dataesr/scientific-paragraphs-categorization huggingface dataset:
355 The CORA analysis salinity standard deviation varies slowly in time, with an order 0. 15 PSU in the surface layer, an order 0. 1 PSU in the 75m depth -125m depth layer and an order 0. 08 PSU in the 275-325m depth layer and below 0. 05 PSU in the deeper layers. This behavior is a consequence of the delayed time mode validation process which reduces drastically the number of random mistakes in the dataset. This variability is probably a function of 360 the local data resolution, the oceanic variability and measurement errors. The slow variability of the CORA salinity standard deviation and its reasonable range suggests that remaining errors in the dataset will not have a large importance. Thus this product is likely to present a low error amplitude. Figure  9  and 10 show time series of the mean temperature standard deviation of the CORA, NRT and RAW analysis.
As anticipated, the mean temperature standard deviation time series is noisy and rather high in the RAW 365 dataset case. The mean amplitude varies almost linearly between 1. 2 °C in the 0-50 m depth layer to 0. 4°C in the 1475-1525 m depth layer, except for the 975-1025 m depth layer with a 1. 2 °C spike. A striking feature is the decreasing mean temperature standard deviation amplitude in time for the RAW analysis. The reason of this behavior is rather unclear. One shall assume that the overall quality of the oceanographic in-situ temperature measurement improves because of improvements in the temperature sensor. On the other hand, it might also be 370 the decrease in the number of deployed XBTs in the 2010s that reduces the number of random errors in the dataset, since the XBT instruments have proved to produce erroneous measurements when they are not handled properly.
The NRT analysis and CORA analysis time series are rather close in all the analyzed layers, except for a 0. A closer look at the vertical profiles of the temperature and salinity mean variability (Figure  9 ) shows that the CORA analysis temperature and salinity variability is far smaller than the RAW analysis and the NRT analysis 405 estimation. The depth variability of the temperature and salinity mean variability is moreover closer to the expected oceanic variability, with a maximum ocean variability at the surface or close at sub surface with decreasing variability below the ocean mixed layer depth. We don't think that the CORA dataset has been over flagged, which could result in artificially decreasing the global ocean variability. Indeed, one should keep in mind that most of the flags applied on these profiles are manually applied by physical oceanographers after receiving a detection alert.
Conclusion The CORA dataset is an extensive dataset of temperature and salinity measurements. Efforts have been made to provide to the scientific community an information as close as possible from the physical measurement and to perform a strict quality control on all profiles. The CORA dataset indeed stands out from the EN4 dataset since 425 the delayed time mode validation is based on automatic detections and systematic PI decision, reducing the number of mistaken bad flags. In addition to that, the profiles are not subsampled and the time series (TSGs and drifters) are distributed. It also stands out from the WOD dataset since all measurements within a profile are validated in delayed time mode, reducing the number of mistaken measurements. Moreover, this study develops an innovative method to assess the overall quality of a dataset.
This method shows 430 the improvements of the dataset quality flags thanks to Coriolis real time QC and the CORA delayed time mode QC frameworks. This method however lacks a comparison with an analysis based on other datasets to ensure that the CORA validation framework is not constraining its description of the ocean variability by over flagging good measurements. This discussion shall be further pursued. Last, this method is based on the mapping of the Ocean variability. It is thus implicit that the ocean sampling is homogeneous and sufficient to perform a monthly analysis.
435 These conditions are met at a global scale and for the ocean measurements from surface to 2000 m depth since the full deployment of the ARGO network. The ocean data coverage is however insufficient to have a global coverage before 2005 (see Fig. 3  for the ocean basin data coverage ratio), especially at depth l rger than 1000 m between 1990 and 2005 and at depth larger than 500 m before 1990, as seen on Fig. 2. The method will thus have to be adapted to the ocean data coverage to provide a synoptic view of the dataset quality. Last, the CORA ocean 440 dataset variability has proven to be rather homogeneous from 2005 to 2016. From this viewpoint, comparison of the ocean variability described by a dataset with a target variability may be an encouraging method for the detection of measurement errors.
