2022
Journal Articles
1.
Lascialfari, Matteo; Magrini, Marie-Benoît; Cabanac, Guillaume
Unpacking research lock-in through a diachronic analysis of topic cluster trajectories in scholarly publications Journal Article
In: Scientometrics, vol. 127, 2022.
@article{Lascialfari2022,
title = {Unpacking research lock-in through a diachronic analysis of topic cluster trajectories in scholarly publications},
author = {Matteo Lascialfari and Marie-Benoît Magrini and Guillaume Cabanac},
url = {https://link.springer.com/article/10.1007/s11192-022-04514-3
https://link.springer.com/content/pdf/10.1007/s11192-022-04514-3.pdf?pdf=button%20sticky},
doi = {https://doi.org/10.1007/s11192-022-04514-3},
year = {2022},
date = {2022-11-01},
urldate = {2022-11-01},
journal = {Scientometrics},
volume = {127},
abstract = {Lock-in and path-dependency are well-known concepts in economics dealing with unbalanced development of alternative options. Lock-in was studied in various sectors, considering production or consumption sides. Lock-in in academic research went little addressed. Yet, science develops through knowledge accumulation and cross-fertilisation of research topics, that could lead to similar phenomena when some topics do not sufficiently benefit from accumulation mechanisms, reducing innovation opportunities from the concerned field consequently. We introduce an original method to explore these phenomena by comparing topic trajectories in research fields according to strong or weak accumulative processes over time. We combine the concepts of ‘niche’ and ‘mainstream’ from transition studies with scientometric tools to revisit Callon’s strategic diagram with a diachronic perspective of topic clusters over time. Considering the trajectories of semantic clusters, derived from titles and authors’ keywords extracted from scholarly publications in the Web of Science, we applied our method to two competing research fields in food sciences and technology related to pulses and soya over the last 60 years worldwide. These highly interesting species for the sustainability of agrifood systems experienced unbalanced development and thus is under-debated. Our analysis confirms that food research for soya was more dynamic than for pulses: soya topic clusters revealed a stronger accumulative research path by cumulating mainstream positions while pulses research did not meet the same success. This attempt to unpack research lock-in for evaluating the competition dynamics of scientific fields over time calls for future works, by strengthening the method and testing it on other research fields.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lock-in and path-dependency are well-known concepts in economics dealing with unbalanced development of alternative options. Lock-in was studied in various sectors, considering production or consumption sides. Lock-in in academic research went little addressed. Yet, science develops through knowledge accumulation and cross-fertilisation of research topics, that could lead to similar phenomena when some topics do not sufficiently benefit from accumulation mechanisms, reducing innovation opportunities from the concerned field consequently. We introduce an original method to explore these phenomena by comparing topic trajectories in research fields according to strong or weak accumulative processes over time. We combine the concepts of ‘niche’ and ‘mainstream’ from transition studies with scientometric tools to revisit Callon’s strategic diagram with a diachronic perspective of topic clusters over time. Considering the trajectories of semantic clusters, derived from titles and authors’ keywords extracted from scholarly publications in the Web of Science, we applied our method to two competing research fields in food sciences and technology related to pulses and soya over the last 60 years worldwide. These highly interesting species for the sustainability of agrifood systems experienced unbalanced development and thus is under-debated. Our analysis confirms that food research for soya was more dynamic than for pulses: soya topic clusters revealed a stronger accumulative research path by cumulating mainstream positions while pulses research did not meet the same success. This attempt to unpack research lock-in for evaluating the competition dynamics of scientific fields over time calls for future works, by strengthening the method and testing it on other research fields.
2.
Franco, Sebastián Fernández; Graña, Juan M; Flacher, David; Rikap, Cecilia
Producing and using artificial intelligence: What can Europe learn from Siemens’s experience? Journal Article
In: Competition & Change, vol. 0, pp. 1–30, 2022.
@article{Franco2022,
title = {Producing and using artificial intelligence: What can Europe learn from Siemens’s experience?},
author = {Sebastián Fernández Franco and Juan M Graña and David Flacher and Cecilia Rikap},
url = {https://www.researchgate.net/profile/Juan-Grana-2/publication/360759657_Producing_and_using_artificial_intelligence_What_can_Europe_learn_from_Siemens's_experience/links/62a739f955273755ebe9963b/Producing-and-using-artificial-intelligence-What-can-Europe-learn-from-Siemenss-experience.pdf},
doi = {10.1177/10245294221097066},
year = {2022},
date = {2022-06-10},
urldate = {2022-06-10},
journal = {Competition & Change},
volume = {0},
pages = {1–30},
abstract = {This paper examines the innovation strategy of Siemens, a key player in Europe’s digital economy, by performing network and lexical analyses using data derived from Siemens’s patents and scientific publications since 1998. We observe that the company’s innovation efforts evolved from a broader attempt to develop internal information and communication technology (ICT) capabilities – alongside its historical industrial priorities – to a strategy focused on developing artificial intelligence (AI) for sector-specific and niche applications (such as life and medical sciences). As a result, it became dependent on tech giants’ clouds for accessing more general AI services and digital infrastructure. We build on the intellectual monopoly literature focusing on the effects of tech giants on other leading corporations, to analyse Siemens’s experience. By abandoning the development of general ICT and given the emergence of tech giants as digital economy intellectual monopolies, we show that Siemens is risking its technological autonomy towards these big tech companies. Our results provide clues to understand the challenges faced by Europe and its firms in relation to US and Chinese tech giants.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper examines the innovation strategy of Siemens, a key player in Europe’s digital economy, by performing network and lexical analyses using data derived from Siemens’s patents and scientific publications since 1998. We observe that the company’s innovation efforts evolved from a broader attempt to develop internal information and communication technology (ICT) capabilities – alongside its historical industrial priorities – to a strategy focused on developing artificial intelligence (AI) for sector-specific and niche applications (such as life and medical sciences). As a result, it became dependent on tech giants’ clouds for accessing more general AI services and digital infrastructure. We build on the intellectual monopoly literature focusing on the effects of tech giants on other leading corporations, to analyse Siemens’s experience. By abandoning the development of general ICT and given the emergence of tech giants as digital economy intellectual monopolies, we show that Siemens is risking its technological autonomy towards these big tech companies. Our results provide clues to understand the challenges faced by Europe and its firms in relation to US and Chinese tech giants.
