dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and sample We obtained the data of all A-share non-financial Chinese firms listed on shanghai and Shenzhen stock exchanges between 2005 and 2018 from the China Stock Market and Accounting Research (CSMAR) database. Sample period starts from 2005 because the data of all variables were not available for earlier years especially the ultimate ownership data to identify SOEs. Consistent with existing studies (e. g. ,  Hope et al. , 2019) , we exclude financial firms because they belong to highly and differently regulated industries. Firms operating in highly regulated industries are significantly different from unregulated sectors due to the higher scrutiny by the government. Finally, we delete observations with missing data, which leads to a final sample of 27,337 firm-year observations.
Materials and methods We retrieved all research articles published between 1 January 2018 and 25 February 2021 in HTML format from the Nature Sustainability website (https://www. nature. com/natsustain) through full access on 25 February 2021 (N = 300 articles). Our analysis was focused on the three article types that focus on original research: Article (a complex story often involving several techniques or approaches, N = 185), Analysis (a new analysis of existing data or new data obtained in a comparative analysis, N = 98) and Brief communication (a concise study with up to 1,500 words, N = 17). The full text of each article was individually screened and classified in one of three categories depending on its geographical implementation (see S1 Fig) : (i) articles without geographical consideration were categorized as 'Concept, modelling and technology' (N = 47), (ii) articles carried out in one specific country were categorized as 'Local studies' (N = 127), and (iii) articles referring to more than one country were categorized as 'International studies' (N = 126).
In the 'Local study' articles, the focal country was identified by reading the full text. For comparative purposes, we also examined publications in Nature for the same period by retrieving all research articles in HTML format from the Nature website (https://www. nature. com/) through full access (N = 2994 articles). The title, abstract, DOI, authors' affiliations, author contribution statements and funding information were automatically extracted from the HTML files using the 'rvest' R package for both Nature and Nature Sustainability publications. Authors' host countries were retrieved from their affiliations. When several affiliations were present, we considered the first affiliation only. Multiple institutional affiliations are a central concern in publication ethics  [13]. There are both 'legitimate' multiple affiliations (i.
All analyses were based on 'authorship events' (N = 2135) and not on author identity, meaning that the same author publishing several times appears several times in the analysis. For Nature Sustainability LLMIC authors (N = 80), we determined the gender of each author based on their name, completed with Internet searches of authors' profile with a photo. For LLMIC authors in Nature (N = 384), 29 had abbreviations as their first name and were excluded from the analysis. The remaining 355 authors were run through the genderizeR package  [14]. GenderizeR provides a probability associated with the gender determined for a given name. We used only authors whose gender was determined with a probability > 0. 75 (N = 303). Finally, we manually inspected authors' contributions in Nature Sustainability publications to determine their contributions to the different research stages in the CRediT author statement.
Discussion Transnational scientific knowledge that provides equal opportunities for every researcher represents a powerful way of moving towards sustainable development. In view of the UN 2030 Agenda, one would expect that sustainability science journals should act as path leaders in term of ethical partnership. However, there is no hard data in the literature about this assumption. By comparing authorship data between the journals Nature sustainability and Nature, our study intends to fill this gap. Overall, these results show that High-Income Countries (HICs) glaringly shape global sustainability scientific production and discourse in Nature Sustainability, reflecting the North-South inequalities found in other journals  [5, 6]. The causes of these inequalities are well documented; among others, lower tertiary education enrolment, scientific 'brain drain', lower research expenditure per capita, weaker institutional support and fewer funding opportunities  [15].
Data To begin, the rate of real per capita GDP growth is applied as a dependent variable and denoted by GROWTH. The data on GROWTH is extracted from the World Bank national accounts. Foreign direct investment inflows are represented by FDI, and it equals the net inflows of foreign direct investment divided by GDP. The FDI is an investment to acquire a lasting management interest (minimum 10 per cent of voting stock) in an enterprise operating in an economy other than the investor's economy. The gross FDI states the total absolute values of inflows, apart from the values of outflows of foreign investments. As we focus on inflows to the economy, we prefer to use the net inflows, as in  Alfaro et al. (2009). The data for FDI is taken from the World Development Indicator. The data on the origin of FDI is obtained by the OECD's International Direct Investment Statistics Yearbook.
