dataesr/scientific-paragraphs-categorization huggingface dataset:
Description of the dataset This dataset provides information on the hospital situation regarding the COVID-19 epidemic. We have opted for the rst proposed le: Hospital data related to the COVID-19 epidemic by department (dep), sex of the patient (sex), number of hospitalized patients (hosp), number of persons currently in intensive care or resuscitation (rea), number of persons currently in follow-up and rehabilitation care (SSR) or long-term care units (USLD), number of persons currently in conventional hospitalization (HospConv), number of persons currently hospitalized in another type of service (autres), cumulative number of persons returning home (rad) or cumulative number of dead persons (dc). The data have been daily updated. For the current application, we considered the data of 28/07/2021, with a total of 150894 observations.
For simplicity reason, we have chosen to just study the department of 'Vienne' database. Therefore, for our application, we served of a dataframe of 1494 observations and 6 variables. Hence, we shall consider the following three models: • Model 1: X = dc and Y = hosp. • Model 2: X1 = sex, X2 = rea, X3 = dc and Y = hosp. • Model 3: X1 = sex, X2 = rea, X3 = dc, Y 1 = hosp and Y 2 = rad. x=119 x=17.
Conclusion and Perspectives In this work, we elaborated a multivariate recursive CCDF estimator. We tackled the asymptotic properties of the proposed estimator by providing the bias as well as the variance in order to demonstrate that our estimator asymptotically follows a normal distribution. Subsequently, we revealed that the use of our recursive estimator with an appropriate choice of the bandwidth and the stepsize enables us to get closer to the true conditional cumulative distribution function rather than non-recursive one. The basic merit of recursive estimators resides in the fact that one can update the estimation with each additional new observation. Therefore, instead of re-running the data each time, it is possible to rewrite our considered estimator as a combination of two (or more) estimators, where each estimator is based on separate datasets, which can be very interesting to keep the computational cost reasonably low.
Information about customers consists of 86 variables and includes product usage data and sociodemographic data derived from zip area codes. The data are supplied by the Dutch data mining company Sentient Machine Research and rest on a real world business problem. The training set involves over 5000 descriptions of customers, including the information of whether or not they have a caravan insurance policy. A test set includes 4000 customers whom only the organizers know if they have a caravan insurance policy. This corresponds to a Dataset to train and validate prediction models and build up a description (5822 customer records). Each record consists of 86 attributes, incorporating sociodemographic data (attribute 1-43) and product ownership (attributes 44-86). The sociodemographic data are derived from zip codes.
Problem formulation We consider a dose-finding clinical trial where patients are followed up for DLT within an observation window [0, t * ], with the additional issue of a non negligible risk of treatment discontinuation due to disease progression, that precludes the complete and reliable assessment of DLT. In this time-to-event framework in oncology, data are collected as dates and t * is defined in days, resulting in t * > 1. During the trial, an aggravation of the patient status may occur, related to the progression of the malignancy, leading to treatment discontinuation in order to propose alternative therapies, if available. Similarly, patients who experience toxicity are generally dose reduced or discontinued and are inconsistently followed for efficacy or progression. In this setting, DLT and progression are competing events within [0, t * ], since both result in trial or treatment discontinuation.
31 In the presence of informative censoring, cumulative incidences were estimated as described by Gray. 37 Non parametric estimators for the benchmark were chosen, similarly to the original benchmark proposal for binary endpoints. 30 The OD was estimated from the complete dataset based on the trial objective defined in Equation (2). Details of the benchmark approach are provided in Supporting Web Material (Table  S1 ). For a comparison purpose in dose selection performances, we also applied the TITE-BOIN-ET design  22  to the simulation scenarios. With the TITE-BOIN-ET design, the method used to guide dose escalation is different from the method used for final estimation. Briefly, for OD estimation at the end of the trial, regression models for each of the toxicity and efficacy outcomes are fitted.
