dataesr/scientific-paragraphs-categorization huggingface dataset:
External Validity of Study Results There are also important questions regarding the external validity of our study results, i. e. , whether we can expect the observed effectiveness of the SSC to extend to other populations and surveys in Senegal and elsewhere. The SSH data we collected in the DHS group during the validation study appeared comparable to the SSH data collected during the 2010/2011 Senegal DHS: in particular, age and date heaping patterns were largely similar in the two datasets (Figure  3 ). This similarity suggests that the results of our assessment of the SSC may also extent to other settings, including national surveys. There are, however, several potential threats to external validity. First, we could only trace migrants who had moved to regions of Senegal that were the closest to Niakhar or Dakar (see Figure  2 ).
These migrants likely maintained more ties to their siblings who still resided in the HDSS area than other migrants who moved to less accessible parts of the country. This latter group would, however, be included in national surveys. If the quality and accuracy of retrospective SSH reports depends on the frequency of interaction between siblings, then the extent of reporting errors in SSH data may be higher in national studies than what we observed during this validation study. Second, because of repeated interactions with HDSS interviewers during household visits, residents of the Niakhar HDSS may also be more aware of ages and dates of vital events than other populations. If that is the case, then the quality of SSH data collected during the validation study is likely significantly higher than the quality of data collected in national surveys such as DHS.
Third, the data collection protocol we followed differed from the protocol used in DHS and other nationally representative surveys. In particular, during DHS, SSH data are collected after several additional modules on reproductive and child health, sexual behaviors, etc. , so that both interviewer and respondent fatigue may be significantly higher than during our validation study. Finally, even though we hired former DHS interviewers, we could not install the same supervisory structure, and quality control procedures, as in national DHS.
Extensions Future research on the SSC and other SSH data quality improvement approaches should extend in several directions. First, micro-simulations should be used to explore the potential impact of the SSC on (1) adult mortality levels and trends (see above) and (2) estimated gender and socioeconomic differentials in adult mortality. Since the SSC improves data quality particularly for women, it has the potential to modify our assessment of gender differences in adult mortality, including the contribution of pregnancy-related deaths to mortality differentials. Similarly, the SSC appeared to improve the reporting of adult female deaths particularly among the least educated respondents (Figure  5 ). Respondents without formal education may indeed be less familiar with ages and dates than other respondents and may experience more difficulties in completing the DHS questionnaire.
The SSC should be adapted for use on tablets and other mobile data collection supports. Most DHS (and other nationally representative surveys) are now conducted on such supports. Using mobile data collection tools could further increase the ease of SSC data collection. It may also help integrate new probes and improve compliance with SSC instructions, and it may alleviate respondents' concerns about the length of the SSH interview. Third, further validation studies of the SSC should be conducted in diverse epidemiological, societal, and data collection settings. For example, future SSC tests should include populations of southern Africa that are affected by large HIV epidemics, as well as matrilineal societies, in which men move into their wife's family compound (and thus are separated from their own siblings) after marriage.
The effects of the SSC on SSH data quality may be different in such settings. Eventually, tests of the SSC should be nested within national SSH surveys to ascertain whether this data collection tool improves national estimates of adult mortality. In summary, this work suggests a new approach to improving estimates of adult mortality in countries with limited vital registration. Instead of relying solely on analytical procedures to correct reporting errors, demographers should also seek to improve the quality of the input data used in burden of disease models and mortality estimates. We showed here that this can be achieved using simple interviewing tools that have been used successfully in a number of fields of demographic and epidemiological research. Editors' Summary Background. Governments and international health agencies need accurate information on births and deaths in populations to help them plan health care policies and monitor the effectiveness of public health programs.
Supporting Information Why Was This Study Done? This study was conducted to estimate whether modifying a standard siblings' survival history questionnaire could improve the accuracy of data obtained. The researchers conducted a study in Niakhar, Senegal, using a modified siblings' survival history questionnaire to incorporate some innovative techniques for assisting memory recall, such as recall cues, to help prevent omissions. The researchers also introduced an event history calendar format to help with more accurate reporting of dates. This modified questionnaire is called the siblings' survival calendar. What Did the Researchers Do and Find? The researchers randomly assigned men and women registered by the Niakhar Health and Demographic Surveillance System, aged 15-59 years old, to an interview with the basic questionnaire or the modified siblings' survival calendar.
