dataesr/scientific-paragraphs-categorization huggingface dataset:
Every health occupations were represented: the head of the local dispensaries, community health agents, midwives, nurses, and bajenu gox iv. Teachers from the primary and secondary schools were also invited with their respective directors. Heads and members of local women's groups were identified and contacted as well as those from farming associations specialized in agriculture and animal breeding. Finally, all village chiefs with one or two of their councillors and the local religious leaders were invited. Most of the field workers hired on a temporary base by IRD staff or their collaborators were also asked to participate. Selecting the results to be reported. Through the preliminary consultations with the local representatives, a consensus emerged that people needed a better insight on the "path to information": what are the different steps from data collection to the findings and potential actions? Interestingly, despite the fact that there was a consensus among the organizing committee members that these days were a first step towards a more direct share of information with the populations, inevitably, researchers wanted to highlight findings that were important according to them although some of these results would not necessarily interest a majority of participants.
Over 60 photographs sampled in the IRD database and also taken for the purposes of the event by a professional photographer were gathered in a power point file to present the main results in demography, health and environment. For each slide a small text was written in French and then translated into sereer. The comment for each slide was read in situ by the two field-workers members of the organization committee of the event. This put a lot of pressure on them especially as they were not used to that tool. Hence, at first, the comments were said very fast with repetitions and unplanned bits and not always in accordance with the slides. However, as the evaluation will show, what appeared as not appropriate for skilled researchers embedded in their own communication biases, seemed highly appreciated by the audience.
Evaluating the process: Data and methods A month after the event, three groups of participants totalizing 99 people were asked to evaluate the whole process. These groups were: 1) 5 researchers who were part of the organization and those who were invited for the event; 2) 15 fieldworkers and key local informants, some involved in the design and organization of the event, some just part of the audience; and 3) a sample of 79 regular participants to each day. Using a qualitative design, we addressed three main dimensions: first, what were the questions and discussions the participant had before the event about the research conducted in the area? Second, were the topics addressed during the event of interest for the participants? Third were the communication tools appropriate to both feed and awake participants' interest? Finally, did the participant have discussions with other people about the event once over and did they disseminate the information gathered during the event? In order to conduct the evaluation, three interviewers were hired, respectively in charge of one sector (see endnote iii and previous section).
Finding breaks in the income distribution We choose as a starting point the finest income subdivision given by the US Census Bureau (16 subdivisions) and compute the 16 × 16 matrix of E αβ values for all cities. We then perform a hierarchical clustering on this matrix, succesively aggregating the subdivisions with the highest E αβ values. The process, that we implemented in the Python library Marble  [25] , goes as follows: 1. Check whether there exists a pair α, β such that E αβ > 1 + 10 σ (i. e. two categories that attract one another with at least 99% confidence according to the Chebyshev inequality). If not, stop the agregation and return the classes; 2. If there is at least one couple satisfying (1), normalize all E αβ values by their respective maximum values. Find then the pair γ, β whose normalized exposure is the maximum; 3.
We therefore propose the density as a proxy to measure of how 'central' an area is. We thus plot quantities computed over all areal units (blockgroups in this dataset) that have a density population in a given interval [ρ, ρ + dρ] where ρ decreases from its maximum to its minimum value. We illustrate this idea and compare its results to the traditional 'distance to the center' method on Fig. 3. With very anisotropic and polycentric cities such as Los Angeles, the order in which the areal units are considered is very different with both methods. As a result, measurements as simple as the average income will yield very different results. This is particularly striking with the example of Seattle, WA shown on Fig. 3. The average income as a function of the distance to the center (areal unit with the highest density) increases from the center to a peak at roughly 30 km (bottom left figure).
