dataesr/scientific-paragraphs-categorization huggingface dataset:
Scenario part 2: harmonized data collection and data sharing The second part of the scenario focused on harmonized approaches to data collection. The results showed that the OHEJP SimEx allowed different sectors to explore their data sharing procedures and to identify possible gaps that may hinder a coordinated and common data management plan. While 90% (n = 184) of the participants reported to have improved their understanding of the importance of data harmonization practices after conduction, 18% (n = 36) indicated that their institute did not prioritize the implementation of such practices (Figure  4 ). Furthermore, it was interesting to note that the degree of implementation of data sharing routines prior to the exercise conduction varied greatly amongst the participating countries. The majority of the participating countries (73%, n = 8) reported a requirement for an interinstitutional data sharing and data collection platform accessible to all sectors.
Although communication was central to this scenario, an even stronger focus on communication is recommended for future simulation exercises. Indeed, participants identified in some countries the communication staff only had a secondary role during the majority of the OHEJP SimEx. Including from the onset communication experts within the exercise planning team could address this concern. Incorporating more practical tasks (e. g. , data sharing exercises) should also be considered to increase the overall participation and engagement. Based on the weaknesses and strengths identified amongst the different countries it is possible to note common topics requiring improvement to implement the OH strategy to outbreak investigation and management. Countries should strive to set up a OH coordination strategy before a specific need for it is identified, ensuring a wellestablished organization able to support a prompt and efficient response  (1).
Countries needing technical support can reach out to international laboratory networks. The need for a common data sharing platform that can be used across sectors was a common outcome across the participating countries and its implementation is pivotal in achieving a OH approach. Efficient tools are needed for earlier identification of outbreaks and quicker access to data for analytical studies and source attribution. Ideally, new data sharing platforms should build from and integrate already existing databases, be able to support large amounts of data and allow for multiple users to access simultaneously. To assist in the transition to an integrated strategy, institutes could develop guidelines on interinstitutional data sharing practices. Harmonization efforts could start at an institutional level by promoting a standardized use of internal data management tools by the professionals to avoid the vulnerability of a system dependent on a limited number of people.
Outbreak investigations comprise of both epidemiological and pathogen-related data. The majority of recommendations and gaps identified were concerning epidemiological data. However, it is important to raise awareness regarding inclusion of pathogen data, in particular genomic data, in national data sharing plans, which are used for cluster identification and source attribution. Ideally, national databases that connect AH, PH and FS laboratories should be created in countries that lack these databases and extended to include more pathogens in countries that already have them in place. To ensure genomic data is used optimally, the protocols used to generate the data and output formats need to be standardized across the different laboratories prior to implementation. As the demand for better and quicker typing techniques increases, there is a need to invest in WGS technologies and building the capacity of skilled teams that can generate and analyze large amounts of genomic data in real time.
Data visualization tools like the FCL, can prove helpful during an outbreak situation by allowing to visualize and analyze complex food networks, help in data collection, tracing back analysis and source attribution. Nevertheless, some points were raised regarding the complicated and time-consuming process of entering data into the FCL platform, and that it may be hard to adapt the tool to a more complex OH incident where the data is too heterogeneous. For the optimal implementation of the tool, it is necessary to improve its interoperability with other information systems and databases (possibly including sequencing data) and offer training on how to use the platform. Countries that showed interest in implementing FCL in their national action plan for foodborne outbreak investigations were given the opportunity to attend a workshop with the tool designers to help with the process.
Acknowledgments We sincerely thank all national teams who participated in the OHEJP SimEx, in particular NELs, LELs and LEs for their dedication throughout the different phases of this project. We would also like to extend our gratitude to the members of the  OHEJP SimEx Advisory Board  for all the advice and guidance provided. Finally, we thank the  One Health EJP Project Management Team and Communications Team  for their support and help.
