Ground truth data set of Gas Chromatography Mass Spectrometry (GCMS) analysed synthesised methylamphetamine

TL;DR

This paper presents a dataset of GCMS analyses from 152 synthesized methylamphetamine samples using five different methods, useful for characterizing impurities and training machine learning models.

Contribution

The novel contribution is a comprehensive GCMS dataset of methylamphetamine impurities from five synthesis routes, enabling automated detection and method characterization.

Findings

GCMS data from 152 methylamphetamine samples synthesized via five methods were collected.

The dataset supports automated detection of synthesis methods using machine learning.

The data is structured using a standardized template for accessibility and automation.

Abstract

Controlled substances are typically subjected to chemical analysis for the purpose of identifying them and, in certain instances, determining their purity using a comprehensive characterisation. This requires the analysis of chemical impurities that may be present in a drug sample due to the synthesis process. This process, known as impurity or drug profiling, can be applied to drugs that are chemically synthesised and/or subsequently chemically modified. The profiling of impurities can offer insights into the synthetic methods employed and, at times, the initial chemicals utilised. Our article focuses on data obtained from the repetitive synthesis (n = 152) of methylamphetamine using five distinct synthetic routes or pathways: Rosenmund Reduction, Birch Reduction, Moscow route, Hypophosphorous route and Emde route. Each sample has been analysed using Gas Chromatography Mass Spectrometry (GCMS) producing a chromatogram, which is presented in various formats.

A template for publishing GCMS data is given in [1] which provides a systematic and robust method to organise GCMS data that is amenable for automated handling by data scientists and accessible to practitioners. The template has been previously applied to MDMA [2]. This paper is the third in the series and is addressing the impurity of methylamphetamine samples using the five pathways listed above. The data can be used for methylamphetamine characterisation according to the synthesis method used, the automated detection of the synthetic method using machine learning, and for training purposes.

Figures

Pathways for methylamphetamine synthesis and batch production. Each box contains the total number of samples for the category that it represents.Fig. 1: dummy alt text

Visualisation of methylamphetamine chemical composition of a sample synthetise with the Rosenmund route: (a) total ion chromatogram, (b) total ion spectrum heatmap.Fig. 2: dummy alt text

An example of a sample folder structure.Fig. 3: dummy alt text

Full text

Specifications TableSubjectHealth Sciences, Medical Sciences & PharmacologySpecific subject areaCollection of chromatographic profiles for a repetitive chemical synthesis of a series of methylamphetamine samples following a series of five known synthetic chemical pathways.Type of dataTable, Graph, Raw, Figure, Filtered]Data collectionData acquisition occurred in three distinct phases. Phase I involved the repetitive synthesis of methylamphetamine using five chemical synthesis routes (Rosenmund Reduction, Birch Reduction, Moscow route, Hypophosphorous route and Emde route) extracted at pH 6.0 and pH 10.5, except for the Hypo route which was extracted at pH 10.5. This resulted in 152 samples, Figure 1.Samples were prepared in house following literature methods. Analysis was undertaken using GCMS to provide chromatographic data in relation to the chemical impurities associated with each method of preparation. Mass spectrometry and chromatography information was extracted via standard GCMS techniques. GCMS standard operating procedures were adhered to for sample analysis, including temperature, run times, carrier gas and internal standards. Samples were grouped into classifications based on the synthetic method used in their production, pH and sample batch number.In phase II, the data was processed and analysed using an Agilent gas chromatography mass spectrometry (GCMS) Hewlett-Packard (HP) 6890/5973 MS Chemstation (version B.00.01 Hewlet Packard, Agilent Technologies).In Phase III, the data was engineered using Agilent’s proprietary software and open source software: OpenChrom 1.4 [3], Julia 1.9 [4], Linux (Ubuntu 22.04) [5] and Python 3.11.2.Data source locationInstitution: University of DundeeCity: DundeeCountry: Scotland, United KingdomData accessibilityRepository name: A Ground Truth Data Set of Gas Chromatography mass spectrometry (GCMS) Analysed Synthesised MethylamphetamineData identification number: doi:10.15132/10000183.Direct URL to data: https://doi.org/10.15132/10000183The dataset (8.78 Gb) is available online.Related research article

Value of the Data

1

• •Drug impurity profiles allow for addressing potential sample-to-sample linkages of synthesised controlled substances. This data can bring to light information about the synthetic methods used to prepare the samples which is potentially valuable in a criminal investigation. The data in this article is ground-truth data because the provenance of the data is known, and each set of synthetic batches were produced by a single chemist under carefully controlled conditions.
• •The data is beneficial for forensic chemists because it enables the development of automated sample comparison tools which can make use of pattern-matching algorithms which can be trained and tested using the impurity profiles presented in the data.
• •The development of machine learning methods for pattern matching requires large datasets. The data presented provides a basis against which further methylamphetamine chemical impurity profiles can be added to increase sample size.

