dataesr/scientific-paragraphs-categorization huggingface dataset:
Remote influences on anomalous rainfall over North, Central and South Vietnam during extended summer (May-October) are explored using a 38-year (1979-2016)  global dataset. Composites of wet and dry events with lags of up to two weeks are assembled for rainfall indices over these three subregions. The moisture budget and the large-scale dynamics associated with these events are described. The moisture budget for composite precipitation events is examined using vertically integrated moisture flux convergence, vertically integrated tendency and evaporation. Variations are mainly explained by the flux convergence, which is seen to serve as a reasonable proxy for intraseasonal variability in rainfall. Rainfall extremes of opposing signs show markedly asymmetrical largescale precursors and different pathways of influence.
Further examples of combined effects include cold surges associated with expansion of the Siberian High and tropical depressions associated with the MJO  (Yokoi and Matsumoto, 2008; Chen et al. , 2012; Abdillah et al. , 2018). This type of combined influence appears to have varying impacts on different regions, so the exact nature of the remote influence will be sensitive to how these regions are defined and how the data is processed (thresholds, time filters, etc). In this paper we will use moisture flux convergence as a proxy for rainfall ISV, since it is available from a consistent dataset and it is a quantity that is closer to the large scale dynamics. The relationship between flux convergence and rainfall will therefore be examined in the context of the regional moisture budget. Then the main focus of this paper is on the asymmetries in large-scale dynamical precursors to wet and dry episodes in selected sub-regions of Vietnam.
Data We use 38 years  of ERA-Interim global reanalysis  (Dee et al. , 2011)  from May to October. Four-times daily data of atmospheric variables including zonal wind (u), meridional wind (v), specific humidity (q), geopotential height (gph) are spectrally analysed at T42 and stored on sigma levels (relative to msl pressure) for later use in a dynamical model. Daily gridded 1x1 degree data of precipitation (PPT) and evaporation (EVP) are also used from ERA-Interim, taken from the Frequent Rainfall Observations on GridS (FROGS) dataset  (Roca et al. , 2019). Four-times daily Vertically Integrated Moisture flux Convergence (VIMC) is calculated following using u, v, and q at all levels, and then averaged to daily values for comparison with precipitation and evaporation. The daily PPT, EVP and VIMC are smoothed with 10-day running mean, and a smoothed annual cycle is removed based on average by calendar date.
Integral to this plan has been the hiring of new Knowledge Transfer Leads per TA, to ensure a deep understanding of products, TA, and other critical medical information for all relevant staff. These roles proved challenging to fill, as few corollaries exist, but are rapidly making an impact. As new core training modules come online, the team is working with colleagues in TAs, publications, communications, and medical information to pilot effective ways of providing real-time updates, such as priority data at congresses, new manuscripts, and collective insights. Periodic assessment will measure skill development against defined pillars, and delivery of content in terms of scientific excellence, clarity, and objectivity. This structure will be important to equip staff, and also to ensure staff feel equipped, to provide value in their interactions.
Materials and methods In order to comprehend the differences between African countries, we collected epidemiological data from 45 African countries. Due to the quality of the data, this list was reduced to 30 countries distributed between North, South, East and West Africa. These countries are: Algeria, Angola, Burkina Faso, Cameroon, Chad, Ivory Coast, Egypt, Ethiopia, Guinea, Guinea-Bissau, Kenya, Libya, Madagascar, Mali, Mauritania, Morocco, Mozambique, Namibia, Niger, Nigeria, RDC, Rwanda, Senegal, Somalia, South Africa, Sudan, Tanzania, Tunisia, Zambia, and Zimbabwe. Up to March, 2022, With the exception of Tanzania, Madagascar, Chad, and Burkina Faso, which had three waves, and Kenya, Algeria, Tunisia, and Zambia, which had five waves, nearly all of the thirty African countries analyzed had four waves.
