dataesr/scientific-paragraphs-categorization huggingface dataset:
Response-time data was analysed with a 3x4 repeated measures ANOVA (3 levels of luminance contrast vs. 4 hues), which yielded a significant main effect of luminance contrast ((1. 49, 28. 28) = 67. 56,  < 0. 001,  2 = 0. 78) and interaction with hue ((6, 114) = 3. 56,  = 0. 003,  2 = 0. 16) -hue itself did not have an effect ((1. 88, 35. 79) = 2. 93,  = 0. 07). We deconstructed the interaction by performing separate repeated measures ANOVAs at each luminance contrast: while at isoluminance there was a significant effect ((3, 57) = 6. 19,  = 0. 001,  2 = 0. 25) driven by slower RTs for green (vs. red  = 0. 019; vs orange  = 0. 008, vs. turquoise  = 0. 003), there were no differences at 45% luminance contrast ( = 0. 16) or at 90% luminance contrast ( = 0. 11). After the completion of the EEG experiment, participants rated each colour on a 9-point Likert scale for its representativeness of its category (red, orange, green or turquoise).
Current study In the present study, we evaluated the efficiency of the previously describe metrics on the eye tracking data from novice and expert pilots. Our main hypothesizes were that expert pilots should exhibit different visual behaviors than novices, including more numerous dwells and shorter dwell times, following the idea that superior perceptual encoding processing comes with expertise. We expected also a sensitivity of all advanced metrics to expertise, with more visual scanning complexity (as evaluated by the Lempel Ziv complexity and the visual pattern lengths), and a more regular visual scanning (as evaluated by the transition entropy) in experts. We also assumed that the pilots' expertise could be classified (using machine learning) in their way that they switched from an instrument to another, using transition matrices.
Data processing Flight simulator and eye-tracking data were analyzed using MATLAB R2019b with custom homebuilt scripts. The data were recorded from the beginning of the landing scenario to touch-down. Because the landing duration depends on the pilot's actions, landing durations could differ by a few seconds. As a consequence, the beginning of the scenarios has been cut out to obtain the same duration for each participant, corresponding to 14,000 frames sampled at 60 Hz for the eye-tracking data and 233 frames at 1 Hz for the flight simulator. Eye tracking data. Fig 4  shows the entire eye tracking pipeline analysis. Each AOI was coded using numbers from 1 to 10 corresponding to the flight instruments (see  Fig 3). Only AOI-based data were extracted in this experiment and concatenated to obtain two chronological vectors containing the indices of the visited AOIs (from 1 to 10) and the time spent on them.
Flight simulator data. The flying performances were examined to quantify the ability of the pilot to comply with the specific flying instructions given by the experimenter. As presented in Fig  5 , Root Mean Square Errors (RMSEs) were calculated for 3 different flight parameters: speed, vertical speed, and heading. In this experiment, the predicted values corresponded to the different specific threshold given by the experimenter (i. e. , speed 130 kt; vertical speed below -500 ft/min and above +800 ft/min; heading different from 143˚) and the observed values corresponded to actual pilots' performances. The deviations were calculated following the formula: RMSE k;kþ1 ¼ ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi 1 n X n i¼1 ðO i À P i Þ 2 s ð3Þ as where for n data points between points k and k + 1, Pi was the predicted value and Oi the observed value.
These eye metrics can be used to evaluate pilots during their training program. For example, it might be possible to follow the evolution of their scanning strategies and determine whether they tend to resemble that of expert pilots. In the future, it might be possible to assess cockpit monitoring during real flight  [76, 77]. In this sense, recent studies investigated the possibility to use an eye tracking assistant to warn pilots using a database of the visual behaviour of expert pilots  [78] [79] [80]. Our results suggest that such on-board eye tracking could be customized based on pilot experience. Finally, we believe that the eye metrics employed in this study can be also useful for practitioners and researchers in other fields such as air traffic control and automotive.
| Strengths and limitations Our study included only publications from PubMed. Other databases such as EMBASE, SCOPUS and PsycINFO and grey literature (theses and dissertations, research and committee reports, government reports, conference papers, and current research) were not included. We believe that an article research of the PubMed database is sufficiently extensive to illustrate the various definitions of BDZ highdose that can be found in the scientific literature. Moreover, this research is the first narrative review of its kind to identify all the used definitions associated with benzodiazepine high-dose.
