PMID- 36819899
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230224
IS  - 2352-3409 (Electronic)
IS  - 2352-3409 (Linking)
VI  - 47
DP  - 2023 Apr
TI  - A ground truth data set of gas chromatography mass spectrometry (GCMS) analysed 
      synthesised methylenedioxymethylamphetamine (MDMA).
PG  - 108931
LID - 10.1016/j.dib.2023.108931 [doi]
LID - 108931
AB  - 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.
CI  - (c) 2023 The Author(s).
FAU - Miller, Jonathan
AU  - Miller J
AD  - Leverhulme Research Centre for Forensic Science, School of Science and 
      Engineering, University of Dundee, Nethergate, Dundee DD1 4HN, Scotland, United 
      Kingdom.
FAU - Puch-Solis, Roberto
AU  - Puch-Solis R
AD  - Leverhulme Research Centre for Forensic Science, School of Science and 
      Engineering, University of Dundee, Nethergate, Dundee DD1 4HN, Scotland, United 
      Kingdom.
FAU - Buchanan, Hilary Ann Scott
AU  - Buchanan HAS
AD  - Leverhulme Research Centre for Forensic Science, School of Science and 
      Engineering, University of Dundee, Nethergate, Dundee DD1 4HN, Scotland, United 
      Kingdom.
FAU - Nic Daeid, Niamh
AU  - Nic Daeid N
AD  - Leverhulme Research Centre for Forensic Science, School of Science and 
      Engineering, University of Dundee, Nethergate, Dundee DD1 4HN, Scotland, United 
      Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20230125
PL  - Netherlands
TA  - Data Brief
JT  - Data in brief
JID - 101654995
PMC - PMC9929198
OTO - NOTNLM
OT  - GCMS
OT  - MDMA
OT  - Machine learning data
OT  - Statistical modelling data
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships which have, or could be perceived to have, influenced the 
      work reported in this article.
EDAT- 2023/02/24 06:00
MHDA- 2023/02/24 06:01
PMCR- 2023/01/25
CRDT- 2023/02/23 10:08
PHST- 2022/11/10 00:00 [received]
PHST- 2023/01/10 00:00 [revised]
PHST- 2023/01/18 00:00 [accepted]
PHST- 2023/02/23 10:08 [entrez]
PHST- 2023/02/24 06:00 [pubmed]
PHST- 2023/02/24 06:01 [medline]
PHST- 2023/01/25 00:00 [pmc-release]
AID - S2352-3409(23)00049-5 [pii]
AID - 108931 [pii]
AID - 10.1016/j.dib.2023.108931 [doi]
PST - epublish
SO  - Data Brief. 2023 Jan 25;47:108931. doi: 10.1016/j.dib.2023.108931. eCollection 
      2023 Apr.