dataesr/scientific-paragraphs-categorization huggingface dataset:
As done for analyzing the impact of SSM errors on VOD estimation, a synthetic database of σ 0 soil and of σ 0 tot was computed using the calibrated IEM combined with the Water Cloud Model  [23, 30]  for Hrms values between 1 and 3. 5 cm (step of 0. 1 cm), NDVI between 0. 4 and 0. 8, SSM fixed to 20 vol. %, and θ was fixed to 35 •. From each reference element of the synthetic database composed of θ, Hrms, SSM, and NDVI, 1000 elements containing Hrms-values noisy with a Gaussian noise of 1 cm mean and 1 cm standard deviation were generated. Figure  13  shows the reference VOD values (without bias on Hrms) and noisy VOD values (1 cm Hrms overestimation). Results show that a 1 cm Hrms overestimation leads to a slight overestimation of VOD of 0. 03 (with an RMSE of 0. 04). In addition, the impact of 1 cm Hrms overestimation on VOD estimation was analyzed according to the NDVI for values between 0.
Acknowledgments: The authors wish to thank the  French Space Study Center  ( CNES, TOSCA program ) and the  National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA)  for supporting this work. Moreover, the authors wish to thank the  European Spatial Agency (ESA)  for the distribution of Sentinel-1 and Sentinel-2 data, and the  French land data center (Theia)  for providing Sentinel-2 images corrected for atmospheric effects. Finally, authors wish to thank the  General Direction of Rural Development of the Department of Agriculture, Livestock, Fishing and Food of the Generalitat of Catalunya  for providing the SIGPAC data and in particular  Maria Jose Escorihuela.
Data analyses. Where applicable, data were checked for normality using Shapiro-Wilk tests prior to analyses in R version 3. 5. 3  63 within the RStudio interface ver. 1. 0. 153  64. For all tests, the level of statistical significance α was set at 0. 05, and results are reported as means ± SD. To investigate potential intraspecific life-history variabilities in neonates and temperature differences across habitats, mean L PC , M TB and water temperatues were compared with two sample t-tests, and frequency histograms were subsequently constructed. Total body mass for a given L PC was used to estimate body condition, assuming that individuals in a good condition would be heavier than those in poorer condition of the same length. Thus, we determined allometric length-mass relationships by using the formula log y = log a + b log x.
Acknowledgements This research was gratefully funded by the  Save Our Seas Foundation  ( SOSF Keystone Grant 290 ) and the  Basler Stiftung für biologische Forschung  awarded to OCW. IAB was supported by a  James Cook University   Postgraduate Research Scholarship. JLR was supported by an  Australian Research Council (ARC)   Early Career Discovery Fellowship  [ PDE150101266] (2015-2017 ) and the  L'Oréal-UNESCO Women in Science Foundation  Fellowship ( 2015-2016 ) concomitantly with an infrastructure and research allocation from the  ARC Centre  of.
However, the severity of coronary artery disease (CAD) has been poorly described in patients with FH hospitalized for an acute MI  [16]. Moreover, no data are available on the prevalence and characteristics of French patients with FH and hospitalized for MI. The detection of FH at time of an acute coronary event is not systematically performed in France, although these patients are at high risk of recurrence of cardiovascular events  [17]. Therefore, in the present study, we aimed to investigate the prevalence of FH, the risk factor burden and the severity of CAD at the time of admission in a coronary care unit (CCU) from a large database of a regional French registry (RICO registry) of patients with acute MI.
Study population This study was performed with the framework of the RICO (obseRvatoire des Infarctus de Côte d'Or) survey designed to include all consecutive ≥ 18 years patients hospitalized for an acute MI since 2001 in a multicenter registry of all the CCU of the region of Côte d'Or in France. Details regarding the methods of the RICO survey were previously reported  [18]. Briefly, baseline characteristics including demographic data, risk factors, treatments for hypercholesterolemia, family history of CAD (defined as premature CAD in first-degree relative < 55 years for men or 65 years for women), cardiovascular history and chronic treatments were prospectively collected. At admission, the following data were also collected: Killip class, ECG signs and hemodynamic parameters (heart arte (HR), systolic (SBP) and diastolic (DBP) blood pressure).
