dataesr/scientific-paragraphs-categorization huggingface dataset:
The spatial units SU_PMSI and SU_INSEE differ, as do the IDs Spatial_Id_PMSI and Spatial_Id_INSEE. The goal of our method is to create a mapping table for the IDs Spatial_Id_PMSI and Spatial_Id_INSEE, in order to build a final database that includes both medical data from the PMSI and ecological data from the INSEE. The PMSI medical database provides information on each hospital stay for each patient, which are aggregated for each Spatial_Id_PMSI spatial unit. In this illustrative example, the healthcare event of interest is an in-hospital birth. This event was detected by screening for (1) hospi-tal admissions from home, (2) a patient age of 7 days or less, (3) admissions from another hospital with a body-weight below 2500 g, and (iv) admissions from another hospital, with a patient age below 30 days.
37 km 2 and a mean population of 11,174. 2. The ecological database was produced by the INSEE [41]. The INSEE acts as France's census office, and collects a vast range of demographic, social, eco-nomic and housing-related data. Most of the data are publicly available on the INSEE website. The data are summarized for various spatial units: the com-mune, the canton, the département and the région (in increasing hierarchical order; see Additional File 2 for details). Most frequently, the data are summarized on the scale of the commune (SU_INSEE), which is characterized by the spatial ID Spatial_Id_INSEE. In 2014, there were 36,594 communes (SU_INSEE) in France. SU_initial versus SU_reference1 SU_initial versus SU_reference2 SU_initial 3. SU_analysis 1. The medical database is the PMSI. Collection of these data has been approved by the French National Data Protection Commission (Commission Nationale de l'Informatique et des Libertés; authorization 1754053).
Possible solutions (and their potential shortcomings) As outlined above, there is no consensus about the most appropriate endpoint for RCTs of antibiotic therapies in severely ill patients. Mortality endpoints are not very sensitive, while clinical cure endpoints are subjective, microbiological cure is not viable, and antibiotic-or ventilation-free days are ambiguous (Table  2 ). It will not be easy to reach consensus, but Delphi processes like the one recently published on endpoints for BSIs  [3]  are a step in the right direction. In this review, the authors concluded that it is unlikely for one endpoint to capture all relevant information and proposed to use composite endpoints. In this article, we will discuss three promising strategies: composite endpoints, which are, for example, used in several ongoing, open-label RCTs evaluating antibiotic therapies, combining mortality and clinical endpoints (NCT02634411, NCT02365493, NCT02575495).
Funding For all authors, the research leading to this manuscript has received support from the Innovative Medicines Initiative Joint Undertaking under Grant agreement No. 115523 [Combatting Bacterial Resistance in Europe projects (COMBACTE)], resources of which are composed of financial contribution from the European Union's 7th Framework Programme (FP7/2007 ± 2013) and the European Federation of Pharmaceutical Industries and Associations (EFPIA) companies' in-kind contribution. MW has also received funding from the German Research Foundation (Deutsche Forschungsgemeinschaft) under Grant No. WO 1746/1-2. DW belongs to an EFPIA member company in the IMI JU and costs related to his part in the research was carried by the respective company as an in-kind contribution under the IMI JU scheme. The funders had no role in data collection and analysis, decision to publish, or preparation of the manuscript.
Methodological notes Three main sets of data are used in the empirical analyses presented in section 3, about financial integration, foreign exchange (FX) markets, and productive-economy related indicators. Data on financialized integration is taken from IMF's International Investment Position. Financial integration refers to the value of liabilities owned to foreigners plus assets held abroad, deducted for reserves. This exclusion was done to differentiate public and private decisions and given the large amount of reserves among EMEs. FX market data are based on the Bank of International Settlement's (BIS) Triennial Surveys of FX Markets. The changes in the structure of FX markets are analyzed by comparing the results from the last five surveys  : 2004, 2007, 2010, 2013 and 2016. Changes from one survey to the other are presented in line graphs for ease of reading, but no data for "missing" years were included.
For most indicators, the latest data available in this database is 2015. These are the ones used to build "2016" indicators with BIS 2016 data. One exception is New Zealand, whose trade data is for 2014. The WB has no data for Taiwan. EMEs are defined as the developing countries that are most integrated into the international financial system. In line with  Chesnais (1997) , who points out that financial globalization is defined by the decisions of portfolio managers, the article proposes a 'financial-markets oriented' operational definition for EMEs. Specifically, it uses the countries who are part of the MSCI index as a benchmark, a broadly used indicator of financial returns in EMEs. As the list of countries in this index changes over time (which is in line with the theoretical definition of EMEs) (4) The use of a month as a representative of the year has an important drawback in financial market data given their fickle nature.
