dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Turunen and Henninger 2022). Utilizing an unstructured approach, it was possible to follow the emerging phenomena and avoid a predefined focus which would have limited the scope of the findings  (Eskola and Suoranta 1998). Such a technique has been found to be valuable in situations where circumstances are changing rapidly and the unfolding of ensuing developments is hard to predict  (Hirsj€ arvi et al. 2004 ). In ethnography, the aim is to understand the situation as a whole and not as different or separated variables  (Eskola and Suoranta 1998) , and in our case we applied the method to follow the changes triggered by the pandemic on business practices as well as on design and manufacturing. Additional data from Twitter focused especially on issues pertaining to social responsibility in global supply chains and we reviewed newspaper and online articles more generally about the COVID-19 crisis and its effects on Finnish companies.
Literature review The methodology employed in the literature review was a qualitative, non-systematic analysis of papers published between 2012 and 2022. The search was conducted using the Scopus database and relevant combinations of keywords such as "cyber-physical system", "digital interoperability", "Physical Internet", "logistic networks", "real-time information and analytics", "cloud computing", "dynamic resource allocation", "machine learning", and "smart material handling system". The literature review was conducted to identify the most relevant papers on these topics. In order to ensure comprehensive coverage, the review employed snowballing to discover additional sources in the identified papers, leading to a more thorough analysis of the literature. After the content analysis, 52 papers were included in the final analysis.
, 2019) , the ML models can be trained at the node level without having to share sensitive information between nodes (when they belong to different organisations). Therefore, this decentralised approach allows the whole logistics network to build the same ML models while maintaining data privacy and security  (Li et al. , 2020). The global ML model would take inputs from each node, such as the node and network KPIs and performance metrics to decide the routing of shipments between nodes in order to level the capacity of the network. While federated learning can be both collaborative and adversarial, in this context we discuss the former, while the latter is a matter of cyber-security which is valid any time Industry 4. 0 topics are discussed. However, these aspects are not addressed in this paper.
Specifically, we hypothesize and empirically show that entrepreneurs are less inclined to emphasize companion marriage norms, while family entrepreneurs are more traditional and more likely to emphasize companion marriage norms. To test our hypotheses, we utilize the German Pairfam Panel (pairfam), a contemporary panel dataset on spousal and family relationships that is representative of the entire population in Germany. This dataset uniquely allows us to identify entrepreneurs and provides information about the intimate relationships of the population that is not found in other entrepreneurship-related panels (such as the Panel Study of Entrepreneurial Development). Our findings confirm that there is substantial heterogeneity among different types of entrepreneurship with regard to marriage norms.
Sample To test our hypotheses, we leveraged the "Panel Analysis of Intimate Relationships and Family Dynamics" (pairfam) dataset, an annual survey representative of all people living in private households in Germany who were born within one of four birth cohorts  : 1971 : -1973 : , 1981 : -1983 : , 1991 : -1993 : , and 2001 : -2003 : (Huinink et al. , 2011)). Because pairfam contains the responses of randomly selected individuals from all over Germany who were surveyed in multiple waves, it offers a unique opportunity to analyze the development of spousal relationships at different life stages and different occupational roles, including those involved in entrepreneurship. Our analysis draws from the pairfam waves 4 through 10 (between 2011 and 2017) because data related to our research question can be found in these waves.
Empirical strategy We used regression analyses to test our results. Our regressions with the full sample included 14,820 observations of 6397 individuals, which we used to test Hypothesis 1. To test Hypotheses 2 and 3, this sample was reduced to the subsample of entrepreneurs to investigate the impact of different entrepreneurial contexts (nonfamily and family entrepreneurs) on marriage norms. The subsample consisted of 903 observations pertaining to 474 individuals. To analyze our panel dataset, we employed random-effects estimators using robust standard errors to avoid heteroskedastic results and to allow for inferences in case of an unchanged status in the variable of interest  (Huber, 1967; White, 1980). Since family entrepreneurs and co-working spouses are stable conditions for virtually all individuals in the sample, this approach was suitable.