2021
Journal Articles
3.
Testoni, Federico E.; Carrillo, Mercedes García; Gagnon, Marc-André; Rikap, Cecilia; Blaustein, Matías
Whose shoulders is health research standing on? Determining the key actors and contents of the prevailing biomedical research agenda Journal Article
In: PLoS ONE, 2021.
@article{Testoni2021,
title = {Whose shoulders is health research standing on? Determining the key actors and contents of the prevailing biomedical research agenda},
author = {Federico E. Testoni and Mercedes García Carrillo and Marc-André Gagnon and Cecilia Rikap and Matías Blaustein
},
editor = {Quinn Grundy},
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0249661},
doi = { https://doi.org/10.1371/journal.pone.0249661},
year = {2021},
date = {2021-04-07},
urldate = {2021-04-07},
journal = {PLoS ONE},
abstract = {Conflicts of interest in biomedical research can influence research results and drive research agendas away from public health priorities. Previous agenda-setting studies share two shortfalls: they only account for direct connections between academic institutions and firms, as well as potential bias based on researchers’ personal beliefs. This paper’s goal is to determine the key actors and contents of the prevailing health and biomedical sciences (HBMS) research agenda, overcoming these shortfalls.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Conflicts of interest in biomedical research can influence research results and drive research agendas away from public health priorities. Previous agenda-setting studies share two shortfalls: they only account for direct connections between academic institutions and firms, as well as potential bias based on researchers’ personal beliefs. This paper’s goal is to determine the key actors and contents of the prevailing health and biomedical sciences (HBMS) research agenda, overcoming these shortfalls.
2020
Journal Articles
4.
Rikap, Cecilia; Flacher, David
Who collects intellectual rents from knowledge and innovation hubs? questioning the sustainability of the singapore model Journal Article
In: Structural Change and Economic Dynamics, vol. 55, pp. 59-73, 2020.
@article{Rikap2020b,
title = {Who collects intellectual rents from knowledge and innovation hubs? questioning the sustainability of the singapore model},
author = {Cecilia Rikap and David Flacher},
doi = {https://doi.org/10.1016/j.strueco.2020.06.004},
year = {2020},
date = {2020-12-01},
urldate = {2020-12-01},
journal = {Structural Change and Economic Dynamics},
volume = {55},
pages = {59-73},
abstract = {While knowledge and innovation are produced in networks involving diverse actors, associated rents are greatly appropriated by global leaders, mostly coming from core countries, that become intellectual monopolies. This raises the question on emerging or peripheral countries companies’ capacity to catch-up, innovate and compete for intellectual rents. The article considers the case of Singapore who has pursued a knowledge hub strategy aimed at: 1) creating world class universities inserted in global knowledge networks of defined fields; and 2) capturing intellectual rents through those institutions’ research and contributing to local firms’ catching up. We show that research universities caught-up. However, we find that foreign companies, particularly multinationals, capture most of Singapore's intellectual rents at the expense of local companies and research institutions. Overall, our findings point to the limitations of Singapore's knowledge hub as a catching-up strategy. The article ends discussing the relevancy of these findings for emerging countries in general.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
While knowledge and innovation are produced in networks involving diverse actors, associated rents are greatly appropriated by global leaders, mostly coming from core countries, that become intellectual monopolies. This raises the question on emerging or peripheral countries companies’ capacity to catch-up, innovate and compete for intellectual rents. The article considers the case of Singapore who has pursued a knowledge hub strategy aimed at: 1) creating world class universities inserted in global knowledge networks of defined fields; and 2) capturing intellectual rents through those institutions’ research and contributing to local firms’ catching up. We show that research universities caught-up. However, we find that foreign companies, particularly multinationals, capture most of Singapore's intellectual rents at the expense of local companies and research institutions. Overall, our findings point to the limitations of Singapore's knowledge hub as a catching-up strategy. The article ends discussing the relevancy of these findings for emerging countries in general.
5.
Rikap, Cecilia
Amazon: A story of accumulation through intellectual rentiership and predation Journal Article
In: Competition & Change, 2020.