Background

2

The data provides analytical information for identifying chemical products that can be utilised for the characterisation of pathways for the synthesis of methylamphetamine. The data provenance is known: the compounds used for the production of the data were synthesised by the authors. The data contributes to the knowledge base in this area and complements previous analyses [1,2] conducted by some of the authors

Data Description

3

Methylamphetamine (d-methylamphetamine) was synthesised from either l-ephedrine, d-pseudoephedrine, or chlorophedrine following five synthetic routes:

• 1.Rosenmund reduction
• 2.Birch reduction
• 3.Moscow method
• 4.Hypo method
• 5.Emde method

In each case, several samples were extracted at pH 6.0 and pH 10.5 and prepared by the same chemist using the same materials and glassware, resulting in 152 samples in total, Fig. 1.Fig. 1. Pathways for methylamphetamine synthesis and batch production. Each box contains the total number of samples for the category that it represents.Fig. 1: dummy alt text

A chromatogram and a heat map were produced for each sample, e.g. Fig. 2.Fig. 2. Visualisation of methylamphetamine chemical composition of a sample synthetise with the Rosenmund route: (a) total ion chromatogram, (b) total ion spectrum heatmap.Fig. 2: dummy alt text

The data set was organised into folders where each folder contained a single sample. Each sample folder was assigned a unique name which contained the defining information of the sample. This prevents data from being allocated to the wrong place while enabling the management of the data programmatically. An example of a sample folder name is:

SynthesisRoute-ROSENMUND_pH-060_BatchNumber-086.D

The folder names have three components separated by an underscore “_”. Each component contains the title of the component and the specific name of the component, separated with a dash ``-‘’. These are described below.

• 1.SynthesisRoute-ROSENMUND. This component contains the name of known synthetic chemical route. The possible names are BIRCH’’, ROSENMUND’’,EMDE’’, MOSCOW’’, HYPO’’. There is no HYPO‘’ with pH 6.0.
• 2.“pH-060”. This component contains the classification of the potential of hydrogen. The possible values are 6.0 and 10.5. These numbers are represented as “060” and “105”.
• 3.“BatchNumber-086.D”. This component contains the batch number, which is a number in the set from “002” to “132”. Not all the numbers in this range are present in the data set.

Each sample folder contains raw data, figures, tables and reports. The name of each file within the folder, to protect data integrity, should also contain the folder name except any raw data files produced by the instrument software. These files are required to be readable by OpenChrom so that the analytical output can be accessed independently of the GCMS instrument. In the data presented, the GCMS instrument software was Agilent Chemstation software which produces the files “ANALYTICALMETHODS METHAMPHETAMINE.M”, “DATA.MS”, “GC01A.CH” and “PRE POST.INI” for each sample analysed. OpenChrom was used to produce a range of files for each sample which were saved into the sample folder. The method for producing these files using OpenChrom is described in the experimental design.

Using these conventions, the tree directory for an example of a methamphetamine sample folder is given in Fig. 3. The description of each file according to its prefix is given below.

