dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, buzzes allow for high rate feedback during prey hunting  [59] , while burst-pulses are used more often in social interactions  [60] [61] [62]. However, they remain close in their structure (i. e. , a series of fast clicks differentiated by the acoustic activity preceding them, such as whistles or accelerating rate of echolocation clicks) and thus difficult to differentiate. To assess the acoustic activity of dolphins (i. e. , whistles + echolocation clicks + BBP), the detection and identification of each of these types of signals is needed. In the following part, the methods used to automatise these detections are presented. Algorithms were coded in Python 3. 9. 7  [63]  and are provided with processed data needed to reproduce the results presented in this article (see Data Availability Statement).
Detection and Identification of Echolocation Click, Buzz and Burst-Pulse To identify click-like signals in recordings, a detector adapted to our dataset was created from an approach used in previous works on marine mammals, mainly sperm whales  [64, 65]. Structurally, a click is a high-energy event of very short duration (usually less than 0. 5 ms). To detect them, a high-pass filter (freq > 50 kHz) was first applied to our raw recordings (Figure  2 ). This prevented whistles and other low-frequency sounds from affecting the detection. Then, the Teager-Kaiser operator  [66]  was applied, which allowed us to estimate the energy contained in an acoustic system, improving the signal-tonoise ratio. Finally, local maxima were selected in the filtered signal (see Figure  2 ) as they correspond to high-energy events, i.
Vocalisation Detection Whistles appear as lines of high energy in the lower frequencies. Selecting by hand all the whistles from 361 min of acoustic recording would be long, laborious and error-prone. Thus, to detect and identify whistles, a vocalisation tracking method is necessary. Selecting vocalisations in an audio track is a complex task, and several techniques have already been developed to achieve it: statistical modelling of whistles  [69] [70] [71] [72] [73] [74] , tracking algorithms based on hand-picked parameters  [75] [76] [77] , image processing approaches  [78] [79] [80] [81] [82] [83]  or deep learning models associated with clustering methods  [84, 85]. For our dataset, we chose to adapt a tracking algorithm developed during the DECAV project  [86]. The original code written in Matlab was optimised and transcribed in Python for the needs of this study.
Data Analysis Firstly, regarding behavioural data from visual monitoring on the boat, nine response variables were considered (see Section 2. 2): four quantitative and five qualitative. The assessment of the effects of the signals emitted by the beacon was performed by comparing the values among treatment sequences with (i) for quantitative data, box plots (distribution of the values of each variable between the sequences), Friedman's non-parametric statistical test implemented for each variable, a permutational multivariate ANOVA (PER-MANOVA  [87] ) on the matrix of Euclidean distances computed among the observations for all variables, and a Principal Component Analysis (PCA  [88] ); (ii) for qualitative variables, the Chi-squared (or Chi2) test of independence on the contingency table crossing the numbers obtained for each of the modalities of a response variable with those of the treatment variable, and a Multiple Factor Analysis (MFA  [89] ) if the null hypothesis H0 of independence among variables was rejected by the Chi-squared test.
and L. L; writing-original draft preparation, L. L. and B. M. ; writing-review and editing, all authors; visualisation (figures production), L. L. , B. M. and O. V. C. ; supervision, B. M. and H. G. ; project administration, B. M. ; project funding acquisition, B. M. , S. B. , O. V. C. , J. S. , W. D. , A. P. , Y. L. G. , Q. S. and T. R. All authors have read and agreed to the published version of the manuscript. Funding: The DOLPHINFREE project coordinated by B. M. is funded by the European Maritime and Fisheries Fund (EMFF) and France Filière Pêche (FFP). L. L. 's PhD grant is provided by Montpellier University. H. G. is supported by ANR-18-CE40-0014 SMILES, and his national Chair in Artificial Intelligence for bioacoustics is funded by ADSIL ANR-20-CHIA-0014-01 DGA and AID. Institutional Review Board Statement: The DOLPHINFREE project had (i) agreement #0-12520-2021/PREMAR_ATLANT/AEM/NP from the French Maritime Prefecture of the Atlantic "to conduct a survey for monitoring groups of common dolphins by means of scientific instruments off the south Finistère coast, following Décret n • 2017-956 of the scientific marine research", (ii) favourable notification from the Ethical Committee in Animal Experiment of Languedoc Roussillon (CEEA-LR) for request #26568 "Behavioural study of wild dolphin groups in response to acoustic signals for limiting by-catch from professional fishery".
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] [2] [3] [4]. The NOR-MAN 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].
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 STOFF-IDENT (https:// water. for-ident. 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.
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. norman-netwo 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 Sus-Dat, https:// www. norman-netwo 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, Comp-Tox) as described below in the methods section, followed by an overview of the current state, implications and outlook in the results and discussion sections.
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] , Chem-Spider identifier (CSID)  [20]  and/or the Distributed Structure-Searchable Toxicity (DSSTox) substance identifier (DTXSID) used in CompTox  [21].
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] , gathered under the NORMAN-SLE community (https:// zenodo. org/ commu nities/ norman-sle/)  [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.
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. epa. gov/ dashb oard/ chemi cal-lists/). 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 cal-lists? search= NORMAN, or through a direct URL composed of the list code (e. g. , https:// compt ox. epa. gov/ dashb oard/ chemi cal-lists/ BISPH ENOLS for the S20 BISPHENOLS list).
