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.
@article{arxiv.2309.09770,
title = {How to Data in Datathons},
author = {Carlos Mougan and Richard Plant and Clare Teng and Marya Bazzi and Alvaro Cabrejas-Egea and Ryan Sze-Yin Chan and David Salvador Jasin and Martin Stoffel and Kirstie Jane Whitaker and Jules Manser},
journal= {arXiv preprint arXiv:2309.09770},
year = {2023}
}
Comments
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmark