dataesr/scientific-paragraphs-categorization huggingface dataset:
The project also aims to generate and maintain a consistent database of downscaled multi-year projections over regions worldwide that can be used for adaptation studies in various sectors, such as agriculture, fire risk, air quality, or heat stress  [45]. These data can be used by the building sector and this article will show a demonstration of this. A large amount of RCM data is available on the CORDEX project platform  [48]. The platform is updated regularly, and new climate data are uploaded often. CORDEX domains are available for all parts of the globe (Figure  3 ). EURO-CORDEX projections are available for Europe, on a grid resolution of 12. 5 km; CORDEX projections are available at a 25 km grid resolution in the Middle East and in North Africa and about 50 km in the rest of the world.
Ramon et al. suggested the use of future multi-year datasets to assess the resilience in extreme weather conditions, and recreated several typical and extreme year methods for comparison  [60]. In the UK, Liu used the UKCP09 British weather generator to generate future probabilistic hot summer years and, more recently, future hot event probabilistic years  [61, 62]. Jentsch et al. developed near-extreme summer reference years (SRY) for the United Kingdom, adapting the UK design reference years (DSY) by adding the solar radiation and cloud cover in the selection process to better represent typical outdoor conditions for buildings overheating (high temperature, high solar radiation, and low cloud cover)  [63, 64]. To the authors' knowledge, reconstituting weather files containing future heatwaves to assess the resilience of buildings during a hot event has only been done by  [62].
More recently, Ouzeau et al. developed a method to detect future heatwaves from a climatological point of view from a EURO-CORDEX dataset  [67]. In this study, we used this method to detect future heatwaves in EURO-CORDEX ensemble multi-year projections to build future climate files containing heatwaves to be used by building practitioners. Since the projections are available for many locations worldwide, this methodology can be used for other countries than France using the CORDEX data for outside of Europe.
The increase at the 0. 5 centile is in accordance with the findings of  [75] , which predict, in France, a mean increase of +2. 1 • C in the near future and of +4. 5 • C in the long-term future. The end of the distribution tail increase is as important as the medium one, since it is when extreme events happen. Heatwaves will be found during these temperatures, and the temperature at a high percentile is also the one used for the design-day of cooling systems. Depending on the standards, methods to choose the design-day differ, and the percentile can be between 0. 90, 0. 95, and 0. 99  [76, 77]. They usually analyse datasets with hourly data points; the one we considered has data points every three hours, at 12 am, 3 pm, and 6 pm; therefore, the temperature peak might be missed. From Table  3 , it is noticeable that the temperature extremes have a higher increase than the temperature median, and for each model.
Data Interpolation and Formatting The data of each climate variable were first interpolated to the 1 h time-step, a typical standard for weather files for building simulations. The dry-bulb temperature, given in Kelvin was converted into Celsius (-273. 15), the cloud cover, given in percentage, was converted into tenths; the specific humidity, given in kg/kg, was multiplied by thousands to convert to g/kg. Polynomial regressions were used for the dry-bulb temperature and the global solar radiation and linear regressions for the other weather variables. With the software EnergyPlus, different time-steps than one hour are possible; however, with the graphical interface DesignBuilder, only 1 h time-step weather files can be used. While using EnergyPlus, if a smaller time-step than 1 h is chosen, the internal EnergyPlus weather convertor linearly interpolates the weather variables to the smaller time-step.
Knowing the water vapour pressure VP and the saturated vapor pressure VP s , the relative humidity RH (%) could be calculated (Equation (  6 )). T > 0 : VP s = Equations (  4 )-(  14 ) and (  17 )-(  18  Average data for the global horizontal radiation (GHR) were available every three hours. First, we set the GHR data points to zero when the sun was down, which was not systematically the case since data points are average (there could be a data point at 07. 30 am, as an average from 06 am to 09 am, even if the sun was in reality down. ) We then interpolated the data to the 30 min time-step and fit a polynomial through the existing data points, and the first and last zero data points each day (to recreate sunrise and sunset). Finally, we kept only the data points every hour to ensure consistency with the other climate data.
