dataesr/scientific-paragraphs-categorization huggingface dataset:
Platforms and activities such as Wikipedia and OpenStreetMap benefit from crowdsourcing in its original sense: that of different citizens arriving at a collective view of what the truth is (with apologies to Pickles  [18] ). This consensual 'Linus's Law' approach generally produces acceptable outcomes, although with some known socio-scientific problems, for example on occasion the versions of the 'super user' or views of the user with the greatest edit persistence will win out despite conflicting or contrary edits in so called 'tag wars'  [19]. In many cases, the capture of crowdsourced data is much less nuanced and detailed than Wikipedia or OpenStreetMap. Consider the Geo-wiki initiative. Anyone can login and evaluate land cover at discrete locations. This may result in people with different backgrounds and experiences identifying different land cover types as being present at the same location  [20].
Data and study area This analysis used Geo-Wiki land cover data. The Geo-Wiki initiative  [15]  collects volunteered data on land cover in order to support a number of activities that range from land cover data validation to land cover data creation  [16]. Geo-Wiki has web and smartphone app interfaces, is open to anyone and volunteers can contribute to different campaigns. In these they allocate what they observe from Google Earth imagery at a series of randomly selected locations, to one of a predefined set of 10 land cover classes. This legend was chosen to be consistent with the generalized land cover classes proposed by Herold et al. [34] , which allows for comparison of different land cover products. More details of these competitions can be found in See et al. [35]. In order to provide some form of validation, the inferred land cover at each location on the 50km sampling grid was compared with GlobCover 2009 2  , reclassified into the 10 classes and resampled to 50km.
The reclassification was the same as that reported in  [4]. It was devised by the Geo-Wiki team led by Steffen Fritz and the class to class relations were agreed by consensus by 3 experts using images and discussing them together. The GlobCover thematic aggregations are shown in Table  1. In this research, data from two Geo-Wiki campaigns were combined from 2011 and 2012 for a South American case study containing some 13,738 data out of a global datasets of 100,808 points. The study area, spatial distribution of Geo-Wiki data and the reclassified, spatially aggregated GlobCover data are shown in Figure  1. Note that geographically weighted approaches, as described below, develop local analyses of data that fall under a moving window or kernel. Thus the 50km grid provide a series of locations at which the local analyses take place, irrespective of the number of data points within each grid cell.
Geographically weighted crowdsourced data A discontinuous geographic kernel was used to select crowdsourced data at each location on a 50km grid as shown in Figure  1. The idea was to use the land cover data points falling under the kernel to infer the land cover present at the centre of the kernel (i. e. a grid point), with data points further away from the kernel centre contributing less to the overall. The geographical extent of the kernel is determined by its bandwidth. In this analysis bandwidths from 5km to 150km at intervals of 5km were examined to explore the interactions of the evidence combination approaches with different scales of aggregation. Each crowdsourced data point records the contributor's opinion of the land cover at that location. It provides evidence in support of a hypothesis of the presence of that land cover class at the centre of the kernel.
At each location the land cover with the greatest belief was identified for each method and was compared with the GlobCover 2009 land cover class. A correspondence matrix was created and the overall correspondence for that method was calculated from the diagonals. Any locations where no land cover class was allocated (ie they were classified as NULL) were omitted for the correspondence analyses. This was done for each of the kernel bandwidths. All of the analyses were undertaken in R 3. 2. 1, the open source software (http://cran. r-project. org) with extensive spatial analysis and mapping functionality. The code and data used in this analysis will be provided on request.
Comparison with reference data The analyses were run for 30 bandwidths of 5km to 150km in intervals of 5km. At each location on a 50km grid and for each evidence combination method, the class with the greatest support was identified and compared with the land cover class at the same location from the aggregated Globcover 2009 dataset. The overall correspondences for each combination method were calculated and the results are shown in Figure  4. This shows a general trend of increasing rates of correspondence with increasing kernel bandwidth for Dempster-Shafer, Bayes and GW Average. The geographically weighted average has the greatest correspondence regardless of bandwidth and Fuzzy Sets decreases with increased bandwidth (and therefore the number of data points being considered at each location) and Possibility plateaus around a kernel bandwidth of 75km.
