We are looking for experts from the fields of Nutrition, Obesity, Eating Behavior, Public Health, and Data Science to serve as mentors to teams competing in our datathon. Mentors can be anyone with expertise in the fields listed above, including post-doctoral fellows. If you aren’t sure if you are qualified, send us an email at email@example.com with a short introduction.
Mentors serve as a source of guidance to teams participating in the datathon, be it in person, through email, or through video-conferencing. For this datathon, data analysis will be conducted by teams of individuals from broad backgrounds, who may need advice or guidance from our mentors on their specific approach to the data and/or to assess the relevance of the question. By signing up as a mentor, you will be expected to be available to answer questions and offer guidance to teams that contact you over the week of October 15-26, as they prepare their data visualizations. Guidance can include suggestions of alternative statistical methods to those proposed by the teams, suggestion of an area of debate in your field of expertise that a team may want to explore in their analysis, correction of methodological errors, etc.
Mentors do not need to be available during the whole week, but should understand the time crunch under-which our teams are operating and make every reasonable effort to respond to questions quickly and succinctly. We expect that mentorship throughout the week will range between an hour and 10 hours depending on the needs of teams.
As a mentor, you are encouraged to attend the inaugural meeting of the Nutrition Obesity Research Club, where the winners of the datathon will be announced. See the main page for more details.
Mentors are not permitted to be on a team and therefore are not eligible to receive prize money.
Get a fresh take on your data. If you have a dataset from your research in the fields of Nutrition or Obesity, you may be able to include your data in the datathon. We are looking for robust data from large-scale studies that have the potential to yield ground-breaking results. See Datasets for details.