dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , rare thrombosis associated with the AstraZeneca and Johnson & Johnson vaccines), vaccine confidence issues may emerge, and individuals become concerned as to why a specific vaccine might be available in one country but not in another  [14, [35] [36] [37]. Other issues reported across Europe include differences in the types of vaccines included in the vaccination schedule, the number of vaccine doses, the group for whom a specific vaccination is approved, and the ages at which vaccines and boosters are recom-mended  [38, 39]. For instance, Sweden decided against vaccinating children aged 5-11 years who were in low-risk groups  [10, 40]. Although Sweden was not included in our ten-nation dataset, this is yet another policy divergence that complicates the COVID-19 vaccination infrastructure within the European region.
If left unaddressed, these policy differences may eventually impede public health outcomes and further erode the public trust in governmental institutions and vaccine-focused science  [41]. Data collection processes, technology and the level of detail recorded all diverged across the ten countries under study. Several groups have called for efforts to harmonize data collection on policy responses to COVID-19 so that lessons can be learned and best practices identified  [42]. Experts have also highlighted the need for interconnected and interoperable data networks based on common standards used in current disease surveillance and response efforts  [1]. Our study highlights the divergences that remain common and reveals the points at which specific changes might be made. Our results provide important insights which may be used to improve pandemic responses and help European countries in their efforts to harmonize and coordinate vaccination efforts against existing and emerging diseases.
Procedures Number of observations. As per our pre-registration, 12  a statistical power analysis determined that we needed to collect data from about 150 Republicans and 150 Democrats in each condition to achieve a statistical power of 80% at an alpha level of 5%. We recruited participants located in the United States through Amazon Mechanical Turk for our online experiment, focusing on individuals who identified as Democrats or Republicans. To maintain equal party representation, we randomly dismissed some Democrat participants (see footnote 4). Despite the complex selection procedure, slightly imbalanced samples in certain conditions did not affect our conservative power analysis. In total, 1,945 participants completed the experiment (see Earnings. All the participants earned $0. 3 for completing the initial questionnaire.
Democrats vs. Republicans in the Seed Session Table  2  reports summary statistics about the behavior and the expectations of participants from the Seed session, distinguishing between Democrats and Republicans. We find no statistically significant difference in behavior and expectations between Democrats and Republicans (see last two columns of Table  2 ). This implies that, in the Endogenous Information treatments, we exposed Democrats and Republicans to information that came from comparable populations. In the rest of the analysis, we pool the data of Democrats and Republicans. We refer to social information that comes from peers with the opposite (same) political orientation as out-group (in-group) information. The aggregate figures of Table  2  show that participants cheated both in Part 1 and Part 2.
Overall, our results suggest that normative expectations are stickier, while behavior and   -2029. 965 -2028. 604 -1935. 355 -1935. 028  Notes: Tobit regressions. Standard errors are in parentheses. The regressions reported in Panel A (B) are based on the data from the Emp-Info (Norm-Info) condition. Models (  1 ) and (3) use specification 1, while Models (  2 ) and (  4 ) are based on specification 2. There is one observation per individual. In all Models, the dependent variable is the inappropriateness switching point of a participant. Controls: age, gender, educational attainment, annual pre-tax income, political orientation, left button above the median, yellow button above the median. * * * p < 0. 01. descriptive expectations appear to be more malleable.
* * p < 0. 05, * * * p < 0. 01. Panel A focuses on the data from the Emp-Info condition, while Panel B on the data from the Norm-Info condition. Models (  1 ) and (  3 ) are based on specification 1, while Models (  2 ) and (  4 ) on specification 2. Table  8  confirms the results from the main experiment regarding the effect of normative information on behavior. The coefficients for Avg-Inf o i in Panel B are positive and statistically significant, indicating that participants behaved more dishonestly as their exposure to more lenient information increased. In line with the results of Table  7 , the effect is, however, less pronounced (the size of the coefficient of Avg-Inf o is much smaller in Table  8  than Table  3 ). In contrast to the main experiment, we do not find an augmented effect from information that came from in-group peers (the interaction Avg-Inf o i × In-Group i is not statistically significant).
