The MUSE data challenge aims to share information, strategies, and best practices for mitigating these issues with the wider community. We will provide a set of raw science observations and associated calibration to any interested team or individual and ask that they apply their ideas to mitigate a given problem. These best efforts will be collated by the SOC/LOC and presented in November in a friendly and collegial manner. As a community, we will discuss the benefits and drawbacks of each approach, and for the first time, compare various techniques in an 'apples to apples' manner with a standardized set of data. We will encourage sharing this knowledge including promoting the outcome of this challenge in a Messenger article, or possible peer-reviewed journal article(s). Most importantly, we hope to understand the impact of various choices on MUSE data products and identify promising new and existing avenues for further work in this field.