dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1990)  and DIAMOND  (Buchfink et al. , 2015)  algorithms. 3. The drawing of synteny maps using a user-provided annotated DNA file in GenBank format. It proceeds by the extraction and matching of the corresponding protein sequences against WASPS centroids using BLAST or DIAMOND. The query sequence and related plasmids from the database are drawn using a consistent symbolic coloring. 4. The drawing of synteny maps using an unannotated raw or Fasta DNA file. It requires the initial six-frame translation of the query sequence using ATG, GTG or TTG as start codons and TAG, TAA or TGA as stop codons. All frames and related plasmids are drawn as above. 5. The similar drawing of plasmid synteny maps from the database according to orthologous clustering using WASPS plasmid and protein accession numbers.
Dedicated hyperlinks connect text and similarity searches with synteny queries. Wide genomic areas can be explored by panning and zooming directly within the browser. All synteny map genes grant access to relevant additional information, to their protein sequences and to all their orthologs from the WASPS database using specific gestures in the web interface (Supplementary Material). Maps can be exported as PNG or SVG. The WASPS updater is fully automated and operates a pipeline in the background to refresh the database at fixed intervals (Supplementary Material).
Results To illustrate WASPS capabilities, we submitted for synteny analysis the unannotated Fasta sequence of the 13 015 bp archaeal plasmid pTN2 (NC_014115. 1) from Thermococcus nautili  (Soler et al. , 2010). Using the option 'primary hits only' and DIAMOND searching algorithm, three hits were detected: plasmid pTN2, already existing in the database, pIRI48 from Thermococcus sp. IRI18 and pCIR10 from Thermococcus sp. CIR10. All three plasmids were selected to generate a plasmid synteny map (Fig. 1 ; Supplementary Material). Interestingly, WASPS synteny results for pTN2 resembled a prior analysis where the same Thermococcus plasmids were aligned and drawn manually  (Krupovic et al. , 2013). In this representation, all plasmids shared orthologous uvrD genes whereas for the WASPS synteny pIRI48 gene YP_007195295.
2010; Dammhahn et al. 2018). Because published among-individual level estimates were largely absent (only 3% of studies reported actual among-individual level estimates; see below), we proceeded to identify which studies in our systematic literature review contained adequate data (repeated measures of behaviour) even if the published statistical analyses were performed at the phenotypic level. We then contacted the authors for the raw datasets. Using the collected datasets, we re-analysed the data at the among-individual level. We tested three key predictions. First, we investigated whether higher levels of bold, active, aggressive and/or explorative behaviour were associated with decreased survival at the among-individual level  (Stamps 2007; Biro & Stamps 2008a; Réale et al. 2010; Dammhahn et al.
METHODS (a) Collection of meta-analytical data We conducted a literature search in Web of Science and Scopus on 26 September 2018 to retrieve papers presenting data on behaviour (with repeated measures, or with single measurements and reporting repeatability estimates for the focal dataset) and survival variables following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach  (Liberati et al. 2009; Moher et al. 2009 ) (electronic supplementary material, Fig. S1 ). We used search terms that would identify papers focusing on among-individual associations (e. g. , "animal personality" and "behavioural syndrome") between behaviour and survival (search terms are presented in electronic supplementary material, Text S1). In our Web of Science search, we used "Zoology", "Behavioral sciences", "Ecology", "Biology", "Evolutionary biology", and "Multidisciplinary sciences" as topic fields.
In our Scopus search, we used "Agricultural and Biological Sciences" as the topic field. Altogether the searches retrieved 674 papers from 2007-2018. We also screened papers cited in Table  1  of  Smith & Blumstein (2008)  to retrieve those studies (published prior 2007) reporting individual-level correlations between behaviour and survival. Because the vast majority of studies (see below) presented repeated measures of behaviour but did not make use of them statistically (i. e. , reported unpartitioned phenotypic correlations instead of among-individual correlations), we contacted the authors of those studies and requested the primary dataset. We also posted a message on Twitter and directly asked colleagues who work on the topic of individual differences in behaviour and survival, to contribute primary datasets (details about datasets can be found in electronic supplementary material, Table  S1 ).