We also employ control variables widely used in the literature as growth rate determinants and briefly described below. Log (initial GDP) refers to value of countries' GDP lagged by four years converted from domestic currencies using constant 2010 U. S. dollars. The data is used to test the convergence hypothesis and attained from the World Development Indicator. Inflation used as an independent variable is measured by the change in the consumer price index. It reflects the annual percentage change in the cost to the average consumer of obtaining a basket of goods and services. The data regarding inflation is from the International Monetary Fund. Openness to trade equals to the ratio of exports plus imports to GDP. The data is obtained from the World Development Indicator. Gross capital form is the ratio of gross capital formation (formerly gross domestic investment) to GDP and equals total investment composed of expenditures on the level of inventories and on the fixed assets of the economy.
The data related to this variable extracted from the World Development Indicator. Gov exp is Government Expenditure which is the ratio of total cash payments of the government's operating activities in providing goods and services to GDP. It also involves employees (such as wages and salaries), interest and subsidies, grants, social benefits, and other expenses such as rent and dividends. The data on government expenditure is also taken from the World Development Indicator. Population growth rate is the annual population growth rate based on the definition, which counts all residents regardless of status and citizenship. Population growth rate is presented by percentage. The data concerning the population is drawn from the World Development Indicator. Landlocked refers to countries which are enclosed completely by land, or their coastal strip lie on closed seas.
I believe, therefore, that the summary indicator is better than the individual variables. Tab. 2 shows the result of the principal component analysis. The first component explains about 81% of the variation of the dependent variable, while the second component accounts for about 17% and the last component corresponds with under 1% of the variation. We, therefore, use the first component as our financial indicator (finance index). Human capital is represented by the school enrollment rate which is the total number of children enrolled in the level of secondary regardless of age divided by the population that officially corresponds to the same level of the age group. The data concerning human capital is taken from the World Development Indicator. The Polity IV dataset is employed as a proxy for political freedom calculated by subtracting the autocracy index from the democracy index.
The Polity Democracy Index takes values from zero to ten, arising from codings of the competitiveness of political participation, the openness and competitiveness of executive recruitment, and constraints on the chief executive. Along the same line, The Polity Autocracy Indices ranges from ten to minus ten and consists of coding of the competitiveness of executive recruitment, the openness of executive recruitment, constraints on the chief executive, regulation of participation, and competitiveness of participation. The data concerning political freedom is obtained from the Integrated Network for Social Conflict Research (INSCR) Database. Finally, rail line is presented by total kilometres length of railways divided by countries total area (square km), and the data are taken from World Development Indicator.
The survey data used derive from 514 observations from a representative sample of decision makers of small and medium-sized enterprises (SMEs) in Austria. Our moderated OLS regression shows that FOMO is positively related to the intention to adopt a new technology and that the relationship is weakened by the prior experience the decision maker has in relation to that new technology. Subgroup analyses identify the boundaries of our results. While the FOMO bias and experience are relevant across all subgroups, the buffering effect of experience for the FOMO bias, applies to the specific subgroups of decision makers who are very open to new technologies, who are well educated (i. e. , who have a university degree) and who work in the sectors of craft and industry. Overall, our findings highlight the empirical relevance of the FOMO bias in technology adoption and of the moderating role of experience.
Data Collection The empirical analysis is based on primary survey data collected from a regionally stratified random sample of Austrian SMEs. Regional stratification is necessary because in Austria institutional settings differ considerably among regions. Our regional stratification followed the approach suggested by  Kibler, Kautonen, and Fink (2014)  and involved collecting data in 27 municipalities randomly sampled in three categories-urban, semi-urban, and rural (Statistik Austria 2011)-across all nine federal states of Austria early in 2019. We identified 185,535 firms in the Aurelia database provided by Bureau van Dijk with valid contact information, with at least one but not more than 249 employees, that were active in industry, craft, service & trade, research, training, and administration.
Among those firms, 151,361 were SMEs and 52,845 were registered in the 27 selected municipalities. We extracted a random sample of 7,286 firms from those 52,845 firms that was weighted for firm size (one-third from each size category: micro, small, and medium). Each sampled firm was contacted to identify an interview partner with decision power on the manufacturing technologies used in the firm. In micro and small firms, that contact was likely to be the founder and/ or CEO, while in medium-sized firms we welcomed members of the top management team as respondents. Data were collected with computeraided telephone interviews. To give the firms a similar chance to participate in the survey, we made three contact attempts with each firm, which resulted in 585 responses. The response rate of 8 % is a little below average but in line with similar studies that randomly addressed decision makers with detailed questionnaires  (Poynton, DeFouw, & Morizio, 2019; Sauermann & Roach, 2013).