Defining a notion of well-posed weak solutions for the master equation in MFG is one of the important problems in the theory. A step in this direction is a recent paper of Bertucci  [7]  on finite state models which introduced the notion of monotone solutions for MFG with finite state space and studied its well-posedness. The work of  [7] , which is based on a uniqueness technique developed by Lions in  [32] , brought to bear techniques from the theory of viscosity solutions although the actual notion of solution is not related to them. The very recent work  [8]  by Bertucci extends  [7]  to the continuous state space and for several noise structures, and relies on a regularity assumption on the solution which is known only for problems with idiosyncratic noise. The second author was partially supported by the National Science Foundation grants DMS-1266383 and DMS-1600129, the Office for Naval Research grant N000141712095 and the Air Force Office for Scientific Research grant FA9550-18-1-0494.
In addition, pan-Mycoplasma polymerase chain reaction was performed before and after the antigen production to confirm the lack of mycoplasma infection of cells. Then, thus validated SARS-CoV-2, HCoV-229E, and OC43 antigens; noninoculated HCT8, MRC5, and Vero cells; Staphylococcus aureus antigen used as a positive control; and skim milk as a negative control were incorporated onto a nitrocellulose membrane (Bio-Rad, Marnes-la-Coquette, France). Nitrocellulose strips were incubated with 20 μl of paleoserum diluted in saturation buffer (PBST-M 0. 5%, 1:10 0 0) at room temperature for 2 hours under agitation before incubation with peroxidase-conjugated AffiniPure Goat Anti-Human immunoglobulin (Ig)A + IgG + IgM (H + L) (Jackson ImmunoResearch, West Grove, PA, USA). The results were quantified using a FUSION FX chemiluminescent imaging system and ImageQuant TL software, as previously reported  [26].
An original metaproteomics cascade search strategy was used to assign MS/MS spectra to peptide sequences with the Mascot Daemon 2. 6. 1 search engine (Matrix Science). Briefly, an National Center for Biotechnology Information (NCBI)nr-derived database containing 45,925 representative taxa as previously described  [28]  , a database restricted to the identified genera, and a database of identified proteins and viruses were successively queried (Supplementary Figure  2 ). Peptides and proteins were identified with a false discovery rate of 1%, including the deamidation of asparagine and glutamine and oxidation of proline and methionine as variable modifications. In parallel, 20 μl of the paleoserum solution collected from individuals BA-I7, BA-I9, and BVN-293, from whom the samples were collected from another archaeological site in Viotte Nord, Besançon, France  [29 , 30]  dating from the 2nd to 3rd century and tested seronegative for coronavirus were processed.
taurus ), dogs, and poultry; all these animal species have been acknowledged as reservoirs of coronaviruses that can also infect human populations  [31] [32] [33] [34] [35]. Paleoproteotyping has proven to be a powerful approach to identify the taxonomic origin of ancient organic materials  [36 , 37]. Here, we have shown that viruses can be traced with tandem mass spectrometry. The main potential weakness of this analytical approach is the completeness of the generalist database used, which should improve over time with continued sequencing of representatives from all branches of the tree of life. This report of what could be considered as the oldest known HCoV infection will hopefully pave the way for additional reports combining the methods reported here to trace the antiquity and dynamics of coronavirus infection in past populations, giving a deeper scientific perspective to the current COVID-19 pandemic.
ual BA-I12 yielded a 9-residue (IVLSEDYKK) peptide that was observed in 9 MS/MS spectra in the extended dataset, corresponding exactly to the polyprotein replicase 1a of the alphacoronavirus porcine respiratory coronavirus (start_end positions: 912 to 938, NCBI accession number KY406735. 1), swine enteric coronavirus (start_end positions: 904 to 930, NCBI accession number KR061459. 1), and canine coronavirus (start_end positions: 911 to 937, NCBI accession number MW591993. 2), and a 17-residue (RG-PEQTQGNFGDQELIR) peptide observed in three MS/MS spectra, a peptide sequence shared within the nucleocapsid protein of the betacoronavirus SARS-CoV-1 (start_end position: 28,680 to 28,730, NCBI accession number KJ473816. 1), SARS-CoV-2 (start_end position: 29,102 to 29,152, NCBI accession number NC_045512.