Several lists on the NORMAN-SLE 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] [61] [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]  [64]). For recent lists, generally the CompTox batch search (https:// compt ox.
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 NORMAN-SLE 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 NORMAN-SLE 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.
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].
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 NORMAN-SLE website and in the latest versions on the NORMAN-SLE 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 NOR-MAN-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 Git-Lab 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] [238] [239] [240] [241] , while 7 articles are external  [242] [243] [244] [245] [246] [247] [248].
Detailed classification content for S77 FCCDB  [89]  is already drafted on the test site. A screenshot of the top portion of the PubChem NORMAN-SLE Tree is shown on the left in Fig. 5. The collision cross section (CCS) content (S50 CCSCOMPEND  [121] , S61 UJICCSLIB  [150]  and S79 UACCSCEC  [187] ) has also been merged and extended in the "Aggregated CCS Classification" tree on PubChem to combine this with the data from CCSbase  [249, 250]  and to allow browsing by adduct categories across all datasets  [251]. All datasets mentioned here can be accessed via hyperlinks available at the NORMAN-SLE Data Source page on PubChem  [73]. Documentation (E) 95-300 S57 GREEKPHARMA 263  [188] ; S96 ECIPFAS 258  [72] ; S11 SWISSPEST 218  [12, 189] ; S54 EFSAPRI 212  [190, 191] ; S30 PHENANTIOX 209  [192] ; S24 HUMANNEUROTOX 190  [56, 59] ; S56 UOATARGPHARMA 185  [193] [194] [195] ; S28 EUBIOCIDES 160  [196] ; S5 KWRSJERPS 159  [99, 197] ; S12 NORMANEWS 156  [198, 199] ; S26 MYCOTOXINS 149  [200] ; S74 REFTPS 146  [71] ; S64 NATOXAQ 130  [201, 202] ; S91 CECTOYS 126  [203, 204] ; S68 HSDBTPS 101  [174, 205] ; S86 TATTOOINK 98  [206] [207] [208] ; S41 CCL4 96  [209, 210] ; S83 CCL5 96  [211, 212] ; S52 THSMOKE 95  [213]  (F) < 95 S95 PFASANEXCH 94  [214, 215] ; S63 UBADWGW 84  [176, 216] ; S59 NPINSECT 83  [217] ; S67 TBUTYLPHENOLS 77  [218] ; S97 UBABPAALT 71  [219, 220] ; S88 UBABIOCIDES 62  [221] [222] [223] ; S93 CECMOUTHING 60  [203, 204] ; S40 ALGALTOX 54  [224] ; S20 BISPHENOLS 52  [225, 226] ; S81 THSTPS 52  [227] ; S45 SYNTHCANNAB 39  [63] ; S90 ZEROPMBOX1 38  [228, 229] ; S44 STATINS 18  [230] ; S98 TIRECHEM 16  [231]  Table  2  Selected overall summary statistics for the NORMAN-SLE, compiled in May 2022 Further details are given in the "stats" subfolder of the ECI NORMAN-SLE GitLab repository  [234].
Category Number Comment Total number of lists 99 S0 to S98 on how to access the data integrated within PubChem is provided on the ECI GitLab pages, including how to find MS  [252]  and CCS  [253]  data for NORMAN-SLE lists via PubChem. This also includes code to retrieve the CCS data  [254] , along with a compiled archive of all CCS values in PubChem (7 June 2022) on Zenodo  [255]. In addition to the deposition and classification, extensive annotation content (i. e. , expert knowledge) provided within the NORMAN-SLE lists has been integrated within PubChem. Various pieces of information from NORMAN-SLE lists now appear on the individual compound records for 21,114 compounds (12 May 2022), with several examples shown as insets in Fig. 5. While the presence of this annotation information in text form in individual PubChem records is useful for readers of the individual chemical records, it also helps in search engine optimization (SEO), i.
e. , the discovery of this information in generalized search engines, beyond the original database. Some categories (PubChem headings indicated in italics) relate to the chemical role, e. g. , the "ATC Code" Table  3  Unique views, downloads and citations for all NORMAN-SLE lists and the Top 5 lists (by unique views), according to the NORMAN-SLE Zenodo Community  [53]  Statistics compiled on 28 April (views/downloads) and 5 May (citations) 2022. The corresponding raw data are given in Additional file 3: Table  S3  [235] and on the ECI NORMAN-SLE GitLab pages  [234]  List  for pharmaceuticals (from S66 EAWAGTPS  [163]  and S76 LUXPHARMA  [155] ), "Agrochemical Category" (S66 EAWAGTPS  [163]  and S69 LUXPEST  [175] ), or "Chemical Classes" (S75 CyanoMetDB  [119] ). Information in the "Use Classifications" and "Uses" categories come from S13 EUCOSMETICS  [108] , S25 OECDPFAS  [60] , S47 ECHAPLASTICS  [170] , S60 SWISSPEST19  [129] , S66 EAWAGTPS  [163] , S69 LUXPEST  [175] , S72 NTUPHTW  [147] , S79 UACCSCEC  [187]  and S80 PFAS-GLUEGE  [124].