dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental data Experimental data (body weights, feed intakes, body composition) were available from birth to slaughter weight for two lines of Large White pigs divergently selected for RFI under ad libitum feeding with the conventional diet. The composition of the conventional diet is reported in the Supplementary Material S2. The selection process concerning low RFI (LRFI, more efficient pigs) and high RFI (HRFI, less efficient pigs) lines are reviewed in  Gilbert et al. (2017). The dataset used in the present study included data from 57 male pigs of the fifth generation of each line fed a conventional diet formulated to cover pig requirements. Growing pigs had ad libitum access to a onephase conventional diet from 10 weeks of age to slaughter (at about 115 kg body weight). The data on individual daily feed intake (DFI) for the whole fattening period were recorded on automatic feeders (ACEMO, Pontivy, France), and back fat thickness (BFT) was measured via ultrasounds on live animals at 23 weeks of age, using an echograph (ALOKA SSD-500, Cergy Pontoise, France).
Line diet optimisation In this study, the diet formulation was optimised to obtain diets with minimum environmental impacts but covering the specific nutritional requirements of the different genetic lines. To diversify the sources of energy and protein available for the diet formulation, six ingredients, oats, triticale, corn, peas, rapeseed meal, and sunflower meal were added to the eight ingredients of the conventional diet (barley, wheat, soybean meal, sunflower oil, and synthetic AA L_ lysine (LLY), L_threonine (LTH), L_tryptophan (LTR), DL_ methionine (DLM)), to formulate new diets. The new ingredients were selected based on their availability at the market and on the accessibility of their characterization data in the embedded database of InraPorc®. The net energy (NE) density and digestible CP and AAs of the ingredients were extracted from the INRA-AFZ database  (Sauvant et al.
Determination of the representative nutritional requirements of the lines The experimental data were imported into InraPorc® to calibrate a growth performance profile for each individual pig. The profiles were calibrated using the recorded daily ad libitum feed intake during the fattening period with the conventional diet, expressed relative to the NE of the diet. The individual digestible CP and AA requirement profiles of the pigs were then obtained as InraPorc® outputs. Pigs are usually fed in groups with a single diet adjusted to the nutrient requirements of a representative pig in the group  (Remus et al. 2019). Accordingly, the five targeted daily requirements for the whole fattening period were extracted from InraPorc® for each individual to identify the representative pig for each line in our dataset.
g. origin), database inventories, manure management and application, farm operations, pig survival rate, and other methodological choices would modify the magnitude of the impacts for all scenarios, while the general conclusions about the scenario differences would hold robust. The results of this study are limited to the simulation tools and further field studies will be required to confirm these outcomes. With weights of 1 for the four impact categories in the environmental score and our list of ingredients, the lines fed their optimised tailored diet had lower GWP, AP and LO than the lines fed a single conventional diet, but not higher EP. Since the phosphorous content of the optimised tailored diets was lower than in the conventional diet, the increased EP in the two lines could be explained by the higher EP of the optimised tailored diet, via higher EP of their ingredients, rather than by increased excretion and leaching of phosphorous during manure storage and spreading.
The higher baseline requirements for LRFI might reflect the higher sensitivity of impacts to higher requirements and underlines the need to adequately capture the nutritional requirements of the targeted animals. The performance traits showed a decrease in growth rate in both lines with the optimised tailored diets compared to the conventional diet  (Soleimani and Gilbert 2020) , leading to approximately three more days required for the pigs to reach 120 kg BW. This is certainly related to the choice of representative requirements to formulate the line constraints: considering the average maximum nutritional requirements in each line would lead to the nonsatisfaction of the requirements in about half the animals in the early stage of the growing phase. In our dataset, this was limited to the very first days of the growing period, but could create a longer delay in reaching slaughter body weight.
Acknowledgements This study is part of a collaborative project supported by the  ERA-NET SUSAN programme  via the  ANR-16-SUSN-0005  contract (SusPig). The authors are grateful to the experimental farm  UE GenESI (INRAE, Surgères, France)  for animal care and data collection as well as InraPorc ® developers. The authors would like to thank  Dr. Garcia-Launay  and  Prof. Ilias Kyriazakis  for the valuable comments and recommendations on this study.
