dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , periods, commas, exclamation points), formality measures (e. g. , swear words, fillers), and meta categories  (e. g. , analytic, authentic, tone; Pennebaker et al. , 2015). Word count is presented as the absolute number of words; all other variables are presented in percentages. Percentages are calculated by counting the number of words belonging to a specific word category and then dividing by word count. In Study 3, we applied models reported in the literature to our two data sets. However, some of these models from the literature were created using older versions of LIWC (LIWC2001 and LIWC2007), which contained different sets of word categories from LIWC2015. To allow for testing of those older models and to ensure compatibility between models, we used LIWC2015 for the main set of word categories and added relevant word categories from previous LIWC versions.
Our final variable set consisted of 103 variables (101 LIWC word categories from three LIWC versions and two additional Twitter variables). Our set contained 92 out of 93 word categories from LIWC2015. The variable semicolon was removed because there were no semicolons present in our two data sets. In addition, we added four word categories from LIWC2007 (inclusive, inhibition, exclusive, humans) and five word categories from LIWC2001 (sensory and perceptual processes, optimism, total first person, total third person, metaphysical). Last, we added two variables comprising the percentages of the symbols "@" and "#" present in each tweet because these variables play a central role in Twitter communication patterns but are not included in any LIWC variable. The complete list of included LIWC variables appears in Table  S1  in the Supplemental Material.
Results Comparing factually correct and incorrect tweets. To test whether language use in factually correct and factually incorrect statements differed, we ran a multivariate analysis of variance (MANOVA). We compared the means of the 101 LIWC variables and two Twitter variables (percentage of @ or # symbols) between the factually correct and the factually incorrect tweets. Using Pillai's trace, this analysis revealed a main effect of veracity, V =. 37, F(103, 365) = 2. 10, p <. 001 (two-sided, as are all the tests reported in this article), η p 2 =. 37. Table  1  provides the means for factually correct and incorrect statements in Data Set 1, the F statistics, p values adjusted with false-discovery-rate (FDR) correction for multiple comparisons  (Benjamini & Hochberg, 1995) , Bayes factors from a Bayesian t test, and Cohen's ds and their 95% confidence intervals.
Method To test the out-of-sample prediction accuracy of the personalized model developed in Study 1, we collected a second data set (Data Set 2) of presidential tweets. Similar to Data Set 1, Data Set 2 comprised all presidential tweets by @realDonaldTrump covering a 3-month period. Because 3 new months' worth of tweets was not yet available when we collected Data Set 2, we focused on the tweets in the 3 months preceding the period covered in Data Set 1 (November 2017-January 2018). In total, 606 tweets were gathered. We applied identical screening methods as in Study 1, correcting misspellings and removing 85 retweets, six duplications, two tweets containing severe spelling mistakes, and 29 tweets containing quotes with more than six words. The final data set comprised 484 tweets with an average length of 29.
The first approach is simply to use. 5. In most deceptiondetection research, a cutoff score of. 5 would be a sensible choice because half of the statements in such studies tend to be truthful and half deceptive  (Levine, 2018). For the test set, this gives an overall accuracy of 77. 69%, a hit rate of 37. 84%, and a false-alarm rate of 10. 46%, whereas for the training set, overall accuracy is 76. 76%, the hit rate is 49. 30%, and the false-alarm rate is 11. 31%. This threshold is very conservative, missing many incorrect statements, and less applicable because outside the lab, truth-lie base rates are often not 50-50  (Levine, 2018). 2. The black curve is associated with Data Set 1 (training set) and the gray curve with Data Set 2 (test set). The diagonal represents random guessing, and the black dot represents the prior used as a random guess.
Second, a longer tweet is simply more likely to contain an inaccuracy. Hence, word quantity may be predictive by itself. By comparing the accuracy of more complex models with the accuracy of this one-variable model, we can see the value of analyzing the content of the tweets beyond their mere length. We also included, as another informative benchmark, random guessing using the prior probability of 30. 28%. For each of the 24 models from the literature, we identified which LIWC variables were included. To allow for a fair comparison between models, instead of relying on coefficients reported in the original articles, we estimated all models on Data Set 1, following the same procedure as for our personalized model in Study 2. We then used the obtained coefficients to compute the accuracy of each model on Data Sets 1 (training) and 2 (test).
