Data – Achieve FAIR Principles Fast

If you want to rush right to the specific FAIR data principles, click here to go to the bottom of the page (it’s a quick read). If you want to understand how you can implement the FAIR principles fast, start here.

The Destination – What would an environment that holds to the FAIR data principles look and feel like?

You are a Scientific Writer & Author for one of the top life sciences companies. You get an automatic notification (from a change in your RIM system) that there has been an update to the quantity of an active ingredient in one of the drugs you manage.

Because active ingredients and quantities/dosage is part of the structured metadata related to your drug, you make your update in a simple to use word doc (that is an overlay to the base XML) one time.

From there, your system tells you all the places that content is now out-of-date. And with a click of a button you can update that component in every label and regulatory submission doc across your RIM system.

In minutes all your labels and regulatory docs across every geography and regulatory agency are updated.

Done.

No copying and pasting one thousand times, no checklist for every document in your RIM system that needs to be updated, no need to have a massive institutional knowledge of where information is kept and how to update it. It’s just one update in one place that you can cascade to all your systems and update every regulatory submission document.

That’s what living the FAIR data principles feels like.

Too simple? Okay, let’s dive a little deeper into each component of FAIR and how this works in practice.

How to achieve Findable?

Data is easily finable when it’s structured, meaning your data has the proper tags and metadata that let a machine know what that data is all about.

This means your data must be structured for it to be findable.

This is exactly how a Google search works. Google indexes public websites, meaning it captures a version of the site and all its public-facing information. Then Google’s algorithm structures the data in a way that a search query related to that website could yield that website in the search results.

That’s exactly what Findable data feels like. Like doing a Google search and the results you are presented with are exactly what you want.

Based on thet criteria, is your data Findable right now?

How to achieve Accessible?

Achieving the accessible principle is about giving the right access to the right people. In IT this is sometimes referred to as “user access management” or “identity access management”.

Once data is Findable, it needs to be able to be accessed by the people who need it. And based on the roles and responsibilities that the user is entrusted with, that user needs to have the appropriate level of permissions to do their job.

Because data in life sciences companies can exist across many tools, systems and locations, living out the Accessible principle can be a significant challenge.

One of the easiest and most cost effective ways to achieve the Accessible principle is to bring in an “overlay” system. This is software that has access to all the key systems in your data lifecycle but does not replace them as a repository for the information itself.

This has a lot of advantages including not needing to rip-and-replace effective data management systems, not needing to force people in different departments to use new tools that are unfamiliar or don’t fit their needs, and many more.

Is your data easily accessible by the people that need to use it?

How to achieve Interoperable?

Now that we can find the data we need and have the proper access to the data, it needs to be easily managed.

That means the data needs a process for storage, analysis, reporting and managing updates.

In our experience the easiest way to achieve this requires 2 solutions to work side by side:

1. Data needs to be stored in a way that it can easily understood and reported on
2. Data needs to be easily and efficiently updated by a user

Most life sciences companies have #1 down. They have a RIM system (or ecosystem) that allows them to store, understand and report on their data.

#2 is where things get tricky. Because so many regulatory bodies (especially the FDA) require XML based submissions, most life sciences companies need their scientific writers and authors to understand and manipulate XML to make content updates. Modifying XML directly is a painful and extremely inefficient process.

That’s why Glemser leverages a Microsoft Word plugin that allows writers and authors to modify base XML through a simple MS word interface. This saves a massive amount of time and no longer requires scientific writers and authors to be XML experts.

How to achieve Reusable?

In the end, reusability of data is the goal of the FAIR Principles. Each principle builds on the last in order to get here.

Now that data can easily be found, accessed and interacted with, it can be organized and managed in a way where it can be reused across different settings.

This is the magic of FAIR. Build once, use many.

Where we see this working best is when you have a layer that we call Controlled Compliant Components. These data components are the basis for how all labels and regulatory documents are constructed.

So when an update to a particular component occurs, you can easily push and approve that update in every place that component resides.

Solution to achieving FAIR fast

Read any article about adopting FAIR data principles and you will see a host of people talking about how real adoption can take years.

ComplianceAuthor™ from Glemser will get R&D departments in life sciences companies to adopt 80% of FAIR in less than 6 months and full adoption in less than a year.

How is that possible?

ComplianceAuthor™ is a technology solution built with the FAIR framework “included”:

• Findable – ComplianceAuthor™, using automation, can transform unstructured data into structured data, essentially creating the metadata each piece of data needs to be findable.
• Accessible – ComplianceAuthor’s™ Google-like search functionality lets users find the exact data they need across their entire RIM system.
• Interoperable – The easy-to-use MS Word based interface makes it simple for scientific writers to do their job without expert knowledge of XML.
• Reusable – Because ComplianceAuthor™ keeps all relevant label and regulatory submission data in Controlled Compliant Components, it is simple to manage updates across all systems, documents, labels and reports. Build once, use many.

If you want more information on how this works, click the contact button in the top menu bar and fill out the form. We’ll get back to you ASAP on a time for us to meet.

What are the FAIR principles?

The FAIR principles are on the Go-fair website. Each of the four FAIR principles has some clauses to help you understand the meaning of each principle:

Findable means that the data itself and the metadata are easy to find for humans and computers:

• F1. (Meta)data are assigned a globally unique and persistent identifier
• F2. Data are described with rich metadata (defined by R1 below)
• F3. Metadata clearly and explicitly include the identifier of the data they describe
• F4. (Meta)data are registered or indexed in a searchable resource

Accessible means that standard protocols are used:

• A1. (Meta)data are retrievable by their identifier using a standardized communications protocol
• A1.1 The protocol is open, free, and universally implementable
• A1.2 The protocol allows for an authentication and authorisation procedure, where necessary
• A2. Metadata are accessible, even when the data are no longer available

Interoperable means that it is easy to combine the data with existing data:

• I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
• I2. (Meta)data use vocabularies that follow FAIR principles
• I3. (Meta)data include qualified references to other (meta)data

Reusable means that the data can be used later on. It includes both good descriptions and clear licenses.

• R1. Meta(data) are richly described with a plurality of accurate and relevant attributes
• R1.1. (Meta)data are released with a clear and accessible data usage license
• R1.2. (Meta)data are associated with detailed provenance
• R1.3. (Meta)data meet domain-relevant community standards