dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , search speed, search distance, and search strategy) with the between-subjects factor group (neglect vs. no neglect) and the within-subjects factor screen size (TS small vs. TS medium vs. TS large). To finally analyze if the three parameters above can be used to reliably predict participants' neglect diagnosis (dichotomized: spatial neglect vs. no neglect), we used Support Vector Machines (SVM). Given that SVM require complete data sets, we first used Multiple Imputation by Chained Equations (MICE;  White, Royston, & Wood, 2011)  to impute missing data for this analysis step only. It entailed that missing values in a given column were estimated using a Bayesian Ridge Regressor, predicting values of the current column from all other columns. MICE was carried out column-wise from the column with the least number of missing values to the column with the most missing values.
The potential impact of the imputation was tested by rerunning all analyses with a dataset where missing values were omitted. In the following sections, only results for the imputed dataset will be reported, because the pattern of results remained identical with and without imputation. Due to the sample size of the present study, we decided to use a dataset containing all screen sizes (TS small, TS medium, TS large) for each participant. To account for the dependence of (http://www. fil. ion. ucl. ac. uk/spm). Normalization of CT or MR scans to MNI space with 1x1x1 mm resolution was performed by using the Clinical Toolbox  (Rorden, Hjaltason et al. , 2012)  under SPM12, and by registering lesions to its age-specific MR or CT templates oriented in MNI space  (Rorden, Bonilha et al. , 2012).
data points in this approach (i. e. , three measures for each participant), we tested our models through Leave-One-Subject-Out Cross-Validation. In this procedure, the machine learning model is trained on data for all participants but one and tested on the participant that was left out for training. This process is then repeated until each participant was predicted once and prediction outcomes (i. e. , balanced accuracy due to the unequal group sizes; Brodersen, Ong, Stephan & Buhmann, 2010) are averaged across predictions for all participants. Hyperparameters (i. e. , the kernel: linear or radial basis function; cost parameter: ranging from 0. 01 to 10) were optimized through a grid search in a nested Leave-One-Subject-Out Cross-Validation (within the training dataset). This procedure was carried out separately for each task (Bells and Letters test) and balanced accuracy scores were obtained across all screen sizes for both tasks.
Lastly, the percent of correctly classified neglect and right-hemispheric control patients were accumulated for each screen size and test. To test if the classification accuracy varied by screen size, chi-square tests of independence were used to compare the distribution of correctly classified neglect and right-hemispheric control patients across screen sizes for each task (Bells and Letters test). All machine learning analyses were conducted in Python using the scikit learn module  (Pedregosa et al. , 2011).
Additional parameters of search behavior Beyond the CoC, the additional variables, search speed, search distance, and search strategy were obtained from the digitized cancelation tasks. Search speed. Data are illustrated in Figure  6. Analysis of the Bells test revealed a significant main effect of group (F(1,15) = 6. 719, p = 0. 02, η p 2 = 0. 309), indicating that control patients found significantly more targets per second (M = 0. 196, SD = 0. 057) than neglect patients (M = 0. 124, SD = 0. 066). The main effect of screen size, on the other hand, was not significant (F(2,30) = 0. 235, p = 0. 792), indicating that a comparable number of targets was found per second in all three screen sizes. The interaction was not significant either (F(2,30) = 1. 983, p = 0. 155). The same analysis applied on the Letters test also revealed a significant main effect of group (F(1,13) = 8.
| Data analysis Data analysis was conducted using IBM SPSS Statistics Version 26. 0 for Mac. A two-tailed significance level was set at 0. 05. First, chisquare tests of independence, one-way analyses of variance (ANOVAs), and Kruskal Wallis H tests were conducted to explore descriptive characteristics and compare differences of participants across three caregiver groups, defined by their care recipients as having multidimensional and enduring brain health conditions (dementia and mental illness), physical health conditions, or both. Additionally, an ordinal logistic regression was conducted to explore factors associated with the increase in care burden. We also performed additional sensitivity analyses that compared caregivers of people with dementia to caregivers of people with mental health issues.
