dataesr/scientific-paragraphs-categorization huggingface dataset:
H1b Concern over the coronavirus will differ between countries See descriptions provided in H1a and §5. We performed MLM to examine the international differences. DV: Concern over the coronavirus ([Corona_concerns]) IV: Country ([country]) (as random and fixed effects) We compared three models as proposed above. Once we found that the fixed effect of the country was statistically significant, we then performed post hoc analysis with Scheffe's method for international comparison. In addition, we performed the same Bayesian MLM with the brms R package (see descriptions provided in H1a). See descriptions provided in H1a. We applied the same criteria to examine whether the effect of the country is non-zero (H1b We compared three models as proposed above. Once we found that the fixed effect of the country was statistically significant, we then performed post hoc analysis with Scheffe's method for international comparison.
H1d Compliance with behavioural guidelines to slow the spread of coronavirus will differ between countries See descriptions provided in H1a and §5. We performed mixed MLM to examine the international differences. DV: Compliance with behavioural guidelines to slow the spread of coronavirus ([Compliance]) IV: Country ([country]) (as random and fixed effects) We compared three models as proposed above. Once we found that the fixed effect of the country was statistically significant, we then performed post hoc analysis with Scheffe's method for international comparison. In addition, we performed the same Bayesian MLM with the brms R package (see descriptions provided in H1a). See descriptions provided in H1a. We applied the same criteria to examine whether the effect of the country is non-zero (H1d We included [country] as a random intercept in the model.
H3a Across countries, trust in government efforts to slow the spread of coronavirus will be negatively correlated with perceived stress See descriptions provided in H1a and §5. We examined a MLM without interaction effects to test the relationship between trust in government efforts to slow the spread of coronavirus and perceived stress. We included [country] as a random intercept in the model. In addition, we performed the same Bayesian MLM with the brms R package (see descriptions provided in H2a). See descriptions provided in H2a. We applied the same criteria to examine whether the effect of trust is non-zero (H3a We included [country] as a random intercept in the model. In addition, we performed the same Bayesian MLM with the brms R package (see descriptions provided in H2a). See descriptions provided in H2a.
H4 Across countries, concern over the coronavirus will be a predictor of perceived stress See descriptions provided in H1a and §5. We examined a MLM without interaction effects to test the relationship between concern over coronavirus and stress. DV We included [country] as a random intercept and a random slope of perceived stress in the model. The planned multilevel models were run step by step to make sure that each random effect is added by order (i. e. country random intercept, the random slope of perceived stress), so that the model reaches convergence. We kept a more parsimonious model and dropped the random effect in the case of non-convergence. In addition, we performed the same Bayesian MLM with the brms R package (see descriptions provided in H2a). We added the aforementioned interaction effects in the brms model.
To Prior to submitting the RR stage 1, we had not accessed or analysed any data, except following response rates to give all COVIDiSTRESS participant researchers feedback on the recruiting progress. This RR was submitted under the COVID-19 rapid response call for papers in Spring 2020. Upon acceptance of the stage 1 protocol, we analysed the newest weekly data extraction that fit minimum criteria (see above). The full dataset collected is available to any other researcher for further exploration or hypotheses testing via the Open Science Framework. The version of the COVIDiSTRESS dataset analysed here was extracted on 30 May 2020. A total of 173 429 responses were recorded and cleaned using the R-code recommended by the COVIDiSTRESS team (available at https://osf. io/z39us/files/). The cleaned dataset consisted of 125 306 responses.
27%) participants with a p < 0. 001 in the chi-square test were excluded. After these exclusions, the dataset consisted of 116 356 participants (see electronic supplementary material, table  S1 ). In the second step, following our pre-registered quality check plan, we investigated floor and ceiling effects for our independent variables, testing how many participants' self-reported compliance with preventive guidelines as well as composite scores of perceived stress (PSS-10), available social provision (SPS) and concern over coronavirus reached the minimum or maximum value. If more than 15% of respondents reached either the maximum or minimum value for a measure, the ceiling/floor effect was considered present. For SPS, a ceiling effect was detected for two countries: Brazil (N = 81, 16. 0%) and Panama (N = 78, 16.
Thus, we proceeded with the alignment method (see OSF for the results of measurement invariance tests and alignments; links: https://osf. io/qp96b/ and https://osf. io/hu683/). The three measures examined were the corona concern scale, the PSS and the SPS (table 1). We followed four main analytical steps in Mplus for measurement testing. First, we conducted an exploratory factor analysis using maximum-likelihood estimation and geomin rotation for each scale to confirm their dimensionality. One-factor structures were confirmed for all three scales. Second, we examined the extent to which the one-factor model was well-fitting across the entire sample using a pooled dataset. This was achieved by adjusting variables so that cases from each country have equal weighing on the covariance matrix. Third, we conducted a multi-group confirmatory factor analysis testing a configural invariance model to examine the measurement invariance of the one-factor model across countries.
