The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

MohammedTahaetal. Environmental Sciences Europe (2022) 34:104 https://doi.org/10.1186/s12302-022-00680-6 RESEARCH The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European andworldwide collaboration onsuspect screening inhigh resolution mass spectrometry Hiba Mohammed Taha1 , Reza Aalizadeh2 , Nikiforos Alygizakis3,2 , Jean‑Philippe Antignac4 , Hans Peter H. Arp5,6 , Richard Bade7 , Nancy Baker8 , Lidia Belova9 , Lubertus Bijlsma10 , Evan E. Bolton11 , Werner Brack12,13 , Alberto Celma10,14 , Wen‑Ling Chen15 , Tiejun Cheng11 , Parviel Chirsir1 , Ľuboš Čirka16,3 , Lisa A. D’Agostino17 , Yannick Djoumbou Feunang18 , Valeria Dulio19 , Stellan Fischer20, Pablo Gago‑Ferrero21 , Aikaterini Galani2 , Birgit Geueke22 , Natalia Głowacka3 , Juliane Glüge23 , Ksenia Groh24 , Sylvia Grosse25, Peter Haglund26 , Pertti J. Hakkinen11 , Sarah E. Hale5 , Felix Hernandez10 , Elisabeth M.‑L. Janssen24 , Tim Jonkers27 , Karin Kiefer24, Michal Kirchner28 , Jan Koschorreck29 , Martin Krauss12 , Jessy Krier1 , Marja H. Lamoree27 , Marion Letzel30, Thomas Letzel31 , Qingliang Li11 , James Little32, Yanna Liu33 , David M. Lunderberg34,35 , Jonathan W. Martin17 , Andrew D. McEachran36 , John A. McLean37 , Christiane Meier29 , Jeroen Meijer38 , Frank Menger14 , Carla Merino39,40 , Jane Muncke22 , Matthias Muschket12 , Michael Neumann29 , Vanessa Neveu41 , Kelsey Ng3,42 , Herbert Oberacher43 , Jake O’Brien7 , Peter Oswald3 , Martina Oswaldova3, Jaqueline A. Picache37 , Cristina Postigo44,14 , Noelia Ramirez45,39 , Thorsten Reemtsma12 , Justin Renaud46 , Pawel Rostkowski47 , Heinz Rüdel48 , Reza M. Salek41 , Saer Samanipour49 , Martin Scheringer23,42 , Ivo Schliebner29, Wolfgang Schulz50 , Tobias Schulze12 , Manfred Sengl30, Benjamin A. Shoemaker11 , Kerry Sims51 , Heinz Singer24 , Randolph R. Singh1,52 , Mark Sumarah46 , Paul A. Thiessen11 , Kevin V. Thomas7 , Sonia Torres39 , Xenia Trier53 , Annemarie P. van Wezel54 , Roel C. H. Vermeulen38 , Jelle J. Vlaanderen38, Peter C. von der Ohe29 , Zhanyun Wang55 , Antony J. Williams56 , Egon L. Willighagen57 , David S. Wishart58 , Jian Zhang11 , Nikolaos S. Thomaidis2 , Juliane Hollender23,24 , Jaroslav Slobodnik3 and Emma L. Schymanski1* Abstract Background: The NORMAN Association (https:// www. norman‑ netwo rk. com/) initiated the NORMAN Suspect List Exchange (NORMAN‑SLE; https:// www. norman‑ netwo rk. com/ nds/ SLE/) in 2015, following the NORMAN collabora‑ tive trial on non‑target screening of environmental water samples by mass spectrometry. Since then, this exchange © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Open Access *Correspondence: [email protected] 1 Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367 Belvaux, Luxembourg Full list of author information is available at the end of the article Page 2 of 26 MohammedTahaetal. Environmental Sciences Europe (2022) 34:104 Background In environmental analytical chemistry, suspect screening typically involves the use of high resolution mass spectrometry (HRMS) to search for the presence of chemicals in environmental samples based on suspect lists, using the exact mass as a first step in the annotation of detected features [1, 2]. Suspect screening has grown in popularity over the last few years as an efficient way to complement traditional target analysis approaches, where a reference standard is required, without performing a time-intensive non-target screening of the tens of thousands of unknown features typical in environmental samples using extensive compound databases. Several publications describe these approaches in greater detail [1–4]. The NORMAN Association (a network of reference laboratories for monitoring of contaminants of emerging concern (CECs) in the environment—hereafter “NORMAN”) [5] ran the first non-target screening (NTS) collaborative trial on river water in 2013/2014 [4]. The results showed that participants tentatively identified roughly as many chemicals via both suspect and target screening methods, but very few via NTS [4]. This early effort demonstrated that suspect screening approaches were more efficient and popular across the 19 participating institutes, offering a much higher annotation rate than non-target identification. Since then, NORMAN has run further collaborative trials involving suspect screening, including dust [6], passive samplers [7] and biota [8]. Suspect screening has also gained popularity beyond environmental studies and matrices, expanding recently to biomonitoring (e.g., [9, 10]). One major outcome of the 2013/2014 NORMAN NTS collaborative trial was the clear need for a better exchange of chemical information both among and beyond NORMAN members [4], since the 2013/2014 collaborative trial participants used an incredibly wide variety of data sources during the trial (shown in Table3 of [4]). This need had already been identified earlier, for example in the MODELKEY project [11] that included several NORMAN members, but the right implementation strategy remained elusive. A second NTS collaborative trial outcome, discussed in subsequent workshops, was a debate between “screen smart”, versus “screen big”. At the time, the “screen smart” strategy had been employed, for example, to study pesticides [12], pharmaceuticals [13] and surfactants [14] using relatively small lists (185, 980 and 394 entries, respectively), to support of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for “suspect screening” lists. The NORMAN‑SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide. Results: The NORMAN‑SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per‑ and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate down‑ load. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN‑SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN‑SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https:// zenodo. org/ commu nities/ norman‑ sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN‑SLE content is progressively integrated into large open chemical databases such as PubChem (https:// pubch em. ncbi. nlm. nih. gov/) and the US EPA’s CompTox Chemicals Dashboard (https:// compt ox. epa. gov/ dashb oard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN‑SLE, including a classification browser (https:// pubch em. ncbi. nlm. nih. gov/ class ifica tion/# hid= 101). Conclusions: The NORMAN‑SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the “one substance, one assessment” approach. New submissions are welcome via the contacts provided on the NORMAN‑SLE website (https:// www. norman‑ netwo rk. com/ nds/ SLE/). Keywords: Suspect screening, High resolution mass spectrometry, Non‑target screening, Open science, FAIR (Findable Accessible Interoperable Reusable) data, Data exchange, Cheminformatics, Exposomics, Environmental contaminants, Chemicals of emerging concern Page 3 of 26 MohammedTahaetal. Environmental Sciences Europe (2022) 34:104 focussed research questions. In contrast, the “screen big” strategy used very large lists containing thousands of chemicals (e.g., lists of high production volume chemicals registered under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation (EC No 1272/2008)) to find more hits—with the accompanying risk of many more false positives (see e.g., [15, 16]). Naturally, the boundary between these two strategies blurred over time, as some “smart” suspect lists also became quite “big”. For instance, the STOFFIDENT (https:// water. forident. org/# !home) compilation of