The performance of sound event detection methods can significantly degrade when they are used in unseen conditions (e.g. recording devices, ambient noise). Domain adaptation is a promising way to tackle this problem. In this paper, we present VOICe, the first dataset for the development and evaluation of domain adaptation methods for sound event detection. VOICe consists of mixtures with three different sound events ("baby crying", "glass breaking", and "gunshot"), which are over-imposed over three different categories of acoustic scenes: vehicle, outdoors, and indoors. Moreover, the mixtures are also offered without any background noise. VOICe is freely available online (https://doi.org/10.5281/zenodo.3514950). In addition, using an adversarial-based training method, we evaluate the performance of a domain adaptation method on VOICe.