dataesr/scientific-paragraphs-categorization huggingface dataset:
Economic and Social Outcomes Outcome data were obtained from participants' tax return records from 1998 to 2017. 36 Records included details about personal earnings, welfare receipt, intimate partnership status, and children living in the household for each year of follow-up. Employment earnings were defined as all pretax wages, salaries, and commissions, not including income from capital gains. Earnings were averaged for the 5 most recent years (2013-2017) and treated as a continuous outcome; earnings at or above the 99th income percentile were winsorized to remove extreme outliers. All earnings and income variables were converted to US dollars using 2017 purchasing power parity ($1 US = $0. 83 CA). Social assistance support (welfare) is provided by the Canadian government as last resort financial support for people with insufficient income who are ineligible for unemployment insurance, excluding those who are incapable of work.
Control Variables The following variables, which are associated with emotional and behavioral problems in childhood and economic and social outcomes in adulthood, were measured when participants were aged 6 years and adjusted for in the analyses. The child's sex, the parents' age at birth of their first child, parents' total years of education, family structure (intact/2 parent = 0, nonintact/single parent = 1), and the parents' household income (obtained from tax return records for the period 1984-1986). The child's verbal IQ was adjusted for using the Sentence Completion Task administered at age 13 years. 43 e instrument correlates highly with other measures of verbal and nonverbal IQ and educational attainment across respondents by sex, socioeconomic status, and race and ethnicity  44  ; however, data on race and ethnicity were not available for this cohort.
Study Setting and Participants From January 2020 to June 2022, a cross-sectional study was conducted in Hanoi, Vietnam. The eligibility criteria included patients who were 18 years old or above, currently being treated at the Mai Huong Daycare Psychiatric, willing to participate in the study through providing informed consent, and had the physical and mental capacity to answer the interviewer's questions. Patients who had serious cognitive impairment or were unable to answer the data collectors' questions were excluded from the recruitment process. A day psychiatric hospital model is currently being applied by Mai Huong Daycare Psychiatric, making the participants outpatients. The day-hospital model allows patients to spend less time at the hospital, reducing the potential negative effects of prolonged hospital stays, while facilitating faster recovery and improved QoL  [22].
Ethical Considerations This analysis used a part of the data set of a longitudinal study on individuals with and without mental health disorders in Vietnam, Vietnam's Brain and Behavior Cohort, led by authors BXT and RCMH. The study monitors changes in functional near-infrared data and self-reported health outcomes using portable, functional near-infrared spectroscopy and clinical-behavioral assessments among Vietnamese patients, in an effort toward developing an artificial intelligence-based diagnosis system for psychiatric disorders in Vietnam. The protocol of this study was approved by the Institutional Review Board of Hanoi Medical University (58/GCN-HĐĐNCYSH-ĐHYHN). We confirmed that all procedures were performed in accordance with relevant guidelines and regulations. Written informed consent was required from participants before participating in the study.
Strengths and Limitations The main strength of this study is that the majority of participants only received outpatient treatments, allowing for the investigation of variables in general settings such as daily life and the workplace. However, our study had several limitations. First, we were unable to analyze causal relationships due to the cross-sectional study design. As a result, we cannot be certain whether the capacity of the patients was impacted by social support or other reasons. Second, self-reported data may suffer from a limitation of recall bias, and social desirability bias may cause certain answers to be underestimated or overestimated. Furthermore, because the time of data collection varied, recall bias might be a concern that leads to inaccuracies, especially for patients with mental health disorders.
Acknowledgments BXT is the principal investigator of this project, which is a ministerial-level study managed by the  Vietnam Ministry of Health  (decision number  850/QĐ-BYT ) and implemented by the  Institute for Preventive Medicine and Public Health, Hanoi Medical University. This research is supported by  Gia Lam   Urban Development And Investment Company Limited, Vingroup , and by the  Vingroup Innovation Foundation (VINIF)  (grant number  VINIF. 2019. DA14 );  National University of Singapore (NUS) Department of Psychological Medicine  ( R-177-000-100-001/R-177-000-003-001 ); and  NUS iHeathtech Other Operating Expenses  ( R-722-000-004-731 ). The authors would also like to thank the  Vietnam Young Physician Association, Hanoi Medical University , and the research collaborators.