water-relevant contaminants such as pesticides, pharmaceuticals and industrial chemicals [17] includes over 10,500 substances. This list is “smart” with respect to the relevance to the water compartment, but with many pollutant classes and a large proportion of REACH chemicals, the overall number of chemicals is large enough to increase the probability of generating many false-positive results. In the extreme, “screen big” could be extended to candidates from even larger compound databases with millions of entries, which are commonly used in NTS approaches—with the lower success rates (i.e., more false positives) as mentioned above. Since suspect screening approaches typically start with only an exact mass of the expected adduct(s) of the suspects, there is a large burden of proof to confirm that the “suspect hit” is actually present, as discussed elsewhere [2–4]. The exchange of and access to chemical information in an open (i.e., free to access, publicly available) manner [18] has not always been as easy as it appears today. A key breakthrough was achieved in 2004 with the launch of PubChem (https:// pubch em. ncbi. nlm. nih. gov/) [19], currently one of the largest open chemical knowledge bases with extensive information on over 111 million chemicals (July 2022). The ChemSpider collection was released a few years later (http:// www. chems pider. com/) [20] and now contains 114 million chemicals (July 2022). The United States Environmental Protection Agency (US EPA) released the CompTox Chemicals Dashboard (https:// compt ox. epa. gov/ dashb oard/) [21] (hereafter “CompTox”) in 2016 as a smaller collection, currently of 906,511 chemicals (July 2022) related to environmental and toxicology questions. Likewise, in 2016 the term “FAIR” was coined, describing how to make research more Findable, Accessible, Interoperable and Reusable [22, 23]. Together, ensuring that data is both Open and FAIR is a powerful combination [24]. The European Union (EU) is also embracing Open and FAIR principles. The European Chemicals Agency (ECHA) [25] and the European Food and Safety Authority (EFSA) [26] are transitioning their information to be more Open and FAIR, while Joint Research Centre (JRC) has released the Information Portal for Chemical Monitoring (IPCHEM) for the exchange of monitoring data in Europe [27]. Recent initiatives such as the European Partnership for Chemicals Risk Assessment (PARC) [28, 29] and the Environmental Exposure Assessment Research Infrastructure (EIRENE) [30] will strengthen this into the future. In response to the NORMAN NTS collaborative trial outcomes, NORMAN initiated the NORMAN Suspect List Exchange (NORMAN-SLE, https:// www. normannetwo rk. com/ nds/ SLE/) in 2015 as part of the NORMAN Database System (NDS, https:// www. normannetwo rk. com/ nds/) [29, 31] to facilitate the open access exchange of various suspect lists within and beyond Europe. This FAIR, open access, whole community initiative is not limited to NORMAN members. The primary aim of the NORMAN-SLE is to provide a location where suspect lists are publicly accessible, together with appropriate reference information, for interested parties to browse and select as desired (facilitating the “screen smart” approach). The NORMAN-SLE forms the basis for the NORMAN Substance Database (NORMAN SusDat, https:// www. normannetwo rk. com/ nds/ susdat/), a merged and curated data table with additional parameters for use in NORMAN activities (to facilitate the “screen big” approach), which will be described in more detail in a separate article. The present article covers the creation and implementation of the NORMAN-SLE as an Open and FAIR data resource, along with its integration with major open chemistry resources (PubChem, CompTox) as described below in the methods section, followed by an overview of the current state, implications and outlook in the results and discussion sections. Methods NORMAN Suspect List Exchange (NORMAN‑SLE) website The principle behind the NORMAN-SLE is simple: facilitating the exchange of chemical information to support the suspect screening of primarily organic contaminants amenable to liquid or gas chromatography (LC or GC) coupled to mass spectrometry. The website itself (https:// www. normannetwo rk. com/ nds/ SLE/) contains a simple overview of the background behind the NORMAN-SLE and a table containing the suspect lists themselves (with the fields “Number”, “Abbreviation”, “Description”, “Link to full list”, “Link to InChIKey list” and “References”), as shown in Fig.1 and explained further below. Each list has a number (starting with S0 for SUSDAT, the merged collection), increasing sequentially with every contribution, along with an abbreviation for easier integration, access, and recognition. The idea behind the simplicity of this website is to enable public access to various suspect lists as close as Page 4 of 26 MohammedTahaetal. Environmental Sciences Europe (2022) 34:104 possible to the lists used in original publications, but with a reasonable degree of standardization and, where possible, added value to enhance and FAIRify these lists for future use (see below). If major adjustments were made to a submitted list, the original list is provided along with modified versions, so that both sets of information are available. Information content andpreparation ofsuspect lists hosted ontheNORMAN‑SLE The minimum information available in most lists is a name and at least one additional identifier, although in most lists, far more information is available. At least one chemical name (plus other synonyms if available) should be included. The preferred formats for structural information are the simplified molecular-input line-entry system (SMILES) [33] plus the International Chemical Identifier (InChI) in the form of standard InChI and InChIKey [34]. Common database identifiers provided typically include one (or more) of either Chemical Abstract Service (CAS) number(s) [35], EC number [36], PubChem Compound Identifier (CID) [19], ChemSpider identifier (CSID) [20] and/or the Distributed Structure-Searchable Toxicity (DSSTox) substance identifier (DTXSID) used in CompTox [21]. To support suspect screening, the (neutral) monoisotopic masses and molecular formulae are included in many of the lists. This information, along with several other predicted values, is also included in the merged NORMAN SusDat. Several other fields may be present, depending on the context of the suspect list, and are included where available. More details on the chemical structure identifiers and recommended chemical structural data templates are provided elsewhere [24, 37]. The suspect lists (commonly submitted via email to NORMAN contact points, see Fig.2, top left) are processed upon submission, with the subsequent processing steps highly dependent on both the type of submission and the size of the list. While the suspect list number is assigned sequentially, the abbreviation, name and description are assigned following pre-defined conventions, and in discussion with authors. Where necessary, curation is performed on these lists to fill in missing values where at least a chemical identifier and/or structural information and/or (correct) name was provided. For some lists, the missing values are filled using automated workflows covering a variety of web services (depending on the list and contributor) from PubChem [19], ChemSpider [20] and CACTUS (https:// cactus. nci. nih. gov/), typically via RMassBank [38], RChemMass [39] and