dataesr/scientific-paragraphs-categorization huggingface dataset:
Consequences of Non-breeding Strategy We estimated body condition using a scaled mass index (SMI,  Peig and Green, 2009)  with bill length as a linear measurement of body size in relation to body mass. However, given that 53 of the individuals were measured in multiple years, we extended this approach to a mixed-effects model with an individual level random intercept, fitted using the package "lme4" v. 1. 1-18-1  (Bates et al. , 2015). The correlation between body mass and bill length accounting for repeated measures was estimated using the package "rmcorr" v. 0. 3. 0 (Bakdash and Marusich, 2017). SMI allows for the comparison of the relative size of energy reserves of individuals within a population, avoiding the assumption that larger animals have better body condition due to a higher absolute mass  (Peig and Green, 2009).
Consequences of Non-breeding Strategy Female gannets were estimated to be on average 89. 4 g (95% CI; 43. 9, 134. 9) heavier than males (χ 2 1 = 12. 9, P < 0. 001). Scaled mass differed slightly between the four colonies (χ 2 3 = 9. 7, P = 0. 02) and post-hoc comparisons indicated that, when averaging over sex differences, individuals sampled at Grassholm were 179. 6 g ± 58. 0 g lighter than individuals sampled at Bass Rock (z = 3. 1, P = 0. 01) with no other significant differences between colonies. There was no difference in scaled mass between individuals using either the northern or southern non-breeding region (χ 2 1 = 0. 58, P = 0. 44), nor were there differences in scaled mass between birds in the two isotopic clusters (χ 2 1 = 0. 0, P = 0. 96). Data on scaled mass and feather stable isotope ratios in two consecutive years were available for 34 individuals sampled at Bass Rock (n = 21) and Grassholm (n = 13).
ACKNOWLEDGMENTS We thank  Sir Hew Hamilton-Dalrymple  ( Bass Rock ), the  RSPB (Grassholm) , the  Neale family (Great Saltee)  and the  Ligue pour la Protection des Oiseaux and Réserve Naturelle Nationale des Sept-Îles (Rouzic)  for permitting and facilitating fieldwork. We are grateful to  Fraser Bell ,  Nadja Christen ,  Armel Deniau ,  Richard Inger ,  Mark Jessopp ,  Greg & Lisa Morgan ,  Claudia Stauss ,  Alyn Walsh ,  Emma Wood , the  Scottish Seabird Center ,  Maggie Sheddan , and  Venture Jet  for field assistance.
FUNDING This study was funded by the  UK Natural Environment Research Council  ( NERC Standard Grant   NE/H007466/1 ). SB is currently funded by an  ERC  consolidator's grant (310820: STATEMIG). EW is funded by the  NERC  ( Independent Research Fellowship   NE/M017990/1 ). Tissue samples were collected under license from the  UK Home Office  ( PPL 40/3408 ).
b. Acoustic analyses All recordings were analyzed using the Praat© voice analysis software (version 6. 0. 31, Boersma and Weenink, 2018). The mean fundamental frequency (F0) and its variation (F0-SD) were measured using the autocorrelation method with a pitch floor of 75 Hz and a ceiling of 300 Hz (Praat's recommendation), with other settings kept as default. The harmonics-tonoise ratio (HNR, in dB) and the local jitter (%), which corresponds to the average absolute difference between consecutive periods, divided by the average period, and calculated in percentage, were measured across the entire utterance using the same settings as the F0. The local jitter corresponds to the jitter ratio, which is commonly used to describe vocal perturbations  (Jones, Trabold, Plante, Cheetham, and Earis, 2001).
e. , likelihood-ratio chi-square tests, ANOVA type III). Additionally, since some acoustic variables are highly correlated (see Table  2 ), we conducted multicollinearity checks on the GLMMs using the variation inflation factors (VIFs). All statistical analyses were performed under the R software (version 3. 4. 0), using the following packages: 'lme4' to build the generalized linear models with random effects  (Bates, Mächler, Bolker, and Walker, 2014) , 'car' to compute the statistical significance of each predictor and check potential multicollinearity problems for the GLMMs  (Fox, Weisberg, and Fox, 2011)  and 'MuMIn' to compute the pseudo-R 2  (Bartoń, 2018). In order to illustrate the results with figures, we used 'boot' to transform the coefficients of the GLMMs back into probabilities  (Canty and Ripley, 2012) , 'dplyr' to compute the predictions of the model  (Wickham, François, Henry, and Müller, 2018)  and 'ggplot2' for the resulting figures  (Wickham, 2009).
