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Iris recognition is a popular technique for recognizing humans. However, as is the case with most biometric traits, it is difficult to collect data that are suitable for use in experiments due to three factors: 1) the substantial amount of data that is required; 2) the time that is spent in the acquisition process; and 3) the security and privacy concerns of potential volunteers. This paper describes a stochastic method for synthesizing ocular data to support experiments on iris recognition. Specifically, synthetic data are intended for use in the most important phases of those experiments: segmentation and signature encoding/matching. The resulting data have an important characteristic: they simulate image acquisition under uncontrolled conditions. We have experimentally confirmed that the proposed strategy can mimic the data degradation factors that usually result from such conditions. Finally, we announce the availability of an online platform for generating degraded synthetic ocular data. This platform is freely accessible worldwide.