dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1992; Geary, 1995; Dehaene, 1997 Dehaene, , 2001;; Berch, 2005; Gersten et al. , 2005; Jordan et al. , 2010; Chu et al. , 2015; Sowinski et al. , 2015). In the current study, the fact that the items loading the more on the general factor were very fundamental skills gives credit to the possibility that number sense corresponds to this general factor. The last hypothesis is that the general factor would only be a psychometric artifact due to the positive manifold of correlations observed between cognitive test tasks (van der  Maas et al. , 2006 Maas et al. , , 2011;; Borsboom, 2017; Fried et al. , 2017). According to  van Bork et al. (2017) , "the existence of a general factor is not tested against the data because any dataset that features a positive manifold will necessarily support a general factor model, whether or not a general factor underlies the data" (p.
(2020)  have shown the superior fit of a network model of intelligence compared to a higher-order g model and a bifactor-CFA model. As Network Analysis is not implemented in MPlus and currently not available with binary variables within the reference R package Psychonetrics  (Epskamp, 2020a,b) , we did not have the opportunity to compare the ICM-CFA and ESEM models with a Network Analysis model of early numeracy. Anyway, this would not have provided a definitive answer to the question of which statistical model best represents reality, as the aim in the social sciences is to obtain the best theoretically and practically useful representation of what is under investigation. Importantly, authors of the Network Analysis approach have never compared network models with ESEM and bifactor-ESEM models.
Data Sources We use survey data on the electoral behavior of South American immigrants in the United States and Spain. We focus exclusively on first-generation foreign-born South American immigrants who have naturalized and are United States/Spanish citizens at the time of interview. To obtain a large enough sample size in each country, we have postharmonized and pooled a number of surveys that included immigrant respondents in each of the countries. and 2018, all from the Centro de Investigaciones Sociológicas databank: the electoral surveys of  2008, 2011, 2015, and 2016 , as well as numerous political barometers and the Spanish General Social Surveys of  2013, 2015, and 2017. This provides us with a sample of 550 South American-born naturalized immigrants for the United States and a sample of 1,761 South American-born naturalized immigrants for Spain.
Turnout First, we examine the turnout of South American immigrants in both countries. 3 The average declared turnout rate for South Americans is 71% in Spain and 75% in the United States, which is in the expected direction of greater mobilization in the United States, although the gap between the two groups is small. This compares with an average declared turnout rate of 83% in Spain and 79% in the United States for the overall population, which means that the turnout gap with the overall population is considerably larger in Spain. Yet Figure  1  indicates that the variance in turnout is higher in the United States than in Spain, even when focusing solely on presidential elections: It oscillated between more than 90% in 2008 (when Obama first won) and 68% in the 2012 elections. Although the sample size for the United States is too small to allow for meaningful analyses across countries of origin, the analysis of the Spanish data reveal considerable turnout gaps by country of origin, with Argentinians and Venezuelans voting at rates around 80% and Bolivians and Ecuadorians at around 65%.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This article and its open access publication is based upon work funded by  COST Action 16111 EthmigSurveyData  (https://www. cost. eu/actions/CA16111/),supported by  COST (European Cooperation in Science and Technology)  and funded by the  Horizon   2020 Framework Programme  of the  European Union ; the  University of Oxford ; the  Universidad Autónoma de Madrid , and the  Casa de Velázquez in Madrid.
The details of the natural model could be of no use because they have to be rebuilt in the architectural field. Following this trail, we see that Philippe Rahm, for example, declares that the source of his biological knowledge is taken in two books  (Koolman and Henrich Ro ¨hm, 2004; Silbernagl and Despopoulos, 2001)  and mentions Google scholar and Nature in general (rather than a specific publication). The fact that the books are student textbooks published several years before the competition Central Park (and even more before its opening) should lead us to doubt on scientific agenda of this knowledge. Whereas the databases he uses show that these scientific fields are very productive 20. This great amount of publication indicates on the other hand a rather imprecise reference: aren't they any progress, contradiction, refutation in such a productive discipline? In the same vein, Art & Build office takes interest in the nastic motions of plants which, depending on species, react to various stimuli as light temperature and are due to a cell growth  (Doorn and Meeteren, 2003).
