4.1 Article Data Paper

A ground truth data set of gas chromatography mass spectrometry (GCMS) analyse d synthesise d methylenedioxymethylamphetamine (MDMA)

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DATA IN BRIEF
Volume 47, Issue -, Pages -

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ELSEVIER
DOI: 10.1016/j.dib.2023.108931

Keywords

MDMA; GCMS; Statistical modelling data; Machine learning data

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Controlled drug samples are subjected to chemical analysis to determine their identity and purity. In some cases, a broader chemical characterization of drug samples may be necessary, particularly to investigate impurities resulting from synthesis. This article reports on the data generated from repetitive synthesis of ecstasy using different methods, providing insights into synthetic methods and starting materials.
Controlled drug samples are normally chemically analysed to determine their identity and in some cases, their purity. There are also circumstances where a more broad chemical characterisation of drug samples may also be required. This involves investigating the chemical impurities that may be present in a drug sample as a consequence of their synthesis. This impurity or drug profiling can be derived from drugs which are synthesised chemically or extracted from plant materials and then modified chemically. Impurity profiling can provide some insight into the synthetic methods used and sometimes the starting chemicals used. We report on the data generated from repetitive ( n = 18 ) synthesis of ecstasy (methylenedioxymethylamphetamine or MDMA) made by three different synthetic methods. Each data sample is expressed in multiple formats.This article uses the template for publishing GCMS data provided in Miller et al.(2022)[1]. The template provides a robust and systematic approach to organise GCMS data that is both useful for practitioners and amenable for automated data manipulation by data scientists.

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