Search strategy For transparency, initial searches were completed on the 4 th of August 2022 to identify database issues prior to commencement. Database searches commenced on the 16 th of September 2022. A final repeated database search was conducted on the 23 rd of November 2022 which included manual citation and reference searching of included papers, after which no new studies were considered for inclusion. A comprehensive literature search of online databases was completed following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses;  Page et al. , 2021)  guidelines, encompassing Web of Science, PubMed, and EBSCOHost (narrowed to the most appropriate repositories for the research question; SPORTDiscus, PSY-CHInfo, PSYCHArticles, SOCIndex, and institutional database).
Data collection Once the initial database search was completed, results were uploaded to Rayyan (online systematic review program) for automatic duplicate removal followed by manual screening. All stages of article screening from this point forth were completed by two independent reviewers. The second reviewer (RB) was proficient in esports research but had limited previous knowledge of HRV. Prior to review, the second reviewer was briefed by the first author and reviewer (MW) to ensure appropriate understanding of HRV and potential confounding factors. Article title and abstract were initially assessed for inclusion followed by assessment of the full text when inclusion criteria were met. Inter-rater reliability was κ = 0. 85. Citation and reference searching of included articles was then completed to identify additional studies that met the inclusion criteria.
The first author then extracted the following data from each of the included studies; author names and date, participants, protocol, esport(s) used, HRV device and measurement, HRV variables, main findings, and theoretical underpinning (where provided). Theoretical underpinning was identified via manual searching of the text and reference list for citations or demonstrable consideration of recognised HRV theories (i. e. , Vagal Tank Theory;  Laborde et al. , 2018; Neurovisceral Integration Model; Thayer & Lane, 2000; Polyvagal Theory; Porges, 2007)  which the authors are very familiar with. Findings of included studies were analysed via narrative synthesis  (Siddaway et al. , 2019)  due to disparity across all variables of interest. Popay et al. (2006)  define narrative synthesis as a process that "relies primarily on the use of words and text to summarise and explain the findings of the synthesis" (p.
Study selection Following PRISMA guidelines  (Page et al. , 2021) , a detailed copy of article screening was recorded (Figure  1 ). In total, 10732 articles were identified from three databases (Web of Science: 1248, PubMed: 634, and EBSCOHost: 8850). 2326 duplicates were removed using automatic detection via Rayyan. Manual screening for duplicates removed an additional 109 articles leaving 8297 articles. Article title and abstract searching resulted in 8244 articles being removed, leaving 53 articles for examination of the full text. Articles of conflict or uncertainty were discussed based on inclusion criteria (n = 2) and details of decisions can be seen in Supplementary Material 1. Reasons for rejection at this stage can be seen in Figure  1. This resulted in 45 articles being removed, leaving six articles included.
There are many different ways of practicing coworking, and many different forms of CoWorking Spaces (CWSs). In this paper, we define CWSs in economic and spatial terms, and we provide some explanations on the transport mode used to travel to. Is the transport mode used to get to CWSs the same as that which is usually used for travel for work purposes? Does the spatial location of the CWS (in large city -medium-sized town -rural community) have an impact on the transport mode choice? The key issue is determining whether these new working spaces favor a shift in travel behavior toward practices less centered on the car. We use a survey of coworkers conducted in 2019 in the Auvergne-Rhône-Alpes Region (AURA), from which data is processed using a binomial logit model. The estimation results show that the mode choice for traveling to CWSs does not allow us to identify a characteristic that is fundamentally different from mode choice for work purpose.
