dataesr/scientific-paragraphs-categorization huggingface dataset:
Isolates and genome sequencing A total of 565 H. pylori isolate genomes were analysed in this study (Additional file 5: Table  S1 ). This dataset comprised 122 strains isolated from clinical samples including strains isolated in France (from patients from different areas of France enrolled in studies carried out by the GEFH, the GELD and FFCG, and the GELA), Belgium (from patients attending the endoscopy clinic of CHIREC-sites de la Basilique and E. Cavell, Brussels), the UK (biopsies from patients attending for upper GI endoscopy at Nottingham University Hospitals NHS Trust), Sweden (eight hospitals) and Dublin (the Meath Foundation Research Laboratory, Tallaght Hospital, Dublin), and 444 publically available genomes from published papers  [17]  and the NCBI database. Swedish isolates were a subset of the collection assembled by Enroth and colleagues in a previously published study  [37].
For isolates sequenced for this study, bacteria were sampled from patients presenting with gastric cancer, gastritis, gastrointestinal stromal tumour (GIST) or no symptoms, from 1995 to present by gastric biopsy and grown on H. pylori-selective medium (Dent plates) at 37 °C in a microaerophilic environment (CampyGen or microaerophilic cabinet) for 5 to 10 days. Isolates from gastric MALT lymphoma or other non-adenocarcinoma forms of cancer (apart from 1 GIST isolate) were excluded from analysis. Colonies were isolated as single colonies and subcultured on fresh blood agar plates to obtain sufficient growth, and for genomic DNA extraction, DNA was quantified using a NanoDrop spectrophotometer, as well as the Quant-iT DNA Assay Kit (Life Technologies, Paisley, UK) before sequencing. High-throughput genome sequencing was performed using a HiSeq 2500 machine (Illumina, San Diego, CA, USA), and the 100-bp short read paired-end data was assembled using the de novo assembly algorithm, Velvet  [38]  (version 1.
2. 08). The VelvetOptimiser script (version 2. 2. 4) was run for all odd k-mer values from 21 to 99. The minimum output contig size was set to 200 bp with default settings, and the scaffolding option was disabled. The average number of contiguous sequences (contigs) for genomes sequenced in this study was 111 with an average total assembled genome size of 1,630,194 bp and an average N50 length of 55. 98 kbp. Short reads for the 107 genomes sequenced and assembled in Swansea are available from the NCBI short read archive (SRA) associated with BioProject: PRJNA395900. All 565 contiguous assemblies of whole genome sequences were individually archived on the web-based database platform BIGSdb  [39]  and are available at the public data repository figshare (https:// figshare. com/articles/Helicobacter_pylori_from_clini-cal_gastric_infection/5245837).
The remaining 565 strains constituted our working dataset, and the population structure amongst these strains was inferred from genome-wide haplotype data using chromosome painting and fineSTRUCTURE  [11] , as in previously published H. pylori genome analysis  [44]. Briefly, donor and recipient DNA chunks were inferred for each recipient haplotype using ChromoPainter (version 0. 04). The number of recombination-derived chunks from each donor to each recipient was summarized in a co-ancestry matrix. fineSTRUCTURE (version 0. 02) was run with 100,000 iteration burn-in and 100,000 MCMC iterations to cluster isolates based on the co-ancestry matrix. Principal component analysis was carried out on our data using the standard PCA implemented in Eigensoft. Specifically, on all biallelic data after pruning of SNPs with r 2 > 0.
7, Popstats ("GitHub -pontussk/popstats: Population genetic summary statistics," n. d. ) were used to calculate D-statistics and specify previously described H. pylori populations  [17]. Isolate genomes were partitioned into groups based upon metadata from patient information collected as part of this study or taken from existing publications. To be able to identify risk factors of the carcinogenic progression, three groups were applied: (i) isolates from patients with gastric cancer (GC), (ii) isolates from individuals with intestinal metaplasia or atrophic gastritis, which we termed "progressive to cancer" (Prog) and (iii) isolates from individuals with non-atrophic gastritis (NAG). To reduce the impact of the phylogeographic structure  [17]  on identification of disease-associated genetic elements, the remaining analyses focussed on the largest dataset for which patient data and geographic origin were available within one unique fineSTRUCTURE population.
