{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/6a18277cda0413146cf8ec8a/6a18293769630795d8951772?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"Reproducibility in R&D building trust and digital foundations","thumbnail_width":200,"thumbnail_height":200,"thumbnail_url":"https://open-images.acast.com/shows/6a18277cda0413146cf8ec8a/1779968128708-c503c273-cd37-4c1c-b84e-d04e6cb6b2e4.jpeg?height=200","description":"<p><strong>In this episode</strong>, 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.</p><p>&nbsp;</p><p>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:</p><p>&nbsp;</p><ul><li>Why reproducibility underpins trust across partnerships, regulation and translation</li><li>How digital tools and standardised workflows reduce risk, error and inefficiency at scale</li><li>What FAIR and AI‑ready data foundations mean for future‑proofing research</li></ul>","author_name":"Springer Nature"}