The nature of the information (in-group vs. out-group) does not affect this result. -3590. 426 -3589. 380 -3467. 860 -3466. 649  Notes: Tobit regressions. Standard errors are in parentheses. The regressions reported in Panel A (B) are based on the data from the Emp-Info (Norm-Info) condition. Models (1) and (3) use specification 1, while Models (2) and (  4 ) are based on specification 2. There is one observation per individual. In all models, the dependent variable is a participant's belief regarding the average winning number reported by others in Part 2. Controls: age, gender, educational attainment, annual pre-tax income, political orientation, left button above the median, yellow button above the median. * * p < 0. 05, * * * p < 0. 01. Next, we consider the effect of information on normative expectations (see Table  10 ).
In stark contrast to the main experiment and in line with the results of Table  7 , we find that the exposure to more lenient information yielded a significant upward correction of one's normative expectations but only when the information was normative (the coefficient of Avg-Inf o i is positive and significant only in Panel B). This effect was the same for ingroup and out-group information, as demonstrated by the insignificant interaction term in Model (4). These results suggest that normative expectations are not always sticky and individuals may still learn from others when forming their expectations. They are sensitive to the leniency of the normative information received. -2893. 101 -2892. 283 -2761. 976 -2761. 581  Notes: Tobit regressions. Standard errors are in parentheses. The regressions reported in Panel A (B) are based on the data from the Emp-Info (Norm-Info) condition.
F Dynamics of Information Acquisition Table  F1  displays the coefficients of random-effects linear probability models. 35  In order to comply with the pre-registered analysis, we also estimate random-effects probit models. The results are reported in Table  F2. While probit models are more appropriate for analyzing binary choices than linear probability models, the interpretation of the results is more difficult in the presence of interaction terms. Hence, we rely on the results from the random-effects linear probability models for our main analysis. In all models, we pool the data from the Control and the Group Identity conditions. The dependent variable is an indicator variable that takes value 1 if an individual consumed above-median information in a given period of Part 2. Model (1) is estimated on the pooled data of Emp-Info and Norm-Info.
Model (  2 ) is estimated on the data from the Emp-Info condition. Model (3) focuses on the data from the Norm-Info condition. All models contain treatment dummies as explanatory variables, using the Control condition as the baseline category and adding two dummies for the Group Identity condition. One dummy indicates whether the in-group information is above-median. The other dummy indicates whether the in-group information is below-median. In all models, we also include a dummy variable equal to 1 if the participant was classified as a cheater (based on their behavior in Part 1), and interaction terms between this dummy and the treatment dummies. In Model (1), we also include a dummy variable for the Emp-Info condition, and interaction terms between this variable and the other treatment variables.
H Effect of Endogenous Social Information on Behavior and Expectations for Cheaters and Non-Cheaters   -1457. 590 -1456. 351 -1356. 529 -1353. 224  Notes: Tobit regressions. Standard errors are in parentheses. The regressions reported in Panel A (B) are based on the data from the Emp-Info (Norm-Info) condition. Models (1) and (3) use specification 1, while Models (2) and (  4 ) are based on specification 2. There is one observation per individual. In all Models, the dependent variable is the proportion of winning numbers reported in Part 2 by a subject. Controls: age, gender, educational attainment, annual pre-tax income, political orientation, left button above the median, yellow button above the median. * p < 0. 10, * * * p < 0. 01. -1569. 824 -1563. 340 -1471. 487 -1471. 136  Notes: Tobit regressions.
Standard errors are in parentheses. The regressions reported in Panel A (B) are based on the data from the Emp-Info (Norm-Info) condition. Models (1) and (3) use specification 1, while Models (2) and (  4 ) are based on specification 2. There is one observation per individual. In all Models, the dependent variable is the belief of a subject on the average number of winning numbers reported by others in Part 2. Controls: age, gender, educational attainment, annual pre-tax income, political orientation, left button above the median, yellow button above the median. * p < 0. 10, * * * p < 0. 01. -3937. 369 -3744. 859 -4063. 629 -3904. 802 -3487. 195 -3296. 549  Notes: Tobit regressions. Standard errors are in parentheses. One observation per individual. Controls: age, gender, educational attainment, annual pre-tax income, political orientation, left button above the median, yellow button above the median.
