We consider the disclosure problem of a sender with a large dataset of hard evidence. The sender has an incentive to drop observations before submitting the data to the receiver to persuade them to take a favorable action. We predict which observations the sender discloses using a model with a continuum of data, and show that this model approximates the outcomes with large, multi-variable datasets. In the receiver's preferred equilibrium, the sender plays an imitation strategy, under which they submit evidence that imitates the natural distribution under a more desirable target state. As a result, it is enough for an experiment to record data on outcomes that maximally distinguish higher states. A characterization of these strategies shows that senders with little data or a favorable state fully disclose their data, but still suffer from the receiver’s skepticism, and therefore are worse-off than they are under full information. On the other hand, senders with large datasets can benefit from voluntary disclosure by dropping observations under low states.