dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. The DACCIWA data from the Savè supersite and coastal radiosonde stations are available on the SEDOO database (http://baobab. sedoo. fr/DACCIWA/, last access: 3 November 2019;  Derrien et al. , 2016; Handwerker et al. , 2016; Kohler et al. , 2016; Maranan and Fink, 2016; Wieser et al. , 2016)  for scientists interested in boundary-layer studies in southern West Africa. KASS-D is not publicly available. It is maintained by researchers at Karlsruhe Institute of Technology, and anyone interested in the data should contact Andreas Fink. Author contributions. KB wrote the paper, with contributions from all co-authors. The data analysis was mostly conducted by KB and to smaller extent by BA and JFQ. BA, NK, CD, FL and ML participated in the DACCIWA field campaign. Competing interests.
Acknowledgements. The  DACCIWA  project has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 603502. The contribution of Julian F. Quinting was supported by the  Helmholtz Association  (grant no. VH-NG-1243 ). We thank the staff of  KIT (Karlsruhe Institute of Technology)  and  UPS (Université Paul Sabatier)  for helping us to install and run the equipment as well as the staff of INRAB in Savè for allowing us to use their grounds for the experiment. We thank  Andreas Fink  for fruitful discussions and  Anke Kniffka  for providing the KASS-D data. We thank two anonymous reviewers for their useful comments and suggestions. Financial support. This research has been supported by the  European Commission (DACCIWA  (grant no. 603502 )) and the  Helmholtz Association  (grant no.
g. , 2D, 3D), occlusion and noise. Some of these characteristics are more decisive than others. In particular, the occlusion, noise and density characteristics, which have a significant influence on the choice and configuration of algorithms. Besides, these characteristics often vary within the same dataset. For example, one portion of the data may be very dense and low-noise, while another portion may be low-density and high-noise. This information can be defined by the user but is mainly inferred from acquisition knowledge. In other cases of application, other characteristics must be taken into account.
Percentages of respondents usually recommending short antibiotic therapies for at least 50% of the vignettes were calculated for 2016 and 2018 and then compared. Respondents who failed to respond to more than seven clinical vignettes were excluded from the latter analysis. All comparisons were performed using the Chi-square test, with a significance threshold set at 5%. Analyses were performed using the SAS software, version 9. 4 (SAS Institute, Inc. , Cary, NC).
MeThods data sources We analysed national register-based data from eight European countries (Finland, Denmark, England/ Wales, France, Switzerland, Italy (Turin), Estonia, Lithuania), Japan and South Korea. Our research group has collected mortality data by education and occupational class for a wide range of European countries,  [10] [11] [12]  and the present study includes all European countries for which detailed mortality data by occupational class are available. Although mortality data for Italy came from Turin, an urban research report population in Northern Italy, previous studies have shown that patterns observed in Turin are similar to those observed at the national level. 16 17 1] [12]  For Japan and South Korea, information on occupational class for the population denominator came from the census and that for the deceased was reported by the family on the death certificates.
Analysis Age-standardised mortality rates (ASMR) by occupational class were computed using the 2013 European standard population and data in 5 year age intervals. In all countries except Finland, England/Wales and Italy (Turin), the last occupation was unknown for economically inactive men. This may cause bias, because economically inactive men tend to have higher mortality than economically active men, and because men in lower occupational classes have a higher likelihood of being economically inactive. 3] [24]  Online supplementary appendix table 3-2 shows the percentages of men for whom occupational class was unknown in our dataset. To measure inequality in mortality we computed rate differences (RDs) and rate ratios (RRs) by occupational class using upper non-manual workers as reference group.
research report European Commission Horizon 2020 Grant [633666]. Data were partly collected as part of the Developing Methodologies to Reduce Inequalities in the Determinants of Health (DEMETRIQ) project, which received support from European Commission Grant [FP7-CP-FP 278511]. The funder had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication. open access This is an open access article distributed in accordance with the Creative Commons Attribution 4. 0 Unported (CC BY 4. 0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made.
Results For each condition, data distribution was assessed using the Shapiro-Wilk test (please refer to Supplementary Materials S1-T1 for the data). We then compared the selected running speed to the reference running speed using in each case either a t-test when data was normally distributed or a Wilcoxon signed rank test. For all four tests, the alpha level was corrected for multiple comparisons using Bonferroni correction (i. e. , 0. 05/4 = 0. 0125). For all conditions, the selected running speed was not significantly different from the reference running speed after correction. We then tested whether the selected running speed was different based on the optical flow condition. Since data was not normally distributed, a Friedman rank sum test for repeated measures was used. No differences were found for the selected running speed in the four conditions [ # (3) = 3.