Results The tested models are shown in Table  4. We report the rank, article, type of data, type of model, model origin, LIWC variables included in the model, and overall accuracy and AUC scores for the training and test sets, respectively. When several models were reported within one article, we differentiated between them in the model column. The models are ranked in terms of overall accuracy on the test set (Data Set 2). Overall accuracy scores on the test set range from 50% to 74%, whereas AUC scores range from. 443 to. 788. The relatively small differences in classification accuracies of the models on Data Sets 1 and 2 again demonstrate the consistent language use in correct and incorrect tweets. Our personalized model outperformed all models from the literature by 5 percentage points.
e. respiratory and cardiorespiratory severe events). The codes of the International Classification of Diseases 10 (ICD10) included for each hospitalisation definition are presented in Supplementary Table  1. Official sources from the Ministry of Health were used for hospitalisation rates and costs. Other national or regional official sources were used for probability of GP and emergency visit, a subscription database for unit costs of other healthcare resources, and published literature for the remaining inputs  [6, 29, 30, [33] [34] [35] [36]. It is worth mentioning that vaccine costs used in the model are exfactory prices, but vaccines are acquired by health provider in a procurement process through a tender, which may reduce acquisition costs (but these vaccine costs are undisclosed). The costs are presented in 2020 euros and were updated using Spanish Price Consumer Index when necessary.
Materials and Methods In 2012, the Khmer Archaeological Lidar Consortium (KALC) acquired lidar data over 370 km 2 of the Greater Angkor Region. These data revealed details of the urban form in the CCC and high-resolution (mm vertical resolution) topographical information about the region  (Evans, et al. , 2013). Combining this lidar imagery and other forms of remote sensing, including satellite imagery, researchers from the Greater Angkor Project have identified over 25,000 archaeological features in the Greater Angkor Region, including 1,000 temples, 9,000 reservoirs, and 9,000 mounded areas used for occupation. In addition, the temples and occupation mounds have been ground verified, and the reservoirs are very clearly identifiable in the remote sensing imagery. Figures 2 and 3 illustrate these features in the context of the Greater Angkor Region.
, 2018). These data were then combined with other archaeological data, association algorithms, art historical analysis, and radiocarbon dates to predict dates for features associated with temple communities in the AMA and archaeological features in the CCC. In total, researchers have been able to predict dates for over 20,000 of the 25,000 archaeological features that have been mapped in the Greater Angkor Region  (Klassen et al. , 2021). When combined with insights from inscriptions  (Lustig & Lustig, 2019) , this has led to a reasonably well-understood history of the agricultural landscape over time  (Klassen & Evans, 2020). We carried forward these chronological assessments of community temples in this analysis. We acknowledge that the dating is subject to revision as more data are collected;   (Klassen et al.
Estimating Temple Platform, Reservoir, and Moat Volumes The digital terrain model (DTM) derived from the lidar data allows us to estimate the volume of water in reservoirs and moats and the volume of earth moved to create temple platforms and reservoir embankments for those features with lidar coverage. We calculated the depth and height of these features using the Zonal Statistics tool in ArcGIS 10. 7. 1 to the nearest cm. Then, we used the Range function to calculate the maximum value from the DTM raster minus the minimum value of the DTM raster inside of each moat, reservoir, mound, and temple polygon. We argue that the maximum and minimum values represent the original form of the features before they experienced erosion over the last 1000 years. Once we calculated the depths of features within the lidar coverage, we used ordinary least-squares regression to measure the direction and strength of any relationship between feature depth and surface area.