ACKNOWLEDGMENTS We would like to acknowledge all the civic society organizations who supported dissemination of the CLIC survey around the world:  Ireland -Alzheimer Society of Ireland, Inclusion Ireland, Family Carers Ireland, Care Alliance Ireland, Parkinson's Association Ireland ,  Engaging Dementia ,  Respond Housing ,  ALONE , the  Irish Gerontological Society  and memory clinics throughout the country;  USA -Alzheimer Association; UK -Lewy Body Society , the  Alzheimer Society, Join Dementia Research, Age Northern Ireland , and the  Institute of Public Health in Ireland (IPH) ;  New Zealand -Professor Vanessa Burholt, Office of Seniors of the Ministry of Social Development, Health Promotion Agency ,  Age Concern NZ , and  Carers Alliance. Open access funding provided by  IReL.
Methods: This review will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two independent investigators will review English-language, full-text, peer-reviewed papers extracted from PubMed and Web of Science that measure passive smartphone data and levels of depression or anxiety among college students. A preliminary search was conducted in February 2022 as a proof of concept. Results: Our preliminary search identified 115 original articles, 8 of which met our eligibility criteria. Our planned full study will include an article selection flowchart, tables, and figures representing the main information extracted on the use of passive smartphone data to monitor anxiety and depression among college students.
3. Participants were assessed on a clinically validated rating scale, either self-reported scales or clinical diagnosis scales, used within psychiatry and psychology to screen for depression and anxiety. 4. The study reported measurements quantifying the use of passive smartphone data to monitor anxiety or depression. 5. The study was either a cohort study or a cross-sectional study. The third criterion is designed to ensure a broad inclusion of studies reporting anxiety and depression symptoms and has been chosen to include both clinical and nonclinical populations. This is not restricted to a specific rating scale in order to reflect the variety of tools used to assess anxiety and depression symptoms in the literature. However, the inclusion of clinically validated rating scales only, such as Generalized Anxiety Disorder-7  [55] , Patient Health Questionnaire-9  [56] , or Hamilton Depression Rating Scale  [57] , was chosen to restrict the analysis to commonly used and validated scales, which enables comparison and generalization.
The exclusion criteria are as follows: 1. The study was not published in English. 2. The study was not published following a thorough peer-review process. 3. No full text was available. 4. The study was one of the following publication types: trial protocols, editorials, letters, opinions, case reports, case studies, reviews, or meta-analyses. Books, book chapters, and other gray literature materials (eg, government reports and theses) will not be included. 5. The study involved only data from smartphone-connected wearables. This last exclusion criterion is designed to avoid including data from tools that are not as ubiquitous as smartphones, either for financial, cultural, or other reasons. Including data gathered by such tools may lead to a selection bias we want to avoid in the first place, in order to get the most representative sample of college students possible.
Data Sources and Search Strategy Two independent reviewers will search the PubMed and Web of Science electronic databases for published studies meeting the search criteria. No limit to publication dates will be applied. Database-specific search strings will be designed using the Boolean operators "AND" and "OR" in combination with MESH descriptors that might be found in titles or abstracts. The search string will include a combination of terms relating to (1) passive smartphone data and (2) anxiety and depression. The search strings for PubMed and Web of Science were as follows: ("mobile phone location data" OR "mobile phone call data" OR "mobile phone data" OR "cell phone data" OR "cell phone call data" OR "cell phone location data" OR "smartphone location data" OR "smartphone call data" OR " smartphone data" OR "call detail records") AND ("depression" OR "affective disorder*" OR "anxiety" OR "anxiety disorder*" OR "mental health" OR "mood disorder*" OR "unipolar" OR "mental disorder*").
