dataesr/scientific-paragraphs-categorization huggingface dataset:
fMRI Data Recording and Analysis Participants were scanned on a 3-T MEDSPEC 30/80 AVANCE whole-body imager (Bruker, Ettlingen, Germany), equipped with a circular polarized head coil. We first acquired a high-resolution structural T1-weighted image (3D sagittal volume using MPRAGE sequence). For functional imaging, we used a T2*-weighted FID-echo planar sequence (EPI), covering the whole brain with 36 interleaved 3-mm-thick axial slices with a 1 mm gap, parallel to the AC-PC plane (repetition time -2400 ms, echo time -30 ms, flip angle 82 , field of view 192 3 192 mm-matrix 64 3 64, voxel resolution of 3 3 3 3 4 mm). 167 functional images were acquired per session. A fieldmap acquisition was acquired to correct geometrical deformations on EPI images. A three-dimensional gradient echo sequence with two echoes (echo times 3.
7 and 8. 252 ms) was used. One participant was discarded from the analysis due to head movement artifacts in the fMRI data. Data were processed using the spm8 software, according to the general linear model  [Friston et al. , 1995]. The first four functional volumes of each session were removed to eliminate nonequilibrium effects of magnetization. The remaining 163 images were corrected for differences in slice acquisition time, and after this step we also discarded the first volume to prevent invalid temporal interpolation. Fieldmaps were processed for each participant using the FieldMap toolbox implemented in SPM8  [Hutton et al. , 2002]. The images were then realigned to the first image and corrected for interactions between movements and field inhomogeneities using the fielmap  [Andersson et al.
Study procedures The survey was conducted in three stages. The first preparatory phase consisted of a methodological workshop between Togolese and French researchers to discuss the study design, validate the survey instruments, and address relevant ethical issues. A local team of four psychologists was recruited to adapt concepts described in the questionnaires to the local context, train interviewers on the study tool, ethical and privacy terms, and supervise data collection. Second, a pilot study was carried out to test the acceptability, adequacy, and understandability of the recruitment procedures and questionnaire. Survey feasibility data and interviewer's observations were integrated within the final survey procedures and tools. Finally, every working day during the study period, each eligible woman was systematically asked to participate in the survey and to provide signed informed consent to be sequentially enrolled.
Prevalence of physical/sexual violence was estimated as the proportion of women declaring having been exposed to any kind of physical or sexual violent act by their current intimate partner or any previous partner. Additionally, we explored attitudes toward partner controlling behaviors that we summarized in six statements, illustrating different gender submissive situations to which women were asked to agree or not. We developed a scoring system to define a 'submission index' summarizing women's attitudes toward partner controlling behaviors based on the sum of the number of positive answers to six questions detailed in Table  1. We also documented women's sociodemographic characteristics (age, instruction level, and contraceptive use), data on the women's partner (alcohol consumption and frequency of involvement in fights) and couple relationships (polygamy and concurrent relationship), women's employment and financial autonomy (having a financial autonomy to support herself and household without her partner for at least 1 month), and the modalities of women's entry in sexual life (conditions and age at first sexual intercourse).
Both univariate and multivariate analyses were carried out. Variables found to be statistically associated with intimate partner violence with a p-value of B0. 25 were included in the multivariable model. To select the final adjusted model presented in this paper, we used a backward elimination method using a p-value of 0. 05  (20). Adjusted odds ratios (aORs) were estimated using multiple logistic regression modeling and statistical significance was considered at the 5% level. Statistical analyses were generated using SAS software (version 9. 2 for Windows, Copyright 2013 for SAS Institute Inc. , Cary, NC, USA).
Smokers were underrepresented in the majority of countries, as were individuals with diabetes; the representativeness of asthma prevalence and vaccination coverage for 65+ individuals in two successive seasons (2010/2011 and 2011/2012) varied between countries. Conclusions: Existing demographic and national datasets allowed the quantification of the participation biases of a large cohort for influenza-like-illness surveillance in the general population. Significant differences were found between Influenzanet participants and the general population. The quantified biases need to be taken into account in the analysis of Influenzanet epidemiological studies and provide indications on populations groups that should be targeted in recruitment efforts.
Data collection To join the network, users registered on their national platform. Upon registration, the user was asked to complete an intake survey, covering demographic factors (age, gender), geographic factors (location of home and work/school expressed at the municipality or zipcode level), socio-economic factors (household size and composition, occupation, educational level, daily transportation means), and health-related factors (including vaccination status against influenza in the 2011/2012 and previous season, diet, pregnancy status, smoking habits, and medical conditions associated with higher risk of influenza complications). The intake survey was standardized and translated whilst preserving the same type and content of questions and possible answers, as well as the same order of questions within the survey, and accounting for the differences related to specific national standards (e.