Funding This work was supported in part by the  French Ministry for Europe and Foreign Affairs  via the project REPAIR  COVID-19-Africa  coordinated by the  Pasteur International Network association , by  European Union 's  Horizon 2020 FIGURE Correlation circles  for the two first PC planes. research  and innovation program  under grant agreement No. 883441  (STAMINA) and the  Tunisian Ministry for High Education  via  PRF  project  PRFCOV19-D5P1  Evaluer, prédire, agir: stratégie fédérée pour la lutte contre la COVID-19.
The final model was selected using backward selection, based on minimisation of the Akaike Information Criterion (AIC)  [34]. The equation of the model retained was: where p is the probability of belonging, alpha is the constant, Pe is the period (before or after), R indicates whether the visit was followed by readmission within 7 days, V is a visit-related information vector including the triage circuit and the quantity of prescriptions of drugs and exams, and X is a patient-related vector, including the age category, sex, and patient status at arrival at the ED. In parallel, data regarding unscheduled ambulatory care from all healthcare establishments in the Department was recorded based on the statistics of the national health information database. Overall trends in patient flow were studied for the period 2016 to 2019 and put in perspective with the results of our analyses.
64). They went from an average of 58. 18 visits per week in the "before" period to 60. 57 in the "after" period. More than two thirds (4266 visits, 68. 82%) of these visits involved patients who left without waiting for the medical examination. The other patients (1853 visits, 21. 18%) could not be diagnosed due to lack of information or to the nature of stay. The average number of ED visits decreased by 29. 37 visits per week (2. 5%), from 1168. 66±76. 62 visits per week before the opening of the USC, to 1139. 29±51. 83 visits per week afterwards, with a significant linear trend (F-test, p=1. 61E-06). Unscheduled care in the Department increased significantly by 328. 12 visits per week (14. 75%, F-test, p =4. 25E-04) during the study period. Additional analysis of the national healthcare information database illustrated by Figure  4 , showed that in the first 6 months after the opening of the UCS, unscheduled care went from a baseline of 2225.
Furthermore, it raises investors' sensitivity to the climate change-related risks for the targeted industries. In line with prior empirical research  (Griffin et al. , 2017; Matsumura et al. , 2014)  Thomson Reuters Asset4 database. Thus, we are confident that we have considered all the carbon emissions data available through different disclosure channels for the sample firms. We need at least two consecutive years of available carbon emissions data because of our empirical research design for the estimation of carbon emissions. We also collect financial and non-financial data from the databases Compustat and Thomson Reuters Asset4. Table  1  reports how data availability affects our sample size. The final sample of our paper includes 1034 firm-year observations for which carbon emissions data and all other data are available.
They also capture the heterogeneous use of carbon measurement methods in different industries. Table  2  summarizes the measurement and data sources for all variables of the emissions estimation model. As we focus on firms from carbon-intensive industries, we expect their business models to be strongly reflected in the direct carbon emissions that occur at sources owned or controlled by the firm. Therefore, we expect the emission estimation model to have higher explanatory power for direct carbon emissions as the dependent variable than for indirect carbon emissions. We apply year-specific emissions estimation models. For each year, we estimate the carbon emissions, including all firm-year observations up to this year. To illustrate, the estimation of carbon CO2_UNEXPECTED i,t = CO2_TOTAL i,t -CO2_EXPECTED i,t.
We collected novel survey data from a representative sample of French households by age, asset classes and wealth in December 2014 and May 2015. We elicited responses on how they perceive the stock market behavior and information of three circles around them: the inner, financial circle , the overall social circle of friends and acquaintances, and the overall population. Although the average reported size of the social circle is about 53, we document that only about half the sample report having a financial circle, and those who do talk about finances only with about five people. The size of the financial circle correlates positively with being younger, a saver, and with larger wealth. 1  We elicit responses on the subjective probability distributions of future stock market cumulative returns (over a five-year horizon) and of perceptions of recent past cumulative returns (over a three-year horizon), from which we derive expected returns and mean perceptions.
