dataesr/scientific-paragraphs-categorization huggingface dataset:
Liber Quarterly Volume 23 Issue 2 2013 a digital book to the public,[. ]must be brought to the attention of the persons to whom the offer is intended, in an unambiguous, visible and readable. [. ]. In the case of a collective use of the offer, the price is set according to the scale set by the publisher. "10These elements appear in a database (managed by the publisher himself) that "describes each offer and mentions the prices or rates associated with it. ".
MEMPHIS main objective is to overcome the conversion issues between the CAD and the VR systems by managing the meta data of a product and other VR related property data through Meta Data Server. The VR data, which is needed to present 3D models of future products in real time and high quality, can be produced directly from PDM systems. Thus, it reduces the repeated correction integration process between the PLM data to VR data. ManuVAR is a project that develops a prototype combining PLM, virtual reality (VR) and augmented reality (AR) Nantes, France, 6th -10th July 2013 7 and human factors (HF) methods  [13]. It intends to integrate PLM and VR in a single environment. All these studies expect a structured method to enable communication between PDM and VR. They emphasize the difficulty to manage this interoperability issue.
This network continues to expand through increased interaction between the different information it offers. The contribution of a multitude of individuals on a common topic (  9 ) is richer than one person. It is in this perspective that collaborative encyclopedias such as Wikipedia have been created. -The power of data: thanks to the Internet, some companies like Amazon, Facebook and Google have constituted substantial databases. -The end of a release cycle: the user is no longer a passive consumer as it has long been but a full participant. He is likely to become a true codeveloper and innovator. -Lightweight programming models: it is to take what already exists and build innovation upon it. It is useless to create complicated systems. We must create content that are easily accessible and available.
Such leverage improves their connection to the brand and enriches the image of the brand. This also means that when a product or service is disliked, this could help the company improve its offer. These platforms go beyond by creating a true community of actors that are directly involved in the life of the company. Thus companies can create an easier contact with individuals who were perhaps not previously customers of the brand. This is a new way to recruit through a more participatory approach. Finally, such an approach can be in close interaction with customers, consumers and therefore have access to a database constantly updated on their expectation and their perception of the company.
A. Crowdsourcing : Costs Reduction and the choice of prices by the Crowd The current financial and economic crisis has pushed companies to rethink their strategy and business model in a perpetual search for cost optimization, resources and manpower. This bleak picture is forcing companies to innovate and remain competitive. This is why some of the companies shift towards crowdsourcing. Even though companies are shifting toward crowdsourcing for its innovation benefits, they still haven't emphasized its role in the pricing of products or services. Despite the variation of the prices from few dollars to one million dollars depending on the nature of the outsourced task, the fact remains that crowdsourcing allows for substantial savings. The database acquired when internet users are solving problems or to giving ideas will provide the companies with high quality profiles.
We argue that engagement by employees and the firm with external organizations and the firm's commitment to improving the natural environment through the adoption of proactive environmental practices lead to higher organizational performance. We discuss the ways in which engaged organizations can facilitate knowledge sharing and learning, employee motivation and commitment to the organization, and demonstrate increased labor productivity. We test our hypotheses with data obtained from French surveys that detail employee characteristics from 5, 210 respondents for 1,988 firms. Our results show that higher levels of labor productivity are specifically associated with greater financial participation, interpersonal contact, inter-firm partnerships and proactive environmental management practices.
The results are usually available 15 months after the end of the reporting year. In order to obtain information about the adoption of environmental proactive practices, we merged the COI data with The Community Innovation Survey (CIS). The CIS survey was administered by the French Institute for Statistics and Economic Studies over the period 2006-2008; the survey is based on the OECD Oslo Manual. Firms answered questions regarding innovations they had introduced within the previous three years. The questionnaire was sent to 25,000 firms and brought another very high response rate at 81%. The CIS survey is mandatory for firms with more with 250 or more employees, bringing more important representation of firms with more than 250 employees. As a result of the data merges, our sample includes 5, 210 observations for 1, 988 firms.
hypotheses, we used data from the French Organizational Changes and Computerization's (COI) 2006 survey. 2 The COI survey is a matched employer-employee dataset on organizational change and computerization from the National Institute for Statistics and Economic Studies (INSEE) and the Ministry of Labor, and the Center for Labor Studies (CEE). The survey benefited from a high response rate of 86% and contains 7,700 firms that are representative of the population of French firms from all industries except agriculture, forestry and fishing. Each firm fills in a self-administered questionnaire about information technologies and work organizational practices in 2006, and changes that have occurred in those areas since 2003, as well as the goals driving decisions to implement organizational changes and the economic context in which those decisions were made.