@article{Rikap2020,
title = {Amazon: A story of accumulation through intellectual rentiership and predation },
author = {Cecilia Rikap},
doi = {https://doi.org/10.1177/1024529420932418},
year = {2020},
date = {2020-06-17},
journal = {Competition & Change},
abstract = {This article elaborates on intellectual monopoly theory as a form of predation and rentiership using Amazon as a case study. By analysing Amazon’s financial statements, scientific publications and patents, we show that Amazon’s economic power heavily relies on its systematic innovations and capacity to centralize and analyse customized data that orients its business and innovations. We demonstrate how Amazon’s innovation activities have evolved over time with growing importance of technologies related to data and machine learning. We also map Amazon’s innovation networks with academic institutions and companies. We show how Amazon appropriates intellectual rents from these networks and from technological cooperation with other intellectual monopolies. We argue that Amazon, as other data-driven monopolies, predates value from suppliers and third-party companies participating in its platform. One striking characteristic of Amazon is the low rate of reported profits. The centrality of innovations leads us to suggest an alternative calculation that shows that Amazon’s profits are not as low as they appear in Annual Reports. We also argue that lower profits are coherent with Amazon’s rentiership and predatory strategy since they contribute to the avoidance of accusations of excessive market power. Finally, the paper offers preliminary observations on: (i) the complementarities between financial and intellectual rentierism and (ii) how data-driven intellectual monopoly expands big corporations’ political power. Going beyond the specific case of Amazon, we thus contribute to a better understanding of the role of lead firms and power dynamics within innovation networks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This article elaborates on intellectual monopoly theory as a form of predation and rentiership using Amazon as a case study. By analysing Amazon’s financial statements, scientific publications and patents, we show that Amazon’s economic power heavily relies on its systematic innovations and capacity to centralize and analyse customized data that orients its business and innovations. We demonstrate how Amazon’s innovation activities have evolved over time with growing importance of technologies related to data and machine learning. We also map Amazon’s innovation networks with academic institutions and companies. We show how Amazon appropriates intellectual rents from these networks and from technological cooperation with other intellectual monopolies. We argue that Amazon, as other data-driven monopolies, predates value from suppliers and third-party companies participating in its platform. One striking characteristic of Amazon is the low rate of reported profits. The centrality of innovations leads us to suggest an alternative calculation that shows that Amazon’s profits are not as low as they appear in Annual Reports. We also argue that lower profits are coherent with Amazon’s rentiership and predatory strategy since they contribute to the avoidance of accusations of excessive market power. Finally, the paper offers preliminary observations on: (i) the complementarities between financial and intellectual rentierism and (ii) how data-driven intellectual monopoly expands big corporations’ political power. Going beyond the specific case of Amazon, we thus contribute to a better understanding of the role of lead firms and power dynamics within innovation networks.
2019
PhD Theses
6.
Perruchas, François
Green Innovation: an empirical analysis of technology, skills and policy PhD Thesis
Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería del Diseño - Escola Tècnica Superior d'Enginyeria del Disseny , 2019.
@phdthesis{Perruchas2019,
title = {Green Innovation: an empirical analysis of technology, skills and policy},
author = {François Perruchas},
url = {https://riunet.upv.es/handle/10251/119965},
doi = {10.4995/Thesis/10251/119965},
year = {2019},
date = {2019-05-22},
urldate = {2019-05-22},
school = {Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería del Diseño - Escola Tècnica Superior d'Enginyeria del Disseny },
abstract = {The foreseeable outcomes of the transition towards low-carbon economies are multiple and affect in different ways policy-makers, world regions, firms and consumers. It has long been acknowledged that at the core of this transition stand environmental innovations which are developed to enhance the long-term sustainability of economic growth. The main pillars of this study are two. First, environmental challenges are different, and so are the responses that are needed to tackle them. The main consequence of this is that the current focus on green technology as a homogeneous block of undifferentiated entities is misleading. Second, the adaptation of production and distribution systems is ultimately carried through by human labour and analysing the transition to environmentally sustainable societies requires a thorough understanding of how work activities are designed, implemented and changed to accommodate new policy imperatives and/or new technological opportunities. Empirical evidence on either of these two pillars is scant or fragmented. The present thesis seeks to fill these gaps through the development of a database on green innovations, of a measure of the life cycle of green technologies, and of the corresponding explorations to scrutinize the relation between green technology production, the territories' characteristics and skills' base of labour market over time and space. The dataset is created identifying green patent in PATSTAT 2016a database using ENV-TECH classification (OECD, 2016) and geolocalizing their inventors. The result is a database covering green innovation worldwide from the 19$^{th}$ century to 2015, even if the period studied is smaller: 1970-2010. This dataset permits a first overview of green technologies evolution over time and space, where we can see differences in terms of country evolution and among technologies in terms of complexity, maybe related with the presence of an heteregeneous body of emerging and mature technologies. To explore further this hypothesis, we develop a methodology to measure technology life cycle stages, and we apply it to understand the patterns of evolution of green technology production at country level. We find that capabilities are more important than wealth to diversify in green innovation, and mature green technologies are positively associated with specialization. We continue the exploration of the relation between local capabilities, life cycle and patent activity in US federal states where we discover that green innovation is more associated than innovation in general with the recombination of distant knowledge, especially in early phases of the life cycle. Finally, we investigate at US commuting zones level the effects of public procurement on green innovation, taking into account local capabilities again, but using labour market skills instead of knowledge recombination variety. We find that green public procurement has a positive and significant effect, in particular in territories with an important share of abstract skills in labour population.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
The foreseeable outcomes of the transition towards low-carbon economies are multiple and affect in different ways policy-makers, world regions, firms and consumers. It has long been acknowledged that at the core of this transition stand environmental innovations which are developed to enhance the long-term sustainability of economic growth. The main pillars of this study are two. First, environmental challenges are different, and so are the responses that are needed to tackle them. The main consequence of this is that the current focus on green technology as a homogeneous block of undifferentiated entities is misleading. Second, the adaptation of production and distribution systems is ultimately carried through by human labour and analysing the transition to environmentally sustainable societies requires a thorough understanding of how work activities are designed, implemented and changed to accommodate new policy imperatives and/or new technological opportunities. Empirical evidence on either of these two pillars is scant or fragmented. The present thesis seeks to fill these gaps through the development of a database on green innovations, of a measure of the life cycle of green technologies, and of the corresponding explorations to scrutinize the relation between green technology production, the territories' characteristics and skills' base of labour market over time and space. The dataset is created identifying green patent in PATSTAT 2016a database using ENV-TECH classification (OECD, 2016) and geolocalizing their inventors. The result is a database covering green innovation worldwide from the 19$^{th}$ century to 2015, even if the period studied is smaller: 1970-2010. This dataset permits a first overview of green technologies evolution over time and space, where we can see differences in terms of country evolution and among technologies in terms of complexity, maybe related with the presence of an heteregeneous body of emerging and mature technologies. To explore further this hypothesis, we develop a methodology to measure technology life cycle stages, and we apply it to understand the patterns of evolution of green technology production at country level. We find that capabilities are more important than wealth to diversify in green innovation, and mature green technologies are positively associated with specialization. We continue the exploration of the relation between local capabilities, life cycle and patent activity in US federal states where we discover that green innovation is more associated than innovation in general with the recombination of distant knowledge, especially in early phases of the life cycle. Finally, we investigate at US commuting zones level the effects of public procurement on green innovation, taking into account local capabilities again, but using labour market skills instead of knowledge recombination variety. We find that green public procurement has a positive and significant effect, in particular in territories with an important share of abstract skills in labour population.