The modifications included emphasizing the importance of accurate recall before the participant started and asking respondents to list their maternal siblings in the order that they came to the mind instead of birth order. Also, the researchers used supplementary interviewing techniques designed to stimulate the recall of potentially omitted siblings and also used an event history calendar approach for collecting data on ages at, and dates of, vital events that had affected the siblings of a respondent. The researchers compared the results from the two survey instruments and precise data on adult mortality collected by continuous demographic surveillance in a small area of Senegal. They found that the calendar survey improved the sensitivity of survey data in recording adult female deaths.
The work the authors describe was funded by a grant from the  Eunice Kennedy Shriver National Institute of Child Health and Human Development  ( R03-HD071117 , PI:  S. Helleringer ) and by a grant from the  French Agence Nationale de la Recherche  ( ANR-11-BSH1-0007 , PI:  G. Pison ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
doi:10. 1371/journal. pmed. 1001652. t001 two household visits. It is assessed retrospectively if s/he was already present in the population at the time of the initial household census (i. e. , 1962 or 1983) or if s/he first entered the HDSS population after birth, by migration. The age of population members is thus known with varying degrees of precision and certainty depending on the way they entered the HDSS dataset.
Development of a disease modelwork package 5 Work package 5 will include two phases. Firstly, disease and risk data will be collected by reviewing open access papers in PubMed and Science Direct databases, supplemented by Google Scholar search. If necessary additional data will be collected by searching statistical data bases and reports as well as contacting disease registries and individual researchers in the field. For the purpose of this project, epidemiological data need to be stratified by age groups and sex, socioeconomic groups and where possible should be nationally representative. We will collect the most recent incidence, prevalence, mortality, and survival data. In addition, if available, we will collect country-specific remission and recurrence data for the selected diseases. Risk data will be collected in a similar manner.
BMI prevalence data will be collected if, in addition to the inclusion criteria above, they are categorised according to the WHO definition: normal weight (<25 kg/m 2 ), pre-obese (25-29. 9 kg/m 2 ), and obese (≥30 kg/m 2 ). Hypertension data will be included if they are defined according to WHO criteria of 140 mmHg systolic and/ or 90 mmHg diastolic blood pressure. In addition to age and sex specific incidence and prevalence, data on hypertension stages will be included if available. Smoking patterns will be assessed by collecting data on frequency, the length of smoking in years and exposure to second hand smoke. Furthermore, we will identify other possible risk factors for each of the four diseases. The literature assessing the significance of each risk factor will be examined to determine the inclusion of a risk factor into the model.
The data on relative risks will be taken from clinical trials and meta-analyses and assumed to have worldwide applicability. Same data collection procedures will apply to all participating countries. Secondly, the project will develop a demonstration micro-simulation model. The model will apply the methods developed by the National Heart Forum for the English Government Tackling Obesities enquiry  [12, 13]. This model has been subsequently applied to other risk factors and diseases and validated in over 70 countries both nationally and locally including the US, Russia, Mexico and Brazil. In short, a dual-module Markov-style micro-simulation model will be used. Module 1 uses multi-variate non-linear regression to project risk trends to 2030. Module 2 utilises a micro-simulation model to simulate a virtual population and estimate the future incidence, prevalence and mortality of the diseases of interest.
Development of a cost-effectiveness modelwork package 6 Work package 6 will be implemented in three phases. Firstly, the data on both direct and indirect costs for each selected disease will be collected for each of the eight countries. The process of data collection will be the same as the disease data collection procedure. Secondly, the intervention scenarios will be developed using information collected in work package 4 and the suggestions arising from the further discussion with partners and experts. Thirdly, following phases one and two an economic model will be developed. A demonstration model will enable to test the cost-effectiveness of interventions to prevent, screen and treat CHD, COPD, type 2 diabetes and CKD.
This knowledge could be transferred to different settings and contexts and may aid future decision making. Secondly, data availability is one of the limitations mentioned in many research publications. We will work closely with the associate partners to collect and synthesise the most recent disease and risk factor data as well as best available economic data across eight European countries. Data sources will be listed in an official report and the outputs of this research will be made available through scientific publications and a dedicated website allowing our findings to be employed in further scholarly and research work. Thirdly, we will assess the burden of chronic disease in various population groups, including those shown to be most at risk. The effectiveness and the costeffectiveness of public health interventions to reduce the risk of chronic diseases will be tested for the different strata of populations helping to establish the most effective ways in intervening across different population groups.