ACKNOWLEDGEMENTS This work was supported in part by  INRAE (Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement)  and  ANR (Agence National de la Recherche) PathoFood  project (no. ANR-17-CE21-0002 ). The partners of the  ANR PathoFood  are acknowledged for fruitful discussions. C. S. M. was a PhD Research Fellow granted by the  ANR PathoFood. Julien Deschamps  ( INRAE ) is warmly acknowledged for technical assistance with confocal laser scanning microscopy at the Mima2 imaging platform (https://doi. org/10. 15454/1. 5572348210007727E12). The authors are thankful to  Han Lim  for highly valuable discussions and advices in designing the dual fluorescent reporter system pHL60, as a sister genetic amplification system derivative from pHL40.
d. × 5 mm, Thermo Fisher) and then resolved on nanoscale PepMap 100 C18 nanoLC column (3 μm, 100 Å, 75 μm i. d. × 50 cm, Thermo Fisher) at a flow rate of 0. 2 μl per min using a 180 min gradient (5%-40% B) with mobile phases A (0. 1% HCOOH/100% H 2 O) and B (0. 1% HCOOH/100% CH 3 CN). The mass spectrometer was operated in data-dependent acquisition mode with MS and MS/MS acquired at a resolution of 60,000 and 15,000, respectively. Successive selection of the 20 most abundant precursor ions for fragmentation was done on doubly charged ion precursors using a dynamic exclusion of 10 s. MS/MS spectra were interpreted to peptide sequences by the PEAKS De Novo pipeline (Bioinformatics Solutions Inc) and against the NCBInr database using the MASCOT software (version 2. 3. 2) from Matrix Science, using standard search parameters.
Proteomic shotgun analysis of a series of relevant secretomes Taking into account the wide range of enzymatic values in the secretomes, we decided to analyse in more depth the secretomes of 16 isolates that presented at least one exceptionally very high activity, for which we analysed the peptides in these secretomes (see Table  2 ). For this analysis, we performed nextgeneration shotgun exoproteomics  (Armengaud et al. , 2012)  using a high-resolution tandem mass spectrometer equipped with a high-field Orbitrap analyser able to give a precise partial sequence of the polypeptides. As expected, we found that most of the identified peptides (about 50% of total) in the 16 secretomes matched with sequences of hydrolases deposited in databases (Table  2 ). We found that, for a number of strains, the presence of high abundance peptides that matched with specific hydrolases correlated with the high enzymatic activity measured in the secretomes; that is, in the three strains with extremely high CBH activity (Penicillium sp.
ID M19048, Penicillium janthinellum IDs M02031 and M10010), we found that 17% or more of the peptides matched cellobiohydrolases: in strain M08014 (without taxonomical identification) which presented high BXL activity, a high proportion of the identified peptides (10%) matched with β-xylosidases sequences deposited in databases, or in strain P. janthinellum ID M24005 in which 11% of the peptides mapped with endoglucanases. However, in other isolates, we could not correlate the highest activities with the highest number of peptides in the secretome. This was expected as there might be enzymes with high specific activity against certain substrates or proteins more stable than others. Furthermore, glycosyl-and glycoside hydrolases (GHs) are classified on the basis of sequence homology and therefore we could not ascribe the presence of these enzymes with any specific activity.
Xyloglucanases, are mainly included in the CAZy database as GH3, GH5, GH9, GH12, GH16, GH44, GH45 and GH74 families  (Lombard et al. , 2014). In the strain M21041 we identified peptides that mapped with GH3 proteins with the accession numbers gi|927389951 (8. 9% of the total peptides in the secretome) and gi|927420448 (2. 1%) but these proteins have similarity with putative BXL, and BGL respectively, not with XYGL; we also identified peptides that mapped within proteins of the GH16 but their relative abundance is very low (0. 25% of the total petides). In strain M02013 (without taxonomical identification), we also identified peptides with homology with members of the GH3 family, but with putative functions as BXL, BGL or xylanase. We did not identified peptides with homology to any of these GHs in strains M19010, M08026 or M24005.
