dataesr/scientific-paragraphs-categorization huggingface dataset:
Survey An online survey was distributed in July and August 2016 to participants of the Ipsos GmbH online access panel. In total, data were collected from 15,055 respondents from France, Germany, Italy, Poland, Romania, Spain, Sweden, and the United Kingdom. Quota sampling was used to ensure that the sample was representative for age (between 18 and 65), gender, and regional population distribution in each country. To ensure measurement equivalence, the survey items were professionally translated and back translated between English and each of the target languages. The survey included stated adoption of low-energy houses, measures of time and risk preferences, dwelling characteristics, socio-demographic information, as well as questions related to the relevance of various purchasing criteria, environmental identity, and social norms.
Main findings We tested for the role of individual time and risk preferences in households' adoption of low-energy houses. We used data from large demographically representative samples of households in eight EU countries and elicited time and risk preferences through incentivized multiple price lists as well as qualitative self-assessment scales. Our analysis focused on owner-occupiers of houses built in the 21 st century. We find mixed results for the effects of time discounting on low-energy house adoption. Time discounting appears significant only when using the aggregate measure combining experimental-based measures and a qualitative scale recommended by  Falk et al. (2017). It is however also just shy of marginal significance for the experimental-based measures (p-values of 0. 17), and clearly nonsignificant for the more general qualitative scale.
ACKNOWLEDGEMENTS Some of this research was funded by the  European Union 's  Horizon 2020 Framework Programme  under the project  BRISKEE-Behavioral Response to Investment Risks in Energy Efficiency  (project number  649875 ). We thank participants at the  41 st International Conference of the International Association of Energy Economics  ( IAEE 2018 ) and the 6th World Congress of Environmental and  Resource Economists  ( WCERE 2018 ) for constructive feedback on an earlier version of this work.
We use a unique firm-level data set of a representative sample of stock exchanges covering a 21-year period, combining financial data with detailed data on board composition that enables us to conduct a fine-grained analysis of the interaction among corporate governance, demutualization and exchange performance. We explore the potential moderating role of exchanges' corporate governance strategies and how demutualization affects performance. We focus our study on the boards of directors, examining board attributes such as directors' stakeholder status and expertise because, as the literature suggests, the board is a key factor in the governance structure of large firms (e. g. Fama and Jensen, 1983). We also assess the effect of changes in top management and incentive structures on exchange financial performance.
Sample selection We use a unique firm-level (unbalanced) panel of 31 equity stock exchanges, all members of the World Federation of Exchanges (WFE), covering the period from 1995 to 2016. The 31 equity stock exchanges represent 80 percent of the total market capitalization of the stock exchange members of the WFE. The sample comprises 7 exchanges from the Americas, 10 from Europe, one from Africa and 13 from Asia/Pacific. The appendix A provides a sample overview. Stock exchanges are classified into three categories: not demutualized, demutualized but not listed and demutualized and listed. Exchange financial data were manually collected from annual reports. When data were not available, it was completed with Thomson Reuters database. The main source for macroeconomic data is the World Bank database.
We also used the WFE database for exchange general financial data (i. e. their market capitalization). Corporate governance data were also collected for 16 of the 26 demutualized stock exchanges of our sample, covering the period 1995 to 2016. For the rest of the demutualized stock exchanges, corporate governance data were not available or were incomplete. These 16 demutualized exchanges cover an average of 220 board directors per year. Data were manually collected from exchange annual reports. When the profiles of directors were incomplete, we supplemented our database from Bloomberg and Reuters databases.
All searches were conducted between March and July 2018. Two reviewers independently assessed each study for inclusion and disagreements were adjudicated by a third reviewer. Data are now being extracted using an extraction sheet developed within the group of reviewers, based on an initial sample of three manuscripts, but allowing for inclusion of newly identified data items (ethical arguments). Data will be analysed qualitatively using a thematic analysis technique. Potential biases in selection and interpretation of extracted data are mitigated by the fact that reviewers come from a range of different scientific backgrounds and represent different types of stakeholders in this ethical discussion (academia, industry, patient advocacy groups). Ethics and dissemination The study does not require ethical approval.
