2023
Journal Articles
Scherngell, Thomas; Schwegmann, Katharina; Zahradnik, Georg
The geographical dynamics of global R&D collaboration networks in robotics: Evidence from co-patenting activities across urban areas worldwide Journal Article
In: PLOS ONE, 2023.
@article{Scherngell2023,
title = {The geographical dynamics of global R&D collaboration networks in robotics: Evidence from co-patenting activities across urban areas worldwide},
author = { Thomas Scherngell and Katharina Schwegmann and Georg Zahradnik},
editor = {Celine Rozenblat from University of Lausanne SWITZERLAND},
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0281353},
doi = {https://doi.org/10.1371/journal.pone.0281353},
year = {2023},
date = {2023-04-13},
urldate = {2023-04-13},
journal = {PLOS ONE},
abstract = {The focus of this study is on the geography of robotics Research and Development (R&D) activities. The objectives are, first, to identify hotspots in robotics R&D worldwide, and second, to characterise structures and dynamics of global robotics R&D collaboration networks through detailed geographical lenses of global urban areas. We use patents as marker for R&D activities, and accordingly focus on technologically oriented R&D, drawing on information from patents applied for between 2002 and 2016. We employ an appropriate search strategy to identify relevant robotics patents based on detailed levels of the Cooperative Patent Classification (CPC) and assign patents to more than 900 global urban areas based on the inventor addresses. The co-patent networks are examined from a Social Network Analysis (SNA) perspective by means of robotics co-patents, contributing to a global network where urban areas are the nodes inter-linked by joint inventive activities recorded in robotics patents. Global SNA measures illustrate structures and dynamics of the network as a whole, while local measures indicate the specific positioning and roles of urban areas in the network. The results are original in characterising the global spatial emergence of this generic new industry, highlighting prominent urban hotspots in terms of specialisation in robotics R&D, pointing to a global shift reflected by the increasing role of emerging economies, in particular China. The global robotics R&D has grown significantly both in total patenting and also in terms of R&D collaboration activities between urban areas. Also, for the networks, growth is not equally distributed, but is rather characterised by significant spatial shifts, both in terms of cities declining or climbing up the specialisation ranking, but even more in terms of the spatial network structure.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2021
Journal Articles
Shen, Yuanfei; Ji, Ling; Xie, Yulei; Huang, Guohe; Li, Xin; Huang, Lucheng
Research landscape and hot topics of rooftop PV: A bibliometric and network analysis Journal Article
In: Energy and Buildings, vol. 251, pp. 111333, 2021, ISSN: 0378-7788.
@article{Shen2021,
title = {Research landscape and hot topics of rooftop PV: A bibliometric and network analysis},
author = {Yuanfei Shen and Ling Ji and Yulei Xie and Guohe Huang and Xin Li and Lucheng Huang},
url = {https://www.sciencedirect.com/science/article/pii/S0378778821006174},
doi = {https://doi.org/10.1016/j.enbuild.2021.111333},
issn = {0378-7788},
year = {2021},
date = {2021-11-15},
urldate = {2021-11-15},
journal = {Energy and Buildings},
volume = {251},
pages = {111333},
abstract = {Rooftop photovoltaic (PV) system, as part of the renewable energy development strategy to guarantee energy security and reduce greenhouse gas emissions in urban areas, has received a lot of attention during the last decade. To provide an up-to-date and systematic research landscape of the rooftop PV field, this study conducted the bibliometric analysis, collaboration network analysis, co-citation analysis, and hotspots detection based on 595 articles collected from the core collection database of Web of Science. The results showed that the number of publications per year in this field has increased steadily since 2015. The USA was the most important contributor in this research field in terms of quantity (number of publications) and impact (number of citations). The co-authorship communities were obtained by collaboration network analysis, and the international collaboration is expected to be further strengthened according to the research focuses of each community. The key knowledge base and the main hot topics of the rooftop PV research field were identified from co-citation analysis and keywords co-occurrence network. Furthermore, based on the literature review, a detailed analysis of the main topics was provided for a better understanding of the current research trends and opportunities. This study can be served as a strategic review of the rooftop PV field to help relevant researchers carry out in-depth research in the future.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Xu, Xin; Hu, Jiming; Lyu, Xiaoguang; He, Huang; Xingyu, Cheng
Exploring the Interdisciplinary Nature of Precision Medicine:Network Analysis and Visualization Journal Article
In: JMIR Medical Informatics, 2021.