Within each surveyed firm, employees were randomly selected and asked about their personal socio-economic characteristics, as well as information about their job and position within the organization. The original dataset includes 14,369 employees. In order to obtain information on employee value-added activities, production sold and employee profit sharing, the COI survey results were merged with two other databases: the 2006 Annual Enterprise Survey (EAE) and the Elaboration of Enterprise Annual Statistics from 2008 (ESANE). The Annual Enterprise Survey is a mandatory survey conducted by the French Ministry of Industry to collect data on firm characteristics such as business activities, size and location. We used a sample comprising 80,000 enterprises. Additionally, the Annual Statement of Social Data (Déclarations Annuelles de Données Sociales, DADS) is used to obtain information about wages and working hours.
Drawing on prior research (e. g. , Salis & Williams, 2010) , we measure labor productivity as the firm's value added divided by the number of employees. Value added was measured in 2008 and is recorded in the ESANE database; the number of employees was obtained from the COI database. Note that the dependent variable was measured in 2008 and the independent variables in 2006 to help control for reverse causality. Financial Participation. The financial participation is a share of firm profit paid to employees. We introduced a variable representing employee financial participation in Euros. Interpersonal Relations. To assess the level of interpersonal relations inside the firm we use three categories. The first category, working relations, is based on the following information: employee works regularly with (1) supervisor; (2) subordinates; (3) colleagues from the same firm; (4) different departments; (5) people outside the firm.
Labor Productivity. Drawing on prior research (e. g. , Salis & Williams, 2010) , we measure labor productivity as the firm's value added divided by the number of employees. Value added was measured in 2008 and is recorded in the ESANE database; the number of employees was obtained from the COI database. Note that the dependent variable was measured in 2008 and the independent variables in 2006 to help control for reverse causality. Financial Participation. The financial participation is a share of firm profit paid to employees. We introduced a variable representing employee financial participation in Euros. Interpersonal Relations. To assess the level of interpersonal relations inside the firm we use three categories. The first category, working relations, is based on the following information: employee works regularly with (1) supervisor; (2) subordinates; (3) colleagues from the same firm; (4) different departments; (5) people outside the firm.
Implications for Crowdsourcing. Whilst it is clear that an office environment is not essential for this work, it is also clear that both agents and employer benefit from having a well-trained experienced workforce, particularly given the strict quality requirements in the SLA between the client and the outsourcer. For crowdsourcing, we need to think of how to manage the advantage that experience bestows, plus how the learning curve might be accommodated. Approaches could include 1) reducing the required learning; e. g. splitting the form in to sections, reducing the number of rules agents must learn, contextual presentation of rules and embedding training within the dataset  [10] , and 2) encouraging workers to become skilled in particular areas; whilst specialization does not have to be requester specific and indeed one of the founding concepts of crowdsourcing is that it would not be, for the work requester, there is likely to be benefit from being the 'first choice' for the best workers.
The participants were randomly assigned to one of the four scenarios. Data was collected by one of the authors in December 2010. Participants received a questionnaire with the following narrative: After a tough semester at the university, you decided to travel on vacation and you go to a travel agency to look at the possibilities. The narrative sequence then varied to manipulate co-production and information level. Participants who were randomly assigned to a co-production scenario read that the following text: When you arrive at the agency, the agent explains you can design your own trip itinerary, so your travel will have the exact features you want. Note that this is a typical co-production scenario and not a mere customization situation as it allows the consumer to freely participate in the production process instead of giving them a limited choice set defined by the firm, which is typical in the case of customization  (Dellaert & Dabholkar, 2009; Liechty et al.
Design and procedure Participants in this 2 (co-production: co-production and no co-production) x 2 (consumer choice: choice of refund and no choice of refund) factorial design between-subjects experiment were 91 undergraduate students of the business course from a major French University. Similar to Study 1, one of the researchers went to classes and students were given the option to participate in the study, answering a paper-and-pencil questionnaire. No incentive was given to them. Subjects were informed that the objective of the research was to investigate college students' perceptions of their experiences in purchasing various product categories. They were randomly assigned to one of the four cells of the experimental design. Data was collected by one of the authors in January 2011. Participants received the following narrative: You have bought a computer to perform your personal and professional activities and now you need a desk on which you can use it.