The aim of the quasi-experiment was to understand the behavior of these two groups of students in relation to the consumption of learning resources. Specifically, the following research questions were evaluated: (RQ1) How does the use of MMS influence the consumption of SPOC learning resources? and (RQ2) How does the use of MMS correlates with the course passing rate? Section II presents a literature review in relation to the use of SPOCs in BL courses. Section III presents the details of the quasi-experiment by describing the experimental scenario, the collected data and analysis methods. Section IV presents the results of the quasi-experiment. Finally, Section V presents the general conclusions and future work.
All students taking the course were part of 5 programs. For this study, and in order to avoid the influence of the teacher, only 3 programs were selected in which the teacher was the same. MyMOOCSpace (MMS) is the mobile tool that was used as a complement to the SPOC for promoting students' activity with the online resources. This tool works on Android mobile devices only. For the correct execution of MMS during the academic semester the following activities were performed. First, SPOC questions and answers were loaded into MMS database. In this way, the questions and answers of the course were the same as those that students could find in the SPOC. Secondly, the random functionality was applied to show the questions randomly to students in each phase of the game. Each phase of the game was graphically represented by a planet, and each planet contained the contents of a week of the SPOC.
c) Instruments and Data gathering Techniques We used mixed methods to address the research questions RQ1 and RQ2 combining qualitative and quantitative data  (Creswell & Plano Clark, 2011). Table  2  presents a summary of the instruments used for data gathering purposes. All the instruments were validated by the ethics committee of the university of origin of the research (Pontificia Universidad Católica de Chile). • SPOC Database. This refers to the Open edX database where the SPOC of the Systems Programming course is hosted. Information associated with the consumption of the learning resources of the course, time of use of each resource and the dates in which the resources were used was extracted from this database. It is inferred from the SPOC database who are the students that most frequently consulted the contents and each one of their learning resources (videos and training exercises).
• MMS Database. From these data, information associated with the students' behavior when facing each question throughout the different stages of the game was extracted. From this database, it is possible to distinguish those questions that have the greatest access frequency by the students, the time that each team of participants of the game remains in each module of the course, the dates of access to each resource, the scores obtained by the team, a ranking of the teams with greatest participation, among other data. From the MMS database, the group GTrad (students who did not use the tool) and the group GTest (students who requested access to the tool by email) are identified. • MMS Logs. Text file where the level of use and times of each team per planet were extracted. In addition to this file it is possible to extract the students' behavior in the tool in terms of performance and response times.
d) Methods To answer (RQ1) "How does the use of MMS influence the consumption of SPOC learning resources?" we perform a statistical analysis on the delivered responses considering the before and after questionnaires. This analysis was complemented with an analysis of the data recorded in the SPOC and MMS databases to cross check the data of the different students in both platforms. Analysis of the Questionnaire Applied Before the Quasi-Experiment. According to the different answers and the use of a scale that allows tabulating the results obtained, we generated a skill matrix indicating their competencies. According to PISA (OECD, 2016), students can be classified into three proficiency level groups: • Low. Students who respond little or nothing to the requests of their teammates; their contributions have little value.
To this end, the four dimensions proposed by Smith-Jentsch et al. (  1998 ) evaluated, grouped under the name of Team Dimensional Training (TDT). From these dimensions, there are three possible values for team performance: • Low: Team with little or no communication, low level of support among members. Leadership skills and proactivity are not perceived. • Medium: There is an exchange of information, but what is communicated is not fundamental. Some level of support is present among the members, but it must be requested explicitly. There may be a leader, but the rest of the members are not proactive. • High: The exchange of information is efficient, with clear terminology relevant to the context. Support and feedback among team members is present, especially with more complex questions. For the analysis of the data from both databases we used the Wilcoxon test (Figures  1  and 2  show that the data distribution is not normal) comparing the results of the before and after tests of the students belonging to the GTest and the students belonging to the GTrad group.