2016
PhD Theses
7.
Oulion, Marina
The acquisition of technological capabilities by large Chinese industrial companies: between catch-up and engagement in emerging technologies PhD Thesis
LISIS, Paris-Est University, 2016, (HAL Id : tel-01483966 , version 2).
@phdthesis{Oulion2016,
title = {The acquisition of technological capabilities by large Chinese industrial companies: between catch-up and engagement in emerging technologies},
author = {Marina Oulion},
url = {https://tel.archives-ouvertes.fr/tel-01483966},
year = {2016},
date = {2016-09-01},
urldate = {2016-09-01},
school = {LISIS, Paris-Est University},
abstract = {Among the world’s 500 largest firms, one out of five is Chinese. In 2014, 94 Chinese firms were among the world leaders in R&D. Since 2016, China is the first acquirer of foreign firms and is now targeting high-technology firms.These recent developments raise questions about the technological positioning of Chinese firms. Studying this topic requires looking at their conditions of emergence. We can look at China’s development from the perspective of the technological catch-up model (Kim, 1997). China has gone through three phases: a phase of acquisition of foreign technology following the country’s opening in 1978, a period of technological assimilation and production of increasingly complex products, and a period of technological integration characterized by technological improvement and the reconfiguration of existing technologies.The hypothesis we make is that firms are now in the last phase of catch-up, and have entered a period of transition to technology leadership. This leads to two questions. What is Chinese innovation today? This topic broadly refers to innovation in emerging countries. How far are Chinese firms from reaching the technological frontier?We observe the transition through the way major Chinese firms engage in research. The integration of emerging technologies into their research strategies reflect dynamics of technological learning which, if they are not yet visible in the market, indicate a transition. Our results show that the trend is significant, with half of large firms (48%) engaging in nanotechnology research. This proportion indicates that Chinese firms have reached the technological frontier. This, however, does not mean that Chinese firms have reached the frontier in other dimensions, such as the organizational dimension. We also show that there are several modalities of commitment to research. While some large Chine firms engage in research by adopting a model similar to that of American or European firms, other dynamics are at work, which reflect, in particular, their historical legacy, and the impact of their localization.To obtain these results, we have built a unique database of 325 large industrial enterprises, and have looked at their patenting activities in nanotechnology, directly or through their subsidiaries, based on the exploitation of sources in English and Chinese. },
note = {HAL Id : tel-01483966 , version 2},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Among the world’s 500 largest firms, one out of five is Chinese. In 2014, 94 Chinese firms were among the world leaders in R&D. Since 2016, China is the first acquirer of foreign firms and is now targeting high-technology firms.These recent developments raise questions about the technological positioning of Chinese firms. Studying this topic requires looking at their conditions of emergence. We can look at China’s development from the perspective of the technological catch-up model (Kim, 1997). China has gone through three phases: a phase of acquisition of foreign technology following the country’s opening in 1978, a period of technological assimilation and production of increasingly complex products, and a period of technological integration characterized by technological improvement and the reconfiguration of existing technologies.The hypothesis we make is that firms are now in the last phase of catch-up, and have entered a period of transition to technology leadership. This leads to two questions. What is Chinese innovation today? This topic broadly refers to innovation in emerging countries. How far are Chinese firms from reaching the technological frontier?We observe the transition through the way major Chinese firms engage in research. The integration of emerging technologies into their research strategies reflect dynamics of technological learning which, if they are not yet visible in the market, indicate a transition. Our results show that the trend is significant, with half of large firms (48%) engaging in nanotechnology research. This proportion indicates that Chinese firms have reached the technological frontier. This, however, does not mean that Chinese firms have reached the frontier in other dimensions, such as the organizational dimension. We also show that there are several modalities of commitment to research. While some large Chine firms engage in research by adopting a model similar to that of American or European firms, other dynamics are at work, which reflect, in particular, their historical legacy, and the impact of their localization.To obtain these results, we have built a unique database of 325 large industrial enterprises, and have looked at their patenting activities in nanotechnology, directly or through their subsidiaries, based on the exploitation of sources in English and Chinese.
2015
Technical Reports
8.
Kahane, Bernard; Mogoutov, Andrei; Cointet, Jean-Philippe; Villard, Lionel; Larédo, Philippe
A dynamic query to delineate emergent science and technology : the case of nano science and technology Technical Report
2015.
