dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The research leading to these results has received funding from the  European Research Council  under the  European Union Funding   for Research and Innovation  ( Horizon 2020 ) ERC Grant Agreement number  670628 , ' Saadian Intellectual and Cultural Life ' ( SICLE , 2016(SICLE,  -2021 )). The authors are particularly grateful to the Director of the  Qarawiyyin Library, A. Boukchouf , for his generosity and help. They also want to thank  M. Aouad  for his invitation to explore the khurūm collection at the Qarawiyyin.
Acknowledgements The author is a member of the  Instituto de Lenguas y Culturas del Mediterráneo y del Oriente Próximo, CCHS-CSIC, Madrid. This research has been funded by the project 'The  Byzantine author (III )' [ PID2019-105102GB-I00 ]. Thanks are due to  Raúl Estangüi  and  Alfredo Calahorra.
52. 5 Pommerol/Monfrin 1991; Anheim 2006, 1-27; Angotti/Fournier/Nebbiai-Dalla Guarda 2017; Berndt 2018; see also the articles in this volume by Donatella Nebbiai and Jacques Verger. 6 Wijsman 2010; Winter 1985;  Beauvoir 2005, 65-118; Stratford 1994, 187-197;  for the French nobility, see Fourcade 2021; for female book holders in a European context, see the database https://booksofduchesses. com/ (accessed on 20 December 2021).
Acknowledgments This article was catalyzed by an online conference organized by  Birkbeck, University of London  and the  University of Malta  in November 2020, as part of the SUNRISE project. A recording of the event is available at https://arcg. is/1y0vOz. The authors thank all speakers and participants. JONATHAN PUGH is Professor of Island Studies,  Newcastle University , Newcastle upon Tyne NE1 7RU, UK. E-mail: jonathan. pugh@ncl. ac. uk. His work on Anthropocene islands explores how islands have become productive for the development of key strands of contemporary Anthropocene thinking. STACY-ANN ROBINSON is an Assistant Professor of Environmental Studies, Colby College, Waterville, ME 04901. E-mail: smrobins@colby. edu. She researches the human, social, and policy dimensions of climate change adaptation in small island developing states, with a special focus on adaptation finance and climate justice.
4 mm (0. 16 mm 2 ) and averaged the data from the protoconid and/or hypoconid of the first (M1), second (M2), and third (M3) lower molars, as well as for the paracone and/or metacone of the upper M1, M2, and/or M3  (Rieau, 2014; Xafis et al. , 2017). The low magnification observation avoided the recording of taphonomical alterations such as fissures and damaged areas  (King et al. , 1999; El-Zaatari, 2010; Uzunidis et al. , 2021). The microwear observation was performed by a single experienced observer (SJM)  (Mihlbachler et al. , 2011; Semprebon and Rivals, 2007). Five microwear variables were recorded: the number of pits (NP), the number of scratches (NS), the presence/absence of gouges (G), the scratch width score (SWS), and the presence/absence of cross scratches (XS). Pits are features with an oval or circular morphology (uniform width and length) (Fig.
Pits and scratches can also be produced by external particles such as dust and grit (e. g. ,  Merceron et al. , 2016; Gallego et al. , 2020; Schulz-Kornas et al. , 2020 ). An exploratory biplot graph using the total number of scratches and pits was developed using the R Statistical Software package (version 3. 5. 1) and R code from  Rivals (2019). Sheep and goats were grouped when there were fewer than three specimens. Further, after verifying the normality of variables, a one-way ANOVA test was run to evaluate significant differences between taxa and geographical areas (p-value threshold = 0. 05). The statistical procedure was carried out using IBM SPPS statistics software.
, 2011). Browser dietary regimes vary between MWS values of 0 to 2. 00; grazer regimes between 2. 09 and 5. 47; and mixed feeders between 0. 40 and 2. 74  (Fortelius and Solounias, 2000; Rivals et al. , 2007; Mihlbachler et al. , 2011). The presence of significant differences between species was tested with an analysis of variance (ANOVA) run using IBM SPPS statistics software.