For the second research question (RQ2), "How does the use of MMS correlates with the course passing rate?", we analyzed the final grades and cross-checked with the data collected through MMS and SPOC. This data analysis was done to understand the behavior of the students when facing the challenges proposed in MMS and the SPOC regarding whether there was an increase or improvement in the grades of those students who used MMS. To evaluate if there are significant differences between the passing rate of the students of the GTest and the GTrad, we performed the chi-square statistical test. In addition, a frequency analysis of the students' interactions with the course was made from the SPOC database. This analysis provides the percentage of consumption of each learning resource per hour during the experiment in relation to the degree of progress of students in the game.
Also, from the MMS database and the final survey of the students, it is concluded that the students belonging to the GTest group accessed more frequently the learning resources in the SPOC. Figure  6  shows the interaction frequency with the different topics of the course during the weeks of the semester. From this it is observed that the topics "Binary Tree" and "Sorting and Searching", corresponding to topics 4 and 5 respectively of the course, are the most consulted. According to the Wilcoxon test and sensitivity analysis, the numbers are still higher compared to the numbers of those who did not use MMS (see Table  3 ). The chi-square analysis on the passing rate of students using MMS is higher than that of those who did not use it (see Table  6 ).
Acknowledgments This project is partially financed by the  Universidad Bernardo O'Higgins, Santiago 's grant ( UBO 'H Rect. No. 1000/532/14 ), the  Corfo 2030  Project ( 14ENI2-26862 ), the  Conicyt grant  ( 21161264 ) for doctoral studies, the  LALA  Project (grant no. 586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP ) the  FEDER/Ministerio de Ciencia, Innovación y Universidades -Agencia Estatal de Investigación  through  Project Smartlet  ( TIN2017-85179-C3-1-R ), the  Madrid Regional Government through eMadrid Network  ( P2018/TCS-4307 ), and the  European Commission through Erasmus+ projects LALA  ( 586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP ),  InnovaT  ( 598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP ) and  PROF-XXI  ( 609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP ). This publication reflects the views only of the authors and funders cannot be held responsible for any use which may be made of the information contained therein.
15 We conducted 12 sessions at the University of Montpellier in June 2018, with approximately 20 subjects per session. The first session with 20 subjects was a pilot to check our program and is excluded from the analysis. We have complete data from 210 subjects. The subjects, recruited using ORSEE  (Greiner, 2015) , were mostly students and 55% were male. While most had experience in experiments (89%), none had previously participated in a similar experiment. The experiment typically lasted for one hour and average earnings were €8, ranging from €1. 50 to €16. 50. These earnings were added to the show up fee of €2 for students of the Economics Department (where the experimental lab is located) and €6 for those from outside the department.
We thank  Jean-Marc Rousselle  for assistance in programming and running the experiment, and  Olivier L'Haridon  for sharing his experimental instructions (in French) on certainty equivalent elicitation. We are also grateful for helpful comments provided by conference and seminar audiences at the  Toulouse School of Economics , the  BEST Conference on Human Behavior  and  Decision Making , and the  UQ BESC social sciences workshop. Finally Céline Nauges  acknowledges funding from the  French National Research Agency (ANR)  under the Investments for the  Future (Investissements d'Avenir) program , grant  ANR-17-EURE-0010.
