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Reproducibility as non-negotiable
11/18/24 • 25 min
This podcast episode discusses the importance of reproducibility in modern cancer research and data science. Through interviews with experts like Dr. Roger Peng, Dr. Casey Greene, and Dr. Jaclyn Taroni, it explores how making research reproducible - by sharing code, data, and detailed documentation - not only helps verify scientific findings but also makes research more efficient and builds a stronger foundation for future discoveries in cancer treatment. The experts emphasize that while implementing reproducible practices requires upfront effort, it ultimately saves time and protects research integrity.
This podcast episode discusses the importance of reproducibility in modern cancer research and data science. Through interviews with experts like Dr. Roger Peng, Dr. Casey Greene, and Dr. Jaclyn Taroni, it explores how making research reproducible - by sharing code, data, and detailed documentation - not only helps verify scientific findings but also makes research more efficient and builds a stronger foundation for future discoveries in cancer treatment. The experts emphasize that while implementing reproducible practices requires upfront effort, it ultimately saves time and protects research integrity.
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Empowering cancer research through data - ITCR
This episode discusses the dramatic increase in data use in cancer research over the past two decades. It highlights how the data revolution has democratized access to data, creating new challenges in data management and analysis. The episode features insights from Dr. Jeffrey Leek, a Chief Data Officer, and Dr Juli Klemm, the program director for NCI's Informatics Technology for Cancer Research (ITCR) initiative. The discuss with us the importance of data infrastructure and education. The ITCR supports the development of informatics tools for cancer research, addressing the growing need for specialized software and expertise in the field.
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Team Science!
In this episode we discuss how modern cancer research increasingly requires collaboration between traditional lab scientists and computational experts from the earliest stages of study design. The episode challenges the common misconception that data scientists only analyze data after collection, highlighting how early involvement of computational experts can strengthen experimental design and ultimately lead to more robust cancer research.
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