The oxygen and carbon isotope composition of the carbonate fraction of the apatite was measured using a Thermo Delta V + IRMS coupled to a Kiel device. CO 2 was released from the sample through reaction with 105% orthophosphoric acid, and measured against a reference gas. The international isotope reference materials IAEA-603 and NBS-18 were used to perform the calibration, and the values were given as δ 13 C and δ 18 O on the VPDB scale. Duplicates were analysed to assess the reproducibility of ±0. 08‰, and the overall precision of ±0. 30‰. R version 4. 0. 5 and the 'car', 'ggplot2 ′ , 'ggrepel', 'lattice', 'readxl' and 'stats' packages together with the development package 'zoowork' were used to visualise the isotope data  (Fox 2007; Wickham 2011; Slowikowski et al. , 2018; Sarkar et al.
Acknowledgements Authors would like to thank the reviewers for all useful comments. This project has been supported by  LabEx ARCHIMEDE, University Paul Valéry, Montpellier 3  (" Investissement d'Avenir" program   ANR-11-LABX-0032-01 ), the  ERC-StG ZooMWest  ( ERC-StG 716298 ) and a Grant ( FJC2021-046527-I ) funded by  MCIN/AEI/ 10. 13039/ 501100011033  and  European Union NextGenerationEU/PRTR. The authors are grateful to  Éric Gailledrat  (director of the archaeological excavations of  Cayla de Mailhac  and Lattara zone 1),  Alexandre Beylier  (director of  La Monédière and Cayla de Mailhac ),  Réjane Roure  (director of  Le Cailar ),  Dominique Garcia  (director of  La Ramasse ) and  Laurent Schneider  for the access to the faunal material and their availability.
Study design and data source This study is a secondary analysis of age-standardized DALY rate per 100,000 population of NCDs over a 30-year follow-up period-from 1990 to 2019 -of the GBD 2019 study  [47]. One DALY should be interpreted as one lost year of healthy life. DALYs are calculated by adding Years of Life Lost (YLL), a measure of healthy time lost due to premature mortality, and Years Lived with Disability (YLDs), a measure of healthy time lost due to living with disease or injury. We retrieved agestandardized NCD DALY rate by sex, country, and year, using GBD 2019 interactive data visualization tool 'GBD Compare'  [48]  and 'GBD Results'  [49]. The GBD 2019 study offers an extensive global, regional, and national data source for 204 countries, including 30 Member States of EEA (in 2019).
A wide-ranging source of estimates is available for 369 diseases and injuries, 286 causes of death, 3484 sequelae, 87 risk factors, 23 age groups, both sexes, for a time-range from 1990 to 2019  [18, [50] [51] [52]. A detailed description of the GBD methods to calculate DALYs is given by prior publication  [47]. Since the prevalence and incidence of NCDs vary by age, we have chosen to perform our analysis by using global age-standardized rates provided by the GBD tool. Age-standardized rates allow comparing health outcomes across countries and time, are consequently often used for benchmarking disease burden studies  [18]. Specific data were analyzed separately for males and females. Age-standardized DALYs per 100,000 population was used to assess the total amount of healthy life lost due to NCDs level 1 and 2.
Categorization of non-communicable diseases The GBD database organizes the included conditions in 4 different hierarchical category levels. The first level is divided into Group I: communicable, maternal, neonatal, and nutritional diseases; Group II: NCDs; and Group III: injuries. Level 2 diseases are the subdivisions of level 1 groups, there are 22 disease and injury aggregate groupings. Level 3 and 4 include specific causes. For certain diseases, level 3 causes are the most detailed classification, while for others a more detailed categorization is defined at level 4. Our analysis was limited to NCDs at level 1 (Group I and III were excluded) and level 2: cardiovascular diseases (CVDs), chronic respiratory diseases, diabetes and kidney diseases, digestive diseases, mental disorders, musculoskeletal disorders, neoplasms, neurological disorders, sense organ diseases, skin and subcutaneous diseases, substance use disorders, and other NCDs (such as congenital birth defects, gynecological diseases, oral disorders, endocrine, metabolic, blood, and immune disorders).
