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We introduce the GAMMA (Galactic Attributes of Mass, Metallicity, and Age)dataset, a comprehensive collection of galaxy data tailored for MachineLearning applications. This dataset offers detailed 2D maps and 3D cubes of 11727 galaxies, capturing essential attributes: stellar age, metallicity, andmass. Together with the dataset, we publish our code to extract any otherstellar or gaseous property from the raw simulation suite to extend the datasetbeyond these initial properties, ensuring versatility for various computationaltasks. Ideal for feature extraction, clustering, and regression tasks, GAMMAoffers a unique lens to explore galactic structures using computational methodsand is a bridge between astrophysical simulations and the field of scientificmachine learning (ML). As a first benchmark, we applied Principal ComponentAnalysis (PCA) to this dataset. We find that PCA effectively captures the keymorphological features of galaxies with a small number of components. Weachieve a dimensionality reduction by a factor of approximately 200 (3650) for2D images (3D cubes) with a reconstruction accuracy below 5%.

GAMMA: Galactic Attributes of Mass, Metallicity, and Age Dataset