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WiDS News | November 17, 2021

Announcing the 5th Annual WiDS Datathon 2022 Challenge: Using Data Science to Mitigate Climate Change

The 5th Annual Women in Data Science (WiDS) Datathon will launch in January, in advance of the WiDS Worldwide Conference to be held on March 7th, 2022. This year’s WiDS Datathon, organized by the WiDS Worldwide team, Stanford University, Harvard University IACS, and the WiDS Datathon Committee, will address an important way to mitigate the effects of climate change with a focus on energy efficiency. The WiDS Datathon Committee is partnering with experts from many disciplines at Climate Change AILawrence Berkeley National Laboratory (Berkeley Lab)US Environmental Protection Agency (EPA), and MIT Critical Data.

“The WiDS Datathon has consistently featured a social impact challenge”, said Karen Matthys, Co-Director of the WiDS initiative at Stanford University. “This esteemed group of collaborators created a challenge related to one of the most critical issues of our time: climate change.”

Background on the challenge

Climate change is a globally relevant, urgent, and multi-faceted issue heavily impacted by energy policy and infrastructure. Addressing climate change involves mitigation (i.e. mitigating greenhouse gas emissions) and adaptation (i.e. preparing for unavoidable consequences).  Mitigation of GHG emissions requires changes to electricity systems, transportation, buildings, industry, and land use.

According to a report¹ issued by the International Energy Agency (IEA), the lifecycle of buildings  from construction to demolition were responsible for 37% of global energy-related and process-related CO2 emissions in 2020. Yet it is possible to drastically reduce the energy consumption of buildings by a combination of easy-to-implement fixes and state-of-the-art strategies². For example, retrofitted buildings can reduce heating and cooling energy requirements by 50-90 percent³. Many of these energy efficiency measures also result in overall cost savings and yield other benefits, such as cleaner air for occupants. This potential can be achieved while maintaining the services that buildings provide.

The dataset and the challenge

The WiDS Datathon dataset was created in collaboration with Climate Change AI (CCAI) and Lawrence Berkeley National Laboratory (Berkeley Lab). WiDS Datathon participants will analyze regional differences in building energy efficiency, and build models to predict building energy consumption, an important first step in understanding how to maximize energy efficiency. Accurate predictions of energy consumption can help policymakers target retrofitting efforts to maximize emissions reductions. ​“We see building retrofitting as a low-hanging fruit to reduce greenhouse gas emissions”, said Nikola Milojević-Dupont, Chair of the Content Committee at Climate Change AI. “Predicting energy consumption of buildings helps identify retrofitting approaches that can ultimately reduce emissions.”

Who can participate in the challenge?

The dataset and challenge will be accessible to both beginners and experienced 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. ​“We work very hard to design the challenge so that all participants feel they have something substantial to work on,” said Sharada Kalanidhi, Co-Chair of the WiDS Datathon.

The WiDS Datathon aims to provide women with hands-on experiences addressing real-world problems, to inspire women worldwide to hone their data science skills, and to create a supportive environment for women to connect with others in their community who share their interests. Toward these ends, the WiDS Datathon is open to individuals or teams of up to 4; at least half of each team must be individuals who identify as women. Participants can be students, faculty, government workers, members of NGOs, or industry members.

How it works

The WiDS Datathon will run from early January to late February 2022 on Kaggle, an online community of data scientists.

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 Worldwide Conference held in-person, and online, on March 7, 2022. Beyond the leaderboard rankings, individuals and teams will also have an opportunity to interact with our partners at the US Environmental Protection Agency (EPA) and MIT Critical Data, submitting research papers about their work to be eligible for the WiDS Datathon Excellence in Research Award. This award is supported by the WiDS Datathon Committee, the National Science Foundation Big Data Innovation Hubs and Kaggle.

Getting started

Make your plans to join us in this year’s WiDS Datathon. We recommend you:

  • Sign up now to participate. We will send you the link to the dataset on Kaggle.
  • Set up your account on Kaggle, so that you’ll be ready to go when the datathon opens
  • Join the WiDS datathon mailing list to make sure you receive news about the WiDS worldwide conference as well as datathon news.
  • Consider forming a team with new collaborators! Connect with potential teammates on the Kaggle forums or through social media with #WiDSDatathon. We will also share opportunities to join WiDS Datathon workshops hosted by WiDS ambassadors around the world, where you can connect with team members and mentors.

Be creative, and have fun! Good luck to all participants — we are excited to see what you create.


¹IEA (2021), Tracking Buildings 2021, IEA, Paris
²Rolnick, David, et al. “Tackling climate change with machine learning.” arXiv preprint arXiv:1906.05433 (2019).
³Diana Urge-Vorsatz, Ksenia Petrichenko, Maja Staniec, and Jiyong Eom. Energy use in buildings in a long-term perspective. Current Opinion in Environmental Sustainability, 5(2):141–151, 2013.