dataesr/scientific-paragraphs-categorization huggingface dataset:
How can this be seen a warning sign in this weak signal? • Pertinence. Considering ABB as an example of a target, this is a fragmented piece of information. • Surprise. This data was not expected, caught someone's attention, and triggered a process of collective reflection. As of that moment, this data gained the status of information for us. • Importance. Considering ABB as a target and the motivations justifying a process of strategic intelligence, it can be raised the hypothesis that ABB relations could suggest business opportunities for the company interested in it. A manager considering this information observed: "The information thus began to be potentially useful to us. We could enter the Arabian market through ABB. " • Anticipation. Is this information anticipative? It is clear that ABB prize is already a past event.
These hypotheses are tested on the formation of collaboration ties between individual inventors. More specifically, we estimate the formation of links in a large co-invention network. 3 Such networks are particularly well suited to test our hypotheses for several reasons. First, we were able to build a dataset on a large scale networks which we observe longitudinally over a sufficiently long period of time. Further, this dataset has been matched with companies data sets in order to use interesting covariates on top of other covariates built using information contained in the patents. The second reason is that the formation of collaborations between inventors remains understudied since previous studies in the field have mainly considered network ties as exogenous. Moreover, the results obtained concerning network effects on inventive productivity remain contradictory in the literature in particular regarding the effects of closure  (Schilling and Phelps, 2007; Fleming et al.
Once we have disambiguated inventor identities thanks to an original Bayesian methodology, we obtain a population of about one hundred thousand inventors and reliable individual information on them, such as their precise geographic location, technological specialization or patent applicant identities (mostly companies in the EPO system). These applicants are matched against the list of French companies in mandatory annual surveys to gather detailed information on applicants, including their yearly R&D investments and research personnel. We suspect that the omission of some (potentially time-varying) variables could lead to the mistaken conclusion that agents have incentives to form triadic connections. This is likely to occur if, in the true data generation process, these covariates affect both the probability of connecting and the probability of having common friends in the same way.
As is well known in the literature on networks, many network statistics are very sensitive to such errors. Therefore, the use of the information on patent inventors necessitates the correct identification of individual identities in patent data through some reliable, systematic and reproducible methodology. Though a growing literature tackling this issue is emerging, 13 a widely accepted standard has not yet been fixed, and a whole range of more or less ad hoc techniques can be seen. Any disambiguation procedure needs, in particular, to have a filtering step, in which different observable attributes, already listed in the patent dataset, are used to provide similarity scores to determine whether two homonyms refer to one and the same person. Two main issues need to be addressed. First, how much should similarity scores increase when two homonyms have the same modality for some given variable? Should, for instance, information about the city of residence contribute more or less to the similarity scores than information about the technological classes? Second, how should the relative frequencies of each variable modality be taken into account? Clearly, it is not as informative to observe that two homonyms live in Paris as in a small town, and we would like to know what difference this makes exactly.
Variables We now present the variables that will be used in the regressions. They include network variables, geographical and technological distances directly extracted from patent data, and applicant data that rely both on the cleaning of the applicant field of patent data and the match of patent applicants with companies in mandatory national surveys. Descriptive statistics on all variables are presented in Table  2. Since the fixed effect approach we use in our econometric estimations deletes all dyads with only null-dependent variables (no link is ever formed), the data are limited to the yearly observations of the dyads that are eventually formed. Moreover, since the variability in the explanatory variables comes from the observation of at least one previous patent, we only consider the 97, 551 dyads in which an inventor has already invented at least one patent.
Applicants The association of inventors to applicants on a yearly basis was based on the two following principles: i) the inventor is associated with her/his first applicant and permanently if she/he does not switch to another applicant; ii) if the inventor switches to a new applicant, she/he is associated with that new applicant from the year of the application of the new patent. To account for the institutional costs of collaboration, a dummy variable app t ij was created: it is equal to unity if i and j have ever been associated with the same applicant. We also identify public research institutions (universities and other public bodies) among all the applicants of our database. This variable is of interest for us since we hypothesize that, when inventors are in the academic sphere, they may follow different behavioral patterns, somewhat reducing the perceived costs of collaborations.