Data This study is based on the CMIP5 (Coupled Model Intercomparison Project Phase 5) database. We use the output of 47 simulations listed in Table  1. The models are evaluated over the historical period defined as  by comparing their output to observations. The mean seasonal cycle of SST anomalies over this period is constructed for each model grid point as the difference between the monthly mean temperature and the mean annual temperature. When several members of historical simulations are available for a specific model configuration, they are averaged together. However, this has practically no impact on the estimated mean seasonal cycle (not shown). The mean climatological cycle of the CMIP5 models under study is evaluated against the Extended Reconstructed Sea Surface Temperature data set  (ERSST-v3b, Smith et al.
, 2008) , averaged over the same time period. This data set was produced by NOAA at 2 • spatial resolution. It is derived from the International Comprehensive Ocean-Atmosphere Dataset with missing data filled GISS-E2-H CMCC-CMS 32 GISS-E2-H-CC CNRM-CM5 33 GISS-E2-R CNRM-CM5-2 34 GISS-E2-R-CC ACCESS1-0 35 CCSM4 ACCESS1-3 36 NorESM1-M CSIRO-Mk3-6-0 37 NorESM1-ME inmcm4 38 HadGEM2-AO IPSL-CM5A-LR 39 GFDL-CM2p1 IPSL-CM5A-MR 40 GFDL-CM3 IPSL-CM5B-LR 41 GFDL-ESM2G FGOALS-g2 42 GFDL-ESM2M FGOALS-s2 43 CESM1-BGC MIROC-ESM 44 CESM1-CAM5 MIROC-ESM-CHEM 45 CESM1-CAM5-1-FV2 MIROC5 46 CESM1-FASTCHEM HadCM3 47 CESM1-WACCM HadGEM2-CC in by statistical methods. This data set is used as the target to be reproduced and is denoted "observation field" hereafter. In order to deal with data at the same resolution, all model outputs as well as the observation fields were regridded on a 1 • resolution regular grid prior to analysis.
Doing so allows us to take the nonlinearities of the data set into account and to filter the noise, which can make the classification spurious. This reduced number of dataset vectors enables an HAC to determine the highly nonlinear borders between the different SOM clusters. This procedure has been used with success in several studies  (Farikou et al. , 2015; Jouini et al. , 2016; Niang et al. , 2003 Niang et al. , , 2006;; Sawadogo et al. , 2009). Note that the use of an HAC directly on the initial data set would not be efficient in the present study because the number of degrees of freedom (here the grid points of the initial domain) is too large for this method to work efficiently. In the present section, we describe the methodology we developed to score the different climate models with respect to the observations.
The unsupervised classification method The first step of the methodology was to decompose the selected region into different classes (the key sub-regions mentioned above) by using a neural network classifier, the so-called SOM  (Kohonen, 2013). This algorithm constitutes a powerful nonlinear unsupervised classification method. It has been commonly used to solve environmental problems  (Hewitson and Crane, 2002; Jouini et al. , 2013 Jouini et al. , , 2016;; Liu et al. , 2006; Reusch et al. , 2007; Richardson et al. , 2003). The SOM aims at clustering vectors (here the 12 SST seasonal anomalies) of a multidimensional database (D) (here the grid points of the studied domain) into classes represented by a fixed network of neurons (the SOM). The SOM is defined as an undirected graph, usually a two-dimensional rectan-gular grid.
The estimation of the referent vectors w of a SOM and the topological order is achieved through a minimization process using a learning dataset base, here from the observations. The cost function to be minimized is of the form J T SOM (χ , W ) = zi∈D c∈SOM K T (δ (c, χ (z i )))|z i -w c | 2 , where c ∈ SOM indices the neurons of the SOM, χ is the allocation function that assigns each element z i of D to its referent vector w χ(z i ) and δ(cχ (z i )) is the discrete distance on the SOM between a neuron c and the neuron allocated to observation z i. K T a kernel function parameterized by T (where T stands for "temperature" in the scientific literature dedicated to SOM) that weights the discrete distance on the map and decreases during the minimization process. At the end of the learning process, the classification can be visualized onto the SOM and interpreted in terms of geophysics.
