dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017). The items were submitted to a factor analysis (Kaiser-Meyer-Olkin measure of sampling adequacy was 0. 768; Bartlett's test of sphericity was χ 2 = 1230, with df = 21 and p < 0. 001) which split them into two dimensions: trust in science (α = 0. 887) and trust in state (α = 0. 804). The variables were constructed based on the Moroccan case study and were implemented in a similar way to the collection of the Sardinian data set. Later, all those items were measured using a 5-point Lik- ert scale and then dichotomised: participants with responses above the mean were considered to have a high value and those under the mean to have a low value, and the responses are as such used in a binary logistic model, schematically presented in Fig. 5. For more details on the variables chosen, refer to publications by  Ivčević et al.
e. associations (9 interviews). All 25 narratives were recorded and transcribed, and basic discourse analysis was carried out using the software Iramuteq  (Ivčević, 2020). The main parts of the interview guide and the groups interviewed are presented in Table  2. As far as the Sardinian case study is concerned, the secondary literature on risk management at the regional (Sardinia) and national (Italy) level was obtained as public documents available freely from administrative web pages. These sources served to contrast the population's responses with the official directions in managing natural risks. Since this literature was easily accessible, a qualitative study in the form of interviews with the local authorities was not considered to be essential for this research, conversely to the Moroccan case study.
Acknowledgements. The authors are thankful for financial support received and are grateful to the local populations and other local stakeholders that provided useful information for this study. Also, the authors are thankful to the two reviewers, whose comments helped to improve the manuscript. Finally, the first author is particularly thankful to  Matthieu Kervyn  from  Vrije Universiteit Brussel  and to  Marta Banožić  from the  University of Vienna  for their advice on, reading of, editing of and commenting on the manuscript. Financial support. This research has been supported by the  H2020 Marie Skłodowska-Curie Actions  (grant no. 713750 ), the  Regional Council of Provence-Alpes-Côte d'Azur  and the  Agence Nationale de la Recherche  (grant nos. ANR-11-IDEX-0001-02 ,  ANR-11-LABE-0061  and  ANR-1-1E-0001-02 ).
1 ). Another difference in the setup is that the simulations with initial and boundary conditions from the CMIP5 models (WRF_CCSM4 and WRF_ACC) are continuous runs with one year of spin-up starting from 1st of Jan. 1995 for the historical simulation and 1st of Jan. 2090 for the future simulation, while the other simulations (WRF_NNRP and WRF_CESM) have been run in 13-month time slices re-initialized at 1 st of Dec. each year and with the first month disregarded as spin-up. While one month of spin-up should be sufficient for the atmosphere, it means that soil temperature and moisture are more dependent on the initial conditions (coming from the reanalysis or GCM) in the time slice simulations than in the continuous simulations. However, any trends in the data should be captured through the initial conditions, and an earlier study found only small differences in precipitation change when using 1 vs.
Global climate models The GCMs listed in Table  1  have both been used as boundary conditions to the WRF model and been analysed directly for comparison to the regional model results. Data for two of the global climate models, CCSM4 and ACCESS1-0, have been downloaded from the Coupled Model Intercomparison Project phase 5 (CMIP5) archive  (Taylor et al. 2011)  for the experiments "historical" and "rcp85". Ensemble member "r6i1p1" was used for CCSM4 and "r1i1p1" for ACCESS1-0. The third global climate model, the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM1-CAM4)  (Gent et al. 2011) , is essentially the same model as CCSM4, but it is run in-house and the setup is documented in  Hodnebrog et al. (2019). To some extent, the CCSM4 and CESM model simulations can be considered two different realizations of the same model.
The main difference in the setup between the CCSM4 and CESM simulations is the horizontal resolution, which is 1. 25° × 0. 94° (lon x lat) and 2. 5° × 1. 88°, respectively. ACCESS1-0 has a horizontal resolution of 1. 88° × 1. 24°. To convert from the GCMs' output in NetCDF format to the GRIB format needed by the WRF pre-processor, we have modified and used the cam_to_wps utility available at https:// github. com/ short wavet rough/ cam_ to_ wps. In the Results section we also compare with an ensemble mean of 18 different CMIP5 models, and these are ACCESS1-3, bcc-csm1-1-m, BNU-ESM, CanESM2,   1996 -1998 2091 -2093 RCP8. 5 RCP8. 5  WRF_CCSM4_large Same as WRF_CCSM4 but with increased size of the 10 km domain, from 150 × 175 to 250 × 280 grid boxes (D3 vs. D3large in Fig. 1 )  1996 -1998 2091 -2093 RCP8.
