Effective human-like manipulation in robots depends on their capacity to recognize and identify textures in different environments. In unpredictable environments, robots with tactile sensors will have to identify textures through touch-related features. To advance research in texture classification, a comprehensive dataset capturing the physical interactions between a tactile-enabled robotic probe and various textures is necessary. As a result, we are driven to create a dataset from the signals collected by a bioinspired multimodal tactile sensing module, as a robotic probe dynamically makes contact with 12 different tactile textures. This dataset includes signals for pressure, acceleration, angular rate, and magnetic field variations, all captured by sensors embedded within the flexible structure of the sensing module. The pressure signals and the signals from the other sensors were sampled at a rate of 130 Hz. Each texture was explored 25 times, with each exploration involving a sliding motion along the uneven surface, tangential to the surface where the texture was bonded. The dataset comprises a total of 300 exploratory episodes. The tactile texture dataset applies to various projects in object recognition and robotic manipulation, making it particularly valuable for tasks involving tactile texture reconstruction and recognition. Additionally, this dataset offers opportunities to study time series properties generated by the robotic sliding motions during tactile texture exploration.