12 January 2026

After nearly 5 years of work, the research team led by Eiko Fried has finished data collection in the ERC-funded WARN-D project on building a personalised early warning system for depression. But just because they collected the data does not mean they can fit the best models! So in the spirit of open scholarship, they invite the whole community to participate finding the best predictive models with the highest clinical impact.

To do so, their machine-learning competition on predicting depression onset just went live. The team set up the competition on Codabench, where you can find data for around 1,750 young adults, followed for 2 years. The data include a very comprehensive battery of risk and resilience factors; 3 months of intensive longitudinal monitoring via smartwatches and smartphones; and 8 follow-up measurement points every 3 months, with risk and resilience factors, as well as the main outcome to forecast: depression onset.

The deadline is February 27th. The winning 3 teams can join the paper as authors. Details, protocol paper, terms and conditions, codebooks etc are all available on Codabench. For questions, please don’t hesitate to reach out to via WARN-D@fsw.leidenuniv.nl.