Data reduction was achieved by using principle components (PC) analysis; the PC's coefficients were used to generate point-line visual speech motion. An animated schema of the face was generated by displaying the PC's coefficients generated in Matlab The difference in syllable number between A and B, and duration of sentence pairs is shown in the last two columns. The sentences were: ''Did you know, Woolly is a sheep'' (Sheep); ''Yum, clams are round, small, soft and tasty'' (Clams); ''Look, the big red apple fell to the ground'' (Apple) and ''It's tea time, let's feed the white mouse some flower seeds'' (Mouse). doi:10. 1371/journal. pone. 0111467. t001 using a marker visualization tool. Rendering was achieved in Togl and OpenGL  [39] , and each frame was written to an image file and compiled to a video sequence at 25 fps.
Of the 54 games in the cross section, the following recordings (and subsequent analyses) were made: 18 in each club (RFEF, Adarve and Villanueva del Pardillo), 18 on each playing surface (natural turf, artificial turf, soil), 18 in each format (5v5, 7v7, 9v9), 36 for each age group (U-9 and U-14). Each game lasted 20 minutes, with no breaks and no substitutions. A cross section with high representativeness and which covers all of the possible parameters that influence the development of the game was chosen. The Madrid Polytechnic University's Institutional Review Board approved the use of human subjects in this research for the purpose of collecting the data and statistically analysing it, according to the declaration of Helsinki.
Procedures The videos of the 54 games used for this study were recorded on the soccer pitch. All of the games were watched on site and then analysed from the recordings, with the same process being followed at all times, which was carried out by a minimum of three different reviewers. The data was collected as the recordings were watched, with previously defined variables being monitored. The study was conducted using the observational method; it is a descriptive study and was carried out by way of observation that was systematised, prepared beforehand, carried out in a natural setting and of a quantitative nature. The observational method was applied as follows: formulation of a problem, collection and recording of data, analysis and interpretation of the observed data and communication of the results.
Statistical Analyses After applying a normality test, the arithmetic average of each and every variable previously recorded from the footage of the 54 games was calculated. For the percentage variables (time and pitch zones), the percentages for each of them were compared. A Pearson's chisquared test was performed with a level of significance (Pearson's X 2 <. 05). Cohen's Kappa has been analysed for the variables of the ball out play, touches per game and attempts at goal. Standard deviations were also calculated in every figure. SPSS 22. 0 (Chicago, Illinois, USA) pack was used for every statistic calculation.
Acknowledgements The authors thank the players who participated in the study. The study was endorsed by the  Fédération Internationale de Football Association (FIFA)  and has also been supported by the  Royal Spanish Football Federation Association (RFEF). In addition, FIFA donated sports equipment (balls, bibs, cones, markers and mini-goals) to the clubs involved in the study. The authors declare that they have no conflicts of interest related to the contents of the study. No funding from any institution has been granted for this study.
Construction and validation of the decision-support tool Because risk management decision-making processes are of a rather poorly structured nature, their modeling requires a set of specific tools, both different and non-exclusive one from another  (Vidalis 2004). In this set, together with purely quantitative options such as genetic algorithms, stands out expertise modeling, one instantiation of which consists in the development of an expert system. As the principle of such a tool is to model the process by which a human expert implements his knowledge to provide a solution for a problem within his field of specialty  (Turban 1992) , an expert-system architecture consists of three elements: -a knowledge database, that hosts the knowledge acquired from the experts, -a facts database, in which is fed the information on the specific problem to be solved, -an inference engine, which searches through the knowledge base, and then activates, all the problem-solving bits of expertise that are related to the characteristics of the problem as described in the facts database.
The expert-system building stage Two databases need to be designed and fed during the expert-system building stage: the knowledge database and the facts database. Regarding the knowledge database, the first step of its construction consists in knowledge acquisition, whereas the second step deals with knowledge representation. Extraction of knowledge is the most difficult task in the process of knowledge engineering, and the choice of an appropriate method for this purpose is a key success factor in the building of an expert-system. Here, the method which has been implemented is the semi-structured interview, which is the most commonly used to for knowledge acquisition in the field of managerial decision-making; more precisely, the interviews have been conducted with a group of experts, and structured through an initial question: 'What do you use as indicators for targeting containers when you visit such or such link of the supply chain?'.
Given the nature of the risk management knowledge, and its deductive implementation mode, production rules have been retained as the knowledge representation formalism for the proposed tool. The risk scenarios identified by the experts are therefore represented through sequences of 'IF <conditions> THEN <actions>' assertions: e. g. : 'IF the full container has been transshipped only once and the shipper in not AEO and the container has already been checked at the port of export, THEN the container must be scanned' When all the conditions of a given rule are supported by the data describing the transportation operation to be secured, the 'actions' part of the rule is performed, therefore advancing the risk management process to a further step. Parallel to the construction of the knowledge database, the facts database needs also to be built.