• 1.“Open-Source-Mass-Spec_ ”. Agilent, by default, produces GCMS data in its proprietary format with data folders ending in “.D”. This file is the open-source equivalent [6].
• 2.“TIS_ ”. The total ion spectra (TIS) file contains all the information required to visualise the chromatogram and heat map. It is a table with column names “RT(milliseconds)” and “RT(minutes) - NOT USED BY IMPORT”. It then has a variable number of columns with positive integer value names usually starting from “30” and progressing by increments of one, e.g. “31”, “32”,…, “499”. Columns “RT(milliseconds)” and “RT(minutes) - NOT USED BY IMPORT” contain retention times in milliseconds and minutes. The retention times are recorded in intervals on an average of 600 milliseconds that are exported from OpenChrom.
• 3.“Chromatogram_MS_ ”. This file contains data processed from “TIS_” to ease the production of the heat map. Only the TIS matrix with row and column headers is present. The retention time column is labelled “RetentionTimeMin”, and the m/zcolumns are labelled as in “TIS_”.
• 4.“Chromatogram_RT-Abund_”. This file contains data processed from “TIS_” to ease the production of a chromatogram. Only the total ion chromatogram (TIC) value pairs are present. There are two columns, the retention time and the sum of the m/zintensities, titled RetentionTimeMin and Abundance respectively.
• 5.“Report_”. This is a general report of the sample peaks that contains metadata for the sample. It includes the operator’s name (Operator) and the sample and (Data Name).
• 6.“MS_ Raw Heatmap_”. This prefix refers to two figures, in PNG and PDF formats, displaying a heat map produced from values in “Chromatogram-MS_” with no transformation.
• 7.“MS_Log_Heatmap_”. This prefix refers to two figures, in PNG and PDF formats, displaying a heat map produced from values in “Chromatogram-MS ” with logarithmic base 10 scale transformation.
• 8.“TIC_Raw_LinePlot_”. This prefix refers to the TIC plot created from the file with the prefix “Chromatogram-RT-Abund_”. The figure is produced in PDF and PNG formats. The figures are ready for inspection and can be used for automated analysis.
• 9.“TIC_Log_LinePlot_”. This prefix refers to the TIC plot created with a logarithmic base 10 scale of the y-axis from the file with the prefix “Chromatogram-RT-Abund_”. The figure is produced in PDF and PNG formats. The figures are ready for inspection and can be used for automated analysis.
• 10.“Peaklabels_ ”. This file consists of a table containing the name of the detected peaks with respect to the retention time. The table contains columns “Name” and “RT [min]. The other columns present are automatically produced by OpenChrom and are not relevant for sample identification. Fig. 3. An example of a sample folder structure.Fig. 3: dummy alt text

Experimental Design, Materials and Methods

4

The data was produced in three phases. Phase I involved the synthesis of 152 methylamphetamine samples (Fig. 1) using five synthesis pathways. Phase II involved the analysis of the synthesised methylamphetamine samples to generate a chemical impurity profile associated with each sample. Phase III consisted of processing the data produced in phase II to transform it into a set of formats usable for further data analysis. The successful synthesis of methylamphetamine was confirmed in all cases using standard chemical analysis (nuclear magnetic resonance (NMR) and Fourier transform infra-red spectroscopy (FTIR)) and comparison with appropriate literature data.

Phase I: chemical synthesis

4.1

The Rosenmund Reduction [7]

4.1.1

To an autoclave hydrogenation vessel was added ephedrine hydrochloride (4.0 g, 19.8 mmol), 90 mL of glacial acetic acid (4.7 g, 47.2 mmol, 2.36 equiv) of 70 % perchloric acid and (1 g, 2.97 mmol, 0.14 equiv) of palladium on barium sulfate. The vessel was attached to an autoclave hydrogenation apparatus. Air was removed and the flask flushed with hydrogen three times, charged to a pressure of 70 bar with hydrogen and heated at 100 °C with mechanical shaking for 4 hours. The catalyst was filtered and acetic acid was removed in vacuo. Twenty percent sodium hydroxide solution was slowly added until the mixture was strongly alkaline (pH>12) and the crude methylamphetamine base extracted with toluene (3 × 20 mL). The combined organic layers were dried over magnesium sulfate and the volatiles removed in vacuo to reveal the methylamphetamine base as a clear to pale yellow coloured oil. The product was dissolved in ether and anhydrous hydrogen chloride gas was bubbled through to reveal a white precipitate, which was washed again with ether. The solid was dried under high vacuum. Typical yield for this route was 27–54 %.

In total 15 repetitive batches of methylamphetamine are presented in the data set: 8 batches from ephedrine hydrochloride (R79-R86) and 7 batches from pseudoephedrine hydrochloride (R71-R77). Subsamples from each batch were extracted separately at pH 6.0 and at pH 10.5 giving a total of 30 extracts.