A simple justification would be that academics are less likely to perceive each other as competitors and are therefore more likely to be willing to engage in joint research projects. The dummy variable acad t ij captures this: it is equal to unity if the two agents have already invented a patent for which the applicant is a public research institution. About eleven percent of all dyads formed are between academics. A final step in the enrichment of our data proceeded as follows. We matched the patent dataset with the French R&D surveys conducted annually by the French Ministry of Research, using the name and location of applicants-companies as the matching key. These surveys are exhaustive for all the companies employing at least one full-time researcher (whatever their size) and provided us with annual data on companies' internal and external R&D expenditure, number of researchers, as well as more general information, such as total number of employees.
The mean estimated coefficient of common neighbors is -0. 25 (SD is only 0. 07) and are significant for 98 out of 100 runs. The sign is thus correctly estimated but the coefficient is about half of its associated generative coefficient (-0. 505). We suspect this downward bias in magnitude may be due to the systematic inclusion in the sample of agent dyads for all the periods before their first collaboration, even when they actually do not meet. Those dyad-periods have been included to match the very nature of the empirical data. Meetings between inventors are not directly observed as in most real network data. Meetings are however observable in the Monte Carlo generated data allowing us to appreciate to what extent using this information would reduce the estimation bias. In Model 2 agents dyads are excluded for all periods they do not meet.
Conclusion While a large number of empirical studies examine how social networks shape aggregate or individual outcomes, the individual strategies that drive their evolution are often not considered. At the same time, a growing theoretical literature explores the properties of networks that emerge when self-interested agents strategically form their connections  (Jackson, 2009). In this paper, we contribute to a recent empirical literature that aims to fill the gap between these two approaches by studying the formation of links using panel data on large social networks (e. g. Fafchamps et al. , 2010; Snijders, 2017). Specifically, we examine the incentives of inventors to form new research collaborations, with a special focus on the effect of triadic closure and non-redundant connections which have been identified as some of the main drivers of social network formation.
Methodology According to  Raffo and Lhuillery (2009) , any procedure of disambiguation should be performed in three stages i) a parsing stage, finalized in the standardization and cleaning of different data set fields; ii) a matching stage, where different algorithms could be used to group homonyms; iii) a filtering stage, where different sets of information (i. e. observable attributes already listed in patent data sets such as, for instance, technological class) are used to give a similarity score in order to determine whether homonyms refer to the same person. If the two first steps are essentially technical, the third one requires non-trivial methodological issues to be solved. Here, we focus on this third step, since the first two have already been treated. Basically, it consists in establishing criteria for assigning similarity scores between homonyms.
The suggestions of two anonymous referees significantly helped us improving the paper. We are grateful to the  Agence Nationale de la Recherche  (grant  ANR-06-JCJC-0076 ) and the  Aquitaine Region (AAP program , no 20101402006) for their financial supports, as well as the  French Ministry of Higher Education and Research  for providing us with the administrative R&D surveys used in this study. We are grateful to the  Agence Nationale de la Recherche  (grant  ANR-06-JCJC-0076 ) and the  Aquitaine Region (AAP program , no 20101402006) for their financial supports, as well as the  French Ministry of Higher Education and Research  for providing us with the administrative R&D surveys used in this study.
ESI adds a particular focus on the timing of supplier involvement  (Bidault et al. , 1998; Swink, 1999) , which usually refers to involvement of key suppliers at the concept stage or during early feasibility studies. Schiele (2006)  considered early purchasing involvement as important as early supplier involvement. Homfeldt et al. (2017)  examine the role of the purchasing department in the innovation process in the automotive industry, as well as the methods and practices used to benefit from the innovation capacity of key suppliers. According to these authors, the purchasing department contributes to the innovation process in three ways: 1. Identification of innovative ideas in the supply market and promotion of these ideas internally; 2. Management of economic activities; 3. Supplier database management.
After checking who the main actors in the innovation process were, participants in each function involved in the process were asked about their needs and the nature of their relationship with other functions, the role of purchasing in innovation process, and critical success factors for intra-functional coordination (if any). Data was gathered from primary and secondary sources. Our approach thus enables us to obtain a triadic view (marketing, purchasing, R&D/innovation). This should also provide more objective responses to the questions and allows us to compare answers from different points of view. Secondary data consists of publicly available materials (company websites, documents provided by the interviewees). These are mainly annual reviews of projects and success stories, as well as insights into main areas for development in the future.
