dataesr/scientific-paragraphs-categorization huggingface dataset:
The first validation (Sect. 3. 1. 1) has been done considering all the measurements available in the latitude band between 15 • S and 15 • N, providing a statistically significant validation in the tropical region. The following section (Sect. 3. 1. 2) limits the comparison with O 3 soundings over the region directly influenced by ENSO events. In Sect. 3. 2 we analyze the temporal and spatial variability of TCO during the period 2008-2013. The link between sea surface temperature and ozone variability is studied with the OMI-MLS estima-tions (see Sect. 2. 2). The objective is to evaluate how modeled ozone distributions reproduce the observed ozone variability over the Pacific ocean during the normal conditions of the Walker cell and during ENSO events. In Sect. 3. 2. 2, we compare the Ozone ENSO Index (OEI), computed using the previous datasets, to the Niño 3.
In this region of colder SST (Fig. 5a ), higher  is located between the coast of South America and 140 • W (Fig. 5b ). The eastward shift of SST occurring from January 2011 to December 2013 corresponds to the return of normal conditions over the Pacific ocean and impacts TCO with an eastward shift. To compare the three model simulations, with OMI-MLS, we have computed anomalies of tropospheric ozone of each dataset for the period 2008-2013 (Fig. 6 ). The anomalies are calculated by subtracting the time-averaged TCO to each TCO determination and this difference has been divided by the mean TCO. The TCO anomalies are expressed in percentage. The variability of TCO, observed previously with OMI-MLS measurements in Fig. 5 , is also clearly visible with the TCO anomaly (Fig. 6a ). The TCO with values 20 % lower than average are located in the IIO region.
Compared to DM and MLS-a the TCO variability is much better reproduced with IASI-a (Fig. 6d ). The amplitude of the TCO changes caused by El Niño and La Niña compares very well with the OMI-MLS observations. However, small differences appear between IASI-a and OMI-MLS. Over the IIO region during El Niño the TCO anomaly is 10 % lower with IASI-a than with OMI-MLS. In addition, the location of the TCO maximum during La Niña is located in the whole POC region with IASI-a and in the eastern part of the POC region with OMI-MLS. Overall, the anomalies of the TCO reproduced by IASIa are in good agreement with those of OMI-MLS. The improvement compared to the DM and MLS-a simulations is very significant, thus demonstrating the usefulness of the IASI data for the ozone evaluation in the troposphere.
The MLS-a produces an OEI very similar to DM. As already discussed, constraining the ozone profile in the stratosphere has little impact on the quality of the modeled ENSO O 3 signal. With the IASI-a we can quantify the contribution of IASI data in the computed OEI (Fig. 7b , red curve). Compared to DM and MLS-a simulations the IASI-a analysis produces OEI in better agreement with the ones derived from OMI-MLS. The OEI variations are in phase with a very good match of periods of maxima and minima. There is, however, a constant bias of approximately 2. 4 DU between the indices of OMI-MLS and IASI-a. As discussed in Sect. 3. 1. 2, IASI-a bias in the lower troposphere is larger in the POC region than in the IIO region. This difference of biases between POC and IIO regions affects the determination of the OEI.
Vertical structure of O 3 The evaluations of TCO obtained with the OMI-MLS by subtracting stratospheric ozone from MLS from the total ozone from OMI cannot give information on the vertical structure of the O 3 anomalies forced by ENSO. This is clearly an advantage of model assimilations that can give a complete three-dimensional structure of the ozone fields with no gaps due to orbitography and clouds. We focus here on the information brought by the assimilation of IASI and MLS data in describing the vertical ozone response to ENSO in the POC and IIO regions. Figure  8  shows monthly mean ozone profiles for IASI-a, MLS-a and DM, over the 6-year record. The tropopause pressure for the three simulations is about 100 hPa. Ozone concentration in this layer is around 70 ppbv. Due to the limitations of the model and the lack of information brought by the two instruments in the boundary layer, as www.
