dataesr/scientific-paragraphs-categorization huggingface dataset:
Toohey et al. (2016a)  provide technical details about EVA. VolMIP requests that all modeling groups use EVA to generate the specific forcing input data set for their model, using the same sulfur emission estimates to be specified for use in the PMIP-past1000 experiment. Figure  3  provides an overview of the EVA forcing for an estimated SO 2 injection for the 1815 Tambora eruption of 56. 2 Tg to be used in volc-long-eq and volc-cluster experiments. volc-cluster ex-   (Mills et al. , 2016) , MAECHAM5-HAM  (Niemeier et al. , 2009) , SOCOL  (Sheng et al. , 2015) , UM-UKCA  (Dhomse et al. , 2014) , and CAMB-UPMC-M2D  (Bekki, 1995; Bekki et al. , 1996). For models producing an ensemble of simulations, the line and shading are the ensemble mean and ensemble standard deviation respectively. periments also include all eruptions represented in the PMIP-past1000 experiment for the overlapping period.
Similar considerations stand for volc-long-hlS. (Timmreck et al. , 2016b)  as well as between VolMIP and the Radiative Forcing Model Intercomparison Project (RFMIP)  (Pincus et al. , 2016, this issue)  will help to building a scientific basis to distinguish between differences in volcanic radiative forcing data and differences in climate model response to volcanic forcing. VolMIP provides a welldefined set of forcing parameters in terms of aerosol optical properties and is thus complementary to SSiRC, which uses global aerosol models to investigate radiative forcing uncertainties associated with given SO 2 emissions. Precise quantification of the forcing to which models are subject is central for both RFMIP and VolMIP: RFMIP has planned transient volcanic and solar forcing experiments with fixed preindustrial sea-surface temperature to diagnose volcanic and solar effective forcing, instantaneous forcing, and adjustments, which is complementary to the volc-pinatubo experiments of VolMIP.
VolMIP is expected to advance knowledge in all such areas. VolMIP is designed based on a limited number of idealized volcanic forcing experiments. We recognize that an eruption's characteristics are a major source of uncertainty for its climatic impacts. We encourage modeling groups interested in performing sensitivity experiments based on the experiments proposed here and concerning, e. g. , the magnitude and the season of the eruption to use VolMIP as a platform for coordinating such efforts within a multi-model framework. The flexibility of the EVA module is, in this regard, a valuable advantage. Follow-up research must take into account that the design of the simulations reflects necessary constraints on the overall resources required to perform the whole set of mandatory experiments. This implies limitations such as the possibly insufficient representation of the whole range of variability of climate modes not explicitly accounted in the design.
This includes, for instance, the SH annular mode (e. g. ,  Karpechko et al. , 2010; Zanchettin et al. , 2014)  and modes of internal stratospheric variability like the QBO. VolMIP's experiments are designed based on observed or reconstructed forcing characteristics of historical volcanic eruptions (1815 Tambora and 1991 Pinatubo for the Tier 1 experiments). Comparison with observational or reconstructed evidence must, however, take into account the idealized character of VolMIP's experiments, including the simplified setting for generating volcanic forcing parameters provided by the EVA module. Specifically, the evolution of the volcanic aerosol cloud in EVA does not account for the meteorological conditions at the time of the eruption and cannot represent the aerosol properties at anything other than the largest scales.
Along with the data, the provenance of the data will be recorded, and DOIs will be assigned to collections of output so that they can be appropriately cited. This information will be made readily available so that published research results can be verified and credit can be given to the modeling groups providing the data. In order to run the experiments, data sets for natural and anthropogenic forcings defined for the DECK and the CMIP6 historical simulations are required. These forcing data sets are described in separate invited contributions to this special issue. In addition, specific volcanic forcings are required for the VolMIP experiments that are described in this paper. The forcing data sets for the volc-pinatubo experiments will be made available through the ESGF with version control and DOIs assigned.
