dataesr/scientific-paragraphs-categorization huggingface dataset:
Signatories included funders, policy makers, governments and research institutions as well as most CETAF members. Starting July 2012, the European Commission fostered access to and preservation of scientific information: -The European Commission issued a recommendation to make OA the main publishing tool for public funded research; -The European Commission's Horizon 2020 programme made OA mandatory; -All H2020 funded scientific publications must be deposited in the OpenAIRE Green OA repository with the aforementioned 6-to 12-month embargoes. It is not compulsory but strongly encouraged to make underpinning research data publicly available online. International organizations such as the European Commission and the Organisation for Economic Co-operation and Development (OECD) called for a swift implementation of OA through the creation of repositories, the development of compatible OA policies and the sharing of best practices internationally.
Acknowledgements This paper was drafted after the notes by  Patricia Mergen  and  Chloé Besombes  during the two CETAF workshops, to whom the authors are thankful. We are grateful to the reviewers,  Gildas Illien ,  Koen Martens  and  Philippe Bouchet , for their remarks and valuable comments on an earlier version of the manuscript that helped us to improve it. The authors would also like to thank the  CETAF community  for its active participation to the workshops which led to this paper and all the colleagues, scientists, librarians and desk-editors, who provided beneficial remarks and useful corrections. A big special thank you to the EJT team, and in particular  Chloë Chester  and  Kristiaan Hoedemakers , whose patience and professionalism allowed the publication of this article.
Background From 1990 to 2016, the mortality of children younger than 5 years decreased by more than half, and there are plentiful data regarding mortality in this age group through which we can track global progress in reducing the under-5 mortality rate. By contrast, little is known on how the mortality risk among older children (5-9 years) and young adolescents (10-14 years) has changed in this time. We aimed to estimate levels and trends in mortality of children aged 5-14 years in 195 countries from 1990 to 2016. Methods In this analysis of empirical data, we expanded the United Nations Inter-agency Group for Child Mortality Estimation database containing data on children younger than 5 years with 5530 data points regarding children aged 5-14 years. Mortality rates from 1990 to 2016 were obtained from nationally representative birth histories, data on household deaths reported in population censuses, and nationwide systems of civil registration and vital statistics.
Research in context Evidence before this study We searched PubMed with the terms "child mortality", "mortality", "adolescent" for papers published between Jan 1, 1970, and April 1, 2018, in English or French. In most low-income and middle-income countries, civil registration and vital statistics systems are not sufficiently complete to provide timely and accurate child mortality rates. Every year, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) does a systematic analysis of mortality of children younger than 5 years, which is based on a comprehensive database that compiles data from surveys, censuses, and vital statistics systems. Thus far, this effort has been limited to mortality in children younger than 5 years, to monitor progress in improving survival of children.
Added value of this study To our knowledge, this is the first study to provide estimates of mortality in children aged 5-14 years in all countries in the world that were made by use of a comprehensive database of measurements from nationally representative surveys, censuses, and vital registration. We extended the database used by the UN IGME to include older children, and we used a Bayesian penalised B-spline regression model to produce smoothed time series of risks of dying and numbers of deaths, with 90% uncertainty intervals. Our estimates fall between model-based estimates from the Global Burden of Disease Study and the World Population Prospects report. However, in some countries, large deviations were observed from these alternative data sources.
Database construction The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) is led and coordinated by the United Nations Children's Fund (UNICEF) and also comprises representatives of WHO, the UN Population Division, and the World Bank Group. Since 2004, the UN IGME has compiled a comprehensive database of mortality measurements to estimate under-5 mortality. 7 In this international cross-sectional study, we expanded this database by adding data on mortality among children aged 5-14 years. These data were sourced from civil registration and vital statistics systems, birth histories collected in sample surveys, data on household deaths reported in censuses, and other sources (all of which are subsequently described), to compile all publicly available nationally representative data on 10 q 5.