other related packages in the R programming language. Other lists are processed with batch services offered through PubChem [19, 40] and CompTox [21, 41]. Additional chemical structure interconversions (e.g., SMILES to InChI) are performed with OpenBabel (http:// openb abel. org/) [42] or the Chemistry Development Kit (CDK) (usually via R) [43] where necessary. Note that the curation performed on the individual suspect lists is independent of the curation and merging to form the NORMAN SusDat collection (see Fig.2, bottom left), which will be detailed in a separate publication. The processes evolve over time as new technical possibilities Fig. 1 Screenshot of the NORMAN Suspect List Exchange (https:// www. norman‑ netwo rk. com/ nds/ SLE/) [32] Page 5 of 26 MohammedTahaetal. Environmental Sciences Europe (2022) 34:104 arise (e.g., batch searching). The resulting suspect lists are generally provided as Excel (XLSX) and comma separated values (CSV) formats, as standardized as reasonably possible, on the website. The CSV format provides greater interoperability, including allowing import into various libraries, vendor and open software, as well as PubChem (described below). A separate InChIKey file is also provided, as this allows fast screening of suspects within the in silico fragmenter MetFrag [44] and other approaches. For some of the lists, additional files are provided, to disseminate all the relevant details. Finally, references and additional information are given, to acknowledge contributors, but also to provide users quick access to the rationale behind each individual suspect list. Further details on the NORMAN-SLE contents, including references, are given in the Results section. Several suspect lists contain partial, incomplete, or even no structural information, such as the perand polyfluoroalkyl substances (PFAS) lists S9 PFASTRIER [45] (e.g., elemental compositions retrieved from patents where no structural or isomer information was available) and S46 PFASNTREV19 [46, 47] (a compilation of PFAS identification efforts in non-target screening studies), as well as the surfactant isomer list S18 TSCASURF [48]. Nevertheless, these lists still provide vital information for identification by mass and/or molecular formula (see e.g., [14, 49], where whole surfactant classes can be identified via the general formula of a homologous series of several structural isomers). For those lists with partial information, missing values were filled in, where possible, as described above, and were saved in separate files or as multiple sheets in one file. Associated InChIKey lists were only generated for known structures. Dealing with partially characterized molecular features or chemical substances of Unknown or Variable Composition, Complex Reaction Products or Biological Materials (UVCB substances, UVCBs) is a subject of future collaborations beyond the scope of the current article (see e.g., [50, 51]), as discussed further below. NORMAN‑SLE onZenodo The development of the Zenodo repository [52] enabled public archiving, versioning and generation of a Digital Object Identifier (DOI) for each NORMAN-SLE list. Thus, since 2019, the NORMAN-SLE content has been uploaded to and archived on the Zenodo repository [52], Fig. 2 Schematic showing the relationships between submitted suspect lists, the NORMAN‑SLE and downstream resources. Top (orange shading): suspect lists submitted in various formats are curated, then added to the NORMAN‑SLE website (centre) and archived on the NORMAN‑SLE Zenodo community (top right), yielding a DOI and use statistics. Bottom left (green shading): the NORMAN‑SLE serves as an information source for NORMAN SusDat and the NORMAN Database System (NDS). Bottom middle (pink shading): NORMAN‑SLE lists are integrated in CompTox manually. Bottom right (blue shading): NORMAN‑SLE content is harvested from Zenodo via mapping files and integrated into PubChem in an automated workflow Page 6 of 26 MohammedTahaetal. Environmental Sciences Europe (2022) 34:104 gathered under the NORMAN-SLE community (https:// zenodo. org/ commu nities/ normansle/) [53]. Each individual NORMAN-SLE collection has its own Zenodo record and thus a dataset DOI, allowing users to cite the individual lists directly, including specific versions, or all versions. Updates to lists can thus be tracked under the Zenodo version control system, with the master DOI always redirecting to the latest version. The lists are tracked under a versioning system following the pattern NORMAN-SLE-SXX-0.Y.Z, where SXX refers to the list number (as on the NORMAN-SLE website and as described below) and the 0.Y.Z pattern records whether it was a major update (Y is increased incrementally by 1) or minor update (Z is increased incrementally by 1). The leading “0” is currently a buffer. Major updates constitute new entries (e.g., new chemicals, rows, information, updates) to the lists, while minor updates are corrections or adjustments to the current contents without adding major new content (e.g., correcting names, identifiers, typographical errors). The presence on Zenodo has enabled better citation, the tracking of use statistics at an individual list level and additional possibilities for the integration with external resources such as PubChem, as shown in Fig.2 (right) and discussed further below. Figure3 shows the presence of the NORMAN-SLE on Zenodo, including versioning in the inset. NORMAN‑SLE andCompTox Chemicals Dashboard integration Since CompTox [21] is a highly relevant resource for environmental and toxicological information, integration of NORMAN-SLE content is of interest to both parties and is achieved via the “Chemical Lists” functionality (https :// compt ox. e pa . gov/ dashb oard/ chemi callists/). The integration started in 2017 and is performed through the upload of the DTXSIDs associated with the individual NORMAN-SLE lists to the DSSTox database [55] that underlies CompTox. Most lists have the NORMAN keyword associated with it, such that they are accessible through the URL https:// compt ox. epa. gov/ dashb oard/ chemi callists? search= NORMAN, or through a direct URL composed of the list code (e.g., https:// compt ox. epa. gov/ dashb oard/ chemi callists/ BISPH ENOLS for the S20 BISPHENOLS list). Several lists on the NORMANSLE were produced in a collaborative curation effort (e.g., S24 HUMANNEUROTOX [56], S37 LITMINEDNEURO [57] and S43 NEUROTOXINS [58], as part of [59]), or were curated and registered by the DSSTox curation team before uploading to the SLE (e.g., S25 OECDPFAS [60–62]). Some other lists on the NORMAN-SLE were sourced directly from CompTox as they contained entries highly relevant for the NORMAN Database System (e.g., S45 SYNTHCANNAB [63] and S58 PSYCHOCANNAB Fig. 3 The NORMAN Suspect List Exchange Zenodo community (https:// zenodo. org/ commu nities/ norman‑ sle) with inset showing the versioning history of S36 UBAPMT (https:// doi. org/ 10. 5281/ zenodo. 