First that we do not know how reliable the reference data were: a different global dataset may well have generated different results. In a similar vein, the approach here was to 'crisp off' combined belief (essentially soft classifications) into Boolean classes. This is to ignore much of the uncertainty embedded in the belief in different land cover class hypotheses. Further work is needed to compare the inferences arising from combining crowdsourced data with soft references data in the manner suggested by Fisher et al  [39]  and Comber et al  [41]  and to evaluate frames or subsets of classes such as are generated by Dempster-Shafer approaches. Second, that for some land cover classes other evidence combination approaches performed better as in Table  2. This may be because of the greater ambiguity over what, for example, the class of Barren means and uncertain conceptualisations of that class by different contributors, or as a result of different densities of crowdsourced data or because of the interaction of these factors.
The maps and other figures illustrates how they treat There are a couple of further issues to note. First, here a number of bandwidths were explored. Methods exist for selecting the bandwidth automatically, for example using a leave-one-out cross validation procedure which optimises the prediction probability for each individual data point when it is removed from the dataset. As a result, optimal bandwidths may therefore be class-specific which will be explored in future work. However these approaches are based on variance evaluations and may not be appropriate for this kind of analysis with this kind of data. Second, the results only indicate the belief in the hypotheses, that is the extent to which the evidence supports the different hypotheses. Whereas, any uncertainty arising from the evidence is implicitly included in the Fuzzy Sets memberships and is absent in Bayesian Probability which partitions evidence into belief and disbelief (what is there), this is not the case for Dempster-Shafer and Possibility Theory.
The geographically weighted evidence combination methods (Dempster-Shafer, Bayesian Probability, Fuzzy Sets and Possibility Theory, GW Average) provide a suite of approaches for assessing belief and for combining conflicting information when mining large crowdsourced datasets, whether the data are contributed actively such as in Geo-Wiki or passively like much social network data. The application of a geographically weighted kernel explicitly addresses the need to consider Tobler's first law of geography when mining and combining crowdsourced data, reflecting the expectation that similar features and process will be clustered and not randomly distributed. The approach of a geographically weighted framework with evidence combination approaches allow more nuanced inferences about the quality of volunteered information to be generated than simple majorities and support the exploitation of large volumes of crowdsourced data about all kinds of phenomenon.
Data analysis The data analysis was performed concurrently with data collection according to the Grounded Theory approach. This allowed a constant comparison between collected data and analyzed data and to identify new hypotheses. The first three authors (SK, GB and BL) performed the analysis in four steps. They were all trained in qualitative research. First, each author independently broke the transcripts down into words or sentences to obtain open codes. Second, the open codes were classified into subcategories or axial codes. Third, the subcategories were classed into themes. The fourth step was to use the thematic analysis to develop a theory about the factors determining the choice between private and salaried practice. Open coding was performed by the three researchers by using NVivo 8® software (QSR International, 2008, Canada).
A similar study could be led with young doctors in other specialties. This would show whether the factors determining their choices of practice are the same and throw light on what really determines the global primary health care supply in France. didn't create a file with GPs' personal data in our If we submitted the protocol to a privacy protection committee, it would have said that our work falls outside their jurisdiction because it didn't concern medical usual cares. However, we collected consent from the young GPs at various times: oral consent to participate in the semi-structured interviews was collected by telephone. Before each interview, all GPs received a letter reminding them that a meeting was planned, that the interview would be recorded and that the transcripts would be anonymized.