5 RCP8. 5  WRF_CCSM4_noCP Same as WRF_CCSM4 but with cumulus parameterization turned off in 10 km domain  1996 -1998 2091 -2093 RCP8. 5 RCP8. 5  WRF_CCSM4_cpGF Same as WRF_CCSM4 but with cumulus parameterization changed from Kain-Fritsch scheme  (Kain 2004 ) to Grell-Freitas ensemble scheme  (Grell and Freitas 2014 ) 1996 -1998 2091 -2093 RCP8. 5  1 3 CCSM4, CESM1-CAM5, CMCC-CMS, CNRM-CM5, CSIRO-Mk3-6-0, FGOALS-g2, GFDL-CM3, GISS-E2-R, HadGEM2-ES, inmcm4, MIROC-ESM, MPI-ESM-LR, MRI-CGCM3 and NorESM1-M. We also compare with 16 different CMIP6 models  (Eyring et al. 2016 ) for the experiments "historical" and "ssp585": ACCESS-CM2, ACCESS-  and UKESM1 -0-LL. The CMIP5 and CMIP6 data were all for ensemble member "r1i1p1" and regridded to 1° × 1° resolution for the analysis. ESM1-5, CESM2, CNRM-CM6-1, CNRM-ESM2-1, EC-Earth3, EC-Earth3-Veg, FGOALS-g3, INM-CM5-0, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2, MRI-ESM2-0, NESM3, NorESM2-MM.
Reanalysis datasets In addition to the WRF simulations using boundary conditions from GCMs, reanalysis data have been downscaled using boundary conditions from the National Centers for Environmental Prediction (NCEP) / NCAR Reanalysis Project (NNRP)  (Kalnay et al. 1996)  in the simulation named WRF_NNRP (Table  1 ). For comparison with GPCC and the WRF simulations, we have also analysed a different dataset, ERA5-Land (Muñoz-Sabater 2020), which is based on the 5 th generation reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5. ERA5-Land is a replay of the land component in ERA5 at a finer spatial resolution of approximately 9 km × 9 km (regridded to 0. 1° × 0. 1°), compared to 30 km × 30 km in ERA5.
In central Chile, WRF and ERA5-Land compare very well to the GHCN-Daily precipitation observations (Fig. 2 ), but WRF has more precipitation in the mountainous eastern part of Chile compared to the GPCC observations (see Fig. 1  for elevation map). However, GPCC precipitation is based on spatial interpolation from station observations, and very few stations are available in this area. Thus it is difficult to know the actual precipitation amounts, but the precipitation pattern in the WRF simulations agree relatively well with the ERA5-Land reanalysis (Fig. 2 ). Lack of high-quality surface precipitation observations in the Andes has also been mentioned previously  (Alvarez-Garreton et al. 2018; Bozkurt et al. 2019). In Peru, the very dry conditions near the coast are seen in all datasets; GPCC, GHCN-Daily, ERA5-Land and all WRF simulations (both forced with reanalysis and with free-running GCMs).
(2019)  performed a comparison of runoff-precipitation covariability using observations of discharge, and found indications of precipitation underestimation in gridded observational datasets. Comparison with individual GHCN-Daily station observations shows very good agreement for WRF_NNRP for central Chile, with slightly better performance for the 10 km run compared to the 50 km run (Supplementary Fig. S3 ). For Peru, WRF_NNRP at 10 km resolution performs better than 50 km resolution for the high altitude stations while  the 10 km resolution run seems to overestimate precipitation amounts at some of the low altitude stations with low observed precipitation amounts. In southern Brazil, both the monthly amounts and the temporal distribution of the observed seasonal precipitation cycle at individual Hidroweb stations are well reproduced by the WRF_NNRP simulation, and there are no large differences between results from the 10 km and 50 km resolution runs.
WRF simulations using boundary conditions from a reanalysis dataset, as in WRF_NNRP, are expected to perform better than WRF simulations using boundary conditions from free-running GCMs, because reanalyses include assimilation of actual observations. Comparison to GPCC indicates that the seasonal precipitation cycle is exaggerated for Peru and southern Brazil when using GCMs as boundary conditions, particularly for WRF_CESM and WRF_CCSM4, with too low values during austral winter and too high values during austral summer (Fig. 3a ). Again, this may partly be due to lack of observations in the Peruvian mountain regions. Comparison to individual station observations shows a similar tendency of an exaggerated seasonal cycle at some stations in both Peru and southern Brazil (Supplementary Fig.
