The F.A.I.R. principles: A quick introduction

What are the F.A.I.R principles?

The FAIR principles are a set of guidelines for making data Findable, Accessible, Interoperable, and Reusable. These principles were introduced to address the challenges of data sharing and reuse in scientific research, but they can also be applied to other domains.

These principles aim to promote openness, collaboration and the maximization of data’s potential value in scientific research and beyond.

The F.A.I.R. principles are a set of four principles that were introduced to address the challenges of data sharing and reuse in scientific research, but they can also be applied to other domains.

It helps strengthen the process when data are captured, recorded and reused.
Those who are using data every day – which include data scientists and data analysts, are looking for ways to boost the integrity and quality of the data capture process.
These four F.A.I.R. principles provide guidelines to data analysts to help support them to ensure that the data is obtained and recorded in an efficient and systematic fashion. Once these elements have been met, it makes it easier to access the data and re-use it.

These principles aim to promote openness, collaboration and the maximization of data’s potential value in scientific research and beyond.

What it means to be F.A.I.R.

Research data is often not used past its intended purpose. In fact, in many cases, scientific data is not shared or even re-usable.

This could be due to data protection or because the data is difficult to find. Imagine the potential of that data if it was used beyond its original goal. Think of what that could mean for medical breakthroughs in disease, or innovative alternative energy options. The benefits to society would be momentous.

Those dealing with data every day, including researchers, funders, and publishers, are looking to make scientific data more accessible and boost its value. Providing other researchers and scientists access to your data makes new discoveries easier and encourages transparency in research.

To get there, participants of the Lorentz Workshop “Jointly Designing a Data FAIRport” in 2014 came up with ‘The F.A.I.R. Principles for scientific data management and stewardship’ to guide researchers towards optimizing data sharing and ensuring data could be re-used by both humans and machines.

But what exactly does it mean to be F.A.I.R.? In this article, we’ll dig into what F.A.I.R. means and why it matters.


Behind the F.A.I.R. acronym

While this is an acronym, it also forms a real word. In English, anyway. A word that means playing by the rules – an honest approach.

In science, when we refer to ‘F.A.I.R. data’ , it’s along the same lines: researchers filing their data follow the rules. Rules that have been agreed upon to make data easy to find, accessible, interoperable and reusable – making up the acronym F.A.I.R. The principles provide a guide to researchers on how their data should be organized so that it can be easily accessed, understood, exchanged and reused.



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Let’s take a closer look at each part of the whole.


F is for Findable

Sure, you may be able to find your data, but how long does it take you? Can you find it easily?

Every moment spent searching for information is a moment taken away from science and innovation. Data should be found with ease, saving both time and effort.

Let’s take a classic case: scientific research papers are notoriously difficult to find, even if they’re published in open access. The reason for this could be as simple as not having the right key words to enable a search engine such as Google, Bing or Yahoo! to find them.

Researchers should choose their key words carefully. Knowing how they will be used should help; it gives the task a sense of importance and meaning. Taking care to think about these words can make a world of difference when it comes to finding information.

Another key point is consistency. If researchers are going to use different key words and terms to name and store their research, it won’t matter how many keywords are associated with it – they won’t function as they should.

Now extrapolate this to research data in your go-to data management tool. Here, keywords and tags have the same function; they link associated metadata and files and make it easier to find later.

So, take a moment and really think about which words you will use to tag your data. One minute now could save you an hour next week.


A is for Accessible

According to a 2020 study printed in Learned Publishing, researchers in the medical and biological fields are reluctant to share their data. Reasons for this vary from not fully understanding the term ‘scientific data’ and what it could include to being protective of their research findings and the effort that went into the project, fear that others would misinterpret the data, and reluctance due to the initial additional effort it could take to make data more accessible using the associated metadata.

Researchers should consider putting their fears aside and sharing the information, not just publishing it. Open access is important to innovation. Not only does it maximize the potential of your data, it also saves other researchers time and keeps studies moving along.


I is for Interoperable

A major barrier to why researchers in the life sciences don’t share their data is because of the daunting prospect of standardizing research data and the amount of work it presents. Can your data be used across multiple projects and systems? If your data is not compatible and interoperable, it limits how it can be used. Again, researchers should ensure that their data is not restricted in its use.


R is for Reusable

If you want to get the most out of your data, you must ensure it can be reused. This means having the full picture in plain sight: state your sources, make sure you add context, and offer contact information for those that want – or need – more information.

Adding this level of detail and metadata facilitates data mining, where you will be able to search – and find – your data through a document or even a set of documents.

This is especially important in times of evolving Big Data, where data production has risen exponentially but data management is struggling to keep up and organize the data into a coherent picture that offers insight.

Some key funding organizations, including the European Commission, encourage and promote the F.A.I.R. principles to safeguard data integrity and maximize research investment. It’s a simple formula: use quality ingredients to produce a quality product; one that you can use over and over, and it’ll last.


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