The Pistoia Alliance has announced the launch of its FAIR Implementation project, backed by pharmaceutical companies including Roche, AstraZeneca and Bayer.

The ‘toolkit’, will be available to help companies implement the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles for data management and stewardship, expected by the end of 2019.

The FAIR guiding principles help to give the sector clear and practical guidance on how data and relevant metadata is captured and managed to foster greater collaboration and more effective partnerships, but many companies are struggling to implement the guidelines.

The Alliance’s implementation aid will consist of selected tools, best practices, training materials, use cases, and methodology for change management, which will be assembled together on a user-friendly and freely accessible web site.

It will also help organisations to undertake their digital transformation, make preparations for the ‘Lab of the Future (LoTF)’ and to accelerate the application of artificial intelligence (AI) and deep learning.

“Roche’s overarching strategy is to become a data driven organisation, so we are very excited to help shape and lead on this project,” commented Dr. Martin Romacker, principal scientist at Roche.

He continued, “We believe the FAIR guiding principles are vital in helping the entire life science ecosystem benefit from the data the sector is creating. Today, data assets are siloed, stored in varying formats, hard to retrieve and share, and are not interoperable – meaning the knowledge we have already learned can’t be utilised by and extended to a wider audience.

“To follow the FAIR guiding principles is a big task for pharmaceutical companies to undertake, but we know everyone is in the same boat and there is no point undergoing this culture shift alone. The Pistoia Alliance is perfectly positioned to drive this change within the industry, and companies need to act with a sense of urgency to implement FAIR if we are going to realise the value of analytical methods such as deep learning based on high quality data.”

Data needs to be better managed to build a more collaborative research environment, and made shareable and interoperable, if the industry is to continue making breakthroughs.