Left ventricular ejection fraction (LVEF) was measured by echocardiography < 12h following admission. Blood sample was taken at admission for lipid profile. Hospital complications were determined and delays in time to admission as well as prolongation of stay in CCU). For each patient, GRACE (Global Registry of Acute Coronary Events) risk score, as a tool to assess mortality risk in acute MI (http://www. outcomesumassmed. org/grace/acs_risk/acs_risk_content. html) and SYNTAX (SYNergy between PCI with TAXus and cardiac surgery trial) score, as an index of CAD severity, were calculated  [19, 20]. Patients were recruited consecutively after obtaining written informed consent, with the exception of patients who died in the first hours after admission or with a severe cardiogenic shock. The study was conducted in compliance with Good Clinical Practice guidelines and the French data protection law.
The choice for a conservative correction factor for statin therapy (+30%) is driven by the preferential use of moderate intensity statin in France. The presence of tendon xanthomas or corneal arcus, family history of hypercholesterolemia or vascular disease were not recorded in the database. Missing information was counted as zero, as done in previous studies  [6, 9, 15]. A probable/definite FH diagnosis was considered when the DLCN score was ≥ 6, a possible diagnosis when the score was 3-5 and unlikely when the score was < 3 points. The group of patients with probable/definite FH was compared with the group unlikely FH, called "no"FH.
The prevalence of 2. 1% of probable/definite FH in this large database is in excellent agreement with data obtained in Switzerland (1. 6% in the SPUM-ACS cohort)  [6]  and in Denmark (2. 0% in patients with first MI)  [10]. The higher prevalence reported in Australia by Pang et al. [8]  could be due to the larger correction factor used for adjustment of LDL-C in individuals on statins associated with a larger use of statins (68% vs 48%). Indeed the mean correction factor was 2. 0 in the study from Pang et al. [8]  and only 1. 48 in our study. The high prevalence (8. 3%) found in EUROASPIRE IV  [7]  could be a reflection of the differences in recruitment methodology. As for the Danish cohort  [10] , all patients hospitalized for an acute MI were included in our consecutive study. A study conducted in the Arabian Gulf using the same corrected factor for prior statin use has reported a higher prevalence 3.
Acknowledgments This work was supported by the  University Hospital of Dijon , the  Association de Cardiologie de Bourgogne , and by grants from the  Agence Régionale de Santé (ARS) de Bourgogne Franche-Comté ,  Institut National de la Santé et de la Recherche Médicale (INSERM)  and from the  Regional Council of Bourgogne Franche-Comté.
Empirical studies investigating the determinants of domestic corporate bond market development in Asia are limited in scope. Most of the few existing studies emphasize the importance of economic indicators and legal rights as drivers of bond market development. We extend existing research and consider a larger variety of factors that may influence corporate bond market development in ten Asian countries. We use data on the size of the corporate bond markets as well as on the value of corporate bond issues in the years 1995-2014. Hence, the data cover the periods following the Asian financial crisis of 1997 and the global financial crisis of 2008. The rest of the paper is organized as follows. Section 2 summarizes the literature and presents the development of the corporate bond markets in selected Asian countries since 1997.
Data and descriptive statistics We use a panel data set with annual observations for the following Asian countries: China, Hong Kong, Indonesia, Japan, Korea, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. The country coverage and time dimension choices are primarily based on the availability of corporate bond market data in the AsianBondsOnline database, which tracks the bond market in Asian countries. In addition, we supplement the data using the World Bank database, from which we retrieve most of the independent variables. The definitions of the variables and their data sources are presented in Table  A. 1 in the Appendix. We constructed a panel of 200 country-year observations for the ten Asian countries. The panel covers the years 1995-2014, and it is unbalanced owing to missing data for some years for some of the countries in the sample.
