dataesr/scientific-paragraphs-categorization huggingface dataset:
(Wang et al. , 2022). To identify emerging POPs, VeillePOP (VEILLE POP, 2010) (Veille sur les nouveaux polluants organiques persistants dans les mollusques marins, Prospective monitoring of persistent organic pollutants of emerging concern in marine mollusks) an interannual study is coordinated and conducted; funded by the French Office of Biodiversity (OFB). VeillePOP documents the levels and trends of contaminants of emerging concern (CEC) not routinely followed in monitoring programs, and for which limited (or no) data for the marine environment are available at national level. Started in 2010, intertidal mollusks are collected at different coastal and estuarine sites along the French coast, including those under the influence of French major watersheds. The sought contaminants have constantly evolved including brominated and chlorinated flame retardants (FRs)  (Munschy et al.
This pattern can be exploited to infer the type and number of halogens present in a molecule using the relative ratios of the isotopologues and thus serve as additional confirmatory data. However, halogenation is not a characteristic exclusive to anthropogenic compounds as halogenated natural products (HNPs) possess similar features and structural scaffolds. (Vetter, 2006)  Among the 30,000 marine natural products within the Comprehensive Marine Natural Products Database (CMNPD, https://www. cmnpd. org/) (Lyu et al. , 2021) , 2800+ are halogenated. The presence of HNPs in marine samples have been reported, sometimes at even higher concentrations than halogenated organic contaminants (HOCs). (Wu et al. , 2021; Fernando et al. , 2018 ) How do we then unravel this chemical space, wherein many chemical features remain unknown? Non-targeted analysis (NTA) using high resolution mass spectrometry (HRMS), one of the most effective tools for comprehensively assessing chemical space, has recently revolutionized the ability of scientists to identify chemical contaminants.
Instrumental innovations over the last decade alongside the increasing availability of open science tools and databases, has enabled the development of techniques that allow scientists to characterize chemical space to an extent that was previously onerous. Improvements in the mass accuracy, mass spectral acquisition speed, mass resolving power, sensitivity of HRMS instruments, and resolution of different chromatographic techniques enabled the detection of bioaccumulative chemicals in bivalves  (Goto et al. , 2020) , fish and reptiles  (Teehan et al. , 2022; Renaguli et al. , 2021) , and marine mammals. (Hoh et al. , 2012; Alonso et al. , 2017; Cossaboon et al. , 2019; Cariou et al. , 2021)  Using these capabilities, the mass spectrometry community have improved our ability to translate mass-to-charge data into chemical information including unambiguous chemical formula assignments and structural annotation elucidation.
Typically, GC-HRMS equipped with electron ionization (EI) is used for the measurement of semivolatile and volatile compounds like POPs. However, EI as a strong ionization technique suffers from considerable in-source fragmentation. While the fragmentation of the molecule is informative, it is only efficiently utilized if the compound is present in a database like NIST. Unfortunately, high resolution EI databases are currently limited to a few thousand compounds and is insufficient for NTA and does not provide coverage for HNPs. Moreover, the intensity of the precursor ion when measured in EI mode is relatively low if not present. It is then challenging to perform a database search to look for potential molecules that match the exact mass of the unknown feature. Electrospray ionization (ESI) is a soft ionization technique that has found many applications for the analysis of polar to semi-nonpolar organic contaminants.
, 2022; Megson et al. , 2016; Zacs et al. , 2019; Aalizadeh et al. , 2022; Mesihää et al. , 2017)  specially because it forms molecular ions ([M] + or [M + H] + ) which translates to unambiguous formula prediction. On the other hand, in negative mode (APCI-), polyhalogenated chemicals like PBDEs ionize by the simultaneous loss of a halogen and by gaining an oxygen atom or just the loss of a halogen. (Singh et al. , 2020)  While this won't be a problem for groups of molecules whose ionization mechanism is understood (which most likely means that standards are available), it can prove to be confusing for unknown molecules as we are not sure if what we are dealing with is the mass of an intact molecule or the mass of an adduct. Because of this, we decided to only pursue positive mode. By having access to the intact molecular ion information, workflows that have been demonstrated to work well for LC-HRMS, where database matching usually begins with a search for molecules that match the exact mass, can also be employed.
