dataesr/scientific-paragraphs-categorization huggingface dataset:
The composite "Molecular Formula" representation in S80 PFASGLUEGE  [124]  is also integrated. Taxonomy information (for organisms) has been included under the "Taxonomy" heading for compounds present in S75 CyanoMetDB  [118, 119]  and S29 PHYTO-TOXINS  [132]  from the Toxic Plants-Phytotoxins database  [131] , and also appears on the individual taxa pages. Transformations for 5135 CIDs have been added from the datasets S60 SWISSPEST19  [129] , S66 EAWAGTPS  [163] , S68 HSDBTPS  [205] , S73 METXBIODB  [110] , S74 REFTPS  [71] , S78 SLUPESTTPS  [173]  and S79 UAC-CSCEC  [187] , as described in some of the articles mentioned above  [24, 174, 241]. As a part of this, SEO text snippets describing these relationships have been added to the following headings: Metabolism/Metabolites (S73 METXBIODB  [110]  and S82 THSTPS  [227] ), Drug Transformations (S66 EAWAGTPS  [163] ) and Agrochemical Transformations (S60 SWISSPEST19  [129] , S66 EAWAGTPS  [163]  and S78 SLUPESTTPS  [173] ).
An example Transformations entry is provided in the middle right inset in Fig. 5. The Transformations data are compiled and archived on GitLab  [80]  and Zenodo  [256] , and is integrated in patRoon 2. 0  [257] , an open source software for mass spectrometry based non-target analysis that includes suspect and transformation product screening workflows. Finally, a significant amount of experimental data has also been included in PubChem from NORMAN-SLE contributors. MS/MS and NMR data have been included from several transformation products (TPs) and/or parent compounds of contaminants of emerging concern, including: 13 C NMR, 19 F NMR, 1 H NMR, 31 P NMR, MS/ MS (all from S74 REFTPS  [71]  containing MS/MS data extracted from 4 articles  [258] [259] [260] [261] [262]  and NMR data from 1 article  [258] ).
Many of these CIDs were not available in PubChem previously. Measured CCS values (often for multiple adducts) associated with 1579 CIDs are included in PubChem, from the datasets S50 CCSCOM-PEND  [121] , S61 UJICCSLIB  [150]  and S79 UACCSCEC  [187]  (see also inset at the bottom left in Fig. 5 ). As mentioned above, this data can be retrieved from PubChem, with documentation provided on the ECI GitLab pages  [252] [253] [254] , along with an archive of the CCS data on Zenodo  [255].
Recognition, role and use of the NORMAN-SLE The collection of download and view statistics on Zenodo, along with citation tracking, has helped track the impact of the NORMAN-SLE on the community, as shown in Tables  2  and 3. Since the Zenodo integration only commenced in 2019, these statistics only cover a fraction of the real-world use of the NORMAN-SLE. Several efforts known to the authors that build on NOR-MAN-SLE information are not captured within these statistics, including for instance CECSCREEN  [84] , which retrieved much of the NORMAN-SLE data that was integrated into CECSCREEN via CompTox. While a PubMed query on the NORMAN-SLE and the sub-collections was attempted to discover more citing articles, this did not return sufficiently reliable results for further interpretation (various text queries generated large numbers of false positives without finding true positives); it seems that environmental literature is not sufficiently covered in PubMed.
Guidance is now provided on the NORMAN-SLE website to help users correctly cite the works; it is hoped that this publication will also help to raise awareness of the resource for the wider scientific communityand will highlight the necessity to cite contributions, so that the level of community adoption becomes more visible over time. The unique views, downloads, and citations available on Zenodo revealed some surprising results. While in NORMAN much focus was given to pesticides, pharmaceuticals, REACH registered chemicals and TP lists due to popular demand, the most popular list by far (see Table  3 ) proved to be S13 EUCOSMETICS  [108] , a collection of chemicals employed in cosmetics from EU regulations  [106, 107]. The second most viewed list was a Swiss pesticide and metabolite list, S60 SWISSPEST19  [129] , a quite recent collection by Kiefer et al.
