dataesr/scientific-paragraphs-categorization huggingface dataset:
Dietary assessment At inclusion and every 6 months thereafter (to vary the season of completion and consider seasonal variation in dietary intakes), participants were invited to complete a series of three non-consecutive, web-based 24-h dietary records (validated against an interview by a trained dietitian  23  and against blood and urinary biomarkers  24, 25  ) randomly assigned over a 2-week period (2 weekdays and 1 weekend day). Participants reported all foods and beverages consumed on any eating occasion. Portion sizes were estimated by participants using validated photographs or usual serving containers. 26 To assess daily intakes of macronutrients, micronutrients, alcohol and total calories, dietary consumption data were linked to the NutriNet-Sant e food composition database, which contains >3500 generic items.
Nitrite and nitrate intakes Nitrite and nitrate intakes were estimated combining contributions of food additives: potassium nitrite (e249), sodium nitrite (e250), sodium nitrate (e251) and potassium nitrate (e252). Because they can also naturally occur in food products, we also estimated intakes from natural presence in food and drinking water. Supplementary File 1 (available as Supplementary data at IJE online) describes the determination process of food additive exposure. In brief, for each food consumption declaration, the presence of each food additive (qualitative composition data) and, if relevant, its dose (quantitative composition data) were searched. To determine the qualitative presence/absence of food additives, three complementary databases were used: OQALI,  29  a national database hosted by the French food safety authority (ANSES) to characterize the quality of the food supply; Open Food Facts, an open collaborative database of food products marketed worldwide; 2 and Mintel Global New Products Database (GNPD),  30  an online database of innovative food products in the world.
When several composition data existed for a same product at different dates (reformulations), the date of consumption in the cohort (year) was considered in the matching of composition data (dynamic matching). The quantitative composition of additives was derived from several sources. First, 2677 ad hoc laboratory assays were carried out for the main additive-vector food pairs, among which 700 assays were on nitrites and nitrates (ad hoc assays committed by our laboratory or by the consumers' association 'UFC Que Choisir'). The second step in the absence of data for a given food was the use of doses by generic food categories transmitted by the European Food Safety Authority (EFSA). Last, generic doses from the Codex General Standard for Food Additives (GSFA)  31  were used. The decision tree in Supplementary File 1 (available as Supplementary data at IJE online) describes this process in details.
Case ascertainment Health events were reported through an annual questionnaire, a specific check-up questionnaire every 6 months or at any time through a specific interface on the study website. Each incident cancer reported was reviewed by a physician from the study team who contacted the participants and asked them to provide relevant medical records. When additional information was needed, the patient's physician, hospital or both were also contacted. All medical data were reviewed by a committee of physicians. Besides, the NutriNet-Sant e cohort is linked to the medicoadministrative databases of the national health insurance system (SNIIRAM databases) and to the French national cause-specific mortality registry (C epiDC). Based on these databases, we were able to complete the information concerning health events and deaths, thus limiting any potential bias due to participants with cancer who might not report their disease to the study investigators.
Strengths and limitations Strengths of this study include its large sample size, its prospective design and the detailed assessment of nitrites and nitrates exposure from different sources. Indeed, repeated 24-h records allowed us to collect precise information on a wide range of foods containing nitrites and nitrates, with information about their source: natural presence or contamination vs addition as food additives. As doses of food additives may vary according to brands for the same type of product, commercial names/brands were collected and accounted for, which represents an important asset compared with most nutritional studies worldwide. Three complementary databases were used to determine their qualitative additive composition and thousands of assays were performed and complemented with EFSA and GSFA data to retrieve information on quantitative doses.
Acknowledgements We thank  Thi Hong Van Duong ,  R egis Gatibelza ,  Jagatjit Mohinder  and  Aladi Timera  (computer scientists);  Julien Allegre ,  Nathalie Arnault ,  Laurent Bourhis  and  Nicolas Dechamp  (data-manager/statisticians);  Sandrine Kamdem  (health event validator); and  Maria Gomes  ( Nutrinaute support ) for their technical contribution to the NutriNet-Sant e study. We also thank all the volunteers of the NutriNet-Sant e cohort.
Acknowledgements Financial support for travel and field logistics for NFG and AR was provided by a  GCRF (Global Challenges Research Fund) Agility Grant  through  CIDRA (Centre for International Development at Aberystwyth University ). The participation of Nepalese and Indian students on the fieldwork training at Ponkar Glacier where fieldwork was conducted was supported financially by the  UNESCO   IGCP  Project  672 : " Himalayan Glaciers  and risks to local communities". The drone survey was conducted by a pilot team from  Nepal Flying Labs. We thank all fieldwork participants for their hard work and enthusiasm. Adina Racoviteanu is supported by a Fellowship through the  Marie-Curie COFUND Ser Cymru II scheme. We acknowledge the  Himalayan Research Expeditions in Nepal  for organizing fieldwork logistics on Ponkar.