This included 173 hpEurope isolates (Additional file 5: Table  S2 ). Subpopulations included in hpEurope were based on previous study and included hspEuropeColombia, hspEur-opeN and hspEuropeS  [17]. A phylogeny for 173 isolates was constructed for visualization of the population using the simple and efficient tree building software FastTree v2. 0  [43]  and annotated using iTOL v3ic  [45]  (Fig. 1 ). Input data included 1573 concatenated genes, identified in the 26,695 reference strains, aligned for all isolate genomes.
Genome-wide association studies The genome-wide association study (GWAS) was conducted with a pipeline based on the bugwas package  [8] , as in a recent study  [46]. Briefly, in this k-mer-based approach  [9] , the genome sequence of each isolate was fragmented into unique, overlapping, 31-bp DNA motifs or k-mers. This allowed the identification of nucleotide variation including single nucleotide polymorphisms (SNPs), indels and the presence or absence of a whole gene or gene region associated with different phenotype groups. DNA motifs significantly associated with gastric cancer were explored after accounting for the inter-dependence of the strains and population structure. An n × n relatedness matrix summarized all genetic covariance amongst the isolate genomes, employing statistical tests for each k-mer by the linear mixed regression model, which uses the relatedness matrix to model the background random effect.
Unlike related methods  [9, 47] , this method does not depend on a single clonal tree that is impossible to construct reliably because of the high rate of recombination in H. pylori. A second GWAS, also implemented in the bugwas package  [8] , was carried out based upon SNPs rather than k-mers. Only the SNPs contained in coding sequences were considered. The k-mer and SNP GWAS approaches were applied to bacterial datasets in two binary phenotype association experiments: (i) GC vs Prog and NAG isolates and (ii) NAG vs GC and Prog isolates. This gave a total of four GWAS experiments.
Analysis of associated elements The odds ratio and p value was calculated for associated elements in the GWAS experiments and the position of hits in a reference genome. Specifically, a reference pan genome was produced using Roary software  [48]  with default parameters, and annotation was carried out using Prokka  [49]. GWAS hits, representing both core and accessory nucleotide variation, were then analysed individually to investigate the putative function of the associated genes and the effect of the variations identified in the amino acid sequence. Positions of hits in all analyses were considered using the reference strain ELS37 (GCA_000255955. 1). This reference strain was chosen as being part of the GC isolates used in our study with a closed genome sequence. A limitation of k-mer-based GWAS approaches is that they reveal significantly associated sequence within genes and not the entire gene presence and absence.
For this reason, the prevalence of genes (presence/absence) containing at least one significant k-mer (p value ≤ 10 -5 ) was determined for genomes in our dataset (n = 143) using BLAST. A gene was considered present when the sequence from the genome shared more than 70% sequence homology with the corresponding gene sequence from H. pylori reference strain ELS37. We examined the correlation of prevalence patterns across our dataset for these genes, by using the rcorr function in the Hmisc R package to compute correlation coefficients and the p value of the correlation for all possible pairs of gene presence/absence patterns. The input was a binary matrix of presence/absence of the genes in 143 genomes. All the genes that contained a GWAS hit at p value < 1 × 10 -6 were individually investigated using BioEdit  [50] , based on a global alignment obtained from the GWAS.
Acknowledgements We thank all the researchers worldwide that have whole-genome sequenced Helicobacter pylori isolates and made their data available to us, either by personal connections or by making the data publicly available. We thank  Dr. Youri Glupczynski ,  Dr. Yvette Miendje-Deyi ,  Dr. Deirdre McNamara , and  Dr. Colm O'Morain  for the recruitment of patients and collection of biopsies.
Funding Elvire Berthenet is funded by a grant from  HCRW. Sam K Sheppard  is a principal investigator for the  MRC CLIMB consortium  ( MR/L015080/1 ) and  Daniel Falush  is supported by a fellowship as part of  MRC CLIMB  ( MR/ M501608/1 ). S. K. S. is also funded by  MRC  grant  G0801929 ,  BBSRC  grant  BB/ I02464X/1  and the  Wellcome Trust. Jane Mikhail  received funding from  MITReG, St David's Medical Foundation  and ABMUHB.
Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. Short reads for the 107 genomes sequenced and assembled in Swansea are available from the NCBI short read archive (SRA) associated with BioProject: PRJNA395900. All 565 contiguous assemblies of whole-genome sequences are available at the public data repository figshare (https://figshare. com/articles/Helicobacter_py-lori_from_clinical_gastric_infection/5245837).
Magnetic and electrical measurements. Magnetic measurements were carried out with a Quantum Design MPMS XL SQUID magnetometer. Magnetic susceptibility was recorded in zero field cooled (ZFC) and field cooled (FC) conditions between 2 and 400 K with an applied magnetic field of 1000 Oe. Hysteresis loops were also measured at 2, 230 and 300 K. Electrical resistivity measurements were carried out with a Quantum Design PPMS, between 180 and 400 K. Magnetoresistance hysteresis loops were also measured at 200 K. Powder synchrotron X-ray and neutron diffraction studies. High resolution powder X-ray diffraction data were collected at the ID22 beamline of the ESRF with incident wavelength 0. 3999 Å. The powder was packed into a glass capillary with an outer diameter of 0. 7 mm and spun during data acquisition with temperatures from 80 to 500 K controlled using an Oxford Cryostream system.
High resolution neutron diffraction data were collected at the D2B beamline of the ILL with incident wavelength 1. 5940 Å. 5 g of powder samples were packed into a vanadium can and diffraction patterns were collected at temperatures from 4 to 500 K. Crystal and magnetic structures of CaFe 3 O 5 were Rietveld-fitted using the FullProf Suite  24. A small amount of Fe at the Ca site was found from both synchrotron X-ray [4. 4(3)% Fe] and neutron [4. 0(8)% Fe] refinements of cation site occupancies at 500 K. Crystal structure refinements of the two phases formed below 302 K in CaFe 3 O 5 was possible with both the synchrotron X-ray and neutron powder diffraction data except at temperatures just below the transition (275 and 285 K). Magnetic irreducible representation analysis was carried out using BasIrReps.
Copernicus Sentinels The Copernicus Sentinels program developed by the European Space Agency consists of a family of missions designed for the operational monitoring of the Earth system with continuity up to 2030 and beyond (Figure  1  and Table  1 ). The Sentinels' concept is based on two satellites per mission necessary to guarantee a good revisit and global coverage and to provide more robust datasets in support of the Copernicus Services. On-board sensors include both radar and multi-spectral imagers for land, ocean and atmospheric monitoring: Sentinel-1 is a polar-orbiting, all-weather, day-and-night radar imaging mission for land and ocean services. Sentinel-1A was launched on 3 April 2014 and Sentinel-1B on 25 April 2016. Sentinel-2 is a polar-orbiting, multi-spectral high-resolution imaging mission for land monitoring to provide, for example, imagery of vegetation, soil and water cover, inland waterways and coastal areas.
Long-term, large scales networks of ground measurements are also of paramount importance. For instance, detailed ground based soil moisture information is provided by in situ soil moisture databases such as the International Soil Moisture Network (ISMN)  [51]. Ground-based measurements of soil moisture and temperature from ISMN  [52] [53] [54] [55]  have been used to evaluate the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analyses, forecasts and reanalyses ability to represent soil moisture and temperature. Verification studies using in situ networks contribute to a better understanding of the deficiencies in models and point to processes that need a better representation. Particular attention must also be devoted to rainfall measurements, for which numerous datasets exist combining in situ rain gauge observations from the SYNOP and SYNOP-like national networks with satellite and ground-based remote sensing to obtained gridded products, as recently compared in a study  [56].