Fourth, we tested measurement invariance more properly with the alignment optimization method (see detailed description below). Finally, we conducted Monte Carlo simulations to provide a validation check of the alignment results, indicating whether the rank order of latent factors across countries is trustworthy. The latent factor scores for each scale obtained from the alignment analyses were combined to be used in the key hypotheses testing analyses. We examined data from 104 332 participants from 48 countries. Sample size ranged from 109 (New Zealand) to 20 165 (Finland), with a mean of 2173 and median of 921 participants per participating country. Based on the results reported in detail below, we deemed that measurement invariance of the three measures was achieved. In brief, the results provided strong royalsocietypublishing.
org/journal/rsos R. Soc. Open Sci. 8: 200589 evidence for the one-factor structure of all three measures, that this unifactorial factor structure was confirmed across all 48 countries, that participants from all countries responded to the items relatively similarly, and the Monte Carlo simulations confirmed the trustworthiness of the latent factor scores obtained. The final dataset consisted of 116 356 participants from 48 countries with the mean age of 39. 4 (s. d. = 14. 1), 84 942 (73. 00%) of participants identified as female, 29 869 (25. 51%) as male, and 1283 (1. 10%) provided other responses or declined to respond. For detailed demographic statistics, see electronic supplementary material, (electronic supplementary material, table  S1 ). For the multilevel models (H1a to H5), we dummy coded individual-level categorical variables (participant gender and education level) and centred the continuous variables ( predictors and control variables) on grand means (see electronic supplementary material, table S1 to S4 with descriptive statistics).
Because the ICCs showed that a considerable amount of variability of outcome variables was accounted for by between-country difference (see the Results section), country-mean variables for individual-level predictors were created by aggregating individual scores at the country level and centred on the grand means in order to separate variance of country part from the variance of the individual part (see  [56] ). Country-level covariates ( population size, GDP per capita, Gini coefficient and educational attainment) were acquired from the World Bank World Development Indicators database  [92]. We used PPP-adjusted GDP per capita, which represents each country's level of economic activities controlling for purchasing power (i. e. the differences in price levels between countries; Eurostat). Gini coefficient measures the extent to which the distribution of income among individuals in a given country deviates from a perfectly equal distribution (ranging from 0 to 100-0 represents absolute equality, whereas 100 represents absolute inequality; [92]).
We used the 2020 Oxford Government Response Stringency Index to quantify the degree to which each government's preventive measure was strict (see https://ourworldindata. org/grapher/covid-stringencyindex). We extracted the index data dated Monday, 6 April 2020. Then, we performed one additional MLM and BMLM after modifying the model for H3b. For this exploratory analysis, we added the interaction effect of the individual-level trust in government efforts × country-level stringency index, and the country-level stringency index as a covariate to the initial model. We examined whether the interaction effect was statistically significant in predicting concern over the coronavirus based on the resultant p-value and BF10 by applying the same interpretation criteria (table  2 ). The result of the post hoc analysis demonstrated that there was a statistically significant interaction effect of the individual-level trust in government efforts × country-level stringency index on concern over the coronavirus.
However, our analyses also contribute royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 8: 200589 further, by providing a detailed cross-country comparison for relationships between early psychological and behavioural responses. While the full breadth of between-country differences is too numerous to discuss, interested readers can study these comparisons in figures 1-4. Specific country-level information can also be found in the electronic supplementary material and online datasets (https:// osf. io/z39us/ and  [59] ). Fourth, the results showed that the more trust people had in government efforts to slow the spread of coronavirus, the less stress they experienced. Again, cross-country differences could be rooted in a number of factors ranging from differences in local cultures and political history, to variations in behavioural guidelines, government intervention stringency and the impact of COVID-19 at the time.
Rather than looking at this phenomenon as a function of individual stress levels, this buffering mechanism could come into play when groups oppose government restrictions for political or practical reasons, or when younger adults converge on the need to retain their social life in the face of pandemic curfews. A notable limitation of the present study was the sampling method. While other studies (e. g. [3] ) have collected survey data using paid representative respondent pools, the goal of the COVIDiSTRESS collaboration was to collect data as quickly and conveniently as possible, with researchers in each country deciding their own recruitment strategies. As such, although spanning across a large number of countries, our sample is not representative of the various national populations. For instance, the COVIDiSTRESS dataset has more female respondents, and, presumably due to the use of online media for recruitment, a younger demographic.
