dataesr/scientific-paragraphs-categorization huggingface dataset:
If several authors belong to the same institution, the best position is selected. If there are authors from different institutions on the same paper, the paper is attributed to each of these considering the best position of authors from individual institutions. All institutions using the database were extracted irrespective of whether or not they received MERRI funding (i. e. SIGAPS score corresponding to less than 250000€); the objective of the study was to evaluate production of scientific articles by hospitals in France. The following data were extracted: institution of affiliation, year of publication, category of journal (A, B, C, D, E, or NC), author position (first, second, third, Other, investigator list, Second-to-Last, or last), type of publication (comment, editorial, erratum, meeting, letter, original article, other, review).
Student t-test was performed on a 3 for third degree, a 2 for second degree, and a for first degree coefficients of the polynomials. When the p-value of a 3 is not significant, we can consider that the coefficient is null and is not a third degree polynomial. We applied the same rational for a 2 for second degree, and a for first degree coefficients. The same analysis was performed excluding authors in the investigator list in order to verify the influence of this position. Normal distribution was assessed by Shapiro-Wilk tests. In order to respect this application condition, outliers were eliminated and if the normal distribution was not respected, a logarithmic scale was used. One-way analysis of variance (ANOVA) was performed using Rstudio software (version 3. 2. 2) at a 5% significant level  [9].
Data description A total of 211715 publications between 2004 and 2014 in which at least one author was affiliated to a SIGAPS-eligible hospital were extracted. Only the 192886 publications related to editorials, original articles, or reviews were retained for analysis in this study. These publications corresponded to authors with affiliations to a total 217 hospitals (figure  1 ). Between the year 2004 and the year 2014, the number of publications increased from 13987 to 20210 (144%); the number of hospitals producing at least one publication in 2004 was 174 and in 2014 this was 208 (120%), and there was a notable increase over the same period of for-profit private hospitals [26 to 48 (185%)]. It is of note that not all 217 hospitals produced a publication each studied year (table  1 ). Between the year 2004 and the year 2014, the number of publications increased from 12780 to 18080 (141%) for public teaching hospitals, corresponding to a mean increase of 530 publications per year; from 1353 to 2548 (188%) for cancer centres, corresponding to a mean increase of 120 publications per year; from 1047 to 2095 (200%) for public non-teaching hospitals, corresponding to a mean increase of 105 publications per year; from 550 to 1223 (222%) for not-for-profit private hospitals, corresponding to a mean increase of 67 publications per year; from 247 to 391 (158%) for the public military hospital, corresponding to a mean increase of 14 publications per year; and from 101 to 634 (628%) for-profit private hospitals, corresponding to a mean increase of 53 publications per year.
DISCUSSION The present study describes the number of publications included in the SIGAPS database for all types of hospitals in France from 2004 to 2014. SIGAPS is a tool that was implemented by the public teaching hospital of Lille with the ministry of health in the context of the new hospital funding scheme which is partially based on research activity quantified by the number and the quality of publications. The results of the study show an increase of the number of publications published by all types of French hospitals over recent years, and that all types of hospital publish scientific articles. The number of publications in the SIGAPS database is complete for public teaching hospitals, cancer centres, and the public military hospital. However, the number of publications from the other types of hospital, i.
e. public non-teaching hospitals, not-for-profit private hospitals, and for-profit private hospitals included in the SIGAPS database are far from complete; for example, in 2012, only 10% of public non-teaching hospitals, 4% of not-for-profit private hospitals, and 4% for-profit private hospitals were included in SIGAPS database compared to data from Institut national de la statistique et des études économiques  [10]. It is potentially a source of bias herein but as the publication-based system allocates funding for all types of hospital, all hospitals concerned by publications are included in SIGAPS database, and this is unlikely to greatly affect the conclusions. For public teaching hospitals, the number of publications is approximately the same as that reported in another study  [6] , which provides a certain external validation to the results for public teaching hospitals, but also this validation may be extrapolated to other hospital groups as the data were from the same extraction.