26532 12) [53, 54] Page 7 of 26 MohammedTahaetal. Environmental Sciences Europe (2022) 34:104 [64]). For recent lists, generally the CompTox batch search (https:// compt ox. epa. gov/ dashb oard/ batchsearch) [65] is used to retrieve DTXSIDs on the basis of the user-provided information, which are then provided directly to CompTox along with the list code, name and description for upload. The presence of compounds in NORMAN-SLE lists appear on the individual chemical records in CompTox (see pink entries in the inset in Fig.2) and can also be identified by prefiltering in the CompTox batch search interface and including flags in the export files. Due to the infrequent release of updates to CompTox, it may be many weeks or months before new NORMANSLE lists are available publicly on CompTox. Currently, 88 of the 99 NORMAN-SLE lists are on CompTox (see Additional file1), with 74 listed under the “NORMAN” URL above. Since not all substances in the NORMANSLE are currently present in CompTox, the mapping of NORMAN-SLE lists in CompTox is often incomplete, i.e., the lists on CompTox contain only entries for which DTXSIDs currently exist (further details are provided in Additional file1). NORMAN‑SLE andPubChem integration As one of the largest open chemical databases with millions of monthly users, integration of NORMAN-SLE content in PubChem has great potential to increase the visibility of this community effort. The NORMAN-SLE integration with PubChem [19] (https:// pubch em. ncbi. nlm. nih. gov/) commenced in 2019. The first substance deposition was processed on November 22, 2019. The deposition file is compiled from all lists by the PubChem team, via a mapping file hosted on the Environmental Cheminformatics (ECI) group (University of Luxembourg) GitLab pages [66]. This mapping file contains a link to the latest version of each suspect list (CSV file) on Zenodo, the list details and version, the dataset DOI, extra DOIs (to include related publications), mappings to the columns containing the chemical identifiers (SMILES, InChIKey, InChI, Synonym), the NORMANSLE URL and a comment field. The compiled deposition file is mapped to PubChem Substance Identifiers (SIDs) and PubChem Compound Identifiers (CIDs) via the PubChem deposition system. While SIDs are available for all substances deposited to PubChem (including those with undefined structures), CIDs are only available for all unique chemical structures (i.e., defined chemical structures) extracted from substance depositions via the PubChem standardization process [67]. As a result, the number of compounds (CIDs) will generally be less than the number of substances (SIDs). Any SMILES errors found during deposition are debugged in collaboration with the PubChem team and any dataset-specific causes are fixed in the corresponding NORMAN-SLE datasets by releasing new minor versions on Zenodo (see e.g., descriptions in [68, 69]). Synonyms are currently provided as a small, manually curated file containing the columns CID, InChIKey, Synonym, Reference DOI and Dataset information (114 entries on 30 April 2022, see [70]) to specifically add missing synonyms to PubChem [70]. These are primarily newly deposited structures (i.e., structures not yet in PubChem) associated with S74 REFTPS [71] and S96 ECIPFAS [72]. The PubChem/ NORMAN-SLE deposition is re-run once updates are available and takes minutes to run. The updated data are live on the public PubChem website within hours to days (newly added structures can take longer to index fully). The latest deposition and number of live substances (i.e., the number of substances currently available on the public website) can be retrieved from the NORMAN-SLE data source page in PubChem [73]. The contents of individual NORMAN-SLE lists are available interactively in PubChem via the NORMAN Suspect List Exchange Tree (https:// pubch em. ncbi. nlm. nih. gov/ class ifica tion/# hid= 101, hereafter “PubChem NORMANSLE Tree”) on the PubChem Classification Browser [74]. This is compiled by PubChem from a second mapping file, also hosted on the ECI GitLab pages [75]. For each dataset, this mapping file contains a link to the latest InChIKey file on Zenodo, the list title as it should appear in the tree (e.g., “S00 | SUSDAT | Merged NORMAN Suspect List: SusDat”) and a tool tip, i.e., further details about the list that displays when users click the “?” icon on the Classification Browser (see figure in Results section). The mapping file also contains additional fields defining the content of interest (keywords, annotations) and other information for internal housekeeping. All lists (except S18 TSCASURF, for which no InChIKeys exist) are listed in numerical order in the PubChem NORMANSLE Tree. In addition, certain lists with detailed classification content appear again at the top of the browser. These are mapped via structural information in the CSVs (not the InChIKey files) to profit from the detailed additional information available in these lists. The PubChem Classification Browser can also be accessed programmatically (i.e., in an automated manner), with documentation available on PubChem [67] and the ECI GitLab pages [76]. The PubChem NORMAN-SLE Tree also enables users to download individual lists (or even various combinations thereof via advanced queries) in the variety of formats offered by PubChem, including the structure data format (SDF) not currently offered on the NORMAN-SLE website, see documentation available in e.g., [77]. PubChem has also integrated several categories of annotation content, i.e., detailed information about individual chemicals, into the compound records in PubChem. As of Page 8 of 26 MohammedTahaetal. Environmental Sciences Europe (2022) 34:104 30 April 2022, a total of 17 annotation categories, which equate to headers in the Table of Contents entries in PubChem [78], were integrated. Many relate to the chemical role or use (e.g., the Anatomical Therapeutic Chemical (ATC) Code for pharmaceuticals, Agrochemical Category, Chemical Classes, Use Classifications and Uses) and transformation information (e.g., included in the Transformations, Metabolism/Metabolites, Drug Transformations and Agrochemical Transformations headers). Others relate to chemical information (e.g., molecular formula) and measurement data, such as nuclear magnetic resonance (NMR—13C, 19F, 1H, and 31P), tandem MS (MS/MS) data and collision cross section (CCS) data from ion mobility experiments. Finally, taxonomy information (functionality recently added to PubChem [79] for organisms) has been included for some lists. All files necessary for the integration of the annotation content within PubChem are present in the Zenodo repository for the respective list, supported by additional mapping or annotation files either added in Zenodo, or hosted on the ECI GitLab pages in the “annotations” subfolder [80] where necessary. The latest overview and the entire content integrated in PubChem (in JSON, XML and ASNT formats, accessible programmatically or for download) is available from the NORMAN-SLE data source page in PubChem [73]. Results Overview ofNORMAN‑SLE The NORMAN-SLE includes 99 contributions (starting at S0 SUSDAT, the compilation of all NORMAN-SLE lists, to S98 TIRECHEM) from over 70 contributors as of May 2022, summarized in Fig.4 and Table1. Full details on all lists are available in Additional file1 [81], including list details and chemical numbers across the resources in CSV format, and Additional file2 [82], a May 2022 copy of the NORMAN-SLE website contents. Figure4 and Table1 show the number of entries in each NORMAN-SLE list as present on the NORMANSLE website and in the latest versions on the NORMANSLE Zenodo collection as of May 2022. The number of InChIKeys associated with these lists (as of May 2022) is available in Additional file1 [81]. Additional file1 also includes the number of entries included in PubChem (obtained via the PubChem NORMAN-SLE Tree [74]) and CompTox (via both the CompTox Chemical Lists [232] website as well as via the PubChem EPA DSSTox Tree [233], since the latter can be automated). These statistics were compiled on 4 May 2022. The corresponding files and code are available at the ECI NORMAN-SLE GitLab repository [234] in the “stats” subfolder. Note that the addition of new content to the NORMAN-SLE was put on hold during compilation of this manuscript (May and June 2022), to ensure that the results included here are internally consistent. All statistics presented here reflect the data in this state. Updates resumed 28 June 2022 and will be described in later efforts (see “Future updates” below). Summary statistics oftheNORMAN‑SLE A selection of summary statistics and facts for the NORMAN-SLE is given in Table2. Both the list and citation information were summarized on 4 May 2022 and the NORMAN-SLE PubChem numbers on 12 May 2022. The (cumulative) numbers of unique views and downloads collected from the NORMAN-SLE Zenodo community on 28 April 2022 are summarized in Table 3, along with the citation numbers for all lists and for the 5 most popular lists according to unique views. The “total unique compounds” number indicates how many entries have a defined chemical structure in PubChem, i.e., a PubChem CID. The “total live substances” number indicates how many entries are deposited, i.e., with a PubChem SID. The total number of unique compounds in PubChem is currently larger than S0 SUSDAT due to the different timing associated with the release cycle of NORMAN SusDat (the basis for S0 SUSDAT), as well as differences in the mappings of structures to unique identifiers. Future efforts will aim to close this time gap between NORMAN-SLE and NORMAN SusDat (see “Future updates” below). The data files supporting these statistics, including a breakdown of the DOIs of the citing articles, are archived on the ECI NORMAN-SLE GitLab pages [234] (“stats” subfolder) and are available as Additional file3 [235] (views, downloads, citations per list) and Additional file4 [236] (more detailed citation breakdown). In total, 24 of the SLE lists have citations listed in Zenodo, with 40 citations from 19 articles. A full breakdown is given in Additional file4 [236]. Of these 19 articles, 12 can be considered “internal”, i.e., articles written by authors involved with the NORMAN-SLE, including 5 articles describing SLE datasets [59, 118, 149, 154, 174] and 7 others citing SLE lists [24, 142, 237–241], while 7 articles are external [242–248]. Of the 24 lists cited, 6 lists are cited by external authors: S0 SUSDAT, S13 EUCOSMETICS, S14 KEMIPFAS, S25 OECDPFAS, S46 NTPFASREV19 and S75 CyanoMetDB. NORMAN‑SLE PubChem integration As described above, the NORMAN-SLE content has been integrated into PubChem in a variety of ways. The basis of all further integration is the substance depositions, formed from the compilation of all lists as described in the Methods section. As of 12 May 2022, the substance deposition in PubChem included 117,071 substances Page 9 of 26 MohammedTahaetal. Environmental Sciences Europe (2022) 34:104 Fig. 4 Starburst plots of the 99 suspect lists forming the NORMAN‑SLE contents. Lists with: (A) > 8000 entries; (B) 1700–8000 entries; (C) 800–1700 entries; (D) 300–800 entries; (E) 95–300 entries and (F) < 95 entries (ranges chosen to optimize plotting). The list codes, numbers of chemical entries and references are summarized in Table 1 according to the same groups, with full details in Additional file 1 Page 16 of 26 MohammedTahaetal. Environmental Sciences Europe (2022) 34:104 but will be streamlined and automated further, also to account for possibilities arising from the PubChem integration. Documentation on how to obtain some of this information via PubChem is also available, e.g., for MS/ MS [252] and CCS values [253–255]. Advanced Entrez queries (via PubChem) can be used to limit this to certain measurement modes. Another suggested enhancement related to UVCBs would be to include important substructures such as the head group of surfactants or repeating unit of polymers, which could be linked to MS/ MS fragments. A large focus has been placed on TPs over the recent years. A continuation of ongoing efforts will include adding more TPs, including the extraction of data from literature to fill data gaps [71, 174, 205] and the integration of workflows in patRoon [257] in a manner compatible with other NTS workflows. Over the years, there has been increasing interest to add lists of predicted TPs to the NORMAN-SLE, with submissions including predicted TPs for S6 ITNANTIBIOTICS [159], S71 CECSCREEN [85] (both generated with BioTransformer [111]) and S38 SOLNSLMCTPS [102]. While such lists are valuable for researchers performing NTS, these can cause problems with downstream integration with the NDS, CompTox and PubChem as these predicted structures are not necessarily observed and verified, while the number of entries can be an order of magnitude higher (or more) than the source list. These datasets are generally decoupled from the cross-integration at present. A future discussion for NORMAN will be how best to integrate predicted TP data, with the possibility of a “Transformations” module to be added—potentially to represent both documented transformations (e.g., similarly as shown in the insets in Figs.2 and 5) and predicted transformations. As the NORMAN-SLE list numbers climb, and with several contributions covering related topics (see Table4), further refinements will be needed to group lists together and allow the selection of certain subsets for different use cases, or the sorting of lists by categories. The extensive integration with PubChem and the resulting need for organization of NORMAN-SLE content in both CompTox and PubChem has given rise to categorization and classification efforts, and preliminary functionality allowing this is already integrated into NORMAN SusDat. Since there is great interest in the gathering of “Use” information and categorization in general, NORMAN has already initiated activities within the Prioritization working group [285] to define and collect relevant use information and categories from members. These activities will feed into subsequent future developments within NORMAN, PARC [28, 29], EU projects such as ZeroPM [229] and beyond. The NORMAN-SLE is a community resource built on an incredible amount of volunteer effort and rather limited financial resources. The entire NDS is supported through the NORMAN Association and project funding obtained by individual contributors. The integration with external resources such as PubChem, CompTox and Zenodo provides significant added value beyond the capabilities available to NORMAN. This approach is key to foster cooperation among existing regulatory frameworks, helping to share data and improve chemical risk assessment in the shift towards a “one substance, one assessment” paradigm [286]. With the EU strongly supporting Open and FAIR data, including large initiatives such as PARC [28, 29] and EIRENE [30], along with Green Deal projects such as ZeroPM [229], opportunities for further developments, consolidation and harmonization with broader EU efforts, including the future Open Data Platform appear promising. While the idea behind the NORMAN-SLE has broad support, the current infrastructure and personnel could not currently support, for instance, a requirement to host and thus make all European environmental research data Open and FAIR. If, however, the experiences in building the NORMAN-SLE could help contribute towards establishing such a platform (to which