Discussions Comparison to observations in Sect. 3. 1 shows that WRF_ NNRP, which is WRF downscaled from a reanalysis dataset, generally performs well for all three regions, indicating that the chosen setup is sound. The performance for WRF when using initial and boundary conditions from GCMs is more variable, but this is to be expected since the GCMs are freerunning (i. e. no assimilation of observations) and the quality of regional climate modelling is conditional on the quality of the boundary conditions (e. g. Rummukainen 2010). The difference between results with WRF using CCSM4 and CESM is relatively small. While this can partly be expected because the two models are very similar, their setups differ in terms of horizontal model resolution and spin-up length when used to drive WRF (see Sect.
Availability of data and material The observation data are available at https:// doi. org/ 10. 5676/ DWD_ GPCC/ FD_M_ V2018_ 025 (GPCC), https:// doi. org/ 10. 7289/ V5D21 VHZ (GHCN-Daily) and http:// hidro web. ana. gov. br (Hidroweb). The CMIP5 and CMIP6 model data are available at https:// esgf-node. llnl. gov/, selected CESM data at https:// doi. org/ 10. 11582/ 2019. 00027 and the WRF model output at https:// doi. org/ 10. 11582/ 2021. 00067.
, 2010; Jenkins et al. , 2013). OsHV-1 µVar is a single variant which differs from the reference genome published in 2005 by Davidson (accession number NC_005881. 2) mainly because of sequence variations in a microsatellite locus upstream of the Open Reading Frame (ORF) 4, in ORF4 and in ORF42/43. The term "microvariants" is now used to refer to all haplotypes with mutations in and upstream of ORF 4 and in ORF 42/43. Other variants have also been described, for example OsHV-1-SB which has been associated with mass mortalities of S. broughtonii in China  (Bai et al. , 2015 (Bai et al. , , 2016)). Following the emergence of OsHV-1 µVar, a number of studies have been undertaken to investigate the diversity of OsHV-1 using single-  (Renault et al. , 2014)  or multigenomic regions  (Renault et al.
Sample Collection Samples analyzed in this study come from a combination of sources, including an historical collection and the European and National Reference Laboratory for Bivalve Mollusc Diseases. For the rest of this paper, we refer to "isolates" in the sense of viral populations isolated from infected individual host. Most of the data derives from whole-genome sequencing of infected individual host or pools of infected individual hosts (Table  1 ). The 22 isolates in this study were collected from   1 ). The historical samples were from moribund oysters collected during mortality outbreaks. For spat, juvenile and adults, a piece of mantle or gill was collected and stored in 90% ethanol until DNA extraction. For larvae samples, a pool of individuals was frozen (-20 • C) and stored until DNA extraction.
Regarding the sequencing protocols, three libraries were sequenced on single-end protocol (1 × 100 bp) and remaining libraries were sequenced with paired-end protocol (2 × 150 bp) (Table  1 ), with the exception of the Miseq data, which was generated using V3-600 Miseq specific cartridge. Raw sequence data are available on the NCBI Sequence Read Archive (SRA) under the BioProject ID: PRJNA721248.
Read Mapping and Filtration A reference mapping approach was used to explore the viral genomic diversity within and between samples. This approach has been extensively used for eukaryotic organisms and is best suited to call single nucleotide polymorphisms (SNPs) and genomic structural variation (SV) from a collection of individuals or pooled libraries in comparison to the more widely used de novo assembly strategies in the viral community  (Baaijens et al. , 2017). All sequenced libraries were aligned against the OsHV-1 reference genome (Accession number: NC_005881. 2)  (Davison et al. , 2005)  using BWA MEM v7. 1. 0 (Li and Durbin, 2009; Li, 2013). Only samples with a mean of coverage depth equal to or above 100X have been included for polymorphism calling and 10X for haplotype reconstruction (Figure  1A ).