Data Sources This study used data from the Cambodia Demographic Health surveys (CDHS) conducted in 2000, 2005, 2010, and 2014 (Macro international Inc. , Opinion Research Corporation (ORC Macro), Caverlton, MD, USA). The DHS surveys collect information on household demographic and socioeconomic characteristics and on child anthropometry, child feeding practices, and child health in a nationally representative sample [10-13]. The surveys were based on stratified samples selected at two stages, and each reporting domain was separated into rural and urban areas.
Indicators Used The household wealth index was constructed by principal component analysis, as described by Filmer and Pritchett  [14]. The wealth index is a composite measure of a household's living standard that was calculated using data consistently available over time. It gathers information regarding accessibility and type of water, sanitation facilities, materials used for housing construction, type of fuel used for cooking, and ownership of selected assets such as a radio, television, refrigerator, etc. Anthropometric measurements were collected from children under five years in a subsample of households. Children's heights were measured to the nearest 1 mm. The nutritional status of children was defined by the height-for-age, weight-for-height, and weight-for-age z-scores calculated according to the Child Growth Standard of the World Health Organization (WHO) using the igrowup macro designed for STATA  [15].
Participants Participants included in our study came from the Safe Move cohort, which comprised 1200 drivers over 70 years old. The calibration of their cognitive abilities was determined by comparing their objective and subjective cognitive performances. The objective cognitive performances were determined using the results from the Wechsler Digit Symbol Substitution Test (DSST) and the Trail Making Test (TMT) parts A and B. These objective data were compared with normative data (i. e. values for participants of the same age and same educational level as the participants in the present study). This comparison allowed classification of the participants according to their objective cognitive level as Bhigh^, Bmedium^or Blow^. The subjective cognitive performances were collected from a self-assessment questionnaire described below.
These subjective data allowed classification of participants depending on the perception of their own cognitive abilities as: Bbetter than the others^, Blike the others^, or Bworse than the others^. By crossing the objective and subjective data, three profiles of drivers emerged: UEs (15 % of the cohort, or 180 drivers), correct estimators (CEs, 42 % of the cohort, or 502 drivers), and OEs (43 % of the cohort, or 508 drivers)  [55]. One hundred and twenty drivers (OEs and UEs), recruited from the cohort, were expected to be included in our study. The inclusion criteria were: driving at least three thousand kilometers per year, owner of a computer connected to the Internet, not suffering from motion sickness or vertigo or Menière's disease, and visual acuity higher than 5/10th as measured using a Monoyer chart.
Participant characteristics Of the 80 expected participants included in our study, 67 finished the experiment. As specified in the introduction, this study presents some preliminary results. The data from additional participants will be presented in a forthcoming article. Of the 67 participants, 40 completed the CT (18 UEs and 22 OEs) and 27 the CT + DS program (14 UEs and 13 OEs, Table  2 ). Participants in the two training groups did not differ in age (t(65) = 0. 88; p = 0. 38) or in the time they spent on training (t(65) = 1. 12 ; p = 0. 27). In addition, one participant could not complete the UFOV® during the post-training evaluation because the computer was out of order. 3. For the TMT, a diminution in the number of perseverations was observed after training in both groups (main effect of Time, Table  4 ).
Although there are currently no normative data to quantify the improvement with the TRIP tactical sub-score, a previous study showed that patients who suffered from Parkinson's disease had, on average, four points less than controls  [67]. It would be useful to compare these data with findings from different populations to understand the repercussions of this one-point improvement between the pre-and post-test. In this study, the driving performance evaluation was also conducted with a behavioral observation grid, completed by the experimenter. The analyses showed a significant negative correlation between the total penalty score of the behavioral observation grid and the TRIP total score, which indicated the links between the parameters measured by these two grids. The negative correlation indicated that when the participants improved their overall driving performance, their total penalty score decreased.