Acknowledgements. VolMIP is dedicated to the memory of  Thomas Crowley  ( 1948-2014 ), whose pioneering work on volcanic forcing on climate has inspired many researchers and strongly contributed to the foundation upon which VolMIP was built. We thank the broad scientific community for the stimulating discussions that motivated VolMIP and for their contribution to the definition of the experiments and the comments on this draft. We thank the climate modeling groups who have committed to perform the VolMIP experiments. We are grateful to the  CMIP6 Panel  who guided our work throughout the endorsement process, in particular concerning their recommendation to upgrade the volc-pinatubo-strat/surf experiments, which led to a stronger Tier 1 experimental palette. We thank  Christoph Raible  and an anonymous reviewer for their helpful comments on the manuscript and on the VolMIP protocol.
The volc-cluster-21C experiment was added to the VolMIP protocol following a suggestion by  Ingo Bethke. We thank  Martin Juckes  for his assistance in preparing the CMIP6 data request and  Karl Taylor  for his assistance throughout the endorsement process. We thank  Andrew Schurer  for discussion about solar forcing. We acknowledge the support provided by the  World Climate Research Programme (WCRP) , which is responsible for the CMIP5. M. Khodri  acknowledges grant support from the LABEX L-IPSL, funded by the  French Agence Nationale de la Recherche  under the " Programme d'Investissements d'Avenir "(grant no. ANR-10-LABX-18-01 ), a grant from the  Agence Nationale de la Recherche MORDICUS , under the " Programme Environnement et Société " (rant no. ANR-13-SENV-0002-02 ) and benefited from the IPSL data access PRODIGUER.
C. Timmreck  acknowledges support from the  German Federal Ministry of Education (BMBF) ,  research program  " MiKlip " (FKZ:  01LP1517B/01LP1130A ) and the European Project  603557-STRATOCLIM  under program  FP7-ENV. 2013. 6. 1-2 ;STRAOCLIM also partially supported  S. Bekki 's work. M. Toohey  acknowledges support from the  BMBF, research program  " MiKlip " (FKZ:  01LP1130B ). A. Robock  is supported by  US National Science Foundation (NSF)  grant  AGS-1430051. E. P. Gerber  acknowledges  NSF  grant  AGS-1264195. A. Schmidt was supported by an  Academic Research Fellowship  from the  University of Leeds  and  NERC  grant  NE/N006038/1. W. T. Ball  was funded by the  Swiss National Science Foundation  projects  149182  and  163206. G. Hegerl  is supported by the  ERC  project  TI-TAN  ( EC-320691 ), by  NCAS  and the  Wolfson Foundation  and the  Royal Society  as a  Royal Society Wolfson Research Merit Award  ( WM130060 ) holder.
Data structure for modelling first and recurrent malaria episodes Malaria episode data of each child were checked carefully to identify a first malaria episode as well as any additional malaria episodes after the first malaria episode (i. e. recurrent episodes). For the time at risk, a child treated for a malaria episode was censured for 21 days in order to prevent double counting of episodes and to allow for any prophylactic effect of the antimalarial treatment  [24]. A sub-sample data of four children in Table  1  illustrates the differences in the data structures required for modelling first malaria episodes and recurrent malaria episodes. The time from the start of the follow-up (July 8th 2008) to the first episode (time-to-first episode) in children who presented malaria episodes, and the time to the last censorship in children who did not present malaria episodes was considered to model first malaria episodes.
Statistical analysis Malaria incidence rates (by species) were calculated by dividing the number of episodes during the study period by child-months at risk, and expressed as the number of episodes per 1000 child-months at risk. A child-month at risk corresponds to a child that had at least once weekly visit of the total 4 weeks of a specific month. Correspondingly, 95 % confidence intervals (95 % CI) of the incidence rates were calculated assuming that the number of new malaria episodes was Poisson distributed. Frailty models were applied to calculate hazard ratios (HRs) of species-specific incidence using separately timeto first episode data and time-to-recurrent episode data. The following potential risk factors were included in the univariate and multivariate models: gender, age groups (≤3, 3-7, ≥7 years), household distance to the dam reservoir (in kilometres), and season (long rainy season, dry season, and moderate rainy season).
Acknowledgements We acknowledge the financial support from the  Institutional University Cooperation IUC - JU  project under the umbrella of the  Flemish Interuniversity Council  ( VLIR-UOS ). We thank also  Kibiralem Sisay who is Statistics Department staff in Jimma University  for helping with me in writing R codes to manage the data. We would like to than the two anonymous reviewers and the editor-inchief, for their constructive comments which helped to improve the paper.
