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Past Datathons

The WiDS Datathon 2023 challenge used data science to improve longer-range weather forecasts to help people prepare and adapt to extreme weather events caused by climate change. The dataset was created in collaboration with Climate Change AI (CCAI). WiDS participants submitted forecasts of temperature and precipitation for one year, competing against the other teams as well as official forecasts from NOAA.

The WiDS Datathon 2022 challenge addressed an important way to mitigate the effects of climate change with a focus on energy efficiency. The dataset was created in collaboration with Climate Change AI (CCAI) and Lawrence Berkeley National Laboratory (Berkeley Lab). Participants analyzed regional differences in building energy efficiency and built models to predict building energy consumption.

The WiDS Datathon 2021 challenge focused on creating models to classify whether patients have been diagnosed with a certain type of diabetes which could inform treatment in the ICU. Participants used data from MIT’s GOSSIS (Global Open Source Severity of Illness Score) Initiative.

The WiDS Datathon 2020 challenge created models that predict patient survival. MIT’s GOSSIS community initiative, with privacy certification from the Harvard Privacy Lab, provided a dataset of more than 130,000 hospital Intensive Care Unit (ICU) visits from patients. This data was part of a growing global effort and consortium spanning Argentina, Australia, New Zealand, Sri Lanka, Brazil, and more than 200 hospitals in the United States.

The WiDS Datathon 2019 challenge utilized a dataset of high-resolution satellite imagery to build awareness about deforestation and oil palm plantations. Planet and Figure Eight provided an annotated dataset of satellite images taken by Planet satellites. The task was to train a model that takes as input a satellite image and outputs a prediction of how likely it is that the image contains an oil palm plantation.

The WiDS Datathon 2018 worked with the InterMedia Survey Institute, a grant recipient of the Bill & Melinda Gates foundation in their Financial Services for the Poor program. The dataset for the challenge contained demographic and behavioral information from a representative sample of survey respondents from India about their usage of traditional financial and mobile financial services. Participants analyzed the data and build machine learning and statistical models to predict the gender of each survey respondent.