Clustering A way to distinguish between different spatial arrangements is to measure how clustered the overrepresented areal units are. The ratio of the number N n (α) of αneighbourhoods (clusters) to the total number N o (α) of areal units when the class α is overrepresented (before constructing the neighbourhood as defined above) gives a measure of the level of clustering and the quantity C α = N o (α) -N n (α) N o (α) -1 (10) is such that C α = 0 in a checkerboard-like situation, and C α = 1 when all areal units form a unique neighbourhood. We show on Fig. 7  the distribution of C α for the three classes over all cities in our dataset. As one could infer from the maps on Fig. 6 , the higher-income and lower-income areal units are well clustered, with a respective average clustering of C = 0.
Concentration If a given class is overrepresented in a neighbourhood, it does not necessarily mean that most of the individuals belonging to this neighbourhood are members of this class  [48]. Conversely, the majority of individuals belonging to a class do not necessarily all live in the above- FIG. 4 : Average representation r of the higher-, middle-and lower-income classes over the 929 CBSA as a function of the local density of households. On average, we find that lowdensity regions (the suburbs) are on the richer end, while high density regions (the center) are on the poorer end. This confirms on a large dataset a stylized fact that had previously emerged from local studies. Interestingly, we also find that high income households are on average overrepresented in very large density areas (ρ > 20, 000/km 2 ), suggesting that density may be one relevant factor in the explanation of the differences between neighbourhoods.
The middle-income class corresponds to a smaller income range than what is usually admitted, a curiosity that certainly deserves further investigations. These complex systems can thus be described by considering a small number of categories only. This is an important piece of information which will simplify the description and modelling of stratification mechanisms. For each class α we compute the ratio of the size of the second largest αneighbourhood to the size of the largest α-neighbourhood. The above figure shows the distribution of this ratio for all cities in our dataset. Higher-and lower-income households tend to concentrate in a single neighbourhood, with a secondary center that is respectively 27% and 33% the size of the largest one, on average over all cities. Middle-income households tend to be more dispersed, with a secondary neighbourhood that is on average 54% of the size of the largest.
Data In this study, we use the American Census Bureau's 2014 American Community Survey data on the income of households at the census block-group level, grouped per Core-based Statistical Areas. The households are divided in 16 income categories, ranging from below $10, 000 annual income to above $200, 000. All data of the 2014 American Community Survey are available from the Census Bureau. 2140 delineations of the Core-based Statistical Areas are available from the Office of Budget Management. The reader interested in obtaining a cleaned version of these data ready for analysis and/or reproduce the results of this analysis can consult the online repository (The code necessary to download, assemble the data and reproduce the analysis performed in this article is freely available online at http://github.
This research was approved by the French ethics committee: "Comité de protection des personnes Sud Ouest et Outre-Mer II" number 02-08-22. All patients gave their written informed consent for the protocol and for the medical cost collection. This trial is registered with ClinicalTrials. gov under number NCT01717144. The clinical trial registration was delayed because the sponsor of the study did not consider this work as a clinical trial (as recommended by the FDAAA 801) due to lack of drugs, biologics or devices but as a behavioral intervention. This manuscript reflects perfectly the initial methodology of this work.
Patients psychological status was evaluated using the Hospital Anxiety and Depression Scale (HADS). An economic evaluation (from the healthcare payer's perspective (i. e. the national social health insurance) was performed over one year, taking into account direct medical and non-medical costs. Direct medical costs included medication, hospitalizations, outpatient care (i. e. medical visits, complementary examinations) and direct non-medical costs were limited to transportation costs. Contrary to the initial protocol, instead of collecting health consumption through a patient notebook, the data used were collected retrospectively from the French social health insurance databases and included only PD patients covered by it. A bottom-up approach was used to collect resources utilization. The cost of hospitalization was assessed using the French Disease-Related Groups (DRG) and the national unit cost scale.
Statistical analysis The sample size calculation was based on PDQ-39 values in PD patients  [19]. For the detection of a 10-point difference at the 5% significance level, with 80% power and a standard deviation of 19, 57 patients were needed in each group. Assuming that some of the patients were likely to withdraw from the study, 60 patients were included in each group. Qualitative variables were compared between groups, using the χ 2 -test (or Fisher's exact test if the expected numbers per cell were small). Student's t-test was used to compare the distribution of quantitative data (or the Mann-Whitney U test if the distribution departed from normality or if the assumption of homoscedasticity was rejected). Covariance analysis (ANCOVA) with disease duration as a covariable for the adjusted analysis was used to compare changes to the QOL scores and quantitative clinical parameters.
