dataesr/scientific-paragraphs-categorization huggingface dataset:
05) on the individual 2 -ΔCt values of treated and control samples. DNA methylation analysis. Total extracted DNA was purified using the Genomic DNA Purification and Concentration Kit (Zymo Research, D4010) and quantified using the QubitTM dsDNA HS Assay Kit (Q32851, Invitrogen). DNA was treated with bisulfite using the EZ DNA Methylation-Gold kit (Zymo Research, D5006) and regions of interest were amplified according to  16. Methylation status of the targeted CpGs was calculated after pyrosequencing with PyroMark Q24 ID 2. 5 software (QIAGEN). Bisulfite conversion of control cytosines was above 98%. Availability of data and material. The agilent 8 × 60 K high-density oligonucleotide microarray used in this study is available at https:// www. ncbi. nlm. nih. gov/ geo/ under the accession number GPL32340.
Acknowledgements The authors thank  T. Lorca  and  N. Morin  ( UMR 5237 CNRS, France ) for helpful exchanges and for providing anti-Xenopus Greatwall and CyclinB antibodies. We thank  J. Montfort  ( INRA LPGP ) for providing zebrafish protein ID (ENSDARP) corresponding to goldfish NCBI sequences. Thanks also to  Bernard Joseph  from  INRAE   U3E  and  Pierre Lô Sudan  from  ISC INRAE LPGP , who took care of goldfish rearing. Hélène Jammes  and  Antoine Peigné  performed the DNA methylation assay and analysis. This work benefited from the financial support of the  PIA CRB  Anim  ANR-11-INBS-0003.
ORIGINAL PURPOSE: The original purpose of this data set was to support model intercomparisons and improvements  (Asseng et al. , 2015; Maiorano et al. , 2017)  as part of the Agricultural Model Intercomparison and Improvement Project (AGMIP, http://www. agmip. org/; Rosenzweig et al. , 2013). The field experimental data were selected from the four-year International Heat Stress Genotype Experiment (IHSGE) to cover the full range of temperature of the global network of field experiments. The IHSGE was carried out as part of a collaboration between CIMMYT and key national agricultural research system partners in warm wheat growing environments to identify important physiological traits that have value as predictors of wheat yield at high temperatures  (Reynolds et al. 1994b ).
FIELD EXPERIMENTS: Experimental locations were selected based on a classification of air temperature and relative humidity during the wheat growing cycle. "Hot" and "very hot" locations were defined as having mean temperatures above 17. 5 and 22. 5°C, respectively, during the coolest month. "Dry" and "humid" locations were defined as having mean vapor pressure deficits above and below 1. 0 kPa, respectively. The present data set includes data from six of the original 12 locations that were selected to represent a large range of temperatures (Table  1 ). At Obregon (first year only) and Tlaltizapán (both years), Mexico normal and late sowing dates were used to provide contrasting temperature regimes at the same location. Of the 16 cultivars originally included in the first two-years of the IHSGE, two (Bacanora 88 and Nesser) were selected for the AgMIP-Wheat model intercomparison.
In the first year, maturity dates for the late sown treatments for both cultivars at Obregon, Mexico were not available and were estimated as the average growing degree-days from anthesis to maturity of all other treatments. Similarly, anthesis date for Bacanora 88 in Egypt was not available and was estimated as the average growing degreedays from crop emergence to anthesis of all other treatments. Biomass harvests at anthesis were made on a single date for the whole experiment. Missing data are indicated by "NA". Correlations between replicates overall and the distribution within each cultivar across replicates were calculated. In cases where the cultivar x replicate effect was high and two replicates were much closer to each other than the third, the outlying replicate was excluded. Data are reported as mean and standard deviation.
Daily weather data (global solar radiation, daily maximum and minimum air temperature, rainfall, wind run, dew point temperature, vapor pressure, and relative humidity) are provided in the ICASA format in space delimited text files. All text files are UTF-8 encoded. All data are available in Harvard Dataverse data repository (https://dataverse. harvard. edu/) with the identifiers "doi:10. 7910/DVN/ECSFZG". Table  2. Overview of the main data set tables. All files are provided in space (weather data) or tabulation (all others) delimited text format. The crop management husbandry parameters, the soil description and initial conditions are also provided in the Microsoft Excel xml format used in AgMIP.
