dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of climate and hydrological data Climate data of the weather stations of Tatopani, Lete, Marpha and Ranipauwa (Department of Hydrology and Meteorology (DHM), Government of Nepal) was analyzed. In addition, data of the Global Precipitation Measurement (GPM) mission (https://www. nasa. gov/mission_ pages/GPM/main/index. html) was used. We used GPM IMERGHH version 6B data in 30 min resolution and 0. 1°(~10 km) spatial resolution between 2001 and 2018  (Huffman et al. , 2019). First, time series data was extracted for the catchments of Lete and Tukuche (Fig. 1 ) and reported as catchment mean precipitation. Second, maps were produced by accumulating the 30 min precipitation for each pixel to daily resolution. Hydrological analyses were based on KG river stage gauge at Lete. The station is located in a confined bedrock river channel downstream of the braided river system (incipient epigenetic gorge).
Water stage is recorded in 15 min intervals using an OTT RLS radar level sensor. Water stage readings were converted with a stage-discharge rating curve. The rating curve was cross checked regularly with independent ADCP surveys. Due to high river dynamics and sediment transport rates the quality of the rating curve is associated with large uncertainties. Parts of the data are published in  Struck et al. (2015)  and  Menges et al. (2019). Discharge data was compared to GPM precipitation data. Based on GPM precipitation data from 2001 to 2018 a precipitation index (PrecIndex) was calculated for all months of 2018 and the whole year 2018 by dividing the monthly or annual totals by the average totals of each month 2001-2018 or annual totals of 2001-2018, respectively. Finally, we used MODIS-10A2 Version 6 snow cover data, provided by the National Snow & Ice Data Center NSIDC  (Hall and Riggs, 2016 ).
Here we used the 8-day maximum snow cover extent layer, with 500 m spatial resolution and 8-day integration interval. The data is structured as follows: If snow in one pixel has been recorded within the 8 days this pixel get classified as snow, otherwise pixels get assigned as clear view or cloud covered flag. We used the entire time series from February 2000 to February 2020. We averaged the monthly extent according to the respective months. The raw HDF-EOS files were reprojected to geographic projection with WGS84 using the HDF-EOS To GeoTIFF Conversion Tool (HEG https://wiki. earthdata. nasa. gov/display/DAS). In order to obtain the elevation distribution data of snow within the Lete catchment, we resampled a void filled 90 m SRTM DEM (https:// doi. org/10. 5066/f7f76b1x) to the same resolution of the MODIS-10A2 layer and extracted for each pixel the corresponding elevation information.
Hydro-meteorological drivers and controls In order to better understand major flood events, related bank erosion and landsliding, we used the data recorded at Lete river gauge. Cumulative runoff during the monsoon period 2018 was highest runoff since the gauge was installed in 2012 (Fig. 3 , see also "Supplementary Material, Figs. 3  and 4" ) and comparable to 2013, well above the average. Peak discharge in 2013 was higher but the flood period in 2018 lasted much longer. Five major flood events occurred in July/August 2018, a minor one was recorded on 12th September 2018 (Fig. 3F ). Unfortunately, discharge data in 2018 is only available from 24th May onwards. Cumulative GPM precipitation for the years 2013-2018 shows that the amount and distribution of precipitation in 2018 significantly differed compared to the other years.
Acknowledgements The authors would like to thank the  Earth System Sciences Program (ESS) of the Austrian Academy of Science, University of Paris ,  UMR 8586 PRODIG  and  LABEX DYNAMITE  ( SarDYN Project on Nepal ), France, and  Kadoorie Agricultural Aid Association, Nepal  for their financial assistance for this study. This paper is a contribution to the touRES (Resilience of tourism systems to natural hazards in the Himalayas) project. Christoff Andermann benefitted from the  Helmholtz Postdoc Program  ( PD-039 ) from the  German Helmholtz Association  during the installation of the stream gauging. We used the R software environment (http://www. r-project. org/). We thank  Planet Labs  for the opportunity to analyze Planetscope satellite images online via  Planet Explorer  (www. planet. com/explorer).