Perspective: individual and societal Studies reporting on both the individual and societal perspective will be included. Intervention: secondary AD prevention using predictive modelling Studies will be included if discussing either the predictive modelling method (statistical algorithms) or source data (genetic data, imaging data, cerebrospinal fluid examination, family data, electronic/medical health record, family history, neurocognitive assessment, demographics, etc. ) as a component of secondary AD prevention, aiming to prevent or delay the onset of MCI/AD dementia in presymptomatic individuals at risk of AD. Excluded will be studies discussing secondary prevention but without any component of predictive modelling (neither method nor data source) or which do not discuss secondary prevention of AD (eg, discuss tertiary prevention which is defined as targeting individuals with MCI and later in the disease course).
Electronic databases The literature was retrieved from the following data sources: Embase/Medline, Scopus and PsycINFO chosen collectively by the group of contributors, which included an information specialist. These databases were searched in two rounds, first time on 28 March 2018 and second time from 28 May to 31 May 2018 (coverage from the beginning of time until the search date). The reason for the two rounds of search was identification of additional keywords representing the same clinical phenomenon as well as improvement of syntax strategies which allowed identification of additional manuscripts. The exact search terms used in all databases are described in the online supplementary file 2. The database searches have now been completed. Automatic deduplication was used whenever possible.
data extraction Full texts of manuscripts/documents potentially eligible for inclusion were then retrieved for data extraction. A single, qualitative review is now ongoing using a semistructured extraction sheet developed in a form of an online questionnaire where content (text) extracted by all reviewers uploads in real time into an online spreadsheet for further qualitative data analysis. The semistructured extraction sheet was developed in the following way: First, the following high-level sections of this sheet were predefined based on the research questions of this study and the methodologic requirements: -Other ethical/social issues. ► Reviewer's conclusions and topics/themes to be further explored. ► Assessment of quality and relevance of a given study. Second, each of the high-level section was populated with a specific list of ethical concerns or social implications.
27 In characterising salient ethical arguments the focus is on the claims being made and the arguments supporting them, not on quantitative assessment of the number of times a given claim appears in the literature. Therefore, the frequency will not be treated as a measure of importance. The research questions of this study define the highest level themes. Ethical considerations, as present in the literature, will be inductively aggregated into subthemes which will lead to scrutinising, augmenting and refining the predefined highest level themes. In order to make sure that complex ethical arguments are understood in context, full-text papers will be revisited during the iterative analytic process. Multiple rounds of analysis will be conducted based on extracted data grouped in matrices of hierarchical themes and then discussed, until there is a consensus within the group of collaborators that the analysis is accurate and possibly exhaustive, given the body of evidence.
retail, etc. ), where we would like to use the correlates of a target as predictors to forecast the value of the target for the next time window. Such challenges have been identified by use case scenarios, defined in the scope of the CloudDBAppliance project  (CDBA, 2019) , which aims to provide a database-as-a-service appliance integrating several data management technologies, designed to scale vertically on many-core architectures. These include an operational database, an analytical database, a data lake, and a data streaming engine. To face these requirements, the ParCorr method was implemented as a continuous query for the highly scalable streaming engine and enhanced with regression capabilities to provide for per-window prediction of target values. In this paper, we present the details of this implementation, following a brief overview of the streaming engine and the ParCorr method.
Model Parameters The model can be configured by a number of parameters, which can be roughly divided in two groups: functional parameters and method parameters. Functional parameters are determined by the application requirements. Sliding windows are defined in terms of size of the window (windowSize) and size of a basic window (windowSlide, the step at which a sliding window advances). For example, if we consider a time unit of one minute, a basic window of size 5 and a window of size 60, this means that method execution will be triggered every 5 minutes and will run on time series data for the last 60 minutes. Other functional parameters are the desired correlation threshold minCorr (e. g. , the application needs all pairs with Pearson correlation above 0. 9) and the searchInverse flag that indicates whether highly negative correlations (e.
The  streaming operators. The parallelization of the algorithm is quite straightforwardsketches of time series vectors on parallel data streams are computed in parallel, which is followed by an additional shuffle step that groups together the identifiers of time series that fit in the same grid cell; then groups are explored for discovering frequent pairs. Since the streaming engine operates in a distributed environment, operators have multiple instances, handling different partitions of data in parallel. This requires shuffles of intermediate data across operator instances and the partitioning is based on a key from the schema of the intermediate dataset. In figures, we use the Key=>Value notation to show which fields are used as partition keys.
