dataesr/scientific-paragraphs-categorization huggingface dataset:
The 2018 reviews were led by the VIMC management National Institutes of Health group. These reviews have been repeated annually against the same standards, but with a move R01 GM124280 Benjamin A Lopman National Institutes of Health towards light-touch peer reviews. R01 AI112970 Benjamin A Lopman 2. 2 Model descriptions Hepatitis B -Centre for Disease Analysis Foundation Funding Funder Grant reference number Author Bill and Melinda Gates Foun- OPP1157270 / INV-009125 Jaspreet Toor dation Susy Echeverria-Londono Xiang Li Kaja Abbas Emily D Carter Hannah E Clapham Andrew Clark Database and Author(s) Year Dataset title Margaret J de Villiers Dataset URL Identifier Toor J, Echeverria-Londono S, Xiang Trotter CL, Truelove Tran Minh Q, S, Tam Y, Tanvir H, Sanderson C, Sweet Shearer D, Resch S, A, Razavi H, Razavi-Perkins TA, Portnoy Papadopoulos T, Nayagam S, CJE, Moore SM, Mengistu T, Metcalf Lopman BA, Lessler J, Li X, Klepac P, Kraay A, Karachaliou A, ML, Jean K, Jit M, Huber JH, Jackson WR, Hogan D, Hallett TB, Hinsley Gamkrelidze I, Ferrari M, MJ, Eilertson K, Clark A, De Villiers ED, Clapham H, L, Abbas K, Carter Kirsten Eilertson https://montagu.
Notes 1 For Europe, see data from Eurostat, which use a baseline of monthly averages 2016-19: https://ec. europa. eu/eurostat/web/products-eurostat-news/-/ddn-20210216-2?redirect=%2Feurostat%2Fweb%2Fcovid-19%2Fpopulation-health (accessed 4 March 2021). A global assessment of excess mortality, based on data from Human Mortality Database and World Mortality Dataset with a baseline of monthly averages 2015-19 can be found at https://ourworldindata. org/excessmortality-covid (accessed 4 March 2021). 2 E. g. the UK in April 2020 and Mexico in July 2020. Spain reached 156% excess mortality in April 2020 (according to the latter database, but only ca. 80% according to Eurostat).
Notes 1 For Europe, see data from Eurostat, which use a baseline of monthly averages 2016-19: https://ec. europa. eu/eurostat/web/products-eurostat-news/-/ddn-20210216-2?redirect=%2Feurostat%2Fweb%2Fcovid-19%2Fpopulation-health (accessed 4 March 2021). A global assessment of excess mortality, based on data from Human Mortality Database and World Mortality Dataset with a baseline of monthly averages 2015-19 can be found at https://ourworldindata. org/excessmortality-covid (accessed 4 March 2021). 2 E. g. the UK in April 2020 and Mexico in July 2020. Spain reached 156% excess mortality in April 2020 (according to the latter database, but only ca. 80% according to Eurostat). Human Remains and Violence 7/2 (2021),4-18.
3%. The Latin American Mortality Database (LAMBdA) team estimated that relative completeness (the completeness of death registration relative to population counts) was 86% for the intercensal interval 1950-1960 but had improved to 96% by  2000 -2010 (Beltrán-Sánchez et al. 2020 ; see Table  3. 4). Other countries in the region suffer from similar data quality problems. Consequently, caution is warranted when comparing mortality trends over time for Mexico and other countries because the accuracy of the estimates is unclear. In this paper, we restrict our analysis of the Mexican mortality data to the period since 1990 because although there are notable data quality issues after 1990, the available evidence suggests that data quality was even worse prior to 1990. We aim to address the following research questions: (1) Do unadjusted mortality estimates for 1990-2016 (based on vital statistics and census counts) display unusual patterns that may reflect data quality problems? (2) How do those unadjusted estimates compare with other published estimates (which may be based on adjusted data)? (3) How does Mexican (unadjusted) mortality compare with the historical mortality patterns observed among the 41 populations included in the Human Mortality Database (HMD)? (4) How much do various adjustments affect estimates of life expectancy for Mexico?.
