Summary
In this episode Rowan Cockett, co-founder and CEO of CurveNote and co-founder of the Continuous Science Foundation, talks about building data systems that make scientific research reproducible, reusable, and easier to communicate. He digs into the sociotechnical roots of the reproducibility crisis - from data integrity and access to entrenched publishing incentives and PDF-bound workflows. He explores open standards and tools like Jupyter, Jupyter Book, and the push toward cloud-optimized formats (e.g., Zarr), along with graceful degradation strategies that keep interactive research usable over time. Rowan details how CurveNote enables interactive, reproducible articles that spin up compute on demand while delegating large dataset storage to specialized partners, and how community efforts like the Continuous Science Foundation and initiatives with Creative Commons aim to fix credit, licensing, and attribution. He also discusses the Open Exchange Architecture (OXA) initiative to establish a modular, computational standard for sharing science, the momentum in computational biosciences and neuroscience, and why true progress hinges on interoperability and composability across data, code, and narrative.
Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'm interviewing Rowan Cockett about building data systems that make scientific research easier to reproduce

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what your interest is in reproducibility of scientific research?
What role does data play in the set of challenges that plague reproducibility of published research?
What are some of the notable changes in the areas of scientific process, and data systems that have contributed to the current crisis of reproducibility?
Beyond technological shortcomings, what are the processes that lead to problematic experiment/research design, and how does that complicate the work of other teams trying to build on the experimental findings?
How does a monolithic approach change the types of research that would be possible with more modular/composable experimentation and research?
Focusing now on the data-oriented aspects of research, what are the habits of research teams that lead to friction and waste in storing, processing, publishing, and ultimately consuming the information that supports the research findings?
What are the elements of the work that you are doing at the Continous Science Foundation and Curvenote to break the status quo?
Are there any areas of study that you are more susceptible to friction and siloing of their data?
What does a typical engagement with a research group look like as you try to improve the accessibility of their work?
What are the most interesting, innovative, or unexpected ways that you have seen research data (re-)used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on reproducibility of scientific research?
What are the next set of challenges that you are focused on addressing in the research/reproducibility space?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
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Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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