dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis The distribution of age, gender and education was assessed in each national sample to gauge its representativeness of respective general populations. Since discrepancies were found between the distribution of those variables and the 2017 census data obtained through the World Bank  17  and Eurostat, 18 weights were applied. The 2017 census data on distribution of sex, education and age variables were obtained through the World Bank and Eurostat. Our weight variable was based on the known distribution of the entire population according to age, sex and education. In our case, the weight variable was poststratified to match the entire population in each country to ensure that calculated estimates were representative of the surveyed European populations. Each participant in the survey dataset was, therefore, assigned a unique weight.
Between September 2017 and December 2017, a control of the quality of the database was undertaken and percentages of missing data for each variable were calculated. Full descriptive analyses were conducted of all variables from the database; specifically the distribution of gender, age and education was assessed in each national sample to address representativeness. A weighted sample, as described above, was built in January 2018. Missing data for the overall study were less than 0. 5% and missing variables were not included in the analysis. Prevalence estimates were described as proportions with 95% Confidence Intervals (CIs). Generalised linear models (GLMs) for clustered (on country) and weighted samples were used to test the significance of the differences between individuals with experience of homelessness and their counterparts on sociodemographic characteristics.
There are also some elements to the survey design that limit our interpretation. For example, it is impossible to build a pattern of cumulative periods, individual long periods spent homeless, or the numbers for whom homelessness represents a recurrent or one-off crisis. Our survey was also not able to investigate how periods of homelessness related to unemployment or other forms of social stress. As a result, we were only able to draw inferences between current working status and past homelessness experience. The correlations between homelessness prevalence and sociodemographics were also limited by two aspects of our dataset. First, our sample size prevented effective GLM analysis of past-year prevalence. For certain sociodemographic data, particularly age, this may induce bias (it is likely that cumulative experience will bias lifetime prevalence toward older age groups).
Second, data on ethnicity, sexual orientation and gender identity were not investigated due to legal restrictions on data collection in some of the participating countries, limiting our ability to investigate these well-explored correlates of homelessness. In addition, living area was limited by being defined by the respondents own perception of where they lived, and as a consequence these data are not standardised. For legal reasons, it was not possible for our study to use geocodes or other methods of locating respondents. Finally our survey targeted European citizens. This means that recent migrants and refugees, two groups that are particularly vulnerable to homelessness, will have been excluded from the survey. This also means we cannot use this survey's data to draw conclusions about the interaction between lifetime prevalence and citizenship or residency status, particularly for those exposed to discrimination.
Acknowledgements The authors would like to thank all interviewers who contributed to data collection and M2Consultant firm for its work monitoring the procedures for the telephone survey. Collaborators Members of the  HOME_EU (Horizon   2020 GA/726997 ) Consortium Study Group:  Maria J. Vargas-Moniz ,  Maria F Jorge-Monteiro , ( APPsyCI -Applied Psychology Research Center Capabilities and Inclusion, ISPA-Instituto Universitário ,  Rua Jardim do Tabaco ,  34 ,  1149-041 Lisboa , Portugal);  Ronni M. Greenwood ,  Rachel M. Manning ,  Branagh O'Shaughnessy  ( Department of Psychology, University of Limerick, Limerick ,  V94 T9PX , Ireland);  Inês Almas ,  Teresa Duarte  ( AEIPS-The Association for Study and Psychosocial Integration ;  Housing First  project:  Casas Primeiro ,  Av. António José de Almeida ,  26 ,  1000-043 Lisboa , Portugal);  Francesca Disperati ,  Marta Gaboardi ,  Michela Lenzi ,  Massimo Santinello ,  Alessio Vieno  ( Department of Developmental and Social Psychology, University of Padova, Via Venezia, 8 -35131 Padova, Italy );  Rita P.
Acoustic Pharyngometry Pharyngometry data was collected on the day of the sleep study using the EccoVision Acoustic Pharyngometer (E. Benson Hood Laboratories, Pembroke, MA). Each measurement consisted of a plot of cross-sectional area (square centimeters: Y-axis) as a function of distance (centimeters: Xaxis) from the incisors. For each subject, measurements were obtained using oral breathing with a pediatric mouthpiece at functional residual capacity. At least four curves were obtained, and the data was considered valid if the cross-sectional area differed by 10% or less from one another. X-and Y-values were recorded for the oropharyngeal junction and glottis. The volumes of the oral cavity (between the incisors and oropharyngeal junction) and the pharynx (between the oropharyngeal junction and glottis) were calculated with the mean pharyngeal area.