The controlled profiles are those who have not been controlled in the previous version of CORA. Therefore, most Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2018-144 Manuscript under review for journal Ocean Sci. Discussion started: 21 January 2019 c Author(s) 2019. CC BY 4. 0 License. of the controlled profiles for a given version of CORA are the profiles measured during the previous year, but not controlled for the earlier version. The profiles for which the measurements have been updated or adjusted since 150 the latest version are however controlled. Last, some datasets covering the historical period may have been incorporated in the Coriolis dataset, which are then controlled in delayed time mode in CORA. The delayed time mode validation process is schematized on Figure 4. The profiles to be validated are first checked by the CORA tests.
The checks raise an alert flag on suspicious profiles, which are then visually checked. For CORA, the validation checks are applied until all the tests are successful. If a single check test fails, the profile is 155 put aside for visual check and the following tests are not applied. The profiles undetected by the CORA tests, and thus not visually controlled, are assessed by a second set of tests developed by CLS. The suspicious profiles are also visually controlled and manually flagged. Last, all the tested measurements are gathered in the CORA database with the updated flags. A first quality check aims to complement the real time QC procedure with redundant tests with sharper threshold 160 than NRT. The following tests are concerned. Data-file consistency test This test checks the obviously out of range position (|Lat|>90 and |Lon|>180 and out of range immersion (PRES>12000 decibar and Depth>12000 m or PRES<-2.
185 flagged in order to be monotonous. This test has been requested by the CORA end users, the oceanographic Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2018-144 Manuscript under review for journal Ocean Sci. Discussion started: 21 January 2019 c Author(s) 2019. CC BY 4. 0 License. reanalysis community, to have a user friendly dataset to work with. Most of the detected profiles are indeed measurements with a very slow sinking speed near the surface, giving pressure vector inversion when exposed to the sea surface swell. Most of the detections are thus confined to the surface layer. Exceptions may however occur in the case of black sea ARGO floats for which a recurrent problem of slow sinking speed is found at sub surface 190 due to the low salinity level of the black sea. Last, "hedgehog" type profiles are detected by this test, which are often caused by transmission mistakes on Argo floats.
Each temperature and salinity profile is compared with the minimum and maximum measured value reference profiles. Those profiles originate from reference fields on a gridded mesh with 1 degree resolution horizontal hexagonal cells of 20m thickness. The reference fields are the maximum and minimum measured values on a set of 1. 2 million ARGO profiles, vertically interpolated from the surface to 2000 205 m depth. The field coverage is reinforced, especially in the inner seas and in the Austral Ocean, badly covered by the ARGO network, by CTDs from the World Ocean Database and sea mammals measurements from the MEOP database The CORA 5. 2 measurements are compared to the minimum and maximum reference values of the corresponding cell and the upper and lower adjacent cells. The profiles containing measurements exceeding the reference values are checked by an oceanographer.
last set of delayed mode validation tests has been developed by the CLS research and development team and 220 aims to complement the CNRS validation tests. These tests provide a sharper expertise on bias detection, spike detection and ocean variability in the continental shelf zones. These tests also aim to complement the Coriolis real time quality check tests for measurements directly included in the delayed mode dataset. The CLS tests are divided in two categories. A first set of tests are applied to detect small density inversions in the measurement profiles and non-monotonous pressure/depth vector. These tests are reliable so the detected suspicious levels are automatically 225 Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2018-144 Manuscript under review for journal Ocean Sci. Discussion started: 21 January 2019 c Author(s) 2019.
Therefore, indirectly, one can assess the reliability of the global dataset by estimating the influence of the quality control on global metrics such as the 240 ocean mean temperature and salinity and the associated variability. Two mappings of ocean temperature and salinity based on the CORA dataset measurements are calculated: a raw estimation (GOHCraw) which considers every measurement without taking the ocean flags and a flagged estimation (GOHCflg) which only consider the good and probably good QCs. 245Interpolated fields are calculated following the method presented byForget and Wunch, 2007  that has the advantage of not biasing mean fields and not relying on specifying them. The global ocean is divided in 1° per 1° grid cells with 10 m vertical layers from the surface to 700 m depth.