Database This retrospective study used data from the Disease Analyzer database (IQVIA), a database containing demographic, clinical, biological, and pharmaceutical data obtained in primary care practices  ( Rathmann et al. , 2018 ). These data are collected in an anonymous format from general and specialized practices. Diagnoses are coded using the German version of the International Classification of Diseases, 10 th revision (ICD-10), and prescriptions using the Anatomical Classification of Pharmaceutical Products of the European Pharmaceutical Marketing Research Association (EphMRA). The quality of the data available in the database is regularly assessed on the basis of several criteria such as completeness of documentation and linkage between clinical and pharmaceutical data  ( Rathmann et al.
Strengths and limitations The major strengths of this study are the large sample size and the use of data obtained in more than 960 general practices. However, the study results should be interpreted in the light of several limitations. First, no COVID-19-like symptoms were documented in the database, and it was therefore not possible to assess the prevalence of specific symptoms (e. g. , cough, fatigue, or fever) in the sample and their individual association with COVID-19 diagnosis. Second, there was a lack of data on health behaviors (e. g. , physical activity or adherence to preventive measures), although these behaviors may predict the risk of being diagnosed with COVID-19. Third, a substantial proportion of patients with COVID-19 may have been diagnosed in emergency departments, and the prevalence of COVID-19 may thus have been underestimated in the present study.
Aim: The purpose of this study is to investigate the current state of knowledge about emotional intelligence (EI) in nursing education Background: Human relationships and emotions are an integral part of nursing care and contribute to care quality. This part of the profession must be addressed in nursing education to prepare students and deal with their emotions and the patients's. Research on emotional intelligence (EI) in nursing has been particularly developed recently. A broad understanding of many benefits of EI is crucial to include EI in this training program. Design: Systematic literature review Methods: Undertaken using electronic database (Cairn; PubMed; Science Direct; Wiley online library) and specific search terms. This research covered articles published in English and French between 2007 and 2021.
Data source This systematic review targeted all empirical studies on EI in nursing education. Four search engines were used: Cairn, PubMed, Science Direct, and Wiley Online Library. The search was conducted in September of 2020, reconducted in April 2021 and included different combinations of the following keywords: nursing students, nursing education, nurse education, Emotional Intelligence (EI), emotional skills (ES), and emotional competencies (EC). These keywords were used for each database.
Inclusion and exclusion We limited the search to articles published in the French and English. Research on EI in nursing education is recent so there is no restriction on the year of publication. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines (PRISMA) directed the systematic review process  (Moher et al. , 2009). The search strategy is the same for each database. Keywords are combined (e. g. , nursing students and emotional intelligence) and the titles associated with these keywords are analyzed. Articles were excluded if they did not focus on nursing students and EI and if compares EI between students without other variables. Non-peer-reviewed works such as doctoral dissertations and published abstracts (without a full method section) were excluded (Fig.
Study selection Articles identified in the Fig. 1  as relevant are uploaded. Abstracts reviewed by the researcher and those that met the inclusion and exclusion criteria were fully reviewed. Screening of the articles began with examining methodology, focusing on the EI model used and variables measured. The search identified 465 titles in the databases. After deleting duplicates and unavailable articles, 94 papers are analyzed. After title and abstract screening following by detailed analysis of the full papers, 57 articles met the inclusion criteria and were reviewed.