Moreover,  Becker and Ivashina (2014)  show that companies are more likely to issue public debt during a contraction of bank credit supply. Consequently, the authors find a substitution effect between bank credit and public debt and present a strong pro-cyclical pattern in the debt financing mix of the companies. The ratio of the value of corporate bonds issued to GDP includes debt issued by financial and non-financial companies, even though both types of firms differ strongly in terms of their capital needs. Thus, we use two additional measures to control for corporate bonds issued by non-financial and financial companies. We retrieve the data on the market value of corporate bond issues by non-financial companies and their average maturity from the World Bank. The value of the bonds issued by financial companies is the total value of corporate bonds issued minus the bonds issued by nonfinancial companies.
We find some inconsistencies in the information about the total value of corporate bonds retrieved from AsianBondsOnline and the value of nonfinancial company bonds retrieved from the World Bank. In those cases, we prioritize the information provided by the AsianBondsOnline. We consider that the existing discrepancies between the two data sets do not affect the results of this study. In measuring both corporate bonds outstanding and bond issues, we restrict our focus to public debt issued in local currency. We do so because we are especially interested in the determinants of the development of the domestic public bond market, which helps a country's companies raise funds for future development and potentially decrease the double mismatch dilemma. Moreover, Asian corporate bond markets are characterized by their high proportion of bonds denominated in local currency.
Acknowledgments We would like to thank the Editor  John W. Goodell ,  Cezary Wójcik , the two anonymous referees, and the participants of the  IFABS Asia Conference  and the seminars at the  IÉSEG School of Management, Warsaw School of Economics and University of Warsaw (WES)  for their valuable comments and suggestions. This work was supported by the  National Science Centre (NCN)  under contract  UMO-2015/17/N/HS4/02939.
After the analysis, the coincidence of observed and expected p values was visualized in a QQ plot for each trait. Several authors have used these QQ plots to determine the best fitting models visually  [33] [34] [35]. When looking to the example of a QQ plot for carotenes contents in Figure  1 , it can be seen clearly that the GLM_Q model represented by "stars" is the worst for fitting our data, while deciding visually between the other three models is impossible. Therefore, we developed an equation to calculate the average square distance (d 2 ) of the CG data points from the diagonal of the QQ plot which represents an objective method for determining the best fitting model for each trait. In our study the mixed effects models fitted better for most of our traits, while only a few traits were found to have better associations with GLM models where the K matrix was not taken into account.
Trait Recording Eo × Eg genotypes were planted in 2010 and phenotypic data recording started in 2014. In total, six production traits and 19 quality traits were studied. The phenotypic raw data are shown in Supplementary Material, Table  S4. The evaluated production traits were bunch number (BN; (nº)), bunch weight (BW; (kg)), bunch yield (BY = BN*BW; (kg)), oil percentage in fresh mesocarp (OilfM; (%)), oil percentage in dry mesocarp (OildM; (%)), and oil percentage in the bunch (OilB; (%)). BN and BW data were collected over four years and cumulative data were used for the analysis. OildM data was determined by Soxhlet extractions. OilfM and OilB were calculated according to García and Nañez  [55]  as modified by Arias et al. [19]. The analyzed oil quality traits considered different components of lipids and tocols, as well as carotenoids.
Sequence Processing and Association Analysis Analyses of the obtained sequences were performed using the South Green Bioinformatics Platform http://southgreen. cirad. fr/ [63] , which provides different bioinfomatic tools and methods for sequence analysis. The Fastq files of the three libraries were combined and processed together, since all genotypes had the same MID combination in the three libraries. In order to obtain clean amplicon sequences, trimming and demutliplexing steps were performed. First, each genotype was identified by the combination of MIDs in each read. Sequences were separated in genotype specific files. For this purpose the public "demultiplex. py" Python script  [64]  was used. Then, the "Cutadapt trimming tool" v1. 8. 1  [65]  was applied to remove universal primer parts (UniA, UniB) and the MIDs.
