Why FAIR is important

To be short an philosophical: the global knowledge pool benefits from FAIR data. To be longer and less philosophical, the rest of this page.

FAIR data is useful for both the researcher that makes it and the public at large. For the researcher, it allows them to:

• Produce higher-quality data;
• Avoid losing data due to poor storage techniques;
• Avoid having to delete old data due to forgetting what it was about;
• Be faster when composing methods for papers;
• Immediately fulfill data sharing requirements when publishing;
• Share and pool together data with members of the same group in an easier way;
• Have faster data analysis, and outsource data analysis to third parties in a much easier way.
• Comply with new requirements for funding from the European Union and other funding bodies, as they are increasigly asking to applicants to provide guidelines on how data will be treated FAIRly during the project.
• Find and collaborate with people who are interested in your data or generate data similar to yours.

For the public at large, FAIR data allows to:

• Repurpose data for new endeavours without needing to recollect it, which is potentially expensive and time consuming;
• Run large meta-analysis studies in an easier, faster way;
• Check the rigorousness of data analysis, ameliorating the reproducibility crisis;
• Allow researchers with less funding to still generate knowledge through data repurposing.

As most public reasearch is funded with public money (i.e. by the state), we as scientists also have a responsibility to use this money to produce high-quality, reusable data that the public - who paid for it - can potentially access and reuse.

Are there downsides of following FAIR practices? Well, you need time and some effort to setup a working method that makes, you create FAIR(-er) data, plus you then need to follow the new guidelines in your day to day work.

By reading this handbook, you will learn how to make these efforts as painless and as smooth as possible, especially if you do not have supporting infrastructure to begin with.

What is in it for me?

You might still not be convinced that the game is worth the candle. Your method for handling data might be perfectly fine for you: you’ve never lost any data, and you can understand your files well.

However, consider that you do not live in a bubble. You eventually need to:

• Share your work with others;
• Use other’s data;
• Combine different datasets of your own work;
• Handle other people’s data for, e.g., quality check;
• Find new people who are as interested to your experimental problem as you are.

If everyone produced FAIR data, all of these steps would be extremely easy to carry out and, in some cases, automatic.

Once you embrace FAIR data, you contribute and can tap into a large amount of data present online for your own reputation and benefit. Working with FAIR data is also more efficient: data analysis is faster with such data, and writing papers becomes a breeze.

If all of this still is not enough to convince you, consider that there are efforts to move away from bibliometric indexes to measure a researcher output and therefore evaluate them. These efforts, like COARA, will most likely replace such measures with things like FAIR quantification, research integrity, etc… Being ready for these changes will undoubtedly give you an edge in the future.