Abstract: Human annotators typically provide annotated data for training machine learning models, such as neural networks. Yet, human annotations are subject to noise, impairing generalization performances. Methodological research on approaches counteracting noisy annotations requires corresponding datasets for a meaningful empirical evaluation. Consequently, we introduce a novel benchmark dataset, $\\texttt{dopanim}$, consisting of about $15{,}750$ animal images of $15$ classes with ground truth labels. For approximately $10{,}500$ of these images, $20$ humans provided over $52{,}000$ annotations with an accuracy of circa $67 \\, \\%$. Its key attributes include (1) the challenging task of classifying $\\texttt{dop}$pelganger $\\texttt{anim}$als, (2) human-estimated likelihoods as annotations, and (3) annotator metadata. We benchmark well-known multi-annotator learning approaches using seven variants of this dataset and outline further evaluation use cases such as learning beyond hard class labels and active learning. Our dataset and a comprehensive codebase are publicly available to emulate the data collection process and to reproduce all empirical results.