These various documents allowed us to gather historical and background information about the firms. The data covers also field observations from the production lines and offices of the companies we visited. The combination of data collected enables us to triangulate several sources and assumes internal validity. To strengthen the research reliability, we conducted face-to-face interviews and taperecorded 23 semi-structured interviews with the main actors involved in innovation process. Each interview lasted an average of one hour. All of the interviews were recorded, transcribed, and sent to the industrial participants for their comments and approval. We relied on the Nvivo software for coding process assigning codes to the primary and secondary data. Coding was completed when data saturation occurred according to  Miles and Huberman (2003).
Collaborative design therefore involves the implementation of methods to manage design team performance. Performance can be measured in a global way by focusing on economic data linked to the company. Performance can be also measured from the activity point of view based on production activities, design activities, etc. A performance indicator is defined as a variable indicating the effectiveness and/or the efficiency of a part or whole of the process or system against a given norm/target or plan  (Lohman et al. , 2004). Performance is expressed in relation to the attainment of a fixed objective, by comparison with the measurement. Performance indicators provide management with a tool to compare actual results with a preset target, and to measure the extent of any deviation  (Fortuin, 1988).
, 2016 ) (Lucero et al. , 2014). Using a functional basis  (Hirtz et al. , 2002) , they associate critical flow (CF) and functions as a critical pair to identify relevant performance metrics. Fleche et al. use quantitative and non-intrusive indicators during the management of collaborative design phases. They compute several indicators linked to the completeness of the CAD data in order to assess the performance of a collaborative design project  (Fleche et al. , 2017). Fleche makes the hypothesis that the completeness rate of the project can be measured by taking into account the modification rate of CAD data between two revisions. The less the CAD data have been modified, the more mature the CAD data. But the completeness rate measured by Fleche is dependent on the nonmodification rate of the CAD data.
CONCLUSION In this paper, we have proposed a new approach in order to calculate a use design performance indicator (UDPI) used during collaborative projects. It is based upon data from requirements and use modelling. The approach is supported by a calculation step which is well defined and open for an easy reuse by the community, being based upon a free and universal modelling language (ICF lexicon). Our calculation method is also flexible enough to be well suited for the early steps of the product design process. This means designers can evaluate UF or criteria without any calculation. They can force validation percentage from their personal experience. In addition, the easy method of calculation allows the designers to monitor the changes in the UDPI and other indicators throughout the project, and use iteration of the product.
The diversity of the data collection also allows a consolidation of the external validity of the results  (Yin, 2003). Indeed, the choice of independent labels considered in our study is driven by their heterogeneity (cf. Table  1 ). As we will see further on, parameters such as size, market positioning, and distribution assets are crucial to understand the development of transactional capability in the different actors of the industry. We also increased the internal validity of our results by drawing on a diverse range of sources connected to Sony Music Entertainment. In particular, we focused on the link between one of the most important labels of the Major that we renamed Sony-label for reasons of confidentiality, and the transversal services of the Major that are directly monitored by its managerial structure.
Data Collection and Design Archives, interviews, internal documents, and reports from public institutions or phonographic associations represent the majority of our data collection. Direct observation was also used and formalized in several field notes reporting the internal organization and routines of the Sony-label. Archives were collected from the Sony Music France/US website and archive. org. We also consulted specialized websites and datasets of professional associations in the industry such as the International Federation of the Phonographic Industry (IFPI) and other local organizations such as the Syndicat National de l'Edition Phonographique (SNEP) and the Société Civile des Producteurs de Phonographe en France (SPPF). Semi directive interviews were our main source of data. These interviews enabled us to better understand the industry and the internal organization of the labels considered.
This is particularly true for art and craft companies providing packaging design, graphic charts, and pictures of music products. Majors such as Sony Music constitute important clients which help these companies build up their reputational resources. Their ability to transact repeatedly with these big record companies reduces their need to invest in bargaining. In the same way, other Sony Music resources have been renewed with transactional ones via the accumulation of knowledge and data on the demand side. Among these resources, the supply chain asset can be considered to be highly strategic  (Barney, 1991). Indeed, independent supply-chain infrastructure has been increasingly removed recently because of its low profitability in a context of a drastic reduction in size of the physical music market.
While in the past Sony Music was not really interested in consumer needs it is now directly involved in the sales relationship. Unlike the indie labels, Sony-label now manages direct-to-consumer marketing and selling: mailing lists, databases, newsletters, and satisfaction surveys to prospect consumer needs on a daily basis. This helps to confirm that Sony Music is developing transactional assets in order to increase its sales performance. In the same way, Sony supply-chain staff have created routines aiming at capturing data on consumers: a Dashboard has been set up combining sales statistics, data left by users on social networks, and data collected by marketing and CRM supports. It creates intangible transactional assets that give some insight into what kind of music and products are appreciated by consumers according to parameters of demographic, gender, age, and location.