3 versus 0%). These results were confirmed also in terms of distant brain failure (defined as the appearance of new brain metastases) and local control. An excellent tolerability was recorded. Similar positive results in terms of clinical outcomes and tolerability were reported in other retrospective series  (40, 41). According to the majority of retrospective studies, a period between 4 weeks and 1 months before or after immunotherapy could be defined as the cutoff to differentiate a concurrent or sequential treatment approach  (42). Currently, different phase II clinical trials are ongoing to evaluate, in a prospective study, the impact of radiotherapy and concurrent immunotherapy (NCT04291092, NCT04787185). Another phase II trial is exploring the use of SRS followed by immunotherapy versus immunotherapy followed by radiotherapy (NCT04650490).
IV. FITTING STRATEGY The signal yield and A CP are extracted via an unbinned extended maximum-likelihood fit to the variables M bc , ΔE, and C 0 NN. There are four categories of events fitted: B 0 → D0 π 0 or B þ → D0 π þ signal events (s), continuum events (c), generic B B events (b), and rare B-decay backgrounds (r). These events are described by probability density functions (PDFs) denoted as P s , P c , P b , and P r , respectively. Separate PDFs are constructed for the B 2b and B 3b reconstruction modes, which are fitted as two separate datasets. The data are further divided into events tagged as B 0 and B0 defined as having flavor q ¼ þ1 and q ¼ -1, respectively, based on the charge of the kaon. The physics parameters are determined via a simultaneous fit to the four datasets. The total likelihood is given by L ¼ e - P j N j Q q;d N q;d ! × Y q;d Y N q;d i¼1 X j f j d N j P j q;d ðM i bc ; ΔE i ; C 0i NN ; qÞ ; ð6Þ where N q;d is the number of events with flavor tag q for the dataset d (d ∈ B 2b ; B 3b ), and N j is the number of events in the jth category (j ∈ s; c; b; r) contributing to the total yield.
The fraction of events in the dataset d for category j is f j d , with f j 3b ¼ 1f j 2b. The PDF P j q;d corresponds to the jth category in the d dataset for flavor q measured at M i bc , ΔE i , and C 0i NN for the ith event. The PDF for each component is given by P j q;d ðM bc ; ΔE; C 0 NN ; qÞ ¼ 1 -q × A j CP 2 × P j d ðM bc ; ΔE; C 0 NN Þ: ð7Þ The model accounts for a possible direct CP asymmetry, A j CP , and the fractions of signal and backgrounds expected in each reconstruction mode B 2b (B 3b ). In Eq. (  6 ), the fraction f s d is determined via MC studies of the B 2b and B 3b modes and is fixed in the fit to data, and A j CP ðj ∈ c; b; rÞ is fixed based on studies of detector bias using sideband data (see Sec. V). The 20 free parameters in the fit are the number of signal events N s , signal asymmetry A s CP , the number of continuum events (N c ) and generic B-decay events (N b ), fractions of backgrounds expected in each reconstruction mode f j d (j ∈ c; b; r), shape parameters of the continuum M bc and ΔE PDFs, and the mean and width of the B 2b signal M bc and ΔE PDFs.
(iii) Continuum background: M bc is fitted as an ARGUS function  [32] , and ΔE as a third-order Chebyshev polynomial, in both modes. (iv) Rare B-decay background: As with a generic B and B 3b signal, the M bc and ΔE distributions for rare B-decay background are modeled with a 2D KEST PDF in both modes. This PDF is determined using MðD 0 Þ 1. 841-1. 882 GeV=c 2 Mðπ 0 B 0 Þ½K þ π - 104. 1-163. 1 MeV=c 2 Mðπ 0 B 0 Þ½K þ π -π 0 105. 8-164. 4 MeV=c 2 Mðπ 0 D0 Þ½K þ π -π 0 107. 9-162. 3 MeV=c 2 PðK AE Þ 0. 3-3. 5 GeV=c Pðπ AE Þ 0. 3-3. 5 GeV=c Pðπ 0 B 0 Þ 1. 5-3. 5 GeV=c Pðπ 0 D0 Þ 0. 2-3. 5 GeV=c MC simulations corresponding to 50 times the luminosity of the Belle dataset. To fit C 0 NN , three summed Gaussians are used for all components except continuum background, which employed two summed Gaussians.
