Abstract Images of galaxy-galaxy strong gravitational lenses can constrain dark matter models and the Lambda Cold Dark Matter cosmological paradigm on sub-galactic scales. Currently, there is a dearth of images of these rare systems with high signal-to-noise and angular resolution. The Nancy Grace Roman Space Telescope (hereafter, Roman), scheduled for launch in late 2026, will play a transformative role in strong lensing science with the planned wide-field surveys. With its remarkable 0.281 square degree field of view and diffraction-limited angular resolution of ~0.1 arcsec, Roman is uniquely suited to characterizing dark matter substructure from a robust population of strong lenses. We present a yield simulation of detectable strong lenses in Roman's planned High Latitude Wide Area Survey (HLWAS). We simulate a population of galaxy-galaxy strong lenses across cosmic time with Cold Dark Matter subhalo populations, select those detectable in the HLWAS, and generate simulated images accounting for realistic Wide Field Instrument detector effects. We predict around 160,000 detectable strong lenses in the HLWAS, of which about 500 will have sufficient signal-to-noise to be amenable to detailed substructure characterization. We investigate the effect of the variation of the point-spread function across Roman's field of view on detecting the suppression of the subhalo mass function at low masses and individual subhalos. Our simulation products are available to support strong lens science with Roman, such as training neural networks and validating dark matter substructure analysis pipelines. Description The .h5 file contains the simulated exposures for each filter of the design reference mission HLWAS (F106, F129, F158, and F184), PSFs for 25 positions on each of the 18 WFI detectors for each of the four filters, and signal-to-noise ratio maps. The metadata include parameters of the detectable strong lenses (e.g., Einstein radius, magnitudes, redshifts, etc.) and detector configuration (e.g., SCA number and pixel position, exposure time, etc.). This notebook describes the dataset's structure and provides examples for working with it. The .tar.gz file contains the subhalo populations (called "realizations" in pyHalo). This notebook contains code blocks to untar the .tar.gz file, deserialize the .pkl files, and work with the subhalo realizations using pyHalo. For example, one can retrieve the properties of each subhalo (e.g., position, mass, concentration, etc.) or plot the subhalo mass function.