@techreport{Kahane2015,
title = {A dynamic query to delineate emergent science and technology : the case of nano science and technology},
author = {Bernard Kahane and Andrei Mogoutov and Jean-Philippe Cointet and Lionel Villard and Philippe Larédo
},
url = {http://www.sciences-technologies.eu/images/stories/cv/Dynamic_query%20to_delineate_emergent_science_technology_pub.pdf},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {RISIS},
pages = {47-70},
abstract = {Building a larger and relevant database out of an initial seed without relying, because of potential bias, on experts is a common challenge for those who wish to study or track a scientific or technological field. Publications and patents are not the only, but definitely an important component of knowledge generation and dissemination and one of the potential sources for innovation. Scientists communicate their findings through publications. Similarly, patents are legal documents to claim ownership of an invention but they also build a public paper trail of technology advancement. Thus publications and patents are an important, relevant and useful tool to follow and represent results of scientific and technological endeavours (Huang, 2010). Data mining is the extraction of relevant and useful information from large volume of data. Publication and Patent data systematically collected in worldwide databases such as the WoS and Patstat are used to track science and technology dynamic. Data mining faces an important challenge in a context of emergence when new technologies experience explosive growth, evolve rapidly and often cross and subvert existing scientific and technology fields. Emerging science and technology (biotechnology in the 1980s, nanotechnology today, other science and technology fields tomorrow), which often carry strong implications and potentialities for science, business and society, add to the challenge. Their content and dynamic are difficult to track at a time when they are struggling to define who they are, what they include and exclude and how they organize themselves internally. Such is the case for nanotechnology, where the quest for a relevant reliable and replicable way to extract relevant publications and patents, is an on-going process involving several teams worldwide (Glanzel 2003, Noyons 2003, Mogoutov and Kahane, 2007, Porter et al., 2008, Kostoff 2007, Leydesdorff and Zhou, 2007). Nanotechnology is a rapidly evolving emerging and dynamic field. Analysts argue that it is likely to be a “general purpose technology” (Youtie 2008, Laredo et al. 2010) with a potential impact across an entire range of industries and great implications on human health, the environment, sustainability and national security. The perceived potential value of nanotechnologies has led to the increased will of governments, academic institutions, firms and other societal actors to better understand what is happening in the field, who is active and where. There is thus an important challenge to develop robust methods to track the nanotechnology field while it rapidly develops and evolves. As a matter of fact, good quality and comprehensive extraction of data is a prerequisite for meaningful understanding and analysis. Huang 2010 as well as L'huillery et al. 2010 have compared the different methodologies developed, and reported on their robustness as well as on the similarities and discrepancies of results obtained. They confirmed the robustness and interest of the evolutionary lexical methodology we have developed (Mogoutov and Kahane, 2007). At that time, three requirements were central to the approach developed. First, it should not depend upon experts. Indeed, the on-going and extensive use of expert-based approaches is costly, time-consuming, and challenging to replicate such that the same outcomes result. This is an important restriction when facing a highly dynamic field where borders are constantly evolving requiring terminology requalification at different times. Second, it should allow updates in order to replicate and compare results while the nanotechnology field (and its lexicon) develop and expand. And third, it should be able to track the relative evolution of subfields inside nanotechnologies: in 2007 we translated this into a third requirement of being “modular”. While the initial development of our methodology was performed in order to extract data from 1998 to 2006, we later engaged in producing an update that could expand the database backward and forward in order to cover years 1991-2011. In our initial methodology, the selection of relevant terms was performed with knowledge built and keywords selected on one single year (2003). A simple solution was to reproduce the selection of terms for 2011, driving us to two semantic universes of nanotechnology, respectively built in 2003 and 2011. However Bonaccorsi (2010) has demonstrated that in a dynamic field such as nanotechnology, keywords often display short life and experience a type of Darwinian selection process. Using this approach, the characterisation of the evolution of the field over 20 years would have only relied on two years for the identification of relevant keywords. There would thus be a risk that we miss the richness of the exploration that shapes the dynamics of knowledge production. Not considering transient keywords that might have emerged and then disappeared, would be a serious drawback in such a dynamic field. There are multiple reasons for this. Two are of particular importance. One is about the learning that a stream of research, even if it goes on with a life of its own, has been experimented but proved not to be useful for colleagues at the time. The other lies in the fact that streams of research which for a while turn to be a dead end, can nevertheless reappear later and become a key resource as demonstrated in many instances. Such a limitation becomes even more visible when taking the whole period under review for identifying relevant keywords. This drove us to add a fourth requirement for such an approach: What is needed is a methodology, which allows us to incorporate and discard in real time relevant terms as they appear and disappear in the nanotechnology story. We need a methodology that allows us to track keywords as characters appear and disappear along the storyline in a movie. Thus, using nanotechnology as a showcase, we here report a data search strategy made of three consecutive steps. As in all the data search strategies for nanotechnology, we start with an initial seed built through the nanostring. We then use the same principle that we applied in our previous approach, that is expanding the initial seed through a dual process where additional keywords observed during a given period are sorted according to their internal specificity (e.g. the extent to which they provide value added meaning to a publication) and then tested in the overall database for ‘external specificity’ (e.g. the ratio of articles in the seed vs. articles in the overall database of publications). This selection of keywords is first applied on the whole dataset covering the 20 years, enabling a “static extension”. The third step builds the “dynamic extension” where additional keywords are identified through a yearly analysis of internal specificity within the nanostring, and selected depending upon their ‘external specificity’. Besides being applied in a specific way for nanotechnology, we claim that such a three steps strategy has universal value to describe the dynamics of emergent and fast evolving fields, transcending pre-existing classifications.