* * p < 0. 05, * * * p < 0. 01. -4838. 589 -4663. 793 -5113. 138 -4982. 330 -4241. 931 -4081. 202  Notes: Tobit regressions. Standard errors are in parentheses. One observation per individual. Controls: age, gender, educational attainment, annual pre-tax income, political orientation, left button above the median, yellow button above the median. * * p < 0. 05, * * * p < 0. 01. -1788. 816 -1785. 512 -1682. 267 -1682. 190  Notes: Tobit regressions. Standard errors are in parentheses. The regressions reported in Panel A (B) are based on the data from the Emp-Info (Norm-Info) condition. Models (1) and (3) use specification 1, while Models (2) and (  4 ) are based on specification 2. There is one observation per individual. In all Models, the dependent variable is the belief of a subject on the average number of winning numbers reported by others in Part 2.
Research assistance from  P. Sternal ,  A. Thampi , and  Q. Thevenet  is gratefully acknowledged. This research has benefited from the support of the  INDEPTH program  of  Université de Lyon  within the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ), the project  DECISION  of the  French National Research Agency  ( ANR-19-CE26-0019 ), the  Social and Cultural Evolution Working Group Research Grant , and the  German Research Foundation (DFG)  under Germany's  Excellence Strategy   -EXC 2126/1-390838866. It was performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program  Investissements d'Avenir  ( ANR-11-IDEX-007 ) operated by the  French National Research Agency.
Clinical diagnosis of CNC can be made using the diagnostic criteria and supplemental criteria for CNC summarised by Stratakis et al. 3. Carney complex is caused by a pathogenic variant of the gene encoding the 1A regulatory subunit of protein kinase A (PRKAR1A, OMIM accession number: 188830) 4. Disease-causing mutations can be detected at any site of the gene but most of them map to exonic and splice sites 5. Importantly, there is no mutation hotspot, almost every family has its own pathogenic variant. Significant genotype-phenotype associations have not been observed, however some studies reported that certain mutations in PRKAR1A gene or sequence variants in other genes (i. e. PDE11A) may lead to the development of adrenal and/or testicular tumours  6, 7. Clinical data and genetic results of the largest CNC patient cohort (n = 353) to date revealed some important and significant observations.
Bioinformatical analysis of WES and WGS data Sequencing data processing was performed following the Genome Analysis Toolkit (GATK) best practices guidelines as follows: paired-end sequencing data were obtained in FASTQ file format and reads were trimmed using cutadapt to remove adapters and bases where the PHRED quality value was less than 20  15. The trimmed reads were aligned to Genome Reference Consortium Human Build hg19 using Burrows-Wheeler Aligner (bwa-mem2-2. 0)  16. Picard tools were used to sort, MarkDuplicates and index reads  17. Base Quality Score Recalibration (BQSR) was performed using GATK  9. Variant discovery was performed in two steps: (1) single-sample variant calling was performed using HaplotypeCaller in GATK, (2) followed by Geno-typeGVCFs to combine variants from single-sample gVCFs to the multisample VCF.
Funding Open access funding provided by  National Institute of Oncology. This work was supported by grants from the  Hungarian National Research, Development and Innovation Office  -project  TKP2021-EGA/TKP2021-NVA/TKP2021-NKTA  (MOLORKIV-A. P. ),  National Tumorbiology Laboratory  (A. B, H. B, I. L. , J. P. A. P. ), grant  FK_131916  (M. M. ),  EFOP-3. 6. 3-VEKOP-16-2017-00009  (S. P. ), and the  ÚNKP-20-3-I-SE-24  (S. P. ). H. B. is a recipient of  Bolyai Research Fellowship  of the  Hungarian Academy of Sciences  and  ÚNKP-22-5-SE-1. A. P. , J. T. and J. B. clinical departments are part the European Reference Network on Rare Endocrine Conditions (Endo-ERN) -Project ID No  739572 , M. M. clinical department is Reference Centre on the European Reference Network for rare skin diseases (ERN-Skin).
