In the rapidly evolving field of computational science, researchers face the dual challenge of managing large datasets and extracting meaningful insights from them. The principles of FAIR (Findable, Accessible, Interoperable and Reusable) have become integral to data management, but what does it take for the analysis that is performed on that data to be FAIR and Reproducible (FAIRR Analytics)?
Join us for a webinar examining nuances and practicalities of FAIR Data and FAIRR Analytics, and practical explorations how experimental results can be not only reproducible but reusable as well.
- Exploring the concept of FAIR and Reproducible Analytics and its significance in extracting meaningful insights from biological data.
- Case studies highlighting successful integration of FAIRR Analytics principles in computational biology research.
- Addressing common challenges in implementing FAIRR Analytics and proposing practical solutions.