There does not appear to be a stark contrast between agriculturally productive and non-productive spaces as occupation mounds often included gardens  (Castillo, et al. , 2020) , reservoirs were a source of fish protein, and inscriptions provide provisions to allow pigs to forage in ricefields  (Jacob, 1993) , all of which is included in our calculations of productivity. Construction periods for agricultural temples are from  Klassen et al. , (2018) , as discussed above. We have used these dates to assign temples to each of the six periods, ranging from 37 to 136 years. However, we acknowledge that there is likely to be some error in the period assignment of individual temples due to the absolute average error of 49-66 years in the dating method itself. To calculate the amount of land available to each temple community, we estimated the transverse dimension of each community's territory as the mean of the distance from each of the 921 dated temples in the dataset to its three nearest neighbors, based on their center-point locations.
Although we cannot map these purchases, approximately 10% of new temples constructed during each period are within 250 m of another temple, based on the coordinates of their center points. Because temples and their associated moats were typically 40-80 m across, this implies a gap of less than about 150 m, which seems too close to reflect adjacent independent communities. We, therefore, consider such cases to represent the purchase of an existing temple by a new owner. To take this into account, we aggregated the data (including populations, territories, reservoir volumes, and total productivities) for all occupied temples within 250 m of one another during a given period, assigned these data to the most recently constructed temple in that group, and recalculated the community center location as the average of the contributing temple locations.
While we do not have comprehensive datasets that can be used to definitively model the terminus of occupation across our study region, emerging evidence suggests community temples and their associated infrastructure remained in use from the time of their dedication through the end of our study period in the fourteenth century CE, which is often referred to as the "collapse" of the Angkor. For example, in an extensive survey across the Greater Angkor Region, researchers have identified post-Angkorian ceramic scatters on occupation and temple mounds that indicate continued occupation through the post-Angkorian Period  (Brotherson, 2019) , while surveys within Siem Reap town that suggest the temples were used until at least the seventeenth century CE  (Vitou, 2012). Similarly, Heng and Stark excavated three temple communities (Wat Athvea Area, Wat Chedei Area, Wat Prei Phdau Area) and found that each site had a long and complex occupation history with ceramics that date from the foundation of the temples until the 15th-twentieth centuries CE  (Heng & Stark, 2020).
Notice that the left side of Eq. (  1 ) can also be expressed as Δa * p , leading to a sys- tem of equations: where the left sides of both equations represent the slope of the relationship between land productivity and distance, and the relationship between area per farmer and distance, respectively. Since transport costs are constant, one can solve 2 for and equate them: leading to: where the term in brackets is itself the slope of the relationship between area per farmer a and productivity per hectare p. Thus, Equation (  5 ) can be empirically examined using a regression framework. We use the data for temple communities during Period 6, estimating a as temple territory / temple population, p as temple productivity / temple territory, and x as the straight-line dis- tance of that temple from the Bayon temple in the center of Angkor Thom.
Relationships Between Community Size and Agricultural Outputs As discussed above, we used measurements, chronological assessments, locations, and orientations of archaeological features to estimate water management infrastructure, associated lands, total agricultural output, and net proceeds for temple communities in the AMA. Table  4  summarizes the resulting dataset for temple communities established during each period and for all temple communities for which we have complete data that were occupied during each period. The data summarized in Table  4  shows that the rate of construction of new temples was very fast during the tenth century but slowed to a fraction of a percentage point after that. The table also suggests the average population size of a temple group was in the low hundreds and that this average did not change appreciably over time.
Acknowledgements The authors wish to thank the  APSARA National Authority  for their permission to conduct remote sensing and field investigations. Portions of this research have been supported by grants from the  James S. McDonnell Foundation  (# 220020438 ), the  Social Sciences and Humanities Research Council of Canada Postdoctoral Fellowship , the  National Science Foundation Doctoral Dissertation Research Improvement Awards  (# 1638137 ), and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 639828  and No. 866454 ). The authors also wish to acknowledge and thank the  Khmer Archaeological LiDAR Consortium  and the  Greater Angkor Project  and its members. Thank you to  Willie Hunter  for assistance with zonal statistics to determine the capacity of the water storage infrastructure at Angkor.