Analysis Strategy and Diagnostics We ran a moderated OLS regression using a stepwise approach. In the first step, we entered the control variables in the model. In the second step, we added the three dependent variables, and in the third step, we added the interaction term. In the interpretation of the results, it must be kept in mind that our analysis is based on a cross-sectional dataset, which does not allow for testing causalities but associations. Before performing the final estimations, we examined the model for multicollinearity. The highest variance inflation factor score was 1. 53, which is clearly below the conventional threshold of 10 indicating serious multicollinearity.
Acknowledgements All persons who have made substantial contributions to the work reported in the manuscript (e. g. , technical help, writing and editing assistance, general support), but who do not meet the criteria for authorship, are named in the Acknowledgements and have given us their written permission to be named. If we have not included an Acknowledgements, then that indicates that we have not received substantial contributions from non-authors. This statement is signed by all the authors (a photocopy of this form may be used if there are more than 10 authors):.
5 Agricultural entrepreneurship is quite different from other types of entrepreneurship in terms of business dynamics. In line with the existing literature  (Fareed et al. (2017) ; among others), we differentiate between the two and focus on nonagricultural entrepreneurship. We also use data at the district level such as the Multidimensional Poverty Index (MPI), developed by the United Nations Development Programme (UNDP) based on the Alkire-Foster methodology. This indicator incorporates three dimensions while measuring the district poverty rate: education, health and living standards. A score closer to zero highlights that there are no poor people in that district and a score closer to 100 translates extreme poverty. Multidimensional poverty is higher in rural areas (54. 5%) as compared to urban areas (9.
A 49% of the individuals in our sample are female. A 35% of the total individuals reported having no formal education; this number reaching 43% and 28% for women and men, respectively. The average age of individuals in our sample is about 35. 3 years, and the average household size is 7. 7 individuals. 7 It is important to note that there are also large gaps in terms of access to microfinance across different regions in Pakistan, that is, across different provinces and districts. Pakistan has a total of four provinces, which are further divided into more than 150 districts. We employ the data from Pakistan Microfinance Network (PMN) to provide insight about the outreach of microfinance institutions (MFIs) at the district level. On average, a district in Pakistan has less than two microfinance branches per hundred thousand adults.
20 These results are reported in Column (4) of Table  S7  in the Supporting information. T A B L E 6 Conditional effect of access to microfinance-Results from the linear probability model suitable for entrepreneurial activity. Although our estimates take into account the variation in population densities across different district in Pakistan through district fixed effects, it is also important to control for this at a more granular level. For that purpose, we incorporate settlement size fixed effects in the model. The 'settlement size' variable in the dataset provides much more detailed information pertaining to the total population of the area where the household resides. This variable is categorical and divides the households into different categories with respect to the total population of the area.
Background: As the uptake of health information technologies increased, most healthcare organizations have become producers of big data. A growing number of hospitals are investing in the development of big data analytics (BDA) capabilities. If the promises associated with these capabilities are high, how hospitals create value from it remains unclear. The present study undertakes a scoping review of existing research on BDA use in hospitals to describe the path from BDA capabilities (BDAC) to value and its associated challenges. Methods: This scoping review was conducted following Arksey and O'Malley's 5 stages framework. A systematic search strategy was adopted to identify relevant articles in Scopus and Web of Science. Data charting and extraction were performed following an analytical framework that builds on the resource-based view of the firm to describe the path from BDA capabilities to value in hospitals.
We adopted a systematic search strategy and applied it to two of the largest citation databases: SCO-PUS and Web of Science  [29]. Search terms were chosen to capture a literature that relates to the application of BDA in hospitals. We searched for a combination of two keywords. The first keyword was "hospital" and its derivatives (hospital OR hospitals OR "health org*" OR "healthcare org*" OR "health care org*" OR "health cent*" OR "healthcare cent*" OR "health care cent*" OR "medical cent*" OR "medical inst*") to narrow down the scope of the results to the hospital setting. The second keyword related to the technologies and techniques we were interested in. We settled on two terms: "big data" and "data analytics". These terms were expected to capture most researches on big data and their associated techniques, including the en vogue terms of data mining and machine learning.
Study characteristics Big data analytics emerged in the healthcare research literature in 2008 with the volume of articles exploding in 2014  [9]. With 95% of articles published between 2014 and 2019 our initial search results (1,478 articles) are aligned with this trend. This dynamic is also confirmed in our final dataset with 57 of 94 articles being published in 2018 and 2019. Most articles are experimental (n=69). They are proofs of concepts of different BDA functionalities and techniques to deal with hospital-specific clinical or administrative challenges. They cover both theoretical and practical contributions. Twenty two articles are case studies investigating the realization of BDA capabilities in hospitals, or groups of hospitals, that position themselves as early-adopters of these new technologies  [30].
