dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, it is important that the strategic use of information and knowledge is aligned to the business strategy, and that the data that is collected is the data that is needed to accomplish and support the business strategy. Nielsen had a strong capability in data generation using the diary method. This capability gave Nielsen a sustained competitive advantage for many years. Working backwards from its business strategy, Nielsen's business plan and its knowledge/information strategy were linked and implemented throughout the company. Therefore, the data that was generated by Nielsen was intentionally collected to accomplish the business goals of the companywhich were providing information and metrics to its customers. The diary-collection method that met the needs of Nielsen's customers in the beginning, became a source of dissatisfaction (turbulence) as Nielsen's customers became more sophisticated in their understanding of Nielsen's data collection processes, and demanded higher-quality data.
Acknowledgements: This article has been made possible, financially, thanks to the  University of Nîmes. It owes a great debt to the work of  Alain Findeli , professor emeritus of the  University of Nîmes , as well as to the pedagogical activities undertaken alongside our students in the Licence Design (lid. unimes. fr)and  Master Design Innovation Société  (dis. unimes. fr)programs within the  University of Nîmes.
The development of an efficient and cost-effective solution to solve a complex problem (e. g. , dynamic detection of toxic gases) is an important research issue in the industrial applications of the Internet of Things (IoT). An industrial intelligent ecosystem enables the collection of massive data from the various devices (e. g. , sensor-embedded wireless devices) dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a collaborative sensing intelligence (CSI) framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points.
In this study, based on the massive spatio-temporal data from different devices and different time points, with developing the potential of big data analytics, we design a collaborative sensing intelligence (CSI) framework. This framework facilitates the cooperativity of big data analytics. On the CSI framework basis, an industrial intelligence ecosystem can be constructed with the dynamic collaboration of different objects (an example is illustrated in Figure  1 ). The scientific contributions of this article are listed as follows. • The definitions of both terms, collaborative intelligence (CI) and industrial sensing intelligence (ISI), are proposed under the background of IoT and big data analytics. An industrial intelligence ecosystem. In this ecosystem, different objects (e. g. , humans and machines) are working as an efficient whole with effective dynamic collaboration.
, in the literature  [19] , they have presented user-centred collaborative location and activity filtering (UCLAF) to merge the data from different users together and have applied the collaborative filtering to find like-minded users and like-patterned activities at different locations. IoT-based CI. As the typical application for such CI, optimizing the performance of intelligent systems has attracted attention. In IoT and intelligent system-related studies, the study of intelligent transportation systems is an important aspect. In the literature  [24] , a collaborative framework is proposed for the real-time traffic information collection, fusion and sharing. The real-time traffic information is reported by various front-end devices of intelligent transportation systems, for example a vehicle-mounted GPS receiver.
In the literature  [45] , Ramchurn et al. have presented delivering the decentralized, autonomous and intelligent system, the smart grid, as a grand challenge for computer science and artificial intelligence research. As a typical case that is tightly related to the CSI framework in the smart grid, optimizing the electricity usage of electric vehicles is worth studying. For example, with analysing the spatio-temporal trajectory data from an intelligent transportation system, the routing pattern of electric vehicles can be acquired, and then, a national electric supply company can make time-and area-divisory electricity prices to control the usage of electricity and, therefore, to improve the efficiency of the smart grid.
Why and How Do We Design the CSI Framework Why do we design the CSI framework? This framework can improve the efficiency of IIoT. In industrial production/service, the internal logical processes are intricate and precise  [46]. A large amount of different equipment is involved in these logical processes. For achieving high efficiency industrial production/service, effective collaboration is necessary between different equipment and between different logical processes. The CSI framework can organize multi-sourced data and make different data sources collaborative with each other based on the data. The multi-sourced data are collected from the different equipment and different logical processes of industrial production/service based on the IIoT. Effective collaboration is possible, with the help of massive data.
First, with the application of IoT technology in industry, massive data can be collected by various widely-distributed sensors and wireless devices  [7]. Then, as the natural advantage of data, different data are easily processed and even merged together  [47]. Finally, the effective collaboration between different equipment and processes can be achieved, with processing and merging different data from multiple sources. Based on (i) the requirements of industrial production/service and the benefit of CSI framework and (ii) the feasibility of achieving collaboration, the question of "why" is answered. Moreover, the data-based collaboration can cost-effectively develop the intelligence of industrial production/service  [48]. For example, in the chemical industry, different equipment is used in different production stages, and different data are collected.