The NORMAN SusDat website (https:// www. norman-netwo rk. com/ nds/ susdat/) was visited 120,221 times (20,258 times counting unique IP addresses per day) between 27 Feb. 2020 and 13 July 2022, compared with 26,318 visits to the NORMAN-SLE website (https:// www. norman-netwo rk. com/ nds/ SLE/). The original versions of two highly popular lists, the Food Contact Chemicals database (FCCdb) and the database of Chemicals associated with Plastic Packaging (CPPdb) are also available on Zenodo. These have much higher views and (for FCCdb only) download statistics associated with their original depositions compared with the NORMAN-SLE version (which directs viewers back to the original resource with a request to cite the original dataset). The numbers (10 July 2022) are (unique views/downloads): CPPdb  [103]  (2,082/659), S48/S49 CPPDBLISTA/B  [104, 152]  (594/1041), FCCdb  [88]  (8,612/3,703), S77 FCCDB  [89]  (410/398).
Neither of these original depositions have any citations. The reason for the parallel integration of these lists (i. e. , an original version plus NORMAN-SLE version) is to ensure the maintenance of the full integration with the NORMAN-SLE website, PubChem and CompTox (as these require the preparation and archive of additional files, as well as the ability to edit the depositions and make any necessary adjustments). All NORMAN-SLE lists feed into the merged collection NORMAN SusDat, which forms the basis of the NORMAN Database System (NDS)  [29, 31]  and integration into other NORMAN initiatives such as the Digital Sample Freezing Platform (DSFP)  [266]  and prioritization efforts (see Fig. 2 ). Several NORMAN-SLE lists are associated with NORMAN activities such as collaborative trials  [4, 6]  and NormaNEWS  [184, 199].
FAIR data and chemical curation The decision to deposit the NORMAN-SLE collections on Zenodo helped "FAIRify"  [22, 23, 269]  the NOR-MAN-SLE via the provision of DOIs and versioning control. This helps trace updates and provide static URLs to data files, enabling powerful automatic integration such as that currently performed with PubChem (see Fig. 2 ), as well as providing the citation possibilities and statistics presented above. These are all features that are not currently possible via the infrastructure supporting the NORMAN-SLE website. Version control is important to track changes to the lists; not only in terms of fixing errors (i. e. , curation), but also to keep historical records of lists as they change, since some chemicals that have, e. g. , been phased out in the EU or changed in relevance may still occur in imported products and the environment.
Overall, the data in the NORMAN-SLE is currently reasonably FAIR: Findable via the DOI and InChIKey for deep indexing; Accessible via the download options of Zenodo; Interoperable via the use of SMILES and InChI; and Reusable via the open license (CC-BY 4. 0) and the use of community standards where feasible, exemplified by the PubChem integration. A transition to the standardized templates proposed recently  [24, 37, 270]  will help FAIRify the NORMAN-SLE further; these templates could also form the basis to help propose a set of chemical identifiers needed to establish unique (chemical) identifiers for the future European Open Data Platform. While best efforts are made to map NORMAN-SLE contributions to identifiers correctly, the resources are not available for extensive curation efforts such as those performed by CompTox.
This is coupled with the current "as is" philosophy, where lists are processed to best represent the data as provided. The versioning offered by Zenodo opens options for quality control and updating of lists, however this is still a very manual process and currently decoupled from updates to NORMAN SusDatworkflow and infrastructure upgrades to resolve this are underway. Since NORMAN-SLE lists are both sourced from and deposited to third party systems, and due to the different release cycles (PubChem updates daily, CompTox approximately annually), different versions of the data result-which can cause confusion. A coherent collaborative and timely process to update and circulate updated lists across the various systems would be beneficial; while this currently works well with the automated updates between PubChem and the NORMAN-SLE, it is not yet possible with CompTox.
g. , Fig. 3  in  [51] ), there is room for further developments. Organometallic compounds (e. g. , methylmercury compounds, organolead/organotin compounds, cyclic volatile methylsiloxanes, gadolinium compounds used as contrast agents) are cases that can be handled to an extent with the current approaches (although not in an "MS-ready" form). Upcoming InChI developments will hopefully improve the handling of organometallic species in databases in the near future  [272]. Further examples related to biocides that are currently beyond the scope of the NORMAN-SLE (but are in part covered by the NDS) include microbial preparations or strains used as biocidal products, where metabarcoding or proteomics (peptide biomarkers) could be used for characterization, along with nanomaterials/nanoplastics.