The Birch Reduction [8]

4.1.2

Ammonia gas was condensed using a dry-ice condenser into a 250 mL flask until the flask was about ½ full. The liquid ammonia then allowed to partially evaporate until the volume was approximately 90 mL. l-ephedrine base (3.30 g, 20 mmol) in ether (30 mL) was added dropwise to the ammonia solution over a period of approximately 10 min with stirring. Small pieces of lithium metal (0.42 g, 60.6 mmol, 3 equiv) were rinsed in petroleum ether, patted dry with a paper towel, and added to flask. After 10 min, water was added to the solution to quench any unreacted lithium metal. The ammonia mixture was allowed to warm to room temperature and evaporate from the flask through the side necks. When the ammonia had evaporated, the remaining solution was transferred from the flask to a separating funnel. Thirty to fifty mL of ether was added and shaken to extract methylamphetamine into the organic layer. The aqueous layer was discarded. The ether layer was dried with magnesium sulfate and the solid was removed by filtration. Anhydrous hydrogen chloride gas was bubbled through the ether solution to reveal a white precipitate. The precipitate was filtered and washed with ether. The solid was dried under high vacuum. Typical yield for this route was 60–80 %. In total 10 repetitive batches of methylamphetamine are presented in the data set (B91-B100). Subsamples from each batch were extracted separately at pH 6.0 and at pH 10.5 giving a total of 20 extracts.

The Moscow route [9]

4.1.3

A 100 mL round bottom flask was filled with of either ephedrine hydrochloride or pseudoephedrine hydrochloride (3.0 g, 15 mmol, 1 equiv). Also added to the flask were red phosphorus (1.0 g, 32.5 mmol, 2.2 equiv), iodine (6.0 g, 47.7 mmol, 3.2 equiv) and 6 mL of water. The reagents were mixed, and a condenser was attached to the flask, and the mixture was refluxed for 24 hours. After this time the flask was allowed to cool, and the contents diluted with an equal volume of water. Any remaining red phosphorus was removed by filtration. Twenty five percent 25 % NaOH solution (12.0 mL, 75.6 mmol) was slowly added and the crude methylamphetamine base extracted with toluene (3 × 20 mL). The combined organic layers were dried over magnesium sulfate and the volatiles removed in vacuo to reveal the methylamphetamine base as a clear to pale yellow-coloured oil. The product was dissolved in ether and anhydrous hydrogen chloride gas was bubbled through to reveal a white precipitate, which was washed again with ether. The solid was dried under a high vacuum. The typical yield for this route was 46–77 %.

In total 20 repetitive batches of methylamphetamine were synthesised: 16 batches from ephedrine hydrochloride (M135-M152) and 4 batches from pseudoephedrine hydrochloride (M131- M134) are presented in the data set. Subsamples from each batch were extracted separately at pH 6.0 and at pH 10.5 giving a total of 40 extracts.

The Hypophosphorous route [10]

4.1.4

Pseudoephedrine hydrochloride (2.0 g) was placed into a round bottom flask (100 mL) and mixed with iodine (4.0 g) and hypophosphorus acid (3.6 mL) and a condenser attached. The mixture was refluxed for 8 hours then allowed to cool. Once cool, the mixture was diluted with an equal volume of water. Following an 8 hour reflux the mixture allowed to cool. Once cool, the mixture was transferred to a beaker and diluted with an equal volume of water. A few grams of thiosulfate were added to the beaker together with 25 % sodium hydroxide solution (24 mL) to extract the methylamphetamine free base. The product was dissolved in ether and anhydrous hydrogen chloride gas was bubbled through to reveal a white precipitate, which was washed again with ether. The solid was dried under a high vacuum. The typical yield for this route was 48–86 %.

In total 24 repetitive batches of methylamphetamine were synthesised. Two different manufactured sources (A and B) of ephedrine hydrochloride were used producing 6 batches from ephedrine hydrochloride A (H2-H7) and 5 batches from ephedrine hydrochloride B (H9-H13). 13 batches of methylamphetamine were also synthesised from pseudoephedrine hydrochloride (H14-H26). Subsamples from each batch were extracted at pH 10.5 giving a total of 24 extracts.

The Emde route [11]

4.1.5

In a hydrogenation vessel, sodium acetate trihydrate (4.88 g, 35.86 mmol, 3.89 equiv) was dissolved in 20 mL of water. Glacial acetic acid (190 mL, 3319 mmol, 360.8 equiv) and unreduced palladium on barium sulphate (2.0 g, 5.88 mmol, 0.64 equiv) were then added to the solution. Finally, a mixture of the 1-phenyl-1‑chloro-2-(methylamino)-propane hydrochloride (2.0 g, 9.2 mmol, 1 equiv) was added. This solution was hydrogenated at 43 psi for 3 hours. After the uptake of hydrogen ceased, the catalyst was removed by filtration and washed with water (200 mL). The combined filtrate and water washings were concentrated in vacuo and the resulting oil was dissolved in water (200 mL) and acidified with 5 mL of concentrated HCl (pH 1). The acidic aqueous solution was extracted with chloroform (2 × 50 mL), then made basic (pH 12) with 40 mL of 10 % NaOH. The basic aqueous solution was extracted with chloroform (3 × 75 mL), and the combined chloroform extracts were washed with water (100 mL) and dried over magnesium sulfate. The volatiles removed in vacuo to reveal the methylamphetamine base. The product was dissolved in ether and anhydrous hydrogen chloride gas was bubbled through to reveal a white precipitate, which was washed again with ether. The solid was dried under high vacuum. The typical yield for this route was 70–80 %.