Determining the total OMD and not the macroinvertebrate-specific OMD resulted in a high remaining number of replicates with positive values and thus enabled a direct comparison between the total OMD of leaves and decotabs. Because of the loss of bags, drying of streams, lack of reliable temperature data, or complete decomposition of the substrate on retrieval, we present data from 64 and 66 out of 70 sites for microbial and total leaf decomposition, respectively.
When calculating the sumTU for fungi (hereafter sumTU fungi ), we selected the respective lowest EC50 value of either the most sensitive freshwater invertebrate or algae because EC50 values for fungi are lacking and we anticipated that fungi react more sensitively to fungicides than invertebrates or algae  (Ittner et al. , 2018). For the sumTU for invertebrates (hereafter sumTU inv ), we selected the respective lowest EC50 value of the most sensitive freshwater invertebrate, which proved to be the most responsive measure in a comparative analysis  (Schäfer et al. , 2013). The EC50 values were collected via the R-package Standartox  (Scharmüller et al. , 2020)  and complemented by values from  Lewis et al. (2016). In addition to tri-weekly measurements, temperature (continuous measurements), oxygen concentrations, and conductivity were constantly measured using multiparameter probes (LogTrans7-compact measuring system SENSOdive CTDO2, UIT equipped with O 2 -Log3055-INT and CTD3100-10 loggers; Driesen + Kern).
Data analysis To compare the microbial decomposition of the three substrates (i. e. , leaves, decotabs, and cotton strips) and the total OMD of two substrates (i. e. , leaves and decotabs), we conducted three and one Pearson's correlations, respectively. The inter-replicate variability (%) was calculated as the variability in OMD of one set of replicates divided by its mean. For identifying the environmental variables most influential for the decomposition, we used average values for the environmental variables measured consecutively during the respective deployment periods. Before calculating the averages, all nutrient and trace element concentrations below the limit of quantification (LOQ) were set to half of the LOQ, while values above the qualification range were set to twice the potential highest value.
Using the package "stabs"  (Hofner & Hothorn, 2021) , we selected a cutoff value of 0. 7 (i. e. , a variable was selected in 70% of the fitted models) and a per-family error rate of 1 (i. e. , a maximum of one variable is tolerated to be falsely selected per model). Thus, variables that were selected with a higher frequency can be considered more relevant. Before running the analysis, the explanatory variables were standardized to an average of 0 and a standard deviation of 1 to remove the effect of different ranges and magnitudes. We displayed the relationship of the substrates' decomposition to the respective selected environmental variables using marginal effects plots  (Lüdecke, 2018). Data preparation, statistical analysis, and visualizations were conducted in R (Ver 4. 2. 1; R Core Team, 2022) using additional packages including "ggplot2"  (Wickham, 2016;  full list of all packages and their versions is given in the R code).
Acknowledgments-The project and  Verena C. Schreiner  were funded by the  Deutsche Forschungsgemeinschaft  (DFG-Project number  216374258 ). Furthermore, this project was part of the pilot study on the monitoring of small streams (Kleingewässermonitoring) implemented by the  UFZ-Helmholtz-Centre for Environmental Research  and the  German Federal Ministry for the Environment, Nature Conservation and Nuclear.
| Datasets Dietary data and parasite infection datasets used in this study were collected from turtle doves from three countries, across both wintering and breeding grounds (Appendix 1: Figure  A1 ) as part of a wider study  (Young, 2022). A wintering population of turtle doves was sampled using mist nets at the Beer Sheba Project  (Beer Sheba Project, 2002) , an agricultural resource center encompassing a 100 ha Acacia woodland near Sandiara (14°22′ N, 16°48′ W), Senegal between November and March over four  winters (2014/2015-2017/2018). Birds were caught at two different European breeding locations, in western Europe (France) and eastern Europe (Hungary) in order to sample geographically distinct populations using different migratory flyways  (Marx et al. , 2016) , early in the breeding season.