Moreover, not all databases are accessible in France. Thus, some references could not be accessed in their entirety and be included in the review. However, searches using the major search engines as quoted in this review provide a comprehensive review to the research question. Only articles in French and English were included, corresponding to the languages mastered by the papers's authors. Thus, some studies could not be analyzed. In addition, only the main author carried out the literature review on the subject. The involvement of several researchers could have made it possible to obtain more articles.
| Objectives and hypotheses This study will investigate the clinical efficacy of administration of probiotics (in addition to treatment as usual, TAU) for 6 months in hospitalised adolescents with AN compared with placebo and follow them up at 6 months and 1 year. The core focus of this study is to evaluate whether probiotic supplementation support weight gain, improve eating disorder psychopathology, other psychopathology and gastro-intestinal symptoms. Specifically, our primary and secondary hypotheses are as follows. Furthermore, associations of the MI with structural and functional brain-imaging, serum (e. g. , IgG, inflammatory markers, hormones), neuropsychology and psychopathology will be explored. Details regarding the acquisition and analysis of brain-imaging data will be reported in a separate protocol paper.
| Study design and recruitment The present study is a two-centre, longitudinal, doubleblind, randomised, placebo-controlled trial (RCT) comparing the clinical efficacy of administration of probiotics (in addition to TAU) compared with placebo in 60 patients with AN. TAU consists of psychiatric/ psychotherapeutic inpatient treatment with nutritional rehabilitation aimed at the restoration of a healthy weight and starting a psychotherapeutic process (details on TAU with outcome data can be found in  Mairhofer et al. [2021] ). It is important to include inpatients only, as nutritional rehabilitation needs supervision and very detailed reporting of food, fluid and probiotics/placebo intake. Outcome data will be compared to 60 sex-and age-matched healthy controls who will be followed for one year but do not receive any intervention.
The V3-V4 region of the 16S rRNA gene will be amplified and sequenced on an MiSeq Illumina sequencer. Raw reads will be quality checked using FastQC v0. 11. 6  (Andrews et al. , 2015). QIIME2 will be used to process and analyse the sequence data  (Bolyen et al. , 2019). Paired end sequences will be denoised by using dada2  (Callahan et al. , 2016) , and clustered into amplicon sequences variants (ASVs) using 'vsearch' with an identity of 0. 97  (Rognes et al. , 2016). Bacterial ASVs will be annotated using q2-feature-classifier plugin  (Bokulich et al. , 2014). The ASV table will be read into R v3. 2. 3 for statistical investigation with the vegan package  (Dixon, 2003). Blood and stool samples are stored at BIOBANK General Hospital of Vienna and BIOBANK of the Medical University of Innsbruck.
Introduction In 2015, Dutton and Lynn published a study claiming that intelligence scores in France are decreasing. This study was based on data collected by the publisher of Wechsler's adult intelligence scale (WAIS;  Wechsler, 2000 Wechsler, , 2011)). In the process of developing the WAIS-IV in 2009, the publisher collected a sample of 79 subjects who performed both WAIS-III and WAIS-IV, in order to ensure that the two versions had convergent validity. Dutton and Lynn (2015)  compared performance averages reported in the test manual for this validity sample, and observed that these subjects had higher standardized scores on the WAIS-IV (in reference to its 2009 normative sample) than on the WAIS-III (in reference to its 1999 normative sample). Using the 79 subjects performing both versions as a common reference point, they interpreted this finding as evidence that the 1999 WAIS-III normative sample had higher average ability than the 2009 WAIS-IV normative sample (for details on this reasoning, see  Flynn, 1998b).
Study 1 re-analyzed the same dataset as  Dutton and Lynn (2015) , using the raw data to which they did not have access. To this end, we obtained permission to use the original data from the test publisher. We complemented the data with inferential tests and appropriate effect sizes, so as to determine whether the difference between WAIS-III and WAIS-IV was significant for all subtests, or whether it was driven by subtests with a large Gc loading (Vocabulary, Similarities, Information, Comprehension, Arithmetic), as the data seemed to suggest. We also verified the composition of the sample (the distribution of age and IQ in particular) to ensure that there were no issues that could affect the results. Study 2 aimed to replicate and extend the results of Study 1 by collecting a new dataset (N = 79).
