Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis
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INVESTIGACIONMetadatos
Título
Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysisAutoría
Fecha de publicación
2021-08-24Editor
Nature ResearchISSN
2052-4463Cita bibliográfica
Schulze, B., van Herwerden, D., Allan, I. et al. Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis. Sci Data 8, 223 (2021). https://doi.org/10.1038/s41597-021-01002-wTipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.nature.com/articles/s41597-021-01002-w#Bib1Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in ... [+]
Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments. [-]
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Scientific Data, 8, 223 (2021)Derechos de acceso
info:eu-repo/semantics/openAccess
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