dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The Covid-19 pandemic is one of those rare historical episodes during which the entire world adopted comparable constraints on the human population at the same time. The initial measures against Covid-19 were state-of-emergencies and partial to full lockdowns. Herein, we report a comprehensive database, which provides tangible quantitative and qualitative assessments documenting a wide range of factors mediating the temporal distortions and disorientations experienced during this historical episode. Making accessible the database and the online tools to collect additional data is motivated by the observations that, as we write this paper, we have not yet returned to a global normality level. Our experimental tools have been and can be easily translated to additional languages, providing an easy stepping stone for inclusive cross-cultural studies using multidimensional variables.
We made efforts for the Blursday database to be highly accessible and readable to all researchers. We provide a graphical user interface that researchers without programming background can use for parsing and retrieving the data as required by their research questions. We also include optional outcomes of some of our preliminary analyses as part of the data (e. g. , objective lockdown measures and subjective confinement indices). Herein, we wished to highlight the existence of Blursday as well as showcase the reliability of the collected data on measures like sleep disturbances and anxiety, and well-established empirical facts typically measured in laboratory settings (see Supplementary Information). To this end, we provide a replication and extension of previously reported sleep disturbances and borderline anxiety during the first lockdown in France  36  , showing that questionnaires included in the database can be reliably exploited independently of research interests dedicated to timing research (Supplementary Figure  4 ).
A number of questionnaires in the database could be exploited on their own for epidemiology, demographic, psychiatry, or chronobiology, or as covariates of other experimental measurements provided in the database. We also illustrate the distributions of several dependent variables in some of the timing tasks measured in Blursday, all of which pointing to the psychophysical and psychometric utility of our experimental approach for future investigations dedicated to these timing properties (Supplementary Figure  5 ). Importantly, by exploring the effects of stringency, mobility, subjective confinement, age, and hour of day on several measures of subjective time (Table  1 ), we report novel empirical findings showing that both objective measures of lockdown and subjective measures of confinement influence participants' time perception at different time scales.
Indeed, our analyses were applied to all sessions (in and out of lockdown) over months during which objective lockdown measures were difficult to track. Table 1: Summary of preliminary observations. To illustrate the use-value and richness of the Blursday database, we selected only a few measurements testing participants' time perception at different time scales (from minutes to one month) and orientation (past/future). We explored the effect of stringency (objective index), mobility (objective index), subjective confinement (subjective isolation index), age and hour of day on distinct measures of subjective time. A green checkmark indicates a significant effect of the covariate on the time measurement; a red cross marks an absence of significant effect. This table provides a succinct summary of otherwise more complex effects subjected to the limitation of our selection of relevant factors and choice of statistical models.
Indeed, ongoing work use these measures together with participants' time perspective personality trait (ZTPI) to explore individuals' self-perception and the behavioral consequences of temporal disorientations on delay-discounting (all collected measures in the Blursday database). Two objective indices of lockdown (stringency and mobility) affected retrospective duration estimations in which a somewhat intriguing observation was found. It is now well-known that participants tested on a range of magnitudes display a behavior that conforms to the predictions of a Bayesian observer. Most studies assess this by using a range of magnitudes around a value of interest showing a generalizable pattern of central tendency or regression to the means  26, 28, 40. The central tendency is described as a subjective under-estimation of values below the mean of the distribution and an over-estimation of values above this mean.
In Blursday, the large-scale dataset, the wide-range of durations tested, and the multi-cultural inclusion make this result quite unique, the observation of a central tendency towards a 15 to 20 minutes value at the inter-individual level is a non-trivial novel observation. We can add to this that the central tendency for retrospective duration was significantly less pronounced during lockdown (S1) than outside of it (SC), so that the participants reports were closer to the ideal observer and conformed less to Vierordt's law during lockdown than outside of it. Finally, the highlighted observations by no means exhaustively exploit the rich repertoire of tasks, tests, and questionnaires of the Blursday database. This stands as a limitation of our results considering that we have not fully exploited the numerous factors that were recorded, and which could help further disentangle their weighted contributions to the temporal distortions and disorientations we report.
The Blursday database is amenable to cross-replication studies and feeds ongoing analyses testing specific working hypotheses. For instance, a first cross-cultural analysis revealed differences in anxiety and depression that can be partially attributed to differences in individuals and cultural time perspectives  17. The motor timing tasks, designed to assess whether endogenous rhythms, sensorimotor productions and synchronizations were affected during isolation can be enriched with the personality traits collected on the same individuals as well as their demographics (age, gender). The foreperiod paradigm, which assesses implicit timing, is being explored with respect to some of the lockdown measures but also as a function of age, cultural diversity, and self-perception measures. The assessment of temporal landmarks taps into the possible distortions of temporal cognitive maps induced by the Covid-19 episode in the population, thereby enabling the exploration of how temporal distortions may link to episodic memory and participants' fluency reports.