With all the collected datasets, we estimated (unpublished) among-individual correlations between behaviour and survival. Our search retrieved a total of 125 estimates from 34 studies with primary datasets collected (plus estimates from one published study) from which we had to drop seven studies due to convergence problems (see below). From the remaining 27 studies with primary datasets collected (Table  1 ): five were fully unpublished, 20 were published but did not estimate amongindividual correlations (rather they used the unpartitioned phenotypic correlations), and two published studies did estimate among-individual correlations but used a different statistical approach than a bivariate model. In total from these 27 studies, we collected 41 datasets which we re-analysed to get comparable estimates across all studies.
From these 41 datasets, 18 had information on males, 16 had information on females and in seven datasets, the sex was unknown. These datasets comprised a wide range of taxa, from mammals, fish, insects, molluscs, reptiles and birds (Table  1 , Fig. S2 ). From each dataset we extracted the following information: type of behaviour (e. g. exploration, activity, boldness, aggression), type of fitness component (longevity, survival), sex (male, female, unknown), whether measurements were taken in the laboratory or in the wild, species name and sample sizes (number of individuals and number of observations). Each dataset often comprised several behaviours and/or survival estimates. Following  Niemelä & Dingemanse (2018) , we changed the values of the behavioural trait within the data sets (by multiplying it by -1) to ensure that higher values indicated more risky behaviour (sensu  Réale et al.
(b) Statistical methods for extraction of the estimates for meta-analytic models In order to collect correlation coefficients at the among-individual level (ri) from the data sets described above, we constructed four types of bivariate models depending on the error distribution of behavioural and survival data: 1) binary behaviour and binary survival, 2) binary behaviour and Gaussian survival, 3) Gaussian behaviour and Gaussian survival and 4) Gaussian behaviour and binary survival. For one dataset  (Quinn et al. 2009) , the residuals of longevity data were Poisson distributed, and we changed the link function to Poisson. Since survival is by definition a fixed trait (i. e. it is only expressed once for each individual) and thus does not harbour within-individual variation, we fixed the residual variance of survival to one.
We also fixed the residual variance to be one for behaviours with binary error distribution. Residual covariance between behaviour and survival was restricted to be not estimated. One could argue that non-biological residual variation may still exist in any fixed trait due to the measurement error  (Brommer 2013). However, measurement error should not be generally correlated with the trait value (i. e. , the residual covariation due to measurement error should be zero)  (Brommer 2013) , and thus, is likely not pooled to the among-individual level correlations. In all models, we set the behaviour and survival as the two response variables and individual identity as random effect and estimated the among-individual level correlation coefficients with 95% Credible Intervals. We also had eight data sets where the behaviour was measured only once, but repeatability estimates ( ) for the all the behaviours were available for the focal dataset.
We ran the bivariate models using the R-package MCMCglmm  (Hadfield 2010)  in the statistical environment R 3. 5. 1. (R Core Team 2017). The chain length varied between 2,300,000 to 10,300,000 depending on the model to ensure sufficient sampling of the chain. We always used a burn-in of 300,000 and sampled the chain every 2,000 iterations. We used four different parameter expanded priors tailored specifically for each of the four types of bivariate models used (electronic supplementary material, see Text S2). We had to drop seven data sets due to convergence problems (mostly caused by very low sample sizes in number of individuals). This led to a final sample size of 125 correlation coefficients from 27 studies with primary data collected and one published study (Table  1 ). ). This approach is more accurate than the alternative "transform-then-analyse" approach  (Morrissey, 2016; Nakagawa & Lagisz, 2016) , where correlations are transformed into absolute values prior to analysis.
7. We included data set identity and phylogeny as random effects in all meta-analytic models. Our models are thus controlled for pseudo-replication caused by the inclusion of repeated correlation coefficients from the same data set and, for evolutionary divergence of the species. Our pooled data contained 28 data sets (27 data sets with published and unpublished primary data, plus one published study with published correlation coefficient estimates) and the phylogeny was constructed of 24 different species (Table  1 , Fig. S2 ). The meta-analytical models were run using the R-package MCMCglmm  (Hadfield 2010)  in the statistical environment R 3. 5. 1. (R Core Team 2017). Estimates with 95% Credible Intervals not overlapping with zero were viewed as indicating statistically important effects.
