dataesr/scientific-paragraphs-categorization huggingface dataset:
earthenv. org/DEM. html. The process was performed in a meta-software called PAVICS-Hydro (Power Analytics and Visualization for Climate Sciencehydrological modelling toolbox) being developed for this purpose, available at: https://pavics-sdi. readthedocs. io/. This set includes mean watershed elevation (meters), slope (degrees), aspect (degrees), Gravelius index (unitless) and perimeter (kilometers). The elevation and perimeter are self-explanatory. The slope is the average slope when considering the individual elevation differences between tiles and can be seen as an indicator of the catchment relief, with higher slopes indicating more mountainous regions. The aspect is the main orientation of the catchment, i. e. where the average slope points towards. The Gravelius index is the ratio of the perimeter of the watershed compared to the perimeter of a circle of the same area.
Higher values indicate more elongated or less compact catchments. All the DEM points falling within a watershed boundary were used to compute these characteristics. The database also provides elevation band data for each catchment in 100-meter intervals. This information was extracted from the EarthEnv 90 m DEM by applying a zonal histogram to a reclassified DEM based on 100-meter intervals in the QGIS software. The data are provided in a separate. csv file "HYSETS_elevation_bands. csv" and represent the percentage of catchment area lying below that elevation. Therefore, the curves are cumulative sums of these areas. This will allow users to provide information to more complex routines and models such as the well-known CemaNeige snow accounting and melt model. The elevation bands can also be used to adjust precipitation rates and temperatures per precipitation bands based on the average catchment elevation and the elevations in the bands, depending on the desired lapse rates and correction methods.
The land use percentages reflect which fraction of the watershed is covered in the different classification categories. The North American Land Change Monitoring System (NALCSM) imagery data from 2010 was used for this purpose  36, 37. NALCMS was developed by Canada, the United States and Mexico to track the evolution of land use over time. A static dataset for 2010 is available and contains 19 land use classes values that were combined to form 7 meta-categories: forests, shrubs, croplands, wetlands, water, urban and permanent snow/ice. For example, coniferous, deciduous and mixed forests were combined into the "forest" category. The original data are available at the Commission for Environmental Cooperation website: http://www. cec. org/tools-and-resources/north-american-environmental-atlas/north-american-land-changemonitoring-system For each watershed, the NALCMS raster dataset was queried through the PAVICS zonal statistics toolbox for each of the 19 original categories.
The values were then aggregated to the 7 categories used in the HYSETS dataset and the relative fraction of each was computed. The dataset used to characterize catchment geology is the GLobal HYdrogeology MaPS (GLHYMPS) of subsurface permeability and porosity  38. This dataset provides quantitative estimates of permeability and porosity below the soil horizon. Catchment averages of these two variables were calculated, by considering the contribution of each spatial polygon being weighted by the fraction of a catchment it covers. The arithmetic mean was used for porosity, but for permeability, the geometric mean was taken. The same process as for NALCMS was performed. However, a pre-processing of the GLHYMPS vector data was performed to transform it to a raster format. The same zonal statistics tools from PAVICS were used to extract the average values of both variables for each catchment.
The permeability units are in m 2 whereas the porosity is archived as a fraction. The GLHYMPS dataset is available at: https://dataverse. scholarsportal. info/dataset. xhtml?persistentId=doi:10. 5683/SP2/DLGXYO. For all the above-mentioned properties, if for any reason the extraction process could not be performed (watershed out of product boundaries, unavailability at a given location) the values are replaced by NaNs and a flag was set in the metadata and properties file.
Monthly hydrometeorological data. One aspect that must be noted is that the data are all averaged temporally at the daily scale and spatially at the catchment scale. This means that for large catchments, it is possible that the daily data are not very representative of the localized precipitation and runoff events. For this reason, the HYSETS database also includes monthly-aggregated data for all catchments, which will allow evaluating products in terms of bias and mass balance over longer periods. The daily data are still made available for all catchments and the users are invited to consider which timescale is more appropriate for their use-case. The monthly hydrometeorological data include data from the seven temperature and precipitation data sources as well as for the streamflow. SWE values were not provided at the monthly scale as they are typically not as variable as other variables.
