dataesr/scientific-paragraphs-categorization huggingface dataset:
We changed the name of the database from the WTID to the WID in order to reflect the increasing scope and ambition of the database. In January 2017 we launched a new database and website, WID. world (www. wid. world), with better data visualization tools and more extensive data coverage. The database is currently being extended into three main directions. Firstly, we aim to cover more developing countries (and not only developed countries, which were the main focus of the earlier studies, largely due to data access). In recent years, fiscal information has been released in a number of emerging economies, including China, Brazil, India, and South Africa. In this paper we present some of the findings obtained for China. In the near future WID. world will include new and updated series for other countries.
Wealth Inequality Dynamics: US, China, France, UK Finally, we present findings on wealth inequality on Figure  3. We stress that currently available statistical information on the distribution of wealth and cross-border assets are highly imperfect in today's global economy. More transparency and better access to administrative and banking data sources are sorely needed if we want to gain knowledge of the underlying evolutions. In WID. world, we combine different sources and methods in a very transparent way in order to reach robust conclusions: the income capitalization method (using income tax returns), the estate multiplier method (using inheritance and estate tax returns), wealth surveys, national accounts, rich lists and generalized Pareto curves. Nevertheless, our series should still be viewed as imperfect, provisional, and subject to revision.
Data collection We aimed to follow up all patients at least once per quarter, with follow up data collected during all consultations. During consultations doctors recorded patient prescriptions and their actual intake of medications. They also recorded patients' blood pressures, measured using mercury sphygmomanometers after patients had sat for five minutes. In both arms doctors entered data on patients' blood pressure, height and weight into the routine internet-based public health management information system. Additionally, those participants selected for the panel population underwent an examination, where doctors recorded their BMI, blood pressure, and levels of HbA 1c , TC and LDL at baseline, 12 and 24 months after randomization  [22]. All laboratory specimens and tests results were quality assured at the provincial central reference laboratory.
Statistical analysis All data from the internet-based management system were exported to SPSS1 20. 0 (Chicago, USA) and analyzed using SAS1 9. 4 (Cary NC, USA). Data were analyzed according to the randomized allocation of their township, and irrespective of their adherence to the protocol. Another biostatistician (MKCC) who was responsible for data analysis was blinded to the treatment allocation. Descriptive analyses of means, medians and proportions were used to report baseline socio-demographic and clinical information. For the panel sub-group, the intervention effects on continuous 12 month outcomes (i. e. systolic and diastolic blood pressure, HbA1 c , TC, LDL and BMI) were analyzed using linear mixed-effect models adjusted for clustering (i. e. a random effect of township hospital), and baseline covariates, namely age, gender, education levels, and annual incomes.
RWD can be derived from numerous sources, including disease registries, observational studies and electronic health records  [14, 15]. Due to specific characteristics of RWD (e. g. non-randomised treatment allocation, longer patient follow-up and broader patient populations), it may provide a more generalizable picture of treatment effects in clinical practice  [18]. In contrast, using RWD for decision making presents new methodological and analytical challenges. For example, due to non-randomized treatment allocation, confounding in estimated treatment effects may occur due to an imbalance in the potential known and unknown confounders in the groups of patients being compared  [18]. Moreover, other practical aspects such as missing data in RWD sources and the lack of interoperability across RWD sources with different database infrastructures may affect the quality of data present or may complicate research across different datasets, respectively  [18].
Inclusion criteria were a melanoma indication, publication dates between 1 January 2011 and 31 December 2016, and the availability of at least three reports, published by three different agencies, per drug. The latter criterion ensured that the majority of included agencies had conducted assessments for each drug. Each resubmission or addendum was categorized as a new report. Data extraction from compiled reports was performed independently by AM and AvV using a standardized data extraction form containing open-ended and closed questions (DEF; see ESM Appendix 1). The inclusion of RWD in REAs and CEAs was examined separately. When RWD was included, two aspects were examined: the reason for inclusion [i. e. the parameter(s) it informed] and the source of RWD. Subsequently, agencies' appraisals of the validity of RWD use and the sources chosen for the intended parameter (henceforth RWD appraisal) was examined by identifying corresponding statements in reports and scoring them using the following algorithm: • Positive: statement identifying a positive opinion on validity of RWD use and source.
• Negative: statement identifying a negative opinion on validity of RWD use and source. • Neutral: statement identifying a neutral opinion on validity of RWD use and source. • Unknown: statement that cannot clearly be identified as positive, negative or neutral. • Not identified: no statement regarding appraisal despite RWD inclusion in the assessment. To measure agreement within data extraction and scoring performed by AM and AvV, the inter-rater reliability (IRR) was calculated twice in two different rounds. In each round, authors independently extracted data from four randomly selected reports (see ESM Appendix 2 for reports per round). Authors' extraction for closed questions were compared using the Fleiss' kappa method, whereby a score of 0 indicates poor agreement and a score of 1 indicates perfect agreement  [24].