2), and the bat Rhinolophus affinis coronavirus (start_end position: 29009 to 29059, NCBI accession number KF569996. 1). For individual BA-I5, the analyses yielded a 9-residue peptide (IVLSEDYKK) observed in 11 MS/MS spectra, which is also found within the polyprotein replicase 1a sequence described previously; a 17-residue (RG-PEQTQGNFGDQELIR) peptide in four MS/MS spectra of nucleocapsid protein, as described previously; and a 10-residue (AYN-VTQAFGR) peptide observed in two MS/MS spectra, most closely related to the nucleocapsid peptide of SARS-CoV-2 (start_end position: 29,072 to 29,101, NCBI accession number NC_045512. 2), R. affinis coronavirus (start_end position: 28,979 to 29,008, NCBI accession number KF569996. 1), and the bat coronavirus BM48-31/BGR/2008 (start_end position: 28454 to 28483, NCBI accession number NC_014470.
Protocol and adjustments of the replication The replication idea emerged from discussions in the "Research Network on Economic Experiments for the Common Agricultural Policy," a group of researchers using and promoting experiments for the evaluation of agricultural policy. 3 The idea was shared with researchers who had experience in data collection with farmers and could reasonably offer a sample of participants. These researchers were invited to a series of joint meetings in which the experimental protocols were developed and discussed. Eventually, 11 teams joined the effort and were involved in designing the experiment and data collection in different farming populations. A few adjustments to  Bocquého et al. 's (2014)  protocol were agreed upon. First, we only included the survey parts that were relevant to the study of risk preferences to reduce response time.
Whenever we refer to covariates in the models, we mean these seven variables that were also part of the original study. All teams followed the same procedure when gathering the data. 5  A questionnaire was developed in English and then translated into national languages. 6 A jointly produced, approximately 4 min long, instruction video explaining the task and payment procedures was offered to all online respondents. The video used one of the examples, and the instructions used the same examples as in the original study. The video was also publicly screened in the Spanish and Austrian face-to-face data collections. Note that in some cases (e. g. , France I), additional data were gathered after the experiment, serving other research purposes. We also asked about the respondents' comprehension.
g. , organic farmers in North-West France, members of two olive grower cooperatives in Spain). Note that these strategies limit the comparability with the original study. All data were collected in the summer and fall of 2021. We provide a short narrative with more details for each of our samples in section 3 of the Appendix S1. An overview of all samples is provided in Table  2. Table  3  gives an overview of respondent farm characteristics pooled across all newly collected samples. Note that we include only covariates that were common across all samples and the original study. Disaggregated and additional summary statistics for each sample are presented in section 1 of the Appendix S1. We also discuss how representative of the underlying populations the samples are in section 2 of the Appendix S1.
Analysis for replication The analysis combined the estimation of three pre-registered structural models with further explorative analysis. We obtained the data and code from the authors of the original study (for Stata), and we successfully verified all the analyses (in Stata and R). The original study weighted responses by population level statistics (survey weights in Stata, see footnote 15, page 147 in  Bocquého et al. , 2014). Because we did not use probabilistic sampling, we also did not apply survey weights in our analysis. The original data and code for additional verification is available online.
While the structural models offer a point estimate for the whole sample using an error term that accounts for individual choice errors, the midpoint technique can give direct insights into the distribution of parameters for CPT based on raw choices. Because different estimation approaches can yield different results, we suggest the estimation of a large number of plausible models. This enables readers to assess the robustness and uncertainty associated with an estimate. More importantly, an open science approach is pivotal: the sharing of data and code allows the community to run further robustness tests and to integrate results in meta-analysis. Our findings have important implications for policy. As stated by  Colen et al. (2016) , "behavioral findings (such as evidence of loss aversion), replicated over time and across domains, can safely be assumed to be valid everywhere and at any time and can therefore help understand reactions to policy of a large share of the EU farming population.