4 0 0 990 0 0 feed ) Lysine (g/kg 2. 85 2. 99 3. 24 1. 92 12. 45 13. 5 9. 68 2. 51 25. 02 0 798 0 0 0 feed ) CP (g/kg 80. 5 74. 2 83. 4 69. 8 165. 8 254. 7 273. 5 92. 8 391 0 954 731 853 584 Quantity notation b o T c p r sm w s f LLY LTH LTR DLM Ingredients Barley Oat Triticale Corn Pea Rapeseed meal Sunflower meal Wheat soft Soybean meal Sunflower oil L-Lysine HCL L-Threonine L-Tryptophan DL-Methionine crude protein (CP) and amino acids, and net energy (NE) of the ingredients retained for diet formulation, and their environmental impacts NE density and digestible CP and amino acids (lysine, threonine, tryptophan, and methionine) of the ingredients were extracted from the INRA-AFZ database of feed ingredients. The environmental impacts of diet ingredients (GWP, AP, EP, LO) were obtained from the Ecoalim dataset of the AGRIBALYSE ® database with the Recipe method 2016.
the support of the Dean's biodiversity fund of the School of Environmental and Biological Sciences at Rutgers; Mireille Chiroleu-Assouline acknowledges the support of the Agence Nationale de la Recherche (ANR-17-EURE-0001); Eszter Kelemen has received support from the János Bolyai Research Grant of the Hungarian Academy of Sciences; Daniel Miller acknowledges the support from the John D. and Catherine T. MacArthur Foundation; Graciela M. Rusch acknowledges the support of the Norwegian Institute for Nature Research (NINA) and the Norwegian Environmental Agency; Cindy Isenhour acknowledges the support from the National Science Foundation Convergence Program.
Acknowledgements We would like to thank all the  Egis team  who took the time to help and dedicate their time in the development of the concept of the multifunctional metro, the Sytral for their support during our researches, and all the people interviewed at the  Grand Lyon  for their time, all the information they gave us about urban needs in Lyon and their wise advises on the appropriate functions to investigate. We also thank  ESTP  for their help in the definition and the development of the methodology to develop multifunctional infrastructures.
4 We demonstrate the effectiveness of our methods in section 4. 2, where we obtain estimates of the effects of MFN tariffs and "time to export" (TTE) as representative unilateral and non-discriminatory policies on the importer and on the exporter side, respectively. 5 To perform the empirical analysis, we build a data set of consistently constructed international and intra-national manufacturing trade flows. Intra-national trade flows are calculated as apparent consumption, equal to the difference between the values of gross manufacturing production (which come from UNIDO's INDSTAT2 Industrial Statistics Database) and total exports (which come from the United Nation's COMTRADE database). 6  Our most important result is that we can indeed identify the estimates of the effects of both non-discriminatory trade policy variables  (MFN tariffs and TTE) , in the presence of importer and exporter fixed effects, without any collinearity issues.
The TTE data come from the Doing Business Report within the World Development Indicators (WDI) Database of the World Bank. 6 We offer a detailed description of our data in section 4. 1, where we also note that databases that offer consistently constructed international and intra-national trade flows are more widely available and accessible nowadays. Two examples include the GTAP database and the WIOD database. We use the WIOD database to test the robustness of our methods.
Data In order to perform the empirical analyses, we construct an unbalanced panel data set for 68 countries for the years 2005 to 2012. 27 We start in 2005 because data on one of our main regressors of interest (time to export, TTE) are available only since 2005. We end in 2012 because the production data we need to construct intra-national trade flows end in 2012. Our data cover four key components: (i) international trade flows, (ii) intra-national trade flows, (iii) non-discriminatory trade policies and (iv) standard gravity variables. International trade flows. Data on international trade flows come from the United Nations' COMTRADE database, which is the standard and most comprehensive source for international trade flows data. 28 To create our data set, we keep every country-pair observation that we observe at least twice such that the bilateral fixed effects do not perfectly predict bilateral trade flows by construction.