This result is illustrated on the graph by the height of the final curves' points. In summary, this graph illustrates the need for a unique repository for all the mechatronics system data. This does not mean that no specific teams have to be created, but they have to continuously share design data into the same repository. The graph also illustrates the influence of regular project leaders' decisions, based on consolidated and dynamic indicators. In the next section, a framework to provide such repository, in order to facilitate the multi-discipline integration, and such indicators, in order to dynamically manage the project, is presented. This framework is presented as the anchor to support agile methods for mechatronics system design.
Case Studies This section focuses on case studies. Two parts are studied in this section: evaluating PCMA for a particular unit to get a maturity score; and choosing the right PLM components from the defined business targets. Experimental data and important information have been collected by interviewing the managers of an Italian prefabrication company. We calculate final maturity score per PCMA dimension by calculating the weighted sum of the KPIs in each dimension; and determine the overall PCMA maturity as the mean score of the fifteen dimensions. The result can be seen in the radar chart (figure  2 ) showing the level of achievement for each dimension (limited in the examples given in this paper to T6). Our work developed AHP methodology and derived priorities of PLM components. Describe the components' weight by using the descending order in figure  3.
Yet, nowadays, humanitarian organizations undergo an increased pressure to improve their processes. Humanitarian organizations differ from private companies in many ways. Among others, this sector is historically focused only on its currents action, on the day to day work needed to attend affected people. The funding of operations comes before any long term project, such as the development of Computer Based Information System (CBIS). In addition, the high turnover of staff reduces the possibilities to build knowledge on past experience, especially since few or no data are recorded from past operations. The inconsistent and uncertain environments in which organizations operate also complicate the situation, as data is difficult to collect and analyze. Last, but not least, the decision making process needs to be quick due to the lives at stake.
Acknowledgments. The research activity is part of the  INGéPROD (Productiveness for Product-Process Engineering  in a Design Chain context), which has been funded by  French Automotive Cluster Pôle de Compétitivité Véhicule du Futur. The authors would like to thank  General Electric  for this collaboration and all the financial supports of this research and technology program:  DRIRE de Franche-Comté, Communauté dAgglomération du Pays de Montbéliard ,  Conseil Général du Doubs  and  Conseil Régional de Franche-Comté.
0, every agent in the organization eventually finds the unique best configuration through individual searches. Moreover, since everyone eventually agrees on the best configuration, the value of organizational knowledge shows rapid improvement, as shown in Figure  5  (Panel (a)). Figure  5. The dynamics of the average value of organizational knowledge for two levels of task complexity, K = 0 (left) and K = 8 (right), and two degrees of environmental turbulence, µ = 0. 0 (top) and µ = 0. 005 (bottom). Four values of λ are considered: λ = 0. 0 (solid black), λ = 0. 2 (solid gray), λ = 0. 6 (dashed black), λ = 1. 0 (dashed gray). The communication is active (p = 1. 0). The data is generated by taking the average per-period value of organizational knowledge for each block of 100 periods. The average from 500 simulation runs is reported.
Acknowledgments We are grateful to  Tommaso Ciarli  and  Luigi Marengo , as well as to seminar participants at Hitotsubashi University, participants in the 2009 annual conference of Strategic Management Society, WEHIA 2009, 2009 national conference of the  Academic Association for Organizational Science , and the Contract Theory Workshop in Japan for comments and suggestions. This project is partly financed by  JSPS-ANR bilateral research grant  " BECOA " and  JSPS   Grant-in-Aid for Scientific Research (B)  (No. 21330058 ).
Genotyping, imputation, and quality control The seventeen participating studies used a variety of commercially available SNP genotyping platforms to genotype their participants. Each study performed quality control of their genotypic data and imputed the genotypes of each participant to a common set of approximately 2. 5 million SNPs from the HapMap CEU population. The exceptions to this were THI-SEAS, which only supplied results for directly genotyped SNPs, and HRS, which imputed to the 1,000 Genomes Project Phase I v3 panel. Prior to the meta-analysis, we performed parallel quality control of the association results for each study. SNPs were excluded on the basis of minor allele frequency (MAF,0. 01 or MAF,0. 05 if deemed necessary) and if the imputation quality (a measure of the observed variance divided by the expected variance of the imputed allele dosage from the imputation software output) was less than 0.
Statistical analysis Tetrachoric correlations were used to calculate self-employment correlations for MZ and DZ twin pairs. This analysis assumes a latent normally distributed tendency to engage in self-employment. We estimated the heritability of the tendency to engage in self-employment in the replication study using standard twin study methods, which were implemented in the program Mx  [64]. Only complete twin pairs with data regarding self-employment status were included in the analysis and opposite-sex DZ twin pairs were excluded, resulting in a final sample size of 4,464 individuals. Specifically, for pooled males and females, males only, and females only, we fitted the three following nested models using the maximum likelihood approach on the raw data: (1) a model including an additive genetic effect, a shared common environment effect, and an individual-specific environment effect (the ACE model); (2) a model that included only an additive genetic and an individual-specific environment effect (the AE model); and (3) a model including only a common environment effect and an individual-specific environment effect (the CE model).