Classification of the observations In the present problem we chose to classify the annual cycles of the SST seasonal anomalies observed in the Senegalo-Mauritanian upwelling. The study was made on the "extended region" constituted of 25 × 36 = 900 pixels, but this enlarged region covers a part of the African continent, and 157 pixels are in fact over land. That means that we have truly 743 ocean pixels to deal with. We consider a time period of 30 years  [1975 to 2005]  extracted from the ERSST-V3b database. For a given grid point and a given year and month, the monthly anomaly is the SST of the pixel for which we have subtracted the mean of the considered year. The climatological mean of the anomaly is then computed for each grid point by averaging each climatological month over the 30 years.
Thus, the learning data set D is a set of 743 12-component vectors z, each component being the mean monthly anomaly computed as above. We denote as "SST seasonal cycle" the vector z in the following. We used a SOM to summarize the different SST seasonal cycles present in the "extended region". We found that 120 prototypes (or neurons) can accurately represent the 743 vectors of D. This reduction (or vector quantization) is made by using a rectangular SOM of 30 × 4 neurons. We then reduced the number of neurons in order to facilitate their interpretation in terms of geophysical processes. For this, we applied a HAC using the Ward dissimilarity  (Jain and Dubes, 1988). We grouped the 120 neurons of the SOM into a hierarchy that can contain between 1 and 120 clusters. Then the different classifications proposed by the HAC were applied to the geographical region: each SST seasonal cycle of each grid point of the region is assigned to a neuron and consequently to a cluster (assignment process), thereby defining the so-called region clusters.
Qualitative analysis of the climate models In order to further progress in the selection of the models, the 47 climate models and the observation field were then analyzed by using an MCA. MCA is a multivariate statistical technique that is conceptually similar to principal component analysis (PCA) but applies to categorical rather than continuous data. Similarly to PCA, it provides a way of displaying a set of data in a two-dimensional graphical form. In the following, we apply a MCA to the (47, 7) matrix R = [R m,i ] whose elements represent the skills of the clusters of the models shown in front of the color bars in Fig. 3 : the rows m represent the 47 different models, the columns i the seven region clusters. The MCA, like the PCA, projects the initial matrix onto a new basis in such a way that the new axes are the matrix eigenvectors (PC), the inertia of each axis being the corresponding eigenvalues.
The problem is now to determine a subset of models which has a better skill than Model-All, in other words minimize the distance to the observations. As the number of models is small enough, we chose to cluster them by an HAC according to their projections onto the six axes provided by the MCA and select the optimal jump in the hierarchical tree  (Jain and Dubes, 1988). We recall that the HAC (hierarchical ascending clustering) is a bottom-up algorithm for dataset clustering. The key operation in hierarchical bottom-up clustering is to repeatedly combine the two nearest (according to a certain distance) clusters into a larger cluster. The HAC starts from individuals and combines them according to their similarity (with respect to the chosen distance) to obtain new clusters. The process is repeated up to get one cluster only (the full data set).
Representation of the upwelling in the CMIP5 climate model clusters In this section, we compare the representation of the Senegalo-Mauritanian upwelling system given by the two best model groups identified above (model group 4 and ZModel group 2). For this evaluation, we use two of the five indices used by  Sylla et al. (2019)  to evaluate the full database, namely the intensity of the SST seasonal cycle and the offshore Ekman transport at the coast. The former is specific to the seasonal variability of the Senegalo-Mauritanian upwelling system, and it has been used for the classification. The latter is more general and, although it has recently been shown to partly represent the volume of the upwelled waters  (Jacox et al. , 2018) , it is extensively used in the scientific literature to characterize upwelling regions  (Cropper et al.
Discussion and conclusion This paper proposed a novel methodology for selecting efficient climate models in a specific area with respect to observations and according to well-defined statistical criteria. The present study has specifically focused on the ability of climate models to reproduce the ocean SST annual cycle observed in specific sub-regions of the studied domain during the period 1975-2005 as reported in the ERSST_v3b data set. These sub-regions were defined by a neural classifier (SOM) as clusters with similar seasonal SST cycle anomalies with respect to some statistical characteristics and were therefore named region clusters. They correspond to ocean areas with well-marked oceanographic specificities. We then checked the ability of the different climate models to reproduce the region clusters defined on the observation data set with a SOM.
Such a procedure could advantageously be used by individual modeling groups to identify, analyze and therefore hopefully reduce their model biases in a targeted region. Secondly, from a physical point of view, an identified model group can be used to analyze the targeted region (here the SMUS) in terms of processes, with the advantages of a subset of models which have been selected from quantitative criteria. Such an application has been briefly il-lustrated by showing how the selected model group represents an important additional characteristic of the SMUS not used for the selection, namely Ekman pumping. A promising reduction of biases of the full multi-model mean ensemble has been identified, opening possibilities for process studies based on this sub-ensemble of the CMIP5 database. A third application of the selection lies in the prediction of the future climate.