Spatiotemporal Parameter Analysis For the spatiotemporal gait parameter estimation, the information derived from the OptiTrack system was used. The participants were equipped with two reflective markers, one positioned on each heel. The data analysis was performed on steady gait by removing the first and the last 15 s of each block. The different gait parameters were estimated using the heel marker trajectory and were averaged for each block, as described in Dubois and Bresciani  [43]. Table  1  shows which spatiotemporal gait parameters were estimated and how they were calculated. The distance on the x-axis between the left and right heel For analysis, these values were averaged both for the whole duration of the block as also for each minute of each block.
Acknowledgments: The authors thank  Florian Blunschi  and  Lisa Perissinotto  ( Department of Neuroscience and Movement Science, University of Fribourg, Fribourg, Switzerland ) for recruiting the participants and for providing support in data collection,  Marco Scodeggio  ( Istituto di Bioimmagini e Fisiologia Molecolare, Consiglio Nazionale delle Ricerche, Segrate, Milano, Italy ) for technical assistance in constructing the response device, and  Thibaut Le Naour  ( Department of Neuroscience and Movement Science, University of Fribourg, Fribourg, Switzerland ) for his help in the creation of the representation of the experimental set-up (Figure 1).
Biomarker Data Participants' spot urine samples were collected in the mobile examination center. Urinary Cd was measured as described in the NHANES Laboratory Procedures Manual  [30, 31]. While elevated blood Cd confirms recent acute exposure, it does not correlate well with total body burden. Therefore, urinary Cd, which reflects integrated exposure and total body burden  [32] , was used in this analysis. Urinary Cd concentrations were determined using inductively coupled plasma-mass spectrometry (ICP-MS) and adjusted by creatinine.
Mortality Data Linked to the NHANES We combined NHANES data from the survey years 1988-1994 and 1999-2004 with their linked mortality datasets. During a mean of follow-up of 15 years (through 31 December 2011), 5367 deaths occurred  [33]. NHANES-linked mortality data were ascertained through probabilistic record matching with the National Death Index (NDI). Follow-up time per person was calculated beginning at the time of the NHANES interview date and ending at death or censoring. The causes of death were determined from the 10th revision of the International Statistical Classification of Diseases and Related Health Problems  [34]. The number of deaths, crude mortality rate and age-standardized mortality rate (ASMRs) of all-cause, cancer and CVD according to quartile of urinary Cd were estimated.
Statistical Analysis All statistical analyses were performed using SAS software, version 9. 4 (SAS Institute Inc. , Cary, NC, USA). To account for the complex survey design, the appropriate SAS procedures, weight, strata, and cluster variables were used. Participants were grouped into quartiles based on urinary Cd concentration after adjustment for urinary creatinine and into tertiles based on dietary Zn intake for  1988-1994 and 1999-2004. The frequencies and means of sociodemographic and lifestyle factors according to quartiles of urinary Cd concentration were estimated. Significance testing for the differences in urinary Cd between subgroups was conducted by ANOVA or chi-square test. Poverty income ratio (PIR) was calculated as a ratio of household income to the poverty threshold after accounting for inflation and family size, based on poverty guidelines provided by the Department of Health and Human Services (HHS).
Firstly, estimation of Zn intake was based on one day of dietary recall data, which may not accurately reflect usual intake of individual participants. Secondly, competing risks can be present between CVD and cancer mortality. Thirdly, even though we tried to match variables between datasets of 1988-1994 and those of 1999-2004, there might be systematic error in combining two datasets. Finally, residual confounding factors might be present in this analysis, although we attempted to adjust for all relevant covariates available in the NHANES dataset.
Similar recommendations were made by the International Labor Organization  51). Similar findings were reported from a study that pooled data from several studies across different industries Åkerstedt et al  52). This study found that self-reported sleepiness was most pronounced during the first recovery day, and not during the last working day. While the authors observed different recovery times associated with different working time arrangements (e. g. shift length, job type, shift sequences), they reported their data supported prior findings that one day of recovery is not sufficient, but two days, in most cases, was enough. However, for periods where workers may experience severe circadian disruption, longer periods of rest may be necessary. For example, Kecklund et al. recommended at least 3 d of recovery following 7 consecutive night shifts  53).
The principal investigator (PI) met with stakeholders in the Agincourt area on two separate occasions in order to obtain local inputs and insights. In the first meeting, the PI attended a day seminar hosted by Wits Agincourt at the Wits Rural Facility in Agincourt, where the project was presented and discussed with approximately 50 attendees from the scientific community, the Provincial Department of Health in Mpumalanga, community leaders, NGOs in the area (Buffelshoek Trust and K2C), and a private game reserve (Sabi Sabi). On the second occasion, the PI and Africa Health Placements met with two members from Wits Agincourt and one member from the Provincial Department of Health in Mpumalanga, also at the Wits Rural Facility, in order to plan for the implementation of the healthcare workforce assessment and to discuss sourcing mortality data from Wits Agincourt.
