dataesr/scientific-paragraphs-categorization huggingface dataset:
Our research project combines four data sources. The first dataset reports the address of all bank branches in Poland between 2008 and 2014, and we obtained it from an independent consulting company, Inteliace Research. This information allows us to track bank exits from and entries to counties, as well as the number of banks or branches operating in counties of Poland. Thus, this dataset is the basis for constructing all variables describing changes in the local bank presence between 2009 and 2014 2. To reflect local banking market instability better, we hand-collected information on ownership changes among commercial banks between 2009 and 2014 (10 bank ownership changes involving 2. 3 thousand branches in total) and among cooperative banks between 2010 and 2014 (13 bank ownership changes concerning 40 branches in total).
Therefore, we were able to identify the number of banks and branches affected by ownership changes in individual counties. The county-level information is supplemented by a third dataset provided by the Polish Central Statistical Office describing the local economic condition, urbanization, human capital, and other traits of 380 counties 3. The fourth and last dataset was obtained from the Amadeus database and includes firm-level information about Polish SMEs in individual counties between 2008 and 2014. We identified SMEs following the 2 We drop the observations form 2008 as we are able to calculate increases only starting from 2009. 3 It should be noted that in our county-level regressions the number of counties drops to 379 because one county was created only in 2013.
The analysis of the relationship between women's representation in top management and performance is the focus of this study. More concretely, this paper evaluates the impact of gender diversity-in the board and in top management positions-on business performance, measured via economic and financial accounting-based variables. The empirical application considers a unique dataset of Colombian public businesses during 2008-2015. This setting is attractive because, similar to many developed and developing economies, women account for about 43% of the country's labor force; however, they are under-represented at the top of the corporate hierarchy: 12% of top executives and board members are women, while only 4% of top companies have a woman serving as CEO  (ILO, 2015). Also, different from the case of various mostly European countries (e.
Sampling, data collection and respondents' profile The members of the selected sample had a minimum of five years of fulltime work experience and also had attended a training program in IT project management. For data collection, one of the authors contacted 15 training centres that serve as PMI® Registered Education Provider (R. E. P. ) and provide PMP® certification preparatory courses. QuestionPro was used as a platform for collecting online responses. In each of the centres there was a trainer responsible for coordinating and collecting the data. As observed in Table  3 , the age distribution of the respondents is skewed between 25 and 35, and the gender distributed towards males. This type of job is sometimes an impediment for female employees, given their family expectations in the framework of the Indian culture.
Measures To address RQ3, we used a quantitative model. Shenhar et al. (2001)  identify 13 constructs to derive four dimensions to measure project success. The four dimensions are project efficiency, impact on the customer, business success, and preparing for the future. Based on the geographical context, research scope and informal discussions with panellist of the Delphi method, we adapted ten constructs from the 13, with minor modifications. These ten constructs formed the basis of outcome variables. Ten mono-item constructs, identified from Delphi method were considered as independent variables. A questionnaire was constructed to capture data from 431 respondents. They were asked to indicate on a 5-point Likert scale (1 implying poor and 5 implying excellent) how they rated each of these constructs with reference to their workplace experience.
1. 2. Terms of Reference as provided by the requestor The GMO Panel was requested to carry out a scientific risk assessment on the data submitted in the context of the renewal of authorisation application for the placing on the market of products containing, consisting of, or produced from GM LLCotton25, in accordance with Articles 11 and 23 of Regulation (EC) No 1829/2003. Where applicable, any conditions or restrictions which should be imposed on the placing on the market and/or specific conditions or restrictions for use and handling, including post-market monitoring requirements based on the outcome of the risk assessment and, in the case of genetically modified organisms (GMOs) or food and feed containing or consisting of GMOs, conditions for the protection of particular ecosystems/environment and/or geographical areas should be indicated in accordance with Articles 6(5)(e) and 18(5)(e) of Regulation (EC) No 1829/2003.
Systematic search and evaluation of literature 10 In addition to the eight separate literature searches provided as part of the annual PMEM reports, the applicant performed a systematic literature search covering the period from January 2007 till September 2014, in accordance with the recommendations on literature searching outlined in EFSA (2010, 2017a). However, search terms and electronic bibliographic databases were not consistent throughout the searches, as required in the renewal guidance (EFSA GMO Panel, 2015). As requested on 10 April 2018 by the GMO Panel, a new systematic literature search was performed for LLCotton25 and the newly expressed PAT/bar covering a reporting period from 1 January 2007 until 30 April 2018. Searches against electronic bibliographic databases, screening of reference lists and internet searches to specialist databases were performed to identify relevant publications.
