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We report on two distinct computational approaches to self-consistentlymeasure photospheric properties of large samples of stars. Both proceduresconsist of a set of several semi-integrated tasks based on shell and Pythonscripts, which efficiently run either our own codes or open source softwarecommonly adopted by the astronomical community. One approach aims to derive themain stellar photospheric parameters and abundances of a few elements byanalysing high-resolution spectra from a given public library homogeneouslyconstructed. The other one is applied to recover the abundance of a singleelement in stars with known photospheric parameters by using mid-resolutionspectra from another open homogeneous database and calibrating derivedabundances. Both semi-automated computational approaches provide homogeneityand objectivity to every step of the process and represent a fast way to reachpartial and final results as well as to estimate measurement errors, makingpossible to systematically evaluate and improve the distinct steps.

Two semi-automated computational approaches for spectroscopic analysis  of stellar photospheres: detailed characterization at high resolution and  abundance determination at medium resolution