(Zheng et al. , 2018; MacNeil et al. , 2022; Celma et al. , 2020; Foster et al. , 2022)  However, as the use of IMS in NTA is still at its infancy, there are still limited data on experimentally measured collisional cross section (CCS) values to allow database CCS matching. Moreover, the use of IMS in chemical exposome work has mostly been to complement mass measurements instead of being exploited for CCS as an indicator of chemical relatedness. In this context, we use relatedness to refer to the similarity in the structural backbone of the molecules being studied. Fundamentally, IMS is a measure of how an ionized molecule travels through a specific space under the influence of some force which varies depending on the type of ion mobility cell. The time it takes for a molecule to traverse this cell is a function of, but not limited to, its shape and size.
Instrumental parameters Analyte separation was accomplished using a Scion 456-GC equipped with an Rxi-5Sil MS column (40 m × 0. 18 mm, 0. 18 μm,Restek, Bellefonte, PA, USA, with helium as carrier gas (flow rate 3. 0 mL/min). The injection (2 μL) was carried out at 280 • C in "splitless" mode. Atmospheric pressure chemical ionization (GC-APCI II) ion source (Bruker, Billerica, MA, USA) operating in positive mode was used followed by mass detection via a Bruker Daltonics timsTOF (trapped ion mobility spectrometry coupled with time-of-flight high resolution mass spectrometry) in IMS (or TOF)-full scan and data-dependent MSMS (ddMS2) modes, respectively. HRMS data was acquired in positive mode, over a range of m/z from 100 to 1000 at a resolving power of > 40,000 at m/z 622 (at 3,5 Hz acquisition rate).
The GC oven is programmed from 120 to 300 • C, over a total time of 25 min. One limitation of this work is that it only allows elution of small to intermediate molecules; modifications are needed to cater to higher molecular weight compounds. The GC-APCI source (heated by transfer line) and the transfer line are operated at 300 • C (capillary transfer is at 250 • C) with a corona current of 1. 8 μA and ultra-pure nitrogen (99. 999%) as the sheath and auxiliary gas. The final workflow developed for the identification of halogenated organic compounds in marine bivalves is shown in Fig. 1. Standard mixtures of organochlorine pesticides (OCPs), PCBs, PBDEs, emerging brominated and chlorinated chemicals, and HNPs, were analyzed together with the sample extracts forming our in-house database (n = 152 compounds), and an alkane mix was run for determination of Kovats retention indices.
Data analysis workflow Identification of halogenated features in bivalve samples was performed in two phases. The first phase entailed target screening the data for the features that we have standards for while the second phase involved the use of several open software for identifying and annotating potentially halogenated features. Raw Bruker files (. d format) were converted to the standard open format (. mzML) using MSConvert (a feature of the open tool Proteowizard (version: 3. 0. 21045-7732b6429). (Adusumilli and Mallick, 2017)  HaloSeeker 2. 0, an open tool written in the R-language, was used for automatized peak picking, feature alignment, and identification of potentially chlorinated and brominated features (F2 + ); complete software parameters are outlined in the SI. (Léon et al. , 2019)  Molecular formula were predicted for the unknown features using Visualizer (cheminfo.
org) (Molecular formula from monoisotopic mass, 2022) using the following elements: C 0-100 H 0-200 N 0-20 O 0-20 Cl 0-10 Br 0-10 I 0-4 F 0-20 , considering both [M] + and [M + H] + formation at 10 ppm mass accuracy. Cheminfo. org houses various freely available chemistry tools including formula prediction which function from the interent browser. The proposed formulas were narrowed down using the halogenation profile. Identification confidence levels were assigned using a GC-HRMS specific scoring system  (Koelmel et al. , 2022). The open tool El-Maven  (Agrawal et al. , 2019) , allowed us to query our data without relying on vendor software. The list of masses identified through HaloSeeker was used to manually inspect the extracted ion chromatograms accessed through El-Maven. El-Maven facilitated the processing of ddMS2 data in the.