Another potential source of bias could be that information became more complete and precise over the study period, which could be due to more attention paid by researchers to mention more carefully all participant institutions and authors. In the SIGAPS database, each author confirms that each publication is his/hers. An author knows his/her publications and if a publication does not appear in the database he/she will notice it and rectify the error. Hence, we can consider that all publications were present in the SIGAPS database. In contrast, after the creation of SIGAPS, it is possible this led to greater interest in being cited in a publication as this will contribute to hospital funding. A more general point is that we used the SIGAPS to evaluate publication production in France as this is used for funding allocation, yet other methods could be employed.
It has to be noted that the increase we found cannot be explained by non-indexed online journals as the SIGAPS database only considers journals with an IF or those that are indexed in PubMed and meet rigorous selection criteria  [8]. The general increase in the number of publications over the study period can, however, be explained by the fact that a non-negligible part of the hospital research funding, irrespective of the type of hospital, is based on the number of publications resulting in a strong motivating factor for publication. Moreover, this increase is greater than that reported in France for all types of scientific publications (136% increase from 2004 to 2014, corresponding to a mean increase of 2004 publications per year [14]), but less important than that found world-wide (all disciplines; 165% increase from 2004 to 2014 corresponding to a mean increase of 61593 publications per year) that is mainly attributable to the emerging countries such as China, Brazil, and India [14], as well as the increase of the number of journals  [15].
At the level of individual hospital types, it is interesting to note that, as expected, public teaching hospitals are the most important producers of research but more importantly other hospitals also publish. To provide a more general perspective of this, at the national level and including both hospital and non-hospital research structures there were 738304 indexed original articles published in research fields during the 2004-2014 period [14]. From the extraction of the SIGAPS database used herein there were 117648 (16% of the total production) indexed original articles published by public teaching hospitals and 35955 (5%) such articles published by other hospitals. This includes a certain overlap in cases when both teaching and non-teaching hospitals participated to the studies (data not shown).
Acknowledgments The authors acknowledge  Dr Ariane Galaup-Paci ,  Ministry of Health -Bureau Innovation et Recherche Clinique ;  Dr Marie-Paule Gustin ,  Université Lyon  1, Inserm  U1111 ,  CNRS UMR5308 ,  ENS de Lyon , and  Dr Gabriela Ciuperca  and  Abdoulaye Diabakhaté ,  Université Lyon 1 ,  UMR 5208 ,  Institut Camille Jordan , for their statistical support;  Dr Alexandre Vainchtock  and  Audrey Dauzet, Hevaweb , for their graphic support; and  Dr Philip Robinson ,  DRCI Hospices Civil de Lyon , for help in manuscript preparation. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Acknowledgment The results of this work are supported by the  Ministry of Science and Higher Education of the Russian Federation  in the framework of the research project no. 2. 3208. 2017/4. 6. The second author is partially supported by the  Russian Federal Professor Program , project no. 1. 472. 2016/1. 4 (Ministry of Science and Higher Education of the Russian Federation)  and by the project  XterM-Feder ,  University of Rouen. The results of Sec. 4. and Sec. 5. are supported by the  RSF  grant number  17-11-01049.
They implement surveillance technology and databases to manage hazards  (Silbey and Agrawal, 2011)  and develop "relational regulation practices" in science laboratories  (Huising and Silbey, 2011). Safety officers also use the authority of legal rules to manage risks, influence safe practices, and build good working conditions  (Pélisse, 2017; Borelle et Pélisse, 2017). Environmental, health and safety regulations issued by the Environmental Protection Agency, the Occupational Safety and Health Administration, States and many municipalities are often quite complex and filled with technical requirements and language. They are also vague and overbroad with respect to how to comply. For example, many environmental and safety regulations are not applicable or easily implementable toward nanoparticles and nanomaterials  (Pélisse, 2017).