@article{Xu2021,
title = {Exploring the Interdisciplinary Nature of Precision Medicine:Network Analysis and Visualization},
author = {Xin Xu and Jiming Hu and Xiaoguang Lyu and Huang He and Cheng Xingyu },
doi = {10.2196/23562 },
year = {2021},
date = {2021-01-11},
urldate = {2021-01-11},
journal = {JMIR Medical Informatics},
abstract = {The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine based on co-occurrences and social network analysis. A total of 7544 studies about precision medicine, published between 2010 and 2019, were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence analysis, and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities. The results indicate that 105 disciplines are involved in precision medicine research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in precision medicine mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in precision medicine is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are 7 interdisciplinary communities in the precision medicine collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics and heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in precision medicine. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in precision medicine. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we use evolution graphs to accurately estimate the developmental trend of precision medicine, such as biological big data processing, molecular imaging, and widespread clinical applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Proceedings Articles
Abdelhamid, Sherif; Jalali, Yousef; Katz, Andrew
Factors Associated with Collaboration Networks in ASEE Conference Papers Proceedings Article
In: 2021 ASEE Virtual Annual Conference Content Access, ASEE Conferences, Virtual Conference, 2021, (https://peer.asee.org/37173).
@inproceedings{nokey,
title = {Factors Associated with Collaboration Networks in ASEE Conference Papers},
author = {Sherif Abdelhamid and Yousef Jalali and Andrew Katz},
url = {https://peer.asee.org/factors-associated-with-collaboration-networks-in-asee-conference-papers},
year = {2021},
date = {2021-07-26},
booktitle = {2021 ASEE Virtual Annual Conference Content Access},
publisher = {ASEE Conferences},
address = {Virtual Conference},
abstract = {Research collaborations are essential to advance rigorous scholarship, perform transformative science, and accelerate engineering education innovation. With this in mind, the engineering education community should continue investigating and evaluating the key factors that hinder or promote collaborative research within and across institutions, especially amidst efforts to continue to grow the field. Over the last few decades, research collaborations across institutions have grown significantly—however, few studies have examined the relationship between such collaborations and the institutional characteristics such as ranking, geographic location, or classifications (e.g., the Carnegie Classification of Higher Education Institutions) when studying collaboration networks. Our paper uses social network analysis (SNA) to help fill this gap by examining how some of these institutional characteristics are related to the institutions' collaborations and network positions. Social network analysis has emerged as a useful approach to measure research collaboration by evaluating several types of collaboration networks, including co-authorship networks. In this paper, we consider the institution network. Nodes in this type of network represent institutions, while links represent the pairwise collaboration between two institutions. Each link also has a weight that represents the collaboration frequency. The links form a social space that we can map and analyze to reveal systematic patterns in the broader engineering education community that might otherwise pass unobserved. For this study, we collected information about all papers published between 1996 and 2019 in the American Society for Engineering Education (ASEE) annual conference proceedings. From this dataset, we built the inter-institutional collaboration network and identified structural network properties, connected components, and modularity classes. The network data were then linked to data regarding each institution's (i) Carnegie classification, (ii) rankings based on the 2020 QS World University Rankings, and (iii) geographic location. With this augmented dataset, we were able to answer research questions about factors associated with inter-institutional collaborations through statistical analysis. In doing so, we identify the key patterns, trends, and associations from our networked data. Among the results, we found that a research institution's classification is significantly related to its network positions in the collaboration network, specifically its modularity class. Additionally, we found correlations between the institutions' centrality measures in the network, including the degree centrality, betweenness centrality, and structural holes. Our findings also indicate an association between the institutions' geographical proximity and their research collaborations. Overall, this study provides a lens through which engineering education researchers, faculty members, and administrators can understand the current state of research collaborations within and across institutions. The results can help researchers answer (and raise more) important research questions, support administrators in making decisions on funding and institutional partnerships, and help faculty members design and develop more effective programs that facilitate research collaborations.},
note = {https://peer.asee.org/37173},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
Journal Articles
Matos, Fábio L; Ross, Steve W; ann ida Huvenne, Veerle; Davies, Jaime; Cunha, Marina R
Canyons pride and prejudice: Exploring the submarine canyon research landscape, a history of geographic and thematic bias Journal Article
In: Progress in Oceanography, 2018.
