dataesr/scientific-paragraphs-categorization huggingface dataset:
A typical use case of maps made with VGI is the mashup map with crowdsourced thematic data on top of existing reference data. In this case, the symbol specification for the reference background might have been designed independently from the thematic data; thus the addition of thematic VGI involves three problems: • Management of contrasts: the thematic data should be more legible than the background and the contrast in the background should be altered to optimise the contrast with the thematic data. • Preserving a topographic style: adding a crowdsourced thematic layer should not prevent the map reader from understanding the topographic style of the background. • Visualising imprecision aspects: the thematic layer is both heterogeneous in terms of quality and different from the background.
Crowdsourcing the Symbol Specification Process? The symbol or style specification process is user-driven, as the map purpose and the map user needs are translated into a legend and rendered on the map. Additionally to the use of crowdsourced data in the map, a crowdsourced map could also include a more important interaction with the user during the mapmaking process: for example, a consensus decision among OSM contributors could be reached regarding the colour to use to render the forest areas in the standard display. Research on automated on-demand mapping tries to capture the needs of users through techniques such as ontologies and interactions  (Balley et al. , 2014) , but allowing the users to choose the way crowdsourced data can be rendered in the legend and the map requires a step further in this direction.
Conclusions and Further Work This chapter addressed the challenges of automated mapmaking using VGI as input data. VGI differs from traditional geographic databases because of heterogeneities in quality and LoD, and because of thematic diversity, so existing methods for automated mapmaking have to adapt to this situation. This chapter described a proposition to infer the LoD of VGI features to overcome heterogeneity, and then presented methods that use this inference to make maps at different scales using map generalisation or LoD harmonisation. The paper also proposed techniques to overcome the quality heterogeneity, which can alter the map legibility. Finally, the paper discussed how advanced stylisation techniques could be applied to VGI. There is much more work to be done, as automated mapmaking itself is a large research topic.
The long-term goal is to design adaptive and completely automated cartographic processes, because the amount of data is too large for manual cartography, and the content has to be adapted to different needs and display devices. Beyond continuing to improve the methods presented here, it must be noted that generalisation and harmonisation operations do not handle quality heterogeneities yet, and we should investigate how such processes can adapt to quality information that can be inferred from VGI features similarly to the handling of LoD information discussed above. For instance, a forest imported from Corine Land Cover and one captured precisely with satellite imagery do not require the same simplification algorithms. The future diffusion of web maps will be based on vector maps using vector tiling, such as the OpenScienceMap project that provides a vector mapping of OSM.
Parameter correlation and parameter recovery We calculated the Pearson correlation between the parameters (Fig 5A ) and found no significant correlation when correcting for multiple comparisons (corrected P value = 0. 05Ä6 = 0. 008; lowest uncorrected P value = 0. 01, highest P 2 = 0. 30). The correlation between α CON and α DIS was weak, but positive, which rules out the possibility that the significant difference between these two learning rates was driven by an anti-correlation induced by the model fitting procedure. We then applied the same model fitting procedure to the synthetic datasets and calculated the correlation between the true and the retrieved parameters (Fig  5B ). We found that, on average, all parameters in both experiments were well recovered (0. 70 R 0. 89) and that our model fitting procedure introduced no spurious correlations between the other parameters (|R| 0.
Parameter optimisation and model comparison In a first analysis, we optimised model parameters by minimising the negative log-likelihood of the data, given different parameter settings, using Matlab's fmincon function (ranges: 0<β<Infinite, and 0< α n <1): LL ¼ logðPðDatajModelÞÞ ð6Þ Negative log-likelihoods (LL) were used to compute the Bayesian information criterion (BIC) at the individual level (random effects) for each model, as follows: BIC ¼ logðntrialsÞ Ã df þ 2 Ã LL ð7Þ BIC were compared between biased and unbiased models to verify that the utilisation of the biased model was justified, even accounting for its extra-complexity. As an approximation of the model evidence, individual BICs were fed into the mbb-vb-toolbox  [49] , a procedure that estimates the exceedance probability and the model attributions for each model within a set of models, given the data gathered from all participants.
Parameter correlation and parameter recovery To validate our results, and more specifically to verify that valence-induced differences in learning rates reflected true differences in learning, as opposed to an artefact of the parameter optimisation procedure, we checked the correlations between the free parameters (Experiment 1: β, a þ c ; a À c ; Experiment 2: β, α CON , α DIS ) and the capacity of recovering the correct parameters using simulated datasets. To check the capacity of recovering the correct parameters using simulated datasets, we simulated performance on our behavioural task using virtual participants with parameters values corresponding to those retrieved from our experimental participants  [23]. We simulated N = 100 virtual experiments.