Study We used international prospective observational data of clinical features and outcomes of patients admitted to hospital with COVID-19. The ISARIC/WHO CCP, incorporating Short PeRiod IncideNce sTudy of Severe Acute Respiratory Infection (SPRINT-SARI),  14  is a standardized protocol for investigations of (re-)emerging pathogens of public health interest. All data were stored at a central repository at the University of Oxford, England. Patients with clinically suspected or laboratory-confirmed COVID-19 infection were enrolled in the study. Participating sites used the ISARIC case.
report form  15  to enter data onto a Research Electronic Data Capture (REDCap, version 8. 11. 11, Vanderbilt University, Nashville, TN) database or used local databases (Supplementary Methods, available as Supplementary data at IJE online) before uploading to the central data repository. Centrally collated data were wrangled and mapped to the structure and controlled terminologies of Study Data Tabulation Model (version 1. 7, Clinical Data Interchange Standards Consortium, Austin, TX) using Trifacta V R software. The data collection, aggregation, curation and harmonization process has been previously described. 16 The first patient was enrolled on 30 January 2020. This analysis includes all patients whose data were entered up to 5 January 2022.
Statistical analysis We excluded patients with missing age or sex from all analyses (Figure  1 ). Additionally, we excluded from analyses of symptoms or treatments sites that did not record information on these two sets of variables. We calculated medians and interquartile ranges (IQRs) for continuous variables and proportions for categorical variables. We calculated proportions of patients who met each of the WHO, Centers for Disease Control and Prevention (CDC) of the USA, European Centre for Disease Prevention and Control (ECDC) and Public Health England (PHE) symptom-based case definitions (Supplementary Methods, available as Supplementary data at IJE online). We calculated case-fatality ratios (CFRs) overall, by country and by month, using the method suggested by Ghani et al. 17  We calculated inverse-variance weighted CFRs by country.
The 1606 transplant patients included in our data set make it one of the largest cohorts to date. Overall, risk of death was 52% higher in transplant patients and 34% higher in those on immunosuppressive therapies. Several studies have shown that tobacco smoking is associated with worse COVID-19 outcomes. 28, 29 We found a 10% higher risk of death among smokers; however, smoking information was available only on $30% of patients and detailed information on the quantity and duration of smoking were not available. Obesity is a recognized risk factor for hospitalization and poor outcomes in patients with COVID-19 and has been one of the conditions for which individuals are prioritized for access to vaccines and treatments. Obesity was associated with a 24% increase in risk; obesity status was available for $45% of patients.
Acknowledgements This work uses data provided by patients and collected by the  NHS  as part of their care and support #DataSavesLives. The data used for this research were obtained from ISARIC4C. We are extremely grateful to the 2648 frontline NHS clinical and research staff and volunteer medical students who collected these data in challenging circumstances; and the generosity of the patients and their families for their individual contributions in these difficult times. The  COVID-19 Clinical Information Network (CO-CIN ) data were collated by ISARIC4C Investigators. We also acknowledge the support of  Jeremy J. Farrar  and  Nahoko Shindo.
Data availability The ISARIC-WHO CCP, case report form and consent forms are openly available on the ISARIC website at https://isaric. org/re search/covid-19-clinical-research-resources/clinical-characterisationprotocol-ccp/. The statistical analysis plan is openly available on the ISARIC website at https://isaric. org/research/covid-19-clinical-re search-resources/accessing-covid-19-clinical-data/approved-uses-ofplatform-data/. Most individual patient data are available to researchers approved by the Data Access Committee. The data inventory, application form and terms of access for the COVID-19 Data Platform, hosted by the Infectious Diseases Data Observatory (IDDO), are available at https://www. iddo. org/covid19/data-shar ing/accessing-data. All individual participant data are available to individuals from sites who have contributed to the ISARIC COVID-19 Platform via the ISARIC Partner Analysis Scheme.
For the composite-lepton model with a Z boson, the data exclude a Z boson with a mass below 1. 3 TeV for a composite lepton mass of 500 GeV. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in ref. [62].
many; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK, Türkiye; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias , Colombia; MEYS CR, Czech Republic; DNRF and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG, Ger-.
Method Principle In this work, we elaborate the spatially-resolved XRR technique by considering the growth of graphene flakes on liquid Cu as a model system. In our previous works,  [12, 20]  we presented in situ XRR measurements on bare liquid copper and liquid copper fully covered with a graphene layer grown by CVD at ≈1400 K, where CH 4 was used as the precursor gas in the mixture of Ar/H 2. Here, we use the data obtained during three different growth experiments: growth 1, growth 2, and growth 3, where the CH 4 /H 2 ratio was 13 × 10 -3 (constant flow), 9 × 10 -3 (injection after accumulation for 30 s followed by a constant flow), and 13 × 10 -3 (injection after accumulation for 20 s followed by a constant flow), respectively. The videos of the growths recorded with an optical microscope and a digital camera are available in the Supporting Information.
