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Research Data, Data Management & Planning: FAIR Data

Resources related to Research Data Management & Planning and Finding Research Datasets



FAIR stands for Findable, Accessible, Interoperable and Reusable. The FAIR Data Principles were developed and endorsed by researchers, publishers, funding agencies and industry partners in 2016 and are designed to enhance the value of all digital resources. The FAIR principles do not prescribe any particular technology, standard, or specification, but rather act as a guide to researchers to aid them in evaluating whether their current data curation practices are sufficient to render their data Findable, Accessible, Inter-operable, and Reusable.

FAIR: Findable, Accessible, Inter-operable, Reusable


Making your data FAIR

TO BE FINDABLE: "means that the data can be discovered by both humans and machines, for instance by exposing meaningful machine-actionable metadata and keywords to search engines and research data catalogues. The data are referenced with unique and persistent identifiers (e.g. DOIs or Handles) and the metadata include the identifier of the data they describe." (

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

F2. data are described with rich metadata.

F3. (meta)data are registered or indexed in a searchable resource.

F4. metadata specify the data identifier.

TO BE ACCESSIBLE: "means that the data are archived in long-term storage and can be made available using standard technical procedures. This does not mean that the data have to be openly available for everyone, but information on how the data could be retrieved (or not) has to be available. For example, data can be marked “Access only with explicit permission from the author” and include the author’s contact details. Ideally, though, the information about data accessibility can also be read by machines, e.g. by way of machine-readable standard licenses."

A1. (meta)data are retrievable by their identifier using a standardized communications protocol.

A1.1 the protocol is open, free, and universally implementable.

A1.2 the protocol allows for an authentication and authorization procedure, where necessary.

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


TO BE INTEROPERABLE: "means that the data can be exchanged and used across different applications and systems — also in the future, for example, by using open file formats. It also means that the data can be integrated with other data from the same research field or data from other research fields. This is made possible by using metadata standards, standard ontologies, and controlled vocabularies as well as meaningful links between the data and related digital research objects."

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.


TO BE RE-USABLE: "means that the data are well documented and curated and provide rich information about the context of data creation. The data should conform to community standards and include clear terms and conditions on how the data may be accessed and reused, preferably by applying machine-readable standard licenses. This allows others either to assess and validate the results of the original study, thus ensuring data reproducibility, or to design new projects based on the original results, in other words data reuse in the stricter sense. Reusable data encourage collaboration and avoid duplication of effort. "

R1. meta(data) have a plurality of accurate and relevant attributes.

R1.1. (meta)data are released with a clear and accessible data usage license.

R1.2. (meta)data are associated with their provenance.

R1.3. (meta)data meet domain-relevant community standards.


Making sensitive data FAIR

If you are a researcher who deals with sensitive or confidential data, you can still aim to make your data FAIR. Not all data can be open, but FAIR data does not equate to open data. In these instances, documentation such as open metadata, ReadMes, and data citations can be key. 


Data as increasingly FAIR Digital Objects


FAIR data at the University of Wyoming

WyoScholar, the institutional data repository, uses an platform that strives to make data FAIR. Here is an overview of platform features that adhere to FAIR principles (note: this figure is geared toward a publisher audience, but can still provide helpful information if you need details for. say, a grant application). If you have questions about the FAIR data principles, or how to make your data more FAIR, contact the data management librarian.


Additional Resources

Adapted from the University of Tennessee Health Science Center Library


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Michaela Clark
Coe Library 304D
Subjects: Data, Statistics & Data