In conclusion, we are confident that the Blursday dataset will act as a rich historical record of temporal disorientation and distortion during Covid-19 and serve as an empirical benchmark for future studies set out to use and build on the same tools to assess the effects of social isolation on temporal information processing across cultures and post-pandemic. In this Resource article, we highlighted only a few possible observations drawn from the current dataset. Our study demonstrates the feasibility of a large and ambitious international study in a short time only thanks to the help of community builders such as the Timing Research Forum (TRF; http://timingforum. org). As members of the timing community, we hope that Blursday sets forth an international "TimeLab" that can support and foster multi-cultural large-scale studies in timing and psychological time research.
However, we also contend that even as of March 2022, the world situation cannot be considered a control in a rigorous and empirical sense of the term. A rigorous control would require the same study to have been performed before the Covid-19 pandemic. While it is conceivable that some of the tasks and questionnaires we tested during the pandemic could find matching controls tested before the pandemic, no such data are currently part of the database. Hence, we loosely refer to this group of participants as "control participants" and to the session as "control session" to emphasize, for instance, that learning effects and familiarity to the task in the longitudinal data can be controlled for. It is also noteworthy that our analysis takes into account a more nuanced approach to lockdown by using a continuous index of stringency as opposed to the categorical dichotomy of being "in and out of lockdown" adopted in the existing literature (see Assessment of objective and subjective confinement indices).
In all sessions, participants reporting drug usage and psychiatric disorders were a priori excluded from data collection; some of the included questionnaires otherwise allow for an evaluation of depression, stress, anxiety and attenuated symptoms of psychosis. Data from Colombia, UK, and USA were too few to be included and were a priori discarded from most analyses (although made available in the database). Due to the exceptional nature and speed of change in governmental policies, experiments started during the first lockdown or state-of-emergency of each country in 2020 (Supplementary Figure  1 ) and continued longitudinally at a different pace according to local policies. We report in Supplementary Figure  1  the full demographics of the database during the experiments along with lockdown dates and general timelines of the study.
Outliers in reported analyses In the retrospective duration analysis, we used three criteria for defining outliers: the first criterion rejected data points with a clock duration shorter than 1 minute and longer than 5 hours due to the implausibility of these timings in our study protocol. The second criterion excluded data with subjective retrospective durations shorter than 12 s and larger than 25 hours (i. e. , subjective estimates that were 0. 2 times the shortest possible clock duration and 5 times the longest possible clock duration). The last criterion removed data points with relative duration errors computed as [(   - ) /  )] beyond the 95 central percentiles per country and per session. The application of these criteria discarded 8. 8 % of the original dataset. The passage of time ratings used data collected right after the retrospective duration estimation.
For this reason, we considered that trials in which the clock duration responses were aberrant were also unreliable for the passage of time. Therefore, we applied the same criteria and rejected Passage of Time trials in which the reported clock duration was shorter than a minute, or longer than 5 hours, as well as trials in which the clock duration was beyond the central 95 percentiles. The application of these criteria discarded 8. 3 % of the original dataset. We performed no outlier removal for the analyses of subjective temporal distance (next week, month) beyond missing responses, which were discarded on a per-trial basis. For all VAS measures (passage of time and subjective temporal distances, Figure  2a  and Figure  3c , respectively) the extreme and middle responses of the VAS tended to be overrepresented.
These fluency tasks took the form of a question "Write as many events as possible that occurred last [week/month/year]" for past fluency or "that will happen next [week/month/year]" for the future fluency tasks (Supplementary Figure  6 ). Hence, these fluency tasks tested the scales at which the fluency was assessed, namely over a week, a month, or a year. An additional semantic fluency task inquiring about associations with the word "time" was tested by simply asking participants to report as many spontaneous associations as possible they had with this word. All fluency tasks in the Blursday database were 60 s long and the number of collected items was unlimited.
For the purpose of our concise report, we selected the Transit Station Mobility Index (henceforth referred to as Mobility Index), which quantifies how much time visitors spent in various transit stations (subway, taxi stand, rentals) during a selected period relative to a baseline period. In the Google dataset, the baseline was defined as the median value from a five-week period spanning January 3 rd to February 6 th , 2020. The more negative the mobility index, the less mobility compared to baseline. As visible in Supplementary Figure  1 , while sessions were carefully aligned to the governmental lockdowns and state of emergency rules, they do not strictly map to the level of stringency or to the degree of mobility estimated in a given country. Some of our results and analyses indicate that objective measures of stringency and mobility may be adequate covariates to explore the effect of lockdown on behavior (for instance, see outcomes of our retrospective duration analysis below).
