FAIR guiding principles

This section of the learning unit is from “FAIR principles” by GOFAIR. The website content is licensed under the CC BY 4.0 license.

In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.

A practical “how to” guidance to go FAIR can be found in the Three-point FAIRification Framework.

Findable The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.

F1. (Meta)data are assigned a globally unique and persistent identifier

F2. Data are described with rich metadata (defined by R1 below)

F3. Metadata clearly and explicitly include the identifier of the data they describe

F4. (Meta)data are registered or indexed in a searchable resource

Accessible Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorisation.

A1. (Meta)data are retrievable by their identifier using a standardised communications protocol

A2. Metadata are accessible, even when the data are no longer available

Interoperable The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.

I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.

I2. (Meta)data use vocabularies that follow FAIR principles

I3. (Meta)data include qualified references to other (meta)data

Reusable The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

R1. (Meta)data are richly described with a plurality of accurate and relevant attributes

The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure. For instance, principle F4 defines that both metadata and data are registered or indexed in a searchable resource (the infrastructure component).

RDA Minimal Metadata for Learning Resources

The RDA Education And Training On Handling Of Research Data Interest Group has defined a minimal metadata seta for learning resources that has become a de facto standard for describing FAIR learning materials.

The following table describes the minimal metadata set elements and their definitions:

Element Name Definition
Title The human readable name of the resource.
Abstract / Description A brief synopsis about or description of the learning resource
Author(s) Name of entity(ies) authoring the resource
Primary Language Language in which the resource was originally published or made available
Keyword(s) Keywords or tags used to describe the resource
License A license document that applies to this content, typically indicated by URL
Version Date Version date for the most recently published or broadcast resource
URL to Resource URL that resolves to the learning resource or to a “landing page” for the resource that contains important contextual information including the direct resolvable link to the resource, if applicable.
Resource URL Type Designation of the identifier scheme used for the resource URL, e.g., DOI, ARK, Handle
Target Group (Audience) Principal users(s) for which the resource was designed
Learning Resource Type The predominant type or kind that characterizes the learning resource
Learning Outcome Descriptions of what knowledge, skills or abilities a learner should acquire on completion of the resource
Access Cost Choice stating whether or not there is a fee for use of the resource (yes, no, maybe)
Expertise (Skill) Level Target skill level in the topic being taught; example values include beginner, intermediate, advanced

This table is taken from RDA Minimal Metadata for Learning Resources by Hoebelheinrich, Nancy J; Biernacka, Katarzyna; Brazas, Michelle; Castro, Leyla Jael; Fiore, Nicola; Hellström, Margareta; Lazzeri, Emma; Leenarts, Ellen; Martinez Lavanchy, Paula Maria; Newbold, Elizabeth; Nurnberger, Amy; Plomp, Esther; Vaira, Lucia; van Gelder, Celia W G; Whyte, Angus licensed under the CC BY 4.0 license.

The metadata schema that also defines the type of each element, the allowed values, and constraints is available on the RDA website: RDA Minimal Metadata for Learning Resources Professional and Informal Education Examples