The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons. org/licenses/by/4. 0/. (val1i2) , and "marriage is a lifelong union which should not be broken" (val1i8) in pairfam dataset are averaged for an operationalization of companion marriage 2 Entrepreneur An indicator variable taking the value one if the respondent is currently following an entrepreneurial career as a selfemployed, owner-manager, or as a manager in their family  's business (job2 [d283]  between 10 and 22, and 54, as well as a whole block of job5 or job6 is answered) 3 Family entrepreneur An indicator variable taking the value one if the respondent follows an entrepreneurial career (entrepreneur equals to 1) and when a family member is employed at the same company (at least one of job5i2-job5i3 equals to 1; or at least one of job6i1-job6i3 equals to 1 in conjunction with job5i1 being equal to 1) 4 Co-working spouse An indicator variable taking the value one when in addition to family business setting indicator (see above) being equal to 1, if the respondent employs their spouse in their own business (job6i) or if the spouse is a co-owner of the business (job5i) 5 Male An indicator variable taking the value one if the sex of the respondent is male (sex_gen equals to 1) 6 Age A count variable indicating the respondent age (age) 7 Education (respondent) A set of 10 indicator variables generated from the ordinal variable casmin indicating the educational attainment of the respondent according to CASMIN classification (see  König et al.
Data and Research Methodology The data used in our work is the FDI flow per country and sector 1. The core of our study concerns the effects of the apparent tax rate, calculated as the ratio of observed receipts to observed taxable income. For a strong robustness check, we add the FDI stock as well, as an independent variable. Further, we employ the tax rate differential, computed as the difference from the corporate tax rates' average for all countries, and the effective marginal tax rate, following  Devereux and Griffith (2003)  2. The data sources are the OECD databases Statistics. Moreover, we use development indicators such as R&D, the domestic credit provided by the financial sector, the school enrollment (i. e. , secondary and tertiary), and institutional quality variables, such as rule of law and corruption control.
We build on a unique dataset with both primary data (senior managers' responses) and secondary, time-lagged data. Based on a matched sample of 176 SMEs, our results support our hypotheses that cognitions and values associated with greater tenure and female gender relate positively to knowledge protection. However, education and age are negatively associated with knowledge protection, for which we discuss possible explanations. Our study contributes to the literature on knowledge protection by expanding the view on what managers do  (Bouty, 2000; Jarvenpaa & Majchrzak, 2016)  and the mechanisms that they use (e. g. ,  Norman, 2001; Rouyre & Fernandez, 2019)  toward identifying managers' cognitions and values, as expressed in their demographic characteristics, and associated with effective knowledge protection.
Sample and data collection We tested our hypotheses on a sample of 176 small and medium firms engaged in coopetition. We chose the coopetition context, because competitor firms tend to be extremely cautious about protecting their core knowledge when they collaborate. Further, we chose to focus on SMEs since the role of senior managers leading the coopetitive relationships is of greater relevance in SMEs than in large firms. Finally, knowledge leakages might affect SMEs more severely than large firms with multiple, more diversified business units  (Torrès & Julien, 2005). We collected time-lagged data, using two different sources-survey data collection tool and a secondary database. We used the Swedish Standard Industrial Classification codes (SNI 2007) for the former and randomly selected 900 firms in high-tech and knowledge-intensive industries.
We telephoned each firm, briefly described our project, and inquired whether the firm was involved in coopetition (i. e. , engaged in simultaneous cooperation and competition with another firm). After excluding firms not engaged in coopetition, those which were inaccessible, or which did not want to participate, we sent a link to our online questionnaire (May-Dec. 2015) to the senior managers of the remaining 404 firms actively involved in or responsible for such relationships. After three reminders, the final dataset comprised 225 complete responses, accounting for a 25% response rate. We tested the non-response bias by using the differences between the respondents and the non-respondents  (Armstrong & Overton, 1977)  based on the firm characteristics: size, age, and industry. Since the one-way ANOVA results did not show statistically significant differences, we concluded that non-response bias was not a major concern.