the NORMAN-SLE could contribute), this would be a huge benefit for research and researchers. Conclusions The NORMAN Suspect List Exchange (NORMAN-SLE) was created to provide a service to NORMAN members and the greater scientific community, in response to a clear need identified in the NORMAN Non-target Collaborative Screening Trial [4]. Through the provision of a centralized website to collect various suspect lists and references, information exchange is ensured to apply the “screen smart” strategy on specific scientific questions. This FAIRified resource is archived on Zenodo to give DOIs for each set, allowing the cross-integration with other resources and formal citation of datasets, raising the profile of the research of various contributors. The combined list formed from all NORMAN-SLE contributions, NORMAN SusDat, serves as a basis for chemical management for the entire NORMAN Database System (NDS), including the NORMAN Digital Sample Freezing Platform (DSFP) [266]. The NORMAN-SLE is not intended to replace major open compound databases such as ChemSpider, PubChem or CompTox, but rather offers a specialized, complementary service targeted to the environmental science community, particularly in relation to suspect screening, for integration within these larger resources, as done with CompTox and PubChem. Raising the awareness about relevant suspect screening lists and the Page 17 of 26 MohammedTahaetal. Environmental Sciences Europe (2022) 34:104 quality issues surrounding suspect screening is vital for improving the identification of contaminants of emerging concern in the environment, biota, and products, thereby helping to reduce the number of molecular unknowns in mass spectrometry analyses and to facilitate more comprehensive chemicals assessments. The NORMAN-SLE welcomes new submissions of suspect lists within the scope, along with other ideas and feedback, as described on the NORMAN-SLE website (https:// www. normannetwo rk. com/ nds/ SLE/). Abbreviations ASNT: Abstract Syntax Notation (ASN.1) Text format; ATC : Anatomical Thera‑ peutic Chemical code; CAS: Chemical Abstract Service; CCS: Collision cross section (ion mobility experiments); CDK: Chemistry Development Kit; CECs: Contaminants of Emerging Concern; CID: PubChem Compound Identifier; CPPdb: Chemicals associated with Plastic Packaging database; CSID: Chem‑ Spider Identifier; CSV: Comma Separated Values; DOI: Digital Object Identifier; DSFP: Digital Sample Freezing Platform; DSSTox: Distributed Structure‑Search‑ able Toxicity (database); DTXSID: Distributed Structure‑Searchable Toxicity (DSSTox) substance identifier; EC: European Commission; ECHA: European Chemicals Agency; ECI: Environmental Cheminformatics group, University of Luxembourg; EFSA: European Food Safety Authority; EIRENE: Environmental Exposure Assessment Research Infrastructure; EU: European Union; FAIR: Findable, Accessible, Interoperable, Reusable; FCCdb: Food Contact Chemicals database; FCCmigex: Database on Migrating and Extractable Food Contact Chemicals; GC: Gas chromatography; HRMS: High resolution mass spectrom‑ etry; InChI: International Chemical Identifier; InChIKey: Hashed form of the International Chemical Identifier; IP: Internet Protocol; JRC: Joint Research Centre; JSON: JavaScript Object Notation; KEMI: Swedish Chemicals Agency; LC: Liquid chromatography; MInChI: Mixture InChI; MS: Mass spectrometry; MS/MS: Tandem mass spectrometry; NDS: NORMAN Database System; NMR: Nuclear magnetic resonance; NORMAN: Network of reference laboratories, research centres and related organisations for monitoring of emerging environmental substances; NORMAN SusDat: NORMAN Substance Database; NORMAN‑SLE: NORMAN Suspect List Exchange; NTS: Non‑Target screening; PARC : European Partnership for Chemicals Risk Assessment; PFAS: Per‑ and polyfluoroalkyl substances; PMT: Persistent, mobile and toxic substances; REACH: Registration, Evaluation, Authorisation and Restriction of Chemicals (EU regulation); SDF: Structure Data Format; SEO: Search Engine Optimiza‑ tion; SID: PubChem Substance Identifier; SMILES: Simplified Molecular‑Input Line‑Entry System; TPs: Transformation products; UBA: German Environment Agency (Umweltbundesamt); US EPA: United States Environmental Protection Agency; UVCBs: Substances of Unknown or Variable Composition, Complex Reaction Products or Biological Materials; XML: Extensible Markup Language; ZeroPM: Zero Pollution of Persistent, Mobile Substances (EU project). Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s12302‑ 022‑ 00680‑6. Additional file1: Summary of the NORMAN‑SLE datasets (CSV format) as of 4 May 2022 [81]. Additional file2: Overview of the NORMAN‑SLE website (DOCX format) as of 30 May 2022 [82]. Additional file3: Summary of Zenodo view and download statistics, plus citations (CSV format) as of 28 April 2022 [235]. Additional file4: Summary of Zenodo citations plus DOIs per list (CSV format) as of 1 May 2022 [236]. Additional file5: Authorship contributions and acknowledgements mapped to NORMAN‑SLE lists (XLSX format). Acknowledgements The authors wish to acknowledge all contributors to the NORMAN‑SLE and to the information behind the NORMAN‑SLE who are not otherwise mentioned in this article. All authors thank those who contributed to all the open software and web services used in this study that have underpinned these efforts. We gratefully acknowledge the contributions of those we could no longer contact and/or who made contributions without our explicit knowledge. Specifically, the authors wish to acknowledge Anca Baesu (McGill University, Canada, S74), Barbara Günthardt (formerly Eawag/Agroscope, S29), Jan Oltmanns (Forschungs‑ und Beratungsinstitut Gefahrstoffe GmbH (FoBiG), Germany) and Rosa Sjerps (Oasen, Netherlands, S5, S27) who were all approached to be authors and preferred to be acknowledged, along with Robert Mistrik (HighChem, Slovakia, S19) who was approached to be authors but did not respond. Further, the authors acknowledge Ton van Leerdam (KWR, Netherlands), Sascha Lege (formerly University of Tübingen, Germany, S1), Graham Peaslee (Notre Dame University, USA, S9), Guangbo Qu and Guibin Jiang (Chinese Academy of Sciences, China, S46), Marie‑Léonie Bohlen and Markus Schwarz (FoBIG, Germany, S54), Oliver Licht and Sylvia Escher (Frauenhofer ITEM, Germany, S54), David Fabregat‑Safont, Maria Ibáñez and Juan Vincente Sancho (University Jaume I, Spain, S61), Raoul Wolf (Norwegian Geotechnical Institute, Norway, S90), the PFAS Analytical Exchange Steer‑ ing Group members Alun James, Anna Kärrman, Audun Heggelund, Belén González‑Gaya, Duncan Gray, Griet Jacobs, Leendert Vergeynst, Noora Perkola, Robert Carter, Stefan van Leeuwen and Ulrich Borchers (S95 [215]) as well as Ann Richard, Chris Grulke and the DSSTox curation team (US EPA, USA). This information is also given in Additional file 5. Thanks to the internal reviewers for their helpful comments. Disclaimers PJH retired from NIH NLM in 2020 and is now an NIH Special Volunteer in Toxicology and Environmental Health Sciences at NCBI. Where authors are identified as personnel of the International Agency for Research on Cancer/ World Health Organization, the authors (VN, ReS) alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer / World Health Organization. The views expressed in this manuscript are solely those of the authors and do not represent the policies of the U.S. Environmental Protection Agency or other agencies. Mention of trade names of commercial products