The ion mobilities were extracted then converted to CCS from the raw files using Data Analysis software 5. 3 (Bruker Daltonics, Bremen Germany). For visualization, we plotted KMD (Cl scale) versus CCS. Where applicable, predicted CCS were calculated freely using dmCCS within CCSBase (https://ccsbase. net/dmccs_predictions) (Ross et al. , 2020)  using the SMILE strings of the putative structures. The SMILE strings were obtained through ChemSketch (free from ACD labs) (Free Chemical Drawing Software for Students | ChemSketch. ACD/Labs, 2023). The annotated features were semi-quantified using surrogate standards where applicable or using the peak area of the closest structural analog (details in the SI). The differences in the spatial distribution of the contaminants and natural products found in this work are then described to gain insight into which areas are more anthropologically impacted and which areas are potentially biodiverse.
While several Diels-Alder OCPs (e. g. dechloranes) and legacy POPs were analyzed as part of this work, we only detected them in standard mixtures but not in our samples, and thus will not be discussed further. A summary of the features found (together with their compound class, predicted formula, m/z, mass error in ppm, retention time, retention index, mobility, CCS, and annotation confidence level) and their semi-quantified concentrations in each sampling point are summarized in the spreadsheet SI under 'Feature Analytical Information' and "Analyte Concentration Summary' tabs, respectively. Annotation of unknown features that matched chemicals present in the in-house database was straightforward, however, many of the unknown features were identified by virtue of a KMD (Cl scale) against CCS plot (Fig.
Methoxylated halogenated diphenylethers 10 methoxylated BDEs (4-tetrabrominated and 6-pentabrominated, confidence level 1: 2 compounds, confidence level 2: 8 compounds) (Fig. 2  green circles, KMD: 0. 31) were found to be present in French bivalves. In addition, five of our unknowns demonstrate a halogenation profile consistent with a molecule possessing 3 bromines and 1 chlorine (Fig. 2 , red circles, KMD: 0. 32) for which the closest formula match was C 13 H 8 Br 3 ClO 2. Among the predicted formula matches, only one had a PubChem match C 11 H 5 Br 3 ClN 3 O and has 11 positional isomers. The predicted CCS for this group of compounds is 160. 0 Å 2 which is well within the range of CCS values for the 5 features in our dataset (range: 158. 6-163. 7 Å 2 ). Despite the seemingly good match, PubChem records suggest that this is a precursor for pharmaceutical manufacturing, which did not make sense for the Charente estuary as we are not aware of any such activity in the area.
, 2019; Liang et al. , 2022)  A comparison of the fragmentation spectrum of a methoxylated tetrabromodiphenylether standard that elutes within 0. 43 min of one of the peaks shows similar fragments (Figure  S11 , SI) which supports the hypothesized structural backbone suggested by the CCS of the unknown features. MSMS spectra (acquired by electron ionization) provided by Alonso et al  (Alonso et al. , 2017)  are in agreement with the spectra we obtained from this work (acquired by APCI) despite using different ionization methods. The annotation process of this group of halogenated features highlighted for us the utility of the KMD vs CCS approach for grouping together structurally similar compounds. We also wish to emphasize the importance of verifying that the observed fragmentation spectrum is able to support the proposed structure acquired from database matching.
Despite their resemblance with monomers used in flame-retardant products  (Hanson et al. , 2011) , and like in the Japanese study, we want to argue that this group of compounds has a backbone that is of natural origin due to their presence in French sampling sites that are relatively less impacted by human activity like ABE, ChI, UsI, and WCo. Like the case for bipyrroles, we propose that their molecular scaffolds of natural origin subsequently get halogenated through a series of complex reactions occurring at the air-sea interface. However, unlike the Japanese study which approached their proposed structure from a contaminant point of view, we want to propose a natural product approach. We thought that if it is an HNP, then the dehalogenated backbone of the compound group may be present in the marine natural product database.