When multiple intake surveys were available for a user, in the present study we used the most recently completed one. In the sensitivity analysis, we quantified the type of changes made in the updated surveys and tested the effect of discarding the updates. Influenza-like-illness surveillance data were obtained through weekly symptoms surveys. No data from the weekly symptoms surveys was considered in this study; however the number and frequency of reporting by each user was used to evaluate the user's active participation in the surveillance network. A schematic representation of the Influenzanet data collection is shown in Figure  1.
Census and health data sources We collected national data from a number of sociodemographic datasets and health datasets for all participating countries. In absence of data for the years 2011 or 2012, we relied on the most recent available sources. Demographic and geographic data were taken from the European Commission portal for European Statistics  [25]  and from national institutes of statistics. Georeferenced census data were obtained from the Nomenclature of Territorial Units for Statistics (NUTS), a standard geocode for referencing the subdivisions of countries for statistical purposes, developed by the European Union  [26]. We considered the NUTS2 level, corresponding to basic regions for the application of regional policies. All other socio-economic data were taken from European Statistics and national sources: household size and composition  [27, 28] ; education data  [29] [30] [31] ; employment data  [32] ; transport habits  [33] ; vaccination coverage data  [34] [35] [36] [37] [38] [39] [40] [41] ; diabetes prevalence data  [42] [43] [44] [45] [46] [47] [48] ; asthma prevalence data  [44, [49] [50] [51] [52] [53] ; smoking prevalence data  [54] ; body mass index (BMI) data for France  [55].
Data analysis The representativeness of the Influenzanet population was assessed through the comparison of its characteristics with those of the general population for each country. We used χ 2 -test for non-continuous sociodemographic variables, and Student's t-test for mean comparisons. All comparisons used 2-tailed tests and a 5% cutoff point. To assess whether differences in participation rates between countries were associated with differences in Internet coverage (access and usage  [57] ), a test for association between paired samples was considered, using Pearson's product moment correlation coefficient, Kendall's τ or Spearman's ρ. Statistical analyses were performed using the R software version 2. 13. 2 (R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria, http://www.
r-project. org). Age data were analyzed in 10-years age categories up to an aggregated 70+ class. For France we had a category of 18-19 years old individuals, because of the absence of younger participants during the data collection campaign here analyzed. We additionally split the 60-69 class into two categories, 60-64 and 65-69 years of age, to account for the age definition (65+) of individuals at risk for developing flu-related complications. Georeferenced data from Influenzanet were mapped from zip codes or municipality resolution to NUTS2 level for comparison with national data. Apart from the geographic and demographic characteristics, all other variables were adjusted by age (10-years categories) and gender. The household composition question offered a list of age groups to be ticked, next to open fields where to indicate the number of individuals in the household for each selected age group (Intake Q6 in Additional file 2).
When no number was indicated, we assumed that one individual belonged to the selected age group. Commuting data, extracted from countries' census and from Influenzanet population, were mapped to NUTS2 level. Data were analyzed in terms of networks of nodes and links  [58, 59] , with nodes representing the NUTS2 regions and directed links the commuting movement between regions. A weight w OD was also assigned to each link from origin O to destination D to indicate the number of commuters on that connection. Adjusted analyses by geographic distribution of the population were performed (Additional file 1). We assessed whether the Influenzanet links reproduce the backbone of the census commuting network defined by extracting for each country a portion of census network of the same size of the Influenzanet commuting network containing the highest traffic links.
4% vs. 3. 2%, p < 10 -6 ) and Belgium (3. 95% vs. 2. 8%, p < 10 -4 ). No significant difference was found in France (5. 9% vs. 6%, p = 0. 8). Results are reported in Figure  9. Commuting links are ranked for decreasing probability of occurrence P OD. Median ranks are smaller than the corresponding value for the random sample, and outside of the confidence interval for all countries except Sweden. Influenzanet diabetes prevalence was in agreement with national data for three countries (The Netherlands, Belgium, and France, with p = 0. 8, p = 0. 2, and p = 0. 7, respectively), and lower for the others (p = 0. 02 for Sweden, p = 0. 01 for Portugal, p ≤ 10 -3 for Italy and UK, Figure  9 ). Vaccination coverage against influenza in the 65+ age class during the 2011/2012 season was larger in the Influenzanet participants of France, Portugal and Sweden, whereas it was statistically representative in Italy (57.