The combination of multi-sources observations with models to ensure meteorological consistency is advocated as a way to increase accuracy. However, precipitation estimation for hydrological applications remains a challenge and motivates the continuous effort of the International Precipitation Working Group (IPWG), a permanent Working Group of the Coordination Group for Meteorological Satellites, active since 2001  [57]. Despite the effort of data collection of long term precipitation observations e. g. ,  [58] , a precipitation dataset that can support climate change studies and can be readily considered in reanalysis, remains largely unavailable. Earth system models also rely increasingly on non-atmospheric ground-based observation networks including information on river discharge from river gauging stations such as the databases held by the WMO Global Runoff Data Centre (GRDC) and groundwater levels from wells.
Ocean-Based Networks The ocean in situ observations coordinated under the Global Ocean Observing System (GOOS, http://www. goosocean. org) is a sustained observing system, unified by the Framework for Ocean Observing  [61] , designed to provide access to timely ocean observations. In many cases, data are telemetered to data centers in near real-time for assimilation into NWP. Higher quality data are also available in delay mode and can be used for validation and evaluation of processes. With its unique status within the United Nations system, GOOS has been able to gather a network of independently-managed and independently-funded observing elements. The network includes Argo floats  [62] [63] [64] , surface drifters  [65] , OceanSITES moorings (http://www. oceansites. org), and ships (http://www.
Earth Surface Modelling Advances and Links with EO Datasets In this section, we describe some of the progress that has been made over the last few years in modelling that can be directly linked to EO data availability. For instance, advances in different land-surfaces components introduced in the ECMWF operational forecasts can be linked to availability of key observational datasets. It is acknowledged that surface related developments need to be discussed in the wider context of Earth system modelling. Common areas of development in surface parameterisation going towards environmental applications are presented by clustering the following groups: land-surface reservoirs, land-atmosphere fluxes, land-surface properties, inland and open waters.
The latter product in particular depends critically on the skill of the land surface model. Some of the model developments prior to the launch of SMAP were focused on a revised set of soil texture and soil hydraulic parameters  [93] , as well as on the calibration of the microwave radiative transfer model that converts the modeled soil moisture and temperature values into L-band passive microwave temperatures  [94]. Within the L4SM assimilation system, these modeled radiances were then confronted with the SMAP observations, with deviations between the modeled and observed radiances resulting in adjustments to the modeled soil moisture  [95]. Since the first release of the L4SM product in 2015, new input parameter datasets for land cover, topography, and vegetation height were derived based on recent, high-quality satellite observations  [96, 97].
The evolution of snow mass, depth and density during one winter season at the Fraser forest and open stations is shown in Figure  9 , a research location in the Rocky Mountains, CO, USA. The results show the improvements of the snow scheme revision (NEW) with respect to the version used in ERA-Interim, labelled as control (CTR). The new snow density parameterisation increased the snow thermal insulation, reduced soil freezing, improved the hydrological cycle, and substantially reduced a warm winter bias compared to Siberian screen-level (2m) observations (not shown). The dramatic change in surface reflectivity when snow is present has been widely explored using EO data, in particular in the visible range, and is mainly limited by cloud presence and contamination. The detection of snow on the surface was one of the first applications of EO data associated with surface characteristics and it is currently one of the surface variables with longer observed datasets using remote sensing.
Time-series of the land surface (skin) temperature (LST, Celsius) at the south-pole for in situ observations (BRSN), MODIS satellite data (MODIS), numerical experiment with the bulk (single-layer) snow scheme (CTR) and numerical experiment with the thermal depth of the snow reduced by a factor of 10 (DSN), the latter being a proxy for the potential impact of a multi-layer snow scheme. It is clear that DSN has a larger amplitude LST diurnal cycle than CTR, and a better representation of strong cooling events  [127].
There are continuing efforts to calculate large-scale evapotranspiration using EO data. Globally available products include MODIS  [128] , GLEAM  [129, 130] , SEBS  [131]  and PT-JPL  [132] , FLUXCOM  [133]  or WECANN  [134]. Since evapotranspiration cannot be observed directly from radiative, observable properties of the surface and atmosphere, typically the models use EO data such as radiation, rainfall and soil moisture to train or derive simple models. Refs. [135, 136]  focused on evaluating these products using flux tower and river streamflow under the LandFlux-EVAL initiative. They found significant differences between the products, which limited their use for model evaluation. More recently, Refs. [137, 138]  have assessed the latest products. They conclude that (i) the models tend to overestimate soil evaporation, (ii) the way the models treat soil moisture stress is important and that (iii) finding ways to evaluate the split of evapotranspiration into its three components (transpiration, soil surface and interception evaporation) is currently not possible but a critical task for the future.