This can be explained by the fact that on Figure  9  we are analysing the raw-output data. These results are in line with the ones presented on Figure  8  since the bias-adjusted temperatures for the first two models are actually warmer and for HadGEM_RCA4 they are lower for the summer temperatures. During the future period, the temperatures projected by the model MPI_RCA4 over the period 2041-2070 are really close to the data from RT-2012 (also expected from Figure  8 ). Since bias-adjusted future temperatures for the model IPSL_RCA are warmer than the raw outputs but colder for the During the historical period, the typical year summer hourly temperatures projected by the models MPI_RCA4 and IPSL_RCA4 over the historical period are lower than the observations (RT-2012) and the temperatures predicted by HadGEM_RCA4 are higher over the historical period 1994-2008.
In France, a new method to detect heatwaves was recently adapted to a EURO-CORDEX dataset  [67]. They used the basis of the operational method of heatwaves detection in France since 2006 that they updated to be suitable to any time series and to any spatial scale. The method was initially used for cold spells detections but has been adapted for heatwaves. The method is based on three percentile thresholds Spic, Sdeb, and Sint, which have been calculated based on mortality data due to historical heatwaves. They were originally absolute thresholds, and have been redefined as percentiles of the daily mean temperature distribution over several years in effort to make the method accessible to any dataset. Spic represents the threshold beyond which a heatwave event is detected (99. 5 percentile of the temperature distribution over the historical period).
For the raw-output, the thresholds differ greatly along the models (up to 3 • C difference) so we used each model threshold for heatwaves detection. Using thresholds specific to each dataset provided a consistent detection of the future heatwaves compared to the ones detected with the bias-adjusted temperatures (Figure  11 ). Since all the weather variables are not available bias-adjusted, we use the raw-output data to build the future heatwave weather files. Energies 2020, 13, x 23 of 36 Figure  10. Heatwave detection from  [67]. The Spic threshold is used for heatwave detection, the Sdeb threshold defines the heatwave duration, the Sint threshold ends the heatwave. Global intensity: read area of the plot in degree hours. We detected heatwaves for both the raw-output and bias-adjusted temperatures for comparison.
From Table  6 , for the bias-adjusted data, we found the Spic, Sdeb, and Sint thresholds very similar; therefore, we used the thresholds median of the three models like in  [67]. The thresholds are higher for the city of Paris than for the rest of France. For the raw-output, the thresholds differ greatly along the models (up to 3 °C difference) so we used each model threshold for heatwaves detection. Using thresholds specific to each dataset provided a consistent detection of the future heatwaves compared to the ones detected with the bias-adjusted temperatures (Figure  11 ). Since all the weather variables are not available bias-adjusted, we use the raw-output data to build the future heatwave weather files. In comparison to historical heatwaves, future heatwaves are longer, more severe and with higher intensity.
The HadGEM_RCA heatwave is sunny and dry with the humidity decreasing from the beginning until it reaches a minimal of 40% during the night and 20% during the day. All heatwaves exhibit wind speeds between 2 and 4 m/s. In this section, we presented two methods to build future weather files from the EURO-CORDEX future climate data. The TWY method is standard and already used by many building practitioners. The HWE method is new and can be used for assessing building resilience to overheating under future heatwaves. As the method is percentile based, it allows to be used worldwide on any climate dataset. The French detection based on mortality thresholds is a supplementary indication of how often the heatwave alert would start according to the Heat Health Watch Warning System (HHWWS) included in the French National Heatwave Plan (NHP), if the adaptive capacities of organizations, populations and building infrastructure remained unchanged.