Maps were created with the collected data using ArcGIS Software and publicly available geographic datasets  [25]. adopted by the Influenzanet Consortium. Active participation was observed for 80% of the participants and covered all NUTS2 regions included in the project. Participation varied widely across countries, geographic regions, gender groups, and age classes. This is most likely related to different factors, namely: the reachability of a given portion of the population obtained through communication campaigns; the availability, usage of and familiarity with the Internet (which is used in this study as the mean to collect data); and the self-selection of participants, or 'volunteer effect' , and the underlying interest towards the object of the study  [61]. Results seem to indicate that coverage biases due to the Internet may partly explain the observed variability   Geographic distribution within each country was not representative, and a larger participation was generally observed in those regions hosting the laboratory/Institution conducting the study, likely reflecting a more powerful effect of communication campaigns at the local level.
Acknowledgements We particularly thank all European participants who took part in the  Influenzanet  project in the 2011/2012 season. We also thank  Niel Hens  for useful discussions and for sharing Flemish general population health data, and  Patricia Soares  for help extracting the Portuguese general population data. This work is partly supported by the  U707/InVS  partnership contract  n°1 2-N-MIP20-04 , and the  ANR  contract no. ANR-12-MONU-0018 (HARMSFLU).
Discussion 31 ,Figure 6 316 Figure 6 Comparison between the Influenzanet commuting network (left) and the backbone of the census commuting network (right). The color code associated to the links in the census commuting network is proportional to the adjusted weight (from yellow to dark-red). Both networks are directed, arrows are omitted for the sake of visualization. Maps were created with the collected data using ArcGIS Software and publicly available geographic datasets [25].
In the latter case, it means that they let the company access all the personal data required to calculate their willingness to pay, in exchange for the guarantee that they will never actually pay more than the flat price. If they accept, a monitoring system is installed on all devices used to consume the track, after which the track itself is downloaded. A trial period, during which personal data are collected and analyzed, ensues. The trial period ends when enough data have been collected about consumption of the track itself, as well as other parameters (consumption of other tracks, music in general, etc. ), to calculate accurately the willingness to pay for the track. Interestingly, this means that the trial period may be of a different length depending on the consumer, the artist, the genre of the track, etc.
Indeed, if one considers, as in  Simon (1959) , that individuals have a bounded rationality, which entails a limited of their environment but also of themselves, and limited processing capabilities, the pricing mechanism presented in this article could have a further value, as it can act as a Decision Support System. Aggregating and analyzing a vast amount of data from many consumers would probably lead the system to know each consumer better than they know themselves. It could know, for instance, that for a particular consumer, a particular 'summer hit' is not just a fad, but will be consumed all her life or, in contrast, that a consumer keeps purchasing albums from one particular artist that she, in fact, never or rarely listens to. Furthermore, in light of the massive amount of digital content available to consumers (there are over 30 million songs on the iTunes Store), such a system would be able to accurately recommend new artists or even new genres, which would make it a very valuable service for consumers.
However, smaller firms would probably not have enough data to accurately estimate willingness to pay. The strength of Apple's iTunes Genius or Google's Instant Mix (and their accuracy in predicting what people want to listen to) precisely lies in the vast amount of user data they are able to gather. 14 Hence, the pricing mechanism described in this article would be more likely to be carried out by firms of a significantly large size. There are also practical issues that need to be considered. An important one is the compliance of such a system with existing laws and regulations. For instance, regulatory frameworks, such as the EU 'Directive on privacy and electronic communications' (2002/58/EC), restrict the amount and type of information that can be collected by firms about consumers. As noted in  Goldfarb and Tucker (2011) , the advent of such regulations has had a significantly negative impact on the effectiveness of online advertising.
Indeed, it is quite possible that the data collected as a part of this music pricing scheme could be used to price-discriminate consumers in other markets. Of course, if that were the case and consumers would become aware of it (or simply if they expect this to happen), this would make mutually advantageous price discrimination unachievable. A final practical issue relates to users without data. Indeed, while consumers who have been using iTunes (or a similar software) for a while already have data that can be used to determine their willingness to pay (iTunes records which tracks have been played and when, when tracks were added to the library, etc. ), calculating the willingness to pay for consumers with no data history is a challenge. Of course, the trial period could be extended until sufficient data has been collected, but this would practically turn the service into a (temporary) free-of-charge 'all-you-can-eat buffet' for such users, since they would be able to try any number of tracks for a significant period of time, before enough information has been collected about their consumption behavior.
This contribution addresses both the role of geographical information in participatory research of coastal zones, and its potential to bridge the gap between research and coastal zone management. Over a one year period, heterogeneous data (spatial, temporal, qualitative and quantitative) were obtained which included the process of interviews, storing in a spatio-temporal database. The GIS (Geographic Information System) produced temporal snapshots of daily human activity patterns allowing it to map, identify and quantify potential space-time conflicts between activities. It was furthermore used to facilitate the exchange of ideas and knowledge at various levels: by mapping, simulation, GIS analysis and data collection. Results indicated that both captured data and the participatory workshop added real value to management and therefore it was deemed well managed by stakeholders.