Data and methods Unadjusted life tables for Mexico are computed following the HMD methods protocol  (Wilmoth et al. 2019) , using official vital statistics (Instituto Nacional de Estadística y Geografía (INEGI) 2018b, 2018c) and census counts (Instituto Nacional de Estadística y Geografía (INEGI) 2018a) as published. 6 Following the HMD protocol, birth counts by sex are used mainly for estimating the relative size of individual cohorts. Death counts by sex and detailed age are used for the numerator of the age-specific mortality rates. Deaths, births, and census countsalso by sex and detailed ageare used to derive intercensal population estimates below age 80 and for cohorts aged 80-90 at the end of the observation. Above age 80, population estimates are derived using the extinct cohort method for all cohorts that are extinct and by the survival ratio method for nonextinct cohorts older than 90 years at the end of the observation period.
More importantly, we estimate age-specific mortality rates and compute life tables for every year between 1990 and 2016. 7 There are alternative methods for evaluating the plausibility of data at the oldest ages. For example,  Jdanov et al. (2008)  recommend computing the ratio of T 100 /T 80 and the ratio of deaths at ages 105+ to deaths at ages 100+. These indicators cannot confirm that the data are wrong; they can only indicate that the data appear unusual compared with the patterns observed in other countries. As shown in Figure  S1 , the ratio of deaths at ages 105+ to deaths at ages 100+ is much higher in Mexico than in Sweden or Chile, the only Latin American country in the HMD. Others have described methods for estimating the expected number of centenarians in a Finally, we use death distribution methods (DDM) to estimate the completeness of death registration relative to population counts.
Adjusting child mortality (below age 5) We adjust mortality below age 5 by substituting the estimated death counts at ages 0 and 1-4 from the UN Inter-agency Group for Child Mortality Estimation (IGME, www. childmortality. org) in place of the registered death counts below age 5. These estimates are carefully prepared by highly qualified experts who have access to a massive database maintained at the United Nations. To derive these estimates, IGME begins by compiling all existing nationally representative estimates of child mortality, including data from vital registration, population censuses, household surveys, and sample registration systems (United Nations Inter-agency Group for Child Mortality Estimation 2018b). Then data quality is assessed and data sources that are deemed to have substantial non-sampling errors or omissions are excluded from the analysis (United Nations Inter-agency Group for Child Mortality Estimation 2018b).
In the case of Mexico, IGME used the following data to derive estimates of mortality below age 5: (1) vital registration data for 2005-2017; (2) direct estimates based on full birth histories from the 1976 World Fertility Survey (WFS), the 1987 Demographic and Health Survey (DHS), and the 1992, 1997, and 2014 Encuesta Nacional de la Dinámica Demográfica (ENADID); and (3) indirect estimates based on summary birth histories from the 1979 Contraceptive Prevalence Survey, censuses  (1980, 1990, 2000, 2005, 2010) , the 2006 ENADID, and the 2015 Encuesta Intercensal. The following data sources were excluded from the dataset used to fit the model because they were deemed to have substantial non-sampling errors or omissions (note that IGME did not rely on summary birth histories if full birth histories from the same survey were available): vital registration data for earlier years , direct estimates based on full birth histories from the 2009 ENADID, and indirect estimates based on summary birth histories from the 1976  WFS, 1987 DHS, and 1992 , 2006 , and 2014 ENADID.
To estimate the probability of dying between birth and age 5 (expressed per 1,000 live births), IGME fitted a Bayesian B-splines bias-adjusted (B3) model with global smoothing to the dataset for Mexico (United Nations Inter-agency Group for Child Mortality Estimation 2018a). Infant (age < 1) mortality (per 1,000 live births) was estimated for Mexico based on the Coale-Demeny West model life table with no global smoothing (United Nations Inter-agency Group for Child Mortality Estimation 2018a). Sex-specific trends in child mortality were not estimated because fewer data sources are available by sex. Instead, IGME estimated the time trend in the sex ratio of child mortality. The final death counts were derived by applying the estimated mortality rates to the live birth counts from World Population Prospects (United Nations Inter-agency Group for Child Mortality Estimation 2019).