Next, participants were asked about their intention to be vaccinated, their fear of vaccination, and finally indicated their gender and age. In the prejudice condition, the questionnaire used was identical to the one used in the love condition, with one notable difference: the first five questions, instead of referring to the person they loved the most, dealt with the person who harmed them the most ("Can you remember the person who harmed you the most during your life?"). Once a participant had finished answering the assigned questionnaire and had clicked on the "submit" button, the corresponding data were automatically imported into a Google Sheets spreadsheet. All responses obtained were retained, with the exception of those participants under the age of 16 or over the age of 30. In this study, age was the only exclusion criterion.
Background: Severe Acute Respiratory Syndrome Coronavirus 2 continues to spread globally and in the absence of an effective treatment, the vaccine remains the best hope for controlling this disease. In this study, we seek to find out the extent to which people in Syria accept the Corona vaccine and what are the factors that affect their decision. Methods: This cross-sectional study was conducted in Syria during the period from January 3 to March 17, 2021. A structured self-administered questionnaire was distributed in two phases: The first phase included distributing the questionnaire as a Google Form on social media platforms. In the second phase, a paper version of the questionnaire was handed to patients, their companions, and workers in public hospitals. SPSS v. 25 and R v. 4. 1. 1 were used to analyze the data.
Study design, setting, and participants A cross-sectional survey was conducted aiming at the general population in all major cities in Syria (Damascus, Rif Dimashq, Aleppo, Daraa, Deir ez-Zor, Hama, Al-Hasakah, Qamishli, Homs, Idlib, Latakia, Quneitra, Ar-Raqqah, As-Suwayda, Tartous). The data were collected during the period from January 3 to March 17, 2021. A nationally representative sample of Syrian people was questioned using a structured self-administered questionnaire which was developed based on previously existing literature, and then it underwent some modification to be suitable for the Syrian society  [12] [13] [14]. The questionnaire was then piloted on 15 persons to insure its clarity and adjustments were made based on their feedback. Because of the war that has been going on in Syria for ten years, many residents do not have access to the Internet either because the necessary infrastructure has been destroyed or because of the bad economic situation.
Chain-referral sampling was used in online distribution phase, online respondents were asked to send the questionnaire to their family members, friends, and colleagues. Convenience sampling methods were used in the hard copy distribution phase. The sample size was calculated using Openepi online software available at "https://www. openepi. com/ SampleSize/SSPropor. htm". According to data from United Nations, the estimated number of Syrian population in 2019 is about 18 million [15], based on that, we conducted a statistical power analysis for sample size calculation. With a conservative assumption of 50% vaccine acceptance, an alpha of 0. 05 and a power of 80% and a desired margin of error of 1. 14%, the desired sample size was found to be 7336. Inclusion criteria included that the person is: (1) 18 years old or more, (2) literate, (3) Syrian (or equivalent) and living in Syria, (4) willing to complete the questionnaire.
Statistical analysis Data from the hard copy questionnaires was entered manually by the investigators to the original Google Form online questionnaire that used to collect online data, then t data was extracted from Google Form directly to an Excel spreadsheet. After that, we encoded the raw data in the Excel sheet to make it compatible with the statistics software. We used Statistical Package for Social Sciences version 25. 0 (SPSS Inc. , Chicago, IL, United States) to perform Chi-square univariate analysis, and R v 4. 1. 1 to conduct Logistic Regression model. Categorical variables were reported as frequencies and percentages. Pearson Chisquare test was used to study the associations between categorical groups. p value < 0. 05 was considered statistically significant.
