The eight raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/). HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in two different ratios and four replicates of each Spike/in ratio were measured: Sample HEK E.coli MS method Sample1 2.5 0.15 DIA Sample2 2.5 0.15 DIA Sample3 2.5 0.15 DIA Sample4 2.5 0.15 DIA Sample5 2.5 0.80 DIA Sample6 2.5 0.80 DIA Sample7 2.5 0.80 DIA Sample8 2.5 0.80 DIA Additionally, iRT peptides were added and 1µg of each samples was measured using data independent acquisition with a Q-Exactive Plus mass spectrometer. Briefly, a scan range from 400-1000 m/Z was first covered by an MS1 scan followed by 25 consecutive MS2 scans (each 24 m/z broad). In the next cycle another MS1 scan was acquired followd by 26 MS2 scans (also 24m/z broad) in which the window centers were shifted by 50% compared to the previous cycle of MS2 scans. The resulting raw files contain overlapping MS2 scans. Besides the eight raw files, we uploaded a spectral library, a transition list for the iRT peptides as well as an sample annotation file. Additionally, we uploaded the Galaxy PyProphet score training result files: PyProphet score report and PyProphet score.