It seems like the R&D industry gets excited by some terms and acronyms and organizations and some people tend to hype them. FAIR (Findable, Accessible, Interoperable and Reusable) is one of those acronyms. The problem is the hype is valid, but it also needs follow through… Another problem is only half of the problem is being discussed as many are not including processes in with FAIR data! After all the processes produce the data.

UnFAIR data and processes have evolved to a breaking point in the sciences for a multitude of reasons. It is extremely important to point these out because it is not a technology problem, it goes much deeper than that. The underlying root cause of poor data environments and lower data integrity is a Cultural problem! What I am about to say will most likely ruffle some feathers. There are several critical problems in science today and they are arrogance, ignorance, and financial and peer pressures. This coupled with one of the most complex industries, NME/Drug, and therapy discovery, has led to the inability to drive approaches that would have led to FAIR data and process environments. A true transformation (everyone must change) is needed, starting in academia/learning institutions, and ending in data driven R&D organizations. Number one, a reteaching and relearning of data and process as an asset, and then taking the time to make sure that data is captured, curated/managed, and reused as model-quality data whenever and wherever possible. This takes strategy and agreement in an organization, and it is a change management program, like most journeys in these organizations. This also means sacrifice, commitment, and strong leadership.

So, when we said it’s not a technology problem, it kind of is as well. The in-silico technology approaches are not reaching their greatest potential because unFAIR data prevents these methods from being used! We need this FAIR transformation to happen now, and it will take everyone’s concerted effort.

This is not an unachievable goal as other industries like Telecom, Entertainment, W3C (World Wide Web Consortium), Banking, and Insurance have driven success in their industries by adopting data and process standards.

So, we just touched on three of the Four Pillars, Culture, Data, and Processes, now let us talk about some technology that will help enable the change!

Have you spoken with a lab scientist lately? They are usually terribly busy and must coordinate their time carefully. In many cases they are stressed in their role. The LAST thing you want to do is ask them to do more or work with scientific software solutions that are not intuitive, and simply not augmenting their work. One main tool for all scientists is their notebook, what they are going to do, what they did, how they did it, what results they got, and finally observations and conclusions. This must be the foundation for a FAIR data and process environment.

In the dynamic R&D world Electronic Lab Notebooks (ELNs) exist to capture the scientific method. First generation or earlier renditions of the ELN were focused on IP capture which may have missed the mark on usability and end user enablement, but things had to start somewhere!

ELNs are applicable to every flavor of R&D company. There will be more excuses than facts when it comes to (not) deploying an ELN. Academia, startups and small organization, scientific domains, and then finally the large R&D organization have had a plethora of wins and losses. Academics can get an ELN for free, startups have a lot lose if they are disorganized or even come across disorganized to their investors or collaborators, we do not need an ELN in parts of our research organization because for a multitude of reasons, etc.

It’s now 2021 and the new ELNs are advanced, mostly cloud-enabled, and driving that next-generation experience. Data and process environments are critical for an ELN to be able to drive FAIR principles. They are also a perfect environment for capturing your scientific business processes so that you can execute your experiments from your ELN! This is not a new concept it goes back years to Laboratory Execution Systems and another solution built for a top energy provider, but now we have technology that can capture the processes and version them! Why is this critical? Because companies that are trying to enhance/optimize, and harmonize their processes do business process mapping in other tools when in fact an ELN could be that repository and become a “functional” or “executional” business process map!

Now your ELN is not only capturing your contextualized data, but it has captured the executable processes and the two together give you the complete picture. Why is this important? Bench scientists now have FAIR data and processes! Bench scientists now have tech and knowledge transfer, they now have ability to mine all types of data and integrate with other data, and they now have data for you in silico-first approaches. This means they could drive 40% efficiency gains in areas or your organization which means faster to market and better quality of life for those that need it!

This costing your large organization a lot of money and potential. Price Waterhouse Cooper and the European Union have estimated its costing upwards of €26 billion euros a year for European R&D organizations. We have done our own calculations based on our knowledge and observed level of data wrangling etc. and we think the cost is higher, as a large Biopharma could see 100’s of millions of Return on Investment (ROI) with a properly deployed and adopted ELN.

The transformation needed to become FAIR compliant in your organization is critical as it reduces data wrangling, improves collaboration, will drive in silico-first approaches, and ultimately lead to a much more efficient R&D community. The efficiency gain leads to better products, better medicines, better therapies, and a better quality of life for all, producers, and consumers.

Learn more about Revvity Signals Research Suite and how it helps a range of ELN customers from small startups to large global biopharmas.