GC-EI-MS datasets of trimethylsilyl (TMS) and

In the field of environment and health studies, recent trends have focused on the identification of contaminants of emerging concern (CEC). This is a complex, challenging task, as resources, such as compound databases (DBs) and mass spectral libraries (MSLs) concerning these compounds are very poor. This is particularly true for semi polar organic contaminants that have to be derivatized prior to gas chromatography-mass spectrometry (GC-MS) analysis with electron impact ionization (EI), for which it is barely possible to find any records. In particular, there is a severe lack of datasets of GC-EI-MS spectra generated and made publicly available for the purpose of development, validation and performance evaluation of cheminformatics-assisted compound structure identification (CSI) approaches, including novel cutting-edge machine learning (ML)-based approaches [1]. We set out to fill this gap and support the machine learning-assisted compound identification, thus aiding cheminformatics-assisted identification of silylated derivatives in GC-MS laboratories working in the field of environment and health. To this end, we have generated 12 datasets of GC-EI-MS spectra, six of which contain GC-EI-MS spectra of trimethylsilyl (TMS) and six GC-EI-MS spectra of