In some other cases, we can fit the data by a nonlinear function of the form A = aP τ ( 2 ) where τ is an exponent usually slightly smaller than 1. In the linear case the density is contant ρ = P/A = 1/a and in the nonlinear case the density increases with population ρ ∼ P 1-τ : larger cities are also denser. Empirically, we do observe that cities around the world display very different behaviors as shown in Fig. 3. When we mix together different countries of the world by taking urban areas with population larger than 1million (Fig. 3 (left)), the nonlinear fit predicts an exponent slighly less than one (τ ≈ 0. 95, r 2 = 0. 66) and with this dataset it seems not possible to conclude between a constant density or a slightly increasing function of population (with exponent of order 0. 05). At a smaller scale, in the case of the US for example (Fig.
V. AVERAGING OVER MANY CITIES ? Various studies of cities such as scaling  [29]  use data from different cities at different times and plot some quantity versus population. For many quantities (denoted Y here), we observe a power law of the form Y ∼ P β where P is the population of the city  [29]. As we already discussed above, we can distinguish different behavior according to the value of β, in particular if it is larger than one or not. Once we have measured this scaling form, we could in principle use it for predicting the behavior of an individual city when its population changes. In order to illustrate the possible problems of such an approach we consider the particular case of delays due to traffic congestion and analyze a dataset for 101 US cities in the time range 1982-2014  [56].
This is a particularly interesting dataset as it is both transversalit contains many cities -and longitudinal -for each city we have the temporal evolution of the delay. The scaling form obtained by agglomerating all the available data for different cities and for different years displays a nonlinear behavior, seemingly in agreement with general empirical results about scaling  [29]. More precisely, we can first compute the scaling exponent by mixing together all cities but for a given year. We can also compute this exponent for each year and we observe an exponent whose value varies in the range [1. 2, 1. 4] for years from 1982 to 2014 (see Fig. 9 ). Finally, we can consider all cities and for all years and we then obain a delay in traffic jams scaling as δτ ∼ P β (40) with β ≈ 1. 36 consistent with a superlinear behavior.
3-1 Data Our sample is composed of all French-listed firms (780 firms) from 1998 to 2012. We eliminate financial and banking firms (SIC 3  codes between 6000 and 6999), given that they have different financial, operating, and risk characteristics. Furthermore, companies with missing data are excluded. Due to the small number of French listed companies before 1998, and in order to obtain nine well-diversified portfolios constructed by crossing size and book-to-market ratio, our study period cannot be longer. Our final sample includes 543 firms. We use the database Diane to collect the data required to compute the probability of financial distress one-year-ahead. The construction of risk factors requires accounting and financial data acquired from the Thomson One Banker database and historical stock prices obtained from the Datastream database.
Materials and methods OPTISOINS01 was a multicenter, prospective, observational study conducted in patients from a determined regional health territory around Paris. This study was approved by the French National ethics committee (CCTIRS Authorization No. 14. 602 and CNIL DR-2014-167) for the research conducted at all participating hospitals. This study was registered with Clinical-Trials. gov (Identifier: NCT02813317). The design of this study has been previously published  [14].
Pathways characteristics and resources consumed More than 750 patients were screened for participation in the study between January 2014 and January 2016 and 617 of them were included in the study. After monitoring and lost to followup, data for 604 patients were analysed and 303, 164 and 137 patients were assigned to group 1, group 2, and group 3, respectively (Fig  1). Median age was 58 years. Most patients presented invasive BC (n = 543, 90%) and 23% (n = 142) had axillary lymph node involvement. Less than 40% of patients received adjuvant chemotherapy (n = 235, 39%). Nearly one-half of patients were working at the time of diagnosis (active patients, n = 297, 49%). Patient characteristics and treatments administered are presented in Table  1. The majority of patients (n = 447, 74%) were managed in a CCC, 14% in a GH (n = 83) and 12% in a TH (n = 12%).
