The Pistoia Alliance, a global non-profit that works to lower barriers to innovation in life sciences R&D, has announced the launch of its FAIR 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 (Findable, Accessible, Interoperable, Reusable) guiding principles for data management and stewardship. 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. 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 website. It will help organisations to undertake their digital transformation, make preparations for the Lab of the Future (LoTF) and to accelerate the application of AI and deep learning.
Life science organisations 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 emphasise 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, harmonised data sets for better predictions.
The Pistoia Alliance is inviting more members to join the FAIR Implementation project team to help steer it and ensure it meets industry requirements. The group is also looking for other organisations in the industry to join the Community of Interest and to contribute feedback.
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