dataesr/scientific-paragraphs-categorization huggingface dataset:
Objective The dataset was generated to allow for an institutional analysis of existing innovative contracts for agri-environmental and climate schemes in Europe, focusing on the involved actors and their roles in contract governance  [6]  ( Table  1  ). It was compiled in the context of the Horizon 2020 research and innovation action 'Contracts 2. 0' ( https://www. project-contracts20. eu ) dedicated to investigate four contract types in particular: result-based, collective, land tenure, and value chain contracts. The dataset presents the underlying data basis for all results presented in a companion research paper entitled 'Institutional analysis of actors involved in the governance of innovative contracts for agri-environmental and climate schemes' published in Global Environmental Change ( 10.
Analytical approach Our analytical approach was divided into three steps (cf. [7]  ): In the first step, we identified and selected case examples in order to represent the different innovative contract types for agri-environmental and climate schemes (result-based, collective, land tenure, and value chain) in our sample. The output of this step was a sample of 19 innovative contracts (4-5 per contract type) from six European countries (cf. data file: con-tracts_abreviations_hybridity. xlsx). In the second step, we used Elinor Ostrom's institutional development and analysis (IAD) framework (cf. [3]  ) for a structured data collection per selected case, employing a survey. The survey contained 34 questions to collect information on the same number of variables. We linked each variable to one of the five aspects of the IAD framework, describing: (1) the general characteristics of the contract, (2) the biophysical, socio-economic, and political context conditions under which the contract is applied in a given country and region, (3) the targeted interactions within and between the social and ecological system, i.
Materials and methods Methods and materials used for the first step in our experimental design: To identify potential cases we employed a method mix combining literature review, web search, and expert consultation (cf. [1]  ). For expert consultation, we approached team members of the Contracts2. 0project ( https://www. project-contracts20. eu/ ), under which umbrella the research related to the described dataset was conducted, to list possible case examples for result-based, collective, land tenure and value chain contracts for agri-environment and climate schemes. The final selection of cases for our sample was then guided by the rationale to have several contracts to compare for the same contract type and also different contract types for the same country under similar context conditions.
Nevertheless, several contracts showed a hybrid nature combining elements of more than one contract type (cf. data file: contracts_abbreviations_hybridity. xlsx). Our final sample included 19 case examples representing the four contract types. We focused our selection on European case examples only to keep the overall workload linked to data collection and analysis manageable. Methods and materials used for the second step in our experimental design: For the structured data collection based on the five aspects of the IAD framework we designed a survey in Excel (Microsoft). The information for the survey was collated from different data sources. We included both freely accessible materials and materials with limited accessibility. Mostly, we looked for contract-specific websites, websites of different actors involved in a specific contract, media websites (e.
e. French and German) we translated the information ourselves. For materials in local languages neither of us was familiar with (i. e. Dutch and Flemish), we first used DeepL (DeepL) and Google Translate (Google LLC) and then asked native speakers involved in the Contracts2. 0project to check if we understood the information correctly. Whenever possible, the completed data matrix was again reviewed by an expert involved in the contract to verify entered data and close last data gaps. Methods and materials used for the third step in our experimental design: Based on the information already retrieved for variables no. 9, 11, 13, 15, and 32 (cf. data file: con-tracts_x_variables_[matrix]. xlsx), asking about the involved contracted party (variables no. 9 and 11), the contracting party (variable no.
Acknowledgments This work was supported by the  European Union 's  Horizon 2020 research and innovation action  'Contracts2. 0'funded under grant agreement no. 818190. We like to thank all who contributed to the identification of cases and the data collection:  Sigrid Aubert ,  Carla Barros Erismann ,  Sven Defrijn ,  Lisa Deijl ,  Jennifer Dodsworth ,  Cédric Gendre ,  Johannes Koberstein ,  Annabelle LePage ,  Julia Rex ,  Anne Sallent ,  Lenny van Bussel ,  Korneel Verslyppe , and  Carleen Weebers. Furthermore, we like to thank  Katrin Lück  for creating the location maps for the case narratives.