To evaluate the effect of the education program on QOL, changes in each dimension of QOL between the baseline and the visit after 12 months of follow-up were compared, between the groups, using a two-side binomial exact test. Probabilistic sensitivity analysis was used to construct the 95% confidence intervals around the incremental costs per one-point unit improvement in UPDRS II and III, based on the 2. 5 th and the 97. 5 th percentiles. The output of bootstrap resampling was used to determine graphically, the 95% credible region of the ICER using the 95% confidence ellipse method. All values are expressed as means ± standard deviation. Differences in scores changes are presented with the 95% confidence interval (95% CI). A P-value 0. 05 was significant. Missing clinical data at 12 months of follow up were imputed by the LOCF method and an intentionto-treat analysis was used.
Economic evaluation (Table 3) Only 81 of the 120 patients in the sample (43 in the educated group and 38 in the control group) were included in the French social health insurance database (Fig  1 ). Characteristics of these patients are summarized in Table  3. Mean annual costs did not differ significantly between the groups, at €7323±8560 for the TTBI group and €7372±8022 for the no-TTBI group. The main cost was treatment, in both groups, accounting for 52% of total costs in the TTBI group compared to 53% in the no-TTBI group (P = 0. 97). Antiparkinsonian drugs accounted for 79% of medication costs in both groups. Hospitalization and ambulatory care costs did not differ significantly between the groups. Nevertheless, among the costs of ambulatory care, there was a trend towards lower costs for laboratory testing (P = 0.
Population and sampling The SU. VI. MAX 2 participants were recruited through a postal campaign organized in 2007-2009 among all SU. VI. MAX participants. From the full initial SU. VI. MAX cohort (N = 13,017), 6,850 participants were agreed to participate in the SU. VI. MAX 2 study, and 5,925 participants were older than 55 years at enrollment and received a geriatric assessment  [24, 25]. Sociodemographic characteristics data, morbidities data and quality of life data were available for 5,647 aged 55 years or older for inclusion in the present analyses (Fig  1 ).
Data collection Sociodemographic and comorbidities assessment. Information on gender, date of birth, weight, size, employment status, education level, familial status, employment status and smoking status was collected. In SU. VI. MAX 2, subjects reported their comorbidities by a self-administered questionnaire. Several chronic conditions were identified in 12 categories: cardiovascular diseases or other cardiovascular impairments; cancers; respiratory diseases or impairments; diseases or impairments related to the ears, nose, or throat; digestive diseases or impairments of digestive function; diseases or impairments related to bones and joints; urinary/genital diseases or impairments; endocrine or metabolic diseases; eye-related diseases or impairments; neurological/psychiatric diseases or impairments; infectious diseases; and other health problems.
So multiple linear regression used to examine the association between multimorbidity and HRQoL scores (PCS and Phys; MCS and Ment) were realized for each age group and by gender. Models were adjusted for others sociodemographic variables which p<0. 2 in bivariate analyses. Analyses involved use of SAS 9. 4. Two-sided p < 0. 001 was considered statistically significant (after Bonferroni correction).
Acknowledgments We thank  Younes Esseddik ,  Gwenae ¨l Monot ,  Paul Flanzy ,  Yasmina Chelghoum , and  Than Duong Van  (computer scientists), and  Nathalie Arnault ,  Veronique Gourlet ,  Fabien Szabo ,  Laurent Bourhis , and  Stephen Besseau  (statisticians) for their technical contribution to the SU. VI. MAX study.