Thanks to this flexibility, we could include several research teams with different constraints for data collection and obtain a large dataset. Although crucial materials to replicate are not always accessible  (Palm-Forster et al. , 2019; Palm-Forster & Messer, 2021) , luckily, we could build on the well-documented instructions, codes, datasets, and other material provided in  Bocquého et al. (2014)  and in later communication with the authors. Harmonizing and improving infrastructures for social science research with farmers is an important task, also for obtaining higher quality samples. For instance, some collaborators of this project could collect data through third-party farmer panels (Netherlands, Germany) or a general registry of all farming businesses (Sweden). Others (e. g. , Spain or Italy), in contrast, had to work with convenience and snowball samples.
A stronger grouping of cases and further harmonization of samples within these sub-groups (e. g. , by farming system, region, or sampling procedure) could lead to better benchmarks for comparison by the removal of additional confounds. Although additional challenges, such as different legal treatments and taxation of cash rewards will likely remain, a coordinated effort to build social science research infrastructures and networks for primary data collection with farmers could facilitate cross-national research (see  Lefebvre et al. , 2021  for more discussion on this). As a first step in this direction, farmers who are part of the European Union's Farm Accountancy Data Network could be invited to voluntarily commit to participate in experiments with high cash rewards on a regular basis. Recruitment of and access to farmers representing a target population is a hard task  (Weigel et al.
Notably, two samples that used face-to-face data collection (Austria and Spain) were at the opposite ends of the assessments for most of the questions (Appendix S1, section 7). Self-assessed comprehension and response quality were very high in Austria, whereas participants in Spain faced more difficulties with the task. Finally, Italy (who also used face-to-face data collection) was close to the overall mean for these questions. Hence, we cannot draw very strong conclusions regarding the use of online versus face-to-face data collection, but we note less between-sample heterogeneity on these aspects for online data collection. It is an open question as to how far adjusting tasks for improved simplicity and comprehension, as well as realism and engagement (e. g. ,  Charness & Viceisza, 2016; Menapace et al.
CONCLUSIONS The objective of this study was to verify the analysis of  Bocquého et al. (2014)  and to test the robustness of their results in a replication in 11 European farming systems. Provided with the original code and data from the authors, we succeeded in verifying all parameters drawn from the original study. In line with the original study and the broader social science literature  (Ruggeri et al. , 2020) , we confirmed that CPT provides a better fit to describe farmers' risk attitudes than EUT. This conclusion holds in all additional samples, albeit we also found considerable heterogeneity within and across samples. Similar to the original study, we faced the challenge of different methods yielding substantially different results for the CPT parameters. We conclude that pre-registration of a preferred specification, a wide range of additional robustness tests, and open methods and data are the best ways to deal with these challenges.
Italian team received a grant from the Project PRIN DRASTIC "Driving The Italian Agri-Food System Into A Circular Economy Model," PRIN-MIUR -Call 2017, funded by the Italian Ministry of Education, University and Research (MIUR) under grant number 2017JYRZFF. Macario Rodriguez-Entrena, María Espinosa-Goded, and Jesús Barreiro-Hurlé acknowledge the funding via a specific contract between the European Commission (EC) and the University of Cordoba (JRC/SVQ/2021/VLVP/0333 -Programming and data-gathering for replication of the  Bocquého et al. (2013)  experiment). 3 For more information on the network, the reader is referred to www. reecap. org. For a discussion on experiments for agricultural policy-making see  Colen et al. (2016). 4 Suppose there are two types of respondents: optimists (believing in a high payment) and pessimists (believing in a low payment).
ACKNOWLEDGMENTS We are grateful to the authors of Bocquého et al. (2014). In particular, we thank  Geraldine Bocquého  for sharing all her material and for always responding so fast to our numerous questions. We would like to thank  Ciaran Blanchflower ,  Olivia Stopek ,  Nino Cavallaro , and the  REECAP network  for their continued support of the project. Comments from three anonymous reviewers helped us improve the paper. Jens Rommel  and  Julian Sagebiel  are grateful for funding received from the  Swedish Research Council for Sustainable Development (FORMAS)  for the project " Building resilience for Swedish food production under increasing risk of extreme weather " under registration number  2019-00993. Douadia Bougherara ,  Sophie Thoyer , and  Marc Willinger  acknowledge financial support from the  University of Montpellier  (SRUM) and from the  French National Research Agency  under the Investments for the Future Program, referred to as  ANR-16-CONV-0004  ( #DigitAg ), and are grateful to  F.