Also more clearly visible with IASI-a are the seasonal variations of the ozone content in the IIO region that is quite regular outside ENSO periods. In that region the annual periodicity of ozone is much pronounced in comparison to the more erratic variations shown in the POC region. The regularity of the ozone fluctuation is more pronounced in IASI-a assimilation than in DM and MLS-a simulations. In addition to the influence of atmospheric dynamics, biomass burning and the associated ozone production could trigger the seasonal fluctuations. Such an ozone production detected by the IASI instrument (and therefore visible in IASI-a) cannot be reproduced by the DM and MLS-a simulations due to the simplified chemical scheme. Overall the combination of the IASI ozone tropospheric retrievals and our 4D-Var algorithm produces a very consistent dataset for the study of the influence of ENSO on the ozone distribution from the stratosphere to the middle troposphere.
A second validation has been done over the Pacific ocean and over southern Asia  anomalies of MLS-a (assimilation of MLS stratospheric profiles). The good reproduction of anomalies in terms of location and timing between eastern and western regions in both simulations are due to the transport forced by the winds from the ECMWF meteorological analyses. However, the amplitude of anomalies is lower than in OMI-MLS data. Assimilation of IASI data corrects this behavior, and the anomalies of IASI-a appear very similar to the OMI-MLS anomalies. In particular, the IASI data bring essential information to reproduce the eastward shift of TCO caused by El Niño. In order to study the ability of IASI-a to reproduce the ozone variability caused by El Niño and La Niña phases, we have used the OEI. The OEI represents an essential diagnostic test for models that should be able to represent ozone features linked with ENSO changes in tropospheric dynamics.
OEI from IASI-a shows variations similar to those of OMI-MLS with a small bias corresponding to higher TCO over the POC than over the IIO region. The Direct Model and MLS-a have the same bias. This bias has been located in the boundary layer with the comparison with the ozonesondes. We have also examined the vertical structures of tropospheric ozone in the IIO and POC regions, with the three simulations (Direct Model, MLS-a and IASI-a), in order to show the contribution of IASI tropospheric data in the assimilation. The IASI-a analysis is consistent with the ozone displacements in adequation with subsidences and convergences generated by El Niño and La Niña in both IIO and POC regions. The IASI assimilation gives a very valuable high-resolution dataset suitable to perform analyses of the O 3 variability in the upper and middle troposphere for shortterm and interannual timescales in the tropical band.
One advantage of infrared sounders like IASI for climate studies is their good spectral stability over time, with respect for example to UV instruments. This is an important feature when trying to determine potentially small climate signals hidden by large ozone variability, due for example to ENSO. Finally, using a more detailed chemistry scheme within future ozone reanalyses would also allow further insights into chemical feedbacks in the context of a changing climate. Data availability. IASI L1 data were provided by the Laboratory of Aerology. EOS MLS L2 version 4 is available at https: //mls. jpl. nasa. gov/data/. The OMI-MLS tropospheric ozone as well as the OEI-Z index are available at http://acd-ext. gsfc. nasa. gov/ Data_services/cloud_slice/. The Nino3. 4 index is available from the NOAA website at http://www.
Acknowledgements. We would like to thank  Jerry R. Ziemke  and the  Aura Ozone Monitoring Instrument (OMI)  science team of the  NASA Goddard Space Flight Center  to the availability of the OMI-MLS data and the  OEI-Z Index. We also thank  WOUDC  for providing ozonesonde station data and the  NASA Jet Propulsion Laboratory  for the availability of Aura MLS data. We also acknowledge the mission scientists from the  NASA GES DISC , who provided the GIOVANNI SST data used in this research. In addition, we acknowledge la  Région Midi-Pyrénées  for financial support and the  CNES  ( Centre National d'Études Spatiales ) for financial sources on the  TOSCA  project. Thanks also to  Sean Crowell  for proofreading an earlier version of this paper. Edited by: Jayanarayanan Kuttippurath Reviewed by:  Catherine Wespes  and one anonymous referee.
Ubiquity and dominance of bacteria and archaea Among the 37 phyla (32 bacteria and 5 archaea) and 2028 genera in the SILVA database, the taxonomic assignment of the data set indi cated the presence of 32 bacterial taxa (encompassing 27 phyla and 5 subphyla) and 3 archaeal phyla in sampled soils (Fig. 2 ) and 1355 genera detected in at least two soils. Comparison of the average rela tive abundance and ubiquity of the 35 phyla recorded led to the definition of four groups: major, medium, minor, and rare phyla (Fig. 2  and fig. S1 ). The 15 phyla in the first two groups were recorded in all soils, with an average relative abundance ranging from 14 to 0. 5% of sequences per sample. Bacteroidetes, Alphaproteo bacteria, Actinobacteria, Planctomycetes, Firmicutes, Gammaproteo bacteria, Acido bacteria, and Betaproteobacteria are the most dominant and cosmopolitan phyla.