Funding: SP and the study were supported by a  Marie Sklodowska-Curie Individual European Fellowship  ( PIEF-GA-2012 Grant 328822 ). SP is currently supported by an  ATIP-Avenir grant  ( R16069JS )  Collaborative Research in Computational Neuroscience ANR-NSF  grant ( ANR-16-NEUC-0004 ). GL was supported by a  PHD fellowship of the Ministère de  l'enseignement take into account information that confirms their current choice. Increasing our understanding of learning biases will enable the refinement of existing models of value-based decisionmaking. superieur et de la recherche. EJK is supported by a  Medical Research Council studentship. SJB is funded by a  Royal Society University Research Fellowship , the  Wellcome Trust  and the  Jacobs Foundation. The  Institut d'Etude de la Cognition  is supported financially by the  LabEx IEC  ( ANR-10-LABX-0087 IEC ) and the  IDEX PSL Ã  ( ANR-10-IDEX-0001-02 PSL Ã  ).
Country and Participants Data were collected as part of the Collective Futures and Climate Change research project (see  Bain et al. , 2016). The project coordinators (first three authors) developed the project and recruited an international research team. The countries were selected a priori based on a combination of environmental indicators and geographic region. The goal was to employ convenience sampling to obtain student and non-student samples from each country where viable (target N=200 for each sample). Data were obtained from university students across 25 countries spanning all inhabited continents, plus community samples in 10 countries to establish the generalizability and robustness of findings. Participants completed surveys online in most countries, using a template developed by the authors to maximize consistency in data collection.
Nation Variables We examined whether three nation-level variables would moderate the SDOenvironmentalism relation. The figures for the Gini index and HDI were taken from the 2015 United Nations Human Development Report (see Tables  1  and 3  in the statistical annex of that report). The Gini data was not available for New Zealand and South Korea so we used the most recent Gini data available for these countries from The World Factbook published by the Central Intelligence Agency of the USA. The 2010 Environmental Performance Index was obtained from the website of the Center for International Earth Science Information Network at Columbia University. Greater values for the Gini index, HDI and EPI indicate more inequality, more human development and greater environmental performance in the country, respectively.
Testing robustness and moderation hypotheses We expected that people with higher levels of SDO would be less willing to engage in pro-environmental actions (robustness hypothesis), but this effect was not expected to occur to the same extent across all countries (moderation hypothesis). We calculated the correlations between SSDO and the three environmentalism measures for each country, and then calculated a meta-analytical summary of the correlations. The meta-analyses were performed using an Excel program developed by Piers Steel (University of Calgary) that runs the Schmidt-Hunter method with a random-effects model. It computes the average correlation across all samples weighted by sample size, with a 95% confidence interval indicating the likely range of this correlation, and a Q-statistic indicating whether the magnitude of the correlations varies substantially across samples.
For the measures that showed significant variation across countries (environmental citizenship and private sphere behavior), we used multilevel modeling to explore the reasons for variation. We first analyzed data from the student samples, and ran multilevel models examining the extent to which the selected country-level indicators (Gini, HDI and EPI) would account for the variability in the associations between SSDO and environmental citizenship and private sphere behavior. Multilevel models were run in HLM (student version 7) with restricted maximum likelihood estimation, allowing the slopes to vary across countries, and robust standard errors for the final estimation. We used group-mean centering for level-1 variables and grand-mean centering for level-2 variables. Since age, sex and conservative political orientation are related to SDO, environmentalism or both, we included these variables as covariates at level-1.
Implications of all the available evidence The longitudinal data described in this study and cross-sectional data are complementary. Biostatistical modelling of these datasets facilitates reliable prediction of the proportion of men with seminal fl uid positive for Ebola virus RNA over time after onset of disease and their prevalence during an epidemic. These predictions will assist in decision-making about surveillance and preventive measures in Ebola virus disease outbreaks. Filovirus Screen RT-PCR Kit and contains only the reagents for detection of Ebola virus) on a Rotor-Gene Q thermocycler (Qiagen). 14 We included only the runs with valid internal control in analysis and we used threshold cycle (Ct) as an inverse measure of Ebola virus RNA concentration in the specimen. The high precision of the kit facilitates pooling of Ct values from separate runs for biostatistical analysis (appendix p 1).
We anaesthetised SCID mice with isofl urane and inoculated intraperitoneally with 200 μL of cleaned seminal fl uid. Two mice were used per seminal fl uid specimen. The animals received 1% enrofl oxacin in their drinking water. We took blood samples from mice once per week. At 3-4 weeks from inoculation, mice were killed and their organs were collected. We tested blood and organs for the presence of Ebola virus RNA using the RealStar Zaire Ebolavirus RT-PCR Kit. To further confi rm the presence of infectious virus, we inoculated new SCID mice intraperitoneally with the homogenised lung tissue of selected seminal-fl uidinoculated SCID mice in 100 μL Glasgow's minimal essential medium (Life Technologies, Darmstadt, Germany) supplemented with 2% fetal calf serum. We inoculated three mice per sample; one mouse was killed at day 2 to generate baseline viral-load data and the remaining two were killed at day 30.
