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（又称数据或数据科学黑客松）的兴起为在短时间内协作、学习和创新提供了平台。尽管其潜在收益显著，但由于缺乏应对潜在问题的明确指南和最佳实践，组织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逾80场数据马拉松挑战赛的经验与洞察，本文提供了指导方针与建议，旨在帮助组织者应对数据马拉松中与数据相关的复杂性。我们将提出的框架应用于10个案例研究。