dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Description Based on our reading of the economic measures, we identified 10 standard economic measures that have been taken by governments. For each country-day, we coded the percentage of 2019 GDP spent by the government on different economic measures, usually following a fiscal plan. Coding in percentage, rather than in amounts, allows us to make relevant cross-country comparisons. We used the 2019 GDP as a basis for comparison because countries have been affected very differently by the pandemic. As it is impossible to compute the exact fluxes of government spending on a given day, we coded the percentage disbursed on a given measure at the stated starting day of the plan. When this information could not be retrieved, we used the day of the bill's passage. To capture the real effort of governments, we only considered those amounts that have been disbursed.
As governments can announce policies that are sometimes not funded, particularly in developing countries that sometimes rely on foreign or international financial institutions to fund the announced measures, our dataset captures only those measures that were both implemented and funded by governments. The coded variables are as follows: -fiscal. This is the global amount spent on a given date, which is then divided between various categories. Fiscal includes additional or foregone revenues that had been specifically targeted at dealing with COVID-19. It does not account for delayed revenues such as tax deferrals and off-budget measures that do not directly affect the annual fiscal deficit. The breakdown of fiscal includes wage support, cash transfers, in-kind transfers, sectoral support, tax cuts , and credit schemes.
91%) and adds a bonus for health workers (0. 03% of GDP). We thus coded replace wage = 0. 91 + 0. 03 if iso == "FRA" & tin(15apr2020, 15apr2020). The spreadsheet data are created from the coding script, available on the online repository of the dataset ("script_fiscal. do"). As noted by Porcher  [3]  , there are many advantages to using such a methodology rather than writing directly in the spreadsheet: 1. The script can be run easily, and potential mistakes in the coding can be easily corrected: if the script does not write, or if it writes more than a unit when it is run, then this indicates a mistake in the coding. 2. Potential errors on measures, scales, dates and countries are more visible to the crosscheckers than they are in a spreadsheet format and can be directly corrected. Verifications or errors can be easily flagged.
Technical validation Three different coders worked from January 2021 until June 2021 on the coding. The coding script was cross-checked by each coder on several occasions and several different points in time. This systematic verification process ensures a homogeneous coding method and avoids mistakes, especially those linked to contradictory interpretations of the classification of the different measures. It also allows, when necessary, correction of the script over time in cases where previously announced measures turned out to be misestimated or unimplemented. We used the IMF Fiscal Monitor database and the IMF COVID-19 Policy Tracker to obtain information about the amounts spent. When necessary, we also used alternative sources to more precisely categorize the expenditures. All the extra sources that were used to feed the code are reported in an Excel file ('Source.
Fiscal Policies Database (imf. org) IMF Policy responses to COVID-19: https://www. imf. org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19 OECD URL to data: simonporcher/Fiscal _ Response2covid19: Dataset of governments' fiscal and off-budget measures to respond to COVID-19 (github. com) https://doi. org/10. 5281/zenodo. 7504461.
by the  T ÜB İTAK  grant  118F413. The second author was supported in part by the  ANR  grant  ANR-18-CE40-0009 ENUMGEOM. The third author was supported by the  Research Council of Norway  project no. 250104. Part of this paper was written during the first and second authors' research stay at the  Max-Planck-Institut für Mathematik ; we are grateful to this institution for its hospitality and support. The third author acknowledges support from the  Centre for Advanced Study, Oslo , and the  Motivic Geometry programme.
Acknowledgements: This work has been supported by the  Chair "Modélisation Mathématique et Biodiversité" of Veolia Environnement-Ecole Polytechnique-Museum National d'Histoire Naturelle-Fondation  X. S. M. was partially supported by the  ANR  project  DEEV   ANR-20-CE40-0011-01. V. C. T. also acknowledges support from  Labex Bézout  ( ANR-10-LABX-58 ).
