Let's learn about F.A.I.R data - Episode #1

Ever since I completed my fellowship with FAIRPlus Community, I always wanted to write a simplified but comprehensive coverage of F.A.I.R data and its principle. Never too late to start this, so starting a series of articles on FAIR data and this one will be the introduction to FAIR data.

Before we see What is it all about, let's dig into a critical question on "Why we need FAIR data ?"

The answer is simple - What we are going to do with a data that is not findable, accessible, interoperable or reusable ? Yes, FAIRification of data unlocks a whole bunch of benefits including reusability, improve data quality and more importantly generate quality insights out of the data which ultimately leads to better decision making.

Not all organisations or sectors are concerned about making their data FAIR but those in pharmaceutical sector have a critical need to work on this. Let's take one simple case - All pharmaceutical organisations do engaged with multiple Contract Research Organisations (CROs) to outsource some of its work in the research and development space. If we don't have a standard mechanism of capturing and integrate the data from external partners, it will be difficult to utilize them in an expected way - FAIR principles and standards will be a saviour here. Not just external data can be FAIRified, even the data internal to an organisation can be FAIR (We will see more on this as we travel in this learning journey).

Now we understand the need of FAIR data, let's look at "What is a FAIR data ?"

FAIR here stands for Findable, Accessible, Interoperable and Reusable.

Findability comes when we have enough metadata associated with the actual data. For example, if you want to search for a particular version of a document, the last modified datetime, comments etc would help you to get it right. So, adding metadata will enrich the findability aspect of a data asset.

Accessibility is something comes right after we find the data asset, this will address whether we can access the actual data that we found out using the metadata. Continuing the above example, after finding the right version of the document, do we have permission to access it ? Are we authorized to perform specific actions on it ? All this accounts to this accessibility aspect.

Interoperability helps to ensure the accessed data is in a state that can be integrated or consumed by different workflows or applications. Same example, after we get required access to use the data asset, do we have it in a format that is operable in the applications we have in our computer system ? Can it be transformed or saved in a different compatible format ?

Reusability supports the dataset with optimal reuse of data for possible consumption requirements. The above said file getting catalogued into a central repository with proper attributes and description, alongside clear usage licensing policies will help multiple consumers to use this asset for a varied purpose and this will stand as a single source of information.

Let's see a practical example to understand better. Let's google Rajinikanth ??

You would have got 6,23,00,000 hit results (that's the power of Thalaivar !!), but let us take our preferred wiki reference.

https://en.wikipedia.org/wiki/Rajinikanth

Here Rajinikanth is a unique identifier which helps us to find him easily in Wikipedia. F is done..

I could able to access the above link with no problems, so it is using open, free and shared internet protocol. Okay, let me try editing it..Oops, I'm unable to do it and it is asking for login credentials, so here comes the A part.

Wiki API Catalog( https://api.wikimedia.org/wiki/API_catalog ) is under construction but has already got REST APIs with which we can export the required data in a desired format. So, I is clear now.

The page itself is enriched with multiple hyperlinks to other wiki references and external references. There is a version history tab with details of author and contributors to this page. Now, R completes the FAIR criteria for this wiki article.

Hope you have enjoyed reading this writing and you found it useful to know about FAIR data. Watch out for the next article on a detailed run through on each of the FAIR components.