16 The Ct for negative PCR results was set at >37, which approximates the 95% detection limit of our PCR assay. 14 ecause the SD for Ct 0 in seminal fl uid could not be estimated, we used a value of 30%, which corresponded to the SD of the Ct values for Ebola virus RNA in plasma See Online for appendix log(c rel ) = -0•262 × Ct. at disease onset (t 0 ). For each parameter, we estimated the mean and SD of interindividual variability. The Ct-based clearance rate was transformed into an Ebola virus RNA clearance rate using equation 1; an increase in Ct by 3•8 cycles corresponded to a decrease in Ebola virus RNA concentration by 1 log unit. To estimate the proportion of male survivors with persisting viral RNA, we simulated 1000 replicate datasets for the 26 study participants using the distributions for Ct 0 and α and calculated the proportion of patients with Ebola virus detectable by PCR in seminal fl uid for each day after disease onset (mean and 90% prediction interval [PI]).
Acknowledgments We thank  Christelle Delmas ,  Claire Levy-Marchal ,  Yves Levy ,  Thierry Damerval ,  Jean Francois Delfraissy ,  Henri Dopavogui ,  Sekou Ditin Cissé , and  Patrick Drury  for scientifi c and administrative support, and  N'Faly Magassouba ,  Stephan Ölschläger  as well as the medical and laboratory staff of the treatment centres for sharing data. We also thank  WHO  fi eld teams and the Guinean health authorities for their commitment and excellent cooperation. The EMLab is a technical partner of the  WHO Emerging and Dangerous Pathogens Laboratory Network (EDPLN) , and the  Global Outbreak Alert and Response Network (GOARN)  and the deployments in West Africa have been coordinated and supported by the  GOARN Operational Support Team at WHO/HQ. This work was done in the context of the projects EVIDENT (Ebola virus disease: correlates of protection, determinants of outcome, and clinical management), EVAg (European virus archive goes global), and REACTION!that received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreements nos.
666100 ,  653316 , and  666092 , respectively, and in the context of service contract  IFS/2011/272-372  funded by  Directorate-General for International Cooperation and Development. The study was further supported by grant  C15-20  from the  Institut national de la santé et de la recherche médicale (INSERM) ,  Paris, France , grant  GU 883/4-1  from the  German Research Foundation (DFG) , and by the  Innovative Medicines Initiative   2 (IMI2 )  Joint Undertaking , under grant agreement  115854. IMI2 receives support from the  European Union 's  Horizon 2020 research and innovation programme  and the  European Federation of Pharmaceutical Industries and Associations (EFPIA).
Acknowledgements The authors wish to thank the patients who generously gave their time in helping to develop the patient education materials and we acknowledge the Working Group members of  COST Action   BM1105  for their contributions. Special thanks to  Ms. Virginia Hughes  for her critical input during the development process,  Prof. Linan Cheng  of the  Shanghai Institute of Planned Parenthood Research  for her validation of the Chinese translations and  Ms. Özlem Firat  for her help in coordinating the translated materials. This work was supported by the  Rare Disease Foundation  and the  BC Children's Hospital Foundation  (#  17-26 ) and  COST Action   BM1105.
We are not presenting this experience as necessarily generaliseable, but our study has the merit of having collected qualitative data on the experience of families, which one usually does not have. The relevance of our study will also reside in the longevity of the group, enabling us eventually to see progression (or not) and encouraging us to reflect on the place of psychoanalytic groups for parents in providing care management for this pathology. Regarding our population study, parents of bipolar adult children participated in a small group following information given (date and location) during the larger UNAFAM support group. The group included eight persons who committed to participating for the entire year, with non-attendance permitted only in exceptional circumstances. These participants were parents of bipolar adults aged 19 to 40, from the French middle class, living in rural or urban areas.
Diagnosis of bipolar disorders had been established by a psychiatrist within a medical structure. Group participants accepted the principle of a research study that maintained their anonymity. The sessions took place in a specific room 8 once a month with 90-min sessions. Ten sessions took place during the first year of the support group's implementation. Data were collected via notes taken after sessions. Personal data were kept anonymous. We studied the data with a content analysis method  (Krippendorff, 2004)  in order to define the themes, and a speech analysis method in order to hear the wording specific to each parent, reflecting his or her experiences and psychic movements. In extracts quoted, material has been anonymised. 4. Themes emergent within the group 4. 1. The upheaval related to the first psychiatric crises This period can sometimes be quite explosive due to the specificity of the manic phase.
