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Making Data FAIR

Researchers spend considerable time, money and effort collecting and interrogating data. Making data findable, accessible, interoperable and reusable (FAIR) can accelerate your research impact, including by gaining more citations for your datasets.
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Who should use this resource

This resource is useful toresearchers, including higher-degree research (HDR) students and early- and mid-career researchers (EMCRs). It is also useful to:
  • digital skills trainers
  • infrastructure providers (including research facilities)
  • data custodians/managers
  • research software authors.

What you’ll learn

By the end of reading this resource, you should:
  • know what the FAIR Principles are
  • understand why FAIR is important and their relevance to data sharing
  • find a range of tools and guides that help make your data and other digital research objects, including research software, FAIR
  • learn what the ARDC is doing to support FAIR.

The FAIR Principles

An abbreviation for “findable,accessible,interoperable andreusable”, the FAIR Principles provide a framework for sharing data in a way that maximises its use and reuse.

The FAIR Principles emerged from Wilkinson et al.’s 2016 journal article “FAIR Guiding Principles for scientific data management and stewardship”. Developed by the international research community, these principles aim to:

  • support knowledge discovery and innovation both by humans and machines
  • facilitate data and knowledge integration
  • enable new discoveries through the analysis of multiple datasets 
  • promote the sharing and reuse of data
  • apply across multiple disciplines, including those with sensitive data
  • strive for machine-readable data and metadata.

The FAIR Principles provide guidelines to improve the findability, accessibility, interoperability and reusability of digital assets. Note that applying these principles varies by discipline.

A short introductory video by Maastricht University on the FAIR Principles

What Being FAIR Means

The following information was sourced in part from Turning FAIR into reality, a 2018 report by the European Commission Expert Group, chaired by Simon Hodson.

The data has sufficiently rich metadata and a unique and persistent identifier to be easily discovered by others. This includes:

ensuring it is findable through disciplinary local or international discovery portals – see our guide to choosing a data repository.

The data is retrievable by humans and machines through a standardised communication protocol with authentication and authorisation where necessary.

The data does not necessarily have to be open, given that it can be sensitive due to privacy concerns, national security or commercial interests. When data cannot be open, there should be clear and transparent conditions governing access and reuse. Consideration should also be given to the persistence of the data in the selected repository and whether metadata will remain accessible even after the data is longer available.

Data often needs to be integrated with other data, applications or workflows to facilitate analyses, storage and processing. This integration requires the associated data and metadata to use a “formal, accessible, shared, and broadly applicable language for knowledge representation”. This involves:

  • using community accepted languages, formats and vocabularies in the data and metadata
  • referencing and describing relationships to other data, metadata, and information through identifiers
  • striving toward machine-readability.

Through the WorldFAIR Project, the Committee on Data of the International Science Council (CODATA) and the Research Data Alliance (RDA) are improving interoperability as well as reusability and FAIRness in general of digital research objects, including data. Learn more about interoperability by watching a webinar series by WorldFAIR.

Optimal reuse of data requires levels of description sufficient to allow data to be replicated and/or combined in different settings. The associated metadata needs to provide rich and accurate information, and the data must come with a clear usage licence and detailed provenance information. Reusable data should maintain its initial richness. It should not be diminished for the purpose of explaining the findings in one particular publication. It needs:

  • a clear machine readable licence
  • provenance information detailing how the data was formed
  • use of discipline-specific data and metadata standards to give it rich contextual information that facilitates reuse.

Why FAIR Data Is Important

Adopting the FAIR Principles accelerates the impact of your work by making it easier for other researchers to find and reuse your data. This can lead to increased collaboration with both research and industry, and acknowledgement of your data in other publications. It also benefits research communities, research infrastructure facilities and research organisations.

Well-researched topics provide rich information for deeper and more complex investigations, and making data from these endeavours more FAIR provides insights into less well studied topics. Meanwhile, making data more FAIR in less studied topics can help turn understanding of important topics in health, environment and society into deeper knowledge more quickly.

Benefits of FAIR data include:

  • maximising the potential of data assets
  • increasing the visibility and citations of research
  • improving the reproducibility and reliability of research
  • aligning with international standards and approaches
  • attracting new partnerships with researchers, business, policy and broader communities
  • enabling new research questions to be answered
  • achieving maximum impact from research.
Why FAIR Data Is Important

ARDC Resources for FAIR Data and FAIR Digital Research Objects

The ARDC offers a range of best-practice guides, tools and services for making data FAIR.

Besides research data, the FAIR Principles can be useful for other digital research objects. For example, the FAIR Principles for Research Software (FAIR4RS) were published in 2022 to improve the sharing and reuse of research software. We also offer various tools and guides that help make these digital objects FAIR.

Explore our resources for making data and other digital research objects FAIR:

Further ARDC and Community Support for FAIR Data

Besides offering gued and tools that help you achieve FAIR, the ARDC supports and drives a number of international and national initiatives:

The ARDC has also developed a policy that applies the FAIR Principles to our own and co-invested materials. When the ARDC partners with other organisations, we ask that they follow this policy.

We’ve also curated community resources that ensure best-practice research methods:

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