Authors' extraction for open-ended questions was compared by a third, independent researcher. Once IRR was established, the remaining reports were equally divided among both authors. To verify whether data extracted from reports on RWD inclusion, RWD appraisal scoring and results of analyses accurately reflect practice in the agencies included, a panel of five senior assessors representing the five respective agencies was consulted (see ESM Appendix 3 for panel members). The data extracted from reports of HTA agencies and results of the analyses mentioned below were mailed to the panel members, who then indicated if, for example, reports were missing from the dataset, whether data for specific questions of the data extraction form was missing and where to find it in reports, as well as their feedback on the results of analyses.
The IRR was calculated twice and improved from 0. 60 in the first round to 0. 80 in the second round, corresponding to substantial agreement between AM and AvV  [24]. RWD was included in 28/52 (54%) REAs and was mainly used to estimate melanoma prevalence and/or incidence (28/28 REAs). Additionally, RWD was used to estimate the effectiveness (7/28) and safety (6/28) of the new drug. The majority of the RWD included for estimation of melanoma prevalence/incidence originated from registries. Additionally, national statistics databases, data from observational studies, and claims databases were used. RWD included for effectiveness or safety was mainly derived from observational studies and/or non-randomized phase I/II studies. For a detailed summary of the frequency of RWD use per parameter and RWD source, see Table  1.
For a detailed summary of the studies used to provide RWD on effectiveness and safety, see Table  S1   ) and determine utilities using quality-of-life information (4/22). All CEAs that included RWD to estimate long-term effectiveness derived data from registries. In some reports, this was further supported by RWD from national statistics databases. In that case, registry data was used to extrapolate overall survival until a specific time point beyond trial duration (e. g. 10 or 15 years), while national statistics data was used to extrapolate overall survival from that point forwards until the end of the model's time horizon. Costs were estimated using data from claims databases, observational studies or cost-of-illness studies. Data sources used for resource use and quality-of-life parameters are presented in Table  1.
Discussion This study examined the extent with which RWD was included and its appraisal in HTA reports of seven melanoma drugs from five different agencies. Results demonstrate an overall difference in RWD inclusion between REAs and CEAs, whereby inclusion is more common in CEAs (88%) than REAs (54%). RWD included mainly informed melanoma prevalence and/or incidence in REAs and long-term effectiveness and costs in CEAs. Sources of RWD used to inform those parameters varied and included registries, observational studies, national statistics databases and claims databases. Statements on RWD appraisal were often not found in REAs and CEAs. When identified, the nature of appraisal statements was mostly unknown or negative. Reasons for negative appraisals were manifold, often relating to decision-makers' awareness of biases associated with RWD, as well as the statistical approaches used to incorporate it in effectiveness estimates.
The value of RWD generated in CRSs would play a critical role in the reassessment of drugs (e. g. to confirm previous efficacy estimates, cost-effectiveness ICER estimates or budget impact). According to previous research, policies for CRSs implemented by three agencies indicated that RWD is largely accepted within this context, provided data collection and analysis abide by predefined conditions  [23]. In the current study, only 2 of the 52 reports were initial assessment reports within CRSs, namely those published by ZIN; however, the respective reassessment reports have not yet been published. Moreover, HAS reports examined were not part of CRSs implemented in France. As such, the potential role of RWD in melanoma reports within CRSs could not be assessed. To our knowledge, work is ongoing within ZIN and HAS to reassess melanoma drugs using RWD.
Strengths The study included all 52 reports from five HTA agencies' websites in the analyses, corresponding to the total number of reports published up to and including 31 December 2016. The inclusion of all reports for all five agencies minimised the chances of missing relevant information. The IRR between the two authors responsible for data extraction and scoring was measured twice, based on a randomly selected set of reports. In doing so, authors minimised the probability that results reached were a consequence of inter-author differences in extraction and scoring. Findings generated by this study were presented to an HTA panel, consisting of five senior assessors representing all five agencies included, to verify whether the results accurately represent practice within their agency, thus improving their plausibility.
Definition of health states The duration of the three health states was derived as described below; a schematic is shown in figure  1. TOX was defined as time spent with grade 3 or 4 treatment-related AEs after randomisation and before disease progression or censoring for progression. AEs used in the definition were limited to those known to be related to trifluridine/tipiracil and to have an impact on QoL: these were nausea, vomiting, diarrhoea, fatigue/asthaenia,  anorexia and febrile neutropaenia. For the TOX state, the number of days with grade 3/4 qualifying AEs before disease progression or censoring date was summed for each patient, based on the AE onset and end dates reported in the trial database. TOX counted calendar time: if a patient experienced several qualifying AEs on the same day then the day was counted once.