The TNS 2014 dataset is constructed from a panel of volunteers. TNS proposes a re-weighting so that the final sample is representative of the French population by age, wealth and asset classes. Such re-weighting to recover representativeness of the population does not strongly modify the descriptive statistics, suggesting that the selection bias is very low relative to the widely used INSEE survey, which does not include our key variables of interest. TNS survey waves are not necessarily representative in terms of other socio-economic or demographic characteristics, such as gender, marital status and number of children.
Experimental design The set of lifestyle recommendations to be presented to the respondents was based on an experimental design. Specifically, we used a Bayesian efficient design to maximise the D efficiency of the chosen choice sets using Ngene software (version 1. 1. 1, http:// www. choic emetr ics. com). A Bayesian efficient design aims to maximise the precision of the estimated parameters of the attributes for a given number of choice tasks by incorporating a priori information about the sign and value of parameters. Parameter estimates deriving from a pilot study (n = 10) were used as a priori information to construct the choice sets. Fifteen different choice tasks were created in which respondents were asked, in each case, to inform whether they would implement the lifestyle recommendation (yes/no).
Study population and data collection and ethical approval The study was conducted in seven European countries (Belgium, France, Ireland, the Netherlands, Spain, Switzerland and the UK) between March and October 2012. Patients with or at risk of osteoporosis to whom medication (or lifestyle behaviours) was at least proposed were consecutively recruited during outpatients' clinics. The questionnaire was completed by the patient at the clinic, or at home and returned in a postage-paid envelope. Approval for this study was obtained from the Medical Ethics Committee of the Academic Hospital Maastricht and Maastricht University. A team from this university coordinated the project. Participants gave informed written consent according to the 1964 Helsinki declaration. Additional local ethics approval was obtained from those participating centres that required ethics approval for a DCE study, i.
Statistical analyses Questionnaires were included in the analysis if a respondent completed all choice tasks. Respondent characteristics were analysed using descriptive statistics. The normality of the distribution of the quantitative parameters was investigated using the mean-median comparison, the histogram and quantile-quantile plot and tested with the Shapiro-Wilk hypothesis test. Results were expressed as numbers and frequencies for qualitative variables and as mean and standard deviation (SD) or median and interquartile range (IQR, P25-P75) for quantitative variables. Data derived from the choice tasks were analysed using Nlogit software (version 5) to assess preferences for attributes and levels. A conditional logit regression model (i. e. , multinomial model) was used to estimate patient preferences.
This same study, on patient's preferences, underlined that participants preferred exercising at home in the morning, on their own schedule, with easy to perform exercise, slow paced and easy to remember. Patients also reported the importance of social support, even if they preferred exercising alone. Taking all those patient preferences into account when proposing exercises to protect their bone health could lead to higher initiation of and adherence to a physical activity program. In addition to exercising, dietary measures should also be considered for any patients suffering from or at risk of osteoporosis. Recommendations include a daily calcium intake of between 800 and 1200 mg and sufficient dietary proteins, ideally achieved through dairy products  [6, 8, 32, 33]. Consumption of cheese and yogurt, for example, has been shown to reduce risk of hip fracture, through a recent meta-analysis pooling data from 18 individual studies  [34].
) underlying why patients do not want to adhere to lifestyle programs. Further qualitative research may provide additional important data to better and deeper understand patient preferences and expectations for lifestyle programs. Nevertheless, the present study does provide initial and useful information for optimal non-pharmaceutical osteoporosis management. Awareness of variation in preferences for lifestyle is very insightful for healthcare professionals when starting communication with a patient regarding lifestyle. Second, data collection was performed in 2012, 10 years ago. Health behaviours could vary across years, with developments in health/scientific research and improvement in treatments. It cannot be excluded that patient's preferences could have changed over the course of years.