Vital statistics data WHO member states regularly provide nationally representative vital statistics data. Through reviews, the UN IGME group establish whether these data are sufficiently complete to estimate child mortality. For our analysis, we retained vital statistics estimates for 87 countries. In some countries, such as South Africa, vital statistics data were included when fitting the trend for 10 q 5 , despite being excluded for UN IGME estimates of 5 q 0. There are reasons to believe that civil registration systems capture a larger percentage of deaths of children aged 5-14 years than deaths of younger children, which are more likely to be unreported, particularly when they occur in the first few days after birth. 8 Hence, when WHO estimated that at least 90% of deaths of adults were reported, vital statistics data were also used to estimate 10 q 5 even if they were excluded for 5 q 0.
Full birth histories Birth histories were obtained from 272 DHS from 85 countries. We also included estimates from the Pan Arab Project for Child Development and Pan Arab Project for Family Health surveys (17 surveys), Multiple Indicator Cluster surveys (30 surveys), reproductive health surveys (23 surveys), World Fertility surveys (36 surveys), and other country-specific surveys. Surveys that were excluded from the UN IGME estimation process for mortality of children younger than 5 years due to concerns about data quality were also excluded when we estimated mortality among older children. 7 stimates of 10 q 5 obtained from full birth histories might be affected by a truncation bias. 9 Since full birth histories are typically collected from women aged 15-49 years, the information referring to the 5 years before data collection will refer to women who were aged 10-44 years at that time, only a fraction of whom would have had children aged 5-14 years to report on.
Therefore, child mortality estimates of the time before the survey will disproportionately represent high-risk first-born children and children born to younger mothers. This truncation bias, which probably results in overestimation of mortality, might be counterbalanced by more frequent omissions of deaths in the past. Because of the truncation bias, we limited our analysis to a maximum of 12 years before each survey. This limit was set to obtain three data points. We used 4-year periods (instead of 5-year periods) to reduce possible biases due to rounding. Hence, we computed mortality rates from the full birth history data for three reference periods: 0-3 years before the survey, 4-7 years, and 8-11 years. Because of large random errors, we did not use shorter time periods. Standard errors were calculated with a jackknife variance estimation method.
Adjustments   17  From these data on crisis-related deaths, we estimated excess mortality rates. We excluded the input data referring to years affected by crises from our database, fitted the B3 model to the remaining data, then added back the excess mortality rates to the smoothed trend. The full list of crises explicitly incorporated in the estimates is shown in the appendix. Although this adjustment is of great importance in some countries heavily affected by crises, the number of excess deaths added through the crisis adjustment corresponded to less than 1% of the total deaths in the 5-14 years age group since 1990. This percentage does not include all crisis-related deaths, however, because we did not adjust for crises that were spread over several years and resulted in stalls in the mortality decline, which was already well captured by the B3 model.
For these countries, 10 q 5 was modelled by use of the association between mortality of children aged 0-4 years and 5-14 years, as observed in the 156 countries for which the B3 model was used. A linear model was used to regress log( 10 q 5 ) against log( 5 q 0 ), with region-specific dummies. 10 q 5 between 1990 and 2016 was predicted for countries with inadequate mortality measurements by use of the country-specific 5 q 10 estimates and the coefficients of this regression (appendix). To calculate the number of deaths of children aged 5-14 years, we accounted for the age structure of the population aged 5-14 in each country. 10 q 5 was split into two segments: children aged 5-9 years ( 5 q 5 ), and those aged 10-14 years ( 5 q 10 ). A specific database of mortality Probability 5 q 5 Probability 5 q 10 Probability 10 q 5 Annual rate of reduction in 10 q 5 , % Table  1 : Global and regional probabilities of death among children aged 5-10 years, 10-15 years, and 5-15 years For EM-DAT see https://www.
