Replication Data for: Redefining lameness assessment: Constructing lameness hierarchy using crowd-sourced data

Conversely, our proposed lameness hierarchy constructed from pairwise lameness assessments, achieved a high interobserver reliability (ICC = 0.81) among experienced assessors. We also demonstrated feasibility for the pairwise assessment to be executed by untrained assessors (in this case crowd workers recruited via Amazon MTurk), evidenced by high agreement between hierarchies generated by crowd workers and experienced assessors (ICC = 0.85).

Utilizing a 5-milestone subsampling algorithm, we found that recruiting just 8 crowd workers per video pair is sufficient to construct a reliable lameness hierarchy. This method also decreases the number of pairwise comparisons by 61%, relative to evaluating all possible comparisons between every pair of cows. The proposed lameness hierarchy method facilitates quick and accurate labeling of lameness videos and enables a more granular evaluation of lameness.

We suggest that this approach can be used to create large training datasets suitable for developing reliable automatic lameness detection models.