Practical The present analysis of the COVIDiSTRESS dataset contributes to an understanding of the complexities at play in early psychological effects of the COVID-19 pandemic. Our findings lead us to suggest several implications for practitioners such as health communicators, government officials and policy makers. Foremost, we find that although there are general patterns, between-countries differences in reactions to the crisis and compliance with behavioural guidelines show that interventions should be context sensitive. The actions already taken by governments, as well as the stress and stressors pervading a society, can at any given time lead to very different reactions to the same disease, the same restrictions, or the same recommendations from health or government authorities. Lowering stress in the population may be needed to create optimal conditions for compliance with behavioural guidelines.
Further, in order to successfully communicate and implement protective behavioural measures, it appears important to base messages and channels on whether individuals in a target audience are oriented towards the measures in society more broadly, or the personal support of social groups, where the latter may reduce stress and compliance both (i. e. [103] ). Even though general trends can be detected across the globe, we found notable between-countries variations in all our analyses. With a dataset comparing so many countries, it is infeasible to unpack all of these specific differences in the text properly. Instead, these must be considered individually though scrutiny of the graphs and electronic supplementary material, and possibly explained by future dives into the large open data pool combined with other sources of information.
We have included the stringency of intervention in the post hoc work of the present pre-registered analysis, but many more such variables must be considered in the future. Finally, we believe that it is important to discuss the broad scientific climate of which the COVIDiSTRESS global survey is only a small part. As the global pandemic gained speed, researchers from a plethora of fields felt compelled to send out a barrage of surveys on issues ranging from conspiracy theories about the disease to experiences of parenting and working/learning from home. Indeed, multiple repositories and consortia also cropped up to support collaboration and avoid repetitive research questions in favour of a coordinated global undertaking. While these many research initiatives are bound to vary in quality, and contents will overlap, the period was an amazing example of the scientific community coming together to address a global crisis, and it is likely that the coronavirus pandemic and the lockdowns that ensued will be the most well-studied social event in human history from a perspective of real-time data collection.
6 mg/cc) [ 8 ], thus proving that is possible to velocity of V shock f ront ∼ 39 km/s. To complement the experimental results, preliminary numerical simulations of the experiments are presented in Fig. 5  for single shock at 20 ns. The simulations were performed with the 2-D radiative hydrodynamics code ARWEN [ 19 , 20 ]. A full comparison with the experimental data requires further testing of these simulations with several initial conditions, thus this single output is used to infer characteristic plasma conditions and make first estimates and further work will look at presenting a detailed simulation study. These first simulations were obtained using the initial conditions in the experiments, i. e. an initial argon pressure of P 0 = 1 bar (ρ 0 = 1. 67 mg/cc), a laser drive energy of E laser = 962 J, a duration of t laser = 1 ns and a focal spot of diameter φ laser = 370 µm.
ACKNOWLEDGMENTS This research was supported by  The Royal Society (UK)  through a  University Research Fellowship  ( URF-R-180032 ), a  Research Fellows Enhancement Award  ( RGF-EA-180240 ), an  International Exchanges grant  ( IES-R3-170140 ), and a  Research Grant  ( RG2017-R2 ). The authors would like to thank the operation group of the SG-II laser facility. C. S. acknowledges support from the  French INSU-PNPS program. U. C. acknowledges support by the project  Advanced Research  ( CZ. 02. 1. 01/0. 0/0. 0/16019/0000789 ) from  European Regional Development Fund  (ADONIS). F. S-V. acknowledges the technical support from  Paul Brown ,  Alan Finch  and the entire team at the  Physics Mechanical Instrumentation Workshop at Imperial College London.
Late in 2020, two genetically-distinct clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mutations of biological concern were reported, one in the United Kingdom and one in South Africa. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B. 1. 1. 7 and B. 1. 351 (variant 501Y-V2). We account for potential biases in genomic surveillance efforts by including passenger volumes from location of where the lineage was first reported, London and South Africa respectively. Using the software tool grinch (global report investigating novel coronavirus haplotypes), we track the international spread of lineages of concern with automated daily reports, Further, we have built a custom tracking website (cov-lineages.
Methods To better characterise the international distribution of lineages B. 1. 1. 7 and B. 1. 351 we collated SARS-CoV-2 sequences from GISAID  8, 9  and assigned lineages using pangolin (v2. 1. 6, https://github. com/cov-lineages/pangolin), which implements the nomenclature scheme described in Rambaut et al. ,  10. Genomes are assigned lineage B. 1. 1. 7 if they exhibit at least 5 of the 17 mutations inferred to have arisen on the phylogenetic branch immediately ancestral to the cluster (Table  1 ) 2 ; or to B. 1. 351 if they exhibit at least 5 of 9 lineage-associated mutations (Table  1 )  5. Lineage count and frequency data have been calculated daily using grinch. Using Air Transport Association (IATA) travel data from October 2020, available through bluedot. global, we aggregated and collated the passenger volumes from international airports in London and South Africa to international destinations on same booking.