For the latter, Ref. [139]  showed that the flux attribution is a complex issue. Despite these caveats, it is imperative that developers use observed data to evaluate their models. To that end, iLAMB (integrated Land Atmosphere Model Benchmarking,  [140] ) was developed as a shared framework for large-scale model evaluation. It was used by the EartH2Observe project [141] to evaluate a suite of ten models including the evaluation of evaporation against the GLEAM EO product. As other researchers, they found that the comparison had limited value due to limited accuracy of the EO product. In the ECMWF land surface scheme, the Leaf Area Index (LAI) expresses the phenological phase of vegetation (growing, mature, senescent, dormant). Initially it was kept constant  [142]  and assigned using a look-up table depending on the vegetation type.
CO 2 Natural Ecosystem Exchange More recently, the ECMWF land surface scheme has been extended with a carbon dioxide module based on the A-gs model  [147]. The reason for adopting simple vegetation and carbon dioxide schemes, is that these are suitable for the NWP setup where environmental factors are controlled by meteorological forcing and constrained by data assimilation. The model relates photosynthesis to radiation, atmospheric carbon dioxide (CO 2 ) concentration, soil moisture and temperature. Ecosystem respiration is based on empirical relations dependent on temperature, soil moisture, snow depth and land use  [148]. The CO 2 module parameters are optimised by vegetation type considering the Gross Primary Production (GPP) and the Ecosystem Respiration (Reco). Together, they compose the Net Ecosystem Exchange (NEE) of CO 2 between biosphere and atmosphere.
The FLUXNET-based in situ observations from different climate regimes (http://www. fluxdata. org/), combined with a benchmarking systems similar to iLAMB  [140] , was used for parameter optimization by minimizing flux errors. Subsequently, a different year of the FLUXNET data was used for verification. The seasonal cycle of NEE is illustrated in Figure  12  for six sites with different biomes. Two model configurations are shown: the first uses a stomatal resistance formulation for evaporation that is controlled by the photosynthesis module (C-TESSEL), and the second uses the Jarvis-based stomatal resistance for evapotranspiration (CH-TESSEL). In addition, the CASA climatology (Carnegie-Ames-Stanford Approach,  [149]  ) is shown because it is extensively used in the community and it was previously used as a boundary condition for atmospheric CO 2 in the MACC (Monitoring Atmospheric Composition and Climate) project.
NEE is a small residual of GPP and Reco; therefore, a correlation coefficient above 0. 6 is highly relevant. Again, C-TESSEL and CH-TESSEL show similar performance, with site to site variability attributed to representativity of the model grid-box  [148]. CH-TESSEL has also been evaluated in coupled integration mode for the 2003 to 2008 period. The operational CAMS (Copernicus Atmosphere Monitoring Service) atmospheric CO 2 analysis and forecast system has been using the online NEE fluxes from CH-TESSEL ever since its introduction in the IFS, replacing the offline CASA fluxes from the GFED dataset  [151]. Extensive testing of this online configuration shows that the global CO 2 atmospheric inter-annual variability is well simulated  [152]. The correlation of global CO 2 with observationally based estimates is 0.
Wetlands are the most dominant source of CH 4 , contributing about 30% to the total flux  [160]. Large uncertainties have been identified in both the spatial and temporal distribution of wetland CH 4 fluxes provided by land surface models  [161]. Major impacts can be seen when simulating long-term CH 4 emissions taking into account different vegetation classes  [162]. Ref. [163]  used GOSAT column CH 4 (XCH 4 ) to evaluate wetland emissions from the Joint UK Land Environment Simulator (JULES,  [164] ). The detailed spatial and temporal resolution of GOSAT data permitted evaluation of the model performance over specific wetlands, which provided insight into some of the causes of model uncertainty. Bias in the modelled fluxes highlighted a failure to capture large scale, river fed, wetlands in the Pantanal region during high rainfall years.