Terms

Rules of participation

Collaboration in teams (no restriction on team size) is allowed and should be declared transparently on the Codabench platform (by creating an Organization for submission). Each person may only belong to one team (per challenge). We score the last submission from each team. Each participant must use only one account on Codabench. Multiple account usage leading to multiple submissions will result in disqualification.

Code of conduct

Participants agree to compete fairly and ethically, avoid exploiting system vulnerabilities, and refrain from harassment or misconduct. Violations may result in disqualification and a ban from future competitions.

Leaderboards

The leaderboard in Codabench contains only preliminary results, serving as feedback for competitors. The final results will be published on the official website after each challenge is completed. Note that for awarded teams, user identities can be made publicly visible.

Awards

The top 5 teams per task in the algorithm challenge (Isometric and Dynamic), as well as the top 5 dataset contributions (Data challenge), will be invited to share their solutions in a special issue of the Journal of Electromyography and Kinesiology.

Remark on algorithm evaluations

As MUnitQuest is a scientific competition, the objective is to show progress in motor unit identification methods. Thus, to be eligible for awards, teams need to openly share their code. The organizing team will review all winning submissions to verify compliance with the competition rules. Any attempt to exploit the evaluation process or label information will result in the disqualification of the team.

Remark on data submissions

To ensure FAIR (findable, accessible, interoperable, and reusable) data handling beyond the completion of MUnitQuest, all datasets entering the showdown phase of the algorithm challenge will be released on an open data repository (for datasets not entering the competition’s data collection, this remains optional). For transparency, the peer review of each dataset will be made publicly available upon the completion of the competition.