AIC performs relatively well in small samples but is inconsistent and does not improve performance in large samples whilst BIC, in contrast, appears to perform relatively poorly in small samples but is consistent and improves in performance with sample size  (Acquah, 2010). The appropriate lag section is required because F-statistic varies with lag order of the variables. The lag order of the variables is given in the second column of Table  4. The results reported in Table  4  reveal that our computed F-statistics are greater than the upper critical bounds generated by  Narayan (2005) , which are suitable for a small dataset. We find four cointegrating vectors once electricity consumption, economic growth, financial development and urbanisation are used as predicted variables. This validates that there is a long run relationship between electricity consumption, economic growth, financial development and urbanisation in the case of UAE over the period 1975-2011.
Monte-Carlo methods are widely used by the financial industry to price derivatives, estimate risks, or to calibrate/estimate models. They can also be used to handle big data, in machine learning, to perform online optimization, to study the propagation of uncertainty in fluid mechanics or geophysics. Under the same label Monte-Carlo, one actually finds very different techniques and communities that evolve in different directions. The thematic cycle that we organized from october 2015 to July 2016 aimed at confronting the different viewpoints of these communities and at contributing to a general thinking on how these techniques can be used by the financial industry and the economic world in general. It benefited from the financial support of the Louis Bachelier Institute, the Chaire Risques Financiers, the Chaire Finance et Développement durable, the Chaire Économie des nouvelles données, the Chaire Marchés en mutation, the ANR program ISO-TACE ANR-12-MONU-0013 and the Institut Henri Poincaré.
Acknowledgements This proposal reflects the opinion of the signatories and does not engage the organizations they work for. HC, FH, JAH, AM, LM wrote the paper and the co-authors commented on it and approved the final draft. We also acknowledge the valuable contributions of  Katherine Littler  (The  Wellcome Trust, UK) ,  Peter Mills  (the  Nuffield Council on Bioethics, UK) ,  Isidoros Karatzas  ( Ethics and Research Integrity Sector ,  European Commission DG RTD) ,  Maria-Filipa Ferraz-de-Oliveira  (Ethics Sector,  European Research Council Executive Agency)  and  Virginie Bros-Facer (EURORDIS -Rare Diseases Europe)  which do not engage their organizations. Compliance with ethical standards.
The identification of these gaps could help managers update the sustainable strategy of the value chain if needed. What we sought to define here is the ability of the template to synthesize the sustainability issues of a value chain, to position solutions addressing these challenges, and to highlight contributions to both impacts and improvements of each actor in the value chain. The ability to account for these aspects is central in the Sustainable Food Value Chains. The following subsections provide a general overview on the French pork sector and on the pork value chain case. The data were obtained through a literature review and through interviews with experts. The case study follows the design of the template proposed in 2. 2 This analysis was carried out by us, acting as neutral experts.
Remerciements Cet article a été nourri des réflexions du projet Coopigen, animé par  Julie Labatut et financé par le Méta-programme SELGEN de l' INRA  ( 2013INRA ( -2015 )). Sa rédaction a été soutenue par les encouragements constants de  Sandrine Petit ,  Lucienne Strivay et de Marc Mormont.
Data collection and classification of the nutritional status Data were collected between February and March 2013 using a questionnaire. Information letters, consent forms (in duplicates) and questionnaires were handed to each class teacher who put them in each child's school bag along with their daily homework. Parents had to retrieve these documents and read and sign the information letter. Those who agreed to let their children participate in the study also signed the informed consent forms, filled in the first part of the questionnaire and put them in their child's school bag. These forms were collected back by the teacher and handed to our team. A copy of the signed informed consent was returned to parents through the same means. The questionnaire contained personal and social data on the child (date of birth, birth weight, eating habits including early feeding, physical activity during school and leisure time, means of travel to school, sleeping habits, electronic and television use habits, and receipt of pocket money), their mother (age, education level, smoking and alcohol habits, weight and height), and their father (education level).
Statistical analysis Stata version 13 was used for data analysis. Continuous variables (all approximately normally distributed) were summarized by means and standard deviations, and categorical variables were summarized by proportions. Variables were summarized for the whole group, and also separately for HSES and LSES; the latter were compared using t-tests or chi-square tests as appropriate. The prevalence of overweight/obesity was calculated using proportions with 95% confidence intervals (CI); values are given for the whole cohort, boys and girls separately, each age group separately, and each social class group separately. Associations between each variable and overweight/obesity were assessed using random effects logistic models; random effects were used to account for the dependence of children within the same school.
5% in 2008-2011  [13]. Also, as parental data, birth weight and child demographic data were reported by parents, self-reporting bias is another potential limitation. To overcome this, we cross-checked the data with children's school files in case of doubt. Amongst other potential limitations is the use of neighborhood as the main criterion for SES, as children from one neighborhood may go to school in another part of the city of different SES. In addition, the psychometric properties of the questionnaire have not been assessed independently in other studies. Finally, our sample may not be nationally representative so generalizability is currently unproven.
