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TY - JOUR TI - Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis AU - Schulze, Bastian AU - van Herwerden, Denice AU - Allan, Ian AU - Bijlsma, Lubertus AU - Etxebarria, Nestor AU - Hansen, Martin AU - Merel, Sylvain AU - Vrana, Branislav AU - Aalizadeh, Reza AU - Bajema, Bernard AU - Dubocq, Florian AU - Coppola, Gianluca AU - Fildier, Aurélie AU - Fialová, Pavla AU - Frøkjær, Emil AU - Grabic, Roman AU - Gago-Ferrero, Pablo AU - Gravert, Thorsten AU - Hollender, Juliane AU - Huynh, Nina AU - Jacobs, Griet AU - Jonkers, Tim AU - Kaserzon, Sarit AU - Lamoree, Marja AU - Le Roux, Julien AU - Mairinger, Teresa AU - Margoum, Christelle AU - Mascolo, Giuseppe AU - Mebold, Emmanuelle AU - Menger, Frank AU - Miège, Cécile AU - Meijer, Jeroen AU - Moilleron, Régis AU - Murgolo, Sapia AU - Peruzzo, Massimo AU - Pijnappels, Martijn AU - Reid, Malcolm AU - Roscioli, Claudio AU - Soulier, Coralie AU - Valsecchi, Sara AU - Thomaidis, Nikolaos AU - Vulliet, Emmanuelle AU - Young, Robert AU - Samanipour, Saer T2 - Scientific Data AB - 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. DA - 2021/08/24/ PY - 2021 DO - 10.1038/s41597-021-01002-w DP - www-nature-com.ezproxy.u-pec.fr VL - 8 IS - 1 SP - 223 J2 - Sci Data LA - en PB - Nature Publishing Group SN - 2052-4463 UR - https://www.nature.com/articles/s41597-021-01002-w Y2 - 2021/08/24/21:14:11 ER -