Regression The Verification operator makes sure that the data processed by it is already partitioned by target time series id, i. e. all the predictors of a particular target, together with their data for the last window, are collocated at the same instance with the target. This avoids additional shuffles of intermediate data and makes it easy for Regression to train a linear regression model for each of the target time series, consuming immediately the output of the Verification operator. So, Regression is stateless and is simply implemented as a class that operates within the Verification operator (R box of Figure  6 ). The implementation uses the Statistics package of the Apache Commons Math library 3.
| The initial concept The initial concept for the Code was a contractual framework between the study funder and the primary lead investigator that would seek to guarantee transparency and scientific independence. However, because of the different languages and legal systems existing across Europe, a standard template for the research contract between a research institution and a study funder was not deemed feasible, and the Code was launched as a set of principles and provisions to be integrated in each study contract. The contract was to be signed before the development of the study protocol. A key requirement was the publication of study results, whether negative or positive, under the responsibility of the primary lead investigator. Transparency was granted by the creation of a publicly accessible electronic register, which later became the European Union (EU) Post-Authorisation Studies (PAS) Register (EU PAS Register), in which the study protocol was to be uploaded before data collection and in which the study report was to be uploaded when the study is finalised.
Source of funding, affiliation, and potential conflicts of interests to be presented. Study report or summary of results to be included in publicly accessible database. ICMJE guideline to be followed. Data ownership and sharing. Rights of ownership of data and results to be included in research contract. Rules for access to raw data, processed data, and results to be specified in protocol and research contract. Verification of published results to be allowed Data transformation steps to be described on request. Analytical data set to be shared based on justification of public health interest and compliance to the Code of Conduct or audit by authority. Open and collaborative approach to be adopted. Data to be shared only after study report finalised. Data sharing to be based on a written request justifying public health interest.
Decision to share data to be taken by study team Analysis of shared data to follow ADVANCE CoC. Protection of human subjects, confidentiality Confidential information to be defined in advance and specified in research contract or separate document. Approval by IRB or IEC to be obtained with exceptions in some countries. Confidentiality to be maintained; personal identifiers to be removed or protected. Protocol to describe data protection and incentives for study subjects. Applicable legislation to be followed. Abbreviations: CoC: Code of Conduct; GVP, EU Good Pharmacovigilance Practices; ICMJE, International Committee of Medical Journal Editors; IRB, institutional review board; IEC, independent ethics committee; PI, principal investigator (GPP); PLI, primary lead investigator (ENCePP CoC).
| Contract research organisation perspective The Code was so far not explicit on whether contract research organisations belonging to ENCePP could participate in a study compliant with the Code beyond protocol finalisation. CROs are usually forprofit private organisations providing scientific expertise in the protocol design, recruitment, monitoring, data management and analysis, study report, and publication of results. The new definition for conflict of interest usefully clarified that the outcome of a specific study being in one direction or another is not intended to be a commercial interest of the CRO or academic institution involved in the research contract.
We are interested in this ongoing debate surrounding zombie firms, not just for its significance to understanding Japan's lost decades, but also for its relevance to other economies concerned about zombie firms' allegedly malefic effects and keen to learn from Japan's experience. However, we believe that the current scholarship on zombie firms in Japan suffers from a key deficiency. Namely, it primarily focuses on stock exchange-listed firms that tend to be large-sized enterprises and overlooks the presence of zombie firms among small and medium-sized enterprises (SME), the corporate category that actually represents the majority of Japanese businesses. In this article, we examine the presence of zombie firms among SME in Japan during the lost decades. Our contribution is twofold. First, we employ firm-level data from the Ministry of Economy, Trade and Industry's Basic Survey of Japanese Business Structure and Activities (BSJBSA) to assess the existence of zombie firms among Japanese SME.
Our use of this database allows us to make a relatively broad inference about the zombie ratio among SME, which is an improvement over earlier studies that examined the ratio with a limited sample size  (Imai, 2016). Second, we explore corporate characteristics of these firms, especially the durability of zombie status, and offer preliminary analysis about these firms' market exit and their employment and sales activity at the time of exit based on our limited data. Our findings about these characteristics add refinement to the debate about zombie firms, since there has hitherto been little attention to important zombie attributes like survival time, with the recent exception of  Nakamura (2017). Consequently, they suggest issues for consideration in future research on this topic. We find that many zombie firms exist among SME during our sample period and that the zombie firm ratio increases as firm size decreases, but also that some zombie SME eventually emerge from zombie status.