From a method point of view, the KMD vs CCS space allowed the database-free grouping together of unknown halogenated chemicals using an intrinsic property that is shared among compounds possessing the same molecular scaffold. We believe that this approach can benefit work on the identification of UVCBs in environmental matrices as many of the chemicals found in this work may fall under this chemical category. (Lai et al. , 2022)  This work supports observations by MacNeil et al  (MacNeil et al. , 2022)  suggesting that a data filter may be applied to capture polyhalogenated chemicals (compounds with > 5F atoms, >3Cl atoms, and > 2 Br atoms) in ion mobility paired HRMS data. Taking together the standards and unknowns, 231of the 232 halogenated features had CCS values which are less than 0. 2 × m/z + 100 Å 2 , with the only monochlorocarbazole failing the filter which is not surprising considering that it is two chlorine atoms short of the recommended 3 chlorine atoms.
Acknowledgements The authors would like to acknowledge  Walter Vetter  from the  University of Hohenheim (Germany)  for sharing HNP mixtures that aided in the identification of many halogenated organic chemicals in this work. We are grateful to  Cyril Noel  from  Ifremer Bioinformatics Core Facility  (SeBiMER; https://ifremer bioinformatics. github. io/)and  Dr. Adelene Lai  for providing technical help and scientific support for biostatistics analyses.
Digital technology has widely spread through all aspects of our lives and it is only natural that its amazing potential should be put in the service of research. martin However, if academics can be skilled users, to create proficient software remains the business of digital professionals, pushing new ways of handling the hard documentation at our disposal. But digital technology is not an end in itself either, only a means to facilitate the work of the discipline that embraces it, especially if we are not afraid to revise our research practices and introduce Egyptology in the field of the Digital Humanities. VÉgA is an online digital dictionary of ancient Egyptian, translated into French, English, German and Arabic. 1 It has been developed as a technological program within the LabEx archimede (Laboratory of Excellence for Archeology and History of the Mediterranean and ancient Egypt), supported by the University Paul-Valéry Montpellier 3 and the cnrs, in collaboration with the private company Intactile design, specialized in the creation of digital interfaces.
But already in 1945, A. H. Gardiner was somewhat critical about this "masterpiece" of ancient Egyptian lexicography and the sentiment of final achievement given by the publishers, arguing that "it behooves all students of Egyptian philology to consider what steps could be taken to supply the crying need for a dictionary adequate to the present position of our knowledge. "3 Since then, an everlasting debate has been going on how to make a "good" dictionary, exhaustive and up to date. 4 In the end, a consensus was made on what such a tool should offer: -every known word of the ancient Egyptian lexicon, with translations -every primary source reference, in context, to distinguish the different usages and meanings, while allowing the scholar to check for himself -a commentary with bibliography on the ongoing discussions on lexemes when necessary -a lexical analysis, with semantical and morphological data, along with links between lexemes formed on the same root or of the same lexical field But as anyone who has undergone the process of such a publication can attest, on the scale of the whole lexicon, the volume of data to collect and analyze is such that the need for exhaustivity seems to be in perpetual conflict with the wish for constant updating, and even more so when you have to fix it in a printed book.
"5 Ultimately, the decision on how to process this kind of data is a matter of choices, and in its ambition to be a global platform for ancient Egyptian vocabulary, VÉgA is no exception. It is however fundamental that these choices should be well-grounded and part of an all-encompassing approach on the development of the project (and its associated tool) over the long term. With that in mind, and right from the start in 2008, the digital tool came as a sensible choice for its flexibility, the opportunity to stock and update as much data as needed, and the benefit of having access to all the documentation in one place. But as with any new device, we must learn new ways to adapt. And in order to develop something effective we needed the help of specialists in computer engineering and design, hence our collaboration with the private company intactile design, elaborating and realizing digital interfaces for specialist sectors taking into account the position of the user, and with whom we were able to fashion a tailor-made tool through co-design work and Agile software development meetings.
