dataesr/scientific-paragraphs-categorization huggingface dataset:
Study population and sampling The psychometric properties were evaluated on a sample of 142 parents/caregivers of children between 8 to 10 years old. The study focused on parents accompanying their children. Inclusion criteria were: participants must be able to read, write and self-administer the questionnaire. Parents or caregivers coming from different locations in Paris and even from farer regions were approached with a consent form and the questionnaire through their children who underwent clinical examinations at the clinical centre. Children were divided into three groups before the beginning of the data collection according to their oral health conditions. The groups were: 1. a group of children attending paediatric odontology consultation and who presented no dental or oral/ facial anomaly (healthy group) 2.
Data collection and ethics Two interviewers approached the parents by way of an informative letter, a consent form that they were asked to sign and the French P-CPQ questionnaire. Parents were invited by the two interviewers to self-complete the questionnaire in the waiting room of the odontology centre while their children underwent clinical examination. The study was approved by the Research Project Ethics Evaluation Committee of the Hospital Robert Debré (AP-HP), under approval N°2016/250-2. The original version of the P-CPQ was obtained using an article available with open access, shared by the terms of "Creative Commons License" which allowed its use without restriction, distribution and reproduction, with the necessary condition of citing the authors' works.
Statistical analysis The P-CPQ scores for each participant was calculated by summing the valid responses scores to all items, whereas the domain' scores were obtained by summing the items scores within each of the four domains. As there were 31 items, the final score could vary from 0 (no impact on the children's Oral Health Quality of Life) to 124 (maximum impact). The highest scores indicated the highest levels of negative impact of oral conditions on the child's quality of life perceived by the parents. "I don't know" was permitted as response to an item and was given a score of 0(zero) as a "never" option. The choice of zero was based on data from Jokovic et al. [9]. The SPSS 24. 0 software for Windows was used to process and analyse the data. Initially, demographic data was presented in numbers and percentages.
40 in a particular factor and is considering clean if its absolute difference between loadings exceeds 0. 20  [26]. EFA was followed by a partial confirmatory factor analysis (PCFA) to verify the goodness of fit of the model that was built according to the results of EFA. PCFA was performed to validate the factorial validity of the model derived from the results of the EFA. For this, we have performed PCFA with Maximum Likelihood Estimation method, and by calculating the implied model chi-square (Goodness-of-fit test) and the degree of freedom associated with the residual correlation matrix. The residual correlation resulted was used in SPSS to estimate the value of the Standardized Roots Mean Residual (SRMR) value that would get from a CFA  [27]. Thus, with SPSS, to calculate an incremental close fit index, two chi-square values were required (the chi-square obtained by Barlett's test of sphericity and the implied chi-square) with the respective degree of freedom.
001 meaning that EFA can be applied to the dataset. An exploratory factor analysis was conducted to determine the factor structure of the French-P-CPQ and to explore item placement within factors. Varimax rotation was used. This procedure led to a different grouping from the initial framework and the 31 items of the French-P-CPQ were loaded into 7 different factors with Eigenvalues> 1 and the total variance explained was 65. 23%. The first factor taken alone explains 36. 33% of the total variance of the 31 items in the analysis which included: 1-First factor: consists of the totality of the original emotional wellbeing subscale (7/7 items)  [14] [15] [16] [17] [18] [19] [20] , and social wellbeing subscale (5/10 items)(items: 2, 25, 27, 28, 29) related to "relationship"; 2-Second Factor. "Eating difficulties": represents 4/8 items of functional limitations domain (items 7, 11, 13, 14); 3-Third factor: "oral dental/pain" (items 1, 3, 4, 12); 4-Forth factor: "Oral Symptom and discomfort "(items 5, 9, 24, 26); 5-Fith factor: "functional limitation" (items 8, 10); 6-Sixth factor: "discomfort": (items 22, 30, 31) and 7-Seventh factor: "oral hygiene" (items 2, 6).