(d) Directional bias in the estimates The majority of the datasets we used in our analyses were associated with published studies (five datasets were unpublished, Table  1 ); however our (re-) analysis of these data resulted in new correlation coefficients that are previously unpublished. Indeed, only three out of 125 among-individual correlation coefficients were included in the published (all three estimates came from a single study) studies we used, and so we cannot estimate the publication bias in our correlation coefficients. Instead, our test of "publication bias" represents the general directional bias in our (unpublished) data. To visualize such potential directional bias in our correlation coefficients, we constructed a funnel plot for the main meta-analytic model by fitting precision (i.
e. , the inverse of sampling variance) as a function of meta-analytic residuals  (Nakagawa & Santos 2012). We further used Egger's regression analysis to statically test whether the distribution of correlation coefficients was more asymmetrical than expected by chance  (Stuck et al. 1998). Following  Nakagawa and Santos (2012) , meta-analytic residuals were calculated using the R-package MCMCglmm (Hadfield 2010), and Egger's regression conducted using the R-package metaphor (Viechtbauer 2010).
AKNOWLEDGEMENTS We thank  Alfredo Sánchez-Tójar  and  Raphaël Royauté  for feedback in the methodological approach, all authors of Table 1 for sharing their raw data, and  Jonathan Pruitt ,  Erik Mathyssen ,  Giovanni Polverino ,  Pavla Salandova ,  Lucas Wauters ,  Julia Greenberg ,  Anssi Vainikka ,  Bart Adriaenssens ,  Maren Vitousek ,  Chelsea Blake ,  Mark McCormick ,  Patricia Fleming ,  David Fisher ,  Francesca Santostefano ,  John Quinn ,  Simona Kralj-Fiser ,  Lundy Sparrow Project ,  Alfredo Sánchez-Tójar  and  Wolf Blanckenhorn  for sharing the requested and/or unpublished data. M. M. was funded by a  Marie Skłodowska-Curie Individual Fellowship (PLASTIC TERN , Grant Agreement Number  793550 ); P. T. N. was supported by the  DFG (Deutsche Forschungsgemeinschaft ;  NI 1539/2-1 ).
2015) Melospiza melodia amaka wild (Marshall et al. 2016) Mungos mungo wild (Réale & Festa-Bianchet 2003) Ovis canadensis canadensis wild (van Overveld et al. 2015) Parus major major wild (Quinn et al. 2009) Parus major major wild (Kain & McCoy 2016) Physella acuta lab (Hulthén et al. 2017) Rutilus rutilus caspicus wild (Santicchia et al. 2018) Sciurus vulgaris orientis wild (Morales et al. 2013) Sitophilus zeamais lab 20 (Shackleton et al. 2005) Teleogryllus commodus lab (Monceau et al. 2017) Tenebrio molitor lab (Boulton et al. 2018) Xiphophorus birchmanni lab (Kralj-Fišer et al. 2017) Zygiella x-notata lab Polverino (unpublished) Gambusia holbrooki lab Niemela (unpublished) Gryllus campestris wild Pruitt (unpublished) Strongylocentrotus purpuratus lab Salandova (unpublished) Pisaura mirabilis lab Lundy sparrow project (unpublished) Passer domesticus domesticus wild *footnote: We obtained four datasets from colleagues: 1) Salandova (unpublished data), 2) 606 Fisher et al.
Statistical analyses We used R version 3. 4. 2 (R Core Team, 2017). The correlations between the variables describing egg morphological characteristics and eggshell spottiness are shown in Tables  ESM1  and ESM2  respectively. Because of the high number of variables and of their correlations, we performed two separate principal component analyses (PCA) with package "ade4" version 1. 7-8  (Dray and Dufour, 2007). For both analyses, variables were first standardized and we kept the two principal components that explained more than 70% of the total variation. Then, we analysed the first two principal components (PC1 and PC2) in separate mixed models (lmer procedure from package "lme4"version 1. 1-14 with REML estimation)  (Bates et al. , 2015). Nestbox was defined as a random effect to account for the non-independence of the eggs laid by the same female.
We present type III analysis of variance tables with Satterthwaite approximation for degrees of freedom (package "lmerTest" version 2. 0-33). The estimates are presented as mean ± standard error. The assumptions of normality and homoscedasticity of residuals were fulfilled for all models, except for the model investigating variation in PC1 for eggshell spottiness. However, the residuals of the model followed a normal distribution when we ran the model without the outlier. Post-hoc tests were computed as differences of least squares means between the factors (pairwise comparisons).