80 (10 November 2017). The 1-2 mm 2 pieces of ear skin were used for DNA extraction using Nexttec columns (Nexttec Biotechnology GmbH, Leverkusen, Germany), according to the manufacturer's instructions. Additionally, we included modern samples of Mongolian cattle (MONG_M, n = 41) as a kinship breed with a similar genetic origin, which probably contributed to the development of Kalmyk cattle. Additionally, we included in the dataset samples of modern representatives of the Hereford breed (HRFD_M, n = 26) because of their great contribution to the development of the modern gene pool of Kazakh white-headed cattle.
PCR fragments were separated by capillary electrophoresis on an ABI3130xl genetic analyzer (Applied Biosystems, Beverly, MA, USA) using GeneScan™-350 ET ROX as a fragment standard. The fragment lengths were determined using the Gene Mapper software v4 (Applied Biosystems, Beverly, MA, USA). Allele sizes were standardized according to an ISAG International Bovine (Bos Taurus) STR typing comparison test  (2018) (2019). A modified multiple-tube approach, proposed by Mondol et al. [37]  and Modi et al. [38] , was used to determine the consensus genotypes as previously described  [29]. Briefly, each DNA sample was analyzed in five replicates. Initially, multiplex amplification of microsatellite loci was performed in duplicates and only the samples in which at least six loci were successfully amplified (positive multiplex PCRs) were selected for further analysis.
Data Analysis FreeNA  [42]  and MICRO-CHECKER 2. 2. 3 software  [43]  were used to check the presence of null alleles. We applied GenAIEx 6. 5 software  [44]  to calculate the number and frequency of alleles. The packages diveRsity  [45] , adegenet  [46] , and ggplot2  [47]  of the R software (http://cran. r-project. org, accessed on 12 May 2021) were used for calculating the main statistics, including observed heterozygosity (Ho), unbiased expected heterozygosity (uHe), unbiased inbreeding coefficient (uF IS ), and rarefied number of alleles (A R )  [48] , performing principal component analysis (PCA), and visualizing breed relationships. Pairwise differences among studied populations for uHe and A R estimates were tested using Wilcoxon rank-sum test  [49]  using Statistica 10 software (www.
statsoft. com, accessed on 18 July 2021). The software environment R3. 5. 0 was used to prepare the data files  [50]. Pairwise Jost's D genetic distances  [51]  and paired F ST genetic distances  [52]  were calculated to evaluate the degree of genetic differentiation of the studied breeds. The pairwise Jost's D and F ST matrices were used to construct the phylogenetic trees using the Neighbor-Net algorithm in SplitsTree 4. 14. 5 [53]. STRUCTURE 2. 3. 4 software  [54]  was applied to investigate the genetic structure of studied populations. We used the admixture model with the option of correlated allele frequencies. The burn-in period was set to 10,000 iterations, followed by 100,000 Markov chain Monte Carlo (MCMC) repetitions for each run. We performed 10 independent runs for a number of possible clusters (K) ranging from 2 to 8.
CLUMPAK software  [55] , available at http://clumpak. tau. ac. il (accessed on 7 May 2021), was used to analyze multiple independent runs at a single K value. For measuring the constancy over runs, we calculated average pairwise similarity scores using CLUMPAK  [55]. To infer demographic history with admixture, we applied the TreeMix software  [56]  to the microsatellite data. We computed the mean and variance in length at each microsatellite locus and used them to run TreeMix v1. 13. We tested up to five migration events in twenty iterations per migration edge. The optimal number of migration events was determined using R package "optM"  [57]  based on TreeMix output files. We added the optimal number of migrations to the phylogenetic model and estimated the consistency between migration edges using TreeMix for 20 independent runs.
Study design and patients This is an observational, international multicenter cohort study of all consecutive patients between March 2020 and April 2021 who met the World Health Organization (WHO) criteria for MIS-C associated with SARS-CoV-2  [4] , having cardiac involvement and underwent CMR. The study was approved by the Research Ethics Board of the Assistance Publique des Hôpitaux de Paris (IRB registration: #00011928, ClinicalTrials. gov Identifier NCT04455347, registered on 01/07/2020, retrospectively registered) and conducted according to MR004 conformity.