The graph in Figure  4b  also presents the analysis of the optical microscopy images recorded during growth 1 in parallel with the X-ray measurements, see Video Growth_S1 (Supporting Information). We use MATLAB to extract the following parameters: the lateral size of the flakes (red), the center-to-center distances between the neighboring flakes (green), and the surface coverage with graphene in the field of view (gray). The area of the sample accessible with the optical microscope through the glass window is ≈5. 0 × 3. 7 mm, which is much larger than the beam footprint. Therefore, this technique provides a larger data set. However, since the early growth stages occur in the center (vertex) of the liquid Cu drop where the beam is aligned, the data from both methods can be correlated. Comparing both plots in Figure  4 , we can see that the first flake appears after 4 min of exposure to methane, then the number of flakes increases.
The red and green curves in Figure  5c  are obtained from the measurement during growths 2 and 3 presented in Figure  5a, b. The inclusion of points from either covered or bare parts of the copper surface results in partial splitting of the XRR curves. To illustrate that the measured points belong to two different states of the sample's surface, XRR curves measured with the same approach for bare liquid copper (gray) and the copper entirely covered with graphene (blue) are also shown in Figure  5c. As we can see, the described method allows obtaining two XRR curves corresponding to two coexisting states of the system. The part of the XRR curves belonging to the graphene/Cu (excluding the points belonging to bare Cu) was fitted with a slab model using the Refl1D software. [32] The slabs include Cu (bulk), C (single atomic layer), and a void in between.
Processing the DEM We use 1-m LiDAR-derived DEMs of small (1-10 km 2 ) basins. We process them using the Matlab Topotoolbox  (Schwanghart & Scherler, 2014). First, for 1-m resolution DEMs, we resample them to a resolution of 3 m using bi-linear interpolation. We then filter the DEM in order to remove meter-scale irregularities, which are indeed likely to be fallen boulders or uprooted trees that are erased by long term processes such as hillslope diffusion and sediment transport by rivers and debris flows. We therefore apply the Wiener filter included in the Topotoolbox.
Comparison With Other Methods To assess the skills of CO 2 CHAIN we compare it to two recent channel extraction methods that are implemented in the LSDTopoTools software  (Clubb, Mudd, Milodowski, Grieve, & Hurst, 2017) : the DrEICH and the Wiener methods. The DrEICH method is process-based and extracts the fluvial channels based on their signature in chi-space and their contour curvature  (Clubb et al. , 2014). LSDTopoTools provides several adjustable parameters to calibrate the method. The ones that are specific to this method are the number of network junction over which to perform the chi-analysis and the concavity value of the river network. A pruning drainage area to remove channel heads under a certain drainage area and a minimum length required for a convex feature to be considered as a valley can also be set  (Clubb & Mudd, 2019).
Acknowledgments The author thank  Mikael Attal ,  Jon Pelletier  and an anonymous reviewer for handling and reviewing in details the manuscript. We thank  Simon Mudd ,  Fiona Clubb  and  Boris Gailleton  for their help on using LSDTopoTools,  Vincent Godard  for his help to access the  Haute Provence  DEM as well as  Wolfgang Schwanghart  for developing and distributing the Matlab TopoTool-Box. All four author contributed to the manual channel mapping as well as  Remi Bossis  and  Anne Guyez  who are warmly thanked. AL and OM conceived the study and developed the methodology with input from PM and SC. AL performed all analysis and code development and wrote the manuscript, under the supervision of OM and SC.
Data Availability Statement All the DEMs used are available online. The San Bernardino Mountains, San Gabriel Mountains and Oregon Coast Range basins are found on the OpenTopography website  (DOGAMI, 2011; Heimsath et al. , 2019; Milodowski, 2014). The Haute Provence basin comes from the RGE-altiⓒdatabase (https://geoservices. ign. fr/ rgealti)  (IGN, 2014). The codes for the CO 2 CHAIN method are available online  (Lurin, 2023).
This finding justified the omission of the explicit wave breaking contribution in the Doppler centroid KaDOP model  [31]  (remembering that breakers implicitly contribute to the empirical MTF) and was incorporated in a new DopRIM SAR  [53, 54]. Nonetheless, many details about the sea surface Doppler shifts caused by breakers remain unknown. This investigation started with a manual inspection of breaker Doppler properties  [52]. Following that, a model for the breaker normalized radar cross-section (NRCS) was developed  [55]  using automated methods for breaker detection. In this paper, the same dataset was used to evaluate breaker Doppler shifts at various observation look geometries using computer vision breaker detection with far richer statistics than that for manual detection. Using these data, the observed Doppler velocity was, for the first time, directly compared to the visible (optical) line-of-sight velocity of whitecaps.
Despite the fact that the two variables are connected, the Doppler velocity was systematically lower than the line-of-sight optical velocity. Their mean ratio depends on the incidence angle and azimuth look direction. A basic asymmetric wedge-shaped breaker model with a smooth transition between fast/specular (coherent) and slow/non-specular (non-coherent) scattering mechanisms, whose balance determines the resulting wave breaking Doppler signature, qualitatively explained this relationship. The paper is organized as follows. Section 2 briefly describes the dataset used in the study. Section 3 presents the direct comparison between the optical and Doppler speeds of whitecaps. In Section 4, a simplified model of the breaker is developed to explain the observations. Section 5 summarizes the study and gives possible extensions of the presented framework.