Data Sources There are ten healthcare facilities in the Agincourt sub-district. Africa Health Placements sourced healthcare facility workforce assessment data from eight KI survey interviews with healthcare facility staff. KI interviewees were selected on the basis of their seniority and in-depth knowledge of their respective healthcare facility and workforce needs. Six facilities are classified as public healthcare clinics (PHCs) (Belfast, Cunningmore, Justicia, Kildare, Lillydale, and Xanthia), while two facilities are classified as community healthcare clinics (CHCs) (Agincourt and Bhubezi (not indicated on the map) (see Map 2 below). Access to two PHCs in the vicinity, namely Rholana and Ireagh, was not possible due to gatekeeping constraints. Community healthcare clinics, which are also state-run facilities, generally offer a greater range of services when compared to PHCs facilities, normally operating for 24 hours (as opposed to 8 hours) and often have in-patient facilities.
Data Sources There are ten healthcare facilities in the Agincourt sub-district. Africa Health Placements sourced healthcare facility workforce assessment data from eight KI survey interviews with healthcare facility staff. KI interviewees were selected on the basis of their seniority and in-depth knowledge of their respective healthcare facility and workforce needs. Six facilities are classified as public healthcare clinics (PHCs) (Belfast, Cunningmore, Justicia, Kildare, Lillydale, and Xanthia), while two facilities are classified as community healthcare clinics (CHCs) (Agincourt and Bhubezi (not indicated on the figure) (see Figure  2 below ). Access to two PHCs in the vicinity, namely Rholana and Ireagh, was not possible due to gatekeeping constraints. Community healthcare clinics, which are also state-run facilities, generally offer a greater range of services when compared to PHCs facilities, normally operating for 24 h (as opposed to 8 h) and often have in-patient facilities.
The semi-structured KI interviews were based on a structured questionnaire developed by Africa Health Placements, which assesses services offered, workforce capacity, and facility location and infrastructure. The following domains were assessed to structure and to organise the healthcare workforce investigation: hospital location information; patient/disease burden; referral location; clinical services offered; current staff capacity; and training and supervision. General knowledge concerning climate change associations with health was explored through the open-ended question "Is there anything else you could like to add?", and by asking all KI respondents "What, if any, impact have you noticed on your clients' health due to climate change?", followed by interviewer probes. All raw data was recorded on a laptop during the interviews and transferred to an Excel spreadsheet.
Data Analysis The data was interpreted by means of thematic content analysis. This technique has proven to be beneficial in the formulation of healthcare policy and interventions. Coding is applied in the disaggregation and investigation of central themes. The process of investigation during data analysis involves linking coded categories and concepts to one another through both inductive and deductive reasoning  [61, 62]. Categories are established by eliminating the constellation of words or statements that relate to the same central meaning, also referred to as meaning units, from the open-ended questions. The derived categories, such as medication delivery times, barriers to service delivery, and climate impacts on health, were then coded in order to detect the regular patterns. Themes were derived from categories that are classed together.
Facility, Infrastructure, and Accessibility Factors Data was analysed from eight KI healthcare facility staff survey interviews, namely three acting charge sisters (two PHCs and one CHC), one charge sister (PHC), two professional nurses (two PHCs), one deputy charge sister (PHC), and one technical assistance manager (CHC)). The catchment population in the area ranges between 9900 and 11,900 individuals. No patient is ever turned away from any of the facilities, and the average patient waiting time for assistance varies between 30 min and 2 h. All PHCs operate for eight hours per day, every day of the week, with patient headcounts per month for each facility fluctuating between 2000 and 5400 (see Table  1   Five KI PHC respondents recount that there is insufficient seating for patients, where sufficient seating was regarded as a chair or bench for each patient to sit on.
Acknowledgments: Thanks to former employees of  Africa Health Placements ,  Caroline Simpson  and  Brenda Mashave , for technical support in the implementation of the study. Our appreciation also to  Rhian Twine ,  Wits Agincourt , who assisted in arranging access to the healthcare facilities. We would also like to express our gratitude to the  French government , in particular the  French attaché for Science and Technology in South Africa ,  Jean-Paul Toutain , who under  President Emmanuel Macron's call to 'Make our Planet Great Again ', has supported this initiative post fieldwork.
All individuals born after the 1st of January 1953 had to stay two years longer in school. Previous research has shown that the reform had no effect on earnings  (Grenet, 2013b) , thus any health impact would be net of a permanent income effect. While higher income is one important pathway through which education may impact health, other pathways such as better use of health information and and/or a change in discount rates towards delaying gratification have been posited  (Cutler & Lleras-Muney, 2012). Given the targeted impact of the compulsory schooling "treatment" (affected cohorts very close to the reform time, and those who would have otherwise dropped out of school), large sample sizes are required to identify health effects of the reform. Partially due to this limitation, few datasets have been available to test the impact of reforms on biological markers.