For improving the ability of acquiring information or knowledge and applying the acquired information or knowledge to realize the automation of production, the different equipment collaborating based on the data is an effective and low-cost method. How do we design the CSI framework? Considering the characteristics of industrial problems, integrating CI into ISI is a practicable method to achieve the CSI framework. Various sensors and wireless devices have been widely deployed to industrial equipment, and massive data are collected by these sensors and wireless devices. On this basis, the CSI framework is designed. Figure  2  illustrates the architecture of the CSI framework. x(i, t-1) x(j, t) x(k, t+1) Figure  2. Architecture of the collaborative sensing intelligence (CSI) framework. From the massive data from industrial ecosystems, to mined information/discovered knowledge, which can be used in algorithm design to solve industrial problems.
Moreover, embedding all possible sensors into a detecting system is not cost-effective. • It is difficult to set an optimal threshold for the sensed reading of toxic gas concentration. For example, for a carbon monoxide sensor, the predefined threshold is x, and in an accident, the leaking source of carbon monoxide gas is far away from this sensor. When the sensed reading of this sensor is larger than the predefined threshold x, the carbon monoxide gas has been widely diffused and has already gotten out of control. Based on the characteristics of industrial problems, the CSI framework is designed and used to solve existing problems in industrial systems. It is based on analysing massive spatio-temporal data from various devices in IIoT environments. Figure  4  illustrates an on-going effort, a CSI-based system, which improves the capability of detecting toxic gases in a large-scale petrochemical plant.
As the important two components of this application, Figure  5  provides the details of sensor-embedded wearable wireless devices and static wireless sensor nodes. For example, first, along with the daily walking of workers in a petrochemical plant, massive spatio-temporal data are collected by smart helmets, and the smart helmets collaborate with static sensor nodes via communication-enabled wrist watches. Then, the collected data by smart helmets and static nodes are submitted to a remote monitoring centre. Finally, the massive spatio-temporal data are analysed based on the CSI framework. Such an analysis enables the collaborative networking among different wireless devices to construct a problem-solving network, and analysis results are returned to the wrist watches. For the special problem, the leakage of toxic gases in large-scale petrochemical plants, because of the wide deployment of wireless devices, massive data are collected from these different devices.
Key Challenges The challenges come from these two aspects: data and functionality. Data: • Data analytics  [56, 57] : This is the bottleneck of the CSI framework, due to the lack of scalability for different datasets. Based on the characteristics of industrial problems, CSI analyses spatio-temporal datasets. These datasets are collected from different industrial equipment and different time points, and they have different semantics, different formats, different sizes and different contexts. • Structuring data: Transforming unstructured data into a unified structured format for later analysis is a challenge for the CSI framework. As the basis of our intelligence framework, spatio-temporal data are not natively structured, e. g. , daily running log data of different industrial equipment  [58] , and such unstructured data are typically text-heavy and contain important log information, such as dates, running parameters of equipment and values of these running parameters.
• Data privacy and knowledge access authorization  [59, 60] : Data privacy and knowledge access authorization are important for data owners. However, in the CSI framework, between data owners and data consumers, sharing data and knowledge is needed and important for good collaboration. For example, for two different industrial systems, they are data sources and they belong to different departments. Because of the high correlation of industrial processes, what level is just enough and how to define the level of privacy and access authorization between these two different industrial systems are challenges that are worth studying. • Generic data model  [61] : For making the spatio-temporal data of CSI framework be able to be used in knowledge discovery, a generic data model needs to be designed.
Functionality: • Knowledge discovery  [62] : In the era of big data, for mining the potential of big data analytics, it is vitally important to discover knowledge with understanding the nature (e. g. , correlations, contexts and semantics) of data. However, it is still an open challenge for the CSI framework, because knowledge discovery is a complex process under the dynamic environment of industrial production/service. • Effective and high-efficiency knowledge utilization  [63] : Along with the wide use of sensors and wireless devices in IIoT, data are being produced by humans and machines at an unprecedented rate. This leads many industrial departments to explore the possibility of innovating with the data that are captured to be used as a part of future information and communication technology (ICT) services.
The major challenge is how to release and use the knowledge that is mined from the massive data of industrial departments. • Support for particular applications: In a particular application, specific data mining and training are required to perform knowledge discovery. For example, for detecting the leakage of toxic gases, based on static and wearable wireless node embedded sensors (they generate massive dynamic data: sensing records with time stamps and location tags), real-time data mining algorithms are needed to mine such data and to monitor dynamic industrial environments. The CSI framework is required to have the ability to support these special requirements and to make data owners and data consumers be able to communicate with each other for effective data mining and knowledge discovery.
