dataesr/scientific-paragraphs-categorization huggingface dataset:
9 It also developed a typology for describing public health research structures based on funding streams and strategies. 10 n an early comparative study, Braun analysed health research in the USA, France, Germany and the UK,  11  including a chapter on public health research. Cross-national information about health services research has been collected through a European collaborative study. 12 Health service research and public health research in Denmark, the Netherlands, Norway and the UK have been compared,  13  and reviewed public health for practice in three of these countries. 14 There have been individual reviews of health research performance in Nordic countries, such as Sweden. 15 ECD 16 provides generic data on funding health/medical research in the public sector. In PHIRE, information was collected across European countries to inform national stakeholder meetings on innovation and research.
The Research Council estimates that only 5% of the total Danish research funding goes to the Strategic Research Committee and 7% through the Research Councils. There were no research funding reported from foundations. Financial support for most research is from the Ministry of Education through grants directly to universities. Regional or sub-national funding was often significant in countries where the health services are managed at sub-national level, including Austria, Italy and the UK. For example, the Research Centre for Prevention and Health is funded by the Capital Region of Denmark, and is undertaking 'Inter99', a controlled intervention study for cardiovascular disease with 60 000 people investigating how structural changes in neighbourhoods affect changes in behaviour. Sweden and the UK are among countries with large non-profit foundations funding health research, particularly related to specific conditions such as disability and cancer.
Only in a few countries are health care systems directly involved in developing research agendas. STEPS  17  provided a framework and database on European national public health research structures, and ERAWATCH has more detail across policies (although for research as a whole rather than health research). Although some countries have national databases describing research that has been funded, these rarely allow analysis for public health studies alone. There is a need to develop standard ways of describing public health research within national systems, so that countries can collect data, and report and monitor in the same way.
Design Meta-epidemiological study. Data sources 93 meta-analyses (735 randomised controlled trials) assessing therapeutic interventions with binary outcomes, published in the 10 leading journals of each medical subject category of the Journal Citation Reports or in the Cochrane Database of Systematic Reviews. Data extraction Sample size, outcome data, and risk of bias extracted from each trial. Data synthesis Trials within each meta-analysis were sorted by their sample size: using quarters within each meta-analysis (from quarter 1 with 25% of the smallest trials, to quarter 4 with 25% of the largest trials), and using size groups across meta-analyses (ranging from <50 to ≥1000 patients). Treatment effects were compared within each meta-analysis between quarters or between size groups by average ratios of odds ratios (where a ratio of odds ratios less than 1 indicates larger effects in smaller trials).
Data sources This study combined data from two independent collections of meta-analyses of randomised controlled trials assessing therapeutic interventions with binary outcomes. The first collection included 48 meta-analyses (421 trials) published in the 10 leading journals of each medical subject category of the Journal Citation Reports during two periods: between July 2008 and January 2009 and between January and June 2010 or in issue 4 of the Cochrane Database of Systematic Reviews in 2008. Further details about the search strategy and selection of meta-analyses have been published. 8 Reports of all component trials from included meta-analyses were obtained. The second collection included 45 meta-analyses (314 trials) published in the Cochrane Database of Systematic Reviews between January and July 2011.
Data extraction and risk of bias assessment Using a standardised data extraction form, we extracted the following data for each randomised controlled trial: the date of publication, whether the trial was a single centre or multicentre trial (with at least two different centres), the number of patients with the outcome in each group, and the number of patients randomised in each group. Data for risk of bias were also collected by using the following domains of the risk of bias tool of the Cochrane Collaboration 9 10 : methods for sequence generation and allocation concealment, blinding, and incomplete outcome data. Each domain was rated as having low, high, or unclear risk of bias, according to the recommendations of the Cochrane Collaboration. 9 10 For each trial, the overall risk of bias was classified as low (that is, low risk of bias for all domains), high (that is, high risk of bias for one or more domains), or unclear (that is, unclear risk of bias for one or more domains in the absence of high risk of bias).
Strengths and weaknesses of the study Our results were based on a large meta-epidemiological study of 93 meta-analyses, representing various medical areas published in the leading journals of each medical speciality or in the Cochrane Database of Systematic Reviews. 0] [21]  To explore the influence of sample size on treatment effect, we used several complementary approaches, which all showed consistent results. However, because our results were based on meta-analyses of trials assessing binary outcomes, they cannot be extrapolated to trials assessing continuous outcomes because such trials usually differ in medical condition, risk of bias, sample size, and statistical analysis.
All authors, external and internal, had full access to all the data (including statistical reports and tables) in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. Funding: This study was funded by an academic grant from the Programme Hospitalier de recherche Clinique Régional (AOR10017). Our team is supported by an academic grant (DEQ20101221475) for the programme "Equipe espoir de la Recherche," from the Fondation pour la Recherche Médicale. The researchers declare that they are independent from the funders. The funders did not have any role in the study. treatment effect estimates between trial sample sizes grouped by quarters (from quarter 1 with the smallest trials, to quarter 4 with the largest trials) and by fixed thresholds (according to numbers of patients).
