A data set of 255000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods

Nontargeted mass spectrometry (MS) has become an important method over the last years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of data sets nontargeted, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of nontargeted screening, peak detection (and refinement of it) is arguably the most important step for nontargeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of:

51 converted mass spectral files in mzML format

An .RData-file containing the extracted ion chromtograms (EICs)

The randomly selected subset and the original output table of MZmine in .csv-format

Example .xlsx files for the classification

2 central classification tables

Several tables with additional information about the sampling, chemical analysis and expert jugdement on EICs

For a full description of the experiment and the data set, please read the related Data Descriptor with the title "A data set of 255000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods" in Metabolites (https://www.mdpi.com/journal/metabolites; DOI: to be updated).