Discussion We proposed a methodology to reassemble future weather files for building simulations from EURO-CORDEX data. The method allows to detect future heatwaves from a multi-years dataset based on outdoor temperature thresholds and additional morbidity thresholds (IBMs). A very intense heatwave was found in the re-composed future typical year from one climate model, which might question the weighting of weather variables in the reconstruction method for future typical years, especially for free-running buildings sensitive to external temperature. Nik has proposed a method to reconstruct both future typical years and future extreme years, using only the dry-bulb temperature to select the months  [59]. Using a similar approach would even out the probability to discover a very intense heatwave in the future typical year; however, the future typical year could have particularly low solar radiation, high wind speed or particularly high or low humidity, three important weather parameters that might underestimate future overheating risks or cooling loads in buildings.
Conclusions In this paper, we first described the different methods that are used by the building community to re-assemble or re-create future climate data. The literature review highlighted a need to produce future weather files containing hot temperature extremes, such as heatwaves. We proposed a methodology for assembling ready-to-use future weather files for building simulations. We presented both future typical weather year (TWY) and heatwave event (HWE) weather files, allowing to assess the building's indoor conditions under future typical conditions and future heatwaves. We used the climate multi-year projections from the EURO-CORDEX project, very recently available on a 12. 5 km spatial resolution. For the first time, a methodology was presented to use these climate data for building simulations.
The methodology allowed to interpolate the climate data from datasets every three hours to every hour and calculate missing data to reconstitute future ready-to-use weather files. We reassembled historical and future typical weather files (TWY) and future heatwave events (HWE) based on a climate criterion and additional morbidity data, and differentiated them, not only in terms of dry-bulb temperature, but also relative humidity, direct solar radiation, and wind speed. We modelled a building case-study located in Paris with the software EnergyPlus and used these new future weather files to analyse potential building overheating and building resilience to future heatwaves by comparing future to historical climate projections. Finally, we considered the uncertainties related to the climate models by using three models (one RCM downscaled from three GCMs) to calculate the building's indoor conditions.
Acknowledgments: The authors would like to thank  Jean-Louis Dufresne  for his communication, knowledge, and help to use the CORDEX projections. The authors acknowledge the  World Climate Research Programme's Working Group on Regional Climate , and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. The authors thank the climate modelling groups (listed in Table 2 of this paper) for producing and making available their model output. The authors acknowledge the Earth System Grid Federation infrastructure an international effort led by the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison , the  European Network  for Earth System Modelling and other partners in the  Global Organisation for Earth System Science Portals (GO-ESSP ).
Covariates Patient and tumour characteristics were prospectively collected in an electronic database. Demographics included age, gender, American Society of Anaesthesiologists score, body mass index (BMI), anticoagulant/antiplatelet treatment, Charlson comorbidity index (CCI), Eastern Cooperative Oncology Group score, and surgeon's experience (subjectively categorised as high [>100 cases], medium [50-100 cases], or low [<50 cases]). Tumour characteristics included tumour size, tumour side, and presence of adherent perinephric fat. Pathological data included stage, surgical margins, and histological subtype.
Statistical analyses Descriptive statistics were reported as median and interquartile range (IQR) for continuous variables, and frequencies and percentages for categorical variables. Spearman's rank correlation coefficients were determined between continuously coded scores and quantitative perioperative outcomes. Four separate logistic regression models adjusted for anticoagulant/antiplatelet treatment, BMI, surgeon's experience, and CCI were used to perform univariable and multivariable analyses to identify the best predictor of overall complications. Receiver operating characteristic curve analysis was used to assess the accuracy of each scoring system and surgeons' VAS score for predicting the trifecta achievement. Statistical analyses were performed using Stata 14. 1 statistical software (Stata, College Station, TX, USA).
Even though one may argue that they were not designed to predict the primary endpoint that we used (ie, the trifecta), we feel that our study highlights the limited abilities of nephrometry scores to predict RPN outcomes, and in our opinion, these scores provide only trivial information. Our paper underlines the lack of predictive models, and the necessity to build and validate a tool to assess individual patient risk for perioperative complications after RPN. We did our best to make our results as reliable as possible. First, we collected our data prospectively to avoid biases associated with missing data. Second, radiological scores were calculated before surgery by two independent physicians who were blinded to patients' data. Third, we included all consecutive patients undergoing RPN without any exclusion.