Acknowledgments The authors wish to thank  C. Boesch ,  H. Coqueugniot ,  W. Enard ,  C. Feja ,  F. Grine ,  M. von Harling ,  B. Herzig ,  J. L. Kahn , the  Kyoto University Primate Research Institute ,  N. Lange ,  F. Mayer ,  U. Olbrich-Schwartz ,  K. Spanel-Borowski ,  F. Spoor ,  F. Veillon ,  G. Weber  and  A. Winter  for access to specimens;  P. Scho  ¨nfeld (particularly for the scanning of the frozen neonates),  P. Schubert ,  H. Temming  and  A. Winzer  for CT scanning; and the Ivorian authorities, particularly the  Ministry of the Environment and Forests  and the  Ministry of Research , the  Swiss Centre for Scientific Research Abidjan , and the  University of Zurich  for their support of the Taï chimpanzee study. The authors are especially grateful to the editor and three anonymous reviewers, who greatly assisted in expanding the scope of this paper.
Funding: Funding for this study was provided by the  Max Planck Society  (https://www. mpg. de/en) to all authors. NAS was additionally funded by a  Natural Sciences and Engineering Research Council of Canada   Postgraduate Scholarship  ( PGSD3 410109-11 , http://www. nserc-crsng. gc. ca/Students-Etudiants/ PG-CS/BellandPostgrad-BelletSuperieures_eng. asp) and by a  Konrad Lorenz Institute for Evolution and Cognition Research Writing-up Fellowship  (https://www. kli. ac. at/en/fellowships/fellowship_general_ information). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In a wide collaborative effort, we were able to gain valu- a Indicated that in the absence a line-list, the distribution of the serial interval was taken from WHO Ebola Response Team (2015a). Unknown at the time of challenge, accuracy of data and reports progressively decreased from scenario 1 to scenario 4. able insights into the transmissibility, epidemiology and impact of intervention strategies  (WHO Ebola Response Team, 2015a , 2014 , 2015b; Nouvellet et al. , 2015; WHO Ebola Response Team, 2016; Garske et al. , 2017). We were also involved in producing regular forecast of future incidence (e. g. WHO Ebola Response Team, 2015a , 2014) , using a semi-mechanistic model based on a renewal equation  (Fraser, 2007). As the Ebola epidemic was declining, the Research and Policy for Infectious Disease Dynamics (RAPIDD) program, from the US National Institute of Health's Fogarty International Center, gave eight teams (including us) the opportunity to assess their models against simulated data.
Methods At each of the five time points, and for each scenario, we were provided with a case-count dataset that consisted of weekly counts of newly confirmed cases (Table  1 ). A field report was also provided, containing information on interventions, e. g. : timing of a recently implemented intervention or increased bed capacity (see SI. 1). Our approach was to estimate the current reproduction number (the average number of secondary cases generated by a typical infected individual, R t ) and to use that to forecast future incidence (Figs. 1 2 ). The current reproduction number was estimated using the case-count dataset, assuming constant transmissibility during a chosen time-window (see the Estimation and Forecast sections below). For scenario 1, we were also provided with a line-list. The line-list contained detailed data for each individual and was used exclusively to infer a serial interval distribution and gain epidemiological insights into the current situation (see preliminary analyses and Fig.
Estimation of the reproduction number The reproduction number used to forecast future incidence was estimated from the case-count data. Several methods to estimate the reproduction number exist, e. g. see  Van Kerkhove et al. (2015)  for various methods linked to the estimation of the basic and effective reproduction of Ebola virus. Here we relied on a well-established and simple method that assumed the daily incidence, I t , could be approximated with a Poisson process following the renewal equation  (Fraser, 2007) : I t ∼Pois R t t s=0 I t-s ω s , where R t is the instantaneous reproduction number and ω the serial interval distribution. From this a likelihood of the data given a set of model parameters can be calculated, as well the posterior distribution of R t given previous observations of incidence and knowledge of the serial interval  (Cori et al.
g. case-count dataset shows much higher incidence than the weekly aggregated counts from the line-list). Additionally and especially relevant for realtime forecasting, the most recent few weeks of data in the line-list were even less reliable; this simulated the delays in updating the line-list. Therefore, while we analysed the line-list to gain epidemiological insights, the line-list was not directly used to assess the transmissibility used in the forecast. Then, using pairs of infector-infectees for which dates of symptom onset were known, we computed the observed serial interval (the time between symptom onset of a case and symptom onset of their infector). We fitted a gamma distribution to these observed serial intervals by maximum likelihood (WHO Ebola Response Team, 2014). Using this serial interval distribution, the instantaneous reproduction number (R t ) was estimated on a daily basis using the EpiEstim R package  (Cori et al.