This was partly intended as a replication effort to compensate for the small sample size of Study 1; we conducted analyses both on the new sample, and on the combination of Study 1 and Study 2 samples (for a total N = 155) to ensure stability of the results. Another purpose was to collect item-level data, which were not recorded for the Study 1 dataset: this made it possible to determine whether differences between versions could be driven by DIF on certain items of the WAIS-III, reflecting systematic bias against a recent sample. The French publisher also authorized access to the item-level data for the WAIS-III 1999 normative sample (N = 1104); item difficulty parameters for the subtests demonstrating a decline were estimated, and compared with the new sample collected in 2019 for Study 2.
The dataset The French publisher authorized access and use of the raw data for the validity sample collected to assess convergent validity between the WAIS-III and WAIS-IV. As described in the test manual and in  Dutton and Lynn (2015) , the dataset comprised 79 subjects (mean age = 44. 53 years, SD = 13. 71, range = 30 to 63 years). Each subject completed both the WAIS-III and WAIS-IV in counterbalanced order (40 subjects performed the WAIS-III first and 39 performed the WAIS-IV first). Dutton and Lynn (2015)  raised concerns regarding the interval between the two sessions, but this information was not recorded. Performance on each subtest was computed in reference to the respective normative samples of each version (N = 1104 for the WAIS-III, collected in 1999, and N = 876 for the WAIS-IV, collected in 2009).
Additional information not available in the manual could also be retrieved from the test publisher, and is provided here for archival purposes. This validity sample was collected in the process of developing the WAIS-IV, by psychologists hired by the publisher to collect this validity data on a paid-per-protocol basis. All psychologists received specific training from the publisher prior to data collection; each psychologist sent back protocols to the publisher to ensure that they complied with data collection instructions and that the test was scored correctly. Each subject completed the WAIS-III and WAIS-IV with the same psychologist. Subjects who completed the WAIS-IV first (n = 39) were included in the normative sample for the WAIS-IV, and there was additional information available for them (sample composition should be similar for the other half of the sample).
Re-analysis of the Data, Taking into Account Sample Composition Apart from the additional details on sample composition summarized in the Methods (section 2. 1. 1), two aspects of sample composition invited further scrutiny. The first was the age of subjects in the sample. The descriptive statistics for age, as stated in the test manual and in  Dutton and Lynn (2015) , were M = 44. 53 years, SD = 13. 71 years, range = 30 to 63 years. This is technically correct, but overlooks the very peculiar composition of the sample, represented in Figure  1. As is immediately visible, the data were actually collected in two discrete age groups: 30-to-34 and 55-to-63 year-olds. (Note that the counterbalancing with test order was performed correctly: 20 younger and 20 older subjects performed the WAIS-III first, whereas 21 younger and 18 older adults performed the WAIS-IV first.
) From the point of view of the publisher, recruiting a younger and an older group was a reasonable choice: this validity sample was collected only to ensure that the WAIS-III correlated with the WAIS-IV, and the publisher wished to ensure that this was the case for both younger and older adults; instead of collecting a few subjects in all age groups, they collected more subjects in two extreme age groups and checked that the correlation between versions was high in the two groups. Because this validity sample was never intended to be analyzed for other purposes, there was no particular incentive to make it representative. Thus, the sample in this dataset was definitely not representative of the general population as a whole, but there was no reason to expect that it should be the case (see also  Zhu & Tulsky, 1999).