Column 3 shows the enzymatic activities that were above the established threshold to be considered as outliers. The most abundant peptides in the secretome are shown in column 4 with the percentage over the total of peptides identified shown in parenthesis. The percentage of peptides mapping with carbohydrate-related hydrolases (% Hydrol) are shown in column 5. The last columns show the percentage of the total peptides identified in the secretomes that mapped with different proteins; EG: Endoglucanase; CBH: Cellobiohydrolase; BGL: β-glucosidase; BXL: β-xylosidase; GHs: Glycosyl and glycoside hydrolases; aux: Galacturonases, mannosidases, chitinases, cellulases, exoglucanases, and other auxiliary enzymes. Bold values indicate the cases in which the highest enzymatic activity of the strain correlated with a high percentage of peptides, identified in the secretome,that matched with enzymes in the database with that activity.
PMOs in the secretome of these strains. LPMOs have been included in the CAZy database as AA7 family (formerly GH61) Peptides that mapped with putative PMOs have been identified in the secretomes of Penicillium sp. ID M12025 and Chaetomium aureum ID M15039, Although the hydrolytic activities measured with non-natural commercial colorimetric substrates do not always correlate with the activity of industrial substrates  (Dashtban et al. , 2010 ; Table  S1 ), we cannot forget that the ability to hydrolyse PCS and PSCS is achieved by the appropriate mixture of enzymes. Therefore, the presence of a high level of a particular enzymatic activity, detected by colorimetric substrates, maybe the consequence of a high quantity of this enzyme in the secretome, even resulting in a decrease of the dose of other necessary enzymes for the hydrolysis of complex substrates.
In conditions PA + V t , PA + V b , PA + V dark , PA -V b and PA -V dark , a total number of 13 drops (4 males and 6 females), 13 drops (3 males and 1 female), 3 drops (1 male and 2 females), 13 drops (4 males and 3 females) and 11 drops (5 males and 3 females) were recorded. Note that at each drop, each fly experienced a single experimental condition. Image processing and analysis. The same method as that described in 2 with a Tracker Video Analysis and Modeling Tool was used here (Copyright (c) 2018 Douglas Brown). The other analyses were performed with MATLAB (R2018a, MathWorks, Natick, MA, USA).
Funding We acknowledge the support received from the  Centre National de la Recherche Scientifique (CNRS) ,  Aix-Marseille University  and the  Agence Nationale de la Recherche (ANR)  (in the framework of the  IRIS (Intelligent Retina for Innovative Sensing)  project  ANR-12-INSE-0009  and the  OrigaBot  project  ANR-18-CE33-0008-01 ).
The year 2007 is used for initialization. The MAELIA approach benchmark MAELIA  (Therond et al. 2014 ) is a high-accuracy platform modeling explicitly farmers' practices at a daily time step in each plot  (n = 15,224)  individually for each farm (n = 1143) described in the LPIS database of the study area. It provides a plot-scale modeling of irrigation practices. Technical operation durations are taken into account to constrain the number of farmers' actions within a single day. Irrigation management strategies described through IF-THEN decision rules are defined for different combinations of crops, soils, irrigation equipment, farm types, etc. Each strategy defines a possible period for irrigation, the dose for each irrigation (mm), the conditions triggering irrigation (e. g. , soil moisture and past and future rainfall thresholds, crop water stress), and the minimal temporal interval between irrigation applications in the same field (hereafter called "water turn").
Moreover, actual irrigation withdrawals cannot be directly compared with IWR, because withdrawals can be limited by water availability and restrictions. A comparison between simulated irrigation withdrawals with MAELIA, taking into account water availability and restriction rules, and annual individual farmer reports to French water authorities showed a very good fit  (Martin et al. 2016). The temporal distribution of irrigation over weeks was also assessed through local expert interviews  (Murgue et al. 2016). Therefore, we consider in our work that the MAELIA irrigation demand estimation, without taking into account water availability and restriction rules, is probably the best assessment of daily IWR that one can afford for this region given the available data and it will represent our reference in this study.