Subsequent intergroup comparisons were performed using the Mann-Whitney U-test. Categorical variables were compared using the chi-square test. Correlations were evaluated using Pearson's correlation coefficient. Additional statistical analyses are described in the text. A P value < 0. 05 was deemed significant. No correction for multiple testing was done due to the pathophysiological design of the study  (20). All statistical analyses were performed with Statview 5. 0 software (SAS Institute, Cary, North Carolina, USA). We enrolled 103 consecutive children for this study and evaluated which parameters obtained from acoustic measurements correlated with height or age to determine which parameters needed to be corrected for body growth. We observed a 100% (103/103) success rate for sitting/supine pharyngometry and a 91% (94/103) success rate for sitting/supine rhinometry measurements.
The reproducibility of acoustic diagnostic methods have already been demonstrated (4-7). However, the idea of normalizing data of acoustic measurements for height, age or even gender for children and adolescents have not been addressed. We showed that pharyngeal distances were related to the height of the children, which was expected. Surprisingly, measured cross-sectional areas and pharyngeal volume were found to be unrelated to the height of the children. Instead, 30-40% of the total variance in the minimum cross-section area measurements of the pharynx can be explained by shared familial factors  (21) , which might explain the heritability of OSA. The measurements obtained from acoustic rhinometry were more closely linked to child growth. The MCA1 area was related to height as previously shown  (13).
In details, these in-depth interviews were all structured using a three parts interview guide (coastal risk perception, implication for oneself and means of action) and a descendant hierarchical classification (DHC) analysis was performed on the corpus with the help of the Iramuteq software in order to identify the main elements of the representation of the coastal risks. 20 elements were identified as central in the case of the coastal flooding risk, and 15 elements in the case of erosion. 13 elements were common between both questionnaires; additional elements are about specific consequences or means of protection related to both risks. For each item, participants were asked to indicate on a 5-point scale, whether they considered the item to be characteristic or not of the risk (from 1, not at all characteristic to 5, completely characteristic).
During the meeting, the nurse will be asked to collect their urine samples after several days of work. Therefore, the self-questionnaire plans to collect data on work history the previous 7 days before urine samples collection (type of studied AD handling, accidental exposure event). For each nurse, the study participation lasts 24 hours. Three urine samples will be collected at different times in less than 24 hours (figure  1 ): the first one within the 3 hours before the start of the work to document an  internal contamination following exposure the previous days before the study; the second within 2 hours following the end of the work, to document an internal contamination following exposure during the first hours of the day working day; the third between 7 and 10 hours after the end of the work, to document an internal contamination following exposure at the end of the work.
The time of the third sampling was chosen to take into account a delayed absorption by the cutaneous way as indicated by Hirst et al. 59  A document gathering the date and times of urine samples will be attached to the samples. Urine samples will be sent to the pharmacology and toxicology laboratory of Bordeaux university hospital within 72 hours at +4°C. Then samples will be aliquoted and stored at -20°C until analysis. At the same time, nurses will complete a self-questionnaire concerning their professional activity throughout the AD handling day. The self-questionnaire is a paper document with a detachable flap. This part will be sent by mail (return postage paid envelopes) to the coordinating centre, which will monitor the completed data and the other part will be kept by the nurse. After urine sample reception by the laboratory, the latter will immediately informs the coordinating centre of this reception.
Open access Step 5: urine assays For each urine sample, four extraction methods followed by a validated analytical method will be performed. Moreover, urine creatinine will be analysed for each urine sample to account for dilution. 60 61 The result will be expressed according to the AD concentration level (ng/L and ng/g of urinary creatinine). Participant will be considered as contaminated when at least one of the five studied AD, is detected in at least one of the three collected urine samples. Step 6: data analysis Statistical analysis will be performed using SAS software (SAS Institute, V. 9. 3) by a statistician from the coordinating centre. The rate of internal contamination will be calculated by reporting the number of contaminated subjects by at least one of the studied AD to the total number of subjects included and will be expressed as a percentage.
This paper is structured as follows. We first review the literature around the transition concept, the modes of governance at the inter-organizational level that support knowledge learning and the differences and complementarities between inter-project learning and ER. We then explain how this study was conducted and how data was collected and analyzed. We illustrate our findings through examples gathered from 30 different collaborative projects, highlighting three different dimensions (structural, conjunctural and interstitial) and the systemic effect these dimensions have on each other. Finally, we discuss our results, pointing out our research limitations as well as future research perspectives. We explain how we contribute to temporary organization theory by providing dimensions that enable organizations engaged in collaborative projects to access ER, thus facilitating knowledge reuse in new projects.
Project outcomes Project continuation (or not), major factors facilitating or hampering this transition, configuration of the new project (partners) Presents the elements that favored or hampered the transition as well as the form of this transition (data not used in this article). The third part of the interview specifically aimed to identify how the project led to the establishment of a second collaborative R&D project (or not). Through these interviews, we gathered the description of 30 projects (Appendix A). Among these, 17 experienced a transition process (as defined in our study), while the remaining 13 were closed and did not lead to a new collaborative R&D project. In addition to the 67 in-depth semi-structured interviews we conducted (Table  4 ), we attended five workshops, organized by cluster 4, as non-participatory observers.