Acknowledgements. We thank  Alessandro Verra  for useful conversations on the subject, and  Klaus Hulek  for interesting correspondence about [6] and answering our questions. We also acknowledge funding from  MIUR Excellence Department  Project  CUP E83C180 00100006  (CC), project  FOSICAV  within the  EU Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement n. 652782  (CC, ThD),  GNSAGA of INDAM (CC,CG) ,  Trond Mohn Foundation  (ThD, ALK) and grant n. 261756  of the  Research Council of Norway (ALK).
Demographics In addition to the total completion time, the following demographic data were collected: gender, age, profile (student, resident, physician), level of experience (number of years of studies/residency/practice), involvement in studying another discipline (especially mathematics), country of study/practice, intended (for students) or practiced medical specialty, type of practice (public/ private, hospital/medical office) and relative time shared between clinical practice and research activities. After collecting demographics, participants were presented with the three exercises collecting both answers and confidence judgments for each question. Each exercise focused on a specific statistical topic (vaccine efficacy, p value and test results interpretation). Vaccine efficacy and test results interpretation was applied to the current context of the COVID-19 pandemic.
Le Monde. https:// www. lemon de. fr/ campus/ artic le/ 2019/ 02/ 09/ en-medec ine-plus-de-filles-mais-toujo ursautant-d-enfan ts-de-cadres_ 54214 79_ 44014 67. html 3 https:// www. legif rance. gouv. fr/ jorf/ id/ JORFT EXT00 00442 29585   • Easy claims resulting in a consistent correct response profile across participants (high accuracy and high confidence, overall), • Hard claims resulting in a less consistent erroneous response profile (mostly low-to-high-confident incorrect answers), • Intermediate claims resulting in a cleaving response profile (bimodal distribution of the accuracy with high numbers of correct and incorrect answers and a peak of low-confident uncertain responses). More detailed discussion on these results is available in the Additional file 1. The collected data are here mapped onto a double-sided probability scale ranging from 50% (lowest confidence, the participant answered randomly) to 100% (maximal confidence judgment), both for correct (blue area) and incorrect (red area) answers.
23, p = 2. 1e-10, see Fig. 4 ). proposed claims. The collected data are here mapped onto a double-sided probability scale ranging from 50% (lowest confidence, the participant answered randomly) to 100% (maximal confidence judgment), both for correct (blue area) and incorrect (red area) answers. Also represented on the x-axis, the distance to the correct answer (d) is defined as a score composed both by the accuracy and confidence, ranging from 0: correct answer with maximal confidence to 100: incorrect answer with maximal confidence through 50: "I do not know", d < 50 and d > 50, respectively, corresponding to correct and incorrect answers. Each vertical line stands for a response. The percentage of this exercise's participants who gave a correct answer is indicated in each blue area. True claims are indicated by a green ticked box Interestingly, the sub-group of 80 participants (10%) with a plausibly more advanced level in statistics (with a bachelor/master degree in mathematics, a PhD degree or practicing a research activity for at least 20% of their time) performed significantly better only at the "p value" exercise than the rest of respondents (lower distance to the correct answer: 31.
Discrimination between correct and incorrect answers We investigated participants' discrimination by looking at the difference between confidence in correct vs. incorrect answers at the individual level (see Fig. 5B ). Confidence was analyzed with regard to the correctness of answers (2 levels: correct/incorrect), the performance (12 levels: number of correct answers) and the participants' profile (3 levels: students/residents/physicians) by fitting a linear mixed-effects model. The model also included gender (2 levels: men/women), practice of research (2 levels: yes/no) and the exercise topic (vaccine efficacy/p value) for covariates adjustment. The participant identifier was assigned as a random effect (intercept) to account for the repeated responses by participant. Significance for main effects and interactions of correctness, performance and Values for participants with 0 and 1 correct answers were, respectively, plotted according to 3 and 2 participants' data points.