},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Building a larger and relevant database out of an initial seed without relying, because of potential bias, on experts is a common challenge for those who wish to study or track a scientific or technological field. Publications and patents are not the only, but definitely an important component of knowledge generation and dissemination and one of the potential sources for innovation. Scientists communicate their findings through publications. Similarly, patents are legal documents to claim ownership of an invention but they also build a public paper trail of technology advancement. Thus publications and patents are an important, relevant and useful tool to follow and represent results of scientific and technological endeavours (Huang, 2010). Data mining is the extraction of relevant and useful information from large volume of data. Publication and Patent data systematically collected in worldwide databases such as the WoS and Patstat are used to track science and technology dynamic. Data mining faces an important challenge in a context of emergence when new technologies experience explosive growth, evolve rapidly and often cross and subvert existing scientific and technology fields. Emerging science and technology (biotechnology in the 1980s, nanotechnology today, other science and technology fields tomorrow), which often carry strong implications and potentialities for science, business and society, add to the challenge. Their content and dynamic are difficult to track at a time when they are struggling to define who they are, what they include and exclude and how they organize themselves internally. Such is the case for nanotechnology, where the quest for a relevant reliable and replicable way to extract relevant publications and patents, is an on-going process involving several teams worldwide (Glanzel 2003, Noyons 2003, Mogoutov and Kahane, 2007, Porter et al., 2008, Kostoff 2007, Leydesdorff and Zhou, 2007). Nanotechnology is a rapidly evolving emerging and dynamic field. Analysts argue that it is likely to be a “general purpose technology” (Youtie 2008, Laredo et al. 2010) with a potential impact across an entire range of industries and great implications on human health, the environment, sustainability and national security. The perceived potential value of nanotechnologies has led to the increased will of governments, academic institutions, firms and other societal actors to better understand what is happening in the field, who is active and where. There is thus an important challenge to develop robust methods to track the nanotechnology field while it rapidly develops and evolves. As a matter of fact, good quality and comprehensive extraction of data is a prerequisite for meaningful understanding and analysis. Huang 2010 as well as L'huillery et al. 2010 have compared the different methodologies developed, and reported on their robustness as well as on the similarities and discrepancies of results obtained. They confirmed the robustness and interest of the evolutionary lexical methodology we have developed (Mogoutov and Kahane, 2007). At that time, three requirements were central to the approach developed. First, it should not depend upon experts. Indeed, the on-going and extensive use of expert-based approaches is costly, time-consuming, and challenging to replicate such that the same outcomes result. This is an important restriction when facing a highly dynamic field where borders are constantly evolving requiring terminology requalification at different times. Second, it should allow updates in order to replicate and compare results while the nanotechnology field (and its lexicon) develop and expand. And third, it should be able to track the relative evolution of subfields inside nanotechnologies: in 2007 we translated this into a third requirement of being “modular”. While the initial development of our methodology was performed in order to extract data from 1998 to 2006, we later engaged in producing an update that could expand the database backward and forward in order to cover years 1991-2011. In our initial methodology, the selection of relevant terms was performed with knowledge built and keywords selected on one single year (2003). A simple solution was to reproduce the selection of terms for 2011, driving us to two semantic universes of nanotechnology, respectively built in 2003 and 2011. However Bonaccorsi (2010) has demonstrated that in a dynamic field such as nanotechnology, keywords often display short life and experience a type of Darwinian selection process. Using this approach, the characterisation of the evolution of the field over 20 years would have only relied on two years for the identification of relevant keywords. There would thus be a risk that we miss the richness of the exploration that shapes the dynamics of knowledge production. Not considering transient keywords that might have emerged and then disappeared, would be a serious drawback in such a dynamic field. There are multiple reasons for this. Two are of particular importance. One is about the learning that a stream of research, even if it goes on with a life of its own, has been experimented but proved not to be useful for colleagues at the time. The other lies in the fact that streams of research which for a while turn to be a dead end, can nevertheless reappear later and become a key resource as demonstrated in many instances. Such a limitation becomes even more visible when taking the whole period under review for identifying relevant keywords. This drove us to add a fourth requirement for such an approach: What is needed is a methodology, which allows us to incorporate and discard in real time relevant terms as they appear and disappear in the nanotechnology story. We need a methodology that allows us to track keywords as characters appear and disappear along the storyline in a movie. Thus, using nanotechnology as a showcase, we here report a data search strategy made of three consecutive steps. As in all the data search strategies for nanotechnology, we start with an initial seed built through the nanostring. We then use the same principle that we applied in our previous approach, that is expanding the initial seed through a dual process where additional keywords observed during a given period are sorted according to their internal specificity (e.g. the extent to which they provide value added meaning to a publication) and then tested in the overall database for ‘external specificity’ (e.g. the ratio of articles in the seed vs. articles in the overall database of publications). This selection of keywords is first applied on the whole dataset covering the 20 years, enabling a “static extension”. The third step builds the “dynamic extension” where additional keywords are identified through a yearly analysis of internal specificity within the nanostring, and selected depending upon their ‘external specificity’. Besides being applied in a specific way for nanotechnology, we claim that such a three steps strategy has universal value to describe the dynamics of emergent and fast evolving fields, transcending pre-existing classifications.
2010
Conferences
9.
Tari, Thomas; Caron, Pauline; Breucker, Philippe; Barbier, Marc
Characterising the Localisation of Projects Collaborations in Research Dynamics: methodological requirements and results for new visualisations of heterogeneous networks Conference
2010, (ENID 2010 - Methods and techniques for the exploitation of heterogeneous data sources).