We obtained objective, time-lagged data for the latter in order to measure our dependent variable (firm performance, measured at time t and t + 1) and several controls. This database called Retriever Business contains information on firms, such as their unique organization numbers, number of employees, financial statements, etc. Using each firm's organization number as a unique identifier, we merged the primary and secondary datasets. Twenty-eight firms dropped out due to missing values and twenty-one firms due to their size (>250 employees), leaving us with a final dataset of 176 cases.
Firm performance Following  Rothaermel, Hitt, and Jobe (2006) , we proxied the overall firm performance by using the net annual revenue, averaged over time t and t + 1 (i. e. , 2016 and 2017 respectively) to reduce the potential bias of single year outliers  (Cannella, Park, & Lee, 2008). Further, we used a different source (secondary database) to minimize concerns related to common method bias. We chose this measure, because if a firm is able to protect its core knowledge from being leaked to its competitors, it can maintain its competitive advantage, and would, consequently, gain higher revenues due to its unique, rare, inimitable, and nonsubstitutable products or services in the marketplace  (Barney, 1991).
Control variables We used six covariates, with the first four variables' data being acquired from the Retriever Business, while key informants reported the last two. The first, firm age, is a proxy for a firm's specific resources developed overtime, which will therefore not only impact its ability to protect knowledge, but also its performance. Firm age was measured as the total number of years a firm has been in business since its inception. Second, we included the firm growth as a covariate, because it allowed us to control for the previous growth's effects on the relationships between the demographic variables and the knowledge protection, as well as between the latter and the lagged measure of firm performance. Firm growth was assessed as the employee growth rate over the last two years before we administered the survey.
Data We assembled data from a large-scale administrative database (DADS, 2022) based upon mandatory employers' reports of each employee's gross earnings subject to payroll taxes from 1994 through 2015 in France. From these reports of matched employers-employees, the National Institute of Statistics and Economic Studies of France (INSEE) compiles a panel of all employees born in October in even-numbered years (excluding civil servants). Essentially, every employee is entered into the database when they start working and is followed until they exit the labor market or till the end of the data-collection year. An observation in this initial DADS data includes an identifier corresponding to the employee (i. e. , ID) and an identifier corresponding to the firm (i. e. , SIREN). Each combination of ID-year-SIREN corresponds to an observation in the data.
This means that for every year that an employee works, there is at least one observation. Moreover, every time the employee switches firms, another observation is generated. Thus, for each observation, we have information on the number of days and hours during the calendar year the individual worked in the firm, their gender, date and place of birth, occupation, total net earnings, and the location and industry of the employer. We selected sales employees from the initial dataset using twenty-seven salespeople's and eight sales managers' four-digit occupation codes (PCS Professions et Catégories Socioprofessionnelles, INSEE) within the manufacturing sector that is identified through twenty two-digit industry codes  (NACE rev. 2, 2022) , thereby generating a randomly selected sample of the salespeople's population.
Experience The independent variables (i. e. , sales occupation, firm, and industry experience) are the aggregation of days worked in each sales job, firm, and industry from the date that a worker is first employed (i. e. , the first observation of the individual) until the respective time of the observation. To obtain this measure by year, we divide the number of days by 360 (a full year of work is assigned 360 days in the dataset). Our measure, occupation experience, is the total number of years that an individual accumulated in sales jobs from the beginning of their career until the observation, regardless of the firm or industry. Note that to construct this measure, we treat sales management and salespeople's jobs as separate sales occupations. 3  Likewise, firm experience is the total number of years that an individual worked in the current firm (the focal observation's listed firm) until that observation, regardless of the occupation or industry.
