RESUMO
The data presented in this article comprises human-written samples of keystroke dynamic features for free-text inputs, in the form of sentences written in natural language, together with synthesized samples that share the same text sequences. The human-written samples originate in three publicly available datasets that have been previously used in several keystroke dynamics studies; the corresponding synthesized samples, which have been forged as detailed in the companion article, share the same keystroke sequences as the human-written ones to facilitate comparison. The human-written samples were collected, and the synthesized samples created, with the objective of training and evaluating a liveness detection model. For each human-written sample of each source dataset and each method, 25 synthetic samples were included in the dataset here presented; these were forged using five different methods, a between-subject profile (only samples from users other than the target were available to the attacker) or with varying partial knowledge of the legitimate users' keystroke dynamics that ranged from only 100 keystrokes to all the available information. This dataset can be used by researchers to evaluate the performance of liveness detection methods for keystroke dynamics against a variety of state-of-the-art methods of sample synthesis.