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The accurate and robust identification of the activity of large populations of spinal motor neurons from high-density surface electromyography (HDsEMG) is critical for advancing neuromuscular physiology, rehabilitation, and human–machine interfacing. However, the field lacks shared datasets, standardized validation procedures, reproducible algorithms, and agreed benchmarks, limiting progress and comparability across methods. MUnitQuest addresses this gap through a unique community-driven competition built on open science principles with two intertwined goals: (i) build a diverse, high-quality, openly shared HDsEMG database with labelled spike trains and (ii) rigorously evaluate algorithms on this benchmark in a transparent, reproducible way. The competition will address two distinct algorithmic challenges:

Challenge 1: Isometric contractions. Well-studied, stationary conditions for which multiple motor unit identification methods currently exist.

Challenge 2: Dynamic contractions. Less studied, non-stationary conditions in which algorithmic performance remains limited, motivating the development of novel approaches and interactive exchange of ideas.

The competition is organised into three phases:

Phase 0: Archive. Community members contribute datasets (i.e., experimental or simulated HDsEMG data together with labelled spike trains), which are automatically standardised to the BIDS-EMG format and assessed via a double-blind review process.

Phase 1: Forge. Algorithm developers are provided with training and validation data sets. The training set includes anonymised labels; the validation set is label-free, enabling submissions of predictions for leaderboard feedback. This phase supports iterative development and refinement of methods while maintaining the integrity of the final evaluation.

Phase 2: Showdown.

The top 10 algorithms from Phase 1 are invited to compete on a previously hidden test data set to obtain the final leaderboard. The top 5 dataset contributions and the top 5 algorithms from each challenge will be invited to contribute to a special issue in the Journal of Electromyography and Kinesiology. By the conclusion of MUnitQuest, we anticipate substantial gains for participants, including a training environment, feedback through leaderboards, and the sharing of solutions. Further, the competition will accelerate methodological progress by objectively quantifying the performance of different motor unit identification algorithms, encouraging participation from research teams outside the core research field, and refining future research directions.