The protocol used for the internal standard preparation and interference correction was based on a previous report by  Aries et al. (2000). The 202 analyzed samples corresponded to 102 PM 2. 5 samples and 100 PM 10-2. 5 samples. After ICP-MS analysis, data for 48 elements that had at least 80 % of their concentrations above the detection limit (DL = 3 9 standard error of analytical blanks) were used for further analyses. These elements included major elements (Na, Mg, Al, K, Ca, and Fe), trace elements (P, Sc, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, Ga, Ge, Mn, As, Sr, Rb, Zr, Mo, Ag, Cd, Sn, Sb, Ba, Pb, Hf, Ta, and W) and rare earth elements from La to Lu. The average blank contribution to elemental concentrations was approximately 10 % for most elements. It reached 15 % for Mg, Sc, Co, Sr, Nd, Sm, and Th and ranged from 20 to 80 % for P, Ca, Cr, Ni, Zn, Sb, Hf, W, and U in 50-75 % of the samples, indicating much higher uncertainties in the concentrations of these elements.
Factor analysis It is important to verify that a dataset has a normal distribution and that measurements are independent of each other prior to using factor analysis (FA). It is well-known among applied geochemists that regional geochemical data never show a normal distribution  (Reimann et al. 2008 ) and that the presence of outliers in environmental studies is very common. Therefore, it is important that all variables have a near-normal distribution  (Reimann et al. 2008 ) and that the impact of outliers is reduced by applying a robust FA  (Filzmoser 1999; Pison et al. 2003). The identification and interpretation of PM sources is achieved through studying the factor scores produced. FA has been used to identify the factors, or sources, contributing to measured PM concentrations (Table  2 ).
The children included were to be aged from 6 to 12 years and were not to have undergone surgery in the past 12 months. The French-translated IOFS questionnaires were sent to the parents by post. These questionnaires were accompanied by a short questionnaire about the parents' socio-professional characteristics. Parents were asked to return completed questionnaires to the investigation center by post. Finally, a second IOFS questionnaire was sent 15 days after the first one for the purpose of interobserver reliability assessment. In addition, clinical data about the children (gender, age and type of cleft) were collected using the University Hospital of Strasbourg patient database. Acceptability was assessed on the basis of the proportion of parents answering the first IOFS questionnaire. The total impact score was calculated by summing the results of all items, giving a total score ranging from 15 (no impact) to 60 (maximum impact).
Acknowledgements We would like to thank  Dr Stein R. E. K. , Riessman C. K. and  Dr Jessop D. for their permission to use and to perform a translation of the original version of the IOFS. The Impact on Family Scale (©1978 by  R. E. K. Stein, M. D. , and  Catherine Kohler Riessman, Ph. D. ) is available for use with permission from the first author:  Ruth E. K. Stein , M. D. ,  Department of Pediatrics, Albert Einstein College of Medicine/Children's Hospital at Montefiore Medical Center, 111 East 210 Street, Bronx ,  NY 10467 ; e-mail: rstein@aecom. yu. edu. The French-translated version of the IOFS is also available for use from  Ruth E. K. Stein , M. D.
Study subjects, data collection, variables of interest, and ethics approval Invited for the present study were those 293 individuals, who remained in a Danish population-based study ten years after the initial survey in 2000, and who accepted participation in the present study. They all turned 50 during the year of the study. Participants received identical automated text messages every fortnight over one year (26 times), using the SMS-Track-Questionnaire system  [16]. They received two standardized questions relating to their low back in the past fortnight; one on the number of days that their lower back had bothered (troubled) them and the other on the number of days on sick-leave in the past fortnight. In the present study, only data on LBP were used. Their fortnightly response, which was sent as a return text message to the SMS-Track question, was in relation to the number of days in this period with bothersome LBP, i.
At least one pain FN surrounded by a non-episode on each side was defined as an episode of LBP. In order to get a feeling for the spread of data, a preliminary analysis was performed, where various patterns were grouped until it became clear that episodes could be classified as brief (consisting of only one pain FN) or longer (if there were at least 2 pain FNs in a row). An episode of at least 6 pain FNs in a row (i. e. 3 months) was defined (pre hoc) as a long-lasting episode, because this corresponds with 'chronic' as defined in numerous studies. The spread of data was thereafter identified for each individual using the above definitions. Specific patterns were thereafter searched out through visual analysis. The subgroups that emerged had therefore not been identified à priori but emerged after systematic observations.
