As discussed in the first episode in this two‑part podcast series, building reproducibility at scale starts with strong cultural and digital foundations. In this second episode, our expert guests, Emma Ganley and Robin Padilla, shift the focus to what that looks like in practice, with a particular emphasis on transparency. They explore practical ways to improve transparency and how documentation, digital tools, publishing practices and incentives come together to support reproducible research in real‑world settings, including:

• Document methods as research evolves, maintaining continuity from experiment through to publication.
• Treat methods like a recipe, with enough clarity and specificity for others to follow the process exactly.
• Build consistent habits that capture changes, decisions, and variations, preserving the context behind results.

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In this episode, Ganley and Padilla reflect on the cultural shifts required to ensure that reproducibility becomes the expectation rather than the exception in science. They discuss the role of digital tools and FAIR (Findable, Accessible, Interoperable, Reusable) principles, share perspectives on the opportunities and limitations of AI, and explore what it will take to shape a more connected and reliable research ecosystem for the future.

Below, we’ve curated some of the key insights from this first episode. You can listen to the full podcast conversation down below. This conversation highlights:

• Why reproducibility underpins trust across partnerships, regulation and translation
• How digital tools and standardised workflows reduce risk, error and inefficiency at scale
• What FAIR and AI‑ready data foundations mean for future‑proofing research

Hosted on Acast. See acast.com/privacy for more information.