@conference{Tari2010,
title = {Characterising the Localisation of Projects Collaborations in Research Dynamics: methodological requirements and results for new visualisations of heterogeneous networks},
author = {Thomas Tari and Pauline Caron and Philippe Breucker and Marc Barbier},
url = {https://www.researchgate.net/publication/262494844_Characterising_the_Localisation_of_Projects_Collaborations_in_Research_Dynamics_methodological_requirements_and_results_for_new_visualisations_of_heterogeneous_networks
https://www.researchgate.net/profile/Philippe-Breucker/publication/262494844_Characterising_the_Localisation_of_Projects_Collaborations_in_Research_Dynamics_methodological_requirements_and_results_for_new_visualisations_of_heterogeneous_networks/links/00b49537de9a036323000000/Characterising-the-Localisation-of-Projects-Collaborations-in-Research-Dynamics-methodological-requirements-and-results-for-new-visualisations-of-heterogeneous-networks.pdf},
year = {2010},
date = {2010-01-01},
abstract = {This communication proposes to discuss the construction of methodological requirements on databases building and software development, and aspires to show some concrete results in visualising heterogeneous networks of research dynamics considered through projects ecology.
Our reflection is grounded in the growing needs, either for decision makers or researchers of the STS and SPS communities to relay their analysis of facts on a convenient visualisation of structural relationships between heterogeneous actants. Their configuration in dedicated databases is worthy to focus on as they reflect the endogenous dynamics of research and R&D activities. Our hypothesis is that the aims, perimeter, contents and selected projects of funding programmes represent a relevant account of the un‐going technological and scientific dynamic on the one hand, and a relevant account of the mobilization and choices of scientific communities and science policy “makers” on the other hand. Those configurations rely firmly on spatial‐based organizations, mixing European, national and regional scales in formal and informal clusters. Our perspective in the CorTexT Platform of IFRIS is to enrich the studies of sciences dynamics on customized databases of research and R&D projects that represent Dthrough territories performative associations of laboratories, scientific teams, R&D firms and lead‐users.
Without ignoring the existence of a large array of scientific perspectives in Information Sciences about the measurement of science productions and science dynamics, we situate our work in the branch of analysis and visualisation of social networks. This field as well as indicators are central for evaluation and policy of science (Callon et al., 1986; Law et al., 1988). At present, the evolution of the analysis of scientific networks is largely attached to the question of characterizing collaborative and cognitive dynamics of knowledge production (Powell et al., 2005) and to the emergence of multi or trans‐disciplinary emerging fields of research (Lucio‐Arias, Leydesdorff, 2007) or paradigmatic field of research (Chavalarias, Cointet, 2008). Tracing and mapping knowledge in scientific database or in other electronic sources still represents a huge field of problems for many disciplines dealing with information. More locally, in relation to specific area of research, mapping heterogeneous networks appears to help the understanding of social dynamic of research activities (Cambrosio, Keating, Mogoutov, 2004; Cambrosio et al., 2006; Bourret et al., 2006).
Using co‐word analysis tools (RéseauLu), we have already proposed a social study focused on regime of knowledge production in agricultural science and on the significance of sustainability (Barbier, Mogoutov et al., 2008). We identified two emergent yet lively research themes: biofuels and vegetal fibres, and realized specific bibliometrical studies on those subjects. We then devoted sociological studies based on heterogeneous sources to fibres (Caron et Barbier 2009) and biofuel & bioenergy research (Tari 2009). Bearing in mind this type of overall view on scientific knowledge we wanted to develop an approach on research projects in those domains.},
note = {ENID 2010 - Methods and techniques for the exploitation of heterogeneous data sources},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
This communication proposes to discuss the construction of methodological requirements on databases building and software development, and aspires to show some concrete results in visualising heterogeneous networks of research dynamics considered through projects ecology.
Our reflection is grounded in the growing needs, either for decision makers or researchers of the STS and SPS communities to relay their analysis of facts on a convenient visualisation of structural relationships between heterogeneous actants. Their configuration in dedicated databases is worthy to focus on as they reflect the endogenous dynamics of research and R&D activities. Our hypothesis is that the aims, perimeter, contents and selected projects of funding programmes represent a relevant account of the un‐going technological and scientific dynamic on the one hand, and a relevant account of the mobilization and choices of scientific communities and science policy “makers” on the other hand. Those configurations rely firmly on spatial‐based organizations, mixing European, national and regional scales in formal and informal clusters. Our perspective in the CorTexT Platform of IFRIS is to enrich the studies of sciences dynamics on customized databases of research and R&D projects that represent Dthrough territories performative associations of laboratories, scientific teams, R&D firms and lead‐users.
Without ignoring the existence of a large array of scientific perspectives in Information Sciences about the measurement of science productions and science dynamics, we situate our work in the branch of analysis and visualisation of social networks. This field as well as indicators are central for evaluation and policy of science (Callon et al., 1986; Law et al., 1988). At present, the evolution of the analysis of scientific networks is largely attached to the question of characterizing collaborative and cognitive dynamics of knowledge production (Powell et al., 2005) and to the emergence of multi or trans‐disciplinary emerging fields of research (Lucio‐Arias, Leydesdorff, 2007) or paradigmatic field of research (Chavalarias, Cointet, 2008). Tracing and mapping knowledge in scientific database or in other electronic sources still represents a huge field of problems for many disciplines dealing with information. More locally, in relation to specific area of research, mapping heterogeneous networks appears to help the understanding of social dynamic of research activities (Cambrosio, Keating, Mogoutov, 2004; Cambrosio et al., 2006; Bourret et al., 2006).
Using co‐word analysis tools (RéseauLu), we have already proposed a social study focused on regime of knowledge production in agricultural science and on the significance of sustainability (Barbier, Mogoutov et al., 2008). We identified two emergent yet lively research themes: biofuels and vegetal fibres, and realized specific bibliometrical studies on those subjects. We then devoted sociological studies based on heterogeneous sources to fibres (Caron et Barbier 2009) and biofuel & bioenergy research (Tari 2009). Bearing in mind this type of overall view on scientific knowledge we wanted to develop an approach on research projects in those domains.