Industry experience is the total number of years an individual worked in the current two-digit industry (the focal observation's industry) until that observation, regardless of the firm or occupation. For example, consider an individual who entered the dataset in 1999 and worked until 2009, when she was a salesperson at the time. The total occupation experience for this salesperson in 2009 is the aggregation of the days she worked in sales jobs over 1999-2009 divided by 360. If the employee had two spells as a salesperson until  2009, namely 1999-2002 and 2005-2009 , her sales occupation experience in 2009 is the sum of the days she worked during those two spells regardless of the firm or industry. This index creates a career history measure for each observation consisting of the occupation, firm, and industry experience.
Using an instrumental-variable method Second, our instrumental-variable method, seldom used in sales research, permits us to go beyond simple individual fixed-effect methods to control for individual performance heterogeneity while also accounting for heterogeneity in firms, industries, and occupations and their match to the abilities and motivation of heterogeneous individuals. Given the exceptional breadth and depth of information available in our data (sales employee careers over 22 years across firms, industries, and occupations), we capture patterns in pay levels at the individual career history level of salespersons and sales managers and show how sales career paths affect their compensation, a topic never investigated in sales research to the best of our knowledge.
Acknowledgements The authors appreciate the comments of Michael Ahearne, Anne Coughlan, Moumita Das, Maria Rouziou and Thomas Steenburgh on a previous version of this article. This work was supported by the EDF Chair at HEC and by a public grant overseen by the French National Research Agency (ANR) as part of the « Investissements d'Avenir » program (ANR-11-IDEX-0003/Labex Ecodec/ANR-11-LABX-0047). In addition, the project received funding from the European Research Council (ERC), advanced grant FIRMNET (project ID 741467). Further funding for administrative costs of accessing the data was provided by Maynooth University. The second and fifth authors warmly acknowledge the Desautels Faculty of Management at McGill University (Canada) for the support and hospitality extended to them during their sabbaticals.
Andrade et al. (2019)  added socio-economic variables to this framework to study the application of agricultural technologies in different countries. At the regional scale, defining zones with similar environmental variables is a first step towards understanding the local factors involved in yield gaps in large datasets with spatial variability  (Beza et al. , 2017; Liu et al. , 2017; Van Wart et al. , 2013). While numerous studies have been carried out on yield gaps in arable crops  (Anderson et al. , 2016; Guilpart et al. , 2017; Pradhan et al. , 2015) , very little is known about grapevine yield gaps, with only some very recent studies carried out in the Barossa and Eden valleys by  Bonada et al. (2022) , in which water-limited grapevine yield gaps were analysed. In contrast to other crops, most European wine production must comply with maximum yield requirements set by the geographical quality labels; this is the target yield for many growers  (Stranieri and Tedeschi, 2019).
Purpose of this study In this study, we used climate and soil indicators linked to grapevine yield gap to determine spatial zones within the winegrowing region of 'Languedoc-Roussillon' (South of France, south-eastern part of the French Occitanie region). We collected 9 years' worth of data from grapevine producers under the Pays d'Oc PGI quality label -who are hence all subject to the same maximum yield requirements established by the label -and aggregated them at the municipality level. We calculated, at municipality level, the soil and climate indicators that influence grapevine yield according to the scientific literature. We then kept only those indicators proving to have a significant effect on the grapevine yield at municipality level. Then, we clustered the zones presenting similar indicators, thus facilitating the identification of environmental resources.