The SU. VI. MAX2 study (2003)(2004)(2005)(2006)(2007)(2008) was funded by the  French National Research Agency  (n  ˚ANR-05-PNRA-010 ), the  French Ministry of Health , Mederic, Sodexo,  Ipsen ,  MGEN  and  Pierre Fabre. Mederic and MGEN are French health insurance organizations, which are complementary to the  National Health Insurance System. Ipsen and Pierre Fabre  are private pharmaceutical companies. Sodexo, a food catering company, supported the study by organizing meetings between researchers and study participants. The funding bodies did not have any involvement in the design/conduct of or in data analysis/interpretation, or in writing/ approval of the manuscript. None of the funders had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The massive integration of sustainable energies into electrical grids (non-interconnected or connected) is a major problem due to their stochastic character revealed by strong fluctuations at all scales. In this paper, the scaling behaviour or power law correlations and the nature of scaling behaviour of sustainable resource data such as flow velocity, atmospheric wind speed, solar global solar radiation and sustainable energy such as, wind power output, are highlighted. For the first time, Fourier power spectral densities are estimated for each dataset. We show that the power spectrum densities obtained are close to the 5/3 Kolmogorov spectrum. Furthermore, the multifractal and intermittent properties of sustainable resource and energy data have been revealed by the concavity of the scaling exponent function.
Fourier analysis of sustainable energy data and -5=3 Kolmogorov spectrum The Fourier power spectral density separates and measures the amount of variability occurring in different frequency bands. In this section, the Fourier power spectral densities are estimated for our database in order to detect scale invariance. For a scale invariant signal, the following scaling power law is obtained over a range of frequency f : Eð f Þ ≈ f -β ( 12 ) where β is the exponent spectral. Figure  6  shows the Fourier power spectral densities of databases described above, compared to the -5=3 Kolmogorov spectrum (red straight line), log-log representation. The spectra computed follow a power law of the f -β with β close to 5=3. As expected, the atmospheric wind and the flow velocity spectra demonstrate a scaling behaviour for the respective frequencies from about f ¼ 0:1-10 Hz and f ¼ 0:1-50 Hz with β ¼ 1:67 close to the 5/3 Kolmogorov value  [4, 25].
Multifractal analysis of sustainable energy data In order to qualify the nature of scaling behaviour (monofractal or multifractal), a multifractal analysis using qth order structure functions is applied to sustainable energy data to determine the scaling exponents ζðqÞ. For each dataset, the structure functions are computed on the temporal increments Δx as defined above. The details concerning the scale range of τ and q are given in the following references  [24, [32] [33] [34]. As shown in  [24, [32] [33] [34] , the straight lines of structure functions indicate that the scaling of the relationship is well respected. Consequently, the scaling exponents ζðqÞ are extracted from the slopes of the straight lines using a linear regression. Figure  7  represents the scaling exponents ζðqÞ corresponding to each dataset compared with a model proposed by Kolmogorov, the linear model K41, ζðqÞ¼q=3.
We can see that the scaling exponents ζðqÞ obtained are nonlinear and concave. This highlights the multifractal and intermittent character of considered sustainable data here. Furthermore, the degree of concavity gives an indication on the degree of intermittency: the more concave the scaling exponent curve is, the more intermittent the process. We recall that the intermittency parameter can be estimated by μ ¼ 2ζð1Þ-ζð2Þ with 0 < μ < 1. Table  1  draws up some parameters for each dataset: H the Hurst exponent, ζð2Þ, and the intermittency parameter μ. As shown in Figure  6  and indicated in Table  1 , the global solar radiation G is the most intermittent.
Conclusion This work highlights the intermittency and the scale invariance properties of flow velocity u, atmospheric wind speed v, wind power output P and global solar radiation G data, at all intensities and at all scales, in the fully developed turbulence framework. We have shown for all datasets over the period encountered: • The presence of a scaling regime or power law correlation of the form f -β over a broad range of time scales, in the Fourier space. The exponent spectral β is close to the exact 5/3 Kolmogorov value for all the datasets. • The nature of the scaling behaviour for each dataset is determined using qth order structure functions analysis. The nonlinearity and the concavity of the scaling exponent functions ζðqÞ obtained reveal the intermittent and the multifractal properties of datasets considered in this manuscript.