Data Records All data are available via the Open Science Framework data repository 39 at: https://doi. org/10. 17605/OSF. IO/ RPC3W. Metadata detailing watershed properties are presented in a semicolon delimited text file "HYSETS_ watershed_properties. txt" and include watershed name, source, centroid coordinates, area, physiographic properties and extraction flags to indicate where values are available. A. csv file containing all elevation band data named "HYSETS_elevation_bands_100m. csv" is also available. A zip file named "HYSETS_watershed_boundaries. zip" contains the ESRI shapefile containing all watershed boundaries and projection information. Finally, an individual netCDF file was generated for each of the meteorological data sources as described in Table  1.
technical Validation The data were validated in two steps. First, as described in the methods section, filters were applied during the processing to exclude catchments with regulation structures, with poor (or nonexistent) watershed boundaries, too few hydrometric data points and by carefully indicating that some watershed boundaries were extracted from GSIM instead of from an official agency dataset. Second, the data were displayed on maps to show the similarities and differences between the various datasets and to allow comparison to atlases and other knowledge bases. Figure  2  shows the comparison of the 6 precipitation databases (all except the non-quality-controlled stations). It can be seen that all datasets show the same patterns across North America. Similar comparisons were performed on North America in another study on approximately 3000 catchments and obtained maps that are very similar to the ones in Fig.
2   11. All datasets clearly show the high west-coast precipitation patterns and the drier Midwest and Canadian prairies. Figure  3  shows a similar comparison for daily average temperature. The north-south gradient as well as the high-elevation gradients in the mountain ranges of the Western United States and Canada are clearly seen. There are some small differences between the different products when observing in detail, which corresponds to the various product data sources. These findings are true for all the temperature datasets in this database. Figure  4  presents the average maximum annual SWE amounts over the dataset region. Again, these values represent well the expected values for North America, with no snow in the southern parts of North America and more snow in the North and mountain ranges.
Extents are limited by the SNODAS original extents in the top panel and cover the entire domain with the ERA5-Land product in the bottom panel. Finally, the watershed properties were analyzed and mapped to obtain a visual confirmation of the various fields. Figure  5  presents watershed-averaged values of elevation, slope, soil permeability and porosity and land use fractions for forests and croplands. It can be seen that elevations map well with the actual topography of North America and that the mountainous regions show higher average slopes, as expected. Porosity and permeability are more variable in nature but reflect the data present in the GLHYMPS subsoil dataset. Finally, land cover fractions are also as expected, with large portions of the United States Midwest having cropland as the dominant land use category and major forested areas in Canada, north-east United States and Western United States.
Usage Notes The HYSETS database contains multiple sources of data that can be used to evaluate their relative performance and lead to multi-component analyses. For example, precipitation data from ERA5, ERA5-Land and SCDNA can be compared from a meteorological point of view, considering that SCDNA integrates multiple sources of precipitation data including stations and reanalysis data for infilling. Validation through comparison with station-based (precipitation and temperature) data can also be performed over all of North America, including in highly heterogeneous climates. Performance assessment through hydrological model calibration can be undertaken, and many combination assessments can be performed through multi-input experiments  41. The performances of the Livneh and NRCAN gridded datasets can also be compared in southern Canada, while the impact of the spatial resolution refinement from ERA5 to ERA-Land on precipitation outputs and on hydrological modelling results can be assessed.
Users could want to use the SNODAS and ERA5-Land SWE values to constrain hydrological models during the calibration process and to validate each other in hydrological modelling applications. As the database covers a wide spatial domain with a great diversity in watershed characteristics, the elevation, slope, land use, porosity and permeability data can be used in large-scale regionalization studies, to better assess the performance of various methods for streamflow prediction in ungauged basins. The numerous nested basins can also help in developing transfer functions from gauged to ungauged sites. The wide range of watershed sizes allows exploring and testing different hypotheses on hydrological processes modelling across spatial scales. The database also has possible usages for climate change impact studies.