Code and data availability. The model output used for this study is freely available on the ESGF database, for example following this url: https://esgf-node. ipsl. upmc. fr/search/cmip5-ipsl/ (Taylor et al. , 2012). The SST data were provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their website at https://psl. noaa. gov/ (Smith the al. , 2008)  and the wind data here: https: //podaac. jpl. nasa. gov (Praveen Kumar et al. , 2011). The code developed for the core computations of this study can be found under https://doi. org/10. 5281/zenodo. 3476724 (Mejia and Sorror, 2019). This code allows reproduction of Figs. 2, 3, 6, 7 and 8.
1b ; Supplementary Fig. 3 , transcriptomic data GEO dataset). Conversely, MESP1, and cardiac chamber specific genes TBX5, MEF2c, NPPA, MLC2v were not expressed in HPVCs (see transcriptomes). Comparisons of HPVC transcriptomes with H1 and H9 ESC-derived mesenchymal cells or bone marrow derived mesenchymal cells as well as with E9. 0 AVC cells indicated HPVCs clustered with E9. 0 AVCs and to a lesser extent to previously reported AVCs  28  and displayed little correlation with human ESC-derived or mesenchymal stem cells (Fig. 1b ). The HPVC transcriptome further showed the presence of genes specific to AVC (TBX2) and endocardium (MSX1) as well as genes involved in Notch signaling (JAG1, HEY1) or in endocardial cushion mesenchymal transition (TGFβ, SMAD6) (Fig. 1b ). Immunofluorescence and high content imaging confirmed that HPVCs featured morphology of endothelial cells and showed expression of endothelial CD31 and KDR in 95% of cells (out of 850 scored cells in 2 separate experiments), VE-cadherin (Fig.
1c ), as well as AVC and endocardial cells (GATA5, TBX3, TBX20, NFATc and MSX1) protein markers (Fig. 1d ). To estimate the cell heterogeneity of HPVCs, a single cellsequencing approach was used. SSEA1+ MESP1+ sorted cells were plated on fibronectin-coated dishes and induced with VEGF/FGF8/FGF2 to an endocardial cell phenotype. Cells were then further sorted using an anti-CD31 antibody and used for single cell-sequencing. We performed a two-step data analysis to cluster cells by principal component analysis. Figure  1e  revealed the endocardial phenotype of ~3000 HPVCs at a single cell level. Although the cells were CD31-sorted, the cell population was heterogeneous. Five main cell clusters were identified as expressing a specific gene pattern depending upon their stage in the early process of EMT (Fig.
An attempt to carry out a transcriptome of E9. 5 AVC was reported previously  28. However, a careful analysis of these data revealed that the AVC transcriptome was contaminated by high expression of chamber specific genes such as TnT, or Gja1 (see GEO data set GDS3663) and key AVC specific genes such as Tbx2 were missing (see GEO dataset GDS3663 and Fig. 1d ). This could explain why this previously reported gene signature did not cluster tightly with those of our dissected AVC and HPVCs (Fig. 1e ). One study using a SAGE protocol reported a gene expression profile of mouse cardiac AVC at a slightly later stage (E9. 5) of development  45. The comparison of our microarray data with that of the Tbx3-GFPsorted cells  44  and the SAGE data  45  further revealed the expression of common and major genes in AVC vs.
Genomic integrity was tested at passages 15-20 using digital PCR of copy number variants of main human recurrent genomic abnormalities (stemgenomics, Montpellier, France). Reverse-Transcription Real-time quantitative PCR. RNA was extracted from SSEA1+ cells or VEGF/FGF8/FGF2-treated using a Zymo research kit. RNA (1μg) was reverse-transcribed using the Superscript II reverse transcriptase (Invitrogen, Cergy, France) and oligo (16)dT primers. Q-PCR was performed using SYBR Green and a Light Cycler LC 1. 5 (Roche Diagnostic). Amplification was carried out as recommended by the manufacturer. Data analysis and primers specific for human genes are described in  23  and in Supplementary Data 2. Microarrays. The AVC and the primary ventricles of E9. 0 mouse embryos were dissected out and RNA (triplicates) was extracted with a Zymo research kit.
