# How to Data in Datathons

arXiv: https://arxiv.org/abs/2309.09770 · HTML: https://mlsanity.com/paper/2309.09770/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 79 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?](https://mlsanity.com/paper/1511.06348/) (2015)
- [Data Readiness Report](https://mlsanity.com/paper/2010.07213/) (2020)
- [Data Readiness Levels](https://mlsanity.com/paper/1705.02245/) (2017)
- [How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior](https://mlsanity.com/paper/1706.07637/) (2017)
- [Design choices for productive, secure, data-intensive research at scale in the cloud](https://mlsanity.com/paper/1908.08737/) (2019)

## Abstract (arXiv)

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.