Value creation process from BDA capabilities Big data analytics capabilities The 5 types of capabilities we classified are associated with the 3 main steps of the BDA process  [31] : data acquisition, data analysis and data interpretation. Data acquisition (n=54) Traceability and interoperability are two types of BDAC associated with this first step of the BDA process. They are respectively mentioned in 43 and 28 articles. Traceability is the ability to track data from different complex data sources and/or HIT components. For instance, BDA techniques can help identify and track surgical complications  [32] , drug-drug interactions  [33]  or opioid use among hospitalized patients  [34]  from different sources to generate new datasets for further analysis. Interoperability is essential to aggregate data from multiple sources and locations and to integrate them into a single data structure  [6].
This capability is essential to support the constitution of databases from different sources  [35]  and the linking of databases from different parts of the HIS  [36, 37]. Data analysis (n=94) Analytical capability and predictive capabilities are both focused on data analysis. Analytical capability is the most frequently mentioned capability in our research (n=58). It is the ability to use descriptive data analytics techniques such as data mining, text mining and statistics to generate new knowledge. For example, natural language processing techniques can help automate the extraction of diseasespecific risk factors from unstructured data  [38]  or explore patients' verbal complaints to identify text patterns that can be further analyzed to be associated with specific conditions  [39]. The use of advanced statistics enables to describe population characteristics  [40] , characterize the features of a condition  [41]  or identify correlation between two dimensions such as staffing level and clinical outcomes  [42].
Most of the measured benefits are operational (n=13) with cost-reduction being monitored in 6 articles  [30, 48, 53, 59, 79, 82] , quality improvements  [47, 79, 95, 96]  and service improvement in 4 articles each  [79, 85, 97, 98]  and productivity gains in 2 articles  [95, 99]. Organizational benefits assessed in 6 articles with focus on changing work patterns (n=4)  [63, 90, 96, 99]  and improving communication and collaboration (n=3)  [79, 86, 96]. Finally, managerial and strategic benefits appeared, respectively, in 6 and 4 articles. The limited focus on managerial and strategic impact is coherent with the nature of the literature reviewed. Most articles in our datasets discuss BDA applications at a micro level, hence setting their focus on micro benefits that translate into operational and organizational perspectives.
VT3 -Performance 36 Care organizational complexity limited incentives to share data  [99]  or a lack of shared standards  [42]. Data quality is mentioned 33 times with veracity of data being in question. Veracity is defined as "uncertainty due to data inconsistency and incompleteness, ambiguities, latency, deception, model approximations"  [100]. Inconsistencies are caused by data collection systems and processes that are different from one site to the other  [54] , inconsistent use of medical terminology  [49] , and variance in coding practices between sites or within teams  [50, 101]. Most datasets are incomplete  [102]  with missing variables  [32, 103, 104]  and values  [105, 106]  or underreporting of certain conditions  [66]. While most BDA methods can be generalized, results often cannot  [44, 56]  because of access and quality issues.
Experiments performed on a single site expose models and results to the influence of the context of experiment and introduce variability and hinder their reproducibility  [99]. Solutions should be tested on multiple datasets to ensure a s imilar level of performance can be achieved at independent sites  [108]  which is a prerequisite to portability, adaptability and economic viability of BDA models  [43, 84]. Finally, the lack of talents, or inability to engage them in data analysis activities restricts the ability to create valuable insights (n=17). Many organizations are facing difficulties to attract data scientists  [30]  and, as a consequence, lack the necessary skills to properly analyze and exploit data  [81]. Developing BDA solutions to generate knowledge requires bringing clinical and technical expertise together.
Despite our efforts to ensure objectivity, accuracy and validity of our research, the latest still presents limitations. From a theoretical perspective, if the RBV is a largely accepted theory in strategic management, its use in healthcare is limited empirically although appearing promising  [118]. The main reproach made by scholars is that publicly-funded organizations are more complex than private ones. However, we believe that even most hospitals are non-for-profit, they fundamentally compete to gain access to resources (patients, activity, healthcare professionals/skills, funding/investments) that determine their position in the system. If imperfect, high-level RBV concept can help understand the determinants of success for hospitals' investments in BDA. From a practical perspective, we limited our search to two databases: SCOPUS and Web of Sciences.