Acknowledgements We are indebted to the curators who granted us access to the collections in their care:  A. Folie & I. Jadin  (RBINS),  N. Cauwe & L. Paquay  (RMAH),  J. -L. Schütz  ( Grand Curtius Museum ),  B. Clarys  (CHAPG), and  C. Jungels  ( Préhistosite de Ramioul ). We thank  A. & G. Jourdan ,  E. Dewamme & A. Mathys  for the photos and casts of the Goyet remains,  I. Jadin  for his help with the RBINS archives,  E. Morin  and three anonymous reviewers for their useful comments and suggestions. This research was funded by the  Wenner-Gren Foundation  (Gr. 7837 to H. R. ), the  College of Social and Behavioral Sciences of CSUN , the  CSUN Probationary Faculty Support Program , and the  RBINS. A. G. -O. was supported by a  Marie Curie-IEF research fellowship , the research group  IT834-13  ( Eusko Jaurlaritza/Gobierno Vasco ), and the  Spanish Ministerio de Economía y Competitividad  (projects  CGL2012-38434-C03-01  and  CGL2015-65387-C3-2-P -MINECO/FEDER- ).
These studies justify that in order to identify mechanisms or causal chains linking environmental challenges, AL and subsequent health, a life course approach is required, particularly if interventions are to be implemented. To study such complex mechanisms, implicating direct and indirect effects of adverse exposures over time necessitates rich longitudinal datasets with long follow--ups. Socioeconomic position being a proxy for various exposures, datasets with large panels of variables on socioeconomic and psychosocial environment are particularly precious to disentangle which aspects contained in socioeconomic position influence both health and AL. Another essential ingredient in these datasets is the inclusion of biological samples, repeatedly collected to represent the dynamic nature of AL.
Together, I hope these small reflections can contribute to further thought and discussion on the theory of AL, and I am sincerely looking forward to the authors' response. into the framework of ecosocial theory. Of course many other mechanisms of embodiment deserve further exploration in terms of environmental conditions across the life course, such as behavioural and psychological factors, socioemotional changes or cognitive function. We agree with Gustasfsson that our desire to maintain an AL measure that is 'loyal' to a balance of physiological systems should be applied equally to the environmental factors that the measure attempts to capture. Now that a number of openly accessible longitudinal datasets collecting a large array of environmental and biological variables are available, it has become possible to specify plausible hypotheses to test and unpick many of the concepts raised here  (Kelly--Irving, Tophoven & Blane, 2015).
Acknowledgements All authors contributed to the conceptualisation and design of this paper via dedicated meetings and workshops. Cyrille Delpierre  and  Michelle Kelly--Irving  drafted the manuscript. All authors contributed to the writing of subsequent drafts. This project received funding from  INCa--Cancéropôle   GSO N°2012--E18  and is part of the  IBISS  project supported by funds from The  French National Research Agency  ( ANR--12--DSSA--0004 ). Funding was used for meetings and workshops but there was no role of the study sponsors in the study design or in the collection, analysis and interpretation of the data or in the writing of the report and in the decision to submit the paper for publication.
MATERIALS AND METHODS Several methods were employed to ensure that the search for pertinent studies was all-encompassing. Articles included were published in English, in a peerreview journal (excluding books, theses, and dissertations) after 2000 (before this date, few households had access to computers and Internet). Conference proceedings were excluded. Databases were searched on November 26, 2015. The selection process is illustrated in Figure  1. Step 1: Database search. First, a search was undertaken on Pubmed and Psychinfo via EBSCO and on Science-Direct, using "online poker gambling" as key words. All articles including "poker" in the title or key words were selected. Step 2: Reading abstracts and references. Articles focusing on a sample of online poker players were then selected. Articles focusing on poker Web sites, advertising, the legal framework, an analysis of poker games, or gaming operator data were excluded.