Using Design for Research As a proper digital tool, VÉgA was never perceived as a mere database of ancient Egyptian vocabulary where data is compiled in a way only pertinent to the subject itself and its intrinsic patterns. Its priority should instead be to facilitate the task of the user, putting him at the core of the process. The Egyptologists had then to embrace some of the design philosophy and seek the help of specialists in research by design,7 integrating a multidisciplinary team of designers and computer engineers exploring usages together with the targeted users. At first, they spent some time observing the Egyptologists in their daily routine of library consultation, collecting data and checking of references. They managed to decompose the process in multiple steps and summarize them in technical terms, understanding the different issues needed to be dealt with in this specific field.
3). The team is working on a paper prototype during a co-design meeting. 9 In a "search as you type" manner: for each typed character, the number of results decreases. figure 12. 2 Snapshot of the VÉgA id 775 note. In the "Comments" tab, three links are made towards words constructed on the root ʿḥʿ: ʿḥʿw "location, position; station, stop" (VÉgA id 3560), mʿḥʿ. t "tomb, funerary chapel, cenotaph" (VÉgA id 4896), and sʿḥʿ "to erect, to set up". The last one is still in orange, meaning the headword is not yet recorded in the database and the link is not active. figure 12. 3 Snapshot of the VÉgA desktop with the list of results for the word "natron" in the search field (without filter). The list shows all the entries containing this word in the translation.
Classifying and Display Designers and computer engineers helped the Egyptologists structure all the needed data to be formally added to the database, in order to clearly present each element without clouding the consultation of others. With that in mind, a note is made of different layers, from the basic translation to more "encyclopedic" features focusing on the real meaning of the word, along with semantic considerations, ethnological details, and specific bibliography. A first unit displays the basic information required with the headword in transliteration,11 its main hieroglyphic forms and translations in the four languages of the academic area (French, English, German and Arabic) (Figure  12. 4). The translations presented are generally, for now, the ones given by the dictionaries and glossaries of reference.
3) Comments In this section can be found all the information needed to understand the meaning of a word: grammatical class (when it can be misleading), etymology, primary sources, relevant and/or specific bibliography, characteristic nuances and metaphorical uses, noteworthy expressions and constructions, and more importantly clickable links toward other headwords with similar roots or lexical fields and research leads. This tab is automatically displayed if the lexeme is deemed problematic or false. Except for the "Comments" tab, which is an open field, all features are standardized so that any data can be processed by the database and brought up more easily. And thanks to the flexibility of the digital medium, the structure can be changed at will, for example, if we notice that a certain type of information becomes recurrent despite not being expected beforehand.
But if the sustainability of the database is an obvious issue in our development process, it does not concern immediately the user requiring assurance of the accuracy and consistency of the data provided by VÉgA. To that end, each iteration of a note should be archived in the database. But this feature is not implemented yet, as the team judged the revisions to be still too frequent and would continue to be until the completion of the lexicon perimeter. It would generate too many archives, neither manageable nor useful in the current state. It is nonetheless planned for a future version of the software, as we established that data integrity is a requirement of digital preservation. In the meantime, and as for any online reference, it is necessary to add the date of consultation when citing a VÉgA note, along with its id number.
As a result, there are only a few complete notes available at the moment. In the future, the opportunity to make a complete note shall then be given to the community, VÉgA providing a platform to publish quickly all lexicographical data under the supervision of an international scientific committee. And contrary to present notes being the product of the whole team, a complete note shall also be signed by its author, as any academic work would. Ideally, when envisioning each note as complete, it would result in every single piece of ancient Egyptian texts to be available in the database. In the end, this could resolve one of the biggest flaws of the tool compared to other digital dictionaries at the moment: the absence of textual resources and bodies of texts. But the collaboration with individual scientists is not the only one to be considered.