Local Haplotypes Reconstruction We first aimed to reconstruct local haplotypes. As far as we are aware, no software dedicated to haplotype reconstruction is able to manage long indels, as it is observed in OsHV-1 genomes, with short reads (i. e. , ∼100 bp reads long). To circumvent this limitation, we decided to make local haplotype reconstruction on each gene coding DNA sequence (CDS) that were present in every sample and outside of inverted and repeat regions. Each CDS coordinate were extracted from the GenBank file  (Clark et al. , 2016)  and we use them to subset the bam file for each sample (Locus_tag, Figure  1B ). We feed QuasiRecomb  (Töpfer et al. , 2013) , haploclique  (Töpfer et al. , 2014) , and shorah  (Zagordi et al. , 2010 (Zagordi et al. , , 2011) )  with CDS-split bam files (Figure  1E ).
Only results from Shorah software are showed on the main text because it represents the most conservative short read local assembler, whereas the other software outputs are showed in supplementary figures. Briefly, Shorah employs a modelbased probabilistic clustering algorithm to correct errors, infer haplotypes and their frequencies, and estimate, using a Bayesian approach, the quality of the reconstruction by computing the full joint posterior probability distribution of all parameters of interest  (Zagordi et al. , 2010). Subsequent plotting were made in R Development Core Team (  2008 ) with basics functions and the ggplot2 package  (Wickham, 2016)  (Figure  1H ).
Short and Long Polymorphism Calling The raw bam files were also processed with Freebayes software  (Garrison and Marth, 2012 ; Figure  1D ) to call short polymorphisms. Freebayes is one of the most versatile variants calling software, particularly when the libraries included pool of viral haplotypes as it is in the present study and has been extensively tested and validated on various organisms with different protocols  (Olson et al. , 2005; Wang et al. , 2005). We discarded genomic regions with low complexity (entropy > 1) and regions with a high number of misalignments namely TRL, IRL, IRS, and TRS genomic regions that are duplicated. Variation of coverage depth and, when available, the orientation of the paired-reads against the reference genome were jointly analyzed to detect SV, deletions, duplications, insertions, inversions, and translocations  (Davison et al.
Phylogenomic Analysis of Consensus Sequence We choose to work on the full-length genome (Figures  1C, D, F, G ) because among the Malacoherpesviridae family most of the transcripts and proteins have no functional annotation. All consensus sequences for each samples, plus the publicly available full length OsHV-1 genomes as of March 2021 (MF509813. 1, NC_005881. 2, KY242785. 1, and KY271630. 1) and the genome of the acute viral necrotic virus (AVNV) GQ153938. 1 were merged and aligned with MAFFT  (Katoh and Standley, 2013 ; Figure  1G ). For the multiple sequence alignment, we did not perform any trimming or masking steps as recommended in  Tan et al. (2015). We used a general time reversible evolutionary model with a gamma distribution of the rate of variation among sites and a proportion of invariable sites (GTR + I + G) considered most appropriate according to jModelTest v2.
1. 10 (Posada, 2008)  and based on the Akaike Information Criterion corrected for small sample size (AICc). We assessed whether there is sufficient temporal signal in our data to proceed with phylogenetic molecular clock analysis and checked there were no sequences whose genetic divergence and sampling date were incongruent with TempEst v1. 5. 3  (Rambaut et al. , 2016)  from a maximum likelihood tree build using PhyML v3. 1  (Guindon et al. , 2010). Time-scaled Bayesian phylogenetic trees were estimated in BEAST v1. 10. 4  (Suchard et al. , 2018)  using a GTR + I + G substitution model under a relaxed molecular clock with a lognormal distribution of rates (Figure  1I ). A Gaussian Markov Random Field Bayesian skyride coalescent model was used as the tree prior  (Minin et al. , 2008). A Monte Carlo Markov chain (MCMC) was run for the number of generations needed to achieve stationary distributions (200,000,000 generations) and sampling frequency adjusted accordingly to yield 10,000 samples from the posterior.
We used Tracer v1. 6  (Rambaut et al. , 2016)  to visualize the posterior distribution of each parameter and to obtain an estimate of the effective sample size (ESS) after removal of the initial 10% burnin. We assumed the run had reached sufficient mixing as the ESS of all the parameters was above 200. A maximum clade credibility (MCC) tree was produced after removal of the initial 10% burn-in based on common ancestor heights method  (Heled and Bouckaert, 2013)  using the auxiliary program TreeAnnotator included in the BEAST package.