emdat. be/ measurements in children aged 5-9 years was constructed from the same data sources, but only to calculate mortality rates of children aged 5-9 years. A penalised spline regression model that was similar to the B3 model was used to estimate country-specific trends in the logit transformation of r-ie, (log(r/(1 -r)))-where r is the ratio of the probability 5 q 5 to the median B3 estimate of 10 q 5. This approach ensures that the 5 q 5 is constrained to be lower than 10 q 5. When the database did not include enough data series of 5 q 5 , we used region-specific coefficients to relate 5 q 0 and 5 q 5. When 10 q 5 (for children aged 5-14 years) and 5 q 5 (for children aged 5-9 years) were estimated, we derived 5 q 10 (for children aged 10-14 years). The age-specific probabilities were then converted into mortality rates,  20  and multiplied by the population estimates from the WPP report.
According to our results, there were 1•7 million deaths in 1990 (1•6 million-1•7 million) and 1•1 million in 2010 (1•1-1•1). We can ascribe these differences to the use of regional coefficients by Hill and colleagues and to potential differences in estimating life table probabilities of dying from full birth histories. Overall, discrepancies across series of estimates are likely to decline in the coming years as methods improve and databases are expanded. An important limitation of our analysis was that estimates are still derived from an expected association of 10 q 5 with 5 q 0 for 39 countries. Although we did not use conventional model life tables, these estimates are still inferred from the data of countries with good vital registration data or numerous sample surveys, and they might mask distinctive features of countries without sufficient data.
Acknowledgments We thank all members of the  Technical Advisory group of the UN Inter-agency Group for Child Mortality Estimation (Leontine Alkema ,  Robert Black ,  Simon Cousens ,  Trevor Croft ,  Michel Guillot ,  Kenneth Hill ,  Bruno Masquelier ,  Jon Pedersen ,  Neff Walker ) and the members of the  UN Inter-agency Group for Child Mortality Estimation (Patrick Gerland ,  Dan Hogan ,  Colin Mathers ,  Emi Suzuki ,  Danzhen You ) for providing guidance on estimating mortality among older children. Thanks also go to  Vincent Bremhorst  and  Océane Van Cleemput  for assistance in estimation, and to  Annet Mahanani  and  Jessica Ho  and colleagues in the field offices of  UNICEF  and  WHO  for supporting the country consultation process. The  UN Children's Fund, United States Agency for International Development  ( SC140822 ), and the  Bill & Melinda Gates Foundation  ( OPP1135440  and  OPP1180460 ) provided support for this study.
• Eventual vibrations are mitigated (spring effect) • Low maintenance when it is compared to a mechanical coupling or a universal joint The challenge in this proposal lies in difficulty of implementing two mechanical functions, jointly: the transmission torque function (coupling) at the distance with the hull in between, and the 3D rotational freedom (spherical joint). Seeking it, we have proposed the first RMCT, the spherical one  [1] , with magnets just in one side of the coupling (motor), which results in a low transmitted torque. Recently, we have started to develop a new RMCT version, the radial one  [17]. In  [18] , we presented the flat reconfigurable magnetic coupling thruster (Flat-RMCT, see Figure  1 ), modeling the magnetic torque using partial domain simulation results with a commercial software (Flux3D) achieved by TE2M.
Magnetostatic Numerical Modeling The magnetostatic numerical model is implemented using the finite volume integral method  [20] , from the RADIA  [21]  tool. RADIA works as a library (add-on) for the Mathematica TM [22] software, which calls the solver, receives the results, and manages the optimization process if necessary. The main idea of the method is to represent a magnetic body by polyhedrons (finite volumes) where the magnetization is considered uniform. Both hard (magnets) and soft (iron) magnetic bodies are represented by these volumes, and their magnetization vectors M are determined as a function of the magnetic field strength H. This relation can be linear for isotropic (e. g. , paramagnetic and diamagnetic) and anisotropic (e. g. , permanent magnets) materials, or nonlinear for other isotropic materials (e.