In total 19 repetitive batches of methylamphetamine were synthesised (10 batches from ephedrine hydrochloride (E111 – E115 and E126 – E130) and 9 batches from pseudoephedrine hydrochloride (E116-E124)) are presented in the data set. Subsamples from each batch were extracted separately at pH 6.0 and at pH 10.5 giving a total of 38 extracts.

Phase II: chemical analysis [12,13]

4.2

Sample extraction

4.2.1

Both basic (phosphate buffer, pH 10.5) and acidic (acetate buffer pH 6.0) extractions were used for all samples with the exception of those synthesized using the Hypo method where only pH 10.5 extracts were prepared and analysed.

The phosphate buffer solution was initially at pH 7 and 0.1 M was prepared by combining 1.360 g of KH2PO4 and 1.779 g of Na2HPO4·2H2O in a 100 mL volumetric flask and filling to the mark with distilled water. This solution was made to pH 10.5 by adding 10 % sodium carbonate.

The acetate buffer solution was initially at pH 8 and 0.1 M was prepared by combining 0.820 g of CH3CO2Na in a 100 mL volumetric flask and filling to the mark with distilled water. This solution was made to pH 6 by adding a few drops of acetic acid.

Homogenised methylamphetamine hydrochloride (100 mg) was placed in a centrifugation tube and dissolved in 2.0 mL of buffer. The mixture was sonicated for 5 min and vortexed for 1 min. 400 µL of ethyl acetate containing eicosane, C20 an internal standard (0.05 mg/mL) was added, the mixture centrifuged for 5 min, and the organic layer transferred to a GC vial insert for analysis. All extracts were analysed within 24 hours of extraction.

GCMS analysis

4.2.2

Analysis was undertaken using an Agilent 6890 GC and 5973 mass selective detector (MSD) fitted with a non-polar column (DB-1MS); the oven temperature programme started at 50 °C for 1 min and then increased at 10 °C/min until 300 °C, and held for 10 min; the injector and detector (transfer line) temperatures were set at 250 and 300 °C, respectively; helium was used as a carrier gas at a constant flow rate of 1 mL/min; 1 µL of extract was injected in the split less mode.

Phase III: data engineering

4.3

The Phase III data engineering stage processed the Agilent proprietary data with the opensource data analysis program, OpenChrom. Designed to view and analyse technical data, specifically GCMS formats, OpenChrom is specifically suited to extract raw data from various paywall vendors. It is compatible with macOS, Windows, and Linux platforms [3].

The identification of GCMS data employed the use of Automated Mass Spectral Deconvolution and Identification System (AMDIS) [14], which facilitates the deconvolution of data, separating individual compounds into local peaks. AMDIS is a Windows OS exclusive but can be accessed in Linux machines through the recommended approach to utilize a Windows emulator, such as wine [13]. On Apple computers, users are advised to employ a virtual machine, like VirtualBox [15], where Windows can be installed.

For each sample, the same steps were performed. The processes described here were completed on Linux. These steps align with those outlined in [1,2] but have been adapted for the dataset presented in this article.

• 1.The GCMS ChemStation software names the sample folders it produces with numbers, e.g. “12,345,678.D”. The sample folders were renamed according to the naming conventions introduced above, Fig. 3, e.g. SynthesisRoute-ROSENMUND_pH-060_BatchNumber-086.D.

• 2.OpenChrom and AMDIS can read the sample data folder generated by ChemStation without any modification.

• 3.OpenChrom detect peaks using the sample data and the AMDIS database, which must be installed. OpenChrom detects peaks as follows:

• I.right-click on the chromatogram,

• II.move the mouse pointer over “Peak Detector” and

• III.click “AMDIS (extern)” from the menu.

This produces a number of windows. The peaks are stored in memory and an inverted triangle is displayed on top of the detected peaks.