These calibration factors are applied as mean shifts and width factors to the C 0 NN Gaussians. To account for small differences between MC and data in the width of the ΔE distribution, the 2D KEST PDF for M bc and ΔE in B 3b is modified slightly. This is done by modifying each ΔE data point in the MC dataset with a random shift based on a Gaussian distribution with a mean of 0 GeV and a width of 7 MeV (chosen after testing with a range of widths) and generating a new KEST PDF from this modified data sample. Fits to the B þ → D0 π þ sample are performed to determine the signal yield, A CP;raw , the continuum background yield, the generic B-decay background yield, and the calibration factors. The rare B-decay background yield is fixed to the value expected from MC simulations. From the fit we obtain N sig ¼ 84537 AE 306 and A CP;raw ¼ ð1.
97 AE 0. 36Þ%. The uncertainties listed are statistical. A CP;raw is the output of the fit without a correction to account for sources of bias. Figure  2  shows the fits to data in M bc , ΔE, and C 0 NN. To account for possible bias in A CP , we perform an analysis over a "sideband" region of data defined as 0. 1 GeV < ΔE < 0. 4 GeV and 5. 255 GeV=c 2 < M bc < 5. 27 GeV=c 2. This region consists almost entirely of continuum events and has an expected A CP of zero. Counting the number of events in this region we find A CP;sideband ¼ ð1. 78 AE 0. 38Þ%. We subtract this value from A CP;raw to correct for the detection asymmetry bias. The branching fraction is calculated as B ¼ N sig N B AE × mean f s 2b ϵ 2b ; f s 3b ϵ 3b ; ð8Þ where N B AE is the number of charged B-mesons in the dataset based on the PDG average value of Bðϒð4SÞ → B þ B -Þ ¼ ð51.
4 AE 0. 6%Þ  [22] ; f s d is the fraction of signal events in the dataset d ¼ 2b or 3b (f s 2b ¼ 0. 51, f s 3b ¼ 0. 49); ϵ d is the product of the reconstruction efficiency, the D0 branching fraction B d , and small corrections for particle identification (PID), and charged-track and π 0 reconstruction efficiencies (see Sec. VII) for mode d. The π 0 → γγ branching fraction is accounted for in the MC simulation. The resulting values for ϵ 2b and ϵ 3b are ð1. 19 AE 0. 03Þ × 10 -2 and ð1. 16 AE 0. 05Þ × 10 -2 , respectively. The mean is calculated as a generalized weighted mean  [33, 34]  taking into account correlated and uncorrelated uncertainties in a covariance matrix. This approach is used because the difference in systematic uncertainties between the two D0 -decay modes leads to the need to weight them in order to calculate the final branching fraction and uncertainty correctly.
Using Eq. (  8 ), the PDG value Bðϒð4SÞ → B0 B 0 Þ ¼ ð48. 6 AE 0. 6%Þ  [22] , the fraction of signal events in dataset d, f s 2b ¼ 0. 45, f s 3b ¼ 0. 55, and the efficiencies ϵ 2b ¼ ð1. 00 AE 0. 03Þ × 10 -2 and ϵ 3b ¼ ð1. 21 AE 0. 07Þ × 10 -2 , we determine the branching fraction to be BðB 0 → D0 π 0 Þ ¼ ð2. 70 AE 0. 06 AE 0. 10Þ × 10 -4 ; ð11Þ where the quoted uncertainties are statistical and systematic, respectively. The fitted yield for each respective category is listed in Table  IV. Events /(  10 Events / (0. 5) Events / (0. 5) ] 2 [GeV/c bc M ] 2 [GeV/c bc M E [GeV] Δ E [GeV] Δ ǸN NN C C FIG.
VII. SYSTEMATIC UNCERTAINTIES The systematic uncertainties associated with the measurement of B and A CP are as follows. (i) Number of B B pairs: The uncertainty associated with the measured number of B B pairs in the full dataset collected at Belle is 1. 37%  [35]. (ii) Bðϒð4SÞ → B 0 B0 Þ: Uncertainty from the branching fraction Bðϒð4SÞ → B0 B 0 Þ ¼ ð48. 6 AE 0. 6%Þ  [22]. (iii) DCS mode correction: The uncertainty due to the correction for doubly Cabibbo-suppressed decays is 0. 01% for both B 0 → D0 π 0 and B þ → D0 π þ (see Sec. II). (iv) Charged-track efficiency: The uncertainty associated with a possible difference in efficiency between MC and data for charged-track reconstruction is found to be 0. 35% per track using partially reconstructed  [35]. (v) π 0 detection efficiency: The ratio of data to MC efficiency for π 0 reconstruction is based on a study 3.