, 2013)  with a 2-week sliding window. This reproduction number provided insights into the trend in transmissibility, but was not used for forecasting. Additionally, for each patient, we extracted all delays using the clinical histories provided. To estimate temporal variation in the delays, we calculated for each day the median delay over the previous 2 weeks. Then, for each delay, we plotted the daily delay (median) against the daily R t above (median). The correlations between R t and the various delays were examined visually (and with univariable linear models) to identify possible drivers of changes in transmission. We also quantified the number of times a patient was recorded as being an infector in the line-list. The incomplete nature of the data available precluded direct estimation of the reproduction number from such contact data, but it still allowed us to compare transmission between different groups.
Forecasting scenario 1 In terms of forecast, with the exception of the first time-point, our method performed well (Fig. 2A ). Overall, 11 observed incidence points (out of 20) were included in our predicted IQR (well within the expected range). All observed incidence points were included in our predicted 95% CrI. In retrospect, if we had been able to choose the correct time-windows for every forecasts, up to 16 out of the 20 points could have been included in our predicted IQR. Based on our simple measure of fit, our choice of time-window was optimal for 3 time-points (out of 5) and outperformed the naïve rational choice for 3 time-points (see Fig. S1 -2 in SI. 2). The comparison of the line-list data to the case-count data showed that not all confirmed cases entered the line-list (Fig. 3A ), and these discrepancies were especially important during the most recent few weeks included in the datasets.
We therefore concluded that the case-count data were more reliable than the line-list data for forecasting. However, the line-list data were key in estimating the serial interval at each time-point (Fig. 3B  and Table  1 ), with our best estimate (i. e. based on the most complete dataset) of the median as 12. 7 days (95% CrI  [10. 3; 14. 7] ). Additionally, the line-list data allowed us to better understand the drivers of transmissibility. For instance, increased contact tracing was highly correlated with a reduction in the delay from onset to hospitalisation (Fig. 3D ). Furthermore, cases entering the line-list through contact tracing had a significant lower case reproduction number (result not shown). This allowed us to hypothesise that contact tracing allowed quicker identification of cases, resulting in quicker hospitalisation and isolation.
, 2017,  for a full discussion of the challenges of data analyses in real-time during outbreaks). To name a few: data cleaning remains a significant task (as errors were added to the simulated data) and the choice of using a data-rich line-list versus a more limited case-count dataset (which are not necessarily consistent with one another) for forecasting is challenging (see WHO Ebola Response Team, 2015a, 2014 for instance). Also, the quick pace of analyses and required updates could easily result in human and communication errors. To conclude, the forecasting challenge proved useful to further reflect on the methodology we had used during the recent West African Ebola epidemic, a luxury of time sometimes difficult to achieve in the midst of an emergency. Furthermore, the joint work on this project (e.
Acknowledgements We thank the following for research funding: the  Medical Research Council  ( MR/J008761/1 ), the  Bill and Melinda Gates Foundation , the  MIDAS network of the National Institute of General Medical Sciences  ( U01 GM110721-01 ,  National Institutes of Health) , the  Health Protection Research Units of the National Institute for Health Research , the  European Union  PREDEMICS consortium ( FP7  Grant Agreement no. 278433-PREDEMICS ),  Wellcome Trust  (Project  093488/Z/10/Z , Investigator  200861/Z/16/Z ). ID acknowledges funds from the  Imperial College Junior Research Fellowship. AC and PN acknowledge support from the  United States Agency for International Development (USAID) , and acknowledge that the results of this work does not necessarily reflect the views of  USAID.
