The Pistoia Alliance, a global, not-for-profit alliance that works to support innovation in life sciences R&D, has launched a freely accessible toolkit to help companies implement the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles for data management. The project is funded by large pharmaceutical companies and SMEs, including AstraZeneca, Bayer, Roche, Novartis, Bristol-Myers Squibb, AbbVie and Copyright Clearance Center.

The toolkit contains numerous method tools, training and change management, as well as use cases. As the life sciences industry continues to digitize, the FAIR guiding principles of Findable, Accessible, Interoperable and Reusable data aims to help organizations with their digital transformation, make preparations for the Lab of the Future (LoTF), and accelerate the application of AI and deep learning.

Although organizations have become increasingly aware of data as an asset, data are often siloed, stored in varying formats, and difficult to retrieve or share. Adopting the FAIR principles helps companies break down internal siloes and cope with the growing volume and complexity of data generated. The FAIR guiding principles were published in 2016 (Wilkinson, et al.) as a blueprint for well managed and machine-actionable data to allow computational systems to find, access, interoperate and reuse data with minimal human intervention. However, further research (Wise et al. 2019) found that many companies are still struggling with implementation of the principles.

“At Roche, we know that implementing the FAIR principles can be difficult for biotech and pharma organizations of every size, so we are very pleased to lead on this project and help make the process easier,” said Dr. Martin Romacker, Principal Scientist at Roche. “The toolkit will help to smooth the path to greater data sharing within and between industries, which is critical to future research efforts. We see the FAIR guiding principles as a worthy goal, and one which will help the industry realize the value of technologies like deep learning.”