This dataset was used in the analysis presented in Glusman et al. (2025) (arXiv:2509:08773), which investigates the occurrence rates of giant planets orbiting low-mass stars observed by TESS. It contains astrometric, photometric, and stellar parameter data for 149,316 M dwarfs, compiled from Gaia DR3 and the TESS Input Catalog (TIC), as well as derived quantities such as absolute magnitudes, color indices, and stellar properties computed using the empirical relations of Mann et al. (2015, 2019). Each star is also labeled with its spectral type and a classification output from the TESS-Miner pipeline. The dataset provides the foundation for the sample definition and occurrence rate modeling described in the publication. The tessminer_100pc_sample.csv file contains 302 columns extracted from Gaia DR3 and supplemented with derived stellar and classification parameters. Key headers include: Gaia metadata: solution_id, designation, Gaia_DR3_source_id, ref_epoch. Astrometry: ra, ra_error, dec, dec_error, proper motions, and parallaxes with uncertainties. Photometry: Gaia broad- and narrow-band magnitudes, as well as color indices (e.g., bp_rp, j_k). Derived stellar parameters: absolute magnitudes (absh), stellar mass (MstarMann), stellar radius (RstarMann), stellar density (RhostarMann), and effective temperature (TeffMann). Spectral and classification information: spectraltype, tessminer_classification, and intermediate classification outputs (e.g., tessminer_classification_x, tessminer_classification_y). These columns provide a complete mapping from raw Gaia astrometry to derived stellar characteristics and machine learning–based classifications relevant to the TESS-Miner pipeline. The manual_classification.csv file also contains our manual classification of each object which survived auto-vetting (see Glusman et al. (2025) for details). They are split into planet candidates, EBs, contact binaries, and noise categories.This dataset can be used by researchers to explore stellar populations, refine stellar parameter estimates, and identify candidates for further exoplanetary studies. Researchers can employ this dataset both as a training/validation sample for new classification models and as a resource for population-level investigations.