Target countries The following 30 EEA Member States were included in our study: Austria, Belgium, Bulgaria, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Norway, Poland, Portugal, Republic of Cyprus, Romania, Slovakia, Slovenia, Spain, Sweden, and the United Kingdom. The EEA was founded by the Agreement on the European Economic Area, an international agreement allowing the extension of the EU's single market to the member countries of the European Free Trade Association (Iceland, Liechtenstein, and Norway). The United Kingdom (UK) was part of the EEA in 2019 -for this reason, we included the UK in the analysis. Since the GBD 2019 database does not report data for Liechtenstein, it was excluded from our study.
GC is usually defined based on the Lorenz curve, which plots the cumulative proportion of the DALY rate of the countries by the cumulative proportion of population. The line drawn at 45 degrees thus represents perfect equality. The GC is twice the ratio of the area that lies between the line of equality and the Lorenz curve. GC ranges from 0 to 1, in which 0 represents perfect equality and 1 means total inequality  [53]. GC was calculated by Stata using ineqdeco (Stata module to calculate inequality indices with decomposition by subgroup) with bootstrap resampling to calculate 95% confidence intervals  [54]. The SII was used as an additional measure of health inequality, that was used to estimate the average absolute difference in DALY rate across countries. This measure is based on the beta coefficient (slope) of the linear regression of Pen's Parade, which ranked all countries by their DALY rate from lowest to highest, with the share to the total population of the included countries.
Both measures are used to estimate inequality in DALY rate across countries  [53, 55]. The GBD database provides data with 95% uncertainty intervals (95% UI) which reflect the variability and potential error in the modeling process providing a range of plausible values within the true DALY rate is expected to lie  [47]. The DALY rate estimates are calculated by exponential β coef ficient(LN [X]) -1 = Annual rate of change sampling 1000 draws of the posterior distribution. The DALY rate was reported in the present study as the mean value of the 1000 draws estimate. The 95% UI is represented by the 2. 5th and 97. 5th percentiles of the corresponding distribution. The 95% UI was interpreted as indicating a statistical difference if they did not overlap: if two or more countries had DALYs within the same 95% UI, they were considered to have no statistical difference.
Moreover, relying on Poisson regression to produce p-values and 95% CIs would indicate statistical significance for almost all ratios, given its exclusive focus on DALY rates. In contrast, the adoption of the 95% UI overlapping rule is a more significant measure, particularly as the GBD 2019 study incorporates various other epidemiological metrics into its calculation of UI. The GBD database does not present estimates for microstate Liechtenstein, which was the only country excluded from our analysis of EEA member states. An additional limitation is the assumption of linear change in the annual change estimates, since our analysis showed that, for some former Soviet countries, this assumption might be incorrect. However, this was mitigated by depicting the DALY rate of change over each year for each country visually.
In the present study, we aimed to measure risk misestimation and test whether our interactive website tools would change risk-taking intentions. Here, we report data from 11,169 unique users who interacted with the COVID-19 Event Risk website during a 4-month period (9/22/ 21-1/22/22). In brief, we found that interacting with the COVID-19 Event Risk website reduced willingness to participate in risky events. This increase in risk aversion was most prominent for users who had been underestimating risk and users who viewed information about larger event sizes. Importantly, even among participants who voluntarily sought information on our website (i. e. , individuals who are likely already aware of or interested in COVID risks), we observed substantial risk misestimation, demonstrating the importance of intervention.