Acknowledgements We would like to thank all participating centres: the  Unit for Osteoporosis and Metabolic Bone from Ghent University Hospital (Belgium) ; the  University Center for Investigation in Bone and Articular Cartilage Metabolism in Liège (Belgium) ; the  Fracture Clinic of Maastricht University Medical Center  (the  Netherlands ); the  Bone Unit of Paris Descartes University, Paris (France) ; the  North Western Rheumatology Unit, Our Lady's Hospital, Manorhamilton  and  Sligo University Hospital (Ireland) ; the  Musculoskeletal Research Unit  and RETICEF from the  Universitat Autònoma de Barcelona (Spain) ; the  Division of Bone Diseases  from the  Geneva University Hospitals (Switzerland) ; the  MRC Lifecourse Epidemiology Unit  from the  University of Southampton (UK)  for helping us in data collection;  Ed Porquie , our patient partner;  Wafa Ben Sedrine ,  Ivette Essers  and  Wilco Tilburgs  for data entry; and all the patients for their participation.
The concentration distribution parameters differed from one practical case to another with a GM set to 253, 253 and 58. 4 μg/m 3 and a GSD set to 2. 80, 1. 03 and 2. 80 for cases 1, 2 and 3, respectively. For all scenarios, both the GM and GSD were set at pragmatic random values generated with R software 4. 0. 5® (R Core Team, Vienna, Austria) for Windows 10©. We used the InCaRisk app  (Petit et al. , 2021)  with 1000 simulations runs to generate all of the reference index-based risk distributions R i (r). Briefly, the InCaRisk app is a free user-friendly and interactive web application that allows for cancer risk estimation following inhalation exposure (https://exporisk-timc. imag. fr/InCaRisk/). A total of 305 substances/chemicals with reference values from up to eight agencies are available.
-An online survey was proposed to the participants in the COP 21 open-space technology meeting in order to assess their motivations, interests, life trajectories and attitudes towards science and the environment. This survey was designed with LimeSurvey, and all answers were anonymized. Data treatment was performed by using the R software. Originally, the survey included 42 questions. As part of the current study, we selected 18 relevant questions: i. Four of them collected socio-demographic information (Age, Gender, Education level). ii. Two questions assessed respondents' knowledge of biodiversity: "Are you able to define the term 'Biodiversity'?" (Yes/No); "If yes, please give your definition". A 0 to 5 quantitative scale was constructed on the basis respondents' words and ideas. (We used the following scale: 0-no definition; 1-the definition includes no relevant notion; 2-the definition includes the notion of diversity or that of species; 3-the definition includes the notion of diversity and that of species; 4-the definition includes the notion of diversity and that of species and interactions; 5-the definition includes the notion of diversity at different scales and that of species and interactions under different forms).
OFB Citizen Committees Interviews and participative observations within OFB and ARBs citizen committees revealed great heterogeneity of outcomes for participants. First, our data indicated that some participants did acquire knowledge about the definition of biodiversity, its current state, and the functioning of some ecosystems: "Very rapidly you learn things, you say 'Oh, right, that's the way it works', and then you talk about that" (an OFB participant, interview 20/05/2019). "The Citizen Committee helped me become aware of this renowned life chain, of these ecosystems. I really became aware of them" (an OFB participant, interview, 20/05/2019). Some of them changed their attitudes and behaviors accordingly: for instance, one participant in the OFB committee indicated he had changed some of his gardening practices (such as avoiding killing ants, "who do have a role to play, certainly", 20/05/2021).
Data Data are quarterly and cover the 1999-2018 period. The main dependent variable of the analysis is the bilateral nominal exchange rate -vis-à-vis the SDR-underlying the equilibrium exchange rate (NER * ). We follow the methodology detailed in  Coudert et al. (2019)  to derive the NER * , using the estimated equilibrium exchange rates (ERER) from the EQCHANGE database (CEPII; see  Couharde et al. , 2018)  as well as the included trade weights. 13 We also relied on consumer price index data from the IMF World Economic Outlook database. Finally, the different exchange rates vis-à-vis the SDR are from the IMF International Financial Statistics database.
