  We introduce the GAMMA (Galactic Attributes of Mass, Metallicity, and Age)
dataset, a comprehensive collection of galaxy data tailored for Machine
Learning applications. This dataset offers detailed 2D maps and 3D cubes of 11
727 galaxies, capturing essential attributes: stellar age, metallicity, and
mass. Together with the dataset, we publish our code to extract any other
stellar or gaseous property from the raw simulation suite to extend the dataset
beyond these initial properties, ensuring versatility for various computational
tasks. Ideal for feature extraction, clustering, and regression tasks, GAMMA
offers a unique lens to explore galactic structures using computational methods
and is a bridge between astrophysical simulations and the field of scientific
machine learning (ML). As a first benchmark, we applied Principal Component
Analysis (PCA) to this dataset. We find that PCA effectively captures the key
morphological features of galaxies with a small number of components. We
achieve a dimensionality reduction by a factor of approximately 200 (3650) for
2D images (3D cubes) with a reconstruction accuracy below 5%.