Materials and Methods The field data used in this study were collected between 2009 and 2015 from the Black Sea Research platform. This database includes Ka-band continuous-wave Doppler radar records accompanied by synchronous video records of the radar footprint. The data processing algorithm is described in detail in  [31, 52, 56]. Briefly, the processing algorithm is focused on the detection of whitecap events within the radar footprint. The footprint is evaluated from the calibrated two-way radar pattern and known look geometry of the radar and video camera. The measurements were performed at 25 • , 30  , and 55 • incidence angles, θ. Raw radar in-phase and quadrature signals, digitized by a 14-bit analog-to-digital (ADC) converter at 40 kHz, were averaged over 0. 2 s time intervals, yielding instantaneous Doppler spectra, S( f , t).
Their zeroth and first moments were used to evaluate the instantaneous surface cross-section, σ(t), and Doppler frequency shift, f D (t), respectively: σ(t) = S( f , t)d f , (1) f D (t) = f S( f , t)d f S( f , t)d f. ( 2 ) where t is the time and f is the Doppler frequency. The radar and video camera were synchronized by recording the same background noise audio signal from the camera's microphone. These two identical signals recorded by the radar ADC and video camera were matched with accuracy better than one video frame (1/25 s) using the usual cross-correlation analysis. In contrast to  [31] , the data collected at a large incidence angle, θ = 70 • , were not considered here because, at such high incidence angles, the optical whitecap speed estimates are significantly distorted due to non-planar projection errors.
For the duration of each detected whitecap event, the following radar and sea surface parameters were estimated and stored in the database: Doppler spectra, NRCS, instantaneous Doppler centroid velocity (DV), and whitecap geometrical parameters. The latter include whitecap area, length, speed, azimuth, and whitecap footprint fraction, Q: Q(t) = B(x, y, t)G(x, y)R -4 dxdy G(x, y)R -4 dxdy , ( 3 ) where B is the binary image brightness (B = 1 for whitecaps, B = 0 for a regular surface), G is the radar footprint on the mean sea horizontal plane, (x, y) is the point coordinates on the mean sea surface, and R is the distance between the radar and (x, y)-point. Every breaking event from the database was visualized (Figure  1 ) to gain deeper insights into the studied phenomenon, conduct a "quality control", and discard possible fault detection cases.
However, the vast majority of breaking events were collected at 7-12 m s -1 , with the significant wave height ranging from 0. 5 to 1. 1 m (Figure  2  and Table  1 ). Since only breaking wave cases were considered, any significant impact of the environmental conditions on the breaking wave structure was not expected and ignored in this study. The median breaker speed in the dataset was ∼2. 2 m s -1. Assuming that it corresponds to the phase speed of dominant breakers, this speed suggested breaking of ∼3. 2 m-long waves. In turn, this corresponds to the breaking of ∼3. 75 f p waves, where f p = 0. 2 Hz is the typical wave spectrum peak frequency at the platform site. Shorter breakers were also present, but excluded from the consideration using the Q < 1% threshold. Such short breakers are too short to dominate the radar signal, given the background regular (non-breaking) surface scattering.
The described processing technique retains only quite intense breaking events, and thus, only plunging breakers were considered in this study. The total number of detected breaking events was 1100. It included about 140,000 processed frames (the video frame rate was 25 fps). For further analyses, two types of data were distinguished: • "Instant" dataset: All processed frames were considered independently. One sample in the "instant" dataset was one video frame, with a radar footprint occupied by a breaker. Note that these data involved all breaking stages, including both young active whitecaps and decaying passive foam wakes following active breaker passage. • "Average" dataset: All parameters corresponding to each breaking event, x, were weight-averaged using a time-varying Q, i. e. , x = x(t)Q(t)/Q(t), with the overline denoting the time averaging over the whitecap lifetime.
Results Three typical examples from the processed dataset are shown in Figure  3. They illustrate up-wave, cross-wave, and down-wave observations at θ = 40 • (the up-wave case is the same as that shown in Figure  1 ). The first prominent thing seen from the samples (Figure  3 ) was a Doppler spectrum widening during the peak of breaking events. This was especially the case for up-wave look direction. Both slow and fast populations of scatterers can be distinguished in this particular situation. The spectrum widening was better observed in the logarithmic scale (Figure  3d-f ). Noticeably, the up-wave spectrum was wider than the down-wave and cross-wave ones. Though the fast scatterers were seen by the radar, the instantaneous Doppler velocity (DV), estimated through the first moment of the Doppler spectrum (blue line in Figure  3a-f ), was still dominated by slow motions.