Conclusions Facing the growing demands of industrial production/service on improving the safety, efficiency and eco-friendliness, as well as meeting the cost-effective objectives, based on the IIoT and the characteristics of industrial problems, we have proposed the CSI framework combining CI and ISI. This sensing-and collaboration-based intelligence framework has the potential to improve the performance of industrial systems by providing better awareness and control to dynamic industrial environments and correlated production/service processes, with analysing and integrating massive spatio-temporal data. Moreover, because the spatio-temporal data are collected from things and humans, CSI can achieve improved automated decision making with ISI collaborating with the crowd wisdom of humans.
Acknowledgments: This work is supported by  Guangdong University of Petrochemical Technology 's Internal Project No. 2012RC106 , the  Educational Commission of Guangdong Province, China , Project No. 2013KJCX0131 , the  Guangdong High-Tech Development Fund No. 2013B010401035 , the  2013 Special Fund of Guangdong Higher School Talent Recruitment , the  National Natural Science Foundation of China  under Grant  61401107 , the  2013 Top Level Talents Project in "Sailing Plan of Guangdong Province " and the 2014  Guangdong Province Outstanding Young Professor Project. This work is also supported by the  EU   ITEA3  Project No. 12010 , " Collaborative Analytics Platform" (CAP). M. Guizani  ( IEEE Fellow, Qatar University, Qatar ) and  Trung Q. Duong  ( School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, UK ) gave us great help in the second round of modification.
He proposed studying these activities in terms of classes of situations, that is, situations with similar characteristics (e. g. , tasks to be performed or aspects to be considered), leading to relatively stable activity modalities, and for which actors associate/develop adapted activity schemes and instruments. Such classes may be grouped in activity families related to a similar general purpose, which may be used as a unit of analysis for the professional domain of activity. Examples of instrumental geneses analyses may be found in  Rabardel and Bourmaud (2003) , for dispatchers assigning technicians to tasks;  Trouche (2003 Trouche ( , 2004)) , for students enhancing symbolic calculators; Cerratto Pargman (2003), for collaborative writing;  Folcher (2003) , for hotline operators using a shared database; and  Gueudet and Trouche (2009) , for a community of teachers using documentation systems.
Source identification We decided to focus our research on academic papers published in peer-reviewed journals in the English language listed on the SCOPUS database. Journals are generally considered as the broadest and most common media for the research community and are generally used to disseminate new findings from validated Agency theory (AT) e. g. ,  Jensen and Meckling (1976) ;  Arrow (1985)  , ;  Bergen et al. (1992)  and  Eisenhardt (1989)  Focuses on mutual contractual arrangements between two or more entities, internal or external to the firm. The delegation of authority within a hierarchical relationship is necessary to complete a task and the "agent" works on the "principal's" behalf. Common agency problems arise as a consequence of information asymmetry and goal incongruence between the principal and the agent.
Source selection Within the SCOPUS database, we selected journals that either have an explicit focus on PSM or are general management journals (including PSM) with a high scientific impact. From these journals, we extracted all papers related to PSM. The content and quality criteria adopted for the selection are now described. To identify the topics falling within the PSM domain and to select our sources accordingly, we used the most common PSM textbooks  (van Weele, 2009; Monczka et al. , 2010; Lysons and Farrington, 2006). These textbooks allowed us to formulate a classification framework, which was discussed and revised by the research team through an iterative process (the framework is reported in the appendix). Ultimately, we identified the "why" (i. e. , strategic competitive priorities in relation to PSM), "what" (i.
In addition, some Marketing journals, particularly those focusing on business-to-business relationships (e. g. , Industrial Marketing Management), often address PSM topics, such as customer-supplier relationships. Finally, the third group consists of general management and economics journals that published at least 3 papers in the field of PSM in 2010. To select journals with a high scientific impact, we considered the Source Normalised Impact per Paper (SNIP) provided by the SCOPUS database. In our analysis, we included only journals with a SNIP above the mean  (Moed 2010). SNIP is defined as the ratio of the journal's citation count per paper and the citation potential in its subject field. It allows a direct comparison of sources in different subject fields. Indeed, citation potential is shown to vary not only between journal subject categories (i.
