Have you ever been excited to use an open dataset, only to find yourself lost in a maze of cryptic files, missing context, and dead-end links? You’re not alone. In this episode, we unpack why so much “open data” still isn’t truly reusable—and why that matters for science, machine learning, and policy alike.

Join us as we explore how FAIR² Data Portals are raising the bar, transforming messy spreadsheets and inaccessible metadata into actionable, trustworthy resources. You’ll hear real-world stories of research frustration, learn what it means for data to be FAIR² (Findable, Accessible, Interoperable, Reusable—plus AI-Ready and Responsible), and discover practical features like interactive data explorers, contextualized methods, AI-powered assistants, and machine learning-ready formats.

Whether you’re a scientist, a data steward, an engineer, or just curious about the future of research, this episode shows how a new approach to data infrastructure is multiplying scientific impact—and how you can be part of the movement.

Listen in, share your feedback, and help shape the next era of scientific data!

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