Before the analysis of the ratio of MA enantiomers, the non-reacted PGA molecules were separated from the product mixture by using a precolumn. Then, the product mixture was evaporated and re-dissolved in mobile phase with a composition of 95% heptane, 5% i-propanol and 0. 1% trifluoroacetic acid for analysis by HPLC. All analyses were performed on a Hitachi Chromaster analytical HPLC system equipped with a HPLC pump (5160 pump), an ultraviolet detector (5410 UV detector) and a chiral HPLC column (CHIRALPAK IC, 250 mm Â 4. 6 mm inner diameter) at a flow rate of 1. 0 ml min À 1. The peak fitting function of Chromaster software was used to measure the individual areas of eventually overlapping peaks in the chromatogram (Supplementary Fig. 8 ). Data availability. All relevant data that support the current findings and which are not included in the main manuscript or the Supplementary Information are available the author on request.
Acknowledgements This work was supported by the  French Ministry of Research, CNRS  and  ENSCBP. A. K. thanks the  Institut Universitaire de France  for financial support. The support by a grant from the  Development and Promotion of Science and Technology Talents Project (DPST, Thailand)  is also acknowledged. Chu. W. thanks the  Junior Research Fellowship Program  of the French Embassy in Thailand, sponsored by the  French Ministry Foreign Affairs.
2. All participants received standardized dietary recommendation according to their membership group and their body weight. To avoid muscle catabolism, a high protein sugarfree drink (High Protein 15 g, 20 mL, UHS Bruno, France) was prescribed just before going to bed. The participants had to fill in a food diary during the last week in phase I and phase II. They were instructed to fill in it the most detailed as possible (weighed food, pictures of dishes, details of the use of cooking fat, type and quantity of oil used for dressing…). The diaries were analyzed by the same scientist with the software Nutrilog 2. 31 (Nutrilog SAS, France).
Sleep monitoring During both phases I and II, subjects' sleep patterns (i. e. , sleep quantity and quality) were monitored continuously using an Actiwatch worn on the non-dominant wrist (Cambridge Neurotechnology Ltd. , UK) with the epoch length set to 1 min. Athletes were monitored in their home environment every day during phase I (21 days), and during phase II (21 days). Mean behavioral activity over the entire recording period was automatically calculated using the Sleepwatch software (Actiwatch activity and sleep analysis version 5. 28, Cambridge Neurotechnology, Ltd. ). Wristwatch actigraphy is a non-intrusive tool used to estimate sleep efficiency, which has been validated for reliability  (Sadeh 2011). When compared with polysomnography, results show an accuracy of up to 80 % in sleep disordered patients for total sleep time and sleep efficiency  (Kushida et al.
Statistical analysis All statistical analyses were conducted using the software Statistica 6. 1 (StatSoft). All data are expressed as mean ± SD. Normality of data was tested using a Shapiro-Wilk test. Values at baseline for age, body composition, and experience in endurance sport, MAP, VO 2max and dietary habits were compared between groups (i. e. , sleep low, SL and sleep normal, CON) using a one-way ANOVA. Two-ways (group × time) ANOVA were used to examine differences in dependent variables (i. e. , sleep characteristics, perceived sleep, illness symptoms, blood and saliva markers of immune response) between groups means at each time point of the protocol. When a significant main effect was found, pairwise comparisons were conducted using Newman-Keuls post hoc analysis. Effect sizes were also calculated using partial eta squared (η 2 p ) values.
Real-Time PCR and Melting Curves Analysis Ninety-one primer pairs were deployed to amplify 100-300 bp fragments from as many genes harbored on the three PAIs LEE,  12, and 41  genes from each island, respectively). The genomic sequence of the O157 STEC strain EDL933 (Acc. no. AE005174) was used to design the primer pairs using the Primer-BLAST web-tool available on the NCBI webserver. Some of the primers were degenerated to amplify the target genes in all the STEC strains for which a sequence was available in GenBank. The sequences of primers used in this study and their annealing position on the genomic sequence of EDL933 strain are reported in Table  1. Total DNA was extracted from overnight cultures of the strains with the Nucleospin Tissue extraction kit (Macherey-Nagel, Düren, DE). The Real-Time PCR reactions were performed on the high throughput BioMark Real Time PCR system with 96.
