The Pistoia Alliance, a global, not-for-profit alliance that works to lower barriers to 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 and stewardship. The project is funded by large pharmaceutical companies and SMEs alike, including AstraZeneca, Bayer, Roche, Novartis, Bristol-Myers Squibb, AbbVie and Copyright Clearance Center.
Collated by experts in the field, the toolkit contains numerous method tools, training and change management, as well as use cases, allowing organisations to learn from industry successes. As the life sciences industry continues to digitise, the FAIR guiding principles of Findable, Accessible, Interoperable and Reusable data will help organisations realise their digital transformation, make preparations for the Lab of the Future (LoTF), and accelerate the application of AI and deep learning.
Although organisations 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.
The FAIR toolkit is available now, and can be downloaded at: http://fairtoolkit.pistoiaalliance.org. The Pistoia Alliance has worked with its industry partners to ensure that the toolkit remains compatible with other FAIR data standardization projects, like the IMI FAIRplus project.
Brought to you by Scope e-Knowledge Center, a trusted global partner for digital content transformation solutions - Abstracting & Indexing (A&I), Knowledge Modeling (Taxonomies, Thesauri and Ontologies), and Metadata Enrichment & Entity Extraction.
