dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis In order to determine whether the color of the plates exerted a significant effect on the six attributes in question, a three-way ANOVA was performed on the data to examine the meal session (lunch or dinner), the dessert (A, B or C), the plate (white or black) and the interactions as explanatory variables. In addition, two regression analyses were performed to explore the correlation between the ratings of the color intensity, flavor intensity and sweetness intensity, and the correlation between the ratings for appetizing, liking of the presentation (appearance attributes) and overall final liking (post-tasting). When the effects were significant, honestly significant differences were calculated using Tukey's test. Differences were considered significant when P ≤0. 05. Statistical analyses were performed using XLSTAT 2011 (Addinsoft, NY, USA).
Context and ethical conditions The present data were collected in the context of a longitudinal investigation of food preferences from birth up to 2 years of age within an Observatory of Food Preferences in Infants and Children (Observatoire des Préférences Alimentaires du Nourisson et de l'Enfant, OPALINE). Participating mothers were recruited before the last trimester of pregnancy, using leaflets and posters affixed in health professionals' practices and in day-care centres. To be included in the cohort, both parents had to have reached 18 years of age (legal majority), and infants had to be in good health. The aims and methods of the study were explained to both parents in great detail. For the part of the programme intended to investigate longitudinal changes in infants' reactions to food odours, the parents were extensively informed about the methods and timing of the olfactory tests.
Then, the durations of mouthing were divided by the duration of accessibility to obtain proportional durations of mouthing (called thereafter mouthing). For each odorant, duration data were then transformed into mouthing scores defined as the proportion of time during which a target bottle was mouthed relatively to the added proportions of time this bottle and the matched control bottle were mouthed. For example, the mouthing score for the apple bottle was equal to proportion of mouthing duration to the apple bottle/ (proportion of mouthing duration to apple odorant + proportion of mouthing duration to the control bottle). Mouthing scores equal to 0. 5 indicate the same duration of response to a given odorant bottle and the control bottle, and are interpreted as expressing indifference to the odour.
Acknowledgements The authors thank the infants and parents who took part in this study. The authors also wish to thank  C Laval  ( ChemoSens Platform ) for recruitment;  A Vincent ,  A Fornerol ,  R Bouhalassa ,  E Szleper ,  J Pierard ,  V Feyen ,  F Durey  for data collection;  C Gulluscio  for behavioural analyses;  V Feyen ,  F Durey  ( ChemoSens Platform ) and  C Martin  ( ChemoSens Platform ) for assistance in adult testing; and the whole OPALINE team for support and advice. The present study was carried out with the financial support of the  Regional Council of Burgundy , the  Institut Fédératif de Recherche n°92 , and the  ANR  (n°ANR- 06-PNRA-028 , OPALINE). This study also benefited from financial supports from the following corporations: Blédina, CEDUS,  Nestlé ,  Symrise , and  Valrhona.
R E S U L T S As the present study used the illusory conjunction task with prereader children for the first time, we first provide a general description of the data we collected. The signal detection theory  (Green & Swets, 1966 ; McMillan & Creelman, 2005)  provides a general framework to describe the decisions that prereaders made in the illusory conjunction task, by measuring the discrimination sensitivity threshold (i. e. , the dk criterion) and the response bias (i. e. , the b criterion). In the present study, the discrimination sensitivity threshold reflects the capacity of prereaders to detect the correct color of the target letter among a degree of background noise. Firstly, the criterion dk was superior to 0, indicating that prereaders did not perform the illusory conjunction task by chance.
e. , yes response) or a non-syllabic response (i. e. , no response). Figure  2  presents the b values for the three groups. In the pretest, the b criterion was greater than 1 in the three groups (b=1. 15 in the visual group, b=1. 19 in the visuo-phonological group, and b=1. 11 in the control group), suggesting a slight response bias toward the non-syllabic response. Such unexpected data mean that in the illusory conjunction pre-test, prereaders were slightly more likely to respond with the color of the last letter (i. e. , the A in NAC is reported to be the same color as C), which may result from the sensitivity to the left-to-right reading direction. After the learning session, the b criterion was still greater than 1 (b=1. 14) in the control group. However, the b criterion was less than 1 in the visual group (b=0.