The noisy shape of this high frequency variability is probably a result of ocean monthly variability and the changing locations of the ocean profiles. 390 The 2014-2015 spike in the ocean variability, detected in all the layers except for the deeper one in the NRT analysis, are caused by many XBT profiles. Most of those profiles are deployed in the Indian Ocean across a transect linking the Gulf of Aden to Perth, Australia, corresponding to measurements performed by the ship of opportunity program (Goni et al. 2009). The profiles have been extracted from the World Ocean Database and have thus not been validated with the Coriolis real time validation framework. Many biases and spikes, probably 395 due to issues with the probes or with poor insolation of the XBT wire problems, have been flagged in delayed time mode.
The largest part of the upper layers spikes in the NRT and RAW analysis is a result of these erroneous measurements. In addition to the usual issues with the XBT measurements, the profiles sometimes indicated negative values at subsurface depth or temperature of 36. 269°C at depth located above the maximum functioning depth of the XBT (460 m depth for T-4 and T-6, 760 m depth for Deep Blue). These unrealistic values have not 400 been flagged after the extraction from the WOD dataset, resulting in exponential growth of the local amplitude of temperature standard deviation in the RAW and NRT analysis in the 475-525 m depth and 975-1025 m depth layers.
A last set of delayed mode validation tests has been developed by the CLS research and development team and 220 aims to complement the CNRS validation tests. These tests provide a sharper expertise on bias detection, spike detection and ocean variability in the continental shelf zones. These tests also aim to complement the Coriolis real time quality check tests for measurements directly included in the delayed mode dataset. The CLS tests are divided in two categories. A first set of tests are applied to detect small density inversions in the measurement profiles and non-monotonous pressure/depth vector. These tests are reliable so the detected suspicious levels are automatically 225 Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2018-144 Manuscript under review for journal Ocean Sci. Discussion started: 21 January 2019 c Author(s) 2019.
Study design For each of the three incidence surfaces considered, 50 sets of simulated data were generated. Estimation of the incidence surface was then carried out for each simulated dataset, with the incidence surface modelled using each of the four splines discussed in Table  1. All splines considered have 80 parameters. For thin plate splines, knots are located at intervals of 2 years in the time dimension, and every 6. 5 years in the age dimension (i. e. for a total of 10 and 8 knots in the time and age dimension respectively). For each of the two marginal splines of a tensor product we specified 10 and 8 parameters in the time and age dimension respectively (for a total of 80 parameters), using equidistant knots. The weakly informative priors imposed on the reparameterised coefficients are available in Sect.
g. tprs) and a simulated dataset. Inference is carried out using Stan (version 2. 14), which employs Hamiltonian Monte Carlo methods  (Hoffman and Gelman 2014; Carpenter et al. 2017). Each posterior estimate is obtained using three chains of 2000 iterations with burn-in of 1000. Splines are implemented via the R package mgcv  (Wood 2017) , and the reparameterisations discussed in  Wood (2016)  are implemented for computational efficiency (see online resource, Sect. 2. 3). The weakly informative priors imposed on the reparameterised spline coefficients, δ k,1 and σ 2 k parameters are available in Sect. 3. 5 of the online resource. The approximate running time per scenario is 10 h. Codes are available at https://github. com/frbrz25/Thesis_Codes.
Acknowledgements This work was presented at the symposium in celebration of  Odd Aalen 's 70th birthday, and was supported by: the  Medical Research Council (Unit Programme  No. MC_UU_00002/11 ); the  UK National Institute of Health Research Health Protection Units on Evaluation of Interventions ; and  Public Health England. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
DATA SummARy All sequencing reads generated in this study have been deposited in the European Nucleotide Archive (ENA) (http:// www. ebi. ac. uk/ ena) under project number PRJNA445436. SRA accession numbers can be found in Table  S1  (available in the online version of this article). The reference genome sequence for Salmonella enterica serotype Kentucky strain 201001922 has been deposited in GenBank under accession number CP028357. The phylogeny and associated metadata can be viewed on Microreact: https:// microreact. org/ project/ Hkl7CzEXV.