However, the fact that Sony Music Entertainment owns its own supply chain gives significant flexibility. This allows the synchronization of promotional tasks, music releases, and derivative product distribution. When a music product is in the process of becoming a hit, other associated products must be conceived and distributed very quickly because of their dependent life cycle. This allows the firm to ensure that each subproject is as close as possible to this lifetime in order to transact all products at the optimal moment. Such coordination is not always possible for independent labels since their release agenda is partly controlled by the distributor. Similarly, transactional capability provides Sony Music with a competitive advantage over its independent competitors in the identification process of new corporate clients: not only are consumer and fan data collected from the supply chain o analyzed to better react to music user demand, but they are also turned into transactional assets for opening business with corporate clients (such as sponsorship, endorsements, synchronization, etc.
). Indeed, Sony Music has initiated a business-to-business team to conclude deals with other companies. In a context of weak IP effectivity, such cooperating clients constitute an increasingly profitable market for labels. This new market has required various legal capabilities to reduce transaction costs associated with contract arrangements. Our results show that Indie-labels 1 and 2 have also tried to offer products for corporate clients in order to expand their image and entice new consumers that would otherwise be out of their scope. But once again, the transactional advantage of Sony Music compared with its independent competitors comes from the knowledge that the firm is able to absorb through its data management. Endowed with data, Sony Music can detect the main consumption practices and characteristics of an artist's fans.
Sony Music is now investing in technical tools to collect and analyze information about consumers and user profiles. The huge amount of information involved requires new investment and skills which are being realized jointly with firms specialized in Big Data treatment. The internalization of gig promoter Arachnée was driven by the same motivations. Of course, the spectacular growth of this market has pushed Sony Music to vertically integrate show facilities to align profit incentives and diminish transaction costs. This new focus on music performance is also materialized through various partnerships with festivals: in 2014, Sony Music become producer of the We Love Green festival. A legal and technical module support is entirely dedicated to setting up tours and concerts in order to better capture the economic value of live music.
But, more importantly, this new resource provides information about consumer practices and tastes. As for the supply chain, the fact that Sony Music owns concert facilities helps to guarantee control of the agenda. It also constitutes a test for new artists to meet their audience, thereby giving a general idea of the success of a branding strategy and useful insight to help readjust this in the case of failure. "Concerts are just the very first step after CD sales. When an artist starts to sell records and perform well you may have something which is interesting to exploit in other fields. " Dir_labmaj These last points illustrate the high level of complementarity between the new value proposition and the traditional resources of the firm: traditional channels which are focused on consumers generate a range of data which are in turn converted into transactional insight to perform 360 deals.
This new business model aims to exploit synergistic relationships between a growing number of sectors, industries, and projects. By providing insight into both the supply and the demand side, we have stressed the fact that Sony Music's performance is not only due to its ability to sell finished products but is also linked to its ability to order and purchase relevant inputs especially when these are fairly distant from the firm's resources. Such transversal analysis underlines the synergistic relationship between knowledge flow from the demand and the supply side: permanent connection with providers enables Sony Music to absorb some insight into related industries and offer opportunities to develop new products. At the same time, the collection of data on sales performance and consumer profiles allows the firm to check the validity of the multiproduct strategy.
3, 923-937 at pp. 126-127 (deciding not to re-open discussion of the requirement of substantiality); WIPO Guide to the Rome Convention and Phonograms Convention (Geneva: WIPO, 1981), pp. 97-98 (accessible online at http://www. wipo. int/ edocs/pubdocs/en/copyright/617/wipo_pub_617. pdf) (''national laws and courts have the final decision when a sufficient part is taken to make this damage a reality. ''). 38  intellectual property concerned. A right holder is not guaranteed the opportunity to demand the highest possible remuneration. 40 To this end, the Court could extrapolate from its jurisprudence regarding Art. 7(1) of the Database Directive. 41 hile the points of attachment of the sui generis right in databases and the related rights of phonogram producers differ (investment v. phonogram), the purpose of both rights is the same: databases like phonograms require substantial investment, and the possibility of recouping that ''especially high and risky'' investment can be effectively guaranteed only through adequate legal protection of the right holders concerned.