This model is known as the log-quadratic model. We use the R program provided by  Wilmoth et al. (2012)  9 to compute the estimated sex-specific life tables for Mexico in a given year based on the log-quadratic model. The software uses the estimated coefficients for   ,   ,   , and   from  Wilmoth et al. (2012, Table 3 ). The one-parameter version of the model uses an estimate of child mortality ( 5 q 0 ) as the only input and assumes that  =0. The two-parameter version of the model uses a measure of adult mortality (e. g. , 45 q 15 ) as a second input. As explained by  Wilmoth et al. (2012: 11) , an iterative procedure is used to choose a value of  that reproduces the input value of 45 q 15 exactly. 10  Then the estimated age-specific mortality rates across age are obtained using the following formula: 𝑚 𝑥 = 𝑒 𝑎 𝑥 +𝑏 𝑥 ℎ+𝑐xℎ 2 +𝑣 𝑥 𝑘.
" See the appendix to  Wilmoth et al. (2012)  for more details. For the two-parameter log-quadratic model, we use adjusted values of 45 q 15 (for 1995 and 2005) 11 from LAMBdA as the second input. The LAMBdA estimates are based on a complex methodology that adjusts for relative completeness and age misreporting  (Beltrán-Sánchez et al. 2020 ). More specifically, the LAMBdA methodology involves three steps. First, mortality below age 5 is estimated by modeling (using local area regressions and splines applied to a pooled set of national values including direct estimates based on vital registration and census counts as well as indirect estimates based on birth histories from surveys and microcensus samples). Second, mortality above age 5 is adjusted using the GGB method to adjust census data for completeness; then the SEG method is implemented to further adjust for completeness of death registration; finally, age misstatement is corrected assuming a systematic pattern of overstatement.
Acknowledgments This work was supported by the  AXA Research Fund in France. José Manuel Aburto  was partially supported by a  Newton International Fellowship  from the  British Academy. Andres Barajas Paz  acknowledges a PhD scholarship through the  Modelling, Measurement and Management of Longevity and Morbidity Risk research program  funded by the  Actuarial Research Centre of the Institute and Faculty of Actuaries. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors alone and do not necessarily represent the official views of the funders. We appreciate the assistance and advice we received from  Virgilio Partida Bush ,  Victor Manuel Garcia Guerrero ,  Patrick Gerland ,  Alberto Palloni ,  Hiram Beltrán-Sánchez , and  Piedad Urdinola.
[7À9]  In rapid response to the COVID-19 outbreak, a number of Western and Northern European cohorts created and harmonised a targeted survey to examine the pandemic's effects on common mental-health indicators in general populations. Since then, there has been increased collaboration between these teams to undertake crossnational comparisons of the mental-health impact of the COVID-19 pandemic; these have been facilitated by international networks such as the COVID-MINDS Network, which focuses on the global mental-health impact of the pandemic [www. covidminds. org]. The study presented here documents mental-health outcomes of populations during the first four months of the COVID-19 pandemic, drawing upon data from seven cohorts across four Western and Northern European countries. We overlaid these data with timelines of country-wide government interventions.
Participating cohorts The seven participating cohorts from four countries are presented in Fig. 1. The participating cohorts are described in Supplemental Text 1. A quantitative survey was harmonised and distributed to multiple cohorts in the COVID-MINDS Network. Covering a range of items, the survey included questions on basic sociodemographic measures (age, gender, level of education, cohabitation and occupational status during the pandemic); chronic physical and mental disorders; perceived loneliness; COVID-19-related mental-health indicators; and COVID-19-related precautions and worries. From Denmark, the Danish National Birth Cohort (DNBC) (N = 24,724), the Citizen Science cohort (N = 11,494), and the Epinion time-series data (N = 6,385) were analysed. [3]  From France, the Constances (N = 29,974) and TEMPO (N = 729) cohorts were analysed.
These findings suggest that a coordinated international strategy and increased collaboration would be effective in counteracting the negative impacts of the pandemic and its related lockdowns on mental health. was analysed. [13]  From the UK, the University College London (UCL) COVID-19 Social Study (N = 70,538) was analysed. The total number of unique respondents in our study was 205,084. The DNBC, the UCL COVID-19 Social Study, Lifelines, Constances, and TEMPO each had longitudinal data collections between March À July 2020. The Citizen Science cohort consists of an online survey, completed primarily during a four-day window at the end of March 2020. The Epinion study had a distinct design with independent samples that were surveyed at each of the 20 time-points during the study period. Response-and retention rates for all cohorts are described in detail in Supplemental Table  1.