Study design and study sample This study was part of the SPOTLIGHT project (36), conducted in five urban regions in Belgium, France, Hungary, the Netherlands and the UK. Sampling of neighbourhoods and recruitment of participants has been described in detail elsewhere  (37). Briefly, neighbourhood sampling was based on a combination of residential density and socio-economic status (SES) data at neighbourhood level. Neighbourhoods were defined according to small-scale local administrative boundaries as used in each country except for Hungary. Budapest is divided into districts and suburbs that are highly heterogeneous in terms of population and much larger than the equivalent administrative areas in the other study countries. In order to ensure comparability between study areas, we defined 1 km 2 areas to represent neighbourhoods in Budapest and suburbs.
Across all five locations the average area of a neighbourhood was 1. 5 km 2  , and the mean population density was 2,700 inhabitants per neighbourhood. Detailed characteristics of the neighbourhoods are described elsewhere  (37). Data on residential density were obtained from the Urban Atlas database (European Environment Agency, 2002) using two categories: high and low residential density (>2/3 and <1/3 of areas covered by residential buildings, respectively). Neighbourhoods were classified as low and high SES on the basis of recent data on neighbourhood median income (i. e. the first and third tertiles, respectively) retrieved from each country's national statistics office. This resulted in four types of neighbourhoods: low SES/low residential density, low SES/high residential density, high SES/low residential density and high SES/high residential density.
Presence of destinations in the residential neighbourhood Objective environmental data on destinations in the neighbourhood were collected using the validated SPOTLIGHT Virtual Audit Tool (S-VAT)  (39). Intra-observer reliability of the tool ranged from 92% agreement (kappa = 0. 65) to 100% agreement (kappa = 1. 00), and inter-observer reliability ranged from 79% agreement (kappa = 0. 44) to 99% agreement (kappa = 0. 58). A total number of 4,486 street segments in 59 neighbourhoods (one Hungarian neighbourhood was not covered by Google Street View at the time of the virtual audit) were audited to collect data on the presence of destinations related to physical activity and eating. Street segments were defined as the part of the street between two intersections (with a minimum length of 50 m and a maximum length of 300 m).
Using a standard operating procedure, researchers collected data on 40 different items in each street segment  (39). 'Destinations' recorded in this audit were indoor recreational facilities, outdoor recreational facilities, public parks, café or bars, takeaway restaurants, fast food restaurants, restaurants, on-street vendors of food, convenience stores, wine or liquor stores, street food markets, local shops and supermarkets. Additionally, we had information on the presence of bicycle lanes. Food outlets were classified according to the food outlet classification of Lake et al. (40)  Data collection resulted in a database of the characteristics of street segments in each of the audited neighbourhoods. For the present study, we aggregated data on destinations from the street-segment level to the neighbourhood level by taking the percentage of street segments with destinations in the neighbourhood.
Statistical analyses After exclusion of individuals who could not be allocated to one of the 59 selected neighbourhoods (n = 732), a sample of 5,205 participants was available for analyses. Differences in characteristics of individuals perceiving none, few and many barriers are examined using ANOVA and chi-Square tests. As item non-response ranged from <1% (age) to 26% (individual perceived barriers), complete case analysis was likely to result in biased estimates  (47). Assuming that data were missing at random (i. e. the probability that a variable value is missing depends on other data that are observed in the dataset but not on any of the missing values), multiple imputations were performed. Given the percentage of missing values, 30 imputed datasets were generated, as recommended by Rubin  (48)  and Bodner  (49).
Missing values were imputed using Predictive Mean Matching in SPSS version 22. 0. All variables described in the methods section were entered in the imputation models  (50). Multilevel multivariable logistic regression models with random intercepts were used to explore the associations of number of individual perceived barriers and presence of destinations with obesity-related behaviours of participants (Level 1) nested within neighbourhoods (Level 2). All associations were adjusted for age, gender, education, employment status and household composition. As availability of destinations and residential density tend to coexist  (11) , and our analyses (not shown) also demonstrated that presence of destinations was significantly higher in neighbourhoods with high residential density, we additionally adjusted the analyses with 'presence of destinations' as independent variable for residential density.