1 Specifications table describing the dataset. Subject Nature and landscape conservation Specific subject area Inventory of actors involved in the governance of innovative contracts for agri-environmental and climate schemes and analysis of the different roles that each actor performs in contract governance Type of data Tables Figures Maps Text document How the data were acquired Data format Raw Analyzed Description of data collection In total, 19 contracts were sampled employing purposive sampling (cf. [2] ) based on a list of criteria differentiating between four contract types for agri-environmental and climate schemes (cf. data file: contracts_types. xlsx). For each contract, information on 34 variables was collected (cf. data file: iad_variables. xlsx). The information was either filled in by the authors by retrieving the information from websites and other data sources, or by an expert directly involved in the contract (cf.
, 2017)  workflow called baargin (Bacterial Assembly and Antimicrobial Resistance Genes detection In Nextflow) https://github. com/ jhayer/baargin. It enables to conduct sequencing reads quality control, genome assembly and annotation, Multi-Locus Sequence Typing and plasmid identification, as well as antimicrobial resistance determinants detection, and pangenome analysis. The use of Nextflow, a workflow management system, makes our workflow portable, flexible, and able to conduct reproducible analyses.
The baargin workflow allows the user to perform a complete in silico analysis of bacterial genomes, from the quality control of the raw data, to the detection of AMR genes and mutations, on multiple datasets of the same bacterial species in parallel. It compiles and summarise the results from all the analysis steps, allowing comparative studies. As a last step, baargin performs a pangenome analysis of all the strains provided, producing the basis for the construction of a phylogenetic tree. The use of Nextflow and containers ensures the reproducibility of the data analysis. Only few bacterial genomics workflows are available, like Bactopia  (Petit III, 2020) , which is highly flexible and complete in term of tools available. Therefore, we needed a lighter workflow, with only a few tools and databases installed for our collaborators that have only limited computing resources and storage.
, 2019)  and the contigs classified at the taxonomic level provided by the user (with the taxid, and including the children taxa) are retrieved and therefore named as "deconta" for decontaminated contigs  (Lu et al. , 2022). The dataset containing all the contigs are named as "raw". From here all the steps except the annotation (8) will be performed on both sets of contigs "raw" and "deconta". 5. A quality check of the assembly is conducted using Quast  (Gurevich et al. , 2013)  and BUSCO  (Manni et al. , 2021). For BUSCO, the users have the possibility to specify the taxonomic lineage database to use for searching the housekeeping genes (at the class level of the strain to analyse for example: enterobacterales_odb10) 6. The contigs (raw and deconta) are then screened to identify the sequence type of the strain using the MLST tool (Multi-Locus Sequence Typing)  (Seemann, 2022).
7. The contigs are subsequently submitted to plasmid identification using PlasmidFinder  (Carattoli et al. , 2014)  and additionally with Platon if the user provides a database for it  (Schwengers et al. , 2020). 8. Antimicrobial Resistance Genes (ARGs) are then searched in the contigs using both CARD RGI  (Alcock et al. , 2023)  and the NCBI AMRFinderPlus  (Feldgarden et al. , 2021). For certain species only, AMRFinderPlus can also detect some mutations conferring resistance, if the user provides that option. 9. A genome annotation is performed on the deconta contigs using Prokka by default  (Seemann, 2014) , or using Bakta  (Schwengers et al. , 2021)  if the user provides a database for it. 10. Once all the strains datasets provided are annotated a pangenome analysis is done using Roary  (Page et al.
Output The results are located in a nested folder architecture. For each dataset, a folder with the sampleID is created, and contains 5 subfolders: -qc -assembly -AMR -annotation -plasmids These subfolders contain the main outputs from the concerned analyses. Additionally, at the root of the results folder (indicated by the user) 2 folders are created: pangenome, containing the results for Roary, and compile_results that contains summary files for each analysis where the results for all datasets provided in input are compiled as presence/absence matrices for further comparative analyses.
