dataesr/scientific-paragraphs-categorization huggingface dataset:
Example of the Cereal Sector In cereal processing, the early BVirtual Grain^system  [75]  used a database to gather heterogeneous information concerning the properties of cereal grains and the technical and agronomical routes to their production. The aim was to identify influential factors and potential relationships between morphological, biochemical, histological, mechanical, and technological properties that are usually studied separately. The database is connected to statistical and numerical computing tools, notably a wheat grain mapping tool developed using Matlab. Based on wheat grain properties and information on the distribution of wheat grain components, it proposes a local representation of the properties in each tissue. The Virtual Grain project was devised to explain grain behavior during fractionation and, in the longer term, in a MRE approach, to optimize the fractionation process so as to best meet structural and textural criteria in future food products.
In the overlapping domain of breadmaking technology, BBread Advisor^was a pioneering knowledge software tool for the baking industry  [76, 77]. BBread Advisor,^a tool exclusively based on expert knowledge stored in a database, does not propose experimental data or dynamic prediction, but it does provide three kinds of information: text information on processing methods, a list of possible defects and their causes, and generic messages on the effects of process changes. The cereal sector has built up from these first attempts to develop a mature MRE approach, and is now probably one of the most MRE-advanced food sectors. To illustrate, Thomopoulos et al. [2]  proposed a reverse engineering decision support method guided by the objectives defined for the end-products of the food chain. The Breverse^approach is materialized by Bbackward chaining^logic-based computation and uses argumentation.
) whose parameters are automatically tuned to reproduce the phenomenon represented by a given training dataset. The civil engineering and urban planning sector offers a particularly interesting case, since (a) it also deals with basic needs (housing); (b) it shares common issues regarding multi-criteria, multi-constraints, multi-stakeholders, and uncertainties; (c) some criteria are common (like pollution, energy consumption, cost, and security) while others share a common goal with the food sector, but translated in a different form (sensory/comfort, nutritive quality/quality of use, etc. ); and (d) participatory approaches and consultation are well-developed. Moreover, reverse engineering is also required. Indeed, building design traditionally relies on a forward-path process in which a first design is made by an architect based on expert rules, then progressively refined by engineers, and the final building design is assessed against various different criteria using physical model simulations (mechanics, thermal, acoustics, and so on).
However, finding the Pareto front for a given problem is no trivial task, and the best attainable result often remains an approximation. Multi-objective evolutionary algorithms (MOEAs)  [92]  currently represent the state of the art in multi-objective optimization, with NSGA-II  [93]  the most prominent, being implemented for several programming languages, from MatLab to C++ to Python (see, for example, http://pythonhosted. org/inspyred/ for a Python implementation). MOEAs thus count as a promising family of algorithms for computing MRE. Starting from a set of randomly generated solutions, a MOEA will create new candidate solutions for the problem, randomly changing and merging the existing ones. Ideally, the MOEA will push the solutions far apart in the objective space, in order to explore the compromises as far as possible and eventually approximate the Pareto front.
The methodology consisted of bringing together experts from agronomy, modeling, optimization, and visualization domains in order to interactively explore the fitting of the model on a large tactile display. There are a number of visualization techniques available for multi-dimensional Pareto-optimal fronts  [99] , but the EvoGraphDice approach stands out as it enables domain experts to explore competing objectives and articulates new requirements with regard to the underlying model, the optimization constraints, and the visualization software. More generally, the dynamic confrontation of model simulations, multiobjective optimization, and visualization offers interesting perspectives for exploring, validating, and, ultimately, tuning MRE-implementing models.
1) Are numerical models of the variables to be optimized available? If so, multi-objective optimization could be a candidate method. 2) Are descriptive data of the variables to be optimized available? If so, database querying could be a candidate method. 3) Can the variables be predicted from existing data? If so, machine learning approaches could be candidate methods. 4) Have opinions of the target population and/or other stakeholders been expressed on certain food alternatives and their associated variable values? If so, multi-criteria decision-making and social choice approaches could be candidate methods. 5) Have stakeholders provided explanations for their preferences? If so, argumentation theory could be a candidate method. 3rd step Model analysis. 1) Provide a synthetic presentation of the different outcomes of the model.
Reviewing, refining, and filtering database: The papers identified in the search were then analyzed and evaluated by reviewing abstracts, then reading the full-text of each article. Irrelevant papers were filtered out, based on academic judgment, after being read in full. We also utilized information clustering techniques implemented using CiteSpace software to filter out irrelevant papers. We scanned the references of papers that have been found through the searching process to find relevant papers not identified through the search process. Reference chasing was also performed to find relevant papers not identified through the search process. The reviewing, refining, and filtering of papers not only established a breadth and width coverage, but also captured important elements of the overall picture of SCR literature.
