FAIR Fluids: Integration and Analysis of Thermophysical Data of Aqueous Deep Eutectic Mixtures from Experiments and Simulations

High Resolution Image Download MS PowerPoint Slide FAIR Fluids is a data-driven framework that consists of a data model to collect thermophysical properties of fluids and a data analysis workflow to compare, visualize, and analyze their dependencies on composition and temperature. Experimental and simulation datasets are read in from ThermoML-, CML-, and CSV-formatted documents, analyzed, and uploaded to the local Dataverse installation DaRUS in a ThermoML-compatible JSON exchange format. The FAIR Fluids platform makes thermophysical data findable, accessible, interoperable, and reusable. The usefulness and feasibility of the FAIR Fluids were demonstrated by collecting published experimental and simulated viscosities η of aqueous glyceline mixtures at different temperatures and analyzing their dependencies on temperature and water content. Three different models to describe the temperature dependency of η were compared: the widely used 2-parameter Arrhenius equation and the 3-parameter Vogel–Fulcher–Tammann equation. The parameters were estimated by least-squares fitting and by Bayesian inference. The systematic analysis of experimental data from different sources enabled the detection of outliers, especially in the low-water-content regime. The FAIR Fluids framework is available at https://github.com/FAIRChemistry/FAIRFluids .