We focus on manufacturing trade. The reason is the need to construct proper intra-national trade flows, which we discuss next. Intra-national trade flows. Availability of intra-national trade flows data is crucial for the implementation of our method. We construct domestic trade flows as apparent consumption, i. e. , as the difference between the value of domestic production minus the value of total exports. While it is tempting to obtain aggregate domestic sales as the difference between GDP and total exports, we do not recommend this approach due to the inconsistency between the measure of GDP as value added and the measure of total exports as gross value. In other words, in order to construct consistent intra-national trade flows, we need gross production value data. Therefore, we rely on the UNIDO's Industrial Statistics Database (INDSTAT2) for our main results, which offers cross-country gross production manufacturing data for a large sample of countries.
29 However, the implementation of our method is not limited to the data set that we use for the current analysis. Recently, more and more data sets include consistently constructed international and intranational trade flows. The GTAP database and the WIOD database by  Timmer et al. (2015)  are two prominent examples. Depending on their purpose and preference, researchers may apply our methods to any data set that includes intra-national trade flows. To show the robustness of our method, we also present results using domestic and international manufacturing trade data from the WIOD 2016 Release, which is available only for a smaller subset of countries. A list of the countries in our data set appears in appendix A4. We access UNCOMTRADE via the World Integrated Trade Solution (WITS) website, http://wits.
worldbank. org/default. aspx. UNIDO's INDSTAT2 database can be accessed at www. unido. org/researchers/ statistical-databases. Tariff data. As noted earlier, while we do recognize that, in some cases, countries apply different tariffs (for example various preferential rates) instead of MFN tariffs and that, in principle, while MFN tariffs apply to only WTO member states, in our data set, we apply the MFN tariff to all countries to ensure that this variable really is non-discriminatory across countries. Data on MFN tariffs come from UNCTAD's Trade Analysis Information System (TRAINS), which we accessed via World Integrated Trade Solution (WITS). 30  As a robustness check, we control for the applied preferential tariff in some specifications, which we also source from TRAINS. 31  Non-discriminatory exporter policy variable.
We use time to export (TTE), a part of the World Bank's Doing Business project, which collects information about measures of business regulation including policies that impact imports and exports, for a wide range of countries over time. 32  TTE measures the number of days it takes to actually export goods. This includes three distinct parts: (i) the time it takes to gather all domestic documents needed for exports, (ii) the time to proceed through customs and comply with border regulations and (iii) the time of domestic transport to the final port of embarkment. The data are collected via questionnaires sent out to exporting firms, port and customs authorities as well as domestic freight companies. Time measurement of the raw data is done in hours and is then transformed to days. 33  The TTE variable is available beginning in 2005 only, which is therefore the start year of our panel data set.
In some cases, the bilateral applied tariff rate is therefore larger than the MFN world average for a given importing country. By definition, applied tariffs have to be lower or equal to a country's MFN tariff, not larger. In these cases, we replace applied tariffs with the value of the MFN tariff. This aggregation measurement error can easily be remedied by using more detailed trade and tariff data. To keep the exposition of our method simple, we use aggregate data. cannot use directional bilateral fixed effects in our cross-section regressions. Therefore, in this case, we rely on the set of standard gravity variables from the literature. Specifically, we use data on bilateral distance, common spoken language, contiguity and colonial ties, which are taken from CEPII's distances database (see  Mayer and Zignago 2011).
Columns (4) and (  9  (1) (2) NOTES: This table reports gravity estimates using a PPML estimator for 68 countries in 2012 in columns (  1 ) and (  6 ) and an unbalanced panel from 2005 to 2012 in columns (2), (3), (  5 ), (  7 ), (  8 ) and (10). Columns (  4 ) and (  9  tariff turn significant even when controlling for bilateral fixed effects. This is different from table 2, where the simple average of the applied tariff was not significant. (3) (4) (5) Until now, all our regressions used trade data from COMTRADE augmented with production data from UNIDO's INDSTAT2 database. The main reason for this is that it allowed us to include the largest number of countries possible in our analysis. A potential disadvantage of this approach is that, because the data from COMTRADE and INDSTAT2 are collected independently, measurement discrepancies between the two databases can, in a few cases, lead to negative constructed domestic trade flows.