Genetic diversity was studied in terms He and Ho of the markers using the adegenet  [72]  and hierfstat  [73]  packages in R. Monomorfic markers were eliminated for the following analyses. For studying genetic variances between and within origins, Fst obtained from VCFtools and Fis obtained from the hierfstat package were used. We tested also for HWE using the pegas package  [74]. The null hypothesis (Ho = 0; p value < 0. 05) was that the population is in equilibrium and pairing occurs randomly. fastStructure software v1. 0  [75]  was applied to analyze the population structure. Allele frequencies of each cluster from 1 to 9 were estimated with a 10-fold cross-validation (CV). In order to choose the appropriate number of model components explaining the structure in our dataset, thechooseK.
py script of the fastStructure software was run. The distruct. py script from the fastStructure was used for drawing the distruct plot. Association studies were performed on a single marker basis using GAPIT v 3. 0  [76]  in R environment. Initially, fixed effects GLM were applied to test associations between segregating markers and phenotype for each trait. For this purpose, either Q matrix obtained from fastStructure (K = 6) was used as covariate, or PCA matrix with three components derived from GAPIT was used as covariate (GLM_Q, GLM_PCA). In addition, MLM analyses were performed in order to include both fixed and random effects. In this case, the IBS K matrix obtained from Tassel (v5. 2. 44) was incorporated into the previous models (MLM_Q+K, MLM_PCA+K) in order to reflect relationships among individuals with either the Q matrix or the PCA matrix.
Multiple testing was also considered, since GAPIT provides beside unadjusted p values also FDR using the method of Benjamini and Hochberg  [77]  adjusted p values. The resulting observed and expected p values of each model were visualized separately for each trait in a QQ plot, in order to get a first impression on the fitting of different alternative models. In addition, an equation was developed to measure the average square distance (d 2 ) of the CG data points from the diagonal of the QQ plot for each model: d 2 = ( where, P o and P e are the expected and observed -log(p) values, respectively and n the number of CG data points. The model with the smallest d 2 value was considered as the best fitting model for our data.
, 2015). This explains why it is necessary to introduce an accurate sea state description, from wave model (or database as for example  Rascle et al. , 2007) , which control exchanges between ocean and atmosphere. Waves affect the ocean surface layer through different processes  (Breivik et al. , 2015)  : -Waves induce current in surface via the Stokes drift, rapidly attenuated with depth. The Stokes drift velocity associated with the wave fields adds a term on the Coriolis effect in the momentum equation. This process is called Stokes-Coriolis forcing. -A part of the atmospheric wind stress is used by waves to grow and is not provided to the ocean. This energy quantity must be subtracted from the oceanic wind stress which drives the ocean model. -During wave breaking, turbulent kinetic energy is produced and induces to the ocean surface layer an enhanced turbulent mixing.
The marine iodine cycle has significant impacts on air quality and atmospheric chemistry. Specifically, the reaction of iodide with ozone in the top few micrometres of the surface ocean is an important sink for tropospheric ozone (a pollutant gas) and the dominant source of reactive iodine to the atmosphere. Sea surface iodide parameterisations are now being implemented in air quality models, but these are currently a major source of uncertainty. Relatively little observational data is available to estimate the global surface iodide concentrations, and this data has not hitherto been openly available in a collated, digital form. Here we present all available sea surface (<20 m depth) iodide observations. The dataset includes values digitised from published manuscripts, published and unpublished data supplied directly by the originators, and data obtained from repositories.
8] [9]  ). However, current sea surface iodide parameterisations are known to have biases  10  , are subject to substantial uncertainty  6  , and do not take advantage of recent and substantial increases in the number of available observations (e. g. 11 ). The observational data underpinning iodide parameterisations is sparse, and has hitherto not been publicly available in a collated form. In many cases, iodide observations are not readily accessible in a digital form (i. e. are only presented in graphical format). To facilitate the development and validation of improved sea surface iodide parameterisations, we have compiled all available sea surface iodide observations. The dataset is an extended version of that used in our earlier publication  12  , in which we described the large scale sea surface iodide distribution and presented correlations between iodide and other oceanographic variables, but did not publish the observations themselves.