The multiple temperature and precipitation datasets represent a diversity of reference data for bias correcting global or regional climate model outputs. Since the database covers a time period of up to almost 70 years, it allows performing trend analyses and investigating recent past changes in hydroclimatic variables in different regions of North America. The considerable diversity of physiographic characteristics and hydroclimatic conditions also creates a vast playground for studying, for instance, hydrological model parameters sensitivity and variability as well as hydrological processes and the impact of watershed characteristics on modelling those processes. The dataset currently covers the period 1950-2018, and it is planned that it will be updated regularly as new datasets are created and made available.
We expect to update the database approximately once per year, or more often when relevant new datasets are made publicly available. 1. Codes to download and extract the data. Most of the data were downloaded via requests to the public-facing servers using their API (WSC and USGS hydrometric data, for example) and through their data dissemination mechanisms (ECMWF ERA5 and ERA5-Land). Other data were downloaded from FTPs and thus did not require specific codes (SNODAS snow water equivalent data, GHCND station data). The "ghcnd_access" toolbox  42  was used to extract the GHCND station daily data from the. dly file format into a more accessible format. The "usgsrdbread. m" MATLAB script written by Kelly Kearney was used to extract the USGS streamflow data to the MATLAB format. The code is available here: https://github.
com/kakearney/usgsrdbread-pkg. 2. Codes to process data at the catchment scale. These codes were mainly a) to perform Thiessen polygons to extract meteorological data from stations at the catchment scale, b) average netCDF file outputs at the catchment scale (or take the closest point), and c) extract the physiographic data from the land-cover, elevation and soil properties raster images. PAVICS was used in the creation of this database, however any GIS software could be used to extract the zonal statistics from the raster data for each catchment. 3. Codes to ensure data quality and producing the final output formats. These codes are a set of checks to ensure that missing data was handled properly (set to NaN rather than 0), that catchments outside of a particular dataset's boundaries were excluded for that product, and that basins with smaller areas than 1 km 2 or were misrepresented by the official agencies were excluded.
For example, the USGS NWIS contains data for the island of Guam, which was excluded using these validation tools. The data production tools are simple aggregation tools that convert the data from the MATLAB processing software to more convenient netCDF files. providing ERA5/ERA5-Land reanalyses; the PAVICS team at OURANOS for providing the tools required for the extraction of physiographic data of North American watersheds; NOHRSC for providing SNODAS data products for North American watersheds; the NRCan/CCRS, the USGS, and three following Mexican organizations -the National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía-Inegi), the National Commission for the Knowledge and Use of Biodiversity (Comisión Nacional para el Conocimiento y Uso de la Biodiversidad-Conabio), and the National Forestry Commission of Mexico (Comisión Nacional Forestal-Conafor), as well as the Commission for Environmental Cooperation, for providing NALCMS products; Tom Gleeson of University of Victoria for providing the GLHYMPS data for permeability and porosity.
Filename Data type and source Hydrometeorological netCDF files containing precipitation (pr), maximum temperature (tasmax), minimum temperature (tasmin) and discharge (discharge) data HYSETS_2020_QCstations. nc Quality controlled station data HYSETS_2020_nonQCstations. nc non-Quality controlled station data HYSETS_2020_SCDNA. nc Serially complete temperature and precipitation dataset for North America HYSETS_2020_NRCAN. nc NRCan gridded interpolated gridded dataset HYSETS_2020_Livneh. nc Livneh gridded interpolated gridded dataset HYSETS_2020_ERA5. nc ERA5 reanalysis data HYSETS_2020_ERA5Land. nc ERA5-Land reanalysis data Hydrometeorological netCDF files containing precipitation (pr), maximum temperature (tasmax), minimum temperature (tasmin) and discharge (discharge) at the monthly scale HYSETS_2020_monthly_meteorological_data.