Data Synthesis and Analysis We aim to investigate the use of passive smartphone data to monitor depression and anxiety among college students. We will thus systematically review publications that report any measurements of passive smartphone data and levels of depression or anxiety among college students and that provide any means of using these data to monitor this specific situation. We will report these measurements according to the method used (eg, correlations or regression-based machine learning) and the type of study (cross-sectional or cohort study), with specific attention to the cross-sectional studies, where the causation may be bidirectional. We will compare the results for clinical and nonclinical samples and, notably, verify if the directionality of the association between smartphone features and indicators of depression and anxiety is the same among these two populations.
Results As a proof of concept, a preliminary search and study selection were conducted in February 2022 and are schematized in Figure  1. We identified 153 records, of which 115 remained after the removal of duplicates. Of these potentially eligible studies, only 8 fully satisfied our inclusion criteria. We are currently going through the process of data extraction and analysis. We will provide a flowchart that summarizes the search strategy, as well as tables and figures presenting the extracted data and the results of the study quality assessment. We intend to make our results available to an international audience through publication in a peer-reviewed journal.
Preliminary Results and Future Steps Following our study selection process, we found 8 published articles that satisfied our eligibility criteria  [42, [61] [62] [63] [64] [65] [66] [67]. Three articles studied depression only  [42, 66, 67] , 1 studied anxiety only  [65] , and 4 included both depression and anxiety  [61] [62] [63] [64]. Other mental disorders-or behaviors related to mental disorders-were analyzed in these studies, such as loneliness, disturbed sleep, or stress. Two studies based on the same data sets included 816 participants. The number of participants included in the other 6 studies ranged from 20 to 100. Three studies  [61, 64, 65]  examined the use of passive smartphone data to predict mental health. The remaining studies either examined associations between passive smartphone data and mental health indicators  [62] [63] [64] [65]  or aimed to extract the key features from.
Then, we analyzed the longitudinal data by considering only those participants who filled in all questionnaires. It should be noted that when only one response was missing in a scale, we interpolated the data for this item. We also included the participants not writing any narrative. This allowed us to rescue the data in 13 participants (results were similar without those 13 participants), leading to the longitudinal analysis of 107 participants answering to all questionnaires. Participants who completed all four assessments (P-All) We examined how clinical measures changed over time in the P-All group. Scores of both the DASS-42 and the PQ-16 were the highest on T1 (Fig. 1 ). They were largely correlated with each other on each measure (Table  3 ).
DISCUSSION The aim of our study was to examine the impact of the pandemicinduced lockdowns on mental health using both validated quantitative scales and diary narratives to better capture the emotional experience of the participants. The collection of mental health data started at the very beginning of the lockdown in France. We replicated previous studies showing abnormal levels of psychological symptoms on the beginning of the study and a decrease of symptoms across time, especially anxiety. Our study extends those results to attenuated psychotic symptoms. Several correlations suggest that our data is reliable, despite the limits of online screening questionnaires: first, we replicated previously reported correlations between symptoms of stress, anxiety and depression (the DASS-42) and several indexes of social contact and the feeling of loneliness.
Statistical analysis Separate ANOVA for repeated measures were conducted on mental health scores with time (T1, T2, T3, T4) as a within-subject variable. Post-hoc analyses were conducted using Tukey HSD. The association between mental health scores and living conditions was explored using correlation analyses based on the false discovery rate (FDR) method, a rigorous method of alpha-level adjustment for multiple comparisons  65. Statistical analyses were performed using SPSS Statistic 21. 0 (IBM SPSS Statistics, New York, United States) and JASP 0. 10. 1 (JASP Team, Netherlands).
Objective and literature searches In this article, we update the available knowledge about summer literacy programs for children by examining programs conducted from 2012 to 2021 following the recommendation of a systematic review approach  (Booth et al. , 2016). In order to determine the effectiveness of the programs, we decided to consider only those articles that referred to the presence of a control group. We present all of the examined articles in light of the various characteristics of a successful program identified by  Bell and Carillo (2007)  and Sobeck  (2017). In May and June 2021, we ran a three-stage search in education, psychology and humanities and social science databases (e. g. , ERIC, PsychINFO, PsycArticles, Sociology Source Ultimate, Gale Academic/General/Business, Spring Nature Journals, etc.
