Pistoia toolkit to build better AI, machine learning, and support the Lab of the Future
The Pistoia Alliance has launched the FAIR Implementation project with backing from pharmaceutical companies, including Roche, Astra Zeneca, and Bayer.
The FAIR guiding principles – Findable, Accessible, Interoperable, Reusable – were published in 2016 as a collaborative effort among academia, industry, and others.
The principles for data management and stewardship place “specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals,” the authors write.
However, earlier this year, a paper discussed the challenges to implementing the principles.
With the launch of its FAIR Implementation project, The Pistoia Alliances hopes to help the industry to undertake “digital transformation” with a toolkit it said will improve data management to enable better artificial intelligence (AI) and machine learning, and support the Lab of the Future.
The first project milestone, to be completed by the end of 2019, is the release of a freely accessible toolkit.
Dr. Ian Harrow, a consultant at The Pistoia Alliance, said the combination of the ‘data deluge’ and increasing awareness by life science organizations that data is a powerful corporate asset are both driving the need for FAIR.
“The principles were published in 2016 with the goal of emphasizing machine-actionability (the idea that computational systems can access, interoperate and reuse data with minimal human intervention) as humans increasingly need computational support to manage the vast volume and complexity of data generated daily – which is a particular issue in the life sciences and research sector,” he told us.
Increasing use of AI and a shift toward greater automation is also a significant trend behind FAIR, said Harrow. “Without principles of data stewardship, and clear and practical guidance on how data and relevant metadata is captured and managed, these efforts will fail to deliver,” he added.
Additionally, both AI and machine learning systems will play key roles in advancing the Lab of the Future (LoTF), which requires high levels of automation.
“When AI is used to aid decision-making about health, harmonized, comparable, and trusted data is paramount,” Harrow explained. “In terms of the LoTF, clear data management and storage techniques like FAIR are required to continue to modernize lab environments and help the industry continue to make breakthroughs.”
The Pistoia Alliance is currently inviting additional members to join the project team and is looking for other organizations to join the Community of Interest and to contribute feedback.