Magnetic Spring Effect T z Results and Model According to Figure  6  (where θ is small), it is expected that the spring effect torque T z values are considerably higher than those related to the auto-driving effect ∆T z. To obtain them, the adopted model (Equation (6), for example) shows that the magnetic spring torque is equal to the propeller torque for θ h = 0 • , where θ m becomes equal to θ: T z (α, θ) = T h z (α, θ m = θ). Thus, Figure  8  presents the T z (α, θ) simulations with 252 configuration points computed, which are the black dots. Taking into account the coupling symmetry (Section 2), the numerical experiments are performed with α ∈ [0 • , 30. 5 • ] and θ ∈ [0 • , 90 • ]. The colored surface is obtained by automatic interpolation, using a Mathematica TM software function. Looking from the lower right side (θ axis) of Figure  8 , we see what is presented in Figure  9 , and from the lower left side (α axis) what is presented in Figure  10.
Background: Patient characteristics and survival outcomes in randomized trials may be different from those in reallife clinical practice. The objective of this study was to describe treatment pathways, safety, drug costs and survival in patients with metastatic Renal Cell Carcinoma (mRCC) in a real world setting. Methods: A retrospective analysis was performed using IQVIA real world oncology cross-sectional survey data, a retrospective treatment database collecting anonymized patient-level data in Europe. Data on treatment naïve patients with mRCC who received a first-line targeted therapy in France were extracted for the period 2005-2015. Descriptive analyses were performed on treatment patterns, patient characteristics and safety profiles. Progression Free Survival (PFS) was determined using Kaplan-Meier survival analysis.
Methods This retrospective analysis used IQVIA-RWD Oncology Cross-Sectional Survey Data (IQVIA-RWD), a cancer treatment database collecting anonymized patient-level oncology data in Europe. IQVIA-RWD provides retrospective information on patient characteristics and treatment history from the day the physician completes the case report form until diagnosis. Data are collected on a quarterly basis via a web survey methodology in which each physician provides case histories for at least 15 consecutive patients they personally treat. The cap of included patients per quarter, is related to the physician's specialty and is statistically determined. Physicians complete the case report form using the patient's medical record. In this manner approximately 2% to 4% of the treated prevalence across cancer types is captured  [7].
To ensure that IQVIA-RWD is representative of oncology practice in France, around 210 physicians with different specialties (radiotherapy, dermatology, otolaryngology, gastroenterology, general surgery, gynaecology, haematology, oncology, pulmonology and urology) working in urban and rural regions and in public and private hospitals are included per quarter  [8]. To ensure the liability of the collected data, projection techniques are applied and each quarter the data are validated using external sources such as incidence and prevalence data from Globocan and Eucan, cancer registries and published literature. The data that support the findings of this study were available as a ready to use excel spreadsheet. In addition to standard data quality control techniques performed by IQVIA on a regular basis, we have checked data consistency and applied predefined criteria to account for inconsistent values as filled by the physicians.
Study data are available from IQVIA but restrictions apply to the availability of these data, which were used under license for the current study. Consequently, individual patient data cannot be made publicly available. However, researchers can acquire the data set from IQVIA against fees upon request and after signing the data sharing agreement. For the purpose of this study, we had permission from IQVIA to access the dataset. Data collected in France between October 2005 and September 2015 for patients diagnosed with RCC were extracted. The principal population of interest was patients with metastatic disease treated by a first-line targeted therapy. Targeted therapies included in the analysis were those recommended for the treatment of mRCC and included sunitinib, pazopanib, temsirolimus, everolimus, bevacizumab, axitinib and sorafenib.