The dataset we now present incorporates more than 400 new observations (see Fig. 1 ), including new, basin scale transects from the Indian Ocean (currently unpublished) and the tropical eastern Pacific  11  , both of which were previously undersampled  12. This new extended dataset is freely available via the British Oceanographic Data Centre (BODC; http://doi. org/czhx)  13. We anticipate that the primary use of the dataset will be modelling of ozone deposition to the sea surface and/or associated trace gas emissions to the atmosphere. It has been used to generate new monthly parameterised sea-surface iodide fields (12 × 12 km resolution) using a machine learning approach, described in our accompanying partner publication  10. The dataset may also be of interest in other areas of iodine research.
Methods Data compilation. The data set includes iodide measurements made by a number of different research groups (Online-only Table  1 ). These were collated from the following sources: A. Published manuscripts. Data was digitised from tables and graphics, either by hand or using the free online tool WebPlotDigitizer (https://automeris. io/WebPlotDigitizer). B. Data originators. Data (both published and unpublished) was provided directly by the owners. C. Data repositories. Data was obtained by request or on-demand download from hosting repositories (e. g. BODC, PANGAEA, the US JGOFS Data System). Following the approach adopted previously 12 , 'surface' concentrations are considered to be those from depths of less than 20 m. As discussed in Chance et al. 12  , the ocean is usually considered well mixed to this depth, and to restrict 'surface data' to shallower depths would substantially reduce the number of observations included.
We examined a sub-set of data (n = 93) where observations were available from multiple depths within the upper 20 m of the water column. While significant differences were occasionally found between individual pairs of samples collected from depths of ~1-2 m and ~10 m at a given station, concentrations were within 10 nM in almost 50% of pairs (49. 5%), and 80% were within 26 nM. Statistical analysis (using a paired students t-test) found no significant difference between samples from different depths within the upper 20 m. The exact depth of near surface samples can itself have high relative uncertainty, as factors such as sea swell can lead to metre scale fluctuations to the exact depth of e. g. a ship seawater inlet. Furthermore, the exact depths of such inlets, or the 'surface' sample bottle, was not always stated in the original data sources.
Therefore, we have not included depth as a parameter in our compiled data set and no distinction has been made between samples obtained using a CTD rosette fitted with Niskin bottles (or similar), a pumped underway seawater supply or a manual method (such as bucket sampling). Each data set was entered onto an individual Excel spreadsheet in a standard format. Rarely, source values were below the limit of detection (LoD) for the method used. Where this was the case, we have used a substitute value of 0. 75 x the estimated LoD and the data point was flagged (column 'ErrorMethod'). No further processing has been applied to any of the data. It has not been normalised e. g. to salinity. Required fields from individual 'input' files were then collated into a single comma-separated value (. csv) file using open-source Python code, including the Pandas package  14.
A total of 1342 observations, from 57 individual data sets has been collated (Online-only Table  1 ). This is an increase of 417 observations (45%) on that included in our earlier compilation  12. Locations of individual data points are shown in Fig. 1 , which highlights how the expanded dataset increases spatial coverage. The earliest observations were made in 1967 and the most recent in 2018. For some data points (n = 32) the date of sampling is not specified as this was not given in the original publication. Ten of the input data sets are currently unpublished.
Additional fields. Each iodide observation is associated with the record fields listed in Table  1. In addition to spatial and temporal co-ordinates, the estimated uncertainty and analytical method used to generate the observations are provided. Method. Analytical methods are summarised in Table  2. In the majority of cases (~53%), iodide was measured by cathodic stripping square wave voltammetry (CSSWV) according to the method of Campos  15. However a range of other measurements techniques were also used. Iodide was sometimes measured as the difference between the total inorganic iodine (TII) concentration and the iodate concentration. Uncertainty. Measurements of iodide in seawater are subject to non-trivial analytical uncertainties, which should be considered when using the data set. An estimate of the uncertainty associated with each observation has been included, using either information provided by the data source where available, or comparison to other measurements using the same analytical method.