Study population Our primary dataset is the CONSTANCES cohort, a representative sample of French adults 18-69 launched in 2012, with the aim of collecting data on 200,000 individuals over a 5-year period  (Zins et al. , 2010). Participants were randomly selected to take part in the study and invited to a one-day clinical examination in one of the 22 Health Screening Centres run by the National Health Insurance Agency. At baseline, a range of comprehensive health assessments were carried out by health professionals, and participants were asked to complete questionnaires about their health and socioeconomic circumstances. At the time of this study, clinical data was available for 58,029 respondents. As detailed below, we restricted our analysis to participants born at most three years before or after the cut off for eligibility to the reform (1 st of January 1953, N=18,915).
Anthropometry, blood pressure and blood-based biomarkers Participants were invited to a health examination, where anthropometry, blood pressure and serum were collected. Quality control was in place to ensure that high quality physiological data was collected across the different sites, details of which have been previously described  (Ruiz et al. , 2016). Weight, height as well as waist and hip circumference were measured by the clinical team for all respondents. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Blood pressure measurements (systolic and diastolic blood pressure) were taken from each arm after a one-minute rest. Blood samples were taken from all participants, who were instructed to fast 12 hours prior to collection, which was carried out between 8 and 10 am.
Blood sugar levels were assessed by fasting blood glucose and blood lipid levels by total cholesterol, high-density lipoproteins and triglycerides. Liver function was measured by gamma-glumamyltransferase (gamma GT) and alanine transaminase. Serum creatinine was used as an indicator of kidney function. Hematology measures included counts of white blood cells, haemoglobin, haematocrit and platelets. The biomarker data were screened for outliers and unreasonable values removed  (Magnusson Hanson et al. , 2017). In addition, BMI, triglycerides, glucose, creatinine, gamma GT, white blood cell count and platelet values were log-transformed and haematocrit data was squared to decrease skewness. The scores for each biomarker was standardized to allow comparisons across measures.
Constances only records education as the highest qualification held by the respondents. However, the Berthoin reform increased the minimum school leaving age from 14 to 16, but had little on qualification attainment  (Grenet, 2013). To assess the magnitude of the treatment, i. e. the effect of the Berthoin reform on school leaving age, we used data from the French Labor Force Survey (LFS) collected between 2003 and 2012. The French LFS is a household survey representative of the French population living in private households conducted by the French National Statistical Institute, the INSEE. We used data on the age at which respondents left full-time education to document the impact of the reform on school leaving age. We included in our analytical sample respondents born within three years of the reform.
A major strength of our study is the use of a quasi-experimental design in a large population-based cohort. The data also included a large number of anthropometric and blood-based biomarkers for a large sample, which is unusual for quasi-experimental designs. Our biological markers may be more sensitive to subclinical changes in health risk than traditional measures of chronic health conditions or mortality. Our RDD estimates should also be understood as the overall effect of prolonging compulsory schooling, which presumably primarily affected those who would have otherwise left school. Although this is of substantive interest as we show that the policy targeted children from blue collar families, these estimates provide no information about other settings or other policies which also have the potential to raise education attainment  (Deaton, 2010; Heckman & Urzua, 2010).
Procedures Participants were given a 5 minutes' adaptation period, in which participants ran at the determined velocity of 3. 3 m/s. The treadmill was then stopped for 30 s and participants dismounted the treadmill before mounting the treadmill for data recording in accordance with the   1998 ). When participants indicated that they were ready to begin, the treadmill was restarted and the velocity of the belt was gradually increased until the speed reached 2. 2 m/s (BF 8 and C8) for a first recording of 30 s then the speed was again increased to reach 3. 3 m/s (BF 11 and C11) for a second recording of 30 s. Each participant performed a trial shod (C) and a trial barefoot (BF). During both trials the accelerometer placed on the distal anteromedial portion of the tibia collects the data on the 3 axis acceleration measure.
Introduction Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic disease involving central nervous system and immune system disorders, characterised by severe fatigue lasting for at least 6 months that is medically unexplained and not relieved by resting. This puzzling condition is a challenge for physicians and researchers especially since it is approached differently in different European countries making difficult the sharing of experience and the evaluation of proposed therapeutic strategies. The 'European Network on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome' (Euromene) project aims to establish a homogeneous research network to attempt to synchronise databases, develop common standards and strategies, and initiate new research projects, in order to achieve better understanding of the disease, harmonize diagnosis and assessment methods and contribute to the development of effective treatments in the future.