Updated bioinformatic data 11 At the time of submission of the renewal dossier, the applicant provided a complete bioinformatic dataset for LLCotton25 event (using the corrected sequence) including an analysis of the insert and flanking sequences, an analysis of the potential similarity to allergens and toxins of the newly expressed protein and of all possible open reading frames (ORFs) within the insert and spanning the junction sites, and an analysis of possible horizontal gene transfer (EFSA, 2017b). The outcome of the updated bioinformatic analyses is presented in Section 3. 3.
In these processes, several state variables were estimated online thanks to sensors developed at the INRA. Moreover, an integrated methodology was proposed for process control coupling models from online sensors, databases and ontologies. In the field of food packaging (Section 3), mathematical models were used to describe the evolution of atmosphere inside the packaging headspace based on packaging and food characteristics. These models are used to study food transformation and to help improve packaging design. Packaging improvements were obtained by coupling models with multicriteria optimization. Qualitative models can be seen as a flexible alternative when there is insufficient information to develop a mathematical model. These kinds of models integrate available scientific knowledge and experts' know-how in process description.
Life-cycle assessment (LCA) was used to quantify the system's global environmental impacts, providing opportunities for system eco-design and innovation. At unit operation level, bacterial quality and energy consumptions were simulated during freeze-drying, allowing eco-reasoned process control. Knowledge formalization in an ontology served to move towards a multicriteria analysis combining process conditions, product quality and environmental impact at both unit-operation and whole-system level. As a synthesis, we propose a summary of the issues, methods and results presented in the paper, before concluding with feedback in the form of a global table. 2. Winemaking fermentation: from online monitoring to modeling and information system for data sharing Alcoholic fermentation, a key step of the winemaking process, is likely to change considerably in the future.
, 2004). MOMAF was licensed to a private company and is currently marketed as a simulation software. The model was then completed with a thermal model  (Colombie et al. , 2007)  evaluating the power required to cool the tank during the winemaking process (as fermentation is a very exothermic process). This model opens up new perspectives for the control of alcoholic fermentation, for example to optimize the fermentation duration, tanks scheduling, energy consumption peaks and total energy required. The next challenge in the modeling of enological fermentation was to consider the kinetics of production of fermentative aromas. Online data were acquired in the gas phase. The first step was therefore to model the gas-liquid ratio (k i ) of these volatiles throughout the fermentation process to calculate their liquid concentrations.
It will therefore be necessary to find a compromise in temperature management to get high aroma concentrations in the final wine after acceptable fermentation durations. Online data were also used to build a winemaking fermentation knowledge management system (ALFIS). The objective was to manage a large amount of data to organize it and extract key knowledge. The information system combined the acquisition of (i) online data from 52 fermenters (36 at lab scale and 16 at pilot scale), (ii) offline data from biological analyses (such as cell number or concentration of extracellular metabolites) and (iii) metadata (expert opinions, operation descriptions, faults, etc. ). These data and metadata were formalized in XML and RDF  (Neveu et al. , 2012). Based on controlled vocabulary, the model provides flexibility and a generic method for managing heterogeneous, multisource data.
Data are available through a Web interface. The database contains the measurements from 7090 fermentations. Ontologies were used to classify the data obtained and improve the organization of these knowledge-sets. For example, a specific ontology of events (faults or enological operations) is used to automatically identify wrong online measurements, which clearly improved the understanding and sharing of quality data. Indeed, by mobilizing a machine-understandable controlled vocabulary (such as sampling or cappunching), this ontology enables to automatically avoid misinterpretation of fermentation data  (Neveu et al. , 2012). Moreover, to integrate data sources ahead of further analysis to answer complex questions that require access to various scientific agrifood data sources, we developed the Agri-Food Experiment Ontology (AFEO), a new ontology resource merging two existing ontology resources, i.
… towards multi-criteria tools Mathematical models for food engineering do allow some technical outputs to be computed but are not sufficient for decisionmaking in an industrial world where choice of a packaging material is a multi-criteria decision. To take into account this aspect, the EcoBioCAP tool was developed to choose the most suitable packaging material for respiring produce from in a dedicated database by answering multi-criteria querying (currently four criteria considered in the first prototype)  (Guillard et al. , 2015). This tool is able to answer the kind of query that reads: "I want a packaging material that will maintain the quality of strawberries (i. e. with the permeability properties that match the respiration of strawberries), at a cost of less than €3 per kg, and if possible transparent and derived from renewable resources".