Modeling and solution methodologies Analyses performed in Sections 5. 1-5. 3 allow the modeling approaches considered to be divided into four categories. These categories are as follows: Category I: mathematical and optimization modeling; Category II: structural equations modeling; Category III: Bayesian networks; Category IV: simulation techniques; Category V: multi-criteria decision-making. The solution methodologies are divided into four main categories. Some researchers have tried to solve optimization models using exact methods, which is difficult and limited in terms of largesize problems. Some authors utilized exact solver commercial software like GAMS, CPLEX, Lingo, AnyLogic, and anyLogistix. For large-scale problems, particularly stochastic programming models with large numbers of disruption scenarios, meta-heuristic algorithms have been applied.
China are the second and third most productive in terms of SCR literature. The degree of contribution from other countries can be seen in Figure 2. Notably, AGH University Science & Technology from Poland is the most productive research institute, while MIT and Northwestern University from the U. S. are the second and third most productive research institutes. 3. 1. Geographical distribution of contributing organizations Searching on SCR database An interesting bibliometric analysis is to uncover the geographical distribution of all organizations Category Cluster-ing (research institutes) contributing to SCR literature. We used BibExcel to analyze bibliographical data in SCR research. Figure 2 illustrates the geographical distribution of all contributing organi- Filtering based on Cat-egory clustering zations.
Sample and Procedure We employed the survey method to collect the data, using a self-reported questionnaire completed by the employees of large companies dealing in fast-moving consumer goods. In line with Rupp et al. [23]  and Farooq et al. [15, 49] , we defined employees as those within the lower-management and/or non-management workforce. We focused on this population, and excluded middle and top management, because our interest was in employees as a specific stakeholder group, as opposed to those directly developing and implementing formal CSR policy. After seeking permission from the company, the authors visited the target companies and obtained the data face-to-face from the respondents. The questionnaire was accompanied by a cover letter. To control for social desirability bias, we emphasized anonymity and confidentiality both verbally and in the cover letter  [50].
70). EPDM is defined as the amount of influence that employees have over a company's decisions  [43]  or their opportunities to suggest improvements in the way things are done  [44, 45]. We consider the participation at all levels. High EPDM means that employees are given opportunities to participate in decsions related to their teams, department, and organizations. We translated the instrument into Urdu, the national language of Pakistan, using a forwardbackward translation procedure  [57]. We collected data using a Likert scale, where 1 = extremely disagree and 7 = extremely agree.
Methodologically, we used a convenience sampling method for data collection; thus, the sample may not be truly representative of the population. In addition, data on all the variables were collected from a single source, which may cause common method bias. However, we used multiple methods to test for common method bias, and the results suggest that common method bias was not a serious threat. Finally, the data reported in this study were collected at one point in time, making it impossible to draw inferences of causality  [70].
Scale purification and validation We followed the suggestions of  Bearden, Netemeyer, and Teel, (1989) ,  Churchill (1979), and Henseler, Ringle, and Sarstedt, (2015)  for the scale purification and validation. We used the data from 111 employees in two organizations. Participants were purposively sampled with the help of senior management, who also helped to disseminate the questionnaires. We used iterative loadings, reliability, convergent validity (AVE), and discriminant validity (HTMT) of the constructs (e. g. ,  Dijkstra & Henseler, 2015; Henseler, Hubona, & Ray, 2016). We employed the AOC construct from  Chordiya et al. (2017) , who followed a study conducted by  Mowday, Steers, and Porter (1979). All items were gauged on a five-point Likert scale, ranging from "strongly agree" to "strongly disagree.
Empirical study We used the data from 192 experienced employees of two global strategic partnerships (GSPs), headquartered in Malaysia, involved in service operations and business links/branches in multiple countries (e. g. China, Pakistan, New Zealand, Australia, UK and others European Countries). The lead researcher met the administrative managers of the Malaysian organizations and defined the purpose of the research, and provided 268 copies of the questionnaires as advised. The participants and organizations agreed on cooperation on two conditions. First, there will be no disclosure of persons or organizations, and second, to share the results with them prior to any public dissemination. All data (collected in 2018) were collected based on the experience of employees at the time of the global strategic partnerships, i.