Still, some researchers may prefer using data, such as the WIOD database, that are constructed in a consistent way such that negative domestic trade flows do not occur. We therefore reestimate table 2 using international and domestic merchandise trade data from WIOD (Release 2016). WIOD contains information on 43 countries. After merging with the tariff data and the time-to-export variable, we are left with 31 countries, considerably less than the 68 countries in our baseline data set. Of the countries with constructed negative domestic trade flows in our baseline data set, only Ireland, Latvia and Lithuania are included in WIOD. The correlation between all trade data from COMTRADE and the WIOD trade data is 0. 99, and the correlation between only domestic trade data from COMTRADE and WIOD is 0.
2016; Armona et al. 2019)  beliefs about inflation, as postulated by models of imperfect information (e. g. ,  Woodford 2013)  or of rational inattention  (Sims 2003; Reis 2006; Alvarez et al. 2012; Abel et al. 2007  or  Abel et al. 2013). Recent empirical evidence on 'stock return ignorance' driving US household decisions to invest in stocks has been advanced by  Merkoulova and Veld (2021). The crucial role of heterogeneous subjective beliefs for disciplining heterogeneous agent models and understanding the macroeconomy has been advocated, among others, by  Carroll (2017)  or  Manski (2018). After assessing the high quality of our data, we document a number of new stylised facts about the stock market beliefs that households hold. Households' average forecasts about returns are significantly lower and less volatile than their counterparts computed from long-run historical data (e.
g. ,  Hurd et al. 2011 , Miniaci and Pastorello 2010 or Merkoulova and Veld 2021). Here, we exploit unique data from a new wave of the Taylor Nelson Sofres French survey  (TNS 2007) , which contains information on attitudes, preferences, subjective expectations, perceptions, and socioeconomic and demographic characteristics for a representative sample of 3826 households, by age, wealth, and asset classes  (Campbell 2006). The rest of the paper is organized as follows: in Section 2, we describe the methodology used to elicit perceptions and expectations of returns, and describe sample measures from in the TNS 2007 survey wave as well as the data set. Section 3 presents the econometric specification and reports the main estimation results of the risky asset demands of households. Finally, in Section 4, we conclude.
The Data The 2007 wave of the Taylor Nelson Sofres French survey (TNS 2007) was designed by researchers at the Paris School of Economics (PSE), and administered by Taylor Nelson Sofres, a professional agency paid with research funds from the Agence Nationale de la Recherche (ANR). The first wave, carried in 2002, had no questions related to stock market expectations. The 2007 wave contained very detailed information on attitudes, preferences, and expectations, in addition to wealth, income, and socioeconomic and demographic characteristics for a representative sample of French households. A questionnaire was sent to a representative sample of 4000 individuals, corresponding to an equivalent number of households. Respondents had to fill out the questionnaire, and return it by the post in exchange of around EUR 25 in coupon tickets (bons-d'achat).
Procedure Collection procedure was carried out online via the LimeSurvey software (UNC official license). We disseminated the invitations to participate, which included the link for the online survey, through e-mails, WhatsApp, and various social networks. No personal identification data was asked of participants during the survey, except for an email address and a cellphone number that was required for the follow-up. During September 15 to 30, 2020, participants were contacted to answer the online survey for the second time. In both the first and the second measurements, upon accessing the survey, participants were initially presented with the information sheet and informed consent form. Written informed consent was obtained from all subjects.
