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（datathon，亦称数据或数据科学黑客松）的兴起为在短时间内开展协作、学习与创新提供了平台。尽管其潜在效益显著，但由于缺乏应对潜在问题的明确指引和最佳实践，组织者往往难以有效处理数据。基于自2016年以来我们与超过60个合作组织共同举办80余场数据马拉松挑战赛的经验与洞见，本文提供了旨在帮助组织者应对数据马拉松中数据相关复杂性的指南与建议。我们将所提出的框架应用于10个案例研究。