Overall, confidence in correct answers was significantly higher than in incorrect answers for all levels of performance superior to one correct answer profile was assessed based on Type II Wald Chi-square tests using the function Anova in the car R package. Post hoc pairwise comparisons were carried out on the significant higher-level interactions with the emmeans R package (https:// cran. r-proje ct. org/ web/ packa ges/ emmea ns/ emmea ns. pdf ) to further determine where the differences occur across the subgroups. P values resulting from the post hoc tests were adjusted with the Tukey's method to account for the multiplicity of contrasts. This analysis revealed a three-way interaction correctness × performance × participant's profile (Wald χ 2 = 36. 9, df = 18, p = 0. 005). The observed variation of discrimination across performance appears stronger in students and to a lesser extent among residents compared to physicians (see Additional file 1: Table  S2C  and Fig.
Funding The funding for this work was provided by the  Haute Autorité de Santé  (CL's Ph. D. grant),  INSERM  and the  Paris Brain Institute-ICM (Benjamin Rohaut 's research annual grant). CL is a student from the  FIRE Ph. D. program  funded by the  Bettencourt Schueller Foundation  and the  EURIP Graduate Program  ( ANR-17-EURE-0012 ). MR work is supported by  La Fondation des Treilles  and by a postdoctoral fellowship from the  AXA Research Fund.
Acknowledgements We would like to warmly thank all members of, the PICNIC lab team from ICM (Lionel Naccache, Laurent Cohen, Paolo Bartolomeo, Tal Seidel-Malkinson, Aude Sangare, Fabien Hauw, Esteban Muñoz-Musat, Amina Ben Salah, Rayan Kouzy, Augustin Gonnard with a special mention to Jacobo Sitt for spotting the call for paper of this special issue!), and of the Service des Bonnes Pratiques department from DAQSS-HAS, as well as Raphaël Porcher, Hugo Mercier, Ralph Hertwig, Mathias Pessiglione, Sacha Altay, Raphaël Veil and Pauline Martinot for their precious scientific advice and their help in survey distribution among medical staff. The distribution was also supported by Anne Charon, Mathilde Lefevre and Nathan Peiffer-Smadja. We are also highly grateful to Karim N'Diaye from the PRISME platform and Mathias Antunes from the Data Analysis Core, as well as to Anne-Sophie Hacquin for their strong support in ethically and technically (Qualtrics RedCap) establishing the survey.
Therefore, the entirety of the material was presented after 4 children were tested. Problems were randomly presented within each set. The experiment was run under DMDX software  (Forster & Forster, 2003). Vocal responses were recorded with a voice key and individually checked offline for accuracy using CheckVocal software  (Protopapas, 2007). CheckVocal was also used to manually adjust the latencies recorded by DMDX if necessary. More precisely, for each response recorded, CheckVocal allows for the visualization of the sound played out through a waveform. When, despite precalibration of the voice key for sensitivity, the onset of the response given by participants is not accurately detected, the timing mark can be manually placed on the onset of the sound waveform. This checking and possible manual readjustments ensure a measure of solution time with 1-ms precision.
Results The data sets that were generated and analyzed in Experiment 1 are available in the Open Science Framework (OSF) repository https://osf. io/fpnav/?view_only=1bfa02854f0a4210871add96630dc1d7. Seven children (3 children aged 8-9 years, 3 children aged 10-12 years, and 1 child aged 13-15 years) were excluded from the analyses because their mean solution times differed by more than 2 standard deviations from the mean solution times of their age group. Therefore, the analyses were conducted on a total of 98 participants. Because our critical predictions centered around the presence (or absence) of a priming effect in older (but not younger) children, we present the results of 2 (Problem Size: small or large) Â 2 (SOA: null or negative) analyses of variance (ANOVAs) conducted separately for each age group.
Procedure Children were asked to solve simple additions and to give their responses orally as quickly and accurately as possible. The same 135 possible combinations of one-digit numbers as in Experiment 1 were used to construct addition problems (see Table  1 ). The task was also designed using DMDX software. Vocal responses were recorded with a voice key and individually checked offline for accuracy using CheckVocal software. Each trial began with a fixation signal ''|" displayed at the center of a computer screen for 500 ms. Then, the addition problem was presented in its entirety at the center of the screen and remained on the screen until a response was given orally by the children. Children were presented with three blocks of problems, each containing the 45 problems described in the Method section.