Here only LI, PR, NZ17, and SW6 are shown. The X-axis is the number of CDS found in the reference annotation genome last accessed  (Davison et al. , 2005)  and the Y-axis is a standardized count of mutations found in 1,000 bp if the SNP density where the same in the targeted CDS. Genomic regions with a low complexity content (repeated sequences) were not considered in this analysis (light gray arrows). SNP, single nucleotide polymorphism; INS, short insertion (<50 bp); DEL, short insertion (<50 bp); MNP, short complex polymorphism, dark gray arrows: large deletion (>50 bp). Figures of all samples (detailed Table  1 ) are given in Supplementary Files. purifying selection, would have been eliminated over long timescales  (Ho et al. , 2005 (Ho et al. , , 2007a,b;,b; Ho and Larson, 2006). While conducting selective pressure analyses on a bigger sampling would allow to test that our results were not a consequence of the time dependency of the molecular clock due to short time range of our dataset (i.
ACKNOWLEDGMENTS The authors thank the  UMR 8199 LIGAN-PM Genomics platform (Lille, France)  which belongs to the " Fédération de Recherche" 3508 Labex EGID (European Genomics Institute for Diabetes ;  ANR-10-LABX-46 ) and was supported by the  ANR   Equipex 2010 session  ( ANR-10-EQPX-07-01 ; " LIGAN-PM "). The  LIGAN-PM Genomics platform (Lille, France)  is also supported by the  FEDER  and the  Region Nord-Pas-de-Calais-Picardie. The authors acknowledge the  Pôle de Calcul et de Données Marines (PCDM)  for providing DATARMOR storage, computational resources, and support services (URL: http:// www. ifremer. fr/pcdm)with a special attention to Bioinformatic team:  Laure Quintric  and  Patrick Durand. The authors thank  Isabelle Arzul  for the fruitful discussions about OsHV-1 biology.
FUNDING This work was financially supported by  DGAL (French National Services of Food and Agriculture)  through the  National Reference Laboratory for Mollusc Diseases, Ifremer ,  La Tremblade  and by the  EU  funded project  VIVALDI  ( H2020 program , n • 678589) within the WP 3. 1. 1part about genetic of resistance to OsHV-1 infection.
Acknowledgements We are most grateful to  Javier Parraga , of the  Mus ee d'histoire naturelle du Havre, Seine-Maritime, France , for arranging a loan of the Destombes collection microcrinoid material to ASG and to  Joseph Dunlop  ( SEGG, University of Portsmouth, UK ) for providing access to their SEM. J erome Thomas (UBGD) registered specimens from the Bertrand Matrion collection which will be managed and enhanced by the Recolnat research infrastructure. We also thank  Thierry Tournebise  ( Curator of the Reserve Naturelle Nationale de la Fôret d'Orient ) for access. We also thank the referees for their useful comments.
Data source EBMT is a voluntary organization comprising 583 transplant centers from 63 countries. EBMT membership requires submission of minimal essential data (MED-A form) from all consecutive transplant procedures to a central registry in which patients can be identified by underlying diagnosis and type of transplant. Informed consent for transplantation and data collection was obtained locally according to regulations applicable at the time of transplantation. Since January 2003, all transplant centers have been required to obtain written informed consent prior to data registration following the Helsinki Declaration 1975.
Chronic GVHD (cGVHD) was also analyzed in a competing risks setting with death and relapse as competing events. The database was closed for analysis as of July 2017. Demographics were compared between groups using the X 2 test or Fisher's exact test for categorical variables and Mann-Whitney U test for continuous variables. The probabilities of OS and PFS were estimated using the Kaplan-Meier product limit method and compared using the log-rank test. Estimates of NRM, incidence of relapse/progression and cGVHD were calculated using cumulative incidence curves and compared with Gray's test, to accommodate competing risks. Survival and cumulative incidence results were calculated as estimates and 95% confidence intervals (95%CI). Multivariate analyses were performed with Cox regression models, variables were selected using a backward-selection procedure.
The inclusion period in our analysis has not allowed us to evaluate this aspect as new drugs were not available outside prospective clinical trials. This follow-up study of the EBMT experience has also allowed us to further analyze unique features of the EBMT transplantation activity in this disease and to highlight significant changes overtime. As mentioned previously, the complexity of the HSCT has increased, transplanting older patients and using more URD and TCD. This might explain the higher NRM in the most recent cohort. In addition, there might be other factors that cannot be captured by the EBMT registry, such as the specific type of therapies used prior to HSCT. We have, nevertheless, ruled out that the increasing number of centers performing allogeneic transplantation in MF/SS in recent years may have had a potential center effect on outcomes (data not shown).