Destinations with more than 5,000 passengers from London and more than 300 passengers from South Africa during the month of October are displayed on the cov-lineages. org website and in the underlying data for this publication  11. grinch, with custom python modules that make use of geopandas v0. 9, matplotlib v3. 2 and seaborn v0. 10, combines this information and produces reports with descriptive tables and figures that can be found at https://cov-lineages. org/global_report. html.
Implementation All of the code underlying this daily lineage tracking web-report can be found at GitHub and Zenodo  12. grinch is a python-based tool, the analysis pipeline of which is built on a snakemake backbone  13. Every 24 hours a scheduled cron  14  task runs on our local servers. We download the latest data from GISAID and deduplicate based on sequence names. The sequences are assigned their most likely lineage using pangolin's latest version and model files. All processed metadata is available and maintained on the cov-lineages. org GitHub repository. To run grinch, the user must have access to a GISAID direct download key and a password and provide these within a configuration file for use. The command used to run grinch is grinch -i grinch_config. yaml, using the config file provided at doi:10.
To explore these factors, we analysed the most recent available International Air Transport Association (IATA) travel data (October 2020). We collated the total number of origin-to-destination air journeys between major London international airports and each country. The calculation was repeated for journeys originating in all international South African airports. We focussed on London and South Africa as they are the locations with the first reports and highest reported prevalence of lineages  B. 1. 1. 7 and B. 1. 351 respectively  2, 5. However, due to low SARS-CoV-2 genomic surveillance in many locations, we cannot reject the hypotheses that these lineages initially originated elsewhere. Figure  1d  shows destinations receiving >5,000 travellers in October 2020 from the UK (Figure  2  shows destinations receiving >300 travellers from South Africa).
Of the countries that receive >5,000 travellers from London, 16 have sequenced B. 1. 1. 7. Of the 45 countries that have identified B. 1. 1. 7 (32 in travellers and 13 with local onward transmission), only 6 perform real-time routine genomic surveillance (Denmark, UK, Iceland, The Netherlands, Australia, Sweden), 3 have prioritised sequencing based on S-gene target failure tests  16  , 30 primarily targeted sequencing towards arriving travellers from the UK, and there was no information available for 10 (details at https://github. com/cov-lineages/ lineages-website/blob/master/_data/). Of the 13 countries that have identified B. 1. 351 (four with local onward transmission including South Africa), 4 perform routine sequencing (South Africa, UK, Botswana, Australia), 6 target sequencing of travellers, and there was no information available for 3.
Rob Lanfear Department of Ecology and Evolution, Research School of Biology, Australian National University, Canberra, ACT, Australia This article describes a software tool, grinch, that can be used to produce automated reports on SARS-CoV-2 lineages. The authors apply it to two lineages of concern in the article, and also highlight that the main utility of grinch is not in static one-off reports, but in regularly updated reports available at https://cov-lineages. org/global_report. html. The paper clearly describes the software and demonstrates its utility. I'd like to commend the authors for putting this tool and the associated website together so quickly, for maintaining both to a very high standard, for making sure that all of the work is open and reproducible, and for the huge amount of work and enormous collaborative effort that has gone into this clear and concise report.
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: I am a paid consultant to GISAID, the database on which much of the data analysed in this article is hosted. Reviewer Expertise: Phylogenetics, molecular evolution, bioinformatics. I have a passing familiarity with SARS-CoV-2 data analysis. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
Acknowledgements An earlier version of this article can be found on Virological (url: https://virological. org/t/tracking-the-international-spread-ofsars-cov-2-lineages-b-1-1-7-and-b-1-351-501y-v2/592). We thank  Norelle Sherry ,  Benjamin Howden  and  Michelle Sait  for their contribution to sequencing in Australia. We also include full acknowledgements and details of group authorships at https://doi. org/10. 5281/zenodo. 4704471 19. We would also like to extend our gratitude to everyone involved in the global sequencing effort.
Description of the Inputlog copy task The copy task presented here was developed as a lean instrument to measure and assess typing skills in a series of well-defined transcription conditions within a limited time. The copy task at hand is a combination of components that consist of a short sentence, three-word combinations and letter clusters. The task is developed for online use and is freely available for researchers as part of Inputlog, a keystroke logging program used in writing and translation studies  (Leijten & Van Waes, 2013 ; Inputlog is freely available for researchers at www. inputlog. net; see Appendix A for more details). The copy task was first developed in Dutch  (Van Waes et al. , 2017) , then further developed in collaborations within the European Literacy Network, a European Commission COST action.