Conceptual approach In many studies  (Collet et al. 2015; Smith et al. 2012) , regional irrigation modeling consists of bringing the amount of water (Irr, mm) to fill the soil reservoir, to maintain the Fig. 2  The three modeling approaches used to assess irrigation water requirements (IWRs). The MAELIA (M) irrigation approach distinguishes 232 pedo-climatic zones and each individual plot and farm. The AqYield soil-crop water balance model is coupled to an agent-based model in MAELIA to assess IWR. A Conceptual approach (C) is used to simulate daily irrigation amounts on the basis of data aggregated on 200 pedo-climatic zones crossed with existing crops based on CropWat outputs. A Semi-plot approach (S) in which the simulation units of the C approach are surface-equally divided into irrigation water turn groups irrigated successively.
Limitations to this work and other variation factors to explore We can identify several limitations of our study. First, the benchmark irrigation data we used correspond to modeling outputs. As a consequence, these data are distinct from reality, even if they represent the best reference data existing in our study area and even if MAELIA showed a very good capacity to simulate irrigation withdrawals. Therefore, we could assume that some of our experiments, even if not identified as the best ones, could be more realistic than MAELIA simulations. This supports the need to keep several modeling hypotheses and even to keep modalities that were less performant to reproduce reference simulations if they are considered as realistic and robust. Furthermore, this work was carried out in only one study zone.
This choice is notably justified by the availability of MAELIA outputs and the complexity of the modeling protocol used, which cannot be easily generalized to other areas. Consequently, conclusions drawn in this area should be carefully used for other areas, particularly in very different agroclimatic zones and cropping systems. The modeling approaches tested here aimed at studying different levels of modeling simplifications. However, more simplified models exist. For example, the impact of spatiotemporal aggregation of input data and the reduction in the number of simulated crops could also be explored in further studies. Moreover, the reference model, MAELIA, itself relies on several simplifications, and not all processes are described in detail. For example, the run-on of water and its impact on the redistribution of water between crop simulation units is not taken into account.
Parameter calibration MAELIA crop parameters were calibrated by experts of the AqYield model to fit the Aveyron basin context. To avoid bias linked to the crop parameters, CropWat parameters were estimated based on the MAELIA outputs. Hence, Kc curves were built on the basis of AqYield detailed outputs. However, Kc curves were kept fixed inter-annually unlike AqYield simulations. Moreover, AqYield detailed outputs were obtained for only one CZ due to constraints on data storage and computation time. As a consequence, Kc curves were built to correspond to AqYield evapotranspiration in one CZ and applied to the entire study area for CropWat experiments. A detailed explanation of CZ choice and Kc calibration methodology is given hereafter ("Choice of reference AqYield data for calibration", "CropWat crop coefficients adjustment").
Choice of reference AqYield data for calibration AqYield detailed outputs were obtained in the CZ 2031 (Fig. 8 ). The selection of this CZ was made with different criteria: -each simulated crop is present; -number of plots for every irrigated crop is high; -number of different soils on which cultivated crops are present is high. In CropWat, crop coefficients aim at calculating maximal crop evapotranspiration, cumulating evaporation, and transpiration, while in MAELIA, crop coefficients are designed to be proportional to maximal transpiration only. That is why we compiled for each plot the sum of maximal transpiration and evaporation simulated by AqYield between 2008 and 2014, considered as maximal evapotranspiration. Then, this maximal evapotranspiration was divided by potential evapotranspiration to obtain a crop coefficient curve comparable to the CropWat crop coefficient curve.
Acknowledgements We are very grateful to  Vazken Andreassian ,  Charles Perrin , and  Gaëlle Tallec  for their advice and for their rereading of this manuscript, and to  Renaud Misslin  for his support on the MAELIA platform. We are also thankful to  Météo-France  for providing climate data. Finally, we thank the  French Agriculture Ministry and Sorbonne University  for supporting the PhD of  Myriam Soutif-Bellenger.
QuantiNemo 2 performs summary statistics, while CMP only outputs mono-or multilocus genotype data and seed circulation history. two coexisting situations that are classically encountered by farmers who produce their own seeds. Migration allows to represent situations quite frequently encountered in subsistence farming systems where farmers need to supplement their own seed stocks to sow their entire available field. In this context, they used to acquiring seed locally within their village from relatives, neighbours or friends through gift, exchange or purchase  (Boster, 1986; Wencélius et al. , 2016; Thomas and Caillon, 2016; Violon et al. , 2016). Colonisation coupled with extinction represents more extreme but less frequent situations in which farmers no longer have seed stocks after a poor harvest due to climatic hazards or because of problems with seed storage  (Fenzi et al.