Discussion and conclusions We presented here an easy-to-use workflow for Bacterial Assembly and Antimicrobial Resistance Genes detection In Nextflow: baargin. It allows the users to analyse genomic datasets from short and long sequencing reads, of several bacterial strains from the same species in one command line. The workflow will automatically assemble the genomes, check for contamination and specifically extract the sequences that belong to the expected taxon. It will then identify their sequence type and screen the assemblies for plasmids sequences and ARGs. The fact that baargin is implemented in Nextflow and is based on containers makes the analyses reproducible. Its modular design makes it easy to customise and extend, by adding new modules for new processes.
Introduction Lipids represent a large class of primary metabolites playing vital biological functions to maintain homeostasis in living organisms, serving as energy storage and cell signaling processes  (Züllig et al. , 2020). They are also involved in a large number of heterogeneous diseases including cancer, neurodegenerative and metabolic disorders. Lipids are classified in eight categories according to the Lipid Maps classification system  (Fahy et al. , 2005; 2009; Liebisch et al. , 2020)  available online through the curated Lipid MAPS Structure Database (LMSD) gathering almost 48,000 unique lipid species in 2022 (https://www. lipidmaps. org/). Those categories can be further separated into classes, in which lipids share some building blocks but also differ by the length of alkyl chains and number of unsaturations creating a high diversity of closely related lipid species making their distinction highly challenging  (Quehenberger et al.
The common analysis workflow for those methods is based on the acquisition at the MS level of robust data for biological and technical replicates together with quality control (QC) injections to correct any deviation of the analytical chain. Then the identification of relevant markers or compounds differentiating sample groups is further accomplished by tandem mass spectrometry (MS/MS) following multiple injections into the LC-MS/MS system using targeted MS/MS workflows. MS/MS spectral data are necessary for the elucidation structure of lipids. The major part of the existing lipid databases mostly includes tandem mass spectra acquired under low-energy nonresonant collision-induced dissociation (CID) or higher energy collision dissociation (HCD) conditions on hybrid TOF and Orbitrap-based instruments.
This clearly advantages fast MS/MS scanning instruments such as hybrid TOF, with MS acquisition rate up to 100 Hz in both the MS and MS/MS modes (at 50 K resolution) for the TripleTOF 6,600 and timsTOF instruments, compared to the latest generation of quadrupole-Orbitrap instruments (Orbitrap Exploris series) working at 40 Hz at 7. 5 K resolution at m/z 200 (Perez de Souza et al. , 2021). Neumann et al. proposed a protocol to realize a better sampling in MS/MS mode by focusing on biologically relevant and meaningful features  (Neumann et al. , 2013). First, a LC run at the MS level is performed, with the direct analysis of the data by preprocessing software XCMS for automated annotation and statistical analysis to select significant features. Then methods with inclusion lists are created and performed on pooled samples using targeted MS/MS (tMS 2 ) acquisition mode in order to confirm previous annotations while also limiting the number of LC-MS/MS experiments.
, 2020a; 2020b). In order to improve the association of precursor ions with their corresponding fragments, SWATH (Sequential Window Acquisition of All THeoretical) has been developed  (Gillet et al. , 2012). The principle is to define sequential MS/MS experiments in reduced m/z windows, typically 10-20 Da to cover the whole mass range, all ions within these windows are then fragmented simultaneously (Figure  1 ). It has been initially developed on hybrid TOF instruments since a high-speed acquisition is mandatory, but can be now implemented on Orbitrap-based instruments  (Fenaille et al. , 2017). SWATH experiments were performed to characterize 214 and 140 lipids from immortalized keratinocytes in both positive and negative ionization modes, respectively. As statistical treatment of the data is mainly based on retention times, authors strongly advise to add internal standards to correct any deviation  (Calderón et al.