• 4.Peak areas were obtained in OpenChrom following the steps:

• I.right-click on the chromatogram,

• II.hover over “Peak Integrator”,

• III.select “Peak Integrator Trapezoid”; a window with title “Edit Processor Options” appears,

• IV.keep the default button highlighted “Use System Options”, and

• V.select “Finish” at the bottom right corner of the window.

• 5.OpenChrom created a report that contains the sample information and the peak areas. The report was saved in the file with prefix “Report_” in text format (with extension “*.txt”); the report is obtained following the steps,

• I.right-click on the chromatogram,

• II.select “Chromatogram Reports”,

• III.select “OpenChrom Report (*.txt)”,

• IV.choose the location of the folder to record the text file by selecting the button that is in line with “Export Folder” and to the right-side of the window. The button is rectangular with a single ellipsis,

• V.write the text file name in the box labelled “Filename” following the naming convention, i.e. with prefix “Report_”,

• VI.click “Finish” at the bottom right of the pop-up window.

• 6.To record the TIS as a CSV file,

• I.right-click on the chromatogram,

• II.move the cursor over “Chromatogram Export”,

• III.click on “CSV Chromatogram (*.csv)”

• IV.follow the same process as in item 5, replacing the prefix with “Chromatogram_MS_”

• 7.At this point the sample file names are of the form

• SynthesisRoute-ROSENMUND_pH-060_BatchNumber-086.D,

• where the last block after the “_” represents the batch number of the drug production. For example, the file name in item 1 in this list contains “BatchNumber-086” meaning that it is batch 86.

• 8.The sample file with prefix “Chromatogram_MS_” used to calculate a row wise summation of the m/zion columns. The calculation produced the abundance at each time step as recorded by ChemStation. A CSV file consisting of the retention time and abundance was saved to a file with its name prefixed with “Chromatogram-RT-Abund_”, which contains the data for creating the TIC.

• 9.The file produced from item 7 in this list was modified by removing extraneous columns and a new CSV file was recorded with prefix “Chromatogram-MS_”

• 10.The raw and log-valued TIS data was visualised using the prefixed file “Chromatogram-MS_” and saved to file with prefixes “MS_Raw_Heatmap_” and “MS_Log_Heatmap ”. Both were saved in PDF and PNG formats.

• 11.The raw and log-valued TIC data was visualised using the prefixed file “Chromatogram-RT-Abund_” and saved to file with prefixes “TIC_Raw_LinePlot_” and “TIC_Log_LinePlot_ ”. Both were saved in PDF and PNG formats.

• 12.AMDIS software was used to create a file of detected peak labels. The file was saved with prefix “Peaklabels_” in a comma separated value format with extension “*.csv”; the file is obtained following the steps.

• I.Launch AMDIS software and from the file dropdown select open and locate the sample folder with extension “.D”.

• II.Select the instrument type: Agilent ChemStation.

• III.Select the sample folder and click open to load the chromatogram.

• IV.On the chromatogram panel, click the analyse dropdown menu and select “Analyse GC/MS data” to open the analysis configuration tab. Click “run” on the configuration tab to identify the peaks.

• V.Return to the analysis menu after the peak identification and click “search NIST library”

• VI.NIST search configuration opens, click on “analyse” on the configuration to begin the library search.

• VII.This results in a list peak labels for each peak. Each label contains a compound name. The labels appear in match order, i.e. the closest match is at the top and least match is at the bottom.

• VIII.Click on the file dropdown menu and select “Generate Report”. This opens a configuration tab, select the location where the report is to be saved and click “generate”.

• IX.Th report was initially saved in a “.txt” file format and later saved in “.csv” format for a better readability.

Limitations

Not applicable.

Ethics Statement

There were no ethical requirements for the collection and analysis of the data. All software used to curate and analyse the dataset was open source. We have read and followed the ethical statement requirements for publication in Data in Brief.

Credit Author Statement

Roberto Puch-Solis: Writing - Original draft preparation, Supervision. Farhan Tanvir Santo: Writing, Editing. Jonathan Miller: Writing - Original draft preparation, Data curation, Software. Busayo Ajala: Data curation, Software. Vanita Kunalan: Conceptualisation, Methodology, Sample synthesis, Data curation. Saravana Jayaram: Conceptualisation, Methodology, Sample synthesis, Data curation. Niamh Nic Daeid: Conceptualisation, Methodology, Supervision, Funding acquisition, Writing - Review & Editing.