Data Collection The marine environment has limited data and is viewed as a common resource  [29]. However, this study aimed to identify daily human activity patterns over a one year period. Consequently, heterogeneous data (spatial, temporal, qualitative and quantitative) were obtained and stored in a spatio-temporal database. Furthermore, with the availability of a complete database from the (AIS) Automatic Identification System used by the vessel traffic service, daily precision data could be recorded throughout 2009. The AIS data were stored in a spatio-temporal database (step 1); daily trajectories of each boat were derived from their position monitored every 10 s (step 2). This study considered shipping lanes as polygons, which encompass a representative number of boat trajectories. The boundaries of these polygons were defined by extracting 90% isopleths (arbitrary threshold) of the Kernel density of trajectories (Kernel type = normal bivariate, scaling factor = 1,000,000; smoothing factor (h) = 100; raster resolution = 40 m) (step 3).
During the face-to-face meeting, each informant could directly draw the spatial activity on a tablet PC using GIS based mapping. Simultaneously, temporal, quantitative and qualitative data were also collected. The origin of the qualitative data came from two methodologies: interviews concerning potential interactions between activities-be it positive, neutral or negative-and a retrospective analysis (regional/local newspapers over a 10 year period). Finally, all data were summarized within a stakeholders-based interaction matrix  [33] [34] [35]. Furthermore, the temporal data, with daily resolution, could indicate whether or not a given activity was present whereas the quantitative data would specify the number of boats associated with this activity. Both data sets were collected differently: (i) "Real" data were retrieved from the database of organizations involved in the survey (i.
Spatio-Temporal Database (STDB) The spatio-temporal unit (STU) is an elementary unit associated with a thematic attribute and is consistent with both temporal and quantitative data (i. e. , maritime transportation for each shipping lane would correspond to a STU). Therefore, these heterogeneous data were specifically structured and classified into a spatio-temporal database (STDB)  [36]  (Figure  2 ) to obtain a spatio-temporal perspective. All spatial data containing the STUs were stored in a shapefile. Each STU contains attributes of geographic information, relative to its nature and source, along with the activity and geometry identifiers. Meaning, the daily occurrences of activities associated to quantitative data (i. e. , the date, boat density, data quality, and identifiers) were stored in a table.
Five quality classes have been defined, from "very weak" (stakeholders based data indicating archetypal activity patterns of a typical seasonality) to "very good" (AIS data or nautical center billing databases). To use the STDB would require the application of spatio-temporal queries to associate with various geo-processing tools. Therefore, to have these tasks automated, two tools were developed by ModelBuilder in ArcGIS to (i) identify and map the daily location of activities over a one year period; (ii) calculate and map the boat density distribution (per polygon).
Supporting Discussion and Building Collective Scenarios Phase The participatory workshop consisted of representatives from local commercial fisheries and five local agencies interested in coastal management; including several whom represented the Natura 2000 and ICZM. The session structure appointed an observer and moderator whom managed and recorded the reactions and discussions of all participants. A questionnaire previously prepared by researchers addressed the relevance for different types of data and information on a three-main step ICZM process: diagnosis, planning, concertation. All participants were briefed of the objectives concerning the questionnaire survey procedure. The captured data included the perceptions of methodology, the dynamic GIS and its possible relevance to initiate simulations for the Bay of Brest.
Data Collection Spatio-temporal analysis of the AIS database mapped seven shipping lanes in total, for maritime transportation in 2009. The display capacity for the multi-scale dynamic GIS permitted this study to create geographic data layers on the foundation of scales used by stakeholders, whilst mapping their activity zones. Among the 28 mapping interviews conducted (Table  2 ) for spatial data, 26 key informants successfully controlled the GIS software to map their activity zones. A total of 123 entities corresponding to the location of activities were recorded. All activities were described without.
Maritime Activities in a Spatio-Temporal Perspective The STDB contains 149 STUs which associate to 9346 of daily occurrences describing 29 activities. Potential boat density associated with each occurrence was calculated along with estimation for daily quality indexes, density and quality data of each occurrence. These respectively relied on the quality of daily comparative percentage for each occurrence and density. For instance, if more than 50% of occurrences belong to the quality class "very weak" for any given day, then daily quality index of occurrences would be considered "very weak" for that particular day. The quality of indexes ranged from "good" to "very good" for 84% of the days in terms of occurrences, and 90% of the days for boat density. Thus, using the STDB within the study's GIS provided temporal snapshots over 2009 and a daily time step actually associated with the data related quality indexes.