, 2020; Züllig and Köfeler, 2021). In DIA workflows, such as SWATH, the direct link between a given precursor ion and its corresponding fragments is often lost, which renders MS/MS data interpretation of unknown lipid species much more difficult than that of MS/MS data resulting from DDA protocols. Data treatment step, i. e. the concatenation and alignment of precursor and fragment ions, is indeed often considered as the critical point. This can be performed according to retention times  (Castro-Perez et al. , 2010)  or by specific data filtering through mass defect calculation  (Stagliano et al. , 2010). A correlation-based deconvolution (CorrDec) method that uses correlation of ion abundances between precursor and product ions has been developed to improve the metabolite annotation rate  (Tada et al.
, 2022). Even if DIA approach looks appealing in terms of numbers of annotated lipid species, the annotation steps required the most exhaustive and reliable MS/MS databases including normalized retention times if available. Acquiring MS/MS spectra from pure authentic standards is the best way to generate spectral databases, but this is clearly unrealistic due to the cost and viability of pure compounds. The most feasible strategy is to generate in silico lowenergy fragmentation spectra thanks to the well-documented mechanisms of lipid fragmentation  (Murphy, 2015)  as defined in LipidBlast database. As already documented in the literature, MS/MS   (Goracci et al. , 2017). By Molecular Discovery.
, 2019). Greazy/LipidLama X X X X DDA: Peak detection and identification from set of theoretical phospholipids and fragments  (Kochen et al. , 2016). DecoID X X DDA, DIA: Peak detection, deconvolution of MS/MS spectra (LASSO regression algorithm) and identification according implementable lipid libraries  (Stancliffe et al. , 2021). (Continued on following page) Frontiers in Analytical Science frontiersin. org data are strongly dependent on the type of tandem mass spectrometer and the collision energy used that is challenging to normalize especially in the case of small molecule analysis  (Murphy, 2015). Thus, the information related to the relative intensities of the fragments cannot be easily employed to refine structure annotation making the match scoring process less robust. Several software solutions  (Kitata et al.
, 2021)  include modules capable of achieving the complete pipeline from the conversion of vendor raw files to statistical analysis and through the identification from integrated and/or implementable lipid libraries as exemplified in Table  1. While attributing ion products to the precursor ion is unproblematic in DDA mode, to reconstruct MS/MS data in DIA mode remains highly challenging. To the best of our knowledge, only two robust software tools, i. e. , MS-DIAL and DecoMetDIA, offer a deconvolution step for untargeted analysis in the DIA mode  (Tsugawa et al. , 2019; Yin et al. , 2019; Tada et al. , 2020). Multiple processing strategies for DIA deconvolution emerge in proteomics  (Kitata et al. , 2021)  and are expected to be applied to small compounds in the near future.
On the contrary, DIA significantly increases the number of annotated lipid species but is still far limited by the available software solutions (notably regarding spectra deconvolution). Free software tools are reviewed in the Table  1  and three of them, namely MS-DIAL  (Tsugawa et al. , 2015) , DecoMetDIA  (Yin et al. , 2019)  and DecoID  (Stancliffe et al. , 2021)  propose efficient bioinformatic tools for the deconvolution of mixed tandem mass spectra obtained from SWATH workflows. Also, as exposed by Barbier Saint Hilaire et al. , well-designing SWATH isolation windows can impact significantly the detection rate of targeted compounds  (Barbier Saint Hilaire et al. , 2020). The free software tool SwathTUNER (Table  1 ) permits the automatized design of the variable isolations windows according either the Total Ion Current (TIC) or precursors density  (Zhang et al.
, 2015)  to significantly lower the complexity of tandem mass spectra obtained in the DIA-SWATH mode. Also, the commercial vendor Sciex proposes the SWATH ® Acquisition module available on their TripleTOF ® systems for the automatically designing of variable isolation windows, permitting the all-in-one acquisition workflow (https://sciex. com/technology/swath-acquisition). Since a decade, ion mobility (IM) offers the unique possibility to separate ions not only on their m/z values but also on their shape in the gas phase, thus enabling the measure of collision cross-sections (CCSs). The IM time is fully compatible with the chromatographic time and the fast acquisition time of modern mass spectrometers. Several groups demonstrated that lipid isomers bearing different fatty chains but the same total number of carbons, including Z/E isomers, can be efficiently separated by IM-MS  (Paglia et al.