KINEMATICS ACQUISITION AND ANALYSIS An Optotrak 3020 (Northern Digital Inc. , Waterloo, Ontario Canada) was used to record the spatial position of an active marker (infrared light-emitting diode) at a sampling rate of 250 Hz and a spatial resolution of 0. 1 mm. The marker was placed on the participant's index finger. Raw data were pre-processed using a second-order Butterworth dual-pass filter (cut-off frequency, 10 Hz). Kinematic parameters were assessed for each individual movement using Optodisp software [Optodisp Copyright INSERM-CNRS-UCBL (Marc Thevenet, Yves Paulignan, Claude Prablanc) 2001]. For the two sub-phases of the movement (back and forth), we analyzed the amplitude (Vel 1 and Vel 2 ), and the latency (LatPeak 1 and LatPeak 2 ) of the index velocity peaks (mm/s) as well as movement durations (ms) (Duration total , Duration 1 , and Duration 2 ).
EEG ACQUISITION AND ANALYSIS EEG data were recorded using BrainAmp amplifiers (BrainVision recorder software, BrainProducts GmbH, Munich, Germany). The observer's EEG data were recorded with an EEG device using 32-channel EEG caps with active electrodes (ActiCap BrainProducts) placed according to the international 10-20 system. Reference and ground were situated at Fpz and AFz, respectively, and impedance was maintained below 20 k. The signal was sampled at 500 Hz and a 50 Hz notch filter was used. Moreover, recordings of vertical and horizontal electrooculograms (EOGs) were made from electrodes above and below the left eye to monitor eye movements and blinks. EEG data were analyzed using BrainProducts Analyser 2 software (BrainProducts GmbH, Munich, Germany). EEG data were time-locked to the onset of the actor's movements by adding kinematic markers to the EEG signal.
Data source The data were obtained from the longitudinal cohort surveys being conducted by the International Tobacco Control (ITC) Policy Evaluation Project. The ITC Project is designed to evaluate the effects of national level tobacco control policies in over 20 countries and to examine the causal mechanisms responsible for policy impact  [23]. All ITC Surveys follow the same conceptual framework and methodology, which makes it possible to compare smoking behaviors and attitudes at the population level across countries. The survey and research protocol was reviewed and cleared for ethics by the Office of Research Ethics at the University of Waterloo, and by the relevant local institutional review boards for three of the countries: the Ethics Commission of the Medical Faculty Heidelberg, University of Heidelberg (Germany), the Research and Ethics Committee of the School of Medical Sciences, Universiti Sains Malaysia (Malaysia), and the Ethical Review Committee of the Chinese Center for Disease Control and Prevention and the Institutional Research Review Committee at Cancer Council Victoria (China).
Participants The analyses presented here are from the first two cohorts of ITC Survey respondents in the five countries available at the time of the study where large probability samples of both smokers and non-smokers were surveyed: Scotland, France, Germany, China, and Malaysia. Data were from the first survey wave in each country except for China and Scotland, which were missing the measure of time perspective at Wave 1. Respondents in all countries were adults aged 18 years and older. Respondents were defined as smokers if they reported having smoked more than 100 cigarettes in their lifetime, and currently smoked at least weekly (Malaysia and China) or monthly (Scotland, France, and Germany). The sample sizes and periods of data collection for each country are shown in Table  1.
Data analyses SPSS Version 19 was used for all statistical analyses. Analyses were conducted using rescaled cross-sectional weights for smokers and non-smokers (see  Scott and Wild, 2001 , for a justification of using these weights within a complex sampling design)  [26]. After examining the bivariate relation between time perspective and smoking status, a multivariate analysis with all countries pooled in one dataset was performed using a logistic regression model to predict the likelihood of being a nonsmoker from the predictor variables. Separate multiple logistic regressions were also performed within the individual country samples. Finally, including an interaction term between country and time perspective in a multiple logistic regression using pooled data provided a test of whether the relation between time perspective and smoking status differed across the five countries.