We then present a more precise econometric analysis using a logit binomial model. The data were collected as part of the ANR COWORKWORLDS project, which aimed to investigate the hypothesis that the sustainability of mobility practices among CWS users depends on the social (professional, family) and spatial (regional, residential) configurations in which they are embedded. We will first present a literature review, partly to find out whether CWS users have any specific socioeconomic characteristics, and partly to identify the standard economic foundation for understanding CWSs (Part 2). We will then explain the data used and our method of analysis (Part 3) before presenting and discussing our results (Part 4).
Data To identify CWSs in the AURA Region, we start by collecting data coming from different sources in order to elaborate a first database 3. This database provides an analysis of the spatial repartition of the CWSs in the AURA Region into four regional profiles which approximatively corresponds to the French National Urban Areas of  INSEE in 2010 ( Brutel and Levy, 2011 ). As a consequence, we identify four spatial areas: City center (urban area with more than 400. 000 of inhabitants), cities suburbs (suburbs of urban area with more than 400. 000 of inhabitants), Medium-sized town (cities belong to urban area between 50. 000 and 399. 000 inhabitants), Small rural town (cities belong to urban area less than 50. 000 inhabitants). Note that, concerning transport modal share, AURA Region is quite similar to others Regions in France  ( AURA, 2017 )  Based on this preliminary step, we collect data from a survey carried out from December 2018 to February 2019 in the AURA Region (Auvergne-Rhône-Alpes) of France.
Estimation method In the absence of any such analysis either for France or in the literature covering several regions, we first use a descriptive statistical analysis to obtain an initial univariate analysis of the characteristics of CWS users' mode choice. Then, we use a more sophisticated binomial logit model to explain transport mode choice for travelling to CWSs in more detail. The data are processed using the free software R and the m logit package  ( Croissant, 2015 ). We will now provide some supplementary information about this binomial model. In the model, mode choice for travel to CWSs is the variable to be explained. The variable to be explained is a discrete variable that can take one of two forms. Discrete choice models explain the mode choice of economic agents as a consequence of their preferences among a set of alternatives  (Train 2003 ; for an overview of discrete choice models in the field of transport see, for example, Kamargianni, Ben-Akiva, and Polydoropoulou 2014 and  Kamargianni et al.
Mode choice model results As already mentioned, we will use a logit binomial econometric analysis to understand mode choice in more detail. We want to identify significant variables which explain mode choice for travelling to CWSs. Added to the fact that taking into account several transport modes is finally quite recent in the literature  ( Heinen and Chatterjee, 2015 ; Patil et al. , 2017 ) , our sample is too small for taking more than two modes and to be able to break it down and carry out a mode choice analysis for each of the four geographic areas. As for the models results, the full model that includes all the variables in the database is presented in Table  4. The likelihood statistic shows that the model is significant at 5%, but the Wald test shows that the full model suffers from multicollinearity (F = 0.
Conclusion As until now, information has not been available in the literature and data are lacking in France, the aim of this paper was to contribute to knowledge of transport modes used for traveling to CWSs locating in different spaces. In accordance to literature, we find that CWS users appear to be a specific group with favored people in particular in the City center (only 21% belongs to low income group). CWS users' mobility is consistent with the Regional mobility heterogeneity but steeper according to spatial CWS location. Car mobility is still mainly used by CWS users in the suburb (70%) and in the rural areas (67. 8%) where the modal alternative is probably not enough attractive and quite completely absent regarding walking in the suburbs (1. 7%) and public transport in rural areas (0%).
Stimuli and task Stimulus presentation was controlled with MATLAB 7. 1 (The MathWorks Inc. , Natick, MA), using Cogent2000 Toolbox (Wellcome laboratory of Neurobiology, University College London). The visual stimuli were presented on a black background using a rear projection system (total display size = 20 degrees of visual angle, 1024 x 768 screen resolution, 60 Hz refresh rate). Each trial included the presentation of two colored letters on the left and right side, upper or lower position, of the central xation (e. g. , see Fig  1 ). Stimuli were presented for 1000 ms with an inter-trial interval of 500 ms. The display always contained the to-be-judged target stimulus for the ONG task. The participants responded with the right hand, pressing the left or right button of an MR-compatible button box to indicate whether the ONG target letter was on the left/right side of the screen.