Experimental design Soil samples were obtained from the French Soil Quality Monitoring Network (RMQS), which is a soil monitoring network based on a 16km regular grid across the 550,000km 2 French territory  (5, 16). The RMQS includes 2173 monitoring sites, each located at the center of a 16 × 16-km cell (Fig. 1 ), for which soil profile, site environment, climatic factors, location, vegetation, and land management were described. All details concerning soil sampling, storage, and physicochemical analysis were reported  (16). Available climatic data were monthly rain, ETP (evapotranspiration), and temperature at each node of a 12 × 12-km 2 grid, averaged for the 1992-2004 period. These climatic data were obtained by interpolating observational data using the Analysis System Providing Information Adapted to Nivology (SAFRAN model).
The RMQS site-specific data were linked to the climatic data by finding the closest node within the 12 × 12-km 2 climatic grid for each RMQS site. Land cover was recorded according to the coarse level of the CORINE Land Cover classification (IFEN; www. statistiques. developpementdurable. gouv. fr/donneesligne/li/2539/0/base donneesgeographiquecorinelandcoverclc. html), which consists of a rough descriptive classification into five classes: forest, croplands, grasslands, others, and perennial crops (corresponding to vineyards and orchards). All these data were available in the DONESOL data base  (16, 28).
Polymerase chain reaction amplification and pyrosequencing of 16S rRNA gene sequences A 16S rRNA gene fragment targeting the V3 to V4 regions to char acterize bacterial diversity was amplified using the primers F479 (5′CAGCMGCYGCNGTAANAC3′) and R888 (5′CCGYCAAT TCMTTTRAGT3′) with the method described previously  (53). Homemade bioinformatic programs were developed to design these primers and to search large DNA sequence databases for the presence of primers, including degeneracies, as coded by the IUPAC (International Union of Pure and Applied Chemistry) rules, and also additional mismatches to test primer improvement. The se quences investigated were SILVA, direct extraction of every small subunit rRNA sequence from EMBL (European Molecular Biology Laboratory) using ACNUC, and also a dedicated reference database of 18S eukaryotic sequences, which had been thoroughly analyzed and annotated for in silico match analysis [see table  S2  from Terrat et al.
Bioinformatics sequence analysis Bioinformatic analyses were done using the GnSPIPE pipeline de veloped by the GenoSol platform [Institut National de Recherche Agronomique (INRA)] (availability: https://zenodo. org/record/ 1123425#. WxD4DO6FPIU)  (5). After sequencing, 49,794,516 raw reads were obtained for the 2132 soil samples. First, all raw reads were sorted according to each multiplex identifier sequence (no tolerated error in 10base multiplex identifiers). Then, a prepro cessing step was carried out to filter and delete lowquality reads based on (i) their length (fewer than 350 bases), (ii) their number of ambiguities (deletion of reads with one or more N, or reads with homopolymer of more than seven consecutive bases), and (iii) their primer sequence(s) (the proximal primer sequence had to be com plete and without errors, with a maximum of two mismatches tol erated in the distal primer sequence).
A PERL program was then applied for rigorous dereplication (that is, clustering of strictly identical sequences with same length). The dereplicated reads were aligned using the INFERNAL alignment program (v1. 0. 2, http://eddylab. org/infernal, with the selected parameters: hbanded, sub, dna) to obtain a global alignment against a handcurated database con taining 508 full length 16S rRNA sequences, chosen after careful consideration and aligned with recommended parameters (v1. 0. 2 using selected parameters: rf, ere 1. 4). Then, in agreement with a previous study  (56) , aligned sequences were clustered into OTUs at 95% of similarity using the CrunchClust program (v43 program, https://code. google. com/archive/p/crunchclust) that groups rare reads with abundant ones and does not count differences in homo polymer lengths (default parameters were selected).