Smoking status The majority of respondents in the entire dataset (67. 7%) were smokers, which reflects the oversampling of smokers in each country. The samples from France and China had the greatest proportion of smokers (76. 8% and 76. 6% respectively), and Scotland and Malaysia had the lowest (54. 7% and 56. 3%). These differences reflect the different quotas for smokers and non-smokers within each country.
Acknowledgments This research was funded by the following project grants:  ITC Scotland: Flight Attendants' Medical Research Institute, NHS Health Scotland, Cancer Research UK  ( C312/A3726 ), Scottish Executive. ITC France:  French Institute for Health Promotion and Health Education (INPES) ,  French National Cancer Institute (INCa) ,  French Monitoring Centre for Drugs and Drug Addiction (OFDT). ITC Germany: German Ministry of Health ,  German Cancer Research Center, Dieter-Mennekes-Umweltstiftung. ITC China: U. S. National Cancer Institute  ( P50 CA111236 ;  P01 CA138389 ;  R01 CA125116 ),  Canadian Institutes of Health Research  ( 79551 ). ITC Malaysia: U. S. National Cancer Institute  ( P50 CA111236 ;  P01 CA138389 ),  Canadian Institutes of Health Research  ( 79551 ). Survey development, project management, and data management, ITC Project,  University of Waterloo: U.
Stimuli The trials were displayed on a computer monitor using E-prime software (E-prime Psychology Software Tools Inc. , Pittsburgh, USA) for learning the words. Thirty-six trials were presented (18 in egocentric-updated condition, 18 in allocentric condition). Each word-to-be-learnt was selected from the plant category, as well as the 36 additional filler words used in the recognition phase. Overall, these words had a lexical frequency use of 2. 62 occurrences per a million (SD = 2. 69) according to the word frequency database Lexique 3. 55  (New et al. , 2001 (New et al. , , 2004 , http:// www. lexique. org). Both lists were counterbalanced across spatial conditions. Concerning the spatial tasks, the layout configuration presented in each film was always different and contained an average of five objects ranging from 4 to 6 objects.
Angle errors were recorded on each trial using in-house software (VRML-prime: http://webu2. upmfgrenoble. fr/LPNC/membre_eric_guinet). A control experiment performed on the same participants allowed us to check that both spatial tasks were equivalent in terms of complexity. In this control experiment, the absolute error angle was computed on each trial by comparing the expected angle to the produced angle. Participants performed with an average absolute error-angle size of 35. 5 • (SD = 13. 5). No absolute error-angle size difference (F < 1) was observed between both spatial task conditions (allocentric and egocentric-updated).
MR acquisition and data processing Magnetic resonance scanning was carried out on a 3T MRI Scanner (Bruker MedSpec S300) with a standard head coil. We acquired 39 axial slices (slice thickness, 3. 5 mm) using a gradient gradient-echo/T2 * weighted EPI method (matrix, 72 × 72; field of view, 216 × 216 mm). The main sequence parameters were: TR = 3 s, TE = 30 ms, flip angle = 77 •. The TR was thus asynchronous with the SOA resulting in an effective sampling rate of the BOLD response. An LCD projector back-projected the virtual environment on a screen positioned behind the head coil. Participants lay on their backs in the bore of the magnet and viewed the stimuli binocularly via a 45 • mirror which reflected the images displayed on the screen. To minimize head movements, participants were stabilized with tightly packed foam padding surrounding the head.
The younger adults were recruited at the University of Bordeaux and the older adults were recruited from a Senior University in Bordeaux (''Universitedu Temps Libre''). In addition, the inclusion criteria for the older participants also included not having a global cognitive deficiency and therefore, the older subjects were tested with the Mattis Dementia Rating Scale as an exclusion test (exclusion for a score ,129). Even if this study does not meet the criteria for the CPP-III assessment, each participant signed a consent form in order to obtain the approval of each participant, as recommended by the CPP-III and the Helsinki convention. This document explains the process and the reasons for the study and how the behavioral data collected will be used. A written informed consent was obtained from each participant.