Interventions The intervention group will participate to the optimization program: clinical medication review performed by a pharmacist in cooperation with the clinician. This aim is to identify actual and potential DRP, to decrease the potential iatrogeny of drug prescription and to improve the drug adherence of the patient. This intervention will be standardized across participating centers through a "Drug prescription optimization" form. The pharmacist will complete this report form including the patient data (medical, social, lab results and medication), their synthesis of medication review, and their PI in order to achieve drug optimization and their counseling/specific strategies in order to improve the drug adherence. In our study, the clinical medication review will be at the inclusion, 6 months and 18 months.
-Review of current medication and Pharmacists' intervention The review of current medication performed by the pharmacist, in collaboration with the clinician (specialist physician), will also identify DRP (including pharmacological redundancy, medication overdose, need for a change in dosage form and PIP) taking into account the specificities of drug management in elderly patients. The DRP will be identified through a structured approach for each patient and the pharmacist will perform PI. The medication review will be standardized through various tools, including current national professional guidelines and international recommendations, medications databases, and prescription appropriateness as assessed by a set of validated quality indicators including Screening Tool of Older Persons' potentially inappropriate prescriptions and Screening Tool to Alert doctors to Right Treatment (STOPP-START) and Beers criteria.
Data collection, management, and statistical analysis Data collection methods and data management The clinical data are collected in a case report form (CRF) assigned to each patient, referred by a unique numeric identifier, using the patient medical record and during the consultation, by a trained neuropsychologist or nurse. Primary and secondary outcomes are collected during the assessment at the consultation. Data are then entered into a computerized database by the neuropsychologist or the nurse. Drugs are collected by the clinical pharmacist using the RP prescriptions and those of the specialist physician at hospital after the consultation. Drugs data and pharmaceutical analysis and PI are entered in the computerized database by the clinical pharmacist. The data collected in the CRF and in the database will be verified for accuracy, missing data, and data consistency with the documents 'source' (medical records, visits schedules).
Statistical analysis The analyzes will be performed with an intention-totreat principle, both RP-patients will remain in their group allocation determined by the randomization procedure, regardless of the nature of the intervention that may have been applied during the study. Analysis per protocol will be performed to verify whether the results are similar with the two approaches. The presence of missing data and extreme values will be verified  [46]. Missing data on the primary outcome may be taken into account to some extent in the statistical models used. The impact of missing data on the results will be evaluated as appropriate. Statistical analyzes will be performed using SPSS software (version 19. 0, SPSS Inc. statistics). The analyses will focus on data at the patient level and statistical models will take into account possible correlations between data, such as the RP who can be considered as a hierarchical level above the patient unit  [47].
Comparative analyzes to assess the impact of the intervention on the primary outcome The ICC will be calculated on the primary outcome for the entire population and by groups to verify that the correlation between observations is similar between groups. The effect of the intervention on evolution of the level of autonomy function will be estimated in a generalized linear mixed model. The mixed model will account for both fixed and random effects, hierarchy data (patient characteristics correlated between them for a same RP) and repeated measurements of the primary outcome  [51, 52]. The evolution of the primary outcome will be defined as the dependent variable, explained by the effect of the study group, the time effect and the interaction between these two effects. The intervention will be considered significant if the interaction between time and the study group is significant.
Harms -End of protocol Patients will be excluded from the follow-up of study if they no longer wish to participate in the study at any time of the conduct of the study. Information on the temporary or permanent cessation of the participation of a patient will be collected in the patient file. The previously collected data before exclusion may however be used as part of the study, as stated in the information letter. If a visit is missing during the follow-up of the study, but the patient did not indicate that he would not participate to the study, the patient will still be included in the study for the next follow-up.
Acknowledgements The authors thank all the specialist physicians of the geriatric and memory centers who contribute to inclusion of patients in the study, in particular  Dr. Aurélia Marfisi-Dubost  and  Dr. Ahmed Salmi , the pharmacists who contribute to optimization of drug prescription, in particular  Dr. Corinne Goubier-Vial  and  Marie-Agnès Lepine , as well as  Florian Gatto ,  Floriane Delphin-Combe , and  Lucie Winterstein.
We interpret this as reflecting the transition from persistent low instantaneous dimensions to less persistent, higher dimensions as the atmosphere shifts from a zonal NAO-type flow to a blocked configuration. We argue that these results can be used in an operational forecast system. If our dynamical indicators are indeed linked to predictability, given a certain state they should correlate with the skill of the model in predicting the future states of the field. Here we partially explore this possibility -because we do not compute the full attractor of the climate model -by correlating the values of d and θ for the period 2000-2015 with the output of the 2 nd Generation NOAA Global Ensemble Reforecast data set  18. This is consistent with the current operational NCEP Global Ensemble Forecast System, and consists of 11 ensemble members.