, 2019; Burgers et al. , 2010)  of the model was needed to better understand its behaviour and to ensure that it performs as expected. Sensitivity analyses also provide a better understanding of which input parameters are responsible for the variability of the model's outputs  (Lurette et al. , 2009; Lamboni et al. , 2011). Finally, one can use SAs for a better targeting of a realistic range of parameters to be used in the model. The two objectives of this paper are (i) to introduce the Crop-MetaPop model and software and (ii) to conduct several SAs of the CropMetaPop model in order to check that the model behaves as expected and to assess the relative importance of the parameters in the model based on the variability they induce in the outputs.
Technical features of CropMetaPop CropMetaPop can be seen as a wrapper library for simuPOP specifically designed to easily simulate crop metapopulations. simuPOP  (Peng and Kimmel, 2005; Peng and Amos, 2008 ) is a python library for the simulation of stochastic individual-based model accounting for mutation, genetic drift, migration and selection and for demographic processes such as extinction and colonisation. CropMetaPop provides a useful and practical addition to simuPOP by integrating specific characteristics of crop metapopulations, such as in particular the possibility to generate or import various connection matrices to link the fields together. It also integrates modules to create random networks with specific characteristics. It is therefore designed to represent accurately seed transfer processes with specific parameters.
Special features In CropMetaPop, the focus has been on describing seed circulation practices in order to represent a diversity of socially established rules that underpin the circulation of seed among farmers. Two different processes are considered to model seed circulation: colonisation and migration. Colonisation arises after the extinction of a local population while migration happens when farmers mix intentionally or unintentionally their own seed lot with foreign ones. Unlike other metapopulation simulation softwares, CropMetaPop can model both colonisation and migration in the same simulation, with the possibility to use two different social networks, if necessary, because we consider that farmers can solicit two different social networks depending on the context. In both cases, seed circulation is a stochastic process and seed supply can come from one or from several non-empty fields when farmers are socially connected.
The number of neutral markers (defined as the difference between the total number of markers (nb_marker) and the number of markers associated with a fitness value) was set to 10 to be able to follow the evolution of several loci simultaneously while limiting the simulation time. To assess the impact of the parameters variation, genetic diversity indices (see Section 2. 2. 4) were computed on genotypic data. The protocol was the same for the six SAs. It consisted in the following steps: 1. Creation of the design of experiments with the correct number of parameter (Section 2. 2. 1) 2. Creation of the simulations setting files and of the launcher files for the simulations (Section 2. 2. 2) 3. Launch of the simulations 4. Analysis of the genetic monolocus data (Section 2. 2. 4) launched with the scripts generated in Section 2.
Monolocus data analysis For each SA, after the simulations, the data of each replicate that have the same parameter combination, and that are in separate folders, were gathered in the same file. All the files, i. e. all simulations, were then analysed by a Python script to compute two genetic and one demographic indicators for each generation, including the average within-population expected genetic diversity (expected heterozygosity) (Hs  (Nei, 1987) ), the average genetic differentiation (Gst  (Weir and Cockerham, 1984; Takahata and Nei, 1984) ) and the survival rate (Surv) of the populations. 𝐻𝑠 = ∑ 𝑆 𝑠=1 ∑ 𝐿 𝑙=1 ( 1-∑ 𝐼 𝑖=1 𝑝 2 𝑖,𝑙,𝑠 ) 𝐿 𝑆 𝐺𝑠𝑡 = ∑ 𝑆 𝑠=1 𝐻𝑡 𝑠 -𝐻𝑠 𝑠 𝐻𝑡 𝑠 𝑠 with 𝐻𝑡 = ∑ 𝐿 𝑙=1 ∑ 𝐼 𝑖=1 𝑝 2 𝑖 𝑙 and 𝑆𝑢𝑟𝑣 = 1 - number of non-empty populations total number of populations with  the number of alleles,  the number of loci and  the number of populations.