Acknowledgements The authors are grateful to children, parents and directors of schools who accepted to participate in this study. We also thank the members of the  Douala Research Network  for their contribution in improving the proposal. The 2HD Research Group is supported by a  Cruddas Link Fellowship  to SPC ( Tseu Medical Institute, Harris Manchester College, University of Oxford, UK ) and receives technical assistance from the  NIHR-Bristol Biomedical Research Unit (BRU) in Nutrition, University of Bristol, UK.
The curricula will be finished in spring 2018. A further output of the project is a competence and qualification profiling tool. Again, this tool is derived from the competence framework. This tool is comparable to the profiling tool developed for the Cross-industry Competence Framework for Technical Communication  [3]. It will have several target audiences, but primarily: technical communicators, who can use the tool to identify competencies they have, and those they need to acquire; and companies, which will be able to use the tool to express competencies they expect from technical communicators, and from higher education institutions. Finally, the existing TecDocNet Guideline  [5]  will be updated, based on new data and insights gathered from the project. The TecDocNet Guideline describes professional education and training of technical communicators in Europe and was published in 2005.
For example, a multinational corporation would outsource its production to East Asia. Regardless the human rights violation of its supplier, its goods will be sold in the European market. At the same time, transnational social movements campaigning around the world, tracked its global supply chains for human rights violations, once evidence is found, it is exposed to public scrutiny. This scenario transforms human rights into a reputational risk to the multinational organization. Thus, in a global context in which more transnational organizations engage with human rights, greater is the need to 'manage' human rights. Aiming to examine how management literature has been engaging with human rights topics, we conducted a bibliometric research. This technique of data collection and analysis enables to observe knowledge production and dissemination within a certain field of knowledge or other social media outlets  (Vanti, 2002; De Bellis, 2009; Ferreira, 2010).
Database and refinements We have adopted the Thompson Reuters Web of Science Database (WoS), as the universe, due to its scope and recognition in the international scientific community and its use in similar studies  (Albort-Moran & Ribeiro-Soriano, 2016; Ferreira, Santos, Almeida & Reis, 2014; Rey-Martí, Ribeiro-Soriano & Palácio-Marques, 2016). The WoS is an academic platform that provides access to indexed journals, as well as tools to analyze and manage researches carried out on the database  (Bordons & Zulueta, 1999). Such tools include the number of citations of a given article over time and the Journal Citation Report (JCR), a measure of impact and influence of Thomson Scientific, which owns the ISI (Rey-Martí, Ribeiro-Soriano & Palácio-Marques, 2016). Among the existing areas in WoS platform, three subject categories were selected: management; business and; public administration.
Since it is an international database, we decided to search the term "human rights" in the field "topic" (which includes the title, abstract and keywords) in the selected journals. This criterion yielded a sample of Human Rights in the Management Literature 164 710 publications. The third sample refinement criteria were the exclusion of all publications that were not scientific articles, and therefore, not necessarily went through the double blind review process (i. e. editorials, reviews, summaries, etc. ). The result reached 413 scientific articles. In turn, the fourth and final refinement aimed to select the items relevant to the scientific community in the management field. Such delimitation took place through the setting of a minimum value for the JCR impact factor. Therefore, we excluded articles whose journals had impact factor less than 1,000 and those not indexed in JCR.
Conclusion We have analyzed the human rights path within management studies, by using a bibliometric approach, which examined product and impact indicators. We conclude that human rights are already a management topic and that it arrived at the field through the place of ethics. The interest on the theme has recently grown as a background to understand the relation between businesses and society. It is possible to affirm that there was a significant increase in the number of publications adopting a human rights framework over the past decade, indicating that the subject has been consolidated within this field. It is clear that the production peaked in 2009. The bibliometric data analyzed do not offer conditions to explore the social context that led to such phenomenon, but it is reasonable to assume that the 2008 economic crisis influenced the resumption of discussions that underlie ethical issues in management.
Investigating historical explanations for the human rights emergence is necessary, as it would provide the social contexts that enabled such development. Thus, it is clear that a research agenda on human rights should emphasize the relationship between corporations and its impacts on society, even though the concept of human rights can be controversial, it offers an opportunity to examine the corporate impact on human kind. Finally, it would be interesting to expand this analysis towards other databases (Rey-Martí, Ribeiro-Soriano, Palacios-Marués, 2016). Examples would be to expand the sample using journals that are not listed on the WoS database or to analyze conference proceedings to see how the topic is dealt in meetings that usually have a higher acceptance rate.