Open access and obesity class I to III) was associated with late-onset preeclampsia (≥34 weeks of gestation, LOP, n=1096 cases) in a linear progressive fashion (R 2 0. 93) while early-onset preeclampsia (<34 weeks gestation, EOP, n=491 cases) was not (R 2 0. 14). LOP represents the vast majority of cases of the disease (90% in high-income countries and approximately 70% in medium-low income countries). 6 7 herefore, we sought to investigate in our comprehensive epidemiological population perinatal database if women with an OptGWG 4 (from a birth weight perspective) would also have lower rates of LOP compared with women with an 'inadequate GWG'. As the formula we proposed has been established for term pregnancies (37 weeks onward),  4  only term preeclamptic women were selected for this study ('LOP37').
MATERIAL AND METHODS The hospital records of all women giving birth at the maternity of the University Hospital South Reunion Island from 1 January 2001 to 30 June 2019 were abstracted in a standardised fashion. The study sample was drawn from the hospital perinatal database which prospectively records data of all mother-infant pairs since 2001. Information is collected at time of delivery and at infant hospital discharge and then regularly audited by appropriately trained staff. This perinatal data base contains information on obstetrical risk factors, description of delivery and maternal and neonatal outcomes. For the purpose of this study, records have been validated and used anonymously. All pregnant women in Reunion Island (as part of the French National Health Care System) have prenatal visits, periodic blood tests and ultrasound scans, and anthropological characteristics recorded in a maternity booklet.
Primary outcome We arbitrarily created five categories of GWG using the published formula (-1. 2 ppBMI (kg/m²)+42 ± 2 kg)  4  defined in our population of Reunion Island: ► Optimal GWG range: opt GWG±2 kg. Multiple regression was used to validate the independent association of maternal age and other confounding factors with LOP37. Variables associated with term preeclampsia in bivariate analysis known to be associated with the outcome in the literature were included in the model. A stepwise backward strategy was then applied to obtain the final model. The goodness of fit was assessed using the Hosmer-Lemeshow test. A p value below 0. 05 was considered significant. All analyses were performed using MedCalc software (V. 12. 3. 0; MedCalc Software's, Ostend, Belgium). We considered the following covariates as possible confounders in this analysis: maternal BMI by increment of 5 kg/m², gestational diabetes, chronic hypertension, OptGWG (Yes/No), smoking, primiparity and maternal age by increment of 5 years.
CONCLUSION Our findings indicate that being overweight or obese (class I to III) at the beginning of any pregnancy is not by default associated with increased maternal and perinatal risks concerning LOP: we may help actively to counterbalance the morbid effects of high BMIs by individualised counselling on their GWG. This approach urgently requires adequately powered prospective trials. Contributors P-YR participated at all the stages of the study (data collection, analysis, writings, etc). BB participated at the data collection. TH verified all the epidemiological calculations and participated deeply to the data analysis. GD, MS and TH expertised the analysis, the text and the final writings (and the English Language). MB, as the head of the Sud-Réunion University's maternity, is the cornerstone of the existence of the perinatal data base made in its department and being worried by the obesity problem in la Reunion asked for more research on gestational weight gain.
Study design The ACOPREFERENCE project was a nationwide Spanish multicentre 2-round Delphi study to seek expert opinion on the management of anticoagulation therapy for patients with NVAF. The approval of the Institutional Review Board (IRB) or by equivalent ethics committee(s) was not required as this Delphi study does not involve human subjects research. No patient data were collected for this study, which was completely based on the feedback provided by experts regarding relevant topics on anticoagulation management in NVAF. The Delphi process is a widely accepted scientific method of structured and systematic information gathering from a group of experts (termed the Delphi expert panel) on controversial or complex topics  [25]. Each panel expert provides opinions individually and anonymously without the biasing effect of dominant individuals or group pressure  [24, 26, 27].