Behavioral data For each participant, mean Reaction Times (RTs) and accuracy (ACC) were computed using MATLAB 7. 1 (The MathWorks Inc. , Natick, MA). Statistical analyses were performed with SPSS v. 21. To examine the effects of Load and Focality on the ONG trials, we computed paired-samples T-tests: ONG_Lfoc versus ONG_Lunf (effect of Focality) and ONG_Lunf versus ONG_Hunf (effect of Load), considering RTs and ACC as the dependent variables. The main analysis targeted the PM1 trials, for which we could investigate the interaction between Load and Focality, considering 4 trial types: PM1_Hfoc, PM1_Lfoc, PM1_Hunf and PM1_Lunf. The data analysis comprised two repeated-measures 2x2 ANOVAs with the factors Load (HIGH/LOW) and Focality (FOC/UNF), separately for RTs and ACC. The emerging signi cant interactions among the factors were followed by post hoc analyses with Bonferroni's correction to reduce the Type I error rate.
fMRI acquisition and pre-processing Images were acquired with a Siemens Allegra (Siemens Medical Systems, Erlangen, Germany) 3T scanner. Functional imaging data were acquired using echo-planar imaging (32 contiguous transverse slices covering the entire cerebral cortex with TR=2. 08 sec, echo time = 30 msec, ip angle=70°, 64×64 matrix, voxel size=3×3 mm in-plane, slice thickness=2. 5 mm; 50% distance factor). Head movement was minimized by mild restraint and cushioning. Each participant underwent 4 fMRI runs, each comprising 374 volumes. Data were preprocessed and analyzed with SPM12 (Wellcome Trust Centre for Neuroimaging, London, UK). The rst four scans of each fMRI run were discarded to allow the signal to stabilize. The remaining scans were corrected for motion between slices and for slice-timing acquisition differences using the middle slice as a reference.
20, 38 evious publications on AI face recognition have reported lower recognition accuracies on cohorts of African American and younger subjects (18-30 years old). 30 Demographic factors may largely influence various biometric algorithms, and some degree of bias to certain demographic groups may be due to under-representation of certain cohorts (e. g. , darker skinned populations) leading to dataset bias,  [39] [40] [41] [42]  highlighting the importance of using datasets well distributed across all demographics for algorithmic evaluation. We achieved this by having an initial dataset well distributed in Africa, Asia, and Europe. 8 This highlights the importance of using well-distributed datasets across all demographics from the algorithmic evaluation. Clearly, the different training datasets used in the development on new AI-based automated grading system for men and women  [7] [8] [9] 18  fulfill these requirements by covering different geographical locations (USA, Brazil, India, China, Korea, Japan, South Africa, France) with different ages, phototypes, gender, and ancestries.
The combination of these three gradients forms the 3-d state-space presented in Figure  1. Next, we used these gradients to organize the overt and covert features of cognition derived from our data by projecting the relevant task and experiential maps for each participant into the 3-d space, correlating each map with each gradient dimension. This analytic step resulted in a set of 3 'coordinates' for each task and experiential map for each participant, in which each coordinate describes the location of each map along each gradient dimension. Finally, we used a series of linear mixed models to apply inferential statistics to these coordinates. This analytical pipeline is shown in Figure  1 , and see Methods for further details. The top row depicts the task paradigm. Participants completed a sustained attention task while brain activity was measured using fMRI.
