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.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS
2023) Track on Datasets and Benchmar