In contrast, health care costs in some countries are not covered by national health insurance and patients need to take out private insurance. Out-of-pocket expenditure can therefore become a barrier to care. For example, in the US, the financial impact of BC has been described to prevent underserved BC survivors from accessing follow-up care  [26]. A variability of direct costs is also observed between countries. Most studies in the US have described higher direct medical costs. A study based on a private insurance database reported a total health care cost of US$42,401 per patient for the first year  [27]. In another study of patients covered by private insurance in the US, the annual direct medical cost of BC was US $19,435  [28]. Moreover, expenses as high as US$5,711 per month have also been published for patients covered by the Medicaid system  [29].
More specifically, we take into account explicitly the variations in energy use due to variations in the share of industrial sector in the economic activity. As indicated also by  Hübler and Keller (2010) , this variable measures the reliance of the economy on the industrial sector and it may be expected that a higher industry share raises energy intensity. The variable of IMF programs is drawn from  Dreher (2006)  who provides a comprehensive database, which reported IMF programs, and which is widely used in the empirical works (see  Dreher, 2006). The variable IMF is defined as a dummy that equals one if an IMF program has been in effect for at least five months in a specific year. On the other hand, as mentioned previously, to study the impacts of IMF programs on environmental pollution, carbon intensity of energy use (CI) is also involved in the analysis.
Economic Growth, Energy Consumption, and CO 2 Emissions: A Tale of Three Countries This section briefly presents the background and data to observe the decarbonization (Table  A1 ) trends before and after the introduction of carbon taxation. This will help us understand how taxes and related policies may work as complements in the decarbonization process. Our set of three countries-Sweden, France, and Canada-indeed illustrate the diversity in the long-term patterns of evolution of economic growth, energy consumption, and CO 2 emissions. Against the background of these trends, an in-depth analysis is made of carbon taxation in the three countries in the following chapters.
Economic Growth, Energy Consumption, and CO2 Emissions: A Tale of Three Countries This section briefly presents the background and data to observe the decarbonization (Table  A1 ) trends before and after the introduction of carbon taxation. This will help us understand how taxes and related policies may work as complements in the decarbonization process. Our set of three countries-Sweden, France, and Canada-indeed illustrate the diversity in the long-term patterns of evolution of economic growth, energy consumption, and CO2 emissions. Against the background of these trends, an in-depth analysis is made of carbon taxation in the three countries in the following chapters.
22 In that study, individuals with LBP completed an online survey in which they answered questions regarding their experiences and understanding of LBP flares. Survey results were analyzed using thematic analysis, content analysis, and descriptive statistics. That study provided information of the consumer conceptualization of flare. In the current study, those data were used to derive a draft definition of flare. Five authors (P. H. , N. C. , J. S. , M. F. , J. M. ) met on 3 occasions to discuss the survey results, consider consumers' perspectives, and discuss terminology to reflect the features that distinguish flare from other symptom fluctuations. The initial proposed definition was refined in consultation with an expert consumer writer (T. D. ). Step 2: Incorporation of Experts' Perspectives in Preliminary LBP Flare Definition A workshop was held at the International Forum for Back and Neck Pain Research in Primary Care (Buxton UK, June 2016) with a group of 19 experts in LBP.
Acknowledgments The authors thank all of the expert participants for their valuable contribution to the Delphi survey. Those participants who provided permission to be are either included in the author list or acknowledged here. These include: Associate Professor Alice Kongsted,  Professor Aage Indahl ,  Professor Birgitta € Oberg ,  Professor Chris Maher ,  Dr. Dan Cherkin ,  Professor Fiona Blyth ,  Dr. Gustavo Machado ,  Professor Julie Hides ,  Dr. Francisco Kovacs , Associate  Professor James McAuley ,  Dr. Jenny Strong ,  Professor Lorimer Moseley ,  Professor Lyn March ,  Professor Nadine Foster ,  PT Nicoulaus Eland ,  Dr. Margreth Grotle , Associate  Professor Paulo Ferreira ,  Dr. Per Kristiansson ,  Quinette Louw , Associate  Professor Will Taylor , and  Dr. Zoe Michaleff.