Projections of the B 0 → D0 π 0 fit results into the signal region (5. 275 < M bc < 5. 285 GeV, -0. 12 < ΔE < 0. 07 GeV, -1 < C 0 NN < 6) for M bc (left), ΔE (middle), and C 0 NN (right) split into the B 2b mode (top) and B 3b (bottom). The blue short-dashed curve shows the signal PDF, red dotted curve shows the B B background PDF, green dash-dotted curve shows the continuum background PDF, pink long-dashed curve shows the (almost negligible) rare background PDF, black line is the fit result. Also shown underneath each graph are the residual pulls between the data points and fitted PDF. of τ -→ π -π 0 ν τ decays  [36]. This ratio is ð96 AE 2Þ% per π 0. (vi) MC statistics in efficiency calculation: Uncertainty associated with the reconstruction efficiency is based on the binomial statistics of the MC dataset used.
quoted as the uncertainty. For A CP this is 0. 06 and <0. 01, respectively. (xii) 2D KEST PDFs: B B, rare, and B 3b signal M bc × ΔE PDFs all use a fixed 2D KEST PDF. To estimate the uncertainty from this, an ensemble test is performed running 1000 fits over the data, with each fit using a different Gaussian-fluctuated KEST PDF based on bin statistics. The uncertainty is quoted as the rms of the resulting yield and A CP distributions. This contributes 0. 35% and 0. 05% to the uncertainty in B for B 0 → D0 π 0 and B þ → D0 π þ , respectively. For A CP the contribution is 0. 15 and <0. 01, respectively. (xiii) Fixed rare B-decay background yield: The uncertainty due to the rare B yield is the quadratic sum of the statistical uncertainty based on the size of the MC dataset and the uncertainty in the branching fractions used to generate the MC.
ACKNOWLEDGMENTS We thank the  KEKB group  for the excellent operation of the accelerator, the  KEK cryogenics group  for the efficient operation of the solenoid, the  KEK computer group , the  Pacific Northwest National Laboratory Environmental Molecular Sciences Laboratory computing group  for strong computing support, and the  National Institute of Informatics , and  Science Information NETwork 5 for valuable network support. We acknowledge support from the  Ministry of Education, Culture, Sports, Science, and Technology of Japan , the  Japan Society for the Promotion of Science , and the  Tau-Lepton Physics Research Center of Nagoya University ; the  Australian Research Council  including Grants No. DP180102629 , No. DP170102389 , No. DP170102204 , No. DP150103061 , and No. FT130100303 ;  Austrian Federal Ministry of Education, Science and Research (FWF)  and  FWF Austrian Science Fund  No.
P  31361-N36 ; the  National Natural Science Foundation of China  under Contracts No. 11435013 , No. 11475187 , No. 11521505 , No. 11575017 , No. 11675166 , and No. 11705209 ;  Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS)  Grant No. QYZDJ-SSW-SLH011 ; the  CAS Center for Excellence in Particle Physics ; the  Shanghai Science and Technology Committee  under Grant No. 19ZR1403000 ; the  Ministry of Education, Youth and Sports of the Czech Republic  under Contract No. LTT17020 ;  Horizon 2020 ERC Advanced  Grant No. 884719  and  ERC Starting Grant  No. 947006  " InterLeptons" (European Union ); the  Carl Zeiss Foundation , the  Deutsche Forschungsgemeinschaft , the  Excellence Cluster Universe , and the  VolkswagenStiftung ; the  Department of Atomic Energy  (Project Identification No.
In addition to addressing family policies, our analysis thus contributes to the recently growing literature on general satisfaction, of which economic well-being is an important domain  (Sirgy, 2017; Stanca, 2012; Van Praag et al. , 2003). Longitudinal data from the European Union Statistics on Income and Living Conditions (EU-SILC) survey for almost 128,000 households from 30 countries are used. This extensive dataset is ex-ante harmonised and consequently provides ideal conditions for a comparative study covering the large majority of European countries. We combine panel regression-based mediation analyses with objective and subjective information on economic well-being, thereby providing a novel perspective to the analysis of child-related costs and a critical assessment of SEW. A range of robustness tests is conducted to yield reliable results.