Robustness check: factor analysis Besides the key issues of countries' groupings and choice of the ERR in the perspective of regional integration, identifying the causes of heterogeneity-frequently mentioned in the literature, but rarely documented-is equally essential. Indeed, such an examination is of major interest to establish milestones in the convergence process. To that end, we perform a factor analysis with the aims of (i ) identifying the common features shared by the different countries, and (ii ) double-checking our above findings regarding the partitions of the ECOWAS area. Accordingly, we collect data on several key variables reflecting macroeconomic structures/conditions as well as imbalances, and perform a factor analysis to identify the structural economic differences between the countries emphasized by the cluster analysis.
Numerical experiments In this section, we report our computational experiments in order to demonstrate the added benefit of our MIP formulation and preprocessing step. The numerical experiments were performed on a desktop computer equipped with an Intel(R) Core(TM) i7-8700k 4. 8 gigahertz and a memory of 16 gigabytes. Our model was implemented in Gurobi 9. 0. 1 with default parameters and the other algorithms were coded in multithreaded C++ using the LEMON Graph Library.
Acknowledgements & Funding The authors gratefully acknowledge the financial support of the  Kopernikus-Project  " SynErgie " by the  Federal Ministry of Education and Research of Germany (BMBF)  and the project supervision by the project management organization  Projektträger Jülich (PtJ). This research was funded in part by the  Luxembourg National Research Fund (FNR )  and  PayPal , grant reference "  P17 /IS / 13342933  /PayPal -FNR/Chair in DFS/ Gilbert Fridgen " (PEARL).
We construct a data set of 236 deSPACs announced in the United States between January 2012 and June 2021 for which a merger was consummated by 1 July 2021. We combine multiple sources of SPAC data, manually check several deal characteristics and control for rumours before the official announcement. We first investigate post-merger announcement performance in the short term, using the period around the day a SPAC announces a business combination target to gauge stock market response. Second, we calculate the buy-and-hold-abnormal returns (BHARs) of these SPACs over 6-24 months from the announcement day and from the deSPAC date to examine long-term performance. Our study provides new insights into the performance of deSPACs for public SPAC investors. First, note that prior literature based on earlier sample periods finds abnormal announcement returns of less than +2% on average (e.
| Sample and data sources We obtain our data on SPAC IPOs from three sources: Refinitiv Eikon/SDC Platinum, SPAC Insider (spacinsider. com) and SPAC Research (spacresearch. com). We identify 375 SPAC IPOs issued on either the NYSE or the Nasdaq that announced a business combination between 1 January 2012 and 30 June 2021. We double-check each announcement date carefully and use the earliest date found in the press releases. As of 1 July 2021, 236 of our sample deSPACs had completed a business combination, 127 had announced a merger that was still pending and 12 were later withdrawn and liquidated. This paper focuses mainly on the sample of 236 deSPACs that successfully completed their business combinations (completed deals). The information on SPAC characteristics comes from a variety of sources.
Most data is directly obtained from Refinitiv Eikon/SDC Platinum, SPAC Insider and SPAC Research. Any missing information is manually obtained from the EDGAR database. 9 Our stock data is obtained from Refinitiv Datastream and total returns of the SPACs and merged companies are available until 1 September 2021. The summary statistics are provided in Table  1  and the variable definitions are listed in Table  A1. As reported in Panel (a) of Table  1  and in line with prior research, we find a strong increase in SPAC activity in recent years. The number of deals increased every year, with a total of 100 SPAC business combinations announced in 2020. Our data extends only until 30 June 2021, but the trend appears to be continuing, as we have 156 deal announcements in the first 6 months of 2021.
All event windows include the day of announcement but also capture the business day before the announcement to account for (potential) information leakage or deal rumours. M&A announcements are indeed sometimes anticipated and news reports are released before a formal announcement is made. We, thus, analyze newspaper articles and control for whether we find any information on a potential merger. We control for both names, the SPAC and the target, around the announcement date in the Lexis news database. However, despite checking all SPAC deals manually, we mostly found announcements of nonbinding letters of intent. We, therefore, decided to control our results by using the flag 'Deal Began as Rumour', provided by the SDC platform. This approach has also recently been used by  Carletti et al.