Data description Our dataset consists of the ICE ECX EUA Daily Futures -SETT. PRICE (in Euro per ton of CO 2 ), the Crude Oil-Brent Cur. Month FOB U$/BBL, the ICE Natural Gas 1 Mth. Fwd. P/Therm, the Coal ICE API2 CIF ARA Nr Mth $/MT -SETT. PRICE, and the fuel-switching price; computation is detailed in Section 2. Energy prices are pictured in Figure  1. We obtained 1598 daily prices that spanned the period from 1 January 2010 to 19 May 2016. All price series were converted to US dollars using the bilateral exchange rates given by the European Central Bank and the Federal Reserve Bank of St. Louis. From daily prices, including the fuel-switching price, we computed the price returns at each time t as (p t -p t-1 )/p t-1 , where p t denotes the price at time t 5. 5 Notice in our paper, we are not using logarithmic returns.
, 2017). (B) Time-scale WT representation of E(t) with the third-order analyzing wavelet g (3) (Equation  16 ). The modulus of the WT is coded, independently at each scale a, using 256 colors from black (|T g (3) (t, a)| = 0) to red (max t |T g (3) (t, a)|). (C) WT skeleton defined by the maxima lines of a 10 s portion of E(t). The scale a = α t/δt, where α is an analyzing wavelet dependent constant (α = 8. 6 10 -2 for g (3) with the lastwave software), and δt = 0. 4 ms. (A ′ -C ′ ) same as (A-C) for a numerical time series E(x, t) generated at position x = 75 (in δx = 0. 3 mm units) with our model defined in Equations (  6 )-(  8 ) with the set of parameter values used in Simul #2 (Table  1 ), and a total system length L = 150 (in δx = 0. 3 mm units). In (B ′ ) the white horizontal dashed-dotted lines delimit the range of time scales (2 8.
These representations often assume that the only variable influencing travellers' route choice behaviour is the travel time, ignoring other local factors, related to the network OD configuration, that have been recognised to influence route choice behaviour  [21] [22] [23] [24] [25]. One of the main reasons for the gap between research in individual route choice behaviour and network loading is the lack of observations at large scale over a sufficient number of OD configurations, that would allow discrete choice models to scale-up at the network level and thus enable the design of network equilibrium founded in a more user-oriented approach. The ambition of this study is to fulfil this gap by the selection of OD pairs that are representative of the OD configurations that are found in a transportation network, and then use these OD pairs in computer experiments to collect data on travellers' route choice behaviour.
Therefore, the OD pairs and routes in the network can be represented by elements in the k clusters. A natural choice to represent the elements in a cluster is the mean element in the cluster (cluster centroid), as it is the point with minimum euclidean distance to all the elements in the cluster. Thus, the cluster centroids are chosen as representative of the clusters' elements, and the k clusters' centroids as representative of the OD pairs and routes in the whole network. These OD pairs and routes are then used in computer experiments to collect data on travellers route choice behaviour. Note that the set representative OD pairs and routes found with kmeans is representative of the attributes of the network, so the question that arises here is if a model estimated over this representative set can adequately reproduce the choices in the rest of the OD pairs in the network.
To answer this question, three discrete choice models are estimated with the observations over the representative set. The discrete choice model used in this work is a joint mixed logit model (MXL), which under certain conditions, as is the case in this study, is equivalent to to the panel data formulation of MXL models  [2, [27] [28] [29]. The predictive accuracy of these models are compared against the predictive accuracy of the same models, but estimated with randomly chosen sets of OD pair configurations in a sort of cross validation procedure. The results of the above methodology are that the models estimated with the observations over the representative OD pair configurations are better in predicting the route choices on unseen OD pairs, i. e. , on OD pairs not used for the estimation process.