High Resolution Virulence Allelic Profiling Analysis (HReVAP) The melting temperatures (Tm) of the amplicons were normalized using the Tm of the PCR products amplified from the positive control strain EDL933 present in each array chip. Normalization values were obtained for each array chip minimizing the mean of the Tm standard deviations for the eight normalized Tm measurements over each gene as well as the overall maximum value of the normalized Tm ranges of all genes. The Tm frequency distributions for each gene have been calculated by grouping values in 0. 05 • C intervals. The frequency distributions were then analyzed with the "mix" function of the "mixdist" package in the R software  (RTeam, 2014). This function finds Maximum Likelihood estimates for the proportions, means, and standard deviations of a mixture distribution by applying a Newton-type iterative method  (RTeam, 2014).
The Tm intervals identified by peaks in the distributions obtained from the analysis of each gene were used to identify the alleles, which were labeled with numbers in ascending order according to the position of the peak in the temperature distribution (Supplementary Table  2 ). Each strain was given a numeric allelic signature comprising the alleles of all the genes analyzed. The neighbor software of the Emboss package for samples clustering with default parameters  (Rice et al. , 2000)  was used to compare the allelic signatures and to obtain distance matrices. Each step of the above described procedure has been implemented in dedicated python classes, including the neighbor software that was wrapped together with the TreeGraph software  (Stover and Muller, 2010)  for graphical trees representation.
The HReVAP software package was deployed and used on the public computational framework ARIES operating on the servers of the Istituto Superiore di Sanità and based on the Galaxy bioinformatics platform  (Giardine et al. , 2005; Blankenberg et al. , 2010; Goecks et al. , 2010)  (https://w3. iss. it/site/aries/). The tree files (. tre) produced with the HReVAP clustering algorithm were downloaded and visualized using the FigTree program version 1. 4. 0  (Drummond et al. , 2012).
Whole Genome Sequencing of STEC and Phylogenetic Analyses Thirty-nine out of the 161 STEC strains used for the HReVAP typing were subjected to whole genome sequencing using the Library Preparation Kit by Kapa Biosystems (Wilmington, MA, USA) and a paired end 100 bp protocol on an Illumina HiSeq2500 instrument in fast run mode according to manufacturers' instructions. The sequencing reads have been uploaded in the EMBL-ENA sequence database (EMBL European Nucleotide Archive Study accession no. PRJEB11886). The raw reads were trimmed to remove the adaptors and to accept 27 as the lowest Phred value and assembled using the de novo assembly tool Edena v3  (Hernandez et al. , 2008). The contigs were subjected to in silico Multi Locus Sequence Typing (MLST) with the protocol described by Wirth and colleagues  (Wirth et al.
, 2006). Whole genome Single Nucleotide Polymorphism (WG-SNP) analysis was performed using the ksnp3 pipeline  (Gardner et al. , 2015) , using 19 as kmer size. The optimum value for the kmer size was selected as that producing the highest number of unique kmers of the median length in all the genomes of the dataset and it was calculated by using the kchooser tool included in the ksnp3 pipeline. All the bioinformatics analyses were performed through the ARIES webserver (https://w3. iss. it/site/aries/).
This has been one of the motivations that since the early 2000s have led to the development of the field of coastal altimetry  (Vignudelli et al. 2011) , which aims at recovering more numerous and better measurements of sea surface height in the coastal strip. Processing enhancements and improvements in the corrections is at the basis of a number of improved coastal altimetry datasets now becoming available, than can potentially be used for sea level studies but will need a thorough validation for that purpose. In this contribution, we first review the status of the measurements of sea level in the coastal zone from tide gauges (Sect. 2), and then in Sect. 3 we examine the same issue from the point of view of coastal altimetry, with an overview of the improvements in the retrieval of sea surface height from altimetry in the coastal zone which have been made possible by algorithmic improvements and development of better corrections and data editing.
