Understanding the interplay between surface roughness and material elasticity in haptic texture perception is important. In the real world, these characteristics do not occur isolated from one another, yet, the haptic perceptions of surface features and material properties are often investigated individually. This highlights the need for suitable stimulus material for haptic perceptual experiments. The present research details the manufacturing and validation of a database of stochastically-rough, elastic stimuli tailored for haptic perceptual experiments. The stimulus set comprises 49 3D-printed samples, offering a systematic variation in stochastic microscale roughness and material elasticity, replicating natural surface features without compromising experimental control. The surfaces were generated using an algorithm that produces randomly rough surfaces with well-defined spectral distributions, demonstrating fractal properties over a large range of length scales. Controlled variations in elasticity were implemented via variations of the printing material composition. Finally, we present preliminary perceptual data from two observers, illustrating the discriminability of the stimulus space for roughness and softness discrimination. This database aims to facilitate haptic research on material and texture perception, offering a controlled yet naturalistic set of stimuli to explore the intricate interplay between surface roughness and material elasticity in shaping haptic texture perception.