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How to Data in Datathons

by Carlos Mougan, Richard Plant, Clare Teng, Marya Bazzi, Alvaro Cabrejas-Egea, Ryan Sze-Yin Chan, David Salvador Jasin, Martin Stoffel, Kirstie Jane Whitaker, Jules Manser

(Track on Datasets and Benchmark at the Conference on Neural Information Processing Systems 2023)

The rise of datathons, also known as data or data science hackathons, has provided a platform to collaborate, learn, and innovate in a short timeframe. Despite their significant potential benefits, organizations often struggle to effectively work with data due to a lack of clear guidelines and best practices for potential issues that might arise. Drawing on our own experiences and insights from organizing >80 datathon challenges with >60 partnership organizations since 2016, we provide guidelines and recommendations that serve as a resource for organizers to navigate the data-related complexities of datathons. We apply our proposed framework to 10 case studies.
Paper