Results The data sets that were generated and analyzed in Experiment 2 are available in the OSF repository (https://osf. io/fpnav/?view_only=1bfa02854f0a4210871add96630dc1d7). To examine size and priming effects conjointly, the data sets of the 7 children who were excluded from the analyses in Experiment 1 were also excluded from the current analyses. For this experiment, and contrary to Experiment 1, specific effects depending on age groups were not expected. Therefore, we classically ran ANOVAs including this variable.
, 2014; Link et al. , 2014; Peeters et al. , 2016; Slusser & Barth, 2017). Sometimes trials are averaged such that participants have a single accuracy score associated with their performance, but because accuracy can vary within participants and across trials, analyses that consider individual trial performance can provide further insights into patterns of estimation. One approach to investigate patterns of estimation is to use latent profile analysis. Latent profile analysis, which is a type of latent variable analysis, is based on the assumption that within a dataset there is a mixture of observations from several mutually exclusive profiles  (Lanza & Cooper, 2016). In the case of number line estimation, our mixture of observations is from the various patterns of estimation exhibited by each student.
Only region differed for those with complete versus incomplete data such that there were fewer students from Winnipeg and Montreal who participated at both time points than there were from Northern Ireland and Chile. We speculate that this difference is the result of recruitment differences wherein ethics approval and consent from parents had to be obtained at both waves of data collection in Winnipeg and Montreal whereas it only needed to be obtained once in Northern Ireland and Chile. In the subsequent analyses, data for students who participated at either one or both time points were included (N = 584; see the Results for details). Students were individually tested by trained research assistants in a quiet area of the schools.
Chile. One dataset included students from Chile in Grade 2 at Time 1 and Grade 3 at Time 2. These data were also included in a meta-analysis conducted by  (Ellis et al. , 2021). Students were either in low-SES schools that received subsidies from the government or high-SES schools that were not subsidized. All schools were located in the urban metropolitan area of Santiago, the Chilean capital. Given the segregation of the educational system in Chile  (Mizala & Torche, 2012) , schools catered to students from similar socio-economic backgrounds (either low-or high SES). All students received instruction in Spanish.
Canada. Two datasets included Canadian students from two provinces: Manitoba and Quebec. The Manitoba number line data were also included in  Xu, Di Lonardo Burr et al. (2022). Although both samples were from Canada, Manitoba and Quebec differ both educationally and culturally such that Manitoba and Quebec had the lowest and highest scores of all Canadian provinces on the PISA mathematics assessment, respectively  (OECD, 2018). Participants in both provinces attended public (i. e. , government funded) schools and lived in middle-class suburbs of either large or small cities. In Quebec, the language of instruction was French. In Manitoba, students attended either English-instruction or French-immersion programs, in which the language of mathematics instruction was English or French, respectively.
e. , percentages of students who stayed or transitioned from one profile to another) for both FIML and list-wise deletion (i. e. , only on students who completed the number line tasks at both time points are included) estimation strategies. Thus, data from all students were included in the analyses. Moreover, LMR-LRT values suggest that the two-profile model was significantly better than the one-profile model, whereas the threeprofile model did not significantly improve the model fit at Time 1. Although the three-profile model at Time 2 significantly improved the model fit, further inspection revealed one profile with a small number of students (n = 23) with no consistent estimation patterns. When an additional profile does not improve the model, parsimony is favoured  (Nylund-Gibson & Choi, 2018).
Research question 2: Are the relations among number line estimation and performance on other mathematics tasks similar across regions? Across regions, correlations of similar strength were found between number line estimation and diverse mathematical tasks (i. e. , number comparison, number writing and naming, mathematical word-problem solving, and arithmetic fluency). The mini metaanalyses of the associations between number line estimation and these tasks across four datasets revealed medium effect sizes for all measures. This pattern is consistent with the findings from Schneider et al. 's (2018) meta-analysis, which showed a medium effect size for the relations between number line estimation and mathematical competence. In summary, regardless of where students were educated, students who made accurate estimates were also more likely to perform well on other mathematical tasks.
