How to Data in Datathons
Abstract
The rise of datathons, also known as data or data science hackathons, has provided a platform to collaborate, learn, and innovate quickly. 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 a guide that serves as a resource for organizers to navigate the data-related complexities of datathons. We apply our proposed framework to 10 case studies.
Cite
Text
Mougan et al. "How to Data in Datathons." Neural Information Processing Systems, 2023.Markdown
[Mougan et al. "How to Data in Datathons." Neural Information Processing Systems, 2023.](https://mlanthology.org/neurips/2023/mougan2023neurips-data/)BibTeX
@inproceedings{mougan2023neurips-data,
title = {{How to Data in Datathons}},
author = {Mougan, Carlos and Plant, Richard and Teng, Clare and Bazzi, Marya and Egea, Alvaro Cabrejas and Chan, Ryan and Jasin, David Salvador and Stoffel, Martin and Whitaker, Kirstie and Manser, Jules},
booktitle = {Neural Information Processing Systems},
year = {2023},
url = {https://mlanthology.org/neurips/2023/mougan2023neurips-data/}
}