A dataset comprised of full-waveform (periodically sampled) pulses emulating LiDAR signals is provided. The waveforms were captured at a sampling rate of 20 Gsample/s and over a dynamic range of 45 dB. A simple Python notebook is also given, showing how to properly load the waveforms and capture parameters from the stored .mat file. The dataset was used to demonstrate a new time-frequency estimation method for pulsed LiDAR systems [1]. The dataset was also used to build efficient machine learning (ML) models capable of accurate and precise time-of-flight estimations [2]. For full details, please check the Experimental Setup in [1]. Please contact Daniel Bastos (d.bastos@ua.pt) for any further questions. References: [1] – D. Bastos, A. Brandão, A. Lorences-Riesgo, P. P. Monteiro, A. S. R. Oliveira, D. Pereira, H. Z. Olyaei and M. V. Drummond, , "Time-Frequency Range Estimation Method for Pulsed LiDAR," in IEEE Transactions on Vehicular Technology, vol. 72, no. 2, pp. 1429-1437, Feb. 2023, doi: 10.1109/TVT.2022.3207588. [2] – Daniel Bastos, Bruno Faria, Paulo P. Monteiro, Arnaldo S. R. Oliveira, and Miguel V. Drummond, "Machine learning-aided LiDAR range estimation," Opt. Lett. 48, 1962-1965 (2023), doi: 10.1364/OL.487000.