Statistical Analyses All data was analyzed in RStudio version 3. 6. 2 (R Core Team, 2020). Reproducible code can be found at an online repository  (López-Steinmetz, 2020). The level of significance was set at p ≤ 0. 05. Exact p-values were reported, except for p-values under 0. 001, which were reported as p < 0. 001. The distribution of the outcome variable was in the range of acceptable values for skewness and kurtosis (-1 to 1 and -3 to 3, respectively)  (Brown, 2006) ; thus, parametric statistics were used. There were no missing data to handle. We provided descriptive measures (percentages, mean, and standard deviation). Likewise, we applied the Student's t test, the 2-sample test for equality of proportions, and the Pearson's r correlation coefficient, to test the first hypothesis of this study.
We have set non-orthogonal contrasts for this last predictor. We established the condition no (i. e. , not having got infected with the COVID-19) as the baseline (dummy coded as 0 in all the contrasts). The contrasts compared the baseline versus each one of the remaining conditions of the COVID-19 contagion. The first model only contained the intercept. We have built up the models by adding one predictor at a time in order to test the overall main effect of each predictor, and then, we built up the models to test all the possible interactions for two-predictor combinations. For the analysis of mixed effects modeling, we have used the nlme package  (Pinheiro et al. , 2020)  with the maximum likelihood method. We have compared the fit of the models looking at two criteria: The Akaike information criterion (AIC) and the Log-Likelihood (log-Lik).
Limitations The findings of this study, although valuable, should be considered in the context of some limitations. First, as we discussed earlier in this paper, additional factors not measured in this study may be relevant to thoroughly understand the negative mental health impacts in healthcare workers during the COVID-19 pandemic and further research is needed to address such factors. Second, the sample was not representative of all Argentinean healthcare workers, though it included data of healthcare workers from well-balanced metropolitan and non-metropolitan or rural areas and there was a low attrition between the two measurements. Third, most women participated. Although to some extent this reflects the fact that women are majoritarian among Argentinean healthcare workers (PNUD, 2018), the uneven sex distribution of the sample is a potential bias source.
Methods This retrospective population-based study evaluated cancerrelated hospital admissions recorded by Brazil's National Public Health System (Sistema Único de Sa úde -SUS). Created in 1990, the SUS have as key ethical principles: universality of access, equity, and comprehensive care. Hospital Admission Authorization (Autorização de Internação Hospitalar -AIH) is a nation-wide database recording the hospital admission to monitor the health's system productivity that is publicly published by the Department of Informatics at the Public Health System website (DATASUS, http://tabnet. datasus. gov. br). 12 The aggregated data were extracted from the AIH for the same period in 2019 and 2020 (March to July), pre-and COVID-19 period. SUS classify hospital admission as the health care provided to an individual for clinical or surgical reasons that require hospital admission and utilization of a hospital bed for a period equal to or greater than 24 hours.
Data on cancer-related hospital admissions, at any treatment or disease phase, were available for clinical purposes, for example, chemotherapy or radiotherapy and for surgical procedures excluding biopsy. We also reported COVID-19-related hospital admission from the same database. The estimated population data were obtained from the Brazilian Institute of Geography and Statistics (IBGE, Supplementary Table  1 ). 13 All data are available by five Brazilian regions: north, northeast, midwest, southeast, and south, and by 26 states and one Federal District (Distrito Federal, Supplementary Table  1 ). 13 he monthly average hospital admission rates per 100 000 inhabitants were calculated by period (pre-COVID-19 and COVID-19 period) and for the country, region, and Brazilian state. The 95% confidence intervals (95% CIs) were computed for rates and rate differences (RD).
11, 33 oreover, there is long-standing evidence of the inequality in hospital capacity across the regions in Brazil in terms of health infrastructure. A recent analysis reported that the median number of hospital beds in Brazil is 19 per 10 000, but 5% of the north's micro-regions have only six beds per 10 000. 32 The COVID-19 pandemic has also raised concerns on socioeconomic inequality in cancer outcome. 34, 35 This pandemic may further widen the inequality in cancer outcomes unless policies at the local, state and national levels are implemented to promote equitable access to a high quality of health care. This population-based study used nationwide and established datasets, showing a representative description of cancer-related hospital admissions in Brazil. Yet, due to the nature of the study design, it is impossible to assume a causal effect with the COVID-19 pandemic.