This paper presents a precise definition of numeral classifiers, steps to identify a numeral classifier language, and a database of 3,338 languages, of which 723 languages have been identified as having a numeral classifier system. The database, named World Atlas of Classifier Languages (WACL), has been systematically constructed over the last 10 years via a manual survey of relevant literature and also an automatic scan of digitized grammars followed by manual checking. The open-access release of WACL is thus a significant contribution to linguistic research in providing (i) a precise definition and examples of how to identify numeral classifiers in language data and (ii) the largest dataset of numeral classifier languages in the world. As such it offers researchers a rich and stable data source for conducting typological, quantitative, and phylogenetic analyses on numeral classifiers.
For instance, one of the most important functions of numeral classifiers relates to the count/mass distinction  (Contini-Morava and Kilarski 2013; Jackendoff 1991; Wu and Her 2021). See Supplementary Material A for an extended discussion on the subject. Investigating such hypotheses quantitatively requires a large database of numeral classifier languages. Especially since findings about one classifier language might not be generalized to other classifier languages. For example, a large number of experimental studies of classifiers focused on Mandarin, while a higher diversity would be ideal  (Saalbach and Imai 2012). However, large-scale structured data on numeral classifiers are scarce. As an example, the WALS  (Gil 2013)  provides information on the presence/absence (and obligatoriness) of numeral classifiers, with 140 languages having numeral classifiers in a sample of 400 languages.
As another example, the AUTOTYP database  (Bickel and Nichols 2002)  has data for 272 classifier languages. While such samples are highly valuable, they are a rather small representation of the more than 7,000 existing languages  (Hammarström et al. 2019). Beside databases, individual research papers/books also provide data on classifiers in languages of the world (e. g. ,  Allassonnière-Tang et al. 2021; Greenberg 1972; Greenberg et al. 1990; Nichols 1992 ), however, these contributions generally consider different types of classifiers with varying definitions. A more substantial and precisely defined database of numeral classifier languages worldwide with geographic information is essential to the research on the distribution of classifiers in language families and subgroups, the probable origin of numeral classifiers and the subsequent areal diffusion of this grammatical feature  (Her and Li in press) , the interaction of classifiers with other classification systems, e.
Manual survey of literature and automatic scan of grammars Based on the definitions provided in Section 2, we conducted two parallel surveys to identify languages that have numeral classifiers. During these surveys, we gathered as many language grammars that could be found as an attempt to cover as many languages as possible. First, a manual survey of language grammars was conducted to identify which languages were described as having numeral classifiers. The language examples available in each grammar were then used for applying the definition provided in Section 2. This method is, as far as we know, the most commonly used to construct databases such as WALS  (Dryer and Haspelmath 2013)  and Autotyp  (Bickel and Nichols 2002; Nichols et al. 2013). In parallel, we also conducted an automatic survey in the collection of digitized grammatical descriptions from the DReaM Corpus  (Virk et al.
The precision is near 0. 95 and much higher than the recall (0. 62). Most errors are classifier languages that are not identified as such by the automatic survey. For more details on this quantitative comparison, please refer to  Hammarström et al. (2021, p. 32). Identifying classifier languages of the world are rare, as only 22% of the languages have such a system. Our database also allows us to refine the attested distribution in existing online databases. As an example, Gil (2013) lists 140 numeral classifier languages in a database of 400 languages, which results in a proportion of 35%. Our data provide a more detailed idea of the scarcity of numeral classifier languages in languages of the world. This divergence of distribution can mostly be explained by a difference of coverage and definition.
