<p>This record contains the complete optimization trajectories, potential model, and analysis data for the Nudged Elastic Band (NEB) calculation of the ethylene + N$_2$O cycloaddition, a Grignard rearrangement, and a bicyclobutane  reaction. This dataset supports the accompanying manuscript <em>"Two-dimensional RMSD projections for reaction path visualization and validation"</em> and enables the full reproduction of the reported 2D RMSD landscapes and 1D energy profiles.</p> <p> </p> <p>Transition state or minimum energy path finding methods constitute a routine component of the computational chemistry toolkit. Standard analysis involves<br>trajectories conventionally plotted in terms of the relative energy to the initial state against a cumulative displacement variable, or the image number.<br>These dimensional reductions obscure structural rearrangements in high dimensions and are often history dependent. This precludes the ability to<br>compare optimization histories of different methods beyond the number of calculations, time taken, and final saddle geometry. We present a method mapping<br>trajectories onto a two-dimensional projection defined by a permutation corrected root mean square deviation from the reactant and product configurations. Energy<br>is represented as an interpolated color-mapped surface constructed from all optimization steps using a gradient-enhanced Gaussian Process with the inverse<br>multiquadric kernel, whose posterior variance contours delineate data-supported regions from extrapolated ones. A rotated coordinate frame decomposes the RMSD<br>plane into reaction progress and orthogonal distance. We show the utility of the framework on a cycloaddition reaction, where a machine-learned potential<br>saddle and density functional theory reference lie on comparable energy contours despite geometric displacements, along with the ratification of the<br>visualization for more complex reactions, a Grignard rearrangement, and a conrotatory bicyclobutane ring opening.</p> <p> </p> <p>The provided data encompasses the entire computational workflow: from the pre-trained PET-OMAT machine-learned potential and initial endpoint geometries to the intermediate NEB optimization steps and the final converged reaction path.</p>