Compared with the table highlighting the number of sources that evoke this interaction, similarities in the data appear and thus have no influence on the interpretation of the tables by the researcher. In addition, the use of the 'characteristics' function for the analyzed sources was necessary. Interviews were assigned to a 'transition' characteristic (when the interviewee presented the follow-up of the project) or to 'no transition' when the interlocutor indicated that no follow-up had occurred. When assigning these characteristics within NVivo, the software is then able to show in the matrix all characteristics at once, or each of them separately.
Concretely, ER manifest (1) through modules that are drawn from a first TO and reused in a second, or (2) through artefacts and socialization mechanisms (e. g. , valorization workshops) developed at the cluster level. Examples of such artefacts include the mapping of rare knowhow offered by the textile cluster, while the energy cluster developed a database of 'orphan patents' (patents that are not exploited) that could be used to identify new projects based on the technologies they protect by combining them with results from recent projects. This is in line with  Grabher's (2002a)  explanation that the upper level (i. e. clusters) "provide cognitive preconditions for converting latent pools into productive resources for collaboration by uncovering complementarities". Concerning the reutilization of module, this may imply that the first TO has developed a functional prototype which will then be tested under conditions as yet not fully developed.
Design and sample We used data from the International Tobacco Control (ITC) surveys in three countries: France, Germany and the Netherlands  7, 8. Each of the ITC surveys used a longitudinal cohort design among nationally representative samples of smokers. We used two consecutive survey waves from each country: France 2008 and 2012; Germany 2009 and 2011; and the Netherlands 2009 and 2010. More detailed information about each country's methodology can be found elsewhere  [7] [8] [9] [10]. For the current study, we included respondents who participated in both survey waves and who reported being a smoker in the first survey wave; smokers are defined as those respondents who had smoked at least 100 cigarettes in their lifetime and were currently smoking at least monthly. We pooled the data from the three countries.
At termination, the set Z * consists of the set of non-dominated portfolios Z N D. The pseudocode is presented in Algorithm 1. It has been coded in C++ programming language and linked to GeNIe Modeler, a development environment for reasoning in graphical probabilistic models. Initialization: z = [0,. , 0]; m ← 1; Z * ← ∅; if z ∈ Z F then Z * ← z; end while m > 0 do Forward-loop: while m ≤ M do z m ← 1; if z ∈ Z F and z * z ∀z * ∈ Z * then Z * ← z ∪ {z * ∈ Z * |z z * }; end if m j=1 z j H j + M j=m+1 min{0, H j } > b for any = 1,. , L then Break Forward-loop; end m ← m + 1; end Backtrack step: z M ← 0; m ← max [{j|z j = 1} ∪ {0}]; if m > 0 then z m ← 0; m ← m + 1; end end Z N D ← Z * ; Algorithm 1: The implicit enumeration algorithm for multi-objective optimization.
This paper analyzes the design and implementation of projects and initiatives targeting older people, and discusses a survey of older people examining feelings of loneliness and/or uselessness. We will begin with a brief overview of theories on successful aging, to show how the question of social exclusion eventually came to the forefront. We will then examine the evolution of this notion in French old-age policies. Finally, we will present the findings of the VIEU project, offering avenues of reflection to consider social inclusion from a new perspective. In the 1970s policies were developed including the increase of the minimum income for older people, financial support against housing exclusion and the development of home care services and information services to break down the isolation of older people.
Methodology, the VIEU Project The objective of the VIEU project was first to document the ways in which some major national stakeholders engaged with the issue of social integration, and secondly to survey older people on their experiences of loneliness. The study had an inductive qualitative design  1. https://www. monalisa-asso. fr/monalisa/mobilisation 2. https://www. legifrance. gouv. fr/eli/loi/2015/12/28/AFSX1404296L/jo and can be characterized as grounded theory  [12]. Two main sources of data were used: institutional documents from promoters, stakeholders and think tanks and interviews transcripts. We carried a thematic analysis on the documents and a grounded theory for the interviews. It implies first to code each interviews, then to create grounded categories and to cross the categories of each interview.
18, 19  consensus-based list of drugs of questionable clinical benefit for older adults nearing the end of life was recently published,  20  which provides an opportunity to broaden the scope of previous studies by examining drug utilization patterns across diseases and care settings. Also, these criteria make a clear distinction between the continuation of previously prescribed drugs and the initiation of new drugs during the final months of life, which pose different challenges in clinical practice. 21 In this study, taking advantage of real-world longitudinal data with national coverage in Sweden, we aimed to describe the use of drugs of questionable clinical benefit during the last 3 months of life of older adults who died from life-limiting conditions.
