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.

翻译：数据马拉松（datathons，亦称数据或数据科学黑客松）的兴起，为在短期内协作、学习与创新提供了平台。尽管其具有显著潜在效益，但组织者常因缺乏针对潜在问题的明确指南与最佳实践而难以有效处理数据。基于我们自2016年以来组织逾80场数据马拉松挑战赛、与超过60家合作机构合作的经验与见解，本文提出了一套指南与建议，旨在为组织者应对数据马拉松中与数据相关的复杂性提供资源。我们将所提出的框架应用于10个案例研究。