Publicly available data sources Dates of key events, including societal lockdowns, political announcements, and the easing of restrictions, were extracted from national news outlets and Wikipedia. Data on daily hospitalisations and death rates due to COVID-19 were extracted from the websites of the Statens Serum Institut in Denmark, the Ministry for Solidarity and Health (Minist ere des Solidarit es et de la Sant e) in France, the National Institute for Public Health and the Environment (Rijksinstituut voor Volksgezondheid en Milieu) in the Netherlands, and the Government of the UK.
Statistical analysis Statistical analysis was performed using R for the Danish cohorts, STATA for the UCL COVID-19 Social Study, SPSS and R for Lifelines, and SAS for the French cohorts. National census data from the four participating countries were used to weight individuals from general-population cohorts in order to achieve more representative sample sets for statistical analyses (see detailed descriptions of these methods in Supplemental Text 2). In brief, weighting was performed using the raking method  [16]  for Epinion, Citizen Science and Lifelines, the entropy balancing method  [17]  for the UCL COVID-19 Social Study, and marginal calibration weighting for Constances. [18]  No weighting was performed for DNBC and TEMPO, as these cohorts were not established to be representative samples of the general population.
Nevertheless, it is likely that these high-risk groups would benefit from tailored strategies to help them cope with such crises. For people with a history of mental illness, strategies might include digital interventions, to alleviate stress, uncertainty, and other concerns. Younger individuals might benefit from fewer restrictions in movement, given that they are generally less likely to become seriously ill from the SARS-CoV-2 virus. [30]  This study has several strengths. First, a large, multinational sample was collected; we report on >200,000 individuals from seven cohorts from four European countries. Second, multiple studies had time-series data available from as many as 20 sampling points during the crisis. It is important to acknowledge the diversity of the included cohorts both from a study design (bulk collection, time-series design, prospective independent samplings) and sample size perspective.
Of note, some included cohorts are not representative of general populations by design (the DNBC is a birth cohort of mothers and their offspring, and the TEMPO cohort includes employees of the French national gas and electricity firms), and we did not apply weighting to these cohorts. We recommend that the presented data should be interpreted mostly in light of the longitudinal patterns they show. For accurate prevalence rates for mental ill-health, representative population cohorts are needed. All analysed data were self-reported and thus prone to various biases associated with this type of data collection. The two most important are response bias (systematic error between responses and true values) and nonresponse bias (differences in true values between responders and non-responders). While mental-health outcomes are generally prone to underreporting, it is difficult to speculate whether such biased reporting exists in our study's surveys, as the surveys were largely focused on psychosocial wellbeing during the COVID-19 crisis.
Funding The data collection related to the project 'Standing together À at a distance: how Danes are living with the corona crisis' was supported by the VELUX FOUNDATIONS' special pool for data-collection projects related to studying COVID-19, grant no. 36336. The Danish National Birth Cohort was established with a significant grant from the Danish National Research Foundation. Additional support was obtained from the Danish Regional Committees, the Pharmacy Foundation, the Egmont Foundation, the March of Dimes Birth Defects Foundation, the Health Foundation and other minor grants. The DNBC Biobank has been supported by the Novo Nordisk Foundation and the Lundbeck Foundation. Follow-up of mothers and The funders had no role in the study design, data collection, data analysis, interpretation, the writing of the report or decisions on where to publish.
7). In this study, we took on this challenge by evaluating a professional digital factchecking tool in classroom settings in France, Romania, Spain, and Sweden. The aim of this design-based study was to make the professional plug-in InVID-WeVerify useful in curricular activity in order to improve pupils' skills in evaluating misleading images and videos. We investigated the potential benefits and challenges of implementing the latest advances in image and video verification in collaboration with teachers across Europe. The tool, InVID-WeVerify, is a free verification plug-in that is available in multiple languages used today by professional journalists and fact-checkers to verify images and videos in newsrooms, such as France24, India Today, Canal 1, and Volkskrant  [32]. The plugin has been downloaded across the globe more than 40,000 times, and it is used on a daily basis by, among others, fact-checkers at Agence France-Presse (AFP) to investigate rumours and suspicious content regarding, for example, Covid-19 and politics.
