The 6th Annual Women in Data Science (WiDS) Datathon launches in January 2023, in the lead up to the WiDS conferences in March 2023. In this year’s datathon challenges participants will use data science to improve longer-range weather forecasts to help people prepare and adapt to extreme weather events caused by climate change.
The WiDS Datathon encourages women worldwide to hone their data science skills, creating a supportive environment for women to connect with others in their community who share their interests. Data scientists of all levels are invited to participate in the datathon, including beginners.
Background on the challenge
Extreme weather events are sweeping the globe and range from heat waves, wildfires and drought to hurricanes, extreme rainfall and flooding. These weather events have multiple impacts on agriculture, energy, transportation, as well as low resource communities and disaster planning in countries across the globe.
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.
The dataset and challenge
This year’s datathon, organized by the WiDS Worldwide team at Stanford University, Harvard University IACS, Arthur, and the WiDS Datathon Committee, will focus on longer-term weather forecasting to help communities adapt to extreme weather events caused by climate change.
The dataset was created in collaboration with Climate Change AI (CCAI). WiDS participants will submit forecasts of temperature and precipitation for one year, competing against the other teams as well as official forecasts from NOAA.
Who can participate
The datasets and challenge will be accessible to both beginners and experienced participants from industry, government, NGOs and academia. Whether you’re currently working in the field or just starting to learn about data science, we welcome all participants. For those who have never tried machine learning, we will be releasing a series of guides to help you get started with the algorithms and dataset. Many WiDS ambassadors will host datathon workshops, where participants will be able to receive mentorship, form teams, and hone their data science skills. The datathon is open to individuals or teams of up to 4; at least half of each team must be individuals who identify as women.
How it works and timeline
The datathon will run from January 4 – March 1, 2023. The WiDS Datathon team provides webinars, tutorials, resources, team building opportunities, and other materials to help guide teams. Training and validation sets will be provided for model development; you will then upload your predictions for a test set to Kaggle and these will be used to determine the public leaderboard rankings and the winners of the competition.
Winners will be announced at the WiDS Stanford conference held in-person, and online, on March 8, 2023. Beyond the leaderboard rankings, prizes will also be awarded to the best high school and undergraduate teams.
- Sign up now to participate. We will send you the direct link to Kaggle.
- Set up your account on Kaggle, and review the datathon details, timeline, and FAQ on the WiDS Datathon Kaggle page.
- Form a team with new collaborators by connecting with datathon participants on the WiDS Datathon Slack, at team building events, and local datathon workshops hosted by WiDS ambassadors around the world.
Be creative, and have fun! Good luck to all participants — we are excited to see what you create.
The WiDS Datathon is made possible due to our Global Visionary Sponsors: Gilead, Meta, Total Energies, Walmart Global Tech, and Wells Fargo.