Future updates: new submissions As described above, submissions and updates to the NORMAN-SLE were frozen during preparation of this manuscript. In the meantime, both new submissions and expressions of interest to update existing lists have been registered, partially stimulated by reaching out to all contributors during the writing of this work. Updates have been suggested for S17 KEMIMARKET  [68] , S28 EUBIOCIDES  [196]  with information from ECHA  [273] , S34 EXPOSOMEXPL  [165, 166]  with new data from  [274]  plus new microbial metabolites  [275, 276]  and S75 CyanoMetDB  [118, 119]  (next release due early 2023). Suggestions for new contributions include a list of endocrine disruptors within the activities of PARC, the Proposition 65 (Prop-65) list of chemicals from the California EPA  [277] , Phenol-Explorer  [278] [279] [280] , the Database on Migrating and Extractable Food Contact Chemicals (FCCmigex)  [281] , and finally a shale gas suspect list  [282]  that has been applied in other studies:  [283, 284]  and will fill a long-identified gap with respect to fracking-related content.
Future updates: potential new features Beyond the new submissions and processing updates mentioned in the previous section, several new features have been suggested (and are being considered) for the NORMAN-SLE and/or the broader NORMAN Database System. These can be grouped into four major areas reflected in the following paragraphs: experimental, TPs, categorization/use and regulatory. On the experimental side, additional functionality to account for physical chemical properties such as mass, polarity, likely ionization mode and amenability to either GC or LC would be beneficial, along with the link to available MS/MS data and/or reference standards for further confirmation. This information is included to a large extent in NORMAN SusDat, which provides a centralized access point for this information, along with predicted toxicity values  [264]  and retention indices  [238] , but will be streamlined and automated further, also to account for possibilities arising from the PubChem integration.
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 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.
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 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.
This information is also given in Additional file 5. Thanks to the internal reviewers for their helpful comments. AJW helped curate several lists and is responsible for the ongoing registration of lists into the DSSTox database and for the CompTox integration, in coordination 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.
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.
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  Queensland Alliance for Environmental Health Sciences, The University of Queensland , gratefully acknowledges the financial support of the  Queensland Department 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 University. 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, technological 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 Protection (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.
When Bob comes online, he computes a new ratchet key pair (rchk +1 , Rchpk +1 ). A temporary value  and the chain key ck (0,+1) are calculated from the root key  6  rk  and the Diffie-Hellman product (Rchpk  ) rchk +1 via KDF. Then, the chain and message keys are computed as described in the previous item. From that point onwards, keys evolve by symmetric ratcheting until the speaker changes again. (2) Now assume that the responder was the speaker at stages (•, ); therefore  is odd and at each stage (•, ) the encrypted message includes associated data Rchpk. When Alice comes online, she chooses new ratcheting information rchk +1 , Rchpk +1 and computes a new root key rk +1 and the base chain key ck (0,+1) from the value  computed at stage (0, ) (see the bullet point before) and the Diffie-Hellman product (Rchpk  ) rchk +1.
Implementation details Our implementation relies on the Java implementation of Signal, available at https://github. com/signalapp/libsignal-protocol-java. We used low-level libsignal functionalities but had to re-implement its more abstract layers, to fit with MARSHAL. The libsignal library uses interfaces in order to store and recover keys and session state data; it allows us to abstract the central server and simulate exchanges between two parties within the same process. Cryptographic details. Our implementation uses similar elliptic curve material. For signatures we used XEd25519 on Curve25519. We use similar authenticated encryption algorithms relying on AES with 128-bit keys. However, while Signal uses CBC mode and an hmac256-MAC, we prefer to use AES-GCM with a 12-byte IV and a 16-byte tag.
*    This study was partially supported by the  French ANR , grants  16-CE39-0012  (SafeTLS) and  18-CE39-0019  ( MobiS5 ). There are also the  French government research program  " Investissements d´Avenir " through the  IDEX-ISITE initiative   16-IDEX-0001  ( CAP 20-25 ), the  IMobS3 Laboratory of Excellence  ( ANR-10-LABX-16-01 ), the  French ANR  project  DECRYPT  ( ANR-18-CE39-0007 ) and  SEVERITAS  ( ANR-20-CE39-0009 ). 1 Person-in-the-Middle is a politically-correct version of Man-in-the-Middle. 2 This property is also called healing. 3 We use a different notation of stages from [8].