, 2022). For example, knowledge of magnitudes should help students to decide that 503 is only slightly larger than 500 and so it should be placed very close to the midpoint on the 0-1000 number line. In the future, researchers should explore both number-system knowledge and spatial knowledge as crucial factors in number-line development. In the present study, we used latent transition analysis to investigate the development of number line estimation for students in four regions. Our focus was on empirical patterns of performance and therefore we did not make inferences about underlying representations or strategy use. Other researchers have fit statistical models to similar data to make inferences about theories and strategy use in estimation, for example, using linear and logarithmic functions to infer how children's magnitude representations change (e.
, 2010) , verbal strategy reports  (Xu, 2019) , and eye-tracking data  (Di Lonardo et al. , 2020; Di Lonardo Burr & LeFevre, 2021). Such detailed investigations may provide further insights into cross-cultural differences that result from varying educational experiences and contribute to a better understanding of how representations and strategies develop in number line estimation. Beyond accuracy and profile membership, we examined the relations between number line performance and other mathematical measures. We found similar relations across diverse groups, suggesting that the number line is a useful tool for comparisons across countries. However, because the administration, scoring, and stimuli were not consistent across all four regions for the other numeracy and mathematical tasks, we could not use LTA to determine which tasks predicted the transition from uniform to variable profile membership.
Acknowledgement Support for project was provided by the  Social Sciences and Humanities Research Council (SSHRC) of Canada  through an  Insight Grant   435-2018-1463  to  Jo-Anne LeFevre ,  Erin A. Maloney ,  Helena P. Osana , and  Sheri-Lynn Skwarchuk , by the  Chilean National Fund for Scientific and Technology Development (ANID/CONICYT FONDECYT)  through Grant  FONDECYT 1180675  and  ANID -MILENIO -NCS2021_014  to  María Inés Susperreguy , and by  BA/Leverhulme Small Research  Grant  SG170445  to  Judith Wylie  and  Victoria Simms.
| Data extraction We extracted data on indication, regulatory and clinical trial characteristics, and the reported treatment outcomes for OS, PFS, TR and QoL. For the years 2000 to 2016, we used the previously published information. 5 For this period, data for trial without control arm were extracted by one single reviewer (VG). Data for 2017 to 2020 from trials without control arm and RCTs were extracted by two independent reviewers (VG with either CA, CL, HK, JH, MPL, MWC or TVP). Any disagreement was resolved by consensus or by a third reviewer (AMS, LGH).
We stratified results by cancer type (solid cancers vs hematological cancers), indications that demanded a biomarker vs those that did not, line of treatment and by indications for which results were reported for QoL. Data were analyzed using R software, version 3. 5. 1 (R Foundation for Statistical Computing); RStudio software, version 4. 0. 3 (Rstudio, PBC). 26 | RESULTS.
| Strengths and limitations This study has several limitations. First, we examined indications for cancer drugs approved for the first time and did not look at expanded indications. Cancer drugs could have greater effects in expanded indications, for example, when drugs move to an earlier treatment line. Though, in our analysis, 38% of indications were approved for first line treatment and we did not see stronger effects in first line indications. Second, while this report is the first to present information on QoL data in FDA approval documents, we did not quantify potential benefits or harms. This might be of importance, considering that the tools to assess QoL have been criticized for focusing only on limited aspects of life and limited follow-up times. 38 Third, half of trials were non-controlled trials for which we did not quantify and compare effects.
For those trials, results on response and survival would have to be interpreted in the context of external controls, for example, historical data. Such comparisons are prone to very high risk of bias, requiring a very careful in-depth analysis and assessment of these factors for a period of over 20 years which would go beyond the scope of this project. Fourth, we only included data which were reported in approval documents. While there may be other studies that have investigated the novel cancer drugs in the same indication, we assume that the manufacturer would present the most supportive evidence to the FDA. For this reason, we think there is a low risk that the included trials would underestimate the efficacy of the approved drugs.