Data collection Local physicians identified eligible patients and reviewed their medical records. Detailed information on demographic, clinical, biological, thoracic and cardiac imaging (echocardiography, CMR and computed tomomgraphy (CT) scan) features were recorded anonymously on a centralized database. SARS-CoV-2 was defined as "confirmed" by either positive nasopharyngeal polymerace chain reaction (PCR) or positive serology. Cardiac involvement was defined as clinical suspicion of acute myocarditis or left ventricular (LV) ejection fraction (LVEF) < 55% or cardiogenic shock. Myocardial inflammation was diagnosed in the presence of at least two of the Lake Louse Criteria  [7]  detailed below.
Limitations This study is a retrospective observational multicenter cohort study, with all associated limitations. In some instances, not all the variables were available from contributing centers. In these instances, the total number of datasets is reflected in the denominator in the results tables. However, a recommended CMR protocol was distributed, which allowed for more consistent imaging data. The limited parametric mapping data available and also the lack of standardized pediatric normative data made use of the updated Lake Louise Criteria impractical. Because parametric mapping may be more sensitive for the diagnosis of myocarditis, it is possible that some cases of myocarditis were missed using the original Lake Louise Criteria. Moreover, we should consider that hospitalized patients, as in our series, represent the cohort with the most severe presentation of COVID-19 infection, excluding from our observation less severe forms.
Acknowledgements We thank  Imagine Institute  for the clinical research and informatic support enabled by state funding from the  Agence Nationale de la Recherche  under " Investissements d'avenir" program  ( ANR-10-IAHU-01 ). We also thank particularly  Salma Kotti ,  Didier Beudin ,  Yasmine Ainouz ,  Hassan Faour ,  Nicolas Garcelon  from  Imagine Institute  for their investment.
The French Flooding Prevention Action Program of Saint-Malo requires assessment of coastal flooding risks. The first prerequisite is a knowledge of the topography and bathymetry of the bay of Saint-Malo. In addition to existing topobathymetric data, the acquisition of new multibeam bathymetric data is performed. The combination of these datasets allows the generation of two high resolution topo-bathymetric digital terrain models. Then, to understand the hydrodynamic conditions which cause coastal flooding, a dense and extensive oceanographic field experiment is conducted. Oceanographic data were acquired using a network of 22 moorings with 37 sensors, during winter 2018-2019. The network included 2 directional buoys, 2 pressure tide gauges, 18 wave pressure gauges, 4 single-point current meters, 7 current profilers and 4 acoustic wave-current profilers from mid-depth (25 m) up to the upper beach and the dike system.
, 2020). At the local scale, some initiatives have 1 Topo-bathymetric and oceanographic datasets for coastal flooding risk assessment: French Flooding Prevention Action Program of Saint-Malo also been carried out through the French Flooding Prevention Action Programs (PAPI). Thus, to increase scientific knowledge and public recognition of the coastal flooding hazard, Saint-Malo urban area (SMA) has established a preliminary PAPI, in which the Shom participates, as a national expert in support of public policies for the sea and the coasts. This PAPI requires, as a first step, a collection of topo-bathymetric and oceanographic data. Detailed and accessible topo-bathymetric data is a priority for communities anticipating impacts and preparing strategies in response to coastal risks. This need has increased in recent years and will continue to increase in the near future because elevation data are critical to depict regions prone to climate change impacts.
, 2018). Moreover, geomorphology is marked by the presence The meteorological conditions are characterized by the passage of low pressure systems and cold fronts  (Gaspar et al. 2007 ). These weather conditions generate storm surges and significant wave fields. Combined with high spring tides, these events can 60 lead to coastal flooding  (Cariolet et al. 2010). Despite an existing dike system in front of  Saint-Malo (Bonnot-Courtois and Lafond, 1994) , eight coastal flooding events have been recorded in the literature between 1979 and 2019, and more than 40 have been identified since 1703 (PPRSM Saint-Malo 2016). To improve the knowledge of coastal flooding risks in Saint-Malo bay, an extensive bathymetric and oceanographic campaign was performed in winter 2018-2019. These campaigns allowed the creation of topo-bathymetric and oceanographic datasets.
Topo-bathymetric dataset The purpose of the following sections is to present the different data used for the generation of the TBDTMs (Figure  1. a), the most common problems experienced with combining data and the approach adopted by Shom for their resolution  (Maspataud et al. , 2015; Biscara et al. , 2016). The hydrographic vessels are equipped with a Kongsberg-Maritime EM710 multibeam echo sounder associated with the SIS acquisition system. Depending on the vessel, sound velocity profiles were measured using Sippican XBT probes or a Valeport SVP1000 Sound Velocity Profiler to correct bathymetric data for local variations in sound speed. Real time GPS positioning was collected with the Applanix POS MV inertial unit. Positioning data were post processed in POSPac software using global navigation satellite systems (GNSS) solutions.