Cultures were analysed for strain and plasmid proportions by flow cytometry, as described in  (7). Data acquisition was performed on the Cochin Cytometry and Immunobiology Facility. Optical density at 600nm was measured on a BioPhotometer plus (Eppendorf). Frequencies at the metapopulation level were measured by pooling equal volumes of populations, effectively taking into account differential growth among populations. Frequencies within populations were computed as the mean of each individual frequency within populations, in order to exclude the effect of differential growth among populations.
Acknowledgements We thank all members of  Institut National de la Santé et de la Recherche Médicale   U1001  and  Angus Buckling  for advice and discussions,  John Chuang  for providing JC1191 strain and pZS*2R-GFP,rhlI plasmid,  Didier Mazel  for providing the pOX38::Tc plasmid,  Chantal Lotton  and the  Cochin Cytometry and Immunobiology Facility  for technical help.
I also utilize a data set on millions of criminal sentencing decisions in US District Courts since 1992, linked to judge identity via FOIA-request, and a digital corpus of their opinions since 1923. These data are linked to publicly available Supreme Court datasets, US District docket datasets, geocoded judge seats, biographies of judicial clerks, and administrative data from Administrative Office of the US Courts (date of key milestones, e. g. , oral arguments, when the last brief was filed, etc. ) for measuring priming of identity, peer effects, perfectionism, partisan ways of persuasion, judicial innovation, and the genealogy of ideologies and schools of thought in temporally and spatially disaggregated text. I have also the universe of administrative data on 1 million refugee asylum and 15 million hearing sessions and their time of day across 50 courthouses and 20 years (with randomly assigned judges) and hand-collected biographical data to study 1 After the great transformation, human economic mentalities were changed, and people became more economically rational, behaving as neoclassical economic theory would predict  (Polanyi 1944 ).
2 US Courts of Appeals Database Project (http://www. cas. sc. edu/poli/juri/aubur ndata. htm). gambler's fallacy, implicit egoism, habit formation, racial contrast, mood, extraneous factors, and time of day effects on judges' normative commitments. I have a linked universe of individuals in a federal prosecutor's office over a decade with many stages of random assignment, to measure, e. g. , name letter effects, in-group bias, and the intersection of hierarchy and race. I have also digitized the speech patterns in US Supreme Court oral arguments since 1955-longitudinal data on speech intonation (linguistic turns) are rare. The data are linked to oral advocates' biographies, faces, clipped identical introductory sentences, and ratings of their traits. The data are used to test labor market treatment of mutable characteristics and persuasion, and mimicry between lawyers and Justices and among Justices over time using high-dimensional econometrics.
(2018)  shows that economic thinking of judges is strongly predictive of sentencing harshness. The idea that if legal institutions can not catch suspects, then the judge might increase the sanctions so the expected deterrence is the same. We can also try to predict re-arrest and prosecutor screening decisions using a unique dataset followed from the police arrest report. An algorithm would reduce the re-arrest rates for a set charge rate. We also find that prosecutors seem to be releasing defendants throughout the risk distribution. We can also predict ideology, in particular the political donations of the Supreme Court lawyers, using both their text and their audio. The audio doubles predictive accuracy relative to the text alone. Motivated by the error analysis that found the Congo war to vastly help with predictions, we digitized the Wikileak cables data to predict the asylum grants and claims.
Since 2017, a new resurgence of measles has been observed in France with more than 1,800 cases notified between January 2017 and March 2018, including four encephalitis cases and two deaths, one in a non-vaccinated adolescent and one in a non-vaccinated young adult. A recent analysis of the French death certificates database showed that since 2017, measles had actually caused 21 deaths (data not shown). Most of the measles-related deaths would have been avoided if the herd immunity threshold had been reached. [11]. We thus concluded that a large proportion of the more than 800 cases and 100 deaths seen in France since the introduction of MenC immunisation could have been avoided through either direct protection or herd immunity if the coverage had reached similar levels as in the Netherlands.
