By Clinical Research News Staff

July 26, 2019 | The Pistoia Alliance recently announced the launch of its FAIR (Findable, Accessible, Interoperable, Reusable) Implementation project, backed by pharmaceutical companies including Roche, Astra Zeneca, and Bayer. The first project milestone by the end of 2019 will be the release of a freely accessible toolkit to help companies implement the FAIR guiding principles for data management and stewardship (DOI: 10.1038/sdata.2016.18).

As the life sciences industry continues to transform digitally, the sector needs clear and practical guidance on how data and relevant metadata is captured and managed to foster greater collaboration and more effective partnerships.

The FAIR guiding principles help to meet this need, but many companies are struggling to implement the guidelines, as discussed recently by Wise and co-authors (DOI: 10.1016/j.drudis.2019.01.008). This need will be addressed by development of the FAIR toolkit which 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 help organizations to undertake their digital transformation, make preparations for the Lab of the Future (LoTF) and to accelerate the application of AI and deep learning.

"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 utilized by and extended to a wider audience," Martin Romacker, Principal Scientist at Roche, said in a press release. "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 realize the value of analytical methods such as deep learning based on high quality data."

Life science organizations are becoming increasingly aware that data is a corporate asset, while at the same time, the "data deluge" continues to put scientists under pressure. 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.

The FAIR guiding principles for data management and stewardship were published in 2016 by Wilkinson and collaborators to emphasize machine-actionability of well managed data so that computational systems can find, access, interoperate, and reuse data with minimal human intervention. This allows humans to cope with the daunting scale of the increasing volume and complexity of data being generated now. FAIR is also crucial for AI and machine learning which benefits from large, harmonized data sets for better predictions.