Flexible querying methodologies employed in knowledge engineering were used to develop these tools  (Buche, Destercke, Guillard, Haemmerlé, & Thomopoulos, 2012; Destercke, Buche, & Guillard, 2011). EcoBioCAP retrieves respiration characteristics from the product database and uses this data plus other user-entered characteristics such as pack geometry to compute the optimal permeabilities for the product. These permeabilities are automatically considered as mandatory preferences associated to selection criteria for the query, to which are added other mandatory or optional preferences that are determined by the user. The flexible querying modulus polls the packaging database to retrieve the material that best satisfies the query preferences, and proposes as output a ranking of these materials.
Environmental issues First, the lactic acid bacteria starters production system was analyzed using life-cycle assessment (LCA). This systemic approach provided a quantification of the global environmental impacts (Fig. 5 ) and served to identify hotspots and compare different existing scenarios. The LCA was performed on the basis of data collected at lab scale and completed with databases. Since freezing, freeze-drying and storage can damage bacterial cells, the impact scores were weighted by bacterial quality, i. e. their final physiological state, to obtain meaningful comparisons  (Pénicaud, Monclus, Perret, Passot, & Fonseca, 2016). Fermentation, freeze-drying, and frozen storage were evidenced as the hotspots of the environmental impact of the system. Furthermore, the system could be revised according to the final product's time in storage, especially with the choice of stabilization technology to limit the storage-phase impact.
A database, called BAGATEL, was built according to the [MS] 2 O vocabulary in order to integrate and store the data and therefore make it available for further querying. [MS] 2 O aims to play a key role as the representation layer of the querying system. The ongoing implementation of a user-friendly BAGATEL-[MS] 2 O interface will enable to compare different micro-organism production scenarios and find improvements by taking into account different criteria, typically product quality, process efficiency and environmental impacts. Furthermore, [MS] 2 O has been re-engineered to make it applicable to other domains, as long as they are devoted to transformation processes, but independently of the product submitted to the transformation. The new PO 2 ontology (Process and Observation Ontology) has been successfully applied to dairy gels  (Ibanescu, Dibie, Dervaux, Guichard, & Raad, 2016).
Another example is that of commercial call centers with a financial transaction during the call conversation. After some time from the start of the call conversation, the customer is asked to do an online payment on a website before coming back to the same agent in order to finish the conversation. The online payment requires the customer to locate her credit card, next she enters the credit card numbers, next she goes through the automated safety check with her bank (using SMS for example), which may take some minutes. As an illustration, we provide real-life data from a vehicle glass repair call center company where the service process consists of three phases and a break in the second phase. Figure  1  gives the empirical probability density functions of the three service phase durations for 5986 calls.
Conceptualization of constructs IT infrastructure capability, business experimentation, business flexibility, operational competence, and firm performance are the key constructs of the proposed research model. IT infrastructure refers to the firm's set of shared technological, managerial, and technical IT resources that provide the basis for using multiple IT applications  (Bharadwaj, 2000; Melville et al. , 2004; Benitez & Walczuch, 2012; Benitez et al. , 2018a). Technological IT resources include servers, computers, laptops, operating systems, software, electronic communication networks (email, Intranet, Extranet, wireless devices), and shared customer databases  (Aral & Weill, 2007; Benitez & Ray, 2012). Managerial IT resources refer to IT managers' (IT and business) skills in identifying and supporting IT-enabled business activities, approving IT innovation projects, deploying a portfolio of resources, searching for new business opportunities, and working effectively with business managers to execute the firm's business strategies  (Benitez et al.
, 2018a). Technical IT resources are IT personnel's (IT and business) skills in designing databases, developing new IT applications, improving the efficiency of IT services, and using different programming languages  (Ray et al. , 2005; Benitez & Ray, 2012). IT infrastructure capability indicates the firm's ability to leverage its IT resources by using multiple IT applications to acquire/provide accurate, timely, reliable, secure, and confidential information from/to key users (managers, employees, suppliers, customers, shareholders, and regulators)  (Bharadwaj, 2000; Mithas et al. , 2011; Benitez & Walczuch, 2012). Drawn from  Melville et al (2004) , IT infrastructure capability is a second-order capability determined by technological, managerial, and technical IT resource infrastructure capabilities.
Sample and data Prior IS research has mainly focused on investigating how US or UK firms leverage IT to create business value  (Benitez et al. , 2018a) ; thus, in an attempt to broaden our understanding of IT business value in the field of IS, we focused our sample on Spanish firms. We tested the proposed theory with survey and secondary data for a sample of 203 large firms in Spain drawn from a list of the 1046 most admired firms in Spain in the 2007 Actualidad Economica database (http://www. actualidadeconomica. com/). Actualidad Economica is the premier business magazine in Spain. It plays a role for Spanish executives/scholars similar to that of Fortune and Forbes in the U. S. Actualidad Economica annually designs, publishes, and sells several databases that compile public information on sales, innovation effort, employer brand value, and executive compensation for the most admired firms in Spain  (Benitez & Walczuch, 2012; Benitez et al.
