Experimental data are subject to different sources of disturbance and errors, whose importance should be assessed. The level of noise, the presence of outliers or a measure of the “explainability” of the key variables with respect to the externally-imposed operating condition are important indicators, but are not straightforward to obtain, especially if the data are sparse and multivariate. This paper proposes a methodology and a suite of tools implementing Gaussian processes for quality assessment of steady-state experimental data. The aim of the proposed tool is to: (1) provide a smooth (de-noised) multivariate operating map of the measured variable with respect to the inputs; (2) determine which inputs are relevant to predict a selected output; (3) provide a sensitivity analysis of the measured variables with respect to the inputs; (4) provide a measure of the accuracy (confidence intervals) for the prediction of the data; (5) detect the observations that are likely to be outliers. We show that Gaussian processes regression provides insightful numerical indicators for these purposes and that the obtained performance is higher or comparable to alternative modeling techniques. Finally, the datasets and tools developed in this work are provided within the GPExp open-source package