These results carry considerable generalizability for the broader population of Japanese SME, given their origin in BSJBSA micro-data covering a wide range of firm sizes and behaviors. They also correspond to small firms' widespread utilization of government support policies like credit guarantees and low corporate tax rates which have been a constant presence throughout postwar Japan, and particularly to the period of the Financial Facilitation Act, which eased credit access for many SME between December 2009 and March 2013  (Goto, 2014). The article is structured in the following way. In the "Literature Review" section, we revisit the debate on zombie firms in Japan and elsewhere to show why SME should be investigated for zombie firms. In the "Data and Method" section, we describe the merits of the quantitative database from which we draw our sample of SME for analysis and outline our article's empirical strategy.
Data and method In our empirical analysis we use the METI Basic Survey of Japanese Business Structure and Activities (BSJBSA). This survey contains data about diversification, globalization, and many other aspects of Japanese firms and is generally used by METI for its own economic policymaking. It covers firms with 50 or more employees and whose paid-up capital or investment are higher than ¥30 million in industries including manufacturing, wholesale and retail trade, and other services. Though the BSJBSA does not include data from microenterprises, a SME category which contains many credit guarantee system users, because the survey encompasses all firms that meet these other size criteria, it addresses a large range of firms and provides a strong advantage in the context of our research.
Our basic empirical strategy consists of (i) identifying zombie firms using BSJBSA micro-data in respective firm-size groups, (ii) investigating the characteristics of these zombie firms, especially their durability in zombie status, and (iii) performing preliminary regression analysis on the relationship among the zombie identifier and other covariates. In step (i), the fundamental task is how to identify zombies. Three measures have been proposed in the literature. First, CHK parsimoniously define zombie firms as companies whose interest payments are lower than the hypothetical risk-free interest payments  (Caballero et al. , 2008 (Caballero et al. , , 1948)). Second, FN add two criteria: profitability and evergreen lending. Profitability is important, FN argue, because firms with earnings before interest and taxes (EBIT) that exceed the firms' hypothetical risk-free interest payments are generally healthy, and such firms' good financial standing may in fact explain why their interest payments are lower than what the prime rate would stipulate.
Identification and comparison of zombie firm ratios by different definitions Lines in Fig. 1  show the zombie firm ratios according to the respective definitions of CHK, FN and Imai, which are calculated as a percentage of all firms in our sample. The end year of the sample period is fiscal year 2014, but the start year for each definition's estimate is different: 2009 for CHK, 2010 for FN, and 2012 for Imai. Short-term and long-term bank borrowing and corporate bond issuance amounts are used to identify zombie firms, but the BSJBSA only begins covering this data from 2009. In addition, the start year is delayed for the FN definition because it considers differences with the borrowing amount in the previous year, and for the Imai definition which uses multiple year earnings (three years in this article) to identify zombie firms.
Next, Fig. 2  depicts the situation in Japan's manufacturing and nonmanufacturing industries. It shows that the manufacturing industry contains a higher zombie firm ratio than the non-manufacturing industry. This figure is based on the Imai definition, yet the same outcome is also observed with the CHK and FN definitions. However, care is needed in interpreting these results, since the BSJBSA widely covers the manufacturing industry but is still in the process of expanding its coverage of the non-manufacturing industry. Additionally, it does not include data for microenterprises, many of which are in the non-manufacturing industry, so there is a high possibility of bias. Therefore, while it is possible to point out that many zombie firms exist in the manufacturing industry, one must be cautious about claiming that the ratio is higher for this industry than for the non-manufacturing industry.
In fact, there is a possibility that firms that were zombies in the previous term have exited this term. However, these firms drop out as a target of regression analysis. Therefore, it should be noted that bias exists in a positive direction when current term zombie status is used as the dependent variable. Analysis on the specific issue of market exit could lead to new knowledge about zombie firms. However, the BSJBSA does not collect information on firms' market exit itself. In the following, we offer preliminary analysis within the scope of BSJBSA, recognizing that fullfledged analysis on exit should be performed with data that explicitly captures this event. We regard firms which do not report their data thereafter as having exited. It should be noted that this identification strategy possibly includes firms that simply quit answering the survey in later years.
Conclusion In this article, we analyze the situation of zombie firms among Japanese SME with data from the BSJBSA. Our analysis covers a much larger sample of SME than previous studies, and addresses multiple characteristics of zombie SME, including firm size, firm age, and durability of zombie status. We also offer preliminary analysis about market exit and corporate activity at the time of exit. Our findings reveal that though there remains some discrepancy over the zombie firm ratio based on different definitional criteria, Japanese SME contained a non-negligible amount of zombie firms during the lost decades. This finding suggests that zombies existed to a significant degree in the past, and at a comparably higher level than large-sized zombie firms estimated in other studies  (Nakamura, 2017).