This is the fourth year that the Institute of Data Science hosted the WiDS Maastricht Datathon to inspire women to learn more about data science and to create a supportive environment for women to connect with others in their community. 
WiDS Datathon is open to individuals or teams of up to 4 participants, at least half of each team must be individuals identifying themselves as women. Everyone is welcome to participate in the WiDS Datathon: students, faculty, civil/public servants or anyone coming from the private sector. We encourage everyone to join regardless of their level of knowledge in data science!

View the WiDS Maastricht Datathon 2022 highlights here.

WiDS Datathon Global
The WiDS Datathon is a Stanford University initiative preceding the Women in Data Science Conference (WiDS). WiDS aims at inspiring and educating data scientists worldwide, regardless of gender, and supporting women in the field. The WiDS Datathon builds upon these same objectives by enabling a diverse team to work together in a data-related challenge. 

Stanford University as the initiator will release a data challenge on the Kaggle platform. You can join the WiDS Datathon 2023 as an individual or with a team to tackle this data challenge competing with other teams from all over the world. Aligned with WIDS missions, we mandate at least 50% women in each participating team to encourage more women to learn data science and create a supportive environment for them.

The data challenge for this year is to use data science to improve longer-range weather forecasts to help people prepare and adapt to extreme weather events caused by climate change. 

Accurate long-term forecasts of temperature and precipitation are crucial to help people prepare and adapt to these extreme weather events. Currently, purely physics-based models dominate short-term weather forecasting. But these models have a limited forecast horizon. The availability of meteorological data offers an opportunity for data scientists to improve sub-seasonal forecasts by blending physics-based forecasts with machine learning. Sub-seasonal forecasts for weather and climate conditions (lead-times ranging from 15 to more than 45 days) would help communities and industries adapt to the challenges brought on by climate change.

This year’s datathon is organized by the WiDS Worldwide team at Stanford University, Harvard University IACS, Arthur, and the WiDS Datathon Committee. The dataset was created in collaboration with Climate Change AI (CCAI). Participants will submit forecasts of temperature and precipitation for one year, competing against the other teams as well as official forecasts from NOAA.