They are guiding principles on best practice to make data maximally reusable, but they are not standards. FAIR qualities can be achieved by different standards.
Findable: The first thing to be in place to make data reuable is the possibility of to find them. It should be easy to find the data and the metadata for both humans and computers. Rich metadata provides important context for the interpretation of your data and makes it easier for machines to conduct automated analysis. It is good practice to use general or discipline specific metadata schemes.
Automatic and reliable discovery of datasets and services depends on machine-readable persistent identifiers and metadata. Persistent identifiers are important because they unambiguously identify your data and facilitate data citation. An example would be a Digital Object Identifier (DOI).
Accessible: The (meta)data should be retrievable by their identifier using a standardised and open communications protocol, with restrictions in place if necessary. Also, metadata should be available even when the data are no longer available.
Important: not all data has to be made open. Data can be restricted and still be FAIR. As open as possible, as closed as necessary.
Open or not, keep your data somewhere safe for the long-term. Look for a repository that stores data safely, make data findable, describes data appropriately (metadata), and adds license information. General repositories and subject-specific repositories are available.
Interoperable: The data should be able to be integrated with other data, applications and workflows. The format of the data should therefore be open and interpretable for various tools. Think about not using proprietary software.
The concept of interoperability applies both at the data and metadata level. Use common formats and standards, and controlled vocabularies.
Reusable: Ultimately, FAIR aims at optimising the reuse of data. In order to do this, data should be well-documented, have a clear license to govern the terms of its reuse, and provenance information.
Documentation could be in the form of a README file to help ensure that your data is correctly interpreted and re-analysed by others. Include contact information.