This could be due to factors such as personality traits (which can be assessed with the BFI included in Blurdsay), or social isolation (which can be explored with the remaining items of UCLA and various questions such as changes in social media use, number of people in the household, and changes in habits included in Blursday). For this concise report, we chose a direct and minimal approach using a Confirmatory Factor Analysis (CFA), which showed that responses to the five items selected a priori, could be aggregated along two dimensions, which we refer to as "self-reported loneliness" (capturing responses to statements "I have. ", "I lack. ") and "felt loneliness" (capturing responses to "I feel. "). These dimensions are available in the database but for simplicity, we combined these two estimates of loneliness as a proxy for "subjective confinement", which scored anywhere between 5 (feeling very isolated) to 20 (not feeling isolated).
Code and data availability Our databasing approach follows the FAIR principles  111  stipulating Findability, Accessibility, Interoperability, and Reusability of the data. In line with FAIR, we provide a graphical user interface to researchers, allowing them to easily and conveniently parse the data in a way that best fits their research needs (https://dnacombo. shinyapps. io/Blursday/). Data collected from each task and questionnaire are available at the individual trial/item level per participant in the database and will be incremented with quantified estimates per participant as analyses progress. The individual trial data are given to support modeling efforts that typically consider trial-based data sometimes based on the order of their occurrence. Due to the nature of the tasks, reliable timing is an important factor.
To help researchers estimate the degree of timing uncertainty in data collected online  112  , we provide the participant's operating system and browser information with which data were collected. To improve the readability of the data at this level of presentation, we also provide human-readable readme. txt files for each questionnaire and task in a dedicated OSF repository (https://osf. io/359qm/ in folder Study_design/README/) along with additional sources of information that facilitates the reusability of the data.
Project repository on OSF. io The OSF repository provides a public access platform to published materials, guides, and codes associated with the Blursday database (https://osf. io/359qm/). The OSF hub will be updated and incremented as results using the database get published. Readme files are provided describing the independent and dependent variables for each task, the number of trials, and the number of possible runs. We provide a comprehensive listing of translation and associated references for the validation of the questionnaires used in Blursday. Additional resources such as the dates of the sessions, the timelines, or useful resources for a more detailed assessment of local governmental measures are also provided. Colored dots are individual data points per participant and per session.
Regression lines were estimated from the linear model with the 95% C. I. (gray shade). The more stringent governmental rules were, the shorter retrospective durations were estimated to be. Panel c: Relative retrospective duration estimates (no unit. ) as a function of the mobility index (percent change relative to baseline, prior to lockdown, see main text) for all sessions (colored). Each dot is an individual data point per participant and per session. Black lines are regression lines estimated from the linear model with the 95% C. I. reported in gray shading. The closer to baseline mobility, the shorter retrospective durations were estimated to be. The distribution of future subjective temporal distances obtained for "next week" (pale green) significantly differed from those obtained for "next month" (dark green; F(1, 3169) = 1171.
Acknowledgments We thank the many participants who took part in our extensive study, mostly without compensation and by sheer interest in citizen science. We are grateful to  Bernadette Martins  ( CEA, NeuroSpin ) for her continuous support on the ethical aspects of the protocol ( CER-Paris-Saclay-2020-020 ) and  Maryline Hevin  ( CEA, NeuroSpin ) for her administrative help. We thank numerous communication channels that have relayed and advertised the study:  Christine Doublé  ( CEA, Neurospin ),  Laure Belot  ( Le Monde ), and  Cécile Chevallier (Le Parisien ). We thank  Dean Buonomano ,  Sylvie Droit-Volet ,  Sonja Kotz ,  Natalia Martinelli ,  Ruth Ogden ,  Dan Poole ,  Darren Rhodes , and  Hedderik van Rijn  for their initial interest and support in building momentum to this international project.
We thank  Brill Publishing  for sponsoring participation tokens in Gorilla. A. W. was funded by the doctoral school ED3C "Cerveau, Cognition, Comportement"; M. C. works in a core facility that receives funding from the program " Investissements d'avenir"  ( Agence Nationale de la Recherche , grant numbers  ANR-10-IAIHU-06  and  ANR-11-INBS-006 ). S. G. was funded by the  Natural Sciences and Engineering Research Council of Canada (NSERC) ; Y. Y. was funded by  JSPS KAKENHI  # 19H05308 ,  UTokyo CiSHuB. V. vW. was funded by  CEA  and  ANR-18-CE22-0016. No dedicated funding was received for this specific work.