3. 2. 2. The expert-system validation stage It focuses on the quality of the expert system, essentially on its degree of accuracy and of robustness (O'Keefe and Preece 1996); it thus consists of a triple evaluation (CAracaValente and Morant 1999): -a laboratory evaluation seeks to identify any difference between the acquired knowledge and its representation in the expert-system knowledge database, -a performance evaluation mainly relates to the interaction with the user (ergonomics of the interface, operational responsiveness, ability of the system to explain its reasoning process), -a field evaluation has for primary objective to estimate the quality of the solutions provided by the expert-system as compared to the decisions actually made in real-life situations. The methodology adopted for the present expert-system validation globally fits within this frame.
The universe of discourse used in the writing of the production rules has been applied to the description of a set of real-life situations having formerly been assessed by the experts in terms of being risky or not risky. These descriptions have then been submitted to the expert-system which has consequently provided a number of recommendations. These recommendations have in turn been compared to those previously made by the experts, leading to the calculation of an accuracy rate which has been used as a validation criterion. More precisely, fifty real-life situations have been identified and formally transcribed before being fed into the expert-system's facts database (Table  5 ). Out of these 50 real-life situations which have been submitted to the decision-support tool, 40 have been recognized by the expert-system, which has provided recommendations (check or no check of the container) 100% identical to those made by the experts.
Here, risk occurrence and detection probabilities were both "high", as well as its criticity. A third illustration comes from a situation where an empty container was stored in a yard owned by a non-OEA operator and with no safety / security devices on its premises (the critical control point for these information stands when the booking is validated by the shipping company). As the risk occurrence probability and the risk criticity were both qualified as "weak", and the detection probability was specified as "high", the recommendation supplied by the decision-support system was "no check", just like what had been decided in the real situation. The 10 other scenarios which have not been identified by the expert-system led to a second round of knowledge collection for further filling of the expert-system database ; obviously, this knowledge updating process needs to be conducted on a permanent basis to ensure the proper adjustment of the database content to the characteristics of the various scenarios likely to be encountered.
Data recording and processing A force platform (Win-Posturo, Medicapteurs, Toulouse, France; CE Dekra certification directive 93/42 appendix VI, 16 bits A/D conversion) with three strain gauges (sensitivity: 0. 01 N; hysteresis: 0. 2%; linearity: 0. 2%; active lowpass filter: 106 Hz) measuring the vertical ground reaction force at 40 Hz, was used to obtain a two dimensional analysis of CoP displacements along both the anteroposterior and mediolateral axes of the platform. The size of the rigid plate constituting the upper part of the platform was 460 mm × 460 mm. Transducers initialization was performed prior to each series of recordings. Data were saved and processed via the WinPosture NV 1. 6™ software package. Although many CoP-based variables have been proposed in the literature, we focused on six of them that have already been studied and demonstrated their relevance in hemiplegic stroke patients  [8, [10] [11] [12] [13] [14] [15] 17, 19, 35] : mean and standard deviation of resultant CoP velocity (VEL and SD VEL , respectively, in mm.
s -1 ), mean velocity of CoP along the mediolateral and anteroposterior axes (VEL ML and VEL AP , respectively, in mm. s -1 ), area of the 90% confidence ellipse enclosing CoP (CE AREA in mm 2 ), and absolute value of the mean CoP position along the mediolateral axis (CoP ML in mm). The weight-bearing asymmetry was assessed with CoP ML , and postural sway with the other five CoP-based variables. For each variable, the mean of the 3 trials obtained from each participant in eyes open and eyes closed conditions was used for data analysis.
Discussion This study provides reliability data and proposes consistent assessment of measurement error for interpretation of changes in postural sway or in weight-bearing asymmetry between two postural control assessments in hemiplegic stroke patients. For each of six relevant CoP-based variables, our approach was to determine the level of agreement using ICC, then to determine the extent of measurement error using SEM and SRD, taking into account random error distribution. In the presence of heteroscedasticity of random error distribution, we determined the extent of measurement error using ratio (with SEM R and SRD R ) and tested the use of measurement error expressed as percentages (with SEM P and SRD P ). For each of the six CoP-based variables, reliability data were reported in eyes open (EO) and eyes closed (EC) conditions.