Horizontal positions were referenced to WGS84 or ITRF2014 geodetic systems at the time of the survey. All the bathymetric data acquired were processed with the CARIS HIPS version 9. 1 software. Tidal corrections were made based on the data from the tidal reference station of Saint-Malo and additional tide gauges specifically deployed for the surveys. The soundings were vertically referenced to the chart datum of Saint-Malo. Subset editing was carried out to remove systematic errors and outliers. Processed and cleaned data were subjected to final validation by a qualified hydrographer.
Litto3D® program LIDAR surveys exploited in this study were carried out within the framework of Litto3D® program. This national program is based on a partnership between Shom and the French National Geographic Institute (IGN)  (Louvart and Grateau, 2005). It aims to provide very high resolution coastal altimetric models of metropolitan and overseas French coasts  (Pastol, 2011). Litto3D® surveys are regularly implemented on Shom's data portal (data. shom. fr) under Open Licence. Coastal mapping of the Normand-Breton Gulf was performed by Shom's Litto3D® team between 2016 and 2018, covering approximatively 700 km2 and reaching up to 18 m water depth (Figure  1. c). Topo-bathymetric data were acquired from a Cessna Grand Caravan 208B type aircraft equipped with an airborne lidar topo-bathymetric HawkEye III double hatch (Leica Geosystems).
The data were acquired in relation to the ellipsoid and referenced horizontally with respect to the RGF93 in standard UTM 30N projection. The trajectory of the aircraft was based on the GNSS system and processed by Inertial Explorer. The trajectory data were post-processed using the stations of the RBF (Réseau de Base Français). The points were generated from the processed waveform with Lidar Survey Studio (LSS) and the point cloud was processed using PFMABE version 6. 4. 0. 43 tools. The validation of the cleaned data was finally done by qualified hydrographers. The produced data were finally reported to the IGN69 altimetry reference frame and to the Lambert 93 projection by the Circe Batch (V4-3, Using RAF09 model) conversion tool.
Shom's bathymetric database Complementary bathymetric data used to generate the TBDTMs were extracted from the Shom's bathymetric database (BDBS). They originate from 52 hydrographic surveys ( 490 millions of soundings) conducted between 1829 and 2019 with different sounding methods (lead-lines, single beam and multibeam echosounders). Spatial coverage of each survey is represented as a vector polygon layer (called hereafter bounding polygon) that may adjoin, overlap or supersede older hydrographic surveys. Each survey extracted from the BDBS is associated with metadata, including the acquisition and processing methods, the order survey and the quality of the data.
Other data In addition to these bathymetric data, a bathymetric survey of the inner harbor delivered by the harbor authority of Saint-Malo was used in the present study. The bathymetric survey was carried out in June 2016 by the GEOXYZ society with a multibeam echosounder. Soundings were vertically referenced to the chart datum of Saint-Malo. This bathymetric source was evaluated prior to integration to other datasets. The RGE ALTI® V2. 0, produced by IGN, was exclusively used for the terrestrial domain. The data are available on the IGN's data portal (https://geoservices. ign. fr) in the RGF93 geodetic system, Lambert 93 projection. Vertical datum of the data corresponds to the NGF-IGN69 legal system  (IGN, 2018). The RGE ALTI® v2. 0 products used in the TBDTMs cover the departments of Côtes d'Armor, Ille et Vilaine and Manche at a resolution of 5 m.
Convert data to a common horizontal and vertical datum The key requirement for creating a seamless merged product is the homogeneity of the input datasets in terms of horizontal and vertical datum  (Gesch and Wilson, 2001). The vertical transformation to the ellipsoid was performed with Circe 5. 1 France (IGN) and Bathyelli V2. 0  (Shom)  for topographic and bathymetric data, respectively.