INTRODUCTION Our world is currently experiencing an information explosion. Many figures are available to quantify the exponential rate of the Big Data phenomenon. For example, it is reported that 90% of the worlds data has been produced in just the last two years, and that the amount of data created by businesses doubles every 1,2 years  (Rot, 2015). Organizations typically view Big Data technologies as holding a lot of potential to improve their performance and create competitive advantage. The ease to collect and store data, combined with the availability of analysing technologies (such as NoSQL Databases, MapReduce, Hadoop) has encouraged many of them to launch Big Data projects. However most organisations are actually still failing to get business value out of their data. A 2013 report surveying 300 companies about Big Data revealed that 55% of Big Data projects dont get completed, and many others fall short of their objectives  (Kelly and Kaskade, 2013 ).
Methods Related to Data-mining Data-mining developed in the 1990's with the aims to extract data patterns in structured information (databases) to discover business factors on a relatively small scale. In contrast, Big Data is also considering unstructured data and operates on a larger scale. However a common point from a process point of view is that both require the close cooperation of data scientists and management in order to be successful. Many methodologies and process models have been developed for data mining and knowledge discovery  (Mariscal et al. , 2010). The seminal approach is KDD (Knowledge Discovery in Database). It was refined into many other approaches (like SEMMA, Two Crows, etc) before being standardised under CRISP-DM (Cross Industry Standard Process for Data Mining)  (Shearer, 2000).
Franchise systems from each of the sampled countries were randomly selected from leading online franchise directories in Australia (www. franchisebusiness. com. au), France (www. franchise-magazine. com), India (www. franchisebusiness. in), South Africa (www. whichfranchise. co. za), and the UK (www. franchisedirect. co. uk). A total of 378 franchise systems were selected across the five countries (91, Australia; 91, France; 58, India; 42, South Africa; 96, the UK). The variations in the final sample size were caused by data on system age and size not always being available, and thus these systems were excluded from the analysis. Furthermore, for both India and South Africa, only a limited number of systems advertise in online directories, perhaps due to limited ubiquity of the internet infrastructure, and thus for these two countries the sample essentially reflects the total population of systems advertising through the selected directories.
Franchisors may be less concerned with conveying their EO to consumers, and therefore it is believed that communications aimed at (potential) franchisees are the most appropriate source by which to explore EO rhetoric. However, to ensure the veracity of the directory entries, and as a test of their reliability, data from the franchise web pages for a subsample (64) of the systems were gathered, and analyzed using the same process of content analysis. This number is in keeping with  McKenny et al. (2016)  who suggest a minimum of a 10% subsample for reliability analysis. Significant correlations were found for each of the EO dimensions between the web pages and the directory entries, suggesting that the directories provide a reliable representation of marketing communications to potential franchisees.
Examples of the dictionary words, along with instances from the data are shown in Table  3. As highlighted earlier, this validated word list has been used in previous studies of EO  (Engelen et al. , 2016; Moss et al. , 2011; Short et al. , 2009; Short et al. , 2010; Zachary et al. , 2011a). The scores for each of the EO rhetoric dimensions represent the number of times the directory entry used a word from the relevant dimension's word list. For the French data, the word lists developed by  Short et al. (2010)  were forward and then back translated to ensure consistency  (Degroot, Dannenburg, & Vanhell, 1994). Thus, the French texts were analyzed using the translated French EO dictionary (and the texts remained in French). While the calculation of the scores for each of the EO rhetoric dimensions was automated through DICTION software, an additional check was made by reading through each of the entries to ensure the semantic context was consistent with the relevant EO dictionary words.
The descriptive statistics for each of the EO dimensions are shown for each of the countries in Table  4. Measuring Culture. Measures of culture were drawn from Hofstede data for each country. The Hofstede country scores of the dimensions uncertainty avoidance and masculinity were noted, and from these data, an ordinal scale was developed, where countries were considered to be high, medium, or low on each of the dimensions (see Table  5  for the categorizations used). Cross-cultural studies have tended to use the country itself as a proxy for culture, rather than direct measures  (Engelen, 2010). Given the limited number of countries in this study, using Hofstede's indices themselves was not considered appropriate, but by using these to develop an ordinal scale of culture across the sample countries for the relevant cultural dimensions, it is believed that this overcomes at least some of the limitations associated with country proxies.
Analysis and Results General Linear Modeling was employed as the method of analysis, using SPSS software. To control for possible age, size, and industry affects, these were included as covariates (with age being measured as the number of years the business had been franchised, size measured by the number of franchised outlets, and sectors as retail or nonretail). Franchise systems in retail and nonretail sectors do present some significant differences in terms of main characteristics and strategies adopted  (Perrigot, 2006). Hence, we control for the industry sectors. Firms of different size and age may also exhibit different organizational and   [Brand] 's wide array of services and profit opportunities allows franchisees to position themselves as a resource and partner with the dealer rather than most competitors who are simply viewed as vendors.