should not be interpreted as an endorsement by the U.S. Environmental Protection Agency. This work has been internally reviewed at the US EPA and has been approved for publication. Author contributions ELS founded, coordinates and maintains the NORMAN‑SLE (including the Zenodo and GitLab integration), supported by HMT and PC. JS (host), LC (IT), NA and NG (webmaster) host the SLE website on the NORMAN Database System and provide technical support. RA, NA and NST coordinate predicted values and SusDat merging. VD, JS, JH, NST, NA, ELS, EEB, ELW, PJH, HPA, SF, JaK, TL, MaSe, PvdO, ZW provide(d) strategic input to NORMAN‑SLE developments. HMT, RA, NA, JPA, HPHA, RB, NB, LiB, LuB, WB, AC, WLC, PC, LDA, YDF, VD, SF, PGF, AG, BG, JG, KG, SG, PH, PJH, SEHa, FH, EMLJ, TJ, KK, MiK, MaK, JeK, MHL, ML, TL, JL, YL, DML, JonM, ADM, JMcL, ChM, JeM, FM, CaM, JaM, MM, MN, VN, KN, HO, JOB, PO, MO, JAP, CP, NR, TR, PR, HR, ReS, SaerS, MaSch, IS, WS, TS, MaSe, KS, HS, RaS, MaSu, KVT, ST, XT, APvW, RCHV, JJV, PvdO, ZW, AJW, DSW, NST, JH, JS, ELS have made contributions to the SLE content as outlined in Additional file 5. AJW helped curate several lists and is responsible for the ongoing registration of lists into the DSSTox database and for the CompTox integration, in coordi‑ nation with ELS and HMT. JZ, ELS and EEB designed the PubChem/NORMAN‑ SLE integration, annotation and classification, which was coded and led by JZ, supported by PAT (web services/infrastructure, curation), BAS (deposition, curation), TC (annotation), QL (synonyms/curation) and PC (FAIRifying lists for annotation). ELW and ELS conceptualized the Zenodo deposition. ELS drafted the manuscript, supported by HMT; all authors revised, read and approved the manuscript and submission. Funding The NORMAN‑SLE project has received funding from the NORMAN Associa‑ tion via its joint proposal of activities. HMT and ELS are supported by the Luxembourg National Research Fund (FNR) for project A18/BM/12341006. ELS, PC, SEH, HPHA, ZW acknowledge funding from the European Union’s Page 18 of 26 MohammedTahaetal. Environmental Sciences Europe (2022) 34:104 Horizon 2020 research and innovation programme under grant agreement No 101036756, project ZeroPM: Zero pollution of persistent, mobile substances. The work of EEB, TC, QL, BAS, PAT, and JZ was supported by the National Center for Biotechnology Information of the National Library of Medicine (NLM), National Institutes of Health (NIH). JOB is the recipient of an NHMRC Emerging Leadership Fellowship (EL1 2009209). KVT and JOB acknowledge the support of the Australian Research Council (DP190102476). The Queens‑ land Alliance for Environmental Health Sciences, The University of Queensland, gratefully acknowledges the financial support of the Queensland Depart‑ ment of Health. NR is supported by a Miguel Servet contract (CP19/00060) from the Instituto de Salud Carlos III, co‑financed by the European Union through Fondo Europeo de Desarrollo Regional (FEDER). MM and TR gratefully acknowledge financial support by the German Ministry for Education and Research (BMBF, Bonn) through the project “Persistente mobile organische Chemikalien in der aquatischen Umwelt (PROTECT)” (FKz: 02WRS1495 A/B/E). LiB acknowledges funding through a Research Foundation Flanders (FWO) fellowship (11G1821N). JAP and JMcL acknowledge financial support from the NIH for CCSCompendium (S50 CCSCOMPEND) via grants NIH NIGMS R01GM092218 and NIH NCI 1R03CA222452‑01, as well as the Vanderbilt Chemical Biology Interface training program (5T32GM065086‑16), plus use of resources of the Center for Innovative Technology (CIT) at Vanderbilt Univer‑ sity. TJ was (partly) supported by the Dutch Research Council (NWO), project number 15747. UFZ (TS, MaK, WB) received funding from SOLUTIONS project (European Union’s Seventh Framework Programme for research, technologi‑ cal development and demonstration under Grant Agreement No. 603437). TS, MaK, WB, JPA, RCHV, JJV, JeM and MHL acknowledge HBM4EU (European Union’s Horizon 2020 research and innovation programme under the grant agreement no. 733032). TS acknowledges funding from NFDI4Chem— Chemistry Consortium in the NFDI (supported by the DFG under project number 441958208). TS, MaK, WB and EMLJ acknowledge NaToxAq (European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska‑Curie Grant Agreement No. 722493). S36 and S63 (HPHA, SEH, MN, IS) were funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) Project No. (FKZ) 3716 67 416 0, updates to S36 (HPHA, SEH, MN, IS) by the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protec‑ tion (BMUV) Project No. (FKZ) 3719 65 408 0. MiK acknowledges financial support from the EU Cohesion Funds within the project Monitoring and assessment of water body status (No. 310011A366 Phase III). The work related to S60 and S82 was funded by the Swiss Federal Office for the Environment (FOEN), KK and JH acknowledge the input of Kathrin Fenner’s group (Eawag) in compiling transformation products from European pesticides registration dossiers. DSW and YDF were supported by the Canadian Institutes of Health Research and Genome Canada. The work related to S49, S48 and S77 was funded by the MAVA foundation; for S77 also the Valery Foundation (KG, JaM, BG). DML acknowledges National Science Foundation Grant RUI‑1306074. YL acknowledges the National Natural Science Foundation of China (Grant No. 22193051 and 21906177), and the Chinese Postdoctoral Science Foundation (Grant No. 2019M650863). WLC acknowledges research project 108C002871 supported by the Environmental Protection Administration, Executive Yuan, R.O.C. Taiwan (Taiwan EPA). JG acknowledges funding from the Swiss Federal Office for the Environment. AJW was funded by the U.S. Environmental Protection Agency. LuB, AC and FH acknowledge the financial support of the Generalitat Valenciana (Research Group of Excellence, Prometeo 2019/040). KN (S89) acknowledges the PhD fellowship through Marie Skłodowska‑Curie grant agreement No. 859891 (MSCA‑ETN). Exposome‑Explorer (S34) was funded by the European Commission projects EXPOsOMICS FP7‑KBBE‑2012 [308610]; NutriTech FP7‑KBBE‑2011‑5 [289511]; Joint Programming Initiative FOODBALL 2014–17. CP acknowledges grant RYC2020‑028901‑I funded by MCIN/AEI/1.0.13039/501100011033 and “ESF investing in your future”, and August T Larsson Guest Researcher Programme from the Swedish University of Agricultural Sciences. The work of ML, MaSe, SG, TL and WS creating and filling the STOFF‑IDENT database (S2) mostly sponsored by the German Federal Ministry of Education and Research within the RiSKWa program (funding codes 02WRS1273 and 02WRS1354). XT acknowledges The National Food Institute, Technical University of Denmark. MaSch acknowledges funding by the RECETOX research infrastructure (the Czech Ministry of Education, Youth and Sports, LM2018121), the CETOCOEN PLUS project (CZ.02.1.01/0.0/0.0/15_ 003/0000469), and the CETOCOEN EXCELLENCE Teaming 2 project supported by the Czech ministry of Education, Youth and Sports (No CZ.02.1.01/0.0/0.0/1 7_043/0009632). Availability of data and materials All data integrated in the NORMAN Suspect List Exchange are available from the NORMAN‑SLE website (https:// www. norman‑ netwo rk. com/ nds/ SLE/) and on the Zenodo NORMAN‑SLE community website (https:// zenodo. org/ commu nities/ norman‑ sle) or via the individual DOIs (see Table 1). The merged NORMAN SusDat collection is also available (https:// www. norman‑ netwo rk. com/ nds/ susdat/). Individual lists can be accessed by their code on CompTox, the collection can be found under this search URL (https:// compt ox. epa. gov/ dashb oard/ chemi cal‑ lists? search= NORMAN) or on the NORMAN‑SLE website (https:// www. norman‑ netwo rk. com/ nds/ SLE/). The NORMAN‑SLE is available as data source in PubChem (https:// pubch em. ncbi. nlm. nih. gov/ source/ 23819) and browsable as a classification tree (https:// pubch em. ncbi. nlm. nih. gov/ class ifica tion/# hid= 101). Detailed annotation content is available in several PubChem compound records, with an overview on the Data Source page (https:// pubch em. ncbi. nlm. nih. gov/ source/ 23819). The code supporting the NORMAN‑SLE including documentation is available on GitLab (https:// gitlab. lcsb. uni. lu/ eci/ NORMAN‑ SLE/), along with the code supporting the NORMAN‑ SLE/PubChem integration (https:// gitlab. lcsb. uni. lu/ eci/ pubch em). Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxem‑ bourg, 6 Avenue du Swing, 4367 Belvaux, Luxembourg. 2 Laboratory of Analyti‑ cal Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece. 3 Environmen‑ tal Institute, Okružná 784/42, 972 41 Koš, Slovak Republic. 4 Oniris, INRAE, LABERCA , 44307 Nantes, France. 5 Norwegian Geotechnical Institute (NGI), Ullevål Stadion, P.O. Box 3930, 0806 Oslo, Norway. 6 Department of Chemistry, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway. 7 Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, QLD 4102, Australia. 8 Leidos, Research Triangle Park, NC, USA. 9 Toxicological Centre, University of Antwerp, Antwerp, Belgium. 10 Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, Spain. 11 National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA. 12 UFZ, Helmholtz Centre for Environmental Research, Leipzig, Germany. 13 Insti‑ tute of Ecology, Evolution and Diversity, Goethe University, Frankfurt Am Main, Germany. 14 Swedish University of Agricultural Sciences (SLU), Uppsala, Swe‑ den. 15 Institute of Food Safety and Health, College of Public Health, National Taiwan University, 17 Xuzhou Rd., Zhongzheng Dist., Taipei, Taiwan. 16 Faculty of Chemical and Food Technology, Institute of Information Engineering, Auto‑ mation, and Mathematics, Slovak University of Technology in Bratislava (STU), Radlinského 9, 812 37 Bratislava, Slovak Republic. 17 Science for Life Laboratory, Department of Environmental Science, Stockholm University, 10691 Stock‑ holm, Sweden. 18 Corteva Agriscience, Indianapolis, IN, USA. 19 INERIS, National Institute for Environment and Industrial Risks, Verneuil en Halatte, France. 20 Swedish Chemicals Agency (KEMI), P.O. Box 2, 172 13 Sundbyberg, Sweden. 21 Institute of Environmental Assessment and Water Research‑Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), Barcelona, Spain. 22 Food Packaging Forum Foundation, Staffelstrasse 10, 8045 Zurich, Switzerland. 23 Institute of Biogeochemistry and Pollutant Dynam‑ ics, ETH Zurich, 8092 Zurich, Switzerland. 24 Eawag, Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland. 25 Thermo Fisher Scientific, Dornierstrasse 4, 82110 Germering, Germany. 26 Department of Chemistry, Chemical Biological Centre (KBC), Umeå University, Linnaeus Väg 6, 901 87 Umeå, Sweden. 27 Depar tment Environment and Health, Amsterdam Institute for Life and Environment, Vrije Universiteit, Amsterdam, The Netherlands. 28 Water Research Institute (WRI), Nábr. Arm. Gen. L. Svobodu 5, 81249 Bratislava, Slovak Republic. 29 German Environment Page 19 of 26 MohammedTahaetal. Environmental Sciences Europe (2022) 34:104 Agency (UBA), Wörlitzer Platz 1, Dessau‑Roßlau, Germany. 30 Bavarian Environment Agency, 86179 Augsburg, Germany. 31 Analytisches Forschun‑ gsinstitut Für Non‑Target Screening GmbH (AFIN‑TS), Am Mittleren Moos 48, 86167 Augsburg, Germany. 32 Mass Spec Interpretation Services, 3612 Hemlock Park Drive, Kingsport, TN 37663, USA. 33 State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco‑Environmental Sci‑ ences, Chinese Academy of Sciences (SKLECE, RCEES, CAS), No. 18 Shuangqing Road, Haidian District, Beijing 100086, China. 34 Hope College, Holland, MI 49422, USA. 35 University of California, Berkeley, CA, USA. 36 Agilent Technolo ‑ gies, Inc., 5301 Stevens Creek Blvd, Santa Clara, CA 95051, USA. 37 Department of Chemistry, Center for Innovative Technology, Vanderbilt‑Ingram Cancer Center, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Inte‑ grative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235, USA. 38 Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands. 39 University Rovira i Virgili, Tarragona, Spain. 40 Biosfer Teslab, Reus, Spain. 41 Nutrition and Metabolism Branch, International Agency for Research On Cancer (IARC), 150 Cours Albert Thomas, 69372 Lyon Cedex 08, France. 42 RECETOX, Faculty of Science, Masaryk University, Kotlářská 2, Brno, Czech Republic. 43 Institute of Legal Medicine and Core Facility Metabolomics, Medical University of Innsbruck, Muellerstrasse 44, Innsbruck, Austria. 44 Tech‑ nologies for Water Management and Treatment Research Group, Department of Civil Engineering, University of Granada, Campus de Fuentenueva S/N, 18071 Granada, Spain. 45 Institute of Health Research Pere Virgili, Tarragona, Spain. 46 Agriculture and Agri‑Food Canada/Agriculture et Agroalimentaire Canada, 1391 Sandford Street, London, ON N5V 4T3, Canada. 47 NILU, Nor we ‑ gian Institute for Air Research, Kjeller, Norway. 48 Fraunhofer Institute for Molec‑ ular Biology and Applied Ecology (Fraunhofer IME), Schmallenberg, Germany. 49 Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, P.O. Box 94157, Amsterdam 1090 GD, The Netherlands. 50 Laboratory for Operation Control and Research, Zweckverband Landeswasserversorgung, Am Spitzigen Berg 1, 89129 Langenau, Germany. 51 Environment Agency, Horizon House, Deanery Road, Bristol BS1 5AH, UK. 52 Chemical Contamination of Marine Ecosystems (CCEM) Unit, Institut Français de Recherche pour l’Exploitation de la Mer (IFREMER), Rue de l’Ile d’Yeu, BP 21105, 44311 Cedex 3, Nantes, France. 53 Section for Environmental Chemistry and Physics, Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark. 54 Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands. 55 Technology and Society Labo‑ ratory, Empa‑Swiss Federal Laboratories for Materials Science and Technology, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland. 56 Computational Chemistr y and Cheminformatics Branch (CCCB), Chemical Characterization and Exposure Division (CCED), Center for Computational Toxicology and Exposure (CCTE), United States Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA. 57 Department of Bioinformatics‑BiG‑ CaT, NUTRIM, Maastricht University, Maastricht, The Netherlands. 58 University of Alberta, Edmonton, AB T6G 2G3, Canada. Received: 27 July 2022 Accepted: 24 September 2022 References 1. Krauss M, Singer H, Hollender J (2010) LC–high resolution MS in environmental analysis: from target screening to the identification of unknowns. Anal Bioanal Chem 397:943–951. https:// doi. org/ 10. 1007/ s00216‑ 010‑ 3608‑9 2. Hollender J, Schymanski EL, Singer HP, Ferguson PL (2017) Nontarget screening with high resolution mass spectrometry in the environment: ready to go? Environ Sci Technol 51:11505–11512. https:// doi. org/ 10. 1021/ acs. est. 7b021 84 3. Schymanski EL, Jeon J, Gulde R et al (2014) Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environ Sci Technol 48:2097–2098. https:// doi. org/ 10. 1021/ es500 2105 4. 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