Each plate of MID-tagged PCR product was combined into a single pool of uniquely identifiable samples, with approximately equal concentration per sample. Pools were cleaned using SPRIselect beads (Beckman Coulter) following the manufacturer's instructions, and the library preparation for Illumina sequencing was carried out on this pool using NEXTflex Rapid DNA-seq Kit (Bioo Scientific), following the manufacturer's protocols. The library was sequenced using a V2 chip, with 2 × 250 bp paired-end reads on an Illumina MiSeq sequencer (Illumina). Bioinformatic analyses were carried out using a custom pipeline designed for analysis of diet metabarcoding data from Illumina MiSeq  (Drake et al. , 2021). Full description of bioinformatics analysis is given in Appendix 3. An additional dataset, collected for another study in the UK, was analyzed in order to incorporate the strain Type A, a high-virulence strain which was detected in the previous study in the UK, but did not occur in the samples collected in this study  (Dunn et al.
, 2018; Thomas et al. , 2022). Data were collected in the UK from 12 farmland sites across Essex, Suffolk, Cambridgeshire, and Norfolk in 2011-2014. Sampling and DNA extraction were conducted following the same protocols used in sample collection from Senegal, France, and Hungary, and full details of sampling are given in  Dunn et al. (2018). Due to the application of different bioinformatic processing pipelines and data cleaning in the two datasets, UK data were analyzed separately from data collected on the breeding grounds in this study.
To assess the relationship between diet and infection status, detrended correspondence analysis (DCA) was first carried out to summarize dietary data, based on the presence or absence of dietary taxa within each bird sampled, to capture the source of dietary variation in a small number of variables, to be included in a generalize linear model (GLM). Analysis was carried out using the "decorana" function in the R package vegan  (Oksanen, 2020)  and visualized using functions from the ggplot2 package  (Wickham, 2016). A high proportion of dietary items occur in a very low number of samples, which may mask co-occurrence patterns of plant taxa in dietary samples; therefore, DCA was conducted on a subset of data, containing only frequently consumed taxa. Four different thresholds representing how frequently items occurred in the diet were tested (taxa occurring in the diet of at least 3%, 5%, 7%, and 10% of birds sampled) to determine the most appropriate threshold for defining "frequently occurring taxa" (Appendix 4).
| Dietary overlap Dietary overlap between infected and uninfected birds, and birds infected with different strains of T. gallinae, was visualized using nonmetric multidimensional scaling (NMDS). For the purpose of NMDS analysis, genera occurring in a single dietary sample were removed, as this analysis is liable to outliers skewing the results. The command "metaMDS" from the vegan package  (Oksanen, 2020)  was used to estimate differences between the presence of genera in individual samples, using Jaccard distance. "Ordispider" was used to visualize results from nMDS via plotting with ggplot2. Pianka's measure of overlap  (Pianka, 1986)  was used to assess the extent to which diet overlapped for uninfected and infected birds, and in birds infected with different strains. This measure is given as a value from 0, indicating no dietary overlap, to 1, indicating complete dietary overlap.
Two matrices were created, including plant genera present in the diet, against either infection status (presence or absence of T. gallinae infection) or the presence or absence of each T. gallinae strain, recording counts of the number of times each genus was present in the diet of infected/uninfected birds, or birds infected with a specific strain present. Due to the segregation of dietary items between Europe and Africa, Pianka's overlap statistic was calculated for the breeding and wintering grounds separately. Average pairwise niche overlap was calculated using the "niche_null_model" command in the R package EcoSimR  (Gotelli et al. , 2015) ) with 9999 repeats. Pairwise overlap was also assessed between specific strains. Pianka's niche overlap was repeated as above in individual sampling seasons to assess whether patterns were consistent across years (Senegal) and between countries (Europe).
| Dietary variation and infection status A total of 190 birds sampled from the breeding and wintering grounds, from which diet and T. gallinae infection status data were successfully obtained were assessed to investigate the relationship between diet and parasite infection (Table  1 ). The overall prevalence of T. galliane infection across the dataset was 70%, but varied from 49% to 100% between seasons (Table  1 ). Five strains of T. gallinae were very rare within this study, occurring in just one (Tcl-TD, 26374943, 0, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1002/edn3. 402 by Cardiff University, Wiley Online Library on [05/04/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License GEO-TD3, Type IIIc-TD, ESWD), two (GEO-TD2), three (Ttl-TD), or four birds (GEO-TD), reflecting 0.