Our reflection is grounded in the growing needs, either for decision makers or researchers of the STS and SPS communities to relay their analysis of facts on a convenient visualisation of structural relationships between heterogeneous actants. Their configuration in dedicated databases is worthy to focus on as they reflect the endogenous dynamics of research and R&D activities. Our hypothesis is that the aims, perimeter, contents and selected projects of funding programmes represent a relevant account of the un‐going technological and scientific dynamic on the one hand, and a relevant account of the mobilization and choices of scientific communities and science policy “makers” on the other hand. Those configurations rely firmly on spatial‐based organizations, mixing European, national and regional scales in formal and informal clusters. Our perspective in the CorTexT Platform of IFRIS is to enrich the studies of sciences dynamics on customized databases of research and R&D projects that represent Dthrough territories performative associations of laboratories, scientific teams, R&D firms and lead‐users.
Without ignoring the existence of a large array of scientific perspectives in Information Sciences about the measurement of science productions and science dynamics, we situate our work in the branch of analysis and visualisation of social networks. This field as well as indicators are central for evaluation and policy of science (Callon et al., 1986; Law et al., 1988). At present, the evolution of the analysis of scientific networks is largely attached to the question of characterizing collaborative and cognitive dynamics of knowledge production (Powell et al., 2005) and to the emergence of multi or trans‐disciplinary emerging fields of research (Lucio‐Arias, Leydesdorff, 2007) or paradigmatic field of research (Chavalarias, Cointet, 2008). Tracing and mapping knowledge in scientific database or in other electronic sources still represents a huge field of problems for many disciplines dealing with information. More locally, in relation to specific area of research, mapping heterogeneous networks appears to help the understanding of social dynamic of research activities (Cambrosio, Keating, Mogoutov, 2004; Cambrosio et al., 2006; Bourret et al., 2006).
Using co‐word analysis tools (RéseauLu), we have already proposed a social study focused on regime of knowledge production in agricultural science and on the significance of sustainability (Barbier, Mogoutov et al., 2008). We identified two emergent yet lively research themes: biofuels and vegetal fibres, and realized specific bibliometrical studies on those subjects. We then devoted sociological studies based on heterogeneous sources to fibres (Caron et Barbier 2009) and biofuel & bioenergy research (Tari 2009). Bearing in mind this type of overall view on scientific knowledge we wanted to develop an approach on research projects in those domains.
Proceedings Articles
10.
Tari, Thomas; Barbier, Marc; Breucker, Philippe
Characterising dynamics of new sciences through project collaborations: a project-based scientometrica insight into French bioenergies research Proceedings Article
In: 3. European Network of Indicators Designers Conference: STI Indicators for Policymaking and Strategic Decisions. 2010-03-032010-03-05, Paris, FRA, 2010.
@inproceedings{tari2010characterising,
title = {Characterising dynamics of new sciences through project collaborations: a project-based scientometrica insight into French bioenergies research},
author = {Thomas Tari and Marc Barbier and Philippe Breucker},
url = {http://agris.fao.org/agris-search/search.do?recordID=FR2014006818},
year = {2010},
date = {2010-01-01},
booktitle = {3. European Network of Indicators Designers Conference: STI Indicators for Policymaking and Strategic Decisions. 2010-03-032010-03-05, Paris, FRA},
abstract = {This communication proposes to discuss the construction of methodological requirements on databases building and software development, and aspires to show some concrete results in visualising heterogeneous networks of research dynamics considered through projects ecology. Our reflection is grounded in the growing needs, either for decision makers or researchers of the STS and SPS communities to relay their analysis of facts on a convenient visualisation of structural relationships between heterogeneous actants. Their configuration in dedicated databases is worthy to focus on as they reflect the endogenous dynamics of research and R&D activities. Our hypothesis is that the aims, perimeter, contents and selected projects of funding programmes represent a relevant account of the un-going technological and scientific dynamic on the one hand, and a relevant account of the mobilization and choices of scientific communities and science policy “makers” on the other hand. Those configurations rely firmly on spatial-based organizations, mixing European, national and regional scales in formal and informal clusters. Our perspective in the CorTexT Platform of IFRIS is to enrich the studies of sciences dynamics on customized databases of research and R&D projects that represent through territories performative associations of laboratories, scientific teams, R&D firms and lead-users. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This communication proposes to discuss the construction of methodological requirements on databases building and software development, and aspires to show some concrete results in visualising heterogeneous networks of research dynamics considered through projects ecology. Our reflection is grounded in the growing needs, either for decision makers or researchers of the STS and SPS communities to relay their analysis of facts on a convenient visualisation of structural relationships between heterogeneous actants. Their configuration in dedicated databases is worthy to focus on as they reflect the endogenous dynamics of research and R&D activities. Our hypothesis is that the aims, perimeter, contents and selected projects of funding programmes represent a relevant account of the un-going technological and scientific dynamic on the one hand, and a relevant account of the mobilization and choices of scientific communities and science policy “makers” on the other hand. Those configurations rely firmly on spatial-based organizations, mixing European, national and regional scales in formal and informal clusters. Our perspective in the CorTexT Platform of IFRIS is to enrich the studies of sciences dynamics on customized databases of research and R&D projects that represent through territories performative associations of laboratories, scientific teams, R&D firms and lead-users.
LIST OF SCIENTIFIC WORKS THAT HAVE USED CORTEXT MANAGER
(Sources: Google Scholar, HAL, Scopus, WOS and search engines)
We are grateful that you have found CorTexT Manager useful. Over the years, you have been more than 890 authors to trust CorTexT for your publicly accessible analyzes. This represents a little less than 10% of CorTexT Manager user’s community. So, thank you!
We seek to understand how the scientific production that used CorText Manager has evolved and to characterise it. You will find here our analysis of this scientific production.
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