This section also presents the datasets available for altimetry and coastal altimetry, for the benefit of the potential users of those products. Some particularly promising prospects for the monitoring of sea level in the coastal zone come from the advent of new technologies, whose coastal performance is discussed in Sect. 4: these technologies are Kaband altimetry from AltiKa, and SAR altimetry from CryoSat-2 (and now Sentinel-3). We then present two specific examples of coastal sea level monitoring: in Sect. 5, a case study showing how local sea level can be monitored with altimetry and tide gauges around the coast of the UK and, in Sect. 6, some results on the variations of sea level rise rate as a function of distance from shore along the West African coast. The concluding section translates the science reviewed and results presented into recommendations for refinements to the processing, which should lead to further progress of the field.
e. hourly or more frequent) tidal datasets such as the Global Extreme Sea Level Analysis (GESLA) dataset and its follow-on GESLA-2: this issue is examined in detail in  Woodworth (2010). Since 1933, tide gauge records have been collected and distributed by the Permanent Service for Mean Sea Level (PSMSL). There are records from more than 2000 locations (Fig. 2 ) available from PSMSL via the webpage www. psmsl. org (Holgate et al. 2012 ). An international programme, the Global Sea Level Observing System (GLOSS), is established under the Joint Technical Commission for Oceanography and Marine Meteorology Tide gauge observations are used to measure and predict tides, quantify the size of tsunamis and storm surges. Tide gauge records are widely used in coastal engineering for design of coastal infrastructure.
Sea level datasets from tide gauges are utilized in many scientific disciplines, e. g. , geodesy, oceanography, geology, paleo-oceanography studies and climatology. Selected tide gauges, in particular those located on islands, are also used for altimeter calibration. The most familiar application of tide gauge data is global and regional sea level rise and variability, providing information on long-term changes in global sea level during the last two centuries. Individual tide gauge observations  (Douglas 1997) , global sea level reconstructions using tide gauge data  (Gornitz et al. 1982; Jevrejeva et al. 2006 Jevrejeva et al. , 2008 Jevrejeva et al. , 2014;; Grinsted et al. 2007; Merrifield et al. 2009; Ray and Douglas 2011)  and reconstructions that jointly use satellite altimetry and tide gauge records  (Church and White 2006, 2011)  or apply Bayesian fingerprinting techniques to tide gauge observations  (Hay et al.
2015)  show the evolution of sea level rise for the past 50-100 years. Three of Fig. 2  Locations of tide gauges in the PSMSL database for which annual mean sea level information is available and the number of available annual records in each station these reconstructions, i. e. Church and White (2011) ,  Jevrejeva et al. (2014)  and the 'Kalman smoother' reconstruction by  Hay et al. (2015)  are shown in Fig. 3  alongside the altimeter-derived global mean sea level from the ESA Sea Level Climate Change Initiative (CCI)  (Ablain et al. 2015). This illustrates the encouraging agreement between in situbased and satellite-based quantifications of the global mean sea level.
85 to 0. 98 and RMS errors lower than 0. 4 m. All the above examples demonstrate the capabilities of Ka-band altimetry for the monitoring of coastal ocean dynamics. With a complete reprocessing foreseen in 2017, and with its long-term stability validated with comparison with other altimeters and tide gauges as is being done in the Sea Level CCI project, the 3? years long SARAL/AltiKa record will provide even more valuable data for coastal studies, providing extended observations of sea level over the same 35-day repeat set of orbits sampled by ERS-1, ERS-2 and Envisat between 1992 and 2010.
Here we use along-track altimetry data from Jason-1 and Jason-2 as reprocessed by the coastally adapted ALES retracker  (Passaro et al. 2014 ) described in Sect. 3. 1 and covering the period 2002-2015. We use a total of 58 tide gauge records, of which 46 were obtained from the data archives of the British Oceanographic Data Centre (BODC), 11 from the UK Coastal Channel Observatory (CCO), and 1 from the Port of London Authority (PLA). The temporal resolution of the tide gauge data is 15 min for records stored at the BODC and 10 min for those stored at the CCO and PLA. For consistency with the satellite altimetry data, the atmospheric correction was applied to the tide gauge data. In particular, we used the dynamic atmospheric correction (DAC) provided by AVISO (ftp. aviso. altimetry. fr), which consists of the barotropic response of the ocean to wind forcing and atmospheric pressure as estimated by the Mog2D-G model for periods shorter than 20 days and the inverse barometer (IB) approximation for longer periods.