, 2015). MS E experiments by coupling LC to IM-QTOF were performed for the characterization of non-purified glycolipids extracts from Caenorhabditis elegans  (Witting et al. , 2021). The multidimensional information collected from chromatographic separation (Retention Time), ion mobility (CCS) and MS E acquisition permits extended lipid annotation. Ion mobility coupled to DIA or DDA approaches paves the way for a rather complete structural annotation and (semi-) quantification of deep lipidome including all minor species, while   (Zha et al. , 2018). Lipid Data Analyzer2 (LDA2) X X DDA, DIA: Semi-targeted approach for identification according to decision rules from various mass spectrometers  (Hartler et al. , 2011; Hartler et al. , 2017). LipidMatch X X DDA, DIA: Untargeted approach for identification according in silico lipid libraries.
For DIA, AIF uniquely  (Koelmel et al. , 2017). LipidIMMS X X DDA, DIA: Targeted approach for identification according Retention Time and Collision-Cross Section predictions, MS and MS/MS spectra from integrated lipid libraries  (Zhou et al. , 2019). also significantly improving the quality of both DDA and DIA tandem mass spectra by separating isobaric/isomeric lipid ion species prior to their isolation and fragmentation. The file sizes, from several hundreds of Mo to several Go per injection, the limited software availability for efficiently handling resulting datasets and the difficulty to generate absolute collision cross-sections for highly flexible molecules, such as lipids in the gas phase, strongly restrict the full exploitation of the data acquired by DIA-or DDAion mobility. A last issue in MS/MS is the efficient location of double bonds on lipid structures as dissociations at low-energy are not efficient  (Ma et al.
Emission reduction scenarios can be performed on any type of atmospheric emissions, but they are most commonly used for anthropogenic emission scenarios. Models have their own peculiarities related to the implemented processes and input data. Therefore, using a group of models helps to smooth the uncertainty tied to the aforementioned points by increasing the statistical samples. For instance, creating an ensemble of models is the basis for the CAMS-II initiative  [11]  which aims at providing daily European air quality forecasts. Ref. [12]  presents a review of the advances and future needs for air quality forecasting, highlighting the benefits of an ensemble approach, as well as pointing out that emission estimations and the uncertainty in emission databases are one of the largest uncertainties in this subject.
Only 9 stations have data for the period of the simulations; February 2015; but the number of soundings per station are sufficient; therefore, the results will be included. More details will be given about these comparisons in Section 3. 1. 3. We have also integrated measurements from the EBAS database  [26]  for aerosol components, including ammonium, nitrates and organic aerosols. Meteorological variables have also been added from several datasets. Two sources of data were used for meteorological variable measurements. The ECA&D project provides the E-OBS database  [27] , which contains measurements merged in an ensemble modelling procedure for the whole of Europe. This dataset includes daily values for temperature (average, minimum and maximum), average wind speed, relative humidity and precipitation.
E-OBS is a gridded dataset resulting from an aggregation of observations over several years, gridded using a kriging procedure explained in the aforementioned reference. In our work, we have extracted (bilinear interpolation) the model outputs and E-OBS data at the location of EEA stations. The second dataset used is the British Atmospheric Data Centre network  [28] , providing hourly values for temperature, wind speed and precipitation. Meteorological data from Parisian measurement sites from the Météo-France (MF) network have also been integrated. These data are used for comparisons provided in Section 3. 1. 1.
Mineral dust emissions are estimated by a threshold process presented by  [45]  with the addition of some improvements (see  [46]  for more details). DMS (Dimethyl Sulfide) emissions are also included using the  [47]  scheme; more details provided in the model description article  [46]. For landcover data, the GLOBCOVER database has been used  [48]. Fire emissions are not included. There were no significant fires during the simulation period that would affect the simulation domain. None of the aforementioned parameters change between the reference simulation and the emission reduction scenarios.