| Radiocarbon dating and age-depth modeling A total of 57 samples from seven cores (PAL02-PAL08; see Table  1  and Figure 2a )w e r er a d i o c a r b o n -dated at Poznan Radiocarbon Laboratory. Core PAL01 was not 14 Cd a t e db e c a u s ei ti sa sister core of PAL02 with almost the same stratigraphy. All radiocarbon ages were calibrated in OxCal 4. 4 (Bronk Ramsey, 2009) using IntCal20  (Reimer et al. , 2020)  and are reported in Table  1. Most of the dated samples were composed of organic carbonate mud, organic mud, peat, and occasionally gastropod shells. Age-depth models were constructed based on a Bayesian approach in "Bchron" package  (Haslett & Parnell, 2008 ;P a r n e l l et al. , 2008). T h ec o d ew a sr u nu s i n gRv e r s i o n3. 5. 3( R S t u d i o Team, 2015). We used the "accumulation" output field in "Bchron" to create sedimentation rates for the eight sediment cores.
ACKNOWLEDGMENTS We wish to thank the  Iranian National Centre for Oceanography and Atmospheric Sciences  and particularly its director Prof. Behrouz Abtahi  for all their support from the beginning of this study. Morteza Djamali  and  Hamid Lahijani  wish to thank the  Iranian Vice-Presidency for Science and Technology and Iran's National Elites Foundation  for their support of the scientific cooperation between  INIOAS  and  Institut Méditerranéen de Biodiversité  et d' Ecologie. Morteza Djamali  is grateful to the staff members of the municipal library of Marseille (the Alcazar) Olivier Polère and  Thierry Conti  (Fonds rares et précieux) for their generous help and cooperation in providing access to Pascal-Xavier Coste's archive. We are indebted to two anonymous reviewers and the associated editor of the journal for their invaluable and constructive comments and corrections to our manuscript.
Field investigations were supported by the " PALEO-PERSEPOLIS " project ( 2015-2019 ) funded by the  Agence Nationale de la Recherche  ( ANR-14-CE35-0026-01 ). Radiocarbon datings were funded by the  AQUA-IGNIS  project supported by the  AMIDEX Foundation (Aix-Marseille Université)  and the  LIA  project entitled " HAOMA " supported by  Centre National de la Recherche Scientifique (CNRS). Andrew Parnell 's work was supported by a  Science Foundation   Ireland Career Development Award grant   17/CDA/4695  and an  SFI  centre grant  12/RC/2289_P2.
Methods: We followed the scoping review methodologies developed by Arksey and O'Malley and the Joanna Briggs Institute. After developing search strategies based on keywords (eg, social media, health research), comprehensive searches were conducted in the PubMed/MEDLINE and Web of Science databases. We limited the search strategies to documents written in English and published between January 1, 2005, and April 9, 2020. After removing duplicates, articles were screened at the title and abstract level and at the full text level by two independent reviewers. One reviewer extracted data, which were descriptively analyzed to map the available evidence. Results: After screening 1237 titles and abstracts and 407 full texts, 268 unique papers were included, dating from 2009 to 2020 with an average annual growth rate of 32.
The search strategy, highlighted in Textbox 1, included two sets of search terms: (1) one linked with SM (eg, social media) and (2) one linked with research (eg, health research, biomedical research). In order to capture the evolution of SM uses for health research over the years, databases were searched between January 1, 2005, and April 9, 2020. The term "social network" was also searched, as it is often misused as a synonym of SM. An additional list of 5 relevant articles  [19] [20] [21] [22] [23]  was manually searched to identify any other potentially relevant articles not yet captured. These articles were chosen in order to retrieve more articles about infodemiology, ethical issues, or the use of SM data. A snowball searching technique was adopted with these 5 articles in which citations within articles were searched and kept if relevant to the review.
Documents related to the mining of social media data to detect prescription drug misuse and abuse as well as those related to the use of machine learning methodologies to analyze data were eligible for inclusion. We included full texts that reported on at least one of the following outcomes: (1) SM data analysis, (2) recruitment through SM, (3) methodology for SM research, and (4) ethical issues of using SM for health research. Only English-language articles were retained. The inclusion and exclusion criteria and the search strings are summarized in Textbox 1.
