Virtual Hackathon: Harnessing the Data Revolution ML Challenge

Register to join a virtual hackathon co-hosted by the National Ecological Observatory Network (NEON) and the Environmental Data Science Innovation & Impact Lab (ESIIL). The event will provide an introduction to the NSF Harnessing the Data Revolution (HDR) Machine Learning (ML) Challenge - Scientific Modeling out of Distribution (Scientific-MOOD), and a virtual space to form a team and create a submission. 

This free hackathon includes an optional bootcamp that will assist with account setup and introduction to CyVerse and Codabench platforms on Tuesday, Feb. 10 at 10am MT, and the main hackathon event on Thursday, Feb. 12 from 10am - 2pm MT.  

The goal of the HDR ML Challenge is to predict out-of-domain biological, ecological, and environmental data. Submissions for the challenge are due by Feb 22, 2026. 

Challenge prizes include:

• Total cash prizes $4000
• Cloud credits from AWS and NVIDIA for the winners
• NVIDIA is also providing $400 in cloud credits to teams for training on their model
• Prize from AMD, to be determined
• Special jury prizes include funded invitations to present their solution at award ceremony
  *There may be additional prizes yet to be determined.

The Hackathon event will include presentations on each of the three benchmarks used for this year's challenge, an overview of the Codabench platform, an overview of CyVerse compute resources that will be available to participants, and a code-along session to demonstrate how to create a submission for the challenge. All the remaining time will be dedicated to team formation and trying out the challenge, all with organizers available to answer questions.

 Note on experience: This event is intended to bring together participants with varied areas of expertise (e.g., computer science, environmental science, biology, etc.) for an interdisciplinary problem solving challenge.  Some coding experience (e.g., Python, R, etc.) is recommended, but this event is open to participants from any career stage (e.g., students, postdocs, researchers, faculty, private sector scientists and developers, etc.) and background.