We have the pleasure of announcing that the inaugural JDSSV prize for the best paper has been awarded to Jakob Raymaekers and Peter Rousseeuw for their paper, Handling Cellwise Outliers by Sparse Regression and Robust Covariance. Congratulations to the winners.

The 37 papers that were published between the journal’s inception in 2021 and the end of 2025, were rigorously evaluated by the editors of the journal after which a final selection of four papers was evaluated by an independent expert panel consisting of David Hand (Imperial College, London) and Eduard Gröller (TU Wie) who provided this report.

"The papers we examined were of a consistently high standard, making significant contributions to data science, so that choosing a single one was difficult. However, by generalising away from a narrow focus on marginals, the award goes to "Handling cellwise outliers by sparse regression and robust covariance" by Jakob Raymaekers and Peter Rousseeuw. They present a method to detect cell-level outliers using a lasso-based approach. The technique iteratively updates a robust covariance matrix by alternating detection and imputation, validated on simulations and real data. The work clearly has a potential for significant beneficial impact in many domains, including medical screening and diagnosis.”

The award consists of a commemorative plaque and a monetary prize of 2000 EUR to be presented at Data Science, Statistics, and Visualisation Conference (DSSV 2026) which is being held in Trento, Italy from 29th June to 1st July, 2026. The authors will present their paper at a 30-minute plenary award session at DSSV 2026.