Data Extraction Data were abstracted on (1) the country of origin, (2) the aims of the study (eg, to map ethical issues when using SM for health research), (3) the type of study (eg, recruitment feasibility assessment), (  4 ) the research field (eg, mental health research), (  5 ) the studied population (eg, adolescents), (  6 ) the type of SM (eg, Facebook),  (7)  the methodology (eg, paid advertisement),  (8)  the outcomes of the study (eg, efficiency of recruitment via SM), and (  9 ) the key findings for our scoping review (eg, possibility to recruit on SM). Data were extracted and cleaned by a first reviewer (CB), then verified and approved by a second reviewer (GF).
Strengths and Limitations of This Scoping Review The present work used a rigorous scoping review methodology from the manual by the Joanna Briggs Institute  [16]  throughout the entire process. It was guided by a previously published protocol  [18]. To ensure a broad search of the literature, the search strategy included two electronic bibliographic databases and the snowball technique. There are some limitations to our scoping review process. We may not have identified all relevant articles in the published literature despite attempts to be as comprehensive as possible. We limited our review to documents written in English, which may have led to missed relevant studies. Data were abstracted by one reviewer and verified by a second reviewer because of the important number of included publications.
eXPeRIMeNTAL ReSULTS The proposed method is implemented in C++ builder. The authors have conducted their experiments on real weighted MRI T1 and T2 images of patients with multiple sclerosis. The images were obtained using a matrix of size 181 X 181, number of slices = 20 and thickness slices = 5 mm. The Figures 2, 3, 4 and 5 show the obtained results of AIS approach. Figures 2(1), 3(1), 4(1),  and 5(1) show the original images, Figures 2(2), 2(3), 2(4); 3(2), 3(3), 3(4), 4(2), 4(3), 4(4)  and 5(2),  5 (3), 5(4) show the AIS results and finally Figure  2 (5), 3(5), 4(5) and 5(5) Show the detected lesions. They have compared the proposed approach with their previous approach  (Merzoug et al, 2014)  and another method which segments multiple sclerosis (MS) lesions in WM using a modified algorithm that relies on the histo based fast fuzzy C-means (HFFCM)  (Yaswanth et al, 2016) using 3D simulated MR data (brainweb).
CoNCLUSIoN In this paper, the researchers have proposed an approach of multiple sclerosis lesions segmentation in 3D brain MR images based on three steps. The first one segments brain tissue by artificial immune systems (AIS). Then the second step extracts features and the last one classifies MS lesions by SVM. For further research, the authors can propose to use another features to remove irrelevant and redundant features, and to conduct the experiments on other brain database IRM. Also one could improve the detection of lesions in MS by exploring the techniques based on Fuzzy neural networks.
How essential is trust in science to prevent the spread of COVID-19? People who trust in science are reportedly more likely to comply with official guidelines, implying that higher levels of adherence could be achieved by improving trust in science. However, analysis of a global dataset (n = 4341) suggests otherwise. Trust in science had a small, indirect effect on adherence to the rules. Nonetheless, it predicted people's approval of prevention measures such as social distancing, and bridged political ideology and approval of the measures (conservatives trusted science less and in turn approved of the measures less). These effects were stronger in the USA than in other countries. Even though any increase in trust in science is unlikely to yield strong behavioural changes, given its relationships with both ideology and individuals' attitudes to the measures, trust in science may be leveraged to yield longer-term sustainable social benefits.
Methods Participants. This data was collected as part of a larger project on the normative and social aspects of COVID-19  (Tuncgenc et al. , 2021). A convenience sample was recruited in April and May 2020 via social media, university mailing lists, press releases and blog posts. Participation was not compensated. Overall, 6675 participants completed the survey. However, participants were able to opt out of certain personal questions (e. g. , on political ideology). Further, the operationalisation of "close social circle" (see below) meant that some participants responded that they had no close circle, in which case there is no data for whether they thought their close circle was adhering to COVID-19 measures (our social conformity measure). These two sources of missing data mean that there are 4341 complete responses for the variables reported here.