First, it is possible that the WALS sample of 400 languages might have been coincidentally biased towards having numeral classifiers. Second, while Gil (2013) also considers sortal classifiers, the definition varies with ours. For example, Eyak (Athabaskan-Eyak-Tlingit) is annotated as a classifier language in WALS. However, considering the available references, we observe that "classifiers are strictly verb prefixes" in Eyak  (Krauss 2015: 122). By comparing the two databases, there is a mismatch of annotation for 42 languages. Fifteen languages are annotated as having classifiers in WALS but not in our database, while 27 languages are annotated as not having classifiers in WALS but annotated as having classifiers in our database. If we were to replace these mismatching points with our data, the proportion of classifier languages would further increase in the WALS sample, which hints toward the first possibility that the option was accidentally biased towards classifiers.
We do not engage with this issue within this paper, nevertheless we suggest that our database enables further testing of these observations from different perspectives. Finally, we also visualize the distribution of numeral classifier languages across language families. Numeral classifier languages are found in 56 of the 203 language families included in the data. The proportion of numeral classifier languages of each of these families is listed in Figure  2. We observe that few families consist only of numeral classifier languages. Interestingly, these families are located either in Asia (Japonic and Hmong-Mien) or the Americas (Jodi-Saliban, Huavean, and Haida), which once again matches with the existing literature on the geographic distribution of numeral classifier languages. Furthermore, only 22 out of the 56 families have half or more than half of their languages as numeral classifier languages.
Summary and future development The product of our clarified definition of numeral classifiers and our surveys is a database of numeral classifier languages. While its contents match with the existing literature and provide additional details about the distribution of numeral classifier languages worldwide, we acknowledge that additional details and feedback from the linguistic community are needed to further enlarge and deepen our survey. Therefore, following the FAIR principles (Findable, Accessible, Interoperable, and Reusable), we also aim at releasing the data obtained through our surveys as an online open-access database, which is named The World Atlas of Classifier Languages and abbreviated as WACL. The contents of WACL (Further details in Supplementary Material C) will be published under the CLLD framework  (Forkel 2014 , https://clld.
org/) under the CLDF format  (Forkel et al. 2018 ) and hosted at the locations https://wacl. clld. org/ and http://wacl. thu. edu. tw/one. It will be updated on a yearly basis with a GitHub repository and a Zenodo frozen version. The version included in this paper is version 1. The building of WACL supports crowd science and will welcome comments and suggestions from the linguistic community to correct and/or expand the content of WACL. For example, even though the content of WACL is the result of automatic and manual scans, the content of WACL may be updated based on feedback from the linguistic community. WACL will also be expanded with additional features such as the obligatoriness/optionality of classifiers, detailed examples for each language in the database, differentiation of sub-categories of numeral classifiers (e.
Acknowledgments: The first author offers his heartfelt thanks to the following graduate students and researchers for their help in building the database, including part-time RAs:  Hsieh, Chen-tien; Lai, Wan-Chun; Chen, Meng-Ying; Wang, Wei; Chen, Ching-Perng; Chen, Yun-Ju; Liao, Jia-Yu; Li, Bing-Tsiong; Chia, Cheng-Pin ;  Allassonnière-Tang, Marc  (the third and corresponding author of the paper); Lin,  Kun-Han ;  Huang, Yu-Min; Chen, Chia-Chi; Yeh, Chu-Hsien; Yang, Wen-Chi; Huang, Tsung-Chia; Chen, Shen-An; Jheng, Jhih Siou; Liang, Yu-Ting; Gao, Zhong-Liang; Cao, Zi-Yun; Hsu, Hung-Hsin; Liang, Yung-Ping; Lo, I-Chieh; Chen, Yi-Ju; Chen, Wei-You; Cheng, Yu-Ching; Kuo, Hui-Ting; full-time RAs: Chen, Ying-Chun; Lin, Yen-Tse; Ho, Pei-Hsuan ; and post-docs:  Hsieh, Fu-Tsai; Tsai, Hui-Chin; Hsiao, Pei-Yi ;  Hsu Chi-Pin.