Data release for the DINGO O4a eccentricity paper. It contains the per-event parameter-estimation products, population selection function, and hierarchical-inference posteriors needed to reproduce every figure, table, and number in the paper, plus the trained DINGO neural networks used for the analyses. Event data: eccentric, quasicircular, and precessing per-event posterior samples (posteriors_eccentric.h5, posteriors_quasicircular.h5, posteriors_precessing.h5); slimmed log-uniform-eccentricity-prior posteriors used as the hierarchical-likelihood input (posteriors_log_uniform_eccentric.h5); per-event posteriors reweighted by the population-informed posterior (posteriors_population_reweighted.h5); per-event summary statistics with pre-computed Bayes factors (summary_statistics.h5); e_gw conversions (egw_conversions.h5); and the eccentricity-mean-anomaly prior hull (e_zeta_prior_hull.h5). Selection function: the injection p_draw dataframe with detection probabilities including the analysis-window factor (injection_p_draw.h5), a fixed-injection eccentricity sweep (fixed_injection_ecc_sweep.h5), and matched-filter survival-function data (survival_function.h5). Hierarchical inference: the selection-corrected velocity-dispersion posterior marginalized over the GWTC-4 mass/spin/redshift hyperposterior (sigma_posterior.h5), the capture-eccentricity lookup table (capture_ecc_table.h5), the external GWTC-4 hyperposterior fit (gwtc4_hyperposterior.h5), and the GC/NSC branching-fraction posterior (branching_fraction_posterior.h5). Glitch analyses: glitch-marginalized posteriors for GW190701, GW231114_043211, and GW231223_032836. Networks: trained DINGO networks (SEOBNRv5EHM, SEOBNRv5HM, SEOBNRv5PHM) with their training settings; see MODEL_MANIFEST.md. Zenodo stores files flat; the companion code maps each file into the foldered layout the notebooks expect. Code to download the data and reproduce all figures: github.com/nihargupte-ph/o4a-eccentricity, archived at doi:10.5281/zenodo.21221948.