It is worth to note that several factors affect the time to saturate the ICU beds. In particular, the saturation time depends on the connectivity of the population: the more people are connected within Here we use the regional number of ICU beds reported before the onset of the epidemic. The number of ICU beds was increased throughout the epidemic. The map has been created with GeoPandas library (https://github. com/geopandas/geopandas) a region, the faster the infection diffuses  [13]. Therefore, the risk of ICUs saturation is higher for the most developed and connected regions, and Lombardy is the most connected region of Italy. Moreover, if a lockdown measure is imposed, the ICUs saturation time depends on the incubation time of the disease and on the degree of adherence of the population to the lockdown.
Acknowledgments This manuscript has been written with the hope that the heroic resistance showed by the  city of Bergamo , the Lombardy region and the whole of Italy will not be needed elsewhere. The authors want to thank all the Italian nurses, doctors and health-care professionals that are fighting against an invisible enemy as war heroes in dark times. This manuscript is dedicated to the memory of  Doctor Li Wenliang , who first tried to warn the World about COVID-19, and all the health-care professionals who died fighting against this virus. Authors thank two anonymous reviewers for their useful comments and suggestions. This work is supported by the  Institut Universitaire de France (IUF). This is IPGP contribution # 4179.
Availability of data and materials The Italian COVID-19 data are available through a GitHub repository managed by the DPC (Dipartimento della Protezione Civile -Presidenza del Consiglio dei Ministri): https://github. com/pcm-dpc/COVID-19 For Fig. 1 , we used World COVID-19 data published by the EU agency for Disease Prevention and Control: https://www. ecdc. europa. eu/en/publications-data/download-todays-datageographic-distribution-covid-19-cases-worldwide For Fig. 1 , we also used the dataset maintained by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), for the time series of the Chinese Hubei region: https://github. com/CSSEGISandData/COVID-19 For Fig. 1 , we finally used the dataset maintained by the New York Times, for the time series of the US* (New York State + California State): https://github.
Introduction SARS-CoV-2 is the third human coronavirus (CoV) responsible for severe respiratory syndrome that emerged in the last 20 years, the two previous ones being SARS-CoV in 2002  (Drosten et al. 2003)  and MERS-CoV in 2012  (Zaki et al. 2012). SARS-CoV-2, which causes the COVID-19 disease in humans, spread into a pandemic in early 2020. By November 23, 2020, more than 59 million infections had been reported with at least 1. 4 million deaths. The etiological agent of COVID-19 was rapidly identified and by 26 January 2020, 10 viral genomes had been sequenced (R. Lu et al. 2020). Sequence comparisons revealed a 99. 98% pairwise identity between those genomes, which is characteristic of a recent emergence. When the first SARS-CoV-2 isolates were sequenced, the closest coronaviruses available in databases were bat-SL-CoVZXC21 and bat-SL-CoVZC45 strains, isolated in 2015 and 2017 from bats in the Zhoushan region of eastern China, and whose genomes showed 88% identity with SARS-CoV-2 (R.
SARS-CoV-2: from Yunnan to Wuhan? The origin of SARS-CoV-2 is a matter of debate. Bioinformatic studies revealed that it has a 96. 2% identity with a CoV genome (RaTG13) reconstructed from faeces and anal samples of Rhinolophus affinis bats. Interestingly, these samples were collected in 2013, but the full genome sequence was only published in early February 2020 (P. Zhou et al. 2020a). Unfortunately, the precise location of the sample collection is documented neither in the original article nor in the sequence databases. However, when the current work was led, we found an exact match between RaTG13 and a 370 nucleotide fragment published in 2016 (KP876546), encoding a BtCoV/4991 polymerase domain, which had been sequenced from isolates collected from a mine shaft in Yunnan Province following the death of 3 miners from an atypical pneumonia  (Ge et al.