Data Collection Surveys are widely used in the research of organizational behavior and psychology fields because of their advantages, including saving time and labor, easier quantification of results, applicable for large-scale investigations, and easier analysis of results. In this study, we used a survey to collect data from Chinese manufacturing firms. In order to obtain reliable, first-hand data, we obtained help from the science and technology bureau of the local government in Jiangsu Province. The bureau required the top management of each manufacturing firm to complete the survey after an official meeting. Meanwhile, we went to a high-tech development zone in Sichuan Province, as well as in Shandong Province, where we used a one-on-one approach to survey manufacturing firms. We also obtained help from a bank's top management in Liaoning Province to collect data.
Collinearity Tests and Hypothesis Test Results Before we tested the hypotheses, we used SPSS19 and STATA to make heteroscedasticity and residual normality tests. Our test results show that there is no heteroscedasticity and the normal distribution is respected (see Appendix A: Figure  A1 , Tables  A1  and A2 , Figure  A2 ). And then, we explored collinearity issues and potential autocorrelation  [62]. When considering collinearity, we conducted multicollinearity tests  [33, 61] , essentially meaning that all control variables and independent variables were put into the model and the tolerance and variance inflation factor (VIF) of each variable was analyzed. The Durbin-Watson (DW) method was also adopted to test the sample data for residual independence. The analysis results (see Table  4 ) show that the tolerance of all variables ranges from 0.
Research Findings In the existing literature on external search and type of innovation, there is no unanimous conclusion on how-or even whether-external search influences the type of innovation. Furthermore, little literature uses a process innovation perspective to measure innovation capability. Our study collected reliable, first-hand data from 112 Chinese manufacturing firms and used innovation capability as a mediating variable to analyze how the depth and breadth of external search influences the type of innovation. On the basis of our empirical research results, we drew the following conclusions: (1) The depth of external search was significantly and positively correlated with innovation capability and exploitative innovation. Innovation capability played a full intermediary role between external search depth and exploitative innovation; (2) The depth of external search was significantly and positively correlated with innovation capability and exploratory innovation.
Methodology Theorical framework was constructed based on the identification of the attributes of the wine assiduously evaluated in studies of consumer behavior. Subsequently, the degree of relevance of each attribute was classified by Best-Worst Scaling (BWS). A questionnaire adapted from  Forbes (2008)  and  Groot (2011) , applied to 100 consumers living in the city of Porto Alegre, was used as a data collection. When observing studies carried out in other countries, it is noticed that the quantitative approach has been the most used to research the preferences of the consumers in relation to the attributes of a product. Within this approach, the most used methods were the Ranking, the Pairing Method and the Likert scale. However, more recently, the Best-Worst scale has been suggested, since it allows to study a large set of attributes in a quicker and easier way, avoiding the fatigue of the interviewees that can generate inconsistent and distorted results  (Groot, 2011).
Method of data analysis To analyze the data collected by the BW scale, we initially calculated the level of importance of each attribute, which is the number of times respondents indicate each attribute Source: Database analyse (2019)  as the most important (Countbest) less the number of times that attribute was pointed as less important (Countworst). The Countbest and Countworst found for each of the nine attributes of this study are shown in Table  1. From then on the standard score was calculated, which is the level of importance divided by the number of times each attribute appears in the combination multiplied by the number of questionnaires answered, as can be observed in the following equation: Standard score = COUNTbest -COUNTworst 4n After applying the equation, the results are shown in tables in the following section.
Relevance of wine atributes to Porto Alegre consumers Through the Best-Worst scale the degree of relevance of attributes was identified in relation to their influence on the decision to buy a product. The classification of that divides attributes into: a) important ones -those that are considered important at the time of product choice but do not determine the purchase of the product -and b) determinants -those attributes that influence the decision of the consumer. After analyzing the data collected through the BW scale, 5 (five) of the 9 (nine) investigated attributes had a negative relevance level and therefore were classified only as important. They are: Price, brand, award, alcoholic level and attractive front label as shown in Table  2. Four of the nine attributes of the wine were positively evaluated by the consumers survey, and thus were considered the determinants for the purchase: grape variety, wine origin, harmonization with food and counterlabel information.
We carried out five interviews at the divisional office level and 37 interviews at the store department level (cf. Table  1 ). All interviews were transcribed. We conducted separate coding for the WHOL and RET cases and wrote distinct monographs of their efforts and representation. NVivo software was used to perform a content analysis. A cross-case analysis was then performed. Data was collectively exchanged regarding the first analysis and interpretations discussed progressively to develop a consensual and inter-subjective interpretation of the field  (Hietanen, Mattila, Schouten, Sihvonen & Toyoki, 2016). We then compared the analysis of WHOL with that of RET. This allowed us to discuss what happens in terms of business interaction when the institutional work of wholesalers meets the institutional work of retailers.