, 2015). President, Corporate Development Officer). On average, the key informants had about 13 years of managerial experience working in their firm. We also asked the key informants for a self-evaluation of their degree of expertise and competence needed to answer the survey. The item "In general, the degree to which I am qualified to complete the survey is…" (1: Very low, 7: Very high) was included at the end of the questionnaire  (Tanriverdi, 2005). The average value for this item was 5. 390 (S. D. : 1. 040). Overall, the above information suggests that the key informants had a high level of competence to answer the questions included in the survey. We used survey data to measure IT infrastructure capability, business experimentation, business flexibility, and quality management (control variable).
Operational competence, firm performance, firm size (control variable), and industry (control variable) are measured with information collected from the Actualidad Economica and SABI (https://sabi. bvdinfo. com/) databases. SABI is a wellknown database produced by Bureau van Dijk, which provides abundant financial information for Spanish and Portuguese firms  (Benitez et al. , 2018a). Our study thus combines survey and secondary data to measure exogenous and endogenous variables, an approach preferable to using perceptual data only because it minimizes the problem of common method bias  (Ahmad & Schroeder, 2003).
We measured technological IT infrastructure capability through annual investment in technological IT resource infrastructure per employee  (Ray et al. , 2005). We included a single-item question on annual investment in technological IT resources (servers, computers, laptops, operating systems, software, electronic communication networks, and shared customer databases) in the questionnaire. The annual investment in technological IT resources data were divided by average number of employees in 2007-2008 with information collected from the Actualidad Economica database. As technological IT infrastructure is a single-item measure, specifying it as composite or reflective yields identical results. We measured managerial and technical IT infrastructure capabilities by adapting the scales from  Byrd and Davidson (2003)  and  Ray et al.
(2005). These two constructs were specified as composite at the first-order level. Business experimentation was specified as a composite first-order construct, using a scale adapted from  Chandler et al. (2000). Business flexibility is a composite second-order construct determined by operational, structural, and strategic flexibility  (Benitez & Ray, 2012; Benitez et al. , 2018a). We measured operational, structural, and strategic flexibility by creating a scale based on  Volberda (1996). We specified these three constructs as composite at the first-order level. We measured operational competence as a composite construct through operational excellence and gross margin management  (Tatikonda et al. , 2013; Benitez et al. , 2018b). We measured operational excellence by average rate of operational sectoral excellence (RSE) for the period 2007-2011, with information gathered from Actualidad Economica database.
The RSE, an objective measure of firms' sectoral excellence in sales  (Benitez & Walczuch, 2012; Benitez et al. , 2018a) , can be estimated from secondary data contained in any known sales ranking of firms in the following way: RSE = 1 -(Sales ranking position of firm/Total number of firms in the industry). This rate will range from zero to one (termed the industry's maximum value of operational excellence). The closer the RSE is to the industry's maximum value of operational excellence, the better is the firm's operational excellence. We measured gross margin management through the average gross margin for the period 2007-2011 using information collected from the SABI database  (Tatikonda et al. , 2013; Benitez et al. , 2018b). Firm performance was measured through the average firm return on assets for the period 2007-2011, with information also collected from the SABI database, which is consistent with prior literature that uses profitability-based measures to assess business value of IT  (Sabherwal & Jeyaraj, 2015).
This five-year period for collection and estimation smooths out the bias derived from a good or bad year  (Tanriverdi, 2005; Benitez et al. , 2013). This study controlled for the effect of quality management on business experimentation, operational competence, and firm performance  (Molina et al. , 2007)  as the variance observed in the firm outcomes can be associated with the level of quality management practices performed by the company. We also controlled for firm size and industry on operational competence and firm performance. Quality management was measured with a scale of two composite indicators adopted from  Zhu and Sarkis (2004). We computed firm size as the natural logarithm of the average number of employees for the period 2007-2011 with information collected from Actualidad Economica database  (Benitez & Walczuch, 2012).
Our research design included perceptual data collected in 2007-2008 for business experimentation and business flexibility and secondary data collected in 2007-2011 for operational competence and firm performance. The unit of the analysis of the study was the firm-level. This research design used lagged measurements and enabled us to test and achieve some support to our theory-driven thesis on the sequence between exploration and exploitation. However, this research design precludes us to discriminate at the opportunity-level analysis. 4   This research has four contributions to the IS research. First, this study is one of the first to explain theoretically and examine empirically exploration and exploitation of business opportunities in the field of IS. Prior IS research on IT and on exploration and exploitation has mainly focused on the balanced usage of exploration and exploitation of IT resources  (Subramani, 2004; Gregory et al.