Simulation study Our simulation study compared one randomized trial with a classical sample size calculation, for which investigators need to formulate a hypothesis, to a meta experiment of 3 trials with a fixed-size of 100 patients in each (Fig  1 ). Full details of the algorithms used are in the S1 File. Data simulation and analysis. We considered a two-arm parallel-group framework with a binary outcome. The treatment effect is measured as the logarithm of the odds ratio (OR). Classical sample size calculation: with the classical approach, the sample size is calculated on the basis of an a priori-formulated hypothesis, the randomized trial is conducted, and data are analyzed by a univariate statistical test. The sample size calculation is based on a hypothesized treatment effect and an a priori-assumed success rate for the control group.
Both parameters are pre-specified and are subject to error. We used data from a previously published review  [6]  to calibrate these errors. An error for the assumed success rate for the control group and an error for the postulated hypothesized treatment effect were drawn from two normal distributions. Details of the exact parameters are in the S1 File. Classical approach: we draw a treatment effect value θ from the normal distribution of treatment effects. In the situation of a non-null treatment effect, we used a distribution with mean log (1. 5). Then we draw a success rate p c from the Beta distribution. For each of these 2 parameters, we draw errors from the empirical error distributions previously observed. Combining the values drawn from the theoretical probability distribution and their associated errors, we derived an a priori-hypothesized treatment effect (denoted θ post , a postulated treatment effect) and an a priori-postulated success rate for the control group (denoted p post C , a postulated success rate).
Regulatory acceptance of the methodology is a critical aspect, and the agencies appear to be supportive of an analysis strategy based on recurrent events. The primary endpoint of the PARAGON-HF study (Prospective Comparison of ARNI with ARB Global Outcomes in Heart Failure With Preserved Ejection Fraction, NCT01920711) is the cumulative number of primary composite events of cardiovascular death and total (first and recurrent) heart failure hospitalizations. 53 PARAGON-HF was also designed to have reasonable power on a standard time-to-first event analysis for sensitivity comparisons. This approach allows regulators and investigators to gain experience with a recurrent-events method against a background of traditional time-to-first event analysis.
Acknowledgements This paper was generated from discussions during a  Cardiovascular Round Table (CRT)  Workshop organized on 21-22 January 2015 by the  European Society of Cardiology (ESC). The CRT is a strategic forum for high-level dialogues between industry and  ESC  leadership to identify and discuss key strategic issues for the future of cardiovascular health in Europe. The opinions expressed in this paper are those of the authors and cannot be interpreted as the opinion of any of the organizations that employ the authors. The authors acknowledge the contribution of  Caroline Boulton  for participating in the discussion at the  ESC CRT  workshop from which this paper originated.
John J. McMurray: Nothing  to disclose Bela Merkely: Personal fees (lectures and advisory board membership) from  Orion Pharma Milton Packer: Nothing  to disclose Krishna Prasad: Nothing to disclose. James Revkin: Nothing to disclose. Giuseppe Rosano: Nothing  to disclose  Ransi Somaratne: Employee  and shareholder of  Amgen, Inc. Wendy Gattis Stough: Personal fees from  European Society of Cardiology, Relypsa ,  Covis ,  Stealth Peptides Adriaan A. Voors :  Research grant  from  Novartis Pharmaceuticals ,  Singulex ,  Sphingotec ,  Trevena , and Vifor; personal fees from  Novartis Pharmaceuticals, Servier ,  Bayer ,  Boehringer Ingelheim ,  Cardio3Biosciences ,  Celladon ,  GSK ,  Merck/MSD ,  Singulex ,  Trevena , and Vifor; non-financial support from  Alere  and  Merck/MSD Frank Ruschitzka: Nothing  to disclose.
This figure also illustrates instances of strong trends of EWS that were not followed by any crash, suggesting that they were false alarms. doi:10. 1371/journal. pone. 0144198. g002 analysis is presented in Methods B section. To enable reproducibility of our results, we have deposited the computer codes of analysis along with data at the open source Github repository (https://github. com/tee-lab/indicators-financial-meltdowns). We describe the results below.