Source evaluation To classify the papers in our database, we adopted the classification framework mentioned above. We also registered general metadata (e. g. , authors, year of publication, journal, volume and issue); the research subject in terms of the sector, type of purchase, company size, geographical scope and unit of analysis; and the conceptual and methodological characteristics (e. g. , the research approach, research method and theory used). To perform this step of the methodology, the classification framework was described in detail in a codebook. Three independent researchers performed the validation of the codebook because this step was crucial for ensuring the reliability of the coding activity. To this end, they independently classified all papers published in 2010 by the Journal of Purchasing and Supply Management.
Second, we enlarge the investigated sources considerably to include top journals in adjacent fields, far beyond the few established journals of SCM. Third, we investigate the relation between EGTs and the specific topics and methodologies of PSM papers, as well as the unit of analysis and the intrinsic nature of research conducted. To pursue this general aim, we formulate three key research questions (see Section 1) on the extent and modalities of use of EGTs to support research on PSM. Based on our findings from a dataset of 1055 papers in 20 top journals, we are able to draw concluding remarks regarding our three original research questions. Regarding RQ1, we can affirm that the field of PSM is not grounded extensively in EGTs. This finding is also confirmed in our comparison with the broader SCM field, which, according to  Chicksand et al.
Or again, they might point in the direction of defining new theories, ultimately internal to the field Therefore, with this study we are not in the position to assess the quality of a research basing on the presence or the absence of an EGT. We believe the quality of the articles is ensured (generally speaking) by the peer-review process typical of the academic journals in our sample. Good articles might discuss new and valuable empirical data or describe emerging phenomena unless any theory is explicitly mentioned. Yet, as argued in Section 1, PSM maturity as a research field is positively related to the use of theories  (Kuhn, 1962; Hunt, 1991). Therefore, considering the corpus of PSM discipline, we believe that its maturity is still relatively lower when compared to other disciplines, but it is definitely increasing, as the use of EGTs increases.
Baseline Data In 2009/10, 18,682 students were enrolled in the third year of licence appliquée in Tunisian universities. All these students were invited to submit an application form for the entrepreneurship track in November or December 2009. In total, 1,702 students (or 9. 1 percent of all eligible students nationwide) applied to receive entrepreneurship education. Of those, 1,310 students applied individually and 392 applied in pairs, so that in total, 1,506 projects were registered. Table  1  shows the number of enrolled students and applicants by gender and university. The third column shows the distribution of the application rate. The last two columns present the distribution of all students enrolled in the third year of licence appliquée in 2009/10 and of applicants, by gender and by university respectively.
Two-thirds of the applicants were women. While this is a high participation rate for women, it is not higher than the proportion of women in the overall population of enrolled students in Tunisia. Demand for the program varied across universities. Differences in application rates are likely explained by variations in the implementation of the information campaigns and intensity of advertisement about the program, 6 as well as regional variations in perceived employment opportunities. 7 [Table  1 here]  The baseline data comes from two sources. An application form was collected in November and December 2009. The application form contained information about students' socio-economic background and employment experience, as well as their parents'. Additional information was collected through a phone survey in January and February 2010.
Follow-up data and post-revolution context After the baseline data was collected and the randomization performed, students participated in the entrepreneurship track between February and June 2010. Graduation took place in June 2010. In October 2010, qualitative data was collected to gather students', coaches', and professors' perceptions on the implementation of the intervention. The follow-up data was collected through face-to-face interviews between April and June 2011, approximately nine to twelve months after the end of the academic year. The instrument included similar questions than the baseline, with the same measures of preferences and entrepreneurial traits. Additional modules were introduced, including a detailed labor module similar to the Tunisian labor force survey, a module on business skills related to the content of the training, a module on networks and a module on access to credit.
Specification I Specification II Specification III (1) (2) (3) (4) (5) (6) 1 Most existing studies on entrepreneurship education also face methodological issues since they are based on quasi-experimental data and have limited external validity since they tend to use data from just a few schools. 2 Source: EduStats. 3 Source: Tunisia Labor Force Surveys. 4  This includes the following universities: Ez-Zitouna, Jendouba, Gabès, Gafsa, Tunis, Kairouan, Mannouba, Monastir, Carthage, Sfax, Sousse, Tunis, and Tunis El-Manar. 5 The training lasted on average 6 hours a day, for a total of approximately 120 hours. 6 University professors played an instrumental role in informing students about the program. 84 percent of all applicants heard about the program through professors, 39 percent from posters, and 17 percent from other students, friends, and relatives.