The amount of data in a laboratory is constantly increasing. The number of questions about it is growing just as fast. This also applies to sharing and reusing information to make future research more efficient, faster and better. Fortunately, there is an answer: the FAIR principles. This is what it means for your data management in the laboratory.
Findable
Make data FAIR by making it findable, accessible, interoperable and reusable. That is what the guidelines introduced in 2016 tell us. The first guideline speaks for itself. Before you can reuse data, you must be able to find it at any convenient time. Ensure that one archive is used for this and draw up guidelines.
Accessible
Data must be accessible. This does not mean that anyone can just look around in a dataset. There is a kind of lock on it, to which the owner has the key. Inviting other authorized persons is described in a protocol. Just like the rights they have per person. This way, the data is both accessible and stored securely.
Interoperable
When a Frenchman, German and Spaniard function in the same team, working together is a lot more difficult than when everyone speaks the same language. That's how it works with data. By processing all scientific research information in the same way, you immediately understand each other. No interpreter or dictionary is needed and you convey information effortlessly. All datasets must therefore be described in the same way, or in other words, be interoperable.
Reusable
Just as it is much more difficult to assemble a wardrobe without instructions, other users cannot use your data properly if they do not know how the information fits together. Therefore, always state how your set was created. For example, by adding the codebook and metadata.