Study design and population This was a cohort study of decedents based on routinely collected administrative and healthcare data in Sweden. Older adults (>75 years) who died between 1 January and 31 December 2015 were identified in the Swedish National Cause of Death register (N = 64,715). Individuals were included in the cohort if at least one condition potentially amenable to palliative care was listed as the underlying or contributing cause of death (Supplementary Table  1 ). We also excluded decedents who were missing an exact date of death, those whose cause of death was either unknown (International Statistical Classification of Diseases, 10th Revision (ICD-10) code R99) or not reported, as well as individuals for whom information regarding drug prescribing was unavailable (~2. 5% of all decedents).
Outcome measurement The main outcome was twofold and included both the continuation and the initiation by the prescribers of drugs of questionable clinical benefit during the last 3 months before death (Figure  1 ). These drugs were defined according to a previously published set of consensus-based criteria, which were developed through a Delphi process that involved 40 experts in geriatrics, clinical pharmacology and palliative medicine from 10 different European countries. 20 Although this list also encompasses drugs deemed 'questionable' for use among older adults with a remaining life expectancy of <3 months, we restricted our analysis to drugs considered as 'often inadequate' in order to increase specificity. We determined drug utilization patterns using dispensing data from the Swedish Prescribed Drug Register, which has been described elsewhere.
Decedent characteristics We ascertained the sociodemographic characteristics of decedents through deterministic patient-level record linkage with data from the Swedish Total Population Register (sex, date of birth, marital status) and the Swedish Register of Education (highest educational attainment). Living arrangement 3 months before death was defined as either 'community-dwelling' or 'nursing home resident' based on information retrieved from the National Social Services Register, and the drug dispensing scheme was either 'ordinary prescriptions' (i. e. drugs dispensed manually) or 'multidose' (i. e. drugs dispensed in machinepacked pouches). Decedents were assigned to one of three distinct illness trajectories according to their cause of death ('cancer', 'organ failure' and 'prolonged dwindling'), using an algorithm published elsewhere.
e. between 365 and 92 days before death). Individuals who were potentially exposed to drugs of questionable clinical benefit during the last 3 months of life but did not refill their prescription were considered as having discontinued their treatment. end of life. This modelling strategy was chosen over logistic regression analysis because the latter tends to generate odds ratios that overestimate the underlying risk ratios (RR) in cohort studies, which can lead to misinterpretations of the findings when the outcome is common. 29 R are reported with 95% confidence intervals (CI). Individuals with missing data for education (n = 1302, 2. 2%) were excluded from the models. Analyses were performed with JMP version 14. 1 (SAS Institute) and Stata version 14. 1 (StataCorp).
Explicit criteria are useful to describe prescribing patterns in large populations, but the actual clinical benefit of therapy can only be determined by the prescribing physician caring for a specific individual patient. 66 Other tools such as STOPPFrail 67 would offer a more precise assessment of potentially inadequate drugs, but their use in epidemiological studies requires extensive information about the clinical indication of drugs that are typically not available in routinely collected data. It should also be noted that the Swedish Prescribed Drug Register only contains data about prescription drugs dispensed through community pharmacies; over-the-counter drugs and treatments administered in hospitals or from nursing home drug storeroom are not included (~10% of all defined daily doses dispensed in Sweden annually).
Since the accidents, extensive research on these sites has been performed and databases have been set up for future use. A series if comprehensive reviews of the observed (phenotypic) effects in wildlife in CEZ and Fukushima exclusion zone have recently been published e. g. by  Hinton et al. (2007 ), Geras'kin et al. (2008) ,  Lourenco et al. (2016) ,  Steinhauser et al. (2014) ,  Strand et al. (2014) ;  Batlle (2016)  and  Beresford et al. (2016). In addition, there is considerable "grey" literature and difficult to access Russian literature that still could be investigated and integrated into the databases, further elaborating the knowledge of these sites. It is useful to further work on these two nuclear accident sites as they are unique study areas that will help to understand the long-term impact of ionizing radiation on the environment and gather data that are necessary for modelling.
Another advantage is that "citizen science" i. e. natural history records and species databases such as Butterflies and Moths of North America (https://www. butterfliesandmoths. org/) can be used to establish biodiversity richness before a planned release or in a similar habitat should an accidental release occur. b. Gross measures of ecosystem condition such as transpiration rates, structure of the ecosystem, and the use of drone technology or sophisticated photographic interpretation to detect ecosystem level perturbations (e. g. Li et al. , 2014; Zolfagher et al. , 2017). c. Useful methods that could be developed include approaches involving network analysis and the exploration of nodes and connectivity related to resilience. There is a suggestion that stress, including radiation stress, reduces network complexity (Troncoso-Ponce and Mas, 2012;  Nikitaki et al.