We chose this level of clustering as it corresponds roughly to the genus level, particularly with our primer set (in silico evaluation), and it also cor responds to the level used to define each soil community composi tion. Here, an OTU is defined by the most abundant read, known as the centroid, and every read in the OTU must have similarity above the given identity threshold with the centroid. A filtering step was then carried out to check all reads detected only once and not clustered, which might be artifacts, such as PCR chimeras, based on the quality of their taxonomic assignments. A database of 16S rRNA sequences from SILVA (version r114), filtered, cu rated, and annotated (database is available here: https://zenodo. org/record/1065438#. WxD4L6FPIU) using the USEARCH program (v8. 0.
1623; www. drive5. com/usearch) with specific parameters (maxhits 15, maxaccepts 0, and -maxrejects 0), was used (associ ated PERL program using USEARCH and formatting results are available here: https://zenodo. org/record/1064170#. WxD4S6FPIU). If a read obtained a percentage of similarity lower than 90%, then it was discarded from the data set. This filtering step allowed the deletion of most of the chimeras produced during the PCR process, but also led to the deletion of potential novel minor taxa not current ly included in the used database. A total of 32,634,692 highquality reads (range of sequencing depth: 48 to 49,926 reads by sample) were kept after these steps. The number of highquality reads for each sample was then "rarefied" (that is, 10,000 high quality reads for each sample) by random selection to allow efficient comparison of the data sets and avoid biased community comparisons and rarefac tion curves  (5).
So, 1798 soil samples were kept for subsequent analyses, encompassing a total of 17,980,000 reads. A postprocessing step was then applied to this global data set to filter potentially artifactual reads as already described regarding mi crobial richness across France  (5). Briefly, the 17,980,000 reads from all samples were aligned and clustered at 95% of similarity into OTUs, using the previously described CrunchClust program. There after, all OTUs that occurred only once in the overall data set and encompassed only a single read were removed. This postprocessing step reduced the number of total OTUs from 205,590 to 92,571 (more than 50% lost), but the number of reads only from 17,980,000 to 17,866,981 (less than 1% lost). The number of deleted reads by sample was 62 ± 60 on average (minimum, 10; maximum, 1093).
Finally, all kept reads were then compared to the dedicated reference database originated from SILVA. The same previously described programs and parameters were used to determine independently the composition of each soil community at the phylum or subphylum level (that is, Alphaproteobacteria, Betaproteobacteria, Deltaproteo bacteria, Gammaproteobacteria, and Epsilonproteobacteria) and at the genus level (procedure, database, and programs available online: https://zenodo. org/record/1065438#. WxD4fe6FPIU and https:// zenodo. org/record/1064170#. WxD4ku6FPIU). Unknown sequences represented an average of 0. 47% by sample at the phylum taxonom ic level and an average of 11% by sample at the genus taxonomic level. All raw data sets are publicly available in the European Bioinformatics Institute (EBI) database system (in the Short Read Archive) under proj ect accession no.
Mapping using geostatistics A geostatistical method was used to map the microbial phyla and genera and to characterize their spatial variations (https://zenodo. org/record/1063500#. WxD4t6FPIU). When the relative abundance of the taxa did not follow a normal distribution, a quantile transfor mation was applied before modeling the spatial correlations. In con ventional geostatistical analysis, an estimate of a variogram model is computed on the basis of the observations, which describe the spa tial variation of the property of interest. This model is then used to predict the property at unsampled locations using kriging  (57). A common method for variogram estimation is first to calculate the empirical (socalled experimental) variogram by the method of mo ments  (58) , and then to fit a model to the empirical variogram by (weighted) nonlinear least squares.
Variance partitioning The relative contributions of soil physicochemical parameters, land management, climatic conditions, geomorphology, and space in shaping the patterns of soil bacterial phyla (called "marginal effects") were estimated by variance partitioning (https://zenodo. org/record/ 1063479#. WxD4zu6FPIU). The explanatory variables were selected to reduce the autocorrelation in the models and to obtain the most parsimonious models. Thus, only 12 environmental parameters of the 21 measured were kept in the analyses. These were pH, amounts in clay and silt for the soil texture, C/N ratio, SOC, available phos phorous and total potassium content for the soil nutrient, tempera ture for the climatic factors, latitude and longitude of the location of the sites, elevation for the geomorphology, and four classes along an anthropization gradient (forest, grassland, crop system, and vineyard/ orchard) for the land management.