Yield data Grapevine yield data were obtained from harvest customs declaration data provided by producers under the Pays d'Oc PGI in the former Languedoc-Roussillon region (Figure  1A ). This label is the largest wine label in France in terms of cultivated area, wine produced and number of winegrowers, with over 1100 wine cellars. Between 70,000 and 120,000 ha are declared under this label every year, which represents over 50 % of the grapevine cultivated area in Languedoc-Roussillon (Figure  1B ). The label sets a maximum red and white wine production limit at 90 hl•ha -1 •year -1. Although the maximum yield for rosé wine was increased from 90 to 100 hl•ha -1 •year -1 in 2015, we considered 90 hl•ha -1 •year -1 as the generic yield objective and threshold for all years and wine colours. The dataset contains a total of 96,667 yield data for a period of 9 years (from 2010 to 2018), 58 grapevine varieties and 606 municipalities.
Soil data Two soil indicators were considered, as they can have an important effect on grapevine yield: soil available water holding capacity (SAWC, mm) and soil pH (pH, no units). High SAWC can buffer against transient water deficits, which is particularly relevant in Mediterranean areas during dry periods in spring and summer  (Coipel et al. , 2006; van Leeuwen et al. , 2018). Soil pH can affect grapevine yield negatively, particularly at very low pH levels  (Himelrick, 1991; Tagliavini and Rombola, 2001). We used the latest update of the Soil Available Water Capacity (SAWC) cartography in Languedoc-Roussillon  (Styc and    Hugo Fernandez-Mena et al. Lagacherie, 2019) , which provides SAWC values at three maximum rooting depths (60, 100 and 200 cm). Validation by comparing with local SAWC observations revealed a substantial level of uncertainty in this map  (Styc and Lagacherie, 2021).
However, the map displays pedologically sound spatial patterns of predicted SAWC that justify its use for large scale studies, such as the present study  (Styc and Lagacherie, 2021). We used soil pH data from the Languedoc-Roussillon GlobalSoilMap  (Vaysse and Lagacherie, 2015)  at a spatial resolution of 90m, following the GlobalSoilMap specifications  (Arrouays et al. , 2019). The map had been created using legacy-measured soil profiles associated with a set of soil covariates using random forest and by kriging. The data covers four soil layers at depths of 0-5 cm, 5-15 cm, 15-30 cm and 30-60 cm and shows high prediction performance (R 2 = 0. 71 -0. 73). Both datasets (SAWC and pH) are publicly available for download at https://www. openig. org/.
Data aggregation at the municipality level Yield, climate and soil data were aggregated at the municipality level. Average grapevine yield (hl•ha -1 •year -1 ) was calculated as the area-weighted average of yields over all grapevine varieties in each municipality. SAWC map raster was intersected with the municipalities of the region studied and an area weighted average of SAWC was calculated for each municipality. We calculated the municipality soil pH as the average pH of several soil layers at depths of 0-5, 5-15, 15-30 and 30-60 cm. The weather data were aggregated at municipality level using the nearest neighbour method.
This technique of model selection is also used in earth science studies, such as that of  Cremona et al. (2018)    the predictors of the selected model as a function of their contribution to reducing prediction error  (Strobl et al. , 2008). Statistical analyses were performed using the R software (R Core Team. , 2014) with 'nlme' package  (Pinheiro et al. , 2021)  for LMM and 'corrplot' package for correlation matrix calculations  (Wei and Simko, 2017). Datamining, plots and tables were done using the packages 'dplyr'  (Wickham et al. , 2018) , 'sjPlot'  (Lüdecke, 2021) , and 'ggplot2'  (Wickham, 2016).
Spatial clustering and zone assessment Based on the set of selected indicators as described in Section 2. 2, we clustered the municipalities with similar soil and climate, helping us to create groups of municipalities that we refer to as zones. For the clustering, we used a combination of principal components analysis (PCA) and ascendant hierarchical classification (AHC). PCA and AHC on principal components were performed using the 'FactomineR' R package  (Lê et al. , 2008). The number of clusters in the AHC was defined in collaboration with local viticultural experts (grapevine growers and wine label managers of Pays d'Oc PGI) to capture a representative range of the pedoclimatic regional variability. The Ward's method suggested using three clusters, but this number of clusters was too low and did not include some important factors such as pH, CDI or VHD.