Summary of findings and some considerations This appears to be the first study in which frequent data on the presence/absence of LBP in the general population were collected covering information for an entire year. The results of this study showed that SMS-Track data can be used successfully to classify people from the general population into three meaningful clinical subgroups; those without LBP, those with episodic LBP, and those with persistent LBP. These subgroups were approximately equally large in this particular study population. The results support the findings from a recent literature review, showing that there is a substantial LBP-free subgroup in the general population and that, at least during one year, this group will remain LBP healthy. Our results also confirmed the episodic theory, in that such a subgroup was seen to definitely exist but, at least in this study sample, there was also an at least equally large chronic group, if chronic is defined as experiencing at least some pain each fortnight during a minimum period of 3 months.
Acknowledgements The authors wish to thank  Professor Tom Bendix  for his role in designing the original study and securing primary funding from  Industrial Insurance Company , now topdanmark. We also thank  Professor Claus Manniche  and  The Spine Centre of Southern Denmark  for hosting the entire project and, in particular, for making the third data collection possible by supplying secretarial support and providing imaging of the participants. Charlotte Leboeuf-Yde 's position in Denmark was partially funded by the 'Fonden til fremme for kiropraktisk forsknings og postgraduat uddannelse' until the  31. 12. 2012.
The model is validated with the Chernobyl accident which occurred in Ukraine (ex-USSR) on 26 May 1986 using the emission inventory from  Brandt et al. (2002). This accident has been widely studied since 1986, and a large database has been created containing measurements of atmospheric activity concentration and total cumulative deposition for 137 Cs from most of the European countries. According to the results, the performance of the model to predict the transport and deposition of the radioactive tracer was efficient and accurate presenting low biases in activity concentrations and deposition inventories, despite the large uncertainties on the intensity of the source released. The best agreement with observations was obtained using the highest horizontal resolution of the model (Z19L run).
Today, more than 25 yr after the accident, a better understanding of the fate of radionuclides has been obtained in terms of total deposition. Furthermore, high quality deposition measurements over Europe have become available from the Chernobyl period by the EU Joint Research Centre (JRC) called "the REM database", whereas high resolution maps have been created called "Atlas of caesium deposition on Europe after the Chernobyl accident"  (De Cort et al. , 1998). These data have being continuously collected by the EU since 1986 in the frame of the REM (Radioactivity Environmental Monitoring) project presenting atmospheric activity concentrations and deposition inventories from European countries, and then used in this paper to validate the model ability to represent the spread and deposition of 137 Cs.
All the results have been compared with raw data from the REM database. Given the large global risk of human exposure to radiation, especially in areas around reactors in densely populated regions, notably in West Europe and South Asia, where a major reactor accident can expose around 30 million people to radioactive contamination  (Lelieveld et al. , 2012) , a reliable transport model for radioactive substances would be a benefit. The recent decision by Germany (following the Fukushima Daiichi accident in Japan) to phase out its nuclear reactors will reduce the national risk, though a large risk will still remain from the reactors in the neighbouring countries.
6 ). The ecological half life of 137 Cs is defined as the period of time it takes for 137 Cs burden to decrease by half, affected by processes others that its radioactive decay (radioactive decay of 137 Cs during the 9 months runs was neglected since 137 Cs is a long-lived radionuclide presenting a half-life of 30. 2 yr). Consequently, during the last day of the emissions 28 % of 137 Cs was still present in the atmosphere, whereas at the end of May the respective rate was 1. 1 % (1. 0 PBq) (Table  2 ). However, according to the REM database and previous simulations of the accident (e. g. Brandt et al. , 2002)  the direction depicted by this simulation is inaccurate. This result was expected since the prevailing winds at the surface blow in a very different direction than the ones above. A closer representation of what happened after the accident is reflected by the second run of the model (RG19L) performed after introducing the known sources of 137 Cs at different vertical layers of the model (see Table  1 ).
Comparison with direct measurements of atmospheric activity Data obtained from the four different runs of the Chernobyl accident were compared to real-time measurements of the activity concentrations of 137 Cs. For that purpose, the aforementioned REM database was used. The activity concentrations from the database were divided in three parts according to the regions mostly affected by the radioactive fallout on 30 April 1986 (see Figs. In West-central Europe (Germany, France, UK, Belgium, Switzerland, Austria and Netherlands), the trends of 137 Cs dispersion in the countries examined showed satisfactory results, in terms of activity concentrations and residence times as well (Supplement, Fig. S1 ). However, relatively small inaccuracies were observed on some days (e. g. in Austria and Switzerland).
The precision of the measurement technique used is not indicated in the database. Data from the REM database have been collected using several different techniques (e. g. direct airborne gamma spectrometry, surface pumping through disc filters followed by gamma spectrometry, etc. ) and the specific method used at each station is not specified in the database. Therefore, determination recoveries contrast between different methodologies and this might induce additional uncertainties to the results. Regarding the residence time of 137 Cs in the countries presented here, the model also shows robustness since, in most of the cases, similar levels were observed. Finally, the ending dates of the fallout, where 137 Cs activity concentrations were near the limit of detection (LOD), concur with those of the model.