Participants We collected data from 11,169 unique users who interacted with the COVID-19 Event Risk Tool (covid19risk. biosci. gatech. edu) during a 4-month period (9/22/21-1/22/22). We identified unique users by public IP addresses. Using a modal dialog box, we asked participants to assert that they were �18 years of age, residing in the U. S. , and agreed to share their responses for research purposes. During the data collection period, the website received approximately 247,000 visits from approximately 170,000 unique users. However, not all participants provided responses to all interactive elements of the website; sample sizes differed across analyses (S1 and S2 Tables). We saved and analyzed data from all users (N = 11,169) who agreed to share their data and interacted with the website tools.
Demographic survey To collect more information about our user base, we included a brief demographics survey hosted by Qualtrics. Survey data collection occurred between 11/17/21 and 1/22/22. Participants who completed the optional survey (N = 612) had the opportunity to enter a monthly lottery draw for a $50 gift card. The demographics survey included questions about age, gender, political affiliation, vaccination status, and individual experiences during the COVID-19 pandemic. Results from the demographics survey are provided in the Supporting Information (S3 Text). Additionally, we used leveraged county-level demographic information from the risk quiz. The website tool attempted to automatically detect each participant's location from browser data, but participants were able to manually select their location if automatic detection failed.
Statistical analysis and data sharing All analyses were conducted in R (v. 4. 1. 1) with RStudio (v. 2021. 09. 0). See Supporting Information (S1 Text) for details. Statistical significance was evaluated with two-sided tests and a threshold of p < 0. 05. Continuous variables were standardized and mean-centered before inclusion in regression models (z-scored); however, all figures depict results in original units for ease of interpretation. For analyses that included multiple observations from each participant, we used mixed-effects models with random intercepts for participants. For risk quiz analyses, which were associated with accurate location data, we also included random intercepts for different U. S. counties, because different counties may differ in political leaning, pandemic restrictions and policies, COVID-related attitudes, population density, and population immunity.
These factors may contribute to shared variance among multiple observations from a given county. All data and code are publicly available in a permanent repository hosted by the Open Science Framework (DOI: 10. 17605/OSF. IO/MBH9W). To protect the privacy of our participants, some identifiers have been removed from the raw data (the last two digits of IP addresses and open-ended personal comments from the demographics survey). All code and data sources for the COVID-19 Event Risk website (https://covid19risk. biosci. gatech. edu/) are publicly available on the About page. The study was not preregistered.
Risk quiz: Misestimation of risk Using data from the risk quiz, we tested whether participants misestimated COVID-19 exposure risk. For each participant, we calculated risk estimation error for each event size by subtracting our prevalence-based measure of exposure risk from the participant's guess. This sign of the risk estimation error score indicates the direction of misestimation (positive values indicate risk overestimation, whereas negative values indicate risk underestimation). The magnitude of this error measure indicates the severity of risk misestimation. On average (across all event sizes), there was a small bias towards underestimation of risk, consistent with our prior research (mean risk estimation error = -2. 49 points, t (4840) = -6. 67, p < 0. 0001, Cohen's d = -0. 10, 95% CI [-0.
Risk quiz: Political partisanship We investigated political disparities in user engagement by leveraging location data from risk quiz submissions. Using county-level demographic information, we investigated whether user participation and responsiveness differed depending on political leaning. On the basis of a survey we administered (S3 Text) and prior studies that have investigated political differences in COVID-19-related attitudes and information seeking  [34] [35] [36] , we hypothesized that individuals from conservative-leaning counties would be less likely to visit the website and use the interactive tools. To investigate these questions, we leveraged location data to identify the Reduced willingness to participate in large events. Participants reported being significantly less willing to participate in events of a given size after viewing the corresponding risk map.
Risk quiz: Characterizing changes over time Data collection took place over a 4-month period that included the emergence and rise of the Omicron variant, as well as several major U. S. holidays (Thanksgiving, Christmas, and New Year's Eve). Using data from the risk quiz, we visualized fluctuations in risk misestimation and change in willingness over time. Temporal trends for risk misestimation differed across event sizes (Fig  6A ). For the largest event size (1,000 people), participants consistently underestimated risk throughout the 4-month period. For the other three event sizes assessed in the quiz (20, 50, and 100 people), participants fluctuated between periods of risk overestimation (particularly during November and December 2021) and periods of risk underestimation (particularly during the height of the Omicron wave in January).