InVID-WeVerify Ratings For the ratings on InVID-WeVerify, we present the mean total ratings, with error bars for each language, in Figure  8. There were 17 items in total, each with a 1-7 point rating scale, resulting in a maximum total rating of 119. A higher rating represents a more positive attitude towards the digital tool. Mean ratings (y-axis) with the standard error of mean for statistically significant differences between pre-and post-test for items on news evaluation test (x-axis) for Romanian (A), Spanish (B) and Swedish (C) language versions of the intervention. A one-way ANOVA (Type II sum-of-squares), with language as the independent variable and total mean ratings as the dependent variable, showed a statistically significant main effect (F(3202) = 8. 22, MSE = 322. 65, η 2 p = 0.
Archaeological pottery sampling and capacity measurement Ceramic samples obtained from fifty-two vessels and one foodcrust, adhering to the inside of a vessel, were investigated. Where possible, potsherds selected for analysis were taken from the upper part of the body of each vessel, which generally accumulates the most amount of lipids during cooking  (Charters et al. , 1993). Since all the reconstructed pots and significant isolated sherds were 3D scanned for technological analysis, the destructive sampling for chemical analysis did not cause any loss of information regarding the form and decoration of the vessels. The ceramics were scanned with a portable 3D scanner Artec Spider, and 3D model without texture was obtained for each potsherd. The reconstructions of the vessels -virtual reshaping and creation of a form model -were carried out with the Cyclone 3DR software.
Stable carbon isotope measurements were performed on the two main fatty acids in archaeological extracts: palmitic (C16:0) and stearic (C18:0) acid methyl esters. Values were corrected subsequent to analysis to account for the methylation of the carboxyl group using a mass balance formula. The Δ 13 C value (δ 13 C18:0δ 13 C16:0) was calculated to overcome the influence of animal feed and the environment on stable carbon isotope values and to facilitate the identification of dairy products, ruminant and non-ruminant adipose fats  (Evershed et al. , 2002). The archaeological data were compared with the values obtained for modern animal fats published in the literature  (Cramp and Evershed, 2014; Debono Spiteri, 2012; Dudd, 1999; Outram et al. , 2009; Pääkkönen et al. , 2020; Taché and Craig, 2015)  and the data obtained in this study (cereals and animal fats, Table  S2 ).
Acknowledgement This work is carried out within the framework of the  ANR CIMO  ( ANR-14-CE31-009 ). We thank  Pierre Pradier  and  Egidio Gonzales  for providing us with the modern samples of cereals and animal fats. We are grateful to  Marie Balasse  and  Juliette Knockaert  for the fruitful discussion on the periods of birth of caprines and lactation season during the Neolithic. Finally, we would like to thank the two reviewers for their constructive suggestions which helped to improve the quality of this article.
Data analysis First, we coded the pilot interviews and then continued data collection and coding simultaneously  (Angel et al. , 2018). We used NVivo  (Bazeley & Jackson, 2019)  to generate categories and themes. Data collection and coding continued with comparisons between coders until we reached saturation  (Angel et al. , 2018)  with the 36 th interview. In the second phase, we revisited the nodes and (re)read the interview transcripts to refine and narrow the categories  (Braun & Clarke, 2006). We analyzed eight more interviews from within the same sector and geographical area but these did not generate new codes. Following the Gioia method  (Gioia et al. , 2013)  in the third phase, we reduced 10 categories to four (see Fig. 4 ). Two dimensions of customer service emerged (see Fig. 5 ): perceived relationship building induced by pragmatism and perceived relationship building induced by long-term strategic goals with an overarching theme of compassionate customer service.
2019, p. 219). Based on the inductive coding process with a subset of transcripts to complete the template, we developed additional level one codes such as "perceptions of growth" and refined level two codes such as e. g. , "entrepreneurship out of commitment", "the role of strenghtening informal ties" or "family entrepreneurs' interpretation of success". To link existing theoretical insights on embeddedness with our empirical observations, we created a matrix accounting for horizontal, vertical and spatial patterns of embeddedness. This process was supported by the qualitative data analysis software "MAXQDA," which allows managing the codes and further quantitative/ qualitative analysis (MAXQDA 2020). The flexible pattern matching approach also promotes the formulation of propositions (see discussion section) to further explore phenomena and build theory  (Sinkovics 2018, p.