Round 1. The members of the Delphi expert panel evaluated the inclusion of the items in the consensus using an online questionnaire through a web platform. Panel members were asked to rate their level of agreement with each questionnaire item on a 9-point Likert scale from 1 (completely disagree) to 9 (completely agree). Panellists were also encouraged to provide comments after scoring each item using open-text comment fields included in the online survey. After the analysis of the data obtained from the first Delphi round, the scientific committee experts participated in a teleconference meeting, where the Delphi survey results were presented and discussed. Item selection was based on the acceptance of questionnaire items by �66. 6% of the expert panel and the agreement of the scientific committee.
Statistical analysis Descriptive statistical analysis of the data obtained from the assessment of the Delphi questionnaire items by the expert panel in rounds 1 and 2 was conducted. The distribution of frequencies of panel responses on the 9-point scale was calculated to establish the level of consensus for each questionnaire item. Each item was categorised according to the scores as rejected (scores 1-3), undetermined (scores 4-6), or accepted (scores 7-9). A descriptive statistical analysis of the characteristics of the Delphi expert panel was also performed, including calculation of measures of central tendency and dispersion (mean ± standard deviation, median and interquartile range) for quantitative variables, and frequencies and valid percentages for qualitative variables. The statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 18.
Acknowledgments The authors would like to acknowledge the Delphi panel experts of the  ACOPREFERENCE study  for their participation in the study (S2 Appendix). We also thank  Dr Cristina Vidal  and  Antonio Torres  from  Dynamic Science (Spain)  for their editorial and medical writing support, funded by  Boehringer Ingelheim, Spain. The authors would also like to thank  Boehringer Ingelheim  for sponsoring the study.
Materials and methods We conducted a population-based observational retrospective study using data from 2012 to 2014 obtained from the French hospital discharge database. We included 2,256,797 deliveries and 1,999,453 singleton newborns in mainland France, among which, 6,733 (3. 0‰) were OMD. The adverse outcomes were maternal mortality in hospital or during transport, stillbirth, neonatal mortality, neonatal hospitalizations, and newborn hypothermia and polycythemia. The socio-residential environment was also included in the regression analysis. Maternal and newborn adverse outcomes associated with OMD were analyzed with Generalized Estimating Equations regressions.
Selection of patients The study data comprised all deliveries recorded from 2012 to 2014 in the French hospital database (Programme de medicalisation des systèmes d'information  [PMSI] ). The PMSI collects the discharge abstracts (DA) from all hospitals in France and is 100% exhaustive for inhospital deliveries  [10]. The data included maternal age, gestational age (GA), length of stay, and hospital death. Diagnoses and procedures are coded according to the International Classification of Diseases (ICD-10) and to the French Classification of Medical Procedures (CCMP). The geographic scale used for this analysis was the geographic code of residence (zip codes) recorded in the PMSI. We studied all deliveries in France at or after 24 weeks of gestation (WG). They were identified from the ICD-10 codes Z37 ("Outcome of delivery") and the delivery procedure codes.
OMD were identified when code Z3900 ("Care and examination immediately after delivery out-of-health hospital") was the main diagnosis in the DA following delivery. Liveborn singleton DAs were identified from codes Z38. 0 ("Single liveborn infant, born in hospital") and Z38. 1 ("Single liveborn infant, born outside hospital") and age in days equal to zero. The PMSI database allows the linkage of DAs for consecutive hospitalizations and the linkage of mothers' and children's DAs for singleton pregnancy thanks to a common identifier used for both (Fig  1 ), in the framework of the secure anonymized information linkage in use since April 2012. Our epidemiologic follow-up included all PMSI data from the beginning of pregnancy until 42 days after delivery for women and from the first 28 days for infants.