All data were analyzed anonymously. The younger and elderly groups were divided between ''active'' and ''passive'' groups. All of the subjects had to complete a French version of the Simulator Sickness Questionnaire (SSQ;  [55] ) immediately after the learning phase in the VE. This questionnaire measures the severity of sickness induced by 3D simulators. The questionnaire consisted of a list of 16 symptoms clustered under three factors: Oculomotor, Nausea and Dizziness. Symptoms were associated with presence/absence and with their degree of severity. Answers were scored 0 for ''absence'' of symptoms, 1 for ''mild'' symptoms, 2 for ''present'' and 3 for ''severe''. The weighted totals of the scale scores and conversion formulas are the same as those used by Kennedy et al. [55]. Participants also had to rate their New Technology (NTIC) experience with computers and computer games (  [56] ).
VR-based Material and Procedure All of the material and procedures are derived from previous studies demonstrating the relevance of VR-based applications to study spatial learning  [14, 21, 33, 34, 35, 59]. The VE was a replica of the district near the Bordeaux hospital. It was created using the Virtoolsß software. Significant landmarks (signposts, signs, and urban furniture) were included in the VE. The apparatus used in the VR room was a Dellß personal computer (3 GHz, 5 Gb RAM) with a NVIDIAß Quadro FX 4400 graphics card, a F1+ ß projector, a 261. 88 meter screen. In addition, in order to actively explore the VE, a Thrustmaster joystick was programmed with three degrees of liberty for displacements: forward translational movements (push); (2) angular rotations to the left or (3) to the right (to lean left or right in a 45u angle).
Background: A large number of longitudinal studies of population-based ageing cohorts are in progress internationally, but the insights from these studies into the risk and protective factors for cognitive ageing and conditions like mild cognitive impairment and dementia have been inconsistent. Some of the problems confounding this research can be reduced by harmonising and pooling data across studies. COSMIC (Cohort Studies of Memory in an International Consortium) aims to harmonise data from international cohort studies of cognitive ageing, in order to better understand the determinants of cognitive ageing and neurocognitive disorders. Methods/Design: Longitudinal studies of cognitive ageing and dementia with at least 500 individuals aged 60 years or over are eligible and invited to be members of COSMIC.
These include the use of standardised protocols, meta-analysis, and harmonisation of data  [6]. The use of standardised or even similar protocols is a rare feature of existing collaborations (the 10/66 Dementia Research Group is one exception  [7] ), and meta-analysis is limited to published results. In contrast, data harmonisation offers the potential to explore both existing and novel research questions by a costeffective use of previously-collected data. Harmonising data across studies to create a single, large database helps to minimise the influence of both studylevel (e. g. , methodology) and individual level (e. g. , demographic) factors, while also enabling these to be explored as potential contributors to differences in results  [8, 9]. Other advantages include increased statistical power for detecting effects, and the inherent replication and enhanced generalisability associated with using heterogeneous samples and methodologies  [8].
COSMIC (Cohort Studies of Memory in an International Consortium) is a recently established endeavour that aims to bring together cohort studies of cognitive ageing internationally in order to facilitate a better understanding of the determinants of cognitive ageing and neurocognitive disorders. The two main objectives of this project are to: 1. Harmonise shared, non-identifiable data from cohort studies that longitudinally examine change in cognitive function and the development of dementia in older individuals (60+ years). 2. Perform joint or mega-analyses using combined, harmonised data sets that yield collated results with enhanced statistical power, in addition to comparisons across geographical regions, ethnicities and sociocultural groups. Other collaborations bringing together cohort studies of ageing include the genetics-focused CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology)  [10]  and ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis)  [11].