Included patients were naïve to anticancer systemic treatments (immunotherapy, cytotoxic chemotherapy and targeted therapies). All analyses in this manuscript are conducted on French patients with mRCC who received a first-line treatment by targeted therapies. Progression was defined as treatment cessation due to disease progression (local or distant); patients who stopped treatment due to other reasons were censored. Progression-free survival was estimated only for the first-line. Only adverse events recorded in the patient's medical record are reported in the database. Dosing information was collected for current treatments and in patients who received a first-line targeted therapy. Drug cost per day was obtained by multiplying the mean cost per mg by the daily dose. Cost data were presented by line of treatment.
Furthermore, there is no longitudinal follow-up for patients in the database; therefore, the database does not adequately convey the subsequent progress of the disease. This might lead to an overestimation of the PFS. It would have been interesting to compare PFS and safety between treatment groups, but such comparisons were not performed due to small sample sizes and the uncontrolled nature of the study. Our results paint a broad picture of the management of mRCC but there are challenges related to the use of RWD. Although our results are consistent with published studies, they should be interpreted with caution. Since we used a cross-sectional database, the follow-up duration between patients was variable and the sample size for some agents limited. In addition, sequences of targeted therapies should be interpreted in light of the date of marketing authorization for each molecule compared to the date of the study.
Observation Global and single-cell outputs are recorded at the beginning of the simulation, at regular intervals during the simulation, as well as at the end of the simulation. Global variables calculated at the scale of the entire domain include: mass of DOC, POM, and CO 2 produced in the media, and, for each of the three bacterial species, the number of bacteria, and total bacterial biomass. The state of all the individual bacteria is saved at sampling times. These single-cell data include the position within the domain, species, mass, uptake rate, and mass at reproduction of the individuals. The carbon mass and position of all the POM spots (abiotic agents) are also saved at sampling times. The DOC concentration of the liquid voxels holding at least one bacterium is also recorded at sampling times.
Simulation Results. 9 read sets were generated using GemSIM  46  , ranging from 50 to 25,000X coverage. Each read set was analysed using one of three callers, LoFreq, VarScan or FreeBayes, producing 27 variant call sets, which were given a score between 0 and 1 defined as (Power*Accuracy)*(1-False Positive Rate) (described in full in Material and Methods, Variant Simulation). LoFreq was the best scoring caller (Supp. Fig. 1 ), and used to call heteroplasmies in all empirical datasets. FreeBayes consistently had the highest power, detecting more than 85% of true variants even at coverages as low 100×, however, FreeBayes also exhibited a false positive rate >5% at all coverages. LoFreq and VarScan had extremely low error rates, with each detecting only one false positive in the 27 variant call sets.
Two samples within the 6+ cohort were found to be unduly influential points using Cook's Statistic (Supp. Fig. 3A, B ). As such they were removed and the model fit again. With these two samples removed no significant association with age was found (ANOVA, p = 0. 1), as shown in Fig. 2  and Table  3. Bootstrapping of the three fitted models also showed a decrease in the standard error and bias for the model estimates with the outliers removed, Table  3. Further, the two outlier samples contained primarily oxidative mutations, one of which contains the highest number of oxidative mutations in our data. To investigate any bias that may be introduced by choosing the most recent sample from recaptured individuals, we produced 1000 permutations in which the sample from recaptured individuals of usable age was chosen at random, to produce a similar dataset of 165 unique individuals.
Prior to variant calling BAM files were processed using Picard Tools (v1. 90)  74  and the Genome Analysis Tool Kit (GATK)  75. Briefly, SAM files were sorted and read groups added using Picard Tools. SAM files were then converted to BAM files and indexed. Prior to base quality recalibration a set of raw variants was produced using the chosen low frequency variant caller, either LoFreq Star  76  or VarScan 2  77. These raw variants were used as the "known sites" necessary for the BQSR walker in the GATK. As no databases of variants exist for most non-model organisms, these raw variants provide an ad hoc solution. Final heteroplasmies were called on the recalibrated data again using the caller of choice. A third variant caller, FreeBayes  78  , does not require recalibrated or realigned data, and so these steps were omitted when FreeBayes was used.