Injection height seems to be very essential in terms of transport and deposition of 137 Cs in certain regions. Another noteworthy point that we should focus on here is what we learn from the results of the RG19L(S) simulation, where surface emissions were assumed. These results differ significantly from measurements and also model-versions where real emission altitudes were used. For instance, no 137 Cs was detected in North Europe until the end of May or extreme amount were estimated in South Europe at the start of the same month. This is additional evidence of how the exact height of the emission could affect the subsequent transport of 137 Cs and the importance of the uncertainty induced by the source term. There are several numerical measures that quantify the extent of statistical dependence between pairs of databases.
Here, we used the Spearman correlation method  (Choi, 1977) , which assesses how well the relationship between two variables can be described using a monotonic function. If there are no repeated data values, a perfect Spearman correlation of +1 or -1 occurs when each of the variables is a perfect monotone function of the other. Table  3  shows the respective results of the datasets compared (REM versus RG19L(S), RG19L, RG39L and Z19L, respectively) for the activity concentrations of 137 Cs. According to the table, the Spearman correlation coefficient ranged from 0. 61 to 0. 64 for the runs where the emission altitude was taken into account for 95 % confidence level (p < 0. 05), whereas it was 0. 21 for the RG19L(S) run; thus, the variables are statistically dependent. On the other hand, the data of the simulation with the real emission altitude are also highly dependent presenting coefficients of 0.
05); thus, the "null hypothesis" can be rejected and the two datasets are dependent. The data derived from each simulation were very similar with τ coefficients between 0. 54-0. 68. This can also be seen in Fig. 8 , which depicts the Box and Whisker plot of the data. The range of the datasets for 137 Cs activity concentrations is very similar, whereas the boxes corresponding to 25-75 % of the values were found at same level, although in some cases the model was found to underestimate. Besides statistics, the average relative biases were also calculated and presented in Supplement, Fig. S1  for each version except the one where surface emissions assumed. Despite the large variation in the biases, they present very satisfactory averages, 9. 59, 81. 49 and 3. 81 for the RG19L, RG39L and Z19L run respectively, which are very good in comparison with previously reported ones for 137 Cs activity concentrations of the Chernobyl accident (e.
g. -61 in  Brandt et al. , 2002). The larger positive biases calculated for the 39 levels are a result of the elevation of higher amounts of 137 Cs at greater heights (previously discussed in the manuscript) in conjunction with the resulting larger residence times. Finally, the arrival times of the radioactive fallout of 137 Cs were assessed for the four different simulations (RG19L(S), RG19L, RG39L and Z19L) and they were compared to those obtained from the REM database (REM). As arrival time we define the time after the accident it takes for 137 Cs to reach the activity concentration of 10 -4 Bq m -3 in a specific location, which is the minimum detected value of the REM database. The results are illustrated in a scatter plot in Fig. 9  for 56 measurement stations in 24 European countries (Ukraine, Russia, Poland, Romania, Hungary, Denmark, Belgium, Finland, Norway, Sweden, Germany, Netherlands, France, Italy, Great Britain, Austria, Switzerland, ex-Czechoslovakia, Turkey, Greece, Ireland, Egypt, Syria and Lebanon).
11 ). Despite the overestimations observed in Central Europe and underestimations in the highly contaminated areas, taking into account the heterogeneity of the direct measurements and the method used to create the Atlas map (inverse distance weighted interpolation method), one could note that the model gives remarkably good results. database. Over 4000 measurements from 20 European countries were used to evaluate and assess the modelling results in terms of the total cumulative deposition of 137 Cs. However, no data from Ukraine, Belarus and Russia were available at the EU-JRC website. Table  4  shows the respective results of the statistical tests used in order to examine the relevance of the datasets (REM vs RG19L(S), RG19L, RG39L and Z19L, respectively) in contrast to the real-time measurements for 137 Cs deposition.
The Spearman correlation coefficient was estimated to range from 0. 46 to 0. 57 with 95 % confidence level (p < 0. 05), whereas the Kendall's tau (τ ) rank correlation coefficient was estimated to vary between 0. 33 and 0. 42, (with 95 % confidence level, p < 0. 05) (Table 4), which shows the dependence between model datasets and observations. In fact, the results obtained from the different model runs (RG19L, RG39L and Z19L) were also contiguous presenting high coefficients (> 0. 7). Moreover, Fig. 15  depicts the Box and Whisker plots for the datasets in terms of the total cumulative deposition of 137 Cs. There is an obvious trend of the model to underestimate the deposition of 137 Cs in the countries examined taking into consideration the boxes corresponding to 25-75 % of the values, although these ranges were similar.