Sample Construction The empirical analysis relies on EU-SILC longitudinal data which was collected yearly from 2004 to 2019 by Eurostat in cooperation with European National Statistical Institutes 3  (Eurostat, 2018c). In total, 31 European countries participated in the survey, 30 of which are considered in this study. 4 An important advantage of EU-SILC data is that it is harmonised across all countries and covers a wide range of economic and demographic information about individuals European Commission (2017). The survey is designed as a rotating panel, with most countries following the participants for a maximum of four years. Exceptionally, France provides a nine-year rotating panel and Norway provides an eightyear one. For comparability reasons and due to panel attrition, only the first four years are considered in these two countries.
Yet the sub-sampling results in small numbers of observations for some of the cells, especially in German-speaking and Liberal countries. Moreover, the sub-sample is a very particular group as it only includes couples that will soon have their first child or just had their first child. Thus, for the regression and mediation analyses, a much larger sample is used that also includes couples with no children or more than one child. Moreover, we provide additional analyses by country and income group. With the dataset presented, only the short-term costs of children can be captured. Couples are followed for a maximum of four years. Even if a child is born at the earliest possible time during the panel duration period, namely between the first and second wave, only a maximum of three values of SEW after that birth can be observed.
Labour income of men and women is computed as the respective employee cash or near-cash income with neither taxes nor social contributions being subtracted. 9 It includes income in-cash and in-kind as well as any social insurance contributions paid by the employer. Income from self-employment is added if not missing. 10 For the descriptive analyses, we also investigate the share of women in full-time and part-time employment. It is based on women's self-defined current economic status, where work is defined as any work for pay or profit. Women in maternity leave are considered as employed, while women in parental leave are not. Theoretically, benefits consist of in-kind and in-cash transfers. With the data provided by EU-SILC, only in-cash transfers can be observed, however. In particular, the benefits considered in this analysis include benefits for bringing up children such as birth grants, parental-leave benefits or child allowances received during the respective income reference period.
11 Furthermore, they include housing allowances and financial assistance to individuals who take care of relatives other than children. 12 9 Gross income instead of net income was used because net income is missing for one-third of the observations in EU-SILC. 10 Observations were dropped if they reported labour income from employment below zero. Labour income from self-employment, however, is allowed to be negative, resulting in below-zero values of labour income. 11 Maternity and parental-leave benefits are included in financial support, unless payments cannot be separately identified from labour income. This can be the case if: (i) payments made by the employer are in lieu of salaries and wages through a social insurance scheme, or if (ii) payments are made by the employer as a supplement to payments from a social insurance scheme (European Commission, 2017).
12 Financial assistance to individuals who support relatives other than children cannot be identified separately in the dataset. 8 Data on inflation and purchasing power parities are taken from the Eurostat database. Inflation indices are based on "prc hicp aind" (Eurostat, 2018d), and purchasing power parities on "prc ppp ind" (Eurostat, 2018e). As suggested by  Mack and Lange (2015) , actual individual consumption is used as a base for purchasing power. For all countries, inflation indices and purchasing power parities from the previous year are used, given that income in the dataset refers to the income reference period, which is the previous year (see supplementary material Section A. 1 for details on the panel structure). Since the income reference period of Ireland does not refer to an actual calendar year, taking yearly data on inflation and purchasing power parity is somewhat imprecise for Ireland.
Consequently, the coefficients from Models 1, 2 and 3 T = D + I -B I = T -(D -B) I = 1. 1 -1. 3 Fig. 1  Subjective and objective economic well-being before and after the birth of the first child. Source: EU-SILC longitudinal data 2004-2019. The weighted means and 95% confidence intervals presented in this graph are based on the 6,396 couples in the sample that had their first child but no additional child during the panel duration period. In total, they provide 19,022 observations for "Women in employment" and "Change in subjective economic well-being (SEW)". Less observations are available for "Income" and "Benefits", as income in the dataset refers to the income reference period, which is the calendar year prior to the survey (see supplementary material Section A. 1 for details on the panel structure).
Hence, income and benefits can only be observed one year after the birth was observed, rather than two years after the birth was observed. The x-axis shows the years before and after the child was first observed in the dataset; zero refers to the first survey wave in which a newborn was recorded. The grey area gives the period during which the birth happened-it is wider for income and benefits, again due to the shift in the income reference period. The share of employment is based on women's self-defined current economic status, where work is defined as any work for pay or profit. Women in maternity leave are considered as employed, while women in parental leave are not. The solid lines show the share in full-time or part-time employment, while the dashed line only shows the latter. For better comparability across regions, income is set to 100 at the beginning of the observation period, i.