Acknowledgments We are thankful to all the people and platforms that helped us publish the online survey on social media, especially:  Syrian Researchers Organization ,  Impact Makers Team ,  Syrian Drugs Up To Date Platform, MedDose Organization (NGO). Also, we would like to thank  Prof. Sliva Ishkhanian , and  Prof. Mohamad Morjan  for their support. And we value the statistical consultation provided by  L. Alasadi. Data Collection Group:  Hanaa Wael Zahrawi 1 , Anan Mhd tawfik Bakdounes 1 , Ahmad Nabil Alhouri 1 , Nour Zuhir Abdulal 1 , Rama Adnan Hawari 1 , Mhd Homam Safiah 1 , Sara Omar Alazrak 1 , Rama Ahmad Alsayed Husein 1 , Mohammed Moutaz Alshaghel 2 , Wafik Mayo 2 , Abdallah Aladna 2 , Abdulkader Hajjar Mwaffak 2  ,  Shahd Maarrawi 2  ,  Marah Khalil 2  ,  Lana Almahairi 2  ,  Yaman Mokresh  2 , Mouhammad Saleh Younes 5.
Firstly, it is mirrored by the applicable in Ismaili arbitration On the one hand, Article 15 of the Ismaili Constitution provides that Ismaili arbitral tribunals (see below) can apply Ismaili personal law where this speaks to the issues at play and does not conflict with state law. On the other hand, the Constitution remains silent on the law to be applied when commercial issues are at stake, as in the Jivraj case. It was confirmed by a judicially appointed expert, representative of the Ismaili arbitration system, that Ismaili commercial The official English text is available on the website of The Heritage Society, the Ismaili electronic database and library: http://ismaili. net/Source/ extra1. html, last accessed 25 May 2021. (Penrad 2008, pp. 126, 131, 133). As is the case in Jewish communities (see, e.
Data analysis Respondents were defined as having responded to at least one item of the socio-demographic and perceived information questionnaire. We used Student's t-test for continuous data and Chisquare test for categorical data to compare respondents and nonrespondents on age, having children and having a first-degree relative carrying a BRCA1/2 or PALB2 PV. Principal component analysis identified two sets of items within the questionnaire on BC risk factors, corresponding to GRF (Cronbach's alpha ¼ 0. 79) and NGRF (Cronbach's alpha ¼ 0. 95). This allowed deriving two multi-item scale scores by summing response scores to items in each of the GRF and NGRF set. For all multi-item questionnaire scales, missing data were replaced by the mean per counselee if at least 50% of the items per domain were answered.
Multilevel multivariate linear models were tested on two outcomes: 1) Perceived information received on GRF and 2) Perceived information received on NGRF. A random effect was introduced in the model on the intercept in order to account for the fact that a given clinician could encounter several counselees. Tested associations in bivariate analyses included age, education level, marital status, having children, BC risk perception, anxiety, depression, engagement and disengagement coping, having a first-degree relative carrier of PV and having lost a family member due to cancer. Significant bivariate associations at p-value < 0. 10 were retained in multilevel multivariate linear models. All analyses were performed using the statistical software SAS version 9. 4 (SAS Institute, Inc. , Cary, NC, USA).
A complete list of variables is provided in SI Appendix. Samples and Participants. Data were derived from 46 independent samples, involving 31 countries and regions across the globe (for details, see SI Appendix, Table  S1 ). To target comparable samples across nations, we primarily targeted student populations between 18 and 25 y of age. Overall, we collected responses from 10,353 individuals. After omitting a number of incomplete answers, we were able to compute a valid SoMi score for 8,354 participants (2,916 males, 4,913 females, and 525 did not report), M age = 21. 98 y, SD = 5. 19. Procedure and Materials. Data were collected in the course of 2015. Because a general proficiency in English was expected in most academic settings, the survey was presented in English where possible. However, when deemed necessary by the local research team, the survey was translated the relevant native language.
However, parameters and tests were derived and run on the whole sample. From the available economic indices, we used variables per capita to prevent confounds from the size of the country population. GDP was log-transformed to linearize the relation with SoMi. We standardized variables and ran all mixed models using R (package lme4)  (60)  with country (level 2) as the clustering variable. After comparisons with other models through the Akaike and Bayesian information criteria, we selected the model with the best fit  (61). Data Availability. Data and associated protocols have been deposited on the Open Science Framework (https://osf. io/8w2mg/).