A limitation of the present study is that due to feasibility and user experience constraints, we only collected a single measure of change in willingness to participate in potentially-risky gatherings. However, in our prior intervention study  [5] , we collected separate measurements of willingness pre-and post-intervention; these data offer additional insight into risk tolerance. In this prior study, participants rated willingness to engage in 15 different everyday activities (if hypothetically all public health restrictions were lifted), using a 5-point scale (1 = definitely would not do this. 5 = definitely would do this). Data from this previous study indicated that at baseline, participants were moderately willing to engage in smaller, relatively safer activities like an outdoor picnic with friends (mean willingness rating = 3.
10) or retail shopping while wearing a mask (3. 51), moderately willing to participate in medium-sized gatherings like indoor restaurant dining (2. 38), and least willing to participate in large, crowded gatherings like flying on an airplane (1. 94), or attending a house party (1. 63). It is likely that willingness to take risks was greater in the present study, as we collected data more than a year after the prior intervention study, when most public health restrictions had been lifted. Overall, our findings demonstrate that online, interactive risk assessment tools can effectively communicate current and local information about health risks. The health belief model describes several key factors that influence how individuals make health decisions: perceived susceptibility, perceived severity, and perceived benefits  [12].
User demographics and temporal effects We explored user demographics by leveraging geographical information and conducting a survey. Approximate location data indicated that users from across the U. S. engaged with the website (S2 Fig). Using location data from the risk quiz, we identified political differences in user engagement. There were 2. 8 times more risk quizzes submitted from liberal-leaning counties than conservative-leaning counties, suggesting a substantial political divide in COVID-19 information seeking. Importantly, the efficacy of our risk assessment tools also differed depending on political leaning: The risk quiz feedback beneficially influenced willingness to take risks for participants from liberal counties but was less effective for participants from more conservative counties.
Additionally, we conducted a demographics survey that supported and expanded on these geographically-driven inferences. Survey responses indicated that users of all ages engaged with the website (ranging from 18 to 88), but our sample was biased towards politically liberal individuals (71%) and women (67%) (S3 Text). The proportion of self-identified liberal individuals on the survey (71%) closely aligned with the proportion observed in the county-level political data (74%), supporting the geographical inference. Exploratory demographic analyses with the survey data did not reveal any effects of political attitudes, age, or gender on either risk estimation accuracy or change in willingness (S3 Text). Our results support the idea that liberals are more likely to seek out risk information and more willing to change their risk tolerance when presented with feedback about their risk misestimation.
These partisan effects underscore the importance of targeted risk communication to reach broader audiences and counteract biases. Recent studies have demonstrated that political partisanship has contributed to dramatic differences in risk perception and trust  [34, 36] , quantity and quality of information seeking  [36, 50] , misunderstanding of viral transmission  [47] , behavioral responses to public health recommendations  [35] , vaccine uptake  [51, 52] , and health outcomes  [53]  during the pandemic. In ongoing research, we are developing new strategies for engaging with conservative individuals (as identified by social media activity) to broaden our target audience and enhance the efficacy of our risk assessment tools. A feature of our study is that we collected data prior to and during the Omicron wave in the United States, which encompassed several major holidays (Thanksgiving Day, Christmas, and New Year's Eve).
Limitations This dataset was collected via interactive tools on a public website with a large user base. This approach enabled large-scale online implementation of a prior evidence-based intervention, but necessarily included several limitations. Our sample was subject to self-selection bias; participants voluntarily sought out information about COVID-19 risk. Therefore, individuals who visited our website may be more concerned about COVID or interested in learning about exposure risk, relative to others who did not visit our website. However, we observed substantial risk misestimation in this self-selected sample, demonstrating that correcting risk perception was still necessary and beneficial in this population. In a related study  [59] , we reduced self-selection bias and expanded our reach by conducting large-scale advertising campaigns on social media.