Socioeconomic differences in health have been consistently observed worldwide. Physical health deteriorates more rapidly with age among men and women with lower socioeconomic status (SES) than among those with higher SES. The biological processes underlying these differences are best understood by adopting a life course approach. In this paper we introduce the pan-European LIFEPATH project which uses multiple cohorts -including biomarker data -to investigate ageing as a phenomenon with two broad stages across life: build-up and decline. The 'build-up' stage, from conception and early intra-uterine life to late adolescence or early twenties, is characterised by rapid successions of developmentally and socially sensitive periods. The second stage, starting in early adulthood, is a period of 'decline' from maximum attained health to loss of function, overt disease and death.
Experimental studies to test policy strategies Data from cohorts are complemented by data from the evaluation of two randomised experiments in the United States carried out by MDRC in New York: (a) the Opportunity NYC-Family Rewards study, a randomised experiment of conditional cash transfers (CCT) to help families break the cycle of poverty, and the first CCT program in a high-income country. The program was evaluated through a randomised controlled trial involving approximately 4,800 families and 11,000 children, half of whom could receive the cash rewards if they met the required conditions, and half who were assigned to a control group that could not receive the rewards. Data include extensive measures of social and economic conditions as well as extensive health assessments, which the present project plans to complement with additional biological measures.
(b) The Paycheck Plus, a randomised experiment of tax credits to help adults without dependent children break the cycle of poverty. The Earned Income Tax Credit is a federal program in which low-income individuals and families receive an annual credit that varies according to their working income. The program was evaluated through a randomised controlled trial involving approximately 10,000 participants, with control participants assigned to the current maximum $500 annual return and experimental participants assigned to a maximum $2000 return. Data include extensive measures of social and economic conditions and we will collect data on serum cholesterol, C-reactive protein, glycosylated haemoglobin, blood pressure, abdominal circumference, height, and weight after the intervention. These experimental studies are relevant to the second challenge, the complexity of causal relationships and the demonstration of reversibility of some effects.
Outcomes and definition of healthy ageing Indicators of healthy ageing are being developed using the wealth of individual information collected in the cohorts participating in LIFEPATH (Table  1 ) as well as from publicly available datasets. Four 'har indicators of healthy ageing are being used (incident diabetes, cancer, cardiovascular disease, and death), and several functional and physiological indicators are also available (blood pressure, anthropometry, frailty, physical strength, walking speed and grip strength, eye macular health, sleep, cognitive function including dementia, mental health, bone health, renal function, urinary electrolytes and cardiometabolic measures). The World Health Organisation increasingly emphasises the need to shift from a definition of health based on hard indicators like mortality and diseases, to ability and disability indicators.
In LIFEPATH we have operationally defined healthy ageing with two components. First, through a general statement about healthy ageing and second, through recommendations about variable selection and choice of analyses across the life course using longitudinal datasets; these are provided below: • Statement on Healthy Ageing: "Healthy ageing is the optimal state of performance and wellbeing capable for each particular phase of the lifecourse that can be expected in a society, across social and cultural groups of a population". Key to variable selection is that the component variables be a) appropriate to the life course stage, and b) the wellbeing variables capture something of perception and lived experience.
Risk factors and healthy ageing One of the leading hypotheses of LIFEPATH is that SES operates partially through an unequal distribution of conventional risk factors for poor health across SES strata, but that there is also an additional effect of SES on healthy ageing that is not explained by these conventional risk factors (third challenge above). Therefore, having good information on a number of risk factors for disease is key to ensure the success of the project. Also, crucial is the ability to encompass the whole trajectory that links SES with healthy ageing by modelling risk factors (and omics, see below) with proper analytic tools and study designs. These include mediation analysis, structural equation models, randomised experiments and natural experiments. Once the data are harmonised across cohorts, LIFEPATH will assess:.
The importance of measuring epigenetic patterns across specific cell subtypes also depends on disease status, and subtle differences are likely more important for multifactorial traits. For these reasons, the first analyses of the methylome will be performed in two specific blood cell types in 500 Whitehall II subjects. We will then validate the findings in PBMC in cohorts with cell counts available. Also, the biomarker stock will be enriched by measuring IL6 and other cytokines in 6,600 additional subjects in Whitehall II (funded by the US NIH) and in Skipogh, in which also the methylome and RNA sequencing will be measured (n=750). In some cohorts we also have genetic data (Table  1 ). This allows three types of analyses: first, we can measure the relative importance of genetic predisposition to disease vs environmental variables including SES; second, we can look at gene-environment interactions with our nongenetic variables including SES (low-penetrance susceptibility); third, we can apply the concept of Mendelian Randomisation, i.