Data production in Life Sciences and Biomedicine is at an all time high. Scientists and clinicians have laboratory instruments at their disposal that can deeply explore the nature of life and disease, generating massive amounts of data quickly for relatively little money. As a result, life sciences has become among the top data generators out of all science.
Along with the massive press to advance Artificial Intelligence (AI), the sheer volume and analytics needs of the field now require a better understanding of datasets, the ability to create high-quality cohorts of data quickly, and the ability to utilize large amounts of data from other sources in single meta-analyses.
That’s where the concept of data being FAIR (Findable, Accessible, Interoperable, and Reusable) comes into play. FAIR refers to the ideal state of data management for scientific data. While the industry has been refining and trying different approaches to FAIR for the last decade, it has yet to agree on any data standards or formats, thus making FAIR fleeting. In this webinar, we’ll discuss the drivers of data analytics in biomedicine, where we stand on the journey towards FAIR, cultural considerations relevant to that journey, and the potential future of scientific discovery if we reach the goal of FAIR data.