Bacterial isolates used in this study A total of 97 S. enterica serotype Kentucky ST198 isolates were directly analysed in this study (Table  S1 ), including 68 isolates collected between 1937 and 2013 that were previously studied by conventional molecular methods  [3] [4] [5] 23] , and 29 new isolates collected between 2008 and 2016. These isolates originated from the French National Reference Center for Escherichia coli, Shigella, Salmonella (Institut Pasteur) and several other international laboratories, and were selected on the basis of their diversity (human or non-human source, geographical area and year of isolation, PFGE types, and AMR phenotypes and genotypes). WGS data for a further 24 S. enterica serotype Kentucky isolates was included in genomic analyses as detailed below.
WGS The 97 S. enterica serotype Kentucky ST198 isolates were subjected to WGS with Illumina at GATC Biotech, Germany (Illumina HiSeq) (n=45), the Institut Pasteur, France (PF1 and P2M sequencing platforms, Illumina HiSeq and NextSeq, respectively) (n=43), the Technical University of Denmark, Denmark (n=7, Illumina MiSeq) or at the Institute for Genome Sciences, University of Maryland School of Medicine (IGS-UoM), USA (Illumina HiSeq) (n=2). Paired-end reads varied in read length depending on the sequencing platform/site, from 100 to 146 bp, yielding a mean of 196-fold coverage per isolate (minimum 30-fold, maximum 687-fold) (Table  S1 ). Short-read sequences have been deposited at the European Nucleotide Archive (ENA) (http://www. ebi. ac. uk/ ena), under study accession number PRJNA445436 and the genome accession numbers are provided in Table  S1.
other genomes studied Additional S. enterica serotype Kentucky ST198 WGS data were obtained from the GenomeTrakr project (https:// ftptrace. ncbi. nih. gov/ pathogen/ Results/ Salmonella)  [25, 26]. All 3014 S. enterica serotype Kentucky isolates in the Salmonella project were downloaded from National Center for Biotechnology Information on 06/01/2016, and ST was determined using srst2  [27]. From the 73 available ST198 GenomeTrakr sequences, we excluded those that were missing the source information required for our analysis (source, location and year of isolation), and retained those from geographical regions underrepresented in our own dataset that were non-redundant in terms of source/outbreak (n=24; accession numbers in Table  S1 ), bringing the total number of genomes analysed in this study to 121.
For the final assembly, a 27-fold genome coverage of 454 data and a 30-fold coverage of Illumina sequence data were combined to create a draft genome sequence consisting of 11 scaffolds and a total length of 4. 86 Mbp. Contigs and scaffolds from the draft assembly were concatenated using a linker sequence ( NNNN NCAC ACAC TTAA TTAA TTAA GTGT GTGNNNNN), in order to generate continuous 'pseudochromosomes'. The linker sequence contains START and STOP codons in each frame and orientation, to allow the gene finder to call truncated genes at all contig ends. Contig orders and orientations within the pseudochromosome were determined based on NUCmer v3. 23  [29]  nucleotide sequence comparison to ST152 S. enterica serotype Kentucky strain CVM29188 (SL475) as a reference genome. Protein-coding and RNA gene predictions and functional annotations were carried out with CloVR-Microbe  [30].
mapping and phylogenomic analysis Short reads for all 121 S. enterica serotype Kentucky ST198 isolates were mapped to the reference genome 201001922 using the mapping pipeline RedDog v1b4 (https:// github. com/ katholt/ RedDog) to identify single-nucleotide variants (SNVs), as previously described  [31, 32]. RedDog uses Bowtie2 v2. 2. 3  [33]  with the sensitive local method and a maximum insert size of 2000 to map all genomes to the reference genome. SNVs were then identified using SAMtools v0. 0. 19  [34]  with a Phred score ≥30, and alleles at each locus were determined by comparing to the consensus base in that genome, using SAMtools pileup to remove low quality alleles (Phred base quality ≤20, read depth ≤5 or a heterozygous base call). SNVs were filtered to exclude those present in repeat regions, phage regions or the SGI.