All quantitative (response and explanatory) data were standardized to guarantee an approximated Gaussian and homoskedastic residual distribution of the model. Considering that the largest part of the environmental selection was measured by the previous explanatory variables, we then investigated the effect of neighborhood on the residuals of the variance parti tioning models, using the PCNM approach computed on the spatial coordinates of the sites. The PCNM method (described below) de scribes and identifies the scales of the spatial relationship between samples (40) (see details in the Supplementary Materials). The clas sification of the PCNM vectors into finescale (30 to 100 km) and coarsescale (100 to 350 km) neighborhood effects was chosen to reveal those spatial structures in the residual distributions of phyla, which could be related to short or longdistance dispersal patterns.
Spatial descriptors were then selected from the model residuals (64) using the forward selection step only since all PCNMs are linearly independent. The respective amounts of variance (that is, marginal and shared) for bacterial phyla distribution were determined by canonical variation partitioning and the adjusted R 2 with redundancy analysis  (63). The statistical significance of the marginal effects was assessed from 1000 permutations of the reduced model. All these analyses were performed with R (www. rproject. org/) using the vegan package (https://cran. rproject. org/web/packages/vegan/vegan. pdf). The relative contributions of environmental parameters were evaluated by performing a variance partitioning for each phylum (the applied procedure is detailed in the Supplementary Materials, https://zenodo.
Acknowledgments: We thank all the soil surveyors and technical assistants involved in sampling the sites. Thanks are also extended to  J. Kreplak  for the use of computation capacity and  D. Warwick  for her comments on the manuscript. Funding: This study was granted by  ADEME (French Environment and Energy Management Agency)  and by " France Génomique" through involvement of the technical facilities of Genoscope  (project number  ANR-10-INBS-09-08 ). Because of the involvement of the technical facilities at the  GenoSol platform  of.
, determined the absolute concentration of triacylglycerides (TG), total phospholipids (PL) and total cholesterol in serum samples using quantitative proton NMR spectroscopy and an external standard  [14]. The signals at 0. 68 ppm, 3. 95 ppm and 4. 14 ppm were used for the quantification of total cholesterol, total PL, and total TG respectively. They showed that the concentration of triacylglycerides, phospholipids and total cholesterol was significantly higher in DMD (Duchenne muscular dystrophy) patients as compared to healthy subjects. To quantify more lipid species, Barrileo et al. , developed a new bioinformatics tool for quantitative 1 H NMR lipid profiling  [15]. This tool, LipSpin is a graphical user interface software that allows the quantification of lipids in proton NMR spectra.
Lipspin imports data samples from either raw free induction decays or one-dimensional NMR (1D) processed spectra and NMR signals were quantified with line shape fitting analysis. After the quantification process, signal areas can be converted into molar concentrations by using an internal standard of known concentration, or an external measurement, for example using total cholesterol concentrations determined with other methods. In this study,  Barrilero et al. quantified  in plasma samples total FAs, saturated fatty acid (SFA), ω-9 FA, ω-6,7 FA, ω-3 FA, mono-unsaturated fatty acid (MUFA), arachidonic acid and eicosapentaenoic, docosahexaenoic acid, Linoleic acids, free cholesterol, cholesterol ester, total cholesterol, triacylglycerides, phosphatidylcholine, sphingomyelin, lysophosphatidylcholine and total phospholipids.
Results were compared with those obtained using gas chromatography (GC) coupled to FID and enzymatic methods. Lipoprotein profiling of plasma samples without extraction, can be useful for the study of diseases  [16]. A 1D 1 H diffusion NMR experiment was recorded to detect large molecules (such as lipoproteins, glycoproteins and choline compounds) and lipoprotein profile characterization was performed from NMR data using Liposcale test  [17]. In this tool, the methyl signal was surface fitted with nine Lorentzian functions associated with each lipoprotein subtype: large, medium and small of the main types of lipoprotein-very low density lipoprotein (VLDL), low density lipoprotein (LDL), intermediate density lipoprotein (IDL) and high density lipoprotein (HDL). The area of each Lorentzian function was related to the lipid concentration of each lipoprotein subtype, and the size of each subtype was calculated from their diffusion coefficient.