{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/65e0f5b7b8456c0016989a98/6a06927868dc584eda03d52a?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"Is Science Next in the AI Disruption Cycle? w/ Kevin Moore","thumbnail_width":200,"thumbnail_height":200,"thumbnail_url":"https://open-images.acast.com/shows/65e0f5b7b8456c0016989a98/1778815587957-692033b2-0448-4866-960e-d83d5e4595e3.jpeg?height=200","description":"<p>In this episode of Data Unchained, Molly Presley talks with Kevin Moore, CEO of Quilt Data, about what it takes to make scientific and enterprise data ready for AI, collaboration, and discovery at scale. Kevin explains how life sciences teams are managing massive multimodal data sets across AWS, S3, sequencing workflows, lab notebooks, metadata systems, governance requirements, and regulatory demands. The conversation explores how bench scientists, computational scientists, data engineers, and IT teams can work from the same versioned, contextualized data sets without losing control of access, compliance, or intellectual property. Kevin also shares why metadata, object storage, version control, and flexible data set constructs are becoming essential as organizations move from isolated data silos toward human and AI-assisted research workflows.</p><p><br></p>","author_name":"Hammerspace"}