What is FAIR data and can Life Science organisations ensure data is compliant whilst adhering to these principles? - Arkivum

Blog / 18 Oct, 2019

What is FAIR data and can Life Science organisations ensure data is compliant whilst adhering to these principles?

Life Sciences organisations are becoming big data enterprises, with growing amounts of data being generated from clinical studies, lab equipment and drug development often in silos; the need to implement good long-term data management practices is growing exponentially.

One such practice used primarily by research in academia are the FAIR data principles.

What is FAIR data?

Initially ideated by FORCE11 (a community of scholars, librarians, archivists, publishers and research funders that help facilitate the change toward improved knowledge creation and sharing), the FAIR data principles were created to support further scientific study through good data management.

The key principles of FAIR data are;

  • To be Findable
  • To be Accessible
  • To be Interoperable
  • To be Re-usable

Why is FAIR data important?

A growing amount of research data is being produced in different formats and from different sources within the life sciences industry which creates data silos across the organisation. The result of this is a laborious and challenging process of storing, managing, consolidating and extracting value from the data generated.

When we look at clinical studies as an example, there can be numerous partners that an organisation is working with to generate all the data needed for their specific study. The need to implement FAIR principles in this situation is paramount to being able to re-use the data generated.

Additionally, different areas of the business have different requirements of data, by adhering to the FAIR data principles organisations are able to ensure that stakeholders within the organisation can find and access the data they need for their specific need.

Could I just implement FAIR and not worry about anything else?

FAIR data principles are important; however, they do not cover a lot of the regulatory compliance mandates that life sciences organisations are required to adhere to. It can feel as if every blog that is ever written on data management in life sciences has the words “compliance” or “regulations” somewhere in the text, but this is because it is important. Adherence to regulations ensures the integrity of the end product by ensuring good audit trails and accountability of research and manufacturing processes.

However, the implementation of FAIR data principles does not necessarily mean that you are compliant with other regulations, see our ALCOA + checklist for more information on the principles. You need to ensure that you are also adhering to data integrity guidelines.

Metadata is key to making data Findable

In order to ensure that your data is findable you need metadata and a unified view of your data. Simple metadata such as the source of a specific piece of data, or the subject it relates to is not always enough, so advanced metadata capabilities are needed.

FAIR data principles do not cover the need for research data to be attributable, accurate, contemporaneous and original (from the ALCOA principle), in order to ensure compliance to these principles and therefore Data Integrity regulations mandated by the MHRA and FDA. In order to ensure you comply to these regulations you need to be able to prove the provenance of such data and ensure data (including metadata) is immutable.

Just storing data in a backup does not make it Accessible

One of the most common misconceptions that we come across is that backup is good enough to preserve your data for the long-term. Whilst in many cases backup may be a cheaper option, you pay in the long-term when you haven’t properly preserved your valuable files.

When looking at accessibility we also need to consider file format obsolescence. Backup does not provide file format normalisation as standard, while you could use open source tools to provide normalisation you cannot guarantee access to the original metadata.

Due to the nature of some of the research that happens in life sciences companies it is clear that
not all data can be open and not all data can be accessed by everyone, security roles and restricted access need to be applied.

Thinking about Interoperability

Data silos can cause major headaches when it comes to managing scientific data, especially when it comes to personally identifiable data. If you are having to look in multiple locations, or even multiple folders within the same location it can take time to find what you are looking for, and how do you know you’ve found it all?

In order to efficiently (time and cost) manage your data it is imperative to ensure it is all accessible in one platform.

Is your data Re-usable?

You may be able to get to your data, but can you actually use it? Is the format of that particular set of notes still readable now? Can you still easily include data from that old server that hosted the aging device in the corner?

There is more to re-usability of data than whether it is relevant to further study, you need to ensure that any data management strategy that you have in place includes provision for technology and format obsolescence.

So why don’t I just not use the FAIR data principles? Wouldn’t it be easier?

You may be thinking by now that this all seems like a lot of work! However, it doesn’t have to be. Data plays a critical role in the life sciences industry but more needs to be done to unlock its full potential. You can’t just put it in a backup and expect it to be re-usable years down the line.

By adopting the FAIR data principles of Findability, Accessibility, Interoperability and Re-usability into your data management strategy, life sciences organisations can achieve a faster time to results and a greater agility to respond to changing business demands.

Arkivum and Thermo Fisher Scientific held a webinar on how organisations can securely collaborate and manage data within a long-term data management framework; view the recording below.

View the recording

Emma Davenport

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