Acknowledgements We are grateful to  Claude Rambaud  and  Alain Fischer  for their leadership of the  French Citizen Consultation on Vaccination , and to the professionals of Sante Publique France who contributed to the consultation,  Veronique Mallet ,  David Heard  and  Matthieu Humez  for the organisational support and  Arnaud Gauthier ,  Christine Jestin ,  Anne-Sophie Barret  and  Denise Antona  for the data they provided as well as to  Bruno Coignard  for his constant support.
Acknowledgements. We are grateful to  Ross Purves  and  Flurina Wartmann  for their thought-provoking comments on a draft version of this paper, as well as participants at the workshop in Stels on "the trouble with defining forest: semantics, ontology, territoriality" in June 2016 for their comments and discussion on an earlier draft. We would also like to thank three anonymous reviewers for their constructive comments. Any errors in this paper remain of course our own. Edited by: Benedikt Korf Reviewed by: three anonymous referees.
Predicting people's responses to invitations is an important issue for social event management, as the decision-making process behind member responses to invitations is complicated. The purpose of this paper is to suggest a privacy-friendly method to predict whether and when people will respond to open invitations. We apply the competing risk model to predict member responses. The predictive model uses past social event participation data to infer a network structure among people who accept or reject invitations. The inferred networks collectively show the extent to which people are likely to accept or reject invitations. Validated using real datasets including 31,230 people and 8,885 events, the proposed method not only presents the variables that predict attendance (such as past attendance and social network), but also those that predict faster responses.
We use data from online meeting communities to predict event participation. More specifically, we use information regarding responses to past events to predict responses to new invitations. The literature uses survey-based research to address the issue of participation prediction  (Siebenaler, 2006). For example, an individual's perceived peer influence and previous experience with the event context have been found to be associated with later event participation  (Siebenaler, 2006). Such studies are limited in terms of event context and sample size. Furthermore, previous studies have not fully explored the social network structures existing at these events. Despite these findings, predicting participation is difficult for practical reasons. For example, individual attributes (perceived values) may be unavailable due to privacy concerns  (Elwood & Leszczynski, 2011).
We can therefore infer relationships between people based on common social events. Data concerning such relationships is valuable in predicting product adoption and customer churn  (Fang, Hu, Li, & Tsai, 2013) , and presumably in predicting responses to event invitations. This paper presents an empirical study that predicts member responses to different social invitations, and constructs a social network based on past responses. This work contributes to the literature by developing a novel survival model to forecast member event participation. We validate the model using Area under the Receiver Operator Characteristic (ROC) Curve (AUC) values based on real-life datasets of 31,230 people and 8,885 events. The research model adds to the literature by using Bayesian networks to account for network variables, and a mixture model for member participation decision modeling.
Social Network Analysis People communicate in different ways. Personal connections between people form network-structured data  (Leskovec, Huttenlocher, & Kleinberg, 2010). A social network consists of nodes and edges. A friendship network is formed when people identify each other as friends. People are presented as the nodes. People connect differently to all types of friends. Links between friends are the edges connecting the nodes in the network. Communication networks develop when people make phone calls and/or send emails to each other. Technical partnerships between companies form a cooperation network based on alliances and shared patents  (Gilsing, Nooteboom, Vanhaverbeke, Duysters, & van den Oord, 2008). Social network analysis (SNA) focuses on how the network structure between the objects  (Fang et al.
Data collection This study uses member responses to social event invitations on the website, Meetup (www. meetup. com). Meetup is a platform for communities to form social groups, and organize offline events. Each group holds many social events at different times. They send invitations (RSVPs) to all their members, who can reply yes or no, or can ignore the invitation. Meetup has mechanisms such as attendance approval and fees to organize member attendance. The data collection procedure started on February 20, 2014 and covered events from February 2006 until June 2014. We collected data from social groups in three cities: New York, London, and Los Angeles. Past studies collect data by crawling the website over three months  (Liu et al. , 2012). Such a time frame is too short to be considered as a longitudinal setting.