The persistence, proportional to the inverse of θ is also shown in Extended Data Fig. A. 1. The Lorenz attractor consists of three unstable fixed points: two at the center of the wings and one at the origin of the axes. The three points are well captured by the statistics of θ. The embedding methodologies adopted in the 1980s were unable to estimate high attractor dimensions 2 , thus providing artificially low values for complex systems. To verify that our methodology does not suffer from the same bias, we have applied it to a range of test fields of the same grid-size as the NCEP data set (Extended Data Section B). Sea-level pressure data. In this study we adopt sea-level pressure (SLP) as the meteorological variable to describe the North Atlantic circulation. The major modes of variability affecting the North Atlantic are often defined in terms of the empirical orthogonal functions of SLP  11, 13  , and a wealth of other atmospheric features, ranging from teleconnection patterns to storm track activity to atmospheric blocking can be diagnosed from the SLP field  16, 28.
The results presented in Storm database. We present a database of historical storms (see Supplementary Information) which affected Europe between 1948 and 2015. This database is largely based on the Lamb  30  and the Roberts et al. 31  catalogues. Additional storms have been integrated because of their relevance in terms of human losses, damages or their profile in the media. This results in a total of 336 storm dates and 73 distinct storms or storm clusters. The database is organized in four columns: 1) the day of occurrence in the format yyyymmdd, 2) the name(s) of the storm(s), 3) the countries or the region affected and 4) a reference to a peer-reviewed article, a report or a press article describing the importance of the storm. As a caveat to our methodology, we note that the increasing coverage of both meteorological instruments and technological means of information results in an increasing number of recorded storms with time (whereas the minima of the instantaneous dimensions are equally distributed over time).
This results in most of the events shown in Fig. 4  occurring in the final part of the NCEP/NCAR reanalysis period. We use the database to evaluate the correspondence between the minima of the instantaneous dimensions, which equates to positive NAO-like SLP anomalies, and storminess. A day is said to match a storm if it falls within 2 days of the date specified in the database. This accounts for the fact that the recorded dates are typically those when the storm made landfall and/or caused maximum damage, as opposed to the genesis or peak growth phases. 73 of the storm days recorded in our database (or 22%) and 31 individual storms or storm clusters (or 43%) match one of the 481 instantaneous dimension minima (corresponding to the quantile 0. 02) identified in the NCEP/ NCAR data. This is a very high percentage: if 336 random days are selected (from the extended winter period over which the storms occur), on average only 7 will match an instantaneous dimension minimum.
Acknowledgements D. Faranda  and  P. Yiou  were supported by  ERC  grant No. 338965-A2C2. DF acknowledges  V Lucarini ,  B Dubrulle ,  F Daviaud ,  A Jezequel  and  MC Alvarez-Castro  for useful discussions. ECMWF data were accessed at: http://apps. ecmwf. int/datasets/. NCEP/NCAR data was obtained at: http://www. esrl. noaa. gov/psd/data/reanalysis/reanalysis. shtml.
Data description Our data consist in four futures markets from four different asset classes: stock index, Treasury bond, exchange rate and commodity. While all assets are very liquid (and are therefore suitable for using realized estimators), we had to remove days where the trading activity has not been sufficient to compute these estimators. To this end, we filter our four time series with respect to three parameters: the length of the trading period in the day, the number of zero-returns and the number of transactions. We choose to work with open-to-close returns because overnight returns have shown to follow a very different dynamics. In addition, including overnight returns may alter our analysis when standardizing returns as we work with volatility computed with intraday transaction data.
We discarded candidates coming from the Generalized hyperbolic distribution family: with 5 parameters, this distribution would require 12 parameters to be estimated when adding the threshold parameters a and b. We want to rely on models that stand a chance to pass goodness-of-fit tests while remaining as parsimonious as possible. 1 σ i √ ν i B( 1 2 , ν i 2 ) 1 + x 2 σ 2 i ν i - ν i +1 2 where B is the Beta function (beta using R) and ∀x ∈ R+ (the negative values are obtained by symmetry), Fi(x) = 1 - B σ 2 i ν i x 2 +σ 2 i ν i , ν i 2 , 1 2 2B ν i 2 , 1 2 where B is the incomplete Beta function (Ibeta using the zipfR package) 7 In this case, θi = (ai,  The parameters of each distribution is estimated over each jump dataset full sample by numerically maximizing the log-likelihood associated to each model.