Laure Kuhfuss acknowledges funding received from the  Scottish Government RESAS division  in the framework of the 2016- 2021 Strategic Research Programme. Marianne Lefebvre  acknowledges funding received from the  Pays de Loire Region  through  RFI Alliance Europa  ( 2020 )(2021)(2022). She is also grateful to  Alexandre Perais  and  Ivan Dufeu  for their contributions to survey dissemination. Tanja Šumrada  acknowledges financial support from the  Slovenian Research Agency  (research programme  P4-0022 (B) ) and is grateful to the  Association of Slovenian Rural Youth  for their help with data collection. The Croatian and Swedish teams acknowledge funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  952303  (AgriFoodBoost). The.
36 Euro, France, Grand Est networks, newsletters, ranging from 8. 70 Euro and Normandie) and emails; online to 195 Euro) survey France II Organic farmers Convenience sample of Payments with vouchers 28 (vegetable growers, farmers contacted to all participants livestock and crops) through agricultural (average of 20 Euro, of North-West of chambers and ranging from 13 Euro France networks of organic to 27 Euro) farmers via newsletters and a mailing list; online survey Germany Arable farmers in Randomly selected Bank transfer to all 153 Germany farmers from database participants (average of of a market research 8. 83 Euro, ranging from company; online 2. 90 Euro to 65 Euro) survey Italy Olive growers of Convenience sample; Cash payments to all 130 Apulia region individual face-to-face participants (average of (Southern Italy) interviews 10.
Accepting gifts from sales reps or meeting them is correlated with excessive, more expensive and less rational prescribing  [18]. A recent study confirmed this finding about French GPs using indicators such as the prescription of antibiotics, benzodiazepines and vasodilators  [17]. Free drug samples and gifts of food and drink in the office are not allowed in France, but GPs still receive gifts and invitations to restaurants since the law allows for exceptions. Promotional budgets could therefore be viewed as an investment that ensures an increase in the number of prescriptions  [4, 17, 19]. A report by the French Inspectorate General of Social Affairs (Inspection Générale des Affaires Sociales-IGAS) studied the information GPs receive about drugs. The report concluded that, after the drug database Vidal, sales rep visits were the second most used source of information for practitioners, despite a negative opinion held against detailing visits  [4].
In a phenomenological study, sample sizes are typically small because data saturation is not essential if the objective is to understand the meaning individuals give to their own lived experience  [28]. Semi-structured interviews with general practitioners were conducted face to face as appropriate for the intimacy of the attitude being explored. Each interview was recorded and transcribed verbatim. Field notes were taken during the interviews and immediately after. Transcripts were analyzed one interview at a time to explore individual perspectives. Texts were commented on, then themes were coded, organized and finally formulated to identify common patterns shared by the different experiences  [27, [29] [30] [31]. Results were interpreted by a multi-field team composed of three GPs (AB, IP, TB), a sociologist (IF), and a social psychologist (IM).
This phenomenon can be analyzed through the concept of self-efficacy as described by psychologist Albert Bandura in 1977  [39]. Bandura defines self-efficacy as the sense a person has of their level of ability to achieve a given task. Without this feeling of self-efficacy, individuals tend to adopt a passive behavior, or even quit, while individuals who have a high sense of self-efficacy adopt a pro-active behavior. This well-known concept aligns with our observations in this study and could help explain why some GPs rely on detailing visits. Meeting pharma reps seems to act as a substitute for continuing medical education because of the GPs' inability to use the complex and time-consuming scientific literature. This phenomenon seems to be compounded by the diversity of the pharmaceutical supply: "As for me, I'm absolutely unable to know the whole Vidal [a French drug database] by heart!" It could also be exacerbated by the fact that French GPs are often isolated and overworked  [40].