COSMIC was established in 2012, with progress reported in 2013  [16]. Official enrolment in COSMIC involves a lead investigator having signed a memorandum of understanding that entails a willingness to share non-identifiable raw and/or processed data for joint or mega-analyses. Studies that, for institutional or other reasons, are unable to provide raw and/or processed data may participate in COSMIC as provisional members, if willing to provide results of in-house analyses conducted using COSMIC protocols. At the time of writing there are 14 officially enrolled and 3 provisional members of COSMIC. These studies, and their key demographic characteristics, are shown in Table  1. It is intended that the overall sample size and range of geographical regions and ethnicities represented be extended even further, and thus we ask that any study meeting the eligibility criteria consider contacting us to become a member of COSMIC.
Discussion Challenges General challenges facing large, international consortia have been previously described (e. g. , by CHARGE  [10] ). These include a potential need for additional funding to prolong the use of study data beyond initial anticipations, and timely and effective communication among members across different countries. More specific to COSMIC are challenges associated with harmonisation, many of which have also been previously described  [9, 35]. The major challenge of harmonisation stems from differences between studies in the measurement instruments used and/or differences in how questions from similar instruments are worded and responses provided and categorised, including the effect of language and culture. Attempts to maximise the number of studies contributing to a final dataset can require that complex information from some studies be simplified (e.
g. , converted from a continuous measure to a categorical scale). There is a potential reduction in validity involved in simplifying data, but there are mechanisms by which this can be tested and/or quantified  [9]. Meeting the objectives of COSMIC will require various data types to be harmonised, but data relating to cognitive outcomes such as impairment and decline are likely to be the most challenging (i. e. , more so than demographic and health-related variables). COSMIC member studies have operationally defined cognitive outcomes in vastly different ways. For example, for the purposes of diagnosing MCI, cognitive impairment has been variously defined as abnormal scores on the memory items of two cognitive status instruments (Mini-Mental State Examination and Geriatric Mental State Schedule) in the Zaragoza Dementia Depression Project  [36] , and as a score on any measure from a comprehensive neuropsychological battery 1.
First project The aim of the first COSMIC project is to compare the baseline prevalence of MCI across the COSMIC member cohorts and the different regions and ethnicities represented by these. The project is currently underway, and is being coordinated by the Sydney team. A questionnaire was developed and promulgated, with the information provided guiding a subsequent request for data from the studies on: The receipt of data was followed by communication with data managers and/or study leaders to clarify the nature of data (e. g. , the particular neuropsychological test used or manner of administration) and/or to ask that further data be provided (e. g. , the individual items from a functional test scale in addition to a total score originally provided). Data from 11 studies have been made available for this project (see Table  1 ), and there is a total sample size of more than 23,000 non-demented individuals aged 60 and older.
Future projects A number of future projects utilising COSMIC data are currently planned, and aim to make comparisons across COSMIC cohorts, countries and ethnic groups of: 1. Risk and protective factors for MCI. 2. Rates of cognitive decline. 3. Biomarkers (e. g. , blood, genetic and MRI-derived) of MCI and dementia. Many of the existing member studies have relevant data to contribute to these projects. It is expected that additional projects will address more refined and specific topics addressing the overall objectives of COSMIC. This could include identifying and comparing rates of decline within particular cognitive domains, and establishing associations between untreated hypertension or non-traditional risk factors and cognitive decline. Projects like these will be enabled and facilitated by growing the COSMIC membership base to ensure that there are sufficient relevant data on variables not collected by all studies.
Extended Implications of COSMIC The mechanisms for harmonising measures developed by COSMIC could produce standardised ways of collecting and reporting data that facilitate the comparability of longitudinal studies of ageing. This includes previous or existing studies, for which data could be reformatted and further analysed. It may also guide the choice of measures used or type of data collected by future studies, for which the capacity to directly compare results with those of many other cohorts would greatly enhance their interpretability and relevance. There is also the potential for the COSMIC database to be made available to non-consortium researchers via the website, following consortia-based publications and with the approval of the Research Steering Committee. The scientific benefits of making large databases available to researchers worldwide are demonstrated by the more than 250 publications reported to have arisen from the sharing of ADNI (Alzheimer's Disease Neuroimaging Initiative) data across the internet  [40].