This data is supplied in conjunction with the paper: Magdău, I. B., Arismendi-Arrieta, D. J., Smith, H. E., Grey, C. P., Hermansson, K., and Csányi, G. NPJ Computational Materials, accepted. (2023). "Machine Learning Force Field for Molecular Liquids: Ethylene Carbonate / Ethyl Methyl Carbonate Binary Solvent." The archive contains the final EC:EMC training data and test sets (Volume Scans, Intra/Inter splits), final GAP potential and the MD trajectories described in the paper. The data is accompanied by a Jupyter Notebook: HowTo.ipynb (also compiled as *.pdf and *.html) which explains in detail the structure of the data and how to interact with it. The Notebook also demonstrates how to create volume scans, intra/inter splits and analyze configurations and MD trajectories.