62; CI 95% 1. 40-1. 88), CVD (RR: 1. 34; CI 95% 1. 15-1. 56), and cardiovascular mortality (HR 1. 12; CI 95% 1. 02-1. 24) (8, 9). Pathophysiological mechanisms of long-term effects of noise on cardiovascular system include activation of the autonomic nervous system, which may lead to altered HRV, oxidative stress, and vascular dysfunction (10). Indeed, studies focused on the effects of noise showed both an imbalance of the ANS toward sympathetic activity and an increased risk of CVD  (11, 12). HRV is a non-invasive method to evaluate the balance of sympathetic and parasympathetic branches of the autonomic nervous system  (13, 14). Time and frequency domain of HRV have been described by Schaffer recently  (15). Furthermore, a retrospective analysis of the data collected from the CAST (Cardiac Arrhythmia Suppression Trial) study showed that the deterioration of HRV 1 year after a myocardial infarction was an independent risk factor for cardiac mortality (16).
All measurements were performed by an experimented occupational health technician. Data were analyzed with specialized software B&K 2245 Work Noise Partner. This process was in accordance with the AFNOR standard in ISO 9612 (May 2009). To assess noise exposure, we used two indicators: LAEq [average level of sound exposure per hour in dB(A)] and LPC [peak sound pressure level in dB(A)]. Workers from the two groups wore same type of ears' sound protection.
Measurement of heart rate variability We used an ECG Holter monitor (ELA Medical SYNESCOPE MultiChannel-MultiDay Version 3. 10 Milan, Italy). Workers wore a standard five lead ECG Holter monitor at work. HRV measurement was performed during all workday, began in the morning just before the beginning of the workday. For testing and references' values, the monitoring began by 10 min without physical activity. Digital recordings were analyzed by a trained cardiologist in the Cardiology Department of the University Hospital of Brest. The ECG recordings were made from three orthogonal leads of Frank. The SyneScope ECG Holter analysis software processed the data collected by the PCMCIA Sorin CRM Flash Sorin digital ECG recorders. An automatic and then manual reading of the R-R intervals was performed to ensure the correct identification and classification of each QRS complex as well as the removal of artifacts.
The process for recruitment of the sample can be represented as a flow chart (Fig. 1 ). Among the 2763 parents who accessed the online survey, 1742 provided parental agreement regarding 823 children and 1503 adolescents. Among these children and adolescents, 271 children and 459 adolescents went online, and 220 children and 308 adolescents agreed to participate. Ultimately, completed data were obtained for 188 children and 283 adolescents. The online survey took approximately 20 min to complete. All procedures have been conducted in full compliance with the principles of the Declaration of Helsinki. Approval for the study protocol was obtained from the Commissioner for Data Protection (Comité National Informatique et Libertéregistration 2220408).
Sociodemographic data and other characteristics The survey consisted of two parts to collect parent and child data. The parent questions asked about parents' sociodemographic information; whether parents were at home during the lockdown; whether a relative or acquaintance had been infected with COVID-19; and parental anxiety, assessed by the Generalized Anxiety Disorder-7 (GAD-7)  [22, 23]. There were also questions about the child in the second part. The collected demographic data included the child's/adolescent's age, sex, living arrangements, home location and education level. The children were also asked about their living and learning conditions. These conditions were captured by several indicators, including objective indicators of current residence-"home location", "type of dwelling", and "access to a private outside space"-and subjective indicators, including "time spent on schoolwork", "difficulty isolating at home", "tensions and conflicts at home", and "noises in the residence".
Second, it was a crosssectional study, and a prospective study could have better determined causation. Further prospective studies with larger sample sizes are needed to determine the factors that affect HRQoL. Third, data were collected only from the French Grand Est region, and a larger sample size with individuals from different areas of France is needed to generalize the results. Fourth, despite the large number of determinants included in the analyses, other factors, such as children's sleep behavior and time of screen exposure, were not accounted for in our study and should be included in future studies. Last, the results were discussed in conjunction with the literature available since the current pandemic spread, but the number of studies examining the effects on young populations is still limited.
Patients We included patients with IBS from 4 prospective studies conducted at the Sahlgrenska University Hospital, Gothenburg, Sweden, one large study focusing on the link between pathophysiology and symptoms, and 3 intervention trials (NCT03869359, NCT02970591, NCT01252550), in which baseline data before the intervention were used. In these studies, patients were primarily referred from primary care, through selfreferral, or via advertisements in newspapers or social media. For the present study, we included patients who had completed the IBS QOL Instrument (IBS-QOL). 14 IBS diagnosis was made by gastroenterologists specialized in disorders of gut-brain interaction (M. S. , H. T. ), and all patients fulfilled the Rome criteria for IBS at the time of inclusion (Rome II to IV). 15  The inclusion criteria were the same across the cohorts: IBS patients between 18 and 75 years of age.