It would be necessary to focus primarily on mammalian species whose ACE2 receptor better matches the key characteristics of the human receptor than chiroptera, such as pigs, goats, sheep, cows or cats ( Figure  4B  ). It should be kept in mind that the reliability of the results depends on the quality of sequencing, metagenomic reconstructions, public accessibility of the data and the accuracy of the annotations in sequence databases (Hassanin 2020). The insertion between the S1 and S2 subunits of the S protein created a furin-sensitive proteolytic cleavage site which appears to contribute to its infectivity and/or epidemic propensity in humans. This insertion must be recent since it is absent from all the close relatives of SARS-CoV-2. This observation is crucial as this site probably played a key role in the species barrier crossing and/or in the efficiency of human-to-human transmission, which is a prerequisite for the emergence of epidemics.
Reproducibility of the analyses All the analyses to produce the results and figures of this article follow the FAIR principles (Findable, Accessible, Interoperable and Reusable). The software environment, sequence data, commented code and examples can be downloaded as a github repository ( https://github. com/jvanheld/SARS-CoV-2_origins ), and the main results can be browsed on the github web pages ( https://jvanheld. github. io/SARS-CoV-2_origins/ ). The software environment is fully described in a yaml-formatted conda configuration file, enabling to re-run all the analyses on Linux or Mac OS X operating systems. The release of the code corresponding to this article is available on Zenodo ( https://zenodo. org/record/3931505 ). A few sequences could however not be made available in the github repository because they were downloaded from the GISAID server, which does not allow to redistribute the 16/26 sequences.
Since these sequences were crucial to reproduce some key elements of the current debate about SARS-CoV-2 origins, we incorporated them in our analyses. These sequences can be found on the GISAID server ( https://www. gisaid. org/ ) with the IDs EPI_ISL_412977 (Bat virus metagenome RmYN02), EPI_ISL_410544 (Pangolin virus Gu-P2S_2019) and EPI_ISL_410721 (Pangolin virus genome Gu1_2019), respectively.
PIP profiles Profiles of Percent Identical Proteins were computed with an original R script available on the github repository, which enables to draw PIP profiles for either nucleic or peptidic sequences. For genomic PIP profiles, each viral sequence of interest ("around-cov-2" or "selected" collections) was aligned onto the reference genome (SARS-CoV-2) using the Needleman-Wunsch global pairwise alignment algorithm. PIP profiles for coding sequences were based on translation-based multiple alignments of the nucleic sequences with the R function DECIPHER::AlignTranslation(). The PIP was measured on the resulting aligned nucleic sequences, as well as on the aligned protein sequences (not shown in this article).
Acknowledgements We would like to thank the following colleagues for their careful revision of the early drafts of the French manuscript, and their many suggestions for its improvement:  Cathy Bellan ,  Mathias Bonal ,  Bruno Canard ,  Bruno Coutard ,  Hélène Chiapello ,  Denis Gerlier ,  Catherine Nguyen ,  Nadia Rabah ,  Annick Stevens ,  Denis Thieffry.
Proportions of S. cerevisiae in mixed-culture was established thanks to the green fluorescence produced by the S. cerevisiae 59A-GFP, the forward and side scatters from the BD Accuri™ C6 Plus flow cytometer and machine learning using the caret package in R  [37]. From these population densities (without taking into account viability), we fitted a growth population model (with the growthcurver package in R,  [38] , and determined the carrying capacity (K) and maximum growth rate (mu) for each fermentation. The final concentrations of carbon metabolites in the medium (acetate, succinate, glycerol, alpha-ketoglutarate, pyruvate, ethanol, glucose and fructose) were determined with high-pressure liquid chromatography  [39]. From these metabolite concentrations, we first calculated the consumed sugar concentration as the difference between the final and the initial concentration of either glucose or fructose.
All data files for: Kinetic analysis of yeast-yeast interactions in oenological conditions files are available from the Mendeley database (https://data. mendeley. com/ datasets/wmhcznvgf4/draft?a=c8b0813a-d27a-45c3-88e0-3f6fe5110130, doi: 10. 17632/ wmhcznvgf4. 1).