We also examined the distribution of IQ scores in the sample, which revealed that out of 79 subjects, five reached the threshold for intellectual disability (FSIQ less than 70; the lowest IQ in the sample was 57, about three standard deviations below average) on both the WAIS-III and WAIS-IV. This represented a larger prevalence of intellectual disability than expected in the general population (6. 3% of the sample instead of an expected 2. 5%), and yielded an unbalanced range of ability given that there were no gifted subjects at the other end of the scale (the highest FSIQ in this sample was 123). Again, including these subjects may have made sense for the publisher as a way to ensure that both versions of the test yielded similar conclusions about intellectual disability, but it would make more sense to exclude them from a dataset testing for the Flynn effect -the absence of intellectual disability should reasonably have been an exclusion criterion for such a study.
Participants Study 2 was designed as a replication of Study 1, meaning data collection was planned for the same sample size. A total of 81 subjects, recruited by word of mouth in the community, participated in the study. As in Study 1, subjects were included using the method of quotas, with quotas on gender, age, and socio-economic level. The sample included 42 women and 39 men, mean age = 38. 44 years, SD = 11. 94, range = 20 to 60 years (with a continuous distribution). Socio-economic groups were representative of the general population (1 farmer, 3 artisans, 17 executives and other intellectual professions, 19 workers of intermediate level, 20 employees, 15 laborers, and 6 students or unemployed persons; retired persons counted as per their former profession). All subjects were native French speakers, and none had a history of major neurologic or psychiatric disorder.
Data Analysis As in Study 1, the first series of analyses testing the difference between WAIS-III and WAIS-IV used within-subjects t-tests, with dz effect sizes and FDR correction for multiple comparisons. A second series of analyses combined the Study 1 and Study 2 datasets, so as to increase power to detect differences between WAIS-III and WAIS-IV; these analyses were performed using mixed-design ANOVAs, with test version (WAIS-III vs. WAIS-IV) as a within-subjects variable, and sample (Study 1 vs. Study 2) included as a between-subjects variable to account for average differences of performance, also controlling for the two-way interaction between test version and sample. Taking advantage of the increased sample size for this combined sample, we also computed Bayes Factors representing the likelihood of the alternative hypothesis of a difference between WAIS-III and WAIS-IV over the null (with uniform priors).
Both methods are probably less accurate than the one used here in this context. Stocking's method A works well with limited sample sizes, as was the case for the 2019 sample; it removes the necessity of picking anchor items to link the ability scales of the two groups on the same metric; and it estimates general ability based on all available information, instead of only the items being tested for DIF. The latter propriety meant that the ability estimate was not confounded with measurement bias in the items being tested, and that the same ability estimate was used to test DIF in the five subtests. Estimation of item parameters using this method was performed with the package irtplay 1. 4. 1  (Lim, 2020)  for R (R Core Team, 2020), separately for the reference 1999 normative sample and the current 2019 sample.
Effect of test version: Combining the Study 1 and Study 2 Datasets The next analysis combined the datasets from Study 1 and Study 2, both to ensure the stability of the results, and to ensure that the fact differences between WAIS-III and WAIS-IV were restricted to certain subtests was not due to insufficient power. The results of ANOVAs for this complementary analysis are summarized in Table  4 ; they were very similar to Study 1 and Study 2 considered separately. As in Study 2, the five subtests with significant cultural influence (Vocabulary, Similarities, Information, Comprehension, and Arithmetic) all elicited significantly lower performance in the WAIS-III version than in the WAIS-IV version. There were no differences for the other subtests, apart from a slightly higher performance in the WAIS-IV version of Symbol Search, driven by the Study 2 sample.
Items with significant DIF are in bold. Note. The first column displays the level of ability (standardized) required to obtain half the maximum number of points on the subtest for the 2019 sample, compared to the 1999 sample; the second column displays the predicted raw score obtained with an average level of ability for the 2019 sample, compared to the 1999 sample; the third column displays a coarse estimate of the predicted standard score obtained with an average ability for the 2019 sample; the fourth column displays the difference of average standard scores observed between the WAIS-III and WAIS-IV, combining the Study 1 and Study 2 datasets, for comparison.