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!

We often hear about making AI responsible—but what if the real problem isn’t the model? What if it starts much earlier, with the data we feed into it?

In this episode, we unpack the hidden assumptions baked into the way data is measured, labeled, and used. We explore why objectivity in data is often a myth—and why designing for subjectivity is essential for building trustworthy AI. From mental health dashboards and inconsistent clinical definitions to the neuroscience of neuron behavior and brain simulations, we examine how context—not just content—shapes what AI learns.

We introduce the FAIR² framework: a practical approach to making data not only Findable, Accessible, Interoperable, and Reusable—but also AI-ready, Responsible, and Context-Rich. This isn’t just about fixing bias. It’s about understanding how data is made—and building integrity into the foundation of every AI system.

If you care about scientific rigor, equity in AI, or reproducible research, this episode will shift how you think about data.

In a world shaped by constant change, memory is fragile — and preservation is never automatic.

Inspired by a visit to the Qianlong Stone Steles in Beijing, this episode reflects on how civilizations have fought against impermanence by choosing what must endure.

Today, as our knowledge increasingly rests on unstable digital foundations, we face the same challenge:

Preservation is not a given; it is an act of will and judgment.

What, in our time, do we believe is worth enduring — and what will we choose to carve in stone?

What does it really take to make data usable—not just technically FAIR, but truly findable, accessible, interoperable, and reusable in the ways science needs today?

In this episode of The FAIR² Chronicles, we explore the frustrating reality of pseudo-FAIR data and the critical gap between aspiration and implementation. We introduce FAIR² as a powerful framework that embeds context, clarity, and computability into the data lifecycle—from rich metadata and semantic alignment to transparent workflows and reproducibility tools.

Whether you’re a researcher, funder, policymaker, or data steward, this conversation will help you rethink what it means to share data that’s not just open, but trustworthy, understandable, and ready to drive discovery.

In this episode, we dig into the often-overlooked cornerstone of scientific progress: reproducibility. It’s not flashy, but it’s fundamental. Without it, science loses its grounding—and so does our ability to trust and build upon results.

We explore how FAIR² Data Management is reshaping the way researchers handle, document, and share their data. From clear variable descriptions and transparent processing pipelines to the use of ontologies, version control, and data articles, FAIR² goes far beyond just making data open—it makes it usable, understandable, and truly reusable.

We also introduce tools like the AI Data Steward, designed to support researchers in creating context-rich, FAIR, and AI-ready datasets without adding extra burden.

Whether you’re a seasoned data scientist or just beginning to think about data stewardship, this episode offers a practical and philosophical look at why reproducibility isn’t just a technical detail—it’s the very foundation of trustworthy science.

Every day, researchers generate valuable datasets. But many of them never get shared—let alone reused. Why? Because data sharing still feels hard: confusing formats, missing documentation, and no clear roadmap. For many, it’s not part of their training. It’s just another overwhelming task.

In this episode, we unpack the very real barriers to sharing research data—and introduce a way forward. FAIR² is an open specification designed to make research data not only open, but AI-ready, responsibly shared, and documented with context. And FAIR² Data Management—a service offered by Frontiers and powered by Senscience—helps researchers apply this framework without needing to become data experts.

We dive into how the platform’s AI Data Steward supports everything from structuring spreadsheets to drafting FAIR² Data Articles. You’ll learn why this matters not just for open science, but for recognition, reuse, and real-world impact.

If you’ve ever thought, “I’d love to share my data, but I don’t know where to start,”—this episode is for you.

Links mentioned:

🔗 Learn more about FAIR²: fair2.ai

📬 Subscribe to The FAIR² Pulse: senscience.substack.com

✅ Apply to join the FAIR² pilot: frontiersin.org/about/fair-data-management

In this episode, we explore a growing opportunity for researchers: turning their datasets into recognized, reusable, and citable scientific contributions.

Too often, datasets sit unused—buried in supplemental materials or left unpublished entirely. But what if those datasets could become a first-class research output in their own right?

Enter the FAIR² Data Article, a new article type from Frontiers that focuses not on findings, but on the dataset itself—how it was collected, structured, and how others can reuse it. We discuss how these articles come with structured, AI-ready data packages, interactive portals, and rich metadata that make reuse easy and powerful.

We also dive into one of the most exciting aspects of the FAIR² platform: the built-in AI Data Steward, a smart assistant that helps researchers clean, structure, document, and publish their datasets—without getting bogged down by technical overhead.

If you’ve got tabular data—whether from a past study or an unpublished project—this episode will show you how FAIR² can help you share it responsibly, extend its impact, and get the credit you deserve.

🔗 Apply to join the pilot: frontiersin.org/about/fair-data-management

🌐 Learn more at: fair2.ai

📰 Subscribe to FAIR² Pulse: senscience.substack.com