Fonctionnement de l'outil d'auto-évaluationLe questionnaire initial a été simplifié et restreint aux 30 questions les plus discrimantes en lien avec les 8 variables déterminantes afin d'établir un diagnostic dépérissement. L'outil d'auto-évaluation vient compléter les résultats précédemment acquis en alimentant une base de données commune. L'organisation et le fonctionnement de l'outil sont précisés sur la figure suivante. L'outil d'auto-évaluation permet de distinguer les exploitations viticoles selon trois profils :• exploitations pas ou peu exposées au dépérissement ; • exploitations à risque élevé, partiellement ou non identifié par le viticulteur et nécessitant une analyse plus approfondie ; • situations caractéristiques des exploitations touchées par le dépérissement. Les calculs de classe longévité et rendement et d'odds ratio s'effectuent en temps réel et un rapport d'évaluation est généré et mis à disposition du viticulteur.
4. 1. 1 Fonctionnement de l'outil d'auto-évaluationLe questionnaire initial a été simplifié et restreint aux 30 questions les plus discrimantes en lien avec les 8 variables déterminantes afin d'établir un diagnostic dépérissement. L'outil d'auto-évaluation vient compléter les résultats précédemment acquis en alimentant une base de données commune. L'organisation et le fonctionnement de l'outil sont précisés sur la figure suivante. L'outil d'auto-évaluation permet de distinguer les exploitations viticoles selon trois profils :• exploitations pas ou peu exposées au dépérissement ; • exploitations à risque élevé, partiellement ou non identifié par le viticulteur et nécessitant une analyse plus approfondie ; • situations caractéristiques des exploitations touchées par le dépérissement. Les calculs de classe longévité et rendement et d'odds ratio s'effectuent en temps réel et un rapport d'évaluation est généré et mis à disposition du viticulteur.
The flow of knowledge is closely linked to proximity. While extensive works show that longterm geographic proximity affects work behavior, little is known about the effect of short-term collocation, such as conferences. Using participant data at Gordon Research Conferences, we estimate difference-in-differences and instrumental variable models, which show that attendees who have no prior within-conference collaborations are more likely to collaborate with other attendees, and that the researchers who have worked previously with other attendees are more likely to continue their collaborations. We also find that researchers who are junior, are located closer to the conference venue, and have established prior ties to the conference draw more collaborative benefits from temporary collocation across organizations.
Data and Empirical Strategy To address our three research questions of who is more likely to attend conferences, how temporarily colocating at conferences affects subsequent collaborative behaviors, and how it benefits attendees differently, two sets of data are required: (1) lists of conference participants and (2) publication data on participants' scientific output. We hand-collected and digitized attendance lists for 15 biological 3  GRCs between 1992 and 1995 from the Chemical Heritage Foundation's Beckman Library and manually matched 1,254 unique attendees 4  onto the Authority database  (Torvik et al. , 2009). Table  1  describes each conference, including topic, year held, and location. [Insert Table  1  about here] In the baseline of linking temporary colocation effects at conferences to collaboration, the empirical challenge is to address three endogeneity issues which clouds causal interpretation: (1) the conference committee accepting submissions of more productive researchers, (2) estimating what attendees' collaborative behavior might have been had they not attended the conference, and (3) the decision of researchers to seek to attend a conference.
5 or counterfactual comparison, we matched attendees with a sample of researchers who did not attend the conference using the MedLine bibliometric data. This database covers all researchers whose work appears in Medline, or about nine million researchers, including all of our GRC attendees. We first matched the top three Medical Subject Heading (MeSH) keywords 6  from attendees and non-attendees' five-year prior publications. If the set of potential matched non-attendees by research focus is greater than one, we refined the matching by finding the neighbors nearest to each attendee based on years of experience since first publication, the number of five-year prior publications, collaborations, and forward citations received. Weighing all four dimensions equally, we kept the two nearest neighbors with the shortest Euclidian distance to the attendee being matched and identify for each attendee one to two closest researchers who did not attend the conference with a sample totaling 2,016 individuals.
Variables The data is an individual-year panel with annual observations for five years before and after conference attendance. Table  3  shows the description and summary statistics, including sample size, mean, standard deviation, and minimum and maximum of each variable. Table  A1  in the Appendix respectively show the correlation matrix for the full and conditional on attendance samples. The key dependent variables are all based on publications. [Insert Table  3 about here]  Collaborative ties. We developed two variables to explore the collaborative tie formation of attending a conference. We first counted the overall number of papers on which researchers collaborated with one or multiple coauthors, irrespective of where they met the collaborators (collaborations). We then focused on collaborations formed between conference participants (collaborations between attendees), which has the number of attended-attended collaborations for the attendee group.