For all of the samples, we controlled for a z-score of age by estimating agespecific thresholds. For the pooled sample, we additionally controlled for sex in a similar way. We used the method that was recently developed by Yang et al. [52]  to estimate the proportion of variance in the tendency to engage in self-employment that is explained by all of the common genotyped SNPs. The method is implemented in the GCTA software  [65]  and hinges on the assumption that in a sample of unrelated individuals, environmental factors segregate independently in the pedigree from the degree of genetic relatedness. In contrast to the twin study design, genetic relatedness is not inferred from the pedigree but is estimated directly from genome-wide SNP data. Under the assumption of no confounding by environmental variables, we can then estimate the accounted-for variance by relating the estimated genetic relatedness between pairs of individuals to their phenotypic correlation.
The resulting estimate is actually a lower bound of the heritability that is estimated from classic twin and family studies. The reason for this is that twin and family studies capture the variation that is due to all of the additive causal variants, whereas the more recently developed method only captures the variants that are either directly genotyped or in linkage disequilibrium. We used a combined sample of individuals from one of the discovery studies (RS-I) and the replication study (STR) to estimate the accounted-for variance. We restricted the sample from each study to individuals for whom data regarding selfemployment were available. Additionally, we included only one randomly selected individual from each family in the STR sample. A second round of quality control of the genotypic data was then performed for both studies.
In the RS-I sample, we excluded 3,748 SNPs because they failed a test of Hardy-Weinberg equilibrium at p,1610 26. We removed 24,993 SNPs with minor allele frequencies that were lower than 0. 01 and another 6,665 due to data missingness greater than 5%. In total, 5,374 individuals and 561,466 autosomal SNPs were included in the analysis. In the STR sample, we removed two SNPs because they failed a test of Hardy-Weinberg equilibrium at p,1610 26. Another 628 SNPs with a minor allele frequency lower than 0. 01 were removed, as were two SNPs with data missingness greater than 5%. Therefore, 643,924 autosomal SNPs and 2,589 individuals were included in the analysis. We then estimated the genetic relationships among 7,963 individuals in the combined sample from the 301,115 common autosomal SNPs.
The analyses were performed for pooled males and females, males only, and females only using an additive genetic model, controlling for age (#29 [reference]; 30-39; 40-49;$50) and sex in the pooled sample. To control for population stratification, the first four principal components of the genotypic data were also included if available. We provide details regarding the statistical analysis within each study in Table  S2. Following the association analyses, the genomic inflation factor l was calculated for each sample to quantify any remaining population stratification or cryptic relatedness. The lowest inflation factor was 0. 989, and the highest was 1. 156, although this latter value was for a study that did not include the first four principal components of the genotypic data in the analysis (Table  S3 ).
Genomic control  [69]  was applied in samples with inflation factors that were greater than one by adjusting the test statistics. We next performed fixed-effect meta-analyses of the association results from the discovery studies for pooled males and females, males only, and females only using METAL software  [70]. Although the phenotype was defined as self-employment in each participating study, we could not harmonize the exact wording of the question on which the self-employment measure was based. In addition, the connotations of self-employment may depend to some extent on the level of economic development and culture. This may lead to unobserved gene-environment interactions that could introduce additional noise in the GWAS results pooled across studies. We combined the association results using weighted z-scores that were based on the p-values and the direction of the effects.
Specifically, twelve overlapping sets of SNPs that were nominally associated in the discovery meta-analyses were created for different significance thresholds (p T ,0. 01, p T ,0. 05, p T ,0. 1, p T ,0. 2, p T ,0. 3, p T ,0. 4, p T ,0. 5, p T ,0. 6, p T ,0. 7, p T ,0. 8, p T ,0. 9, and p T #1). These sets were used as inputs for score calculation in the STR. We restricted the STR sample to individuals for whom data regarding selfemployment were available and included only one randomly selected individual from each family, resulting in a final sample size of 2,589 individuals for the prediction analyses. Prior to calculating the scores for each individual in the STR, we followed  [76]  and selected all of the autosomal SNPs, pruning those in strong linkage disequilibrium with other SNPs. This process was performed using a pairwise r 2 threshold of 0.