Conflict resolution Selecting the most reliable source from multi-temporal and multi-sensor data is a fundamental challenge addressed in numerous works  (Macnab and Jakobsson, 2000; Wong et al. , 2007; Maspataud et al. , 2015). This issue is particularly true for hydrographic offices, whose data legacy may be substantial: Shom's hydrographic knowledge counts presently more than 10,000 bathymetric surveys conducted between 1816 and 2019 with many areas characterized by overlapping surveys. Survey depth measurements were collected with different sounding methods and positioning systems whose accuracy has improved over time. Moreover, the data may span decades, introducing temporal and geomorphologic change as a source of error  (Eakins et al. , 2011)  To date, the selection of the most reliable surveys (i.
c). This reference layer will be of benefit to numerous Shom's activities. The generation of TBDTMs in the Normand-Breton gulf benefited from the reliability of all metadata and bounding polygons in the area of interest, which constitute the preliminary step prior to the construction of the Téthys. The deconfliction process was executed on all datasets used for the generation of the TBDTMs using a semi-automated procedure based on GMT routines. For the datasets not integrated in the BDBS for which no status exists, this one was directly defined by the operator on the basis of inherent criteria of the data. The result of the deconfliction process corresponds to the most reliable soundings that can be used as input into the surface modeling.
Interpolation Because multiple sources of data contribute to the construction of the DTM, some datasets have data point spacing larger than the required cell size. Splines functions are generally used for their efficiency to honour variable density data providing a representative smooth and continuous surface. They may be more appropriate for large interpolation distances, which is frequently required for bathymetric data  (Amante (2012)  and references therein). Based on these observations, Shom used the SAGA (System for Automated Geoscientific Analyses;  Conrad et al. (2015) ) software packages for the generation of the TBDTMs. Multilevel B-splines interpolation tool was used to perform surface modelling of the compiled data.
Evaluation DTM coherency is evaluated based on visual inspection (slope, cross-section and 3D views), through additional layers (density, sources diagram) and, if possible, the cross-validation of the DTM using datasets that have not been incorporated into the gener- -the overlapping of some bathymetric surveys with a "complete" status, which lead to a noisy representation of the seafloor. For this type of problem, the status of identified extracted surveys was modified by the operator to generate a smooth surface.
Oceanographic dataset The oceanographic dataset includes water levels, currents and sea states observations from extensive oceanographic surveys conducted by Shom during winter 2018-2019. These data are monitored from different sensors types. These following sections describe the oceanographic surveys and data processing. The moorings were located to accurately describe hydrodynamic conditions from offshore to the coastline (Figure  3 ). The Datawell were deployed offshore at 25 m water depth (Sauvages and Trouvée, Figure  3. a), providing information on the incident wave fields. Other moorings were essentially deployed along four cross shore transects (T1, T2, T3 and T4, figure   1  summarizes the location of each mooring and sensor. For consistency with TBDTMs, the vertical datum used is the Lowest Astronomical Tide (LAT).
Water levels The water levels are monitored by pressure sensors, tide pressure gauge (SBE) and wave pressure gauge (OSSI). These sensors record bottom pressure at different frequencies, bursts and intervals of acquisition (Table  2 ). The tide Pressure gauges record the average bottom pressure every 20 minutes with a 2-min burst. First, the raw tide data file was extracted in ascii format with the seaterm manufacturer software (Seabird). Then, the raw data was processed by the TDB software. The raw data was converted in water level assuming hydrostatic equilibrium (Eq. 1). Finally, the processed data was subjected to final validation by a qualified hydrographer. h = P m -P atm ρg (1) with P m the bottom pressure measurement in Pa, P atm the atmospheric pressure extracted from ERA5 atmospheric reanalysis in Pa, ρ = 1026kgm -3 the averaged water density measured in Saint-Malo bay and g = 9.
81ms -2 the gravitational acceleration. The wave pressure gauges record bottom pressure at high frequency. The bottom pressure was directly recorded in ascii format. First, the raw pressure data of each sensor is calibrated using the pressure slope and offset of the sensor. For foreshore sensors, the pressure sensor was calibrated by comparing the pressure measured when the sensor is out of water to atmospheric pressure. For offshore sensors, the pressure sensor was calibrated at the end of the recording when the instrument was taken out of the water or in the laboratory. Then, the corrected pressure was converted into water level assuming hydrostatic equilibrium (Eq. 1). Finally, to reconstruct water level for the long waves such as tide, the water level was smoothed, with moving average of 10 min, to filter out deformations of water surface related to short waves.