Discussion Our study set out to examine whether experience sampling can be used to provide insights into covert task-relevant states that are hypothesized to play a role in organizing task behavior. To address this question, we used a simple sustained attention task in which participants detected visually presented targets (to provide an index of overt behavioral states) and experience sampling (to provide indices of covert cognitive states). To allow these different features to be examined in a common analytic space, we used a state-space derived from dimensions of brain function variation  12  calculated from the resting state data of the Human Connectome Project  29. These dimensions characterize three different types of brain organization: 1) the distinction between sensory-motor and association cortex, 2) the distinction between motor and visual cortex, and 3) the distinction between the default mode and fronto-parietal networks in association cortex.
Participants. One hundred and seven participants took part in this study. Ninety-one participants participated in a behavioral session (67 females; mean age: 23. 38 years, standard deviation: 4. 53 years, age range: 19-40 years). Sixty-two participants participated in the scanning session (41 females; mean age: 23. 29 years; standard deviation: 4. 51 years, age range: 18-39 years). After excluding 5 participants, 57 remained for fMRI data analysis (due to technical difficulties or excess movement). Forty-six participants participated in both the behavioral and scanning session. All participants had normal/corrected vision and had no history of psychiatric or neurological illness. All scanning participants were right-handed. This cohort was acquired from the undergraduate and postgraduate student population at the University of York.
fMRI acquisition. All MRI scanning was carried out at the York Neuroimaging Centre. Structural and functional scans were acquired using a Siemens Prisma 3T MRI Scanner with a 64-channel phased-array head coil. Structural data were acquired using a T1-weighted (MPRAGE) whole-brain scan (TR = 2300 ms, TE = 2. 26 ms, flip angle = 8°, matrix size = 256 x 256, 176 slices, voxel size = 1 x 1 x 1 mm). Functional data were collected using a gradient-echo EPI sequence with 54 bottom-up interleaved axial slices (TR = 3000 ms, TE = 30 ms, flip-angle = 80°, matrix size = 80 x 80, voxel size = 3 x 3 x 3 mm, 267 vol) covering the whole brain. These details are the same as those described in Konu et al. 11. fMRI data pre-processing. Functional and structural data were pre-processed and analyzed using FMRIB's Software Library (FSL, version 5.
0. 1, http://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FEAT/). Individual T1-weighted structural images were extracted using BET (Brain Extraction Tool). Functional data were pre-processed and analyzed using the FMRI Expert analysis Tool (FEAT). Individual participant analysis involved motion correction using MCFLIRT and slice-timing correction using Fourier space timeseries phase-shifting. In the current study, to control for individual's movement during the scanning period in our inferential analyses, we also calculated each individual's mean movement across all three runs using the MCFLIRT output to include as a nuisance regressor in subsequent analyses (prefiltered_func_data_mcf_abs_mean. rms). After co-registration to the structural images, individual functional images were linearly registered to the MNI-152 template using FMRIB's Linear Image Registration Tool (FLIRT).
Registration from high resolution structural to standard space was then further refined using FNIRT nonlinear registration. Functional images were spatially smoothed using a Gaussian kernel of FWHM 6 mm, underwent grand-mean intensity normalization of the entire four-dimensional dataset by a single multiplicative factor, and had high pass temporal filtering (Gaussian-weighted least-squares straight line fitting, with sigma = 50s). These details are the same as those described in Konu et al. 11.
Principal Component Analysis. Analysis of the mDES data was carried out in SPSS (Version 25, 2019). Principal Component Analysis (PCA) was applied to the scores from the 13 experience-sampling items comprising the probes for each participant. PCA was applied at the trial level in the same manner as in our prior studies e. g. ,  [15] [16] [17] 23, 47, 48, 49. Specifically, we concatenated the responses of each participant for each trial into a single matrix and employed a PCA with varimax rotation. We performed this analysis separately for each session (behavioral and scanning) in order to examine the similarity in the solutions produced across each situation (see Supplementary Figure  2 ). These details are the same as those described in Konu et al. 11. In the current study, we calculated each individual's mean score, across runs, for each PCA component identified for inclusion in inferential analyses.