Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

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Identifiant documentaire 8-3325586
Identifiant OAI 3325586
Auteur(s): Schulze Bastian,van Herwerden Denice,Allan Ian,Bijlsma Lubertus,Etxebarria Nestor,Hansen Martin,Merel Sylvain,Vrana Branislav,Aalizadeh Reza,Bajema Bernard,Dubocq Florian,Coppola Gianluca,Fildier Aurélie,Fialová Pavla,Frøkjær Emil,Grabic Roman,Gago-Ferrero Pablo,Gravert Thorsten,Hollender Juliane,Huynh Nina,Jacobs Griet,Jonkers Tim,Kaserzon Sarit,Lamoree Marja,Le Roux Julien,Mairinger Teresa,Margoum Christelle,Mascolo Giuseppe,Mebold Emmanuelle,Menger Frank,Miege Cecile,Meijer Jeroen,Moilleron R.,Murgolo Sapia,Peruzzo Massimo,Pijnappels Martijn,Reid Malcolm,Roscioli Claudio,Soulier Coralie,Valsecchi Sara,Thomaidis Nikolaos,Vulliet Emmanuelle,Young Robert,Samanipour Saer
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Date de publication 01/01/2021
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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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