RNA was also extracted from HPVCs. cRNAs were profiled using Illumina Mouse WG-6 v2 BeadChips or human Expression HG-U133 arrays and analyzed in Genespring GX 11. 0. Data were quality filtered to exclude signal intensities below background, and expression profile differences of 4-fold or greater at a significance threshold of 0. 01 or less were compiled into upregulated and downregulated transcript lists. Bioinformatically filtered genes were used for downstream analysis in Ingenuity Pathway software to examine network relationships as well as identify overrepresented gene ontologies. Cluster analysis of AVC and mesenchymal stem cell data sets were conducted as follows. Previously published AVC (GDS3663) and mesenchymal array data sets (GDS1288) were downloaded from GEO. HPVC gene expression profiles were examined using HG-U133 arrays and signal values were computed using GCRMA normalization.
All expression signals for each dataset were Log10 transformed and normalized by z-score transformation  66. Three experimental replicates were then averaged for each study, human and mouse orthologs and/or homologs between microarray data sets were determined by matching NCBI gene symbols across experiments and between species. This resulted in 9,385 orthologues that were then clustered using the HOPACH algorithm for gene clustering with the cosangle distance metric  67  or by hierarchical clustering with the Euclidean distance metric. Single cell-sequencing. HiPS cell-derived HPVC and hiPS cell-derived valvular interstitial cells were dissociated with trypsin into single cells and processed with the SingleCell3 Reagent Kit on the Chromium platform as described by the manufacturer (10X genomics).
cDNA libraries were sequenced with a Next-seq Illumina sequencer. A first analysis was performed with the Cell Ranger and loop cell10X genomics softwares. Clusters and subclusters were defined using genes that were differentially expressed in cell clusters in comparison with all other cells with a threshold of Log2 equal to at least 2. A secondary analysis was performed by B Jagla at the Pasteur Institute (Paris) using the SCDE package and scShinyHub (publicly available at https://github. com/baj12/scShinyHub). 1895 genes were detected as expressed in 50% of cells and 4576 in 25% of cells (Supplementary Fig. 2 ). Antibodies. Antibodies used for cell or embryo immunofluorescence were used at a dilution of 1/100 unless otherwise specified. Antibodies were raised against Isl1 (Developmental Hybridoma bank, Iowa University 1/50), Tbx20 (novus biological H00057057-B01), Msx1 (Abcam ab93287), Tbx3 (Santa Cruz sc-178721), aggrecan (Chemicon AB1031), versican (Chemicon AB1033), smooth muscle actin (Sigma A-2547), Filamin A (Epitomics, CA, USA), vimentin dylight 550 (Novus biological, VM452, 1/200), periostin (Abcam ab140141), CD31 (BD pharmigen, WM59), VEcadherin (R&D systems/MAB9381), Sox9 (a gift from Pr Wegner, University of Nurnberg, and Santa-Cruz Sc17431), GATA5 (Abcam ab11877), Hyaluronanbinding protein (Millipore 385911), collagen I (Abcam 34710), anti-Patched1 (Merck-Millipore, France, 06-1102), human Lamin A/C (Novacastra, NCL-LAM-A/C), and anti-NFATc (Santa-Cruz 17844 H10).
Cell imaging. High content imaging was performed in 96 wells plates using an Arrayscan (Cellomics Thermo Fisher Scientific) attached to an inverted microscope (Carl Zeiss, Inc. ) using 20x N-Achroplan objective, NA 0. 45, at room temperature. Other images were observed in epifluorescence microscopy (Zeiss microscope) or using an Ultraview Vox Spinning disk Perkin Elmer confocal microscope driven by the Volocity software or using a TRIO multispectral analysis setup (Caliper, Science). To visualize cilia and Ptch1 staining, stacks of images were acquired using a Zeiss observer epifluorescence microscope using a Plan-Apochromat 63X water objective or a confocal LSM800 Zeiss microscope equipped with an airyscan of 32 detectors. Light was provided by a Colibri 7 source or a laser module 405/488,561 and 640 nm wavelengths, respectively.
Images were acquired using the ZEN ZEISS software. Then some images were deconvoluted using Autoquant and reconstructed in 3D using Imaris software (IMARIS). Quantification of extracellular matrix proteins was performed by thresholding images acquired with a Zeiss observer microscope or a confocal LSM800 equipped with a Colibri7 4LED light source using the same LED intensity and same exposure time for the CCD camera for all images or a laser module using a constant laser power. The surface of the cell field labelled by the antibody was calculated using Image J (NIH image). All details of image acquisition including excitation wavelengths and power of the light source, emission wavelengths and detector sensitivity as well image processing can be found in Supplementary Table  1 , "Cell imaging parameters".