Assembly, annotation and pangenome analysis All reads were filtered using FastXToolKit v0. 0. 14  [43]  to remove all reads containing bases called as 'N' , and Trimmomatic v0. 30  [44]  was used to remove any reads with a mean Phred quality score below 30. Each isolate genome was assembled using SPAdes v3. 5  [45]  using a kmer range of 21, 33, 55, 65 and 75. Scaffolding was performed using SSPACE v3. 0  [46]  and GapFiller v1. 10  [47]  with default settings. All assemblies were ordered against the S. enterica serotype Kentucky ST198 strain 201001922 reference genome using Abacas v1. 3. 1  [48]. Prokka v1. 10 [49]  was used to annotate each assembly using a preferential protein database made up of coding sequences from the 201001922 reference genome, the ARG-Annot resistance database  [50] , and the SGI1, SGI1-K and SGI2 references (accession numbers AF261825, AY463797 and AY963803).
Identification of resistance, virulence and phage genes AMR gene alleles were determined by mapping short reads to the ARG-Annot resistance database  [50]  using srst2  [27]. AMR gene locations were determined by interrogating genome assemblies with blast v2. 3. 0  [52]. Associations between AMR genes and SGI type or geographical regions were determined using two-way contingency tables for each gene. Each region was tested with Fisher's exact test to determine whether the frequency of the gene was positively associated with that specific region compared to all other regions. A P value cut-off of 0. 05 was used to determine significance. Presence or absence of Salmonella virulence genes defined in the vfdb database  [53]  was determined using srst2 to screen the short-read data. All genomes were screened using phaster  [54]  to detect phage regions.
Reconstruction of SGI sequences ISMapper v1  [55]  and the assembly graph viewer Bandage  [56]  were used to piece together segments of the SGI in each genome. To do this, each assembly was queried with blast to identify which contigs contained SGI backbone and AMR genes. Each assembly was also queried for IS26 using ISMapper's assembly improvement mode  [55] , identifying contigs that contained IS26 flanking sequence. Contigs containing flanking IS26 sequence with SGI genes or AMR genes were hypothesized to be part of the SGI. Both pieces of information (blast and ISMapper results) were used in conjunction with the reference SGI1-K reference sequence (accession number AY463797) to determine which contigs could be joined together. In some cases, it was unclear whether IS26-flanked AMR genes were located within the SGI or a plasmid.
Analysis of IncI1 and IncC plasmids All S. enterica serotype Kentucky ST198 genomes were screened for plasmid replicons using srst2 v0. 2. 0 with the version of the PlasmidFinder database  [57]  that is distributed in the srst2 package. Reads from S. enterica serotype Kentucky ST198 isolates containing IncI1 plasmids as well as a set of publicly available IncI1 plasmid sequences (Table  S2 ) were mapped to the IncI1 plasmid pNF1358 (accession number DQ017661). SNVs were called using the same method as described above for chromosomal SNVs. The resulting SNVs were filtered to include only those that were present in core genes (defined as genes present in 100 % of the IncI1 plasmid sequences, see Table  S3 ). The final alignment consisted of 1380 SNVs, which was used to create a maximum-likelihood tree with RAxML v8.
S8 ). We found that this bla NDM-1 region was entirely covered by WGS reads, with no breaks or gaps in coverage, supporting that it is the true structure in this plasmid (Fig. S8 ). This configuration also appears in another pST3 IncC plasmid, pRH-1238, from S. enterica serotype Corvallis (GenBank accession number KR091911), isolated from a wild bird in Germany  [65]. Another source for the phenotypic diversity of S. enterica serotype Kentucky ST198 susceptibility profiles was variations in the SGI1 (Fig. 3d ). Notably, plasmid carriage was significantly associated in the cases where SGI1-P, SGI1-Q (containing few or no AMR genes) or no SGI were detected (Fisher's exact test, P=0. 024, odds ratio=2. 65, 95 % confidence interval=1. 09-6. 64) (Fig. 3b, d  and e ).