Acknowledgments We thank the undergraduate and graduate students  Erika Beaugeard ,  Jeanne Dupuy ,  Eva Du Tien Hat ,  Laura Grosvalet ,  Lucie Mathieu ,  Juliette Rabdeau ,  Marine Ramirez ,  Baptiste Vancostenoble  and  Antonin Waterschoot  for their assistance during field work and  Magalie Chaigneau ,  Thibault Correia ,  Sophie Murarasu ,  Benoît Perez ,  Simon Sandre-Sivan  and  Gaëlle Sobczyk-Moran  for their help during lab analyses. We are grateful to  C. Parenteau  and  C. Trouvé  for their help in conducting hormone assays, and to  Claudy Haussy  for her help with the lysozymes analyses. JB was supported by a grant from the  local government (Regional Council of Île-de-France :  Sustainable Development Network   R2DS , no. 2014-11 ). This work has benefited from technical and human resources provided by  CEREEP-Ecotron IleDeFrance  ( CNRS/ENS UMS 3194 ) and the  Contrat Plan Etat Region (CPER ECONAT).
The CEREEP received financial support from the  Regional Council of Ile-de-France  under the DIM Program R2DS bearing the reference I- 05-098/R, and from the program  " Investissements d'Avenir " launched by the  French government  and implemented by  ANR  with the reference  ANR-11-INBS-0001   AnaEE France. FA and CB were supported by the  Agence Nationale de la Recherche  ( ANR  project  Urbastress ,  ANR-16-CE02-0004-01 ). All applicable institutional and national guidelines for the care and use of animals were followed.
For use in GEOS-Chem, the CarbonTracker initial condition was interpolated vertically in pressure and horizontally in space to the GEOS-Chem grid as described in section 3. 1. 2 CT2016 CO 2 optimized fluxes are partitioned into four flux terms, with five tracers: the background initial condition (no fluxes added), the imposed fossil fuel term, the optimized biological flux, the imposed fire emissions flux, and the optimized oceanic flux. Each of these terms is tracked independently as a tagged tracer. As a result of the optimization procedure, these fluxes are generally consistent with observed atmospheric CO 2 mole fractions. They were created with an inverse modeling system based on TM5 and may have artifacts and inaccuracies associated with that model's atmospheric transport and with assumptions used in the CarbonTracker data assimilation system.
The main hypothesis of  Yu et al. (2018)  is that the regridding to coarse resolutions of increasingly higher-resolution native reanalysis products such as MERRA2, which is archived from 0. 25 • data assimilation products, can result in transport biases. This can lead to a potential loss of resolved convection and an overall low bias in the convective fluxes used in GEOS-Chem. In contrast, TM5 meteorology is regridded from a 80-km meteorological product where presumably more convection is parameterized instead of being directly represented. We do note that ERA-i will be replaced in the near future with ERA5 which will have a resolution of about 31 km, which is much closer to that of MERRA2. Yu et al. (2018)  proposed code updates to GEOS-Chem, not yet implemented, which rediagnose convection from the coarse-averaged meteorology fields, as opposed to directly inputting parent model convective mass fluxes.
Abstract. This work uses a network of GPS stations over Europe from which a homogenized integrated water vapor (IWV) dataset has been retrieved, completed with colocated temperature and precipitation measurements over specific stations to (i) estimate the biases of six regional climate models over Europe in terms of humidity; (ii) understand their origins; and (iii) finally assess the impact of these biases on the frequency of occurrence of precipitation. The evaluated simulations have been performed in the framework of HYMEX/Med-CORDEX programs and cover the Mediterranean area and part of Europe at horizontal resolutions of 50 to 12 km. The analysis shows that models tend to overestimate the low values of IWV and the use of the nudging technique reduces the differences between GPS and simulated IWV.
g. ,  Sun et al. , 2006; Panthou et al. , 2016). A better knowledge of the IWV-precipitation relationship would be a help to better constrain models. Up to now, very few long-term (> 15 years) and homogeneous datasets of water vapor measurements exist, even less at subdaily timescales. These datasets are necessary to understand the humidity variability at regional scales at different timescales. Besides, the colocation of such measurements with independent measurements of precipitation and vertical profiles of temperature provide a strong added value for better climate understanding. Reanalyses are of course a good tool to have these three parameters colocated over long-term and at subdaily timescales; however, precipitation mostly relies on the model physics. Moreover,  Flato et al. (2013)  have shown that even in reanalyses, the relationship between the IWV trend and the temperature trend presents differences between reanalyses and deviates from C-C over tropical oceans.
