The FAIR principles

My name’s Isabel Chadwick. I've got a special interest in research data management. So my job involves helping researchers and research students to look after their data during their projects, thinking about all of the legal, ethical implications of looking after what is sometimes personal data, sometimes sensitive data, and sometimes just really, really big data.

The FAIR data principles are intended to give guidelines on the findability, the accessibility, the interoperability and the reusability of digital assets that are created during the course of research.

They go beyond merely saying that research data should be made open, and rather give more concrete guidance on how that data can be best exploited to enable reuse, replication, verification of results, any kind of scrutiny.

So they're really, really important because a huge amount of money and time is invested into generating research data globally.

And if that data isn't findable, if it can't be accessed, if it doesn't interoperate, then essentially it isn't reusable - and that sort of means that it's a huge financial loss for global research as well as a bit of a setback for research progress.

Sometimes there are legal, ethical or commercial reasons why research data cannot be made publicly accessible.

And FAIR data doesn't always mean open data. So even where data has to be restricted to only allow access to certain people, or maybe even no people, the really important thing is that the metadata that describes that data is made available.

Now, when we use the term metadata, what we mean is all of the information that builds up a picture about what that item or that data set might be. So a really good way of thinking about metadata is thinking about things that you use in your everyday life. So for example, you might have a record collection that you have organised according to different things.

So you might have put it in alphabetical order according to the name of the artist, or you might have put it into different sections for classical, pop, rock, for example. Or you might simply have just done it in a colour order to make it look like a pretty rainbow and all of those are ways of organising information using different types of metadata in order to be able to find things and understand things more easily.

And we do the same things with information that includes data sets, but also would be things like publications, so every published piece of research will have rich metadata assigned to it.

When it comes to research data, that information is really, really important, because in terms of transparency, allowing people to understand how you created your data, what the data is - and pretty key to this is - how they can reuse it, is really important.

To make your data FAIR, there are a few really key steps that come at different points in your research process. So right at the beginning of your research project, before you've even started collecting your data, we would always advise that you write a data management plan. And that plan should outline how you're going to look after your data during your project, and then what's going to happen to it after your project.

In terms of what happens after your project, the best piece of advice for making your data FAIR, would be to deposit it in a trusted digital repository. And you should do this whether your data is going to be openly available to the public, or whether it's going to have a restricted access placed upon it.

The reason why we say that you should put your data into a trusted digital repository is because it will provide you with a persistent identifier like a DOI, which will ensure findability and accessibility of your data.

In terms of reusability, it will also enable you to assign an open license to your data, so that people can understand what the terms of free use are, and they know what they are and aren't allowed to do with that data when they access it.

And finally, in terms of interoperability, what's really important is that you use those DOIs or those persistent identifiers that you are provided with by your repositories, or by your publishers, to link your different outputs. So we want to see you linking your data sets with your publications, with your other outputs, with your software, for example, and your materials.

That's a really important aspect of FAIR.

But, to start from the beginning, the data management plan is a really solid way to start.