In this study, we make use of the Global Positioning System (GPS) IWV dataset that has been processed as done by  Parracho et al. (2018)  and which provides IWV measurements from over 100 European sites covering a period of 5 years or more. The GPS technique accurately measures IWV (accuracy around 1-2 kg m -2 according to  Bock et al. , 2005 , 2013 , and Ning et al. , 2016)  in all weather conditions including rainy situations, which is an important aspect for our study (e. g. ,  Wang et al. , 2007). GPS measurements have been successfully used to better understand atmospheric processes at high resolution  (Bastin et al. , 2005 (Bastin et al. , , 2007;; Bock et al. , 2008; Champollion et al. , 2009). Here we use a GPS IWV dataset to analyze the humidity biases in regional climate models over Europe at interannual, seasonal and daily timescales and to better understand the source of errors in models.
We also use GPS IWV measurements colocated with precipitation and tropospheric temperature measurements from the SIRTA observatory in France to consider the relationships between these parameters. Note they have not yet been considered outside the tropics. We compare observations and regional models output at the site level and extend the analysis of models to other locations over Europe. The paper is organized as follows: Sect. 2 presents the observational datasets and the different simulations used in this study. Section 3 describes the methodology to compare observations and models. In Sect. 4, the ability of models to reproduce the mean value of humidity and its variability over Europe at different timescales is evaluated. The influence of dynamical and physical processes is discussed, and a special focus on the scaling of IWV with temperature is developed.
GPS IWV data The GPS dataset used in this study is based on homogeneously reprocessed GPS delay data produced by the NASA's Jet Propulsion Laboratory in the framework of the first International GNSS Service (IGS) reprocessing campaign. The data cover the period from January 1995 to May 2011 and include more than 400 stations globally. For the present study, the delay data were screened and converted into 6-hourly IWV estimates as described in  Parracho et al. (2018). Then, daily values are computed using this 6hourly dataset. The dataset was restricted to the period from January 1995 to December 2008 and includes 95 GPS stations over Europe as shown in Fig. 1. It represents many more stations than in the study of  Parracho et al. (2018)  because for the purpose of their study which estimates IWV trends, they restricted their selection to stations with only small gaps over the 15-year period from 1995 to 2010.
Observations at SIRTA This study also uses observations collected at the SIRTA atmospheric observatory, located 20 km southwest of Paris  (48. 7  • N, 2. 2 • E at 160 m of altitude; black triangle in Fig. 1 ), from 2003  (Haeffelin et al. , 2005). This observatory has collected many observations, which are now synthesized into the so-called "SIRTA-ReOBS dataset" described in  Chiriaco et al. (2018a)  and used in  Chiriaco et al. (2014)  and  Bastin et al. (2018). After many steps of data quality control and harmonization, the "SIRTA-ReOBS" file contains hourly averages of more than 50 variables at this site. The sample of data varies from one variable to another. Among these variables, the IWV retrieved from GPS measurement since 2008 and the precipitation rate from a single rain gauge from 2003 to the present are available.
A regional-scale precipitation estimate, deduced from the measurements of other Météo-France rain gauges located around Paris area, is also provided. The Météo-France COMEPHORE ("COmbinaison en vue de la Meilleure Estimation de la Precipitation HOraiRE") product is used to allow a fairer intercomparison between models and observations than the single rain gauge  (Chen and Knutson, 2008) : it is an hourly reanalysis of precipitation by merging radar data and rain gauges over France at 1 km × 1 km resolution (more details in  Fumière et al. , 2019 ; see also  Laurantin et al. , 2012). From this product, we can have a better knowledge of the average precipitation rate over a model grid of 50 km×50 km or higher resolution. However, this product only covers the period 1997-2007 (at the time of this study) and is not concomitant with the IWV dataset over SIRTA.