@article{matos2018canyons,
title = {Canyons pride and prejudice: Exploring the submarine canyon research landscape, a history of geographic and thematic bias},
author = {Fábio L Matos and Steve W Ross and Veerle ann ida Huvenne and Jaime Davies and Marina R Cunha},
url = {https://doi.org/10.1016/j.pocean.2018.04.010},
doi = {10.1016/j.pocean.2018.04.010},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Progress in Oceanography},
publisher = {Elsevier},
abstract = {We mapped submarine canyon research using a scientometric approach to define and characterize its scientific landscape based on a comprehensive bibliographic dataset. The abundance of studies covering structural and functional aspects of submarine canyons allowed us to identify the existing knowledge clusters, historical trends, and emergent topics in canyon research. Our analysis documented a network of knowledge clusters of which four were particularly relevant: a strong cluster on “Geology & Geophysics”, well established since the beginnings of canyon research; a cluster on “Biology & Ecology” that gained strength primarily over the past two to three decades; a cluster on “Oceanographic Processes” which occupied a central position in the network and connected strongly to almost all the other clusters and especially to the fourth main cluster on “Modelling”. A smaller, but also well connected, cluster on “Biogeochemistry” related closely to “Biology & Ecology”, and three other small clusters (“Sedimentology”, “Sediments & Tidal Currents”, “Canyon Sampling”) bridged the main clusters. Finally, we identified three small, but specific satellite clusters (“Oil & Gas”, “Chemosynthetic Communities”, “Molecular & Symbionts”). The high-level structure of the knowledge network reflects a latent interdisciplinarity in canyon research. However, the evolution of the research lines over the past nine decades suggests that this pattern arose mostly in the new millennium. Emergent research topics in the last decade also reveal a concern regarding anthropogenic impacts and climate-driven processes. Our results also show a well implemented and international collaboration network, although research efforts have been mainly directed towards only a few canyon systems. A geographical and thematic bias also characterizes canyon research, with specific topics addressed preferentially in particular canyons by different leading research institutions. This spatial and thematic bias, together with the paucity of truly inter-disciplinary studies, may be the most important limitation to integrated knowledge and development in canyon research and hinders a global, more comprehensive understanding of canyon patterns and processes. The scientific landscape mapping and the complementary results are made available as an open and interactive platform that canyon stakeholders can use as a tool to identify knowledge gaps, to find key players in the global collaboration network and to facilitate planning of future research in submarine canyons.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Technical Reports
Aubin, Sophie; Huber, Madeleine
eROSA. e-infrastructure Roadmap for Open Science in Agriculture : bibliometric study results Technical Report
INRA, Horizon 2020 2018, (Ref. Ares(2018)3404573).
@techreport{Aubin2018,
title = {eROSA. e-infrastructure Roadmap for Open Science in Agriculture : bibliometric study results},
author = {Sophie Aubin and Madeleine Huber},
url = {https://zenodo.org/record/1305000/files/Bibliometric%20study%20results.pdf},
year = {2018},
date = {2018-06-28},
urldate = {2018-06-28},
institution = {INRA, Horizon 2020},
abstract = {This study highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. The added value of such a methodological approach is the resulting ability to provide a detailed answer to the question “who does what?” by collecting, processing, analysing and visualising the metadata1 of related scientific publications. The study focuses on articles that have been published in the past 10 years (i.e. during the period 2005-2015). As such, the analysis is a first attempt at delineating, mapping and describing the scientific community that the e-ROSA project seeks to engage with. It neither aims at being exhaustive nor at providing an evaluation on the scientific excellence of identified stakeholders as this is not the goal of the community-building activity under e-ROSA. The specific objectives of the analysis include:
1 - The identification of scientists and related collaboration networks involved in data science for agriculture in order to initiate further contact while building and engaging with the e-ROSA community throughout the project: e.g. these results provide valuable contacts in the context of the desk surveys that will be carried out under Work Package 1 in order to consolidate and reach out to the community, and in the context of the workshops organised under Work Package 2 that seek community-building and co-design of the e-ROSA Roadmap.
2 - The identification of specific domains related to data and computer science that are of interest to identified scientists (i.e. working on agricultural issues).
3 - The identification of related conferences and journals that the e-ROSA project can target in order to effectively reach out to the relevant communities involved in data science issues related to agriculture.},
note = {Ref. Ares(2018)3404573},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
1 - The identification of scientists and related collaboration networks involved in data science for agriculture in order to initiate further contact while building and engaging with the e-ROSA community throughout the project: e.g. these results provide valuable contacts in the context of the desk surveys that will be carried out under Work Package 1 in order to consolidate and reach out to the community, and in the context of the workshops organised under Work Package 2 that seek community-building and co-design of the e-ROSA Roadmap.
2 - The identification of specific domains related to data and computer science that are of interest to identified scientists (i.e. working on agricultural issues).
3 - The identification of related conferences and journals that the e-ROSA project can target in order to effectively reach out to the relevant communities involved in data science issues related to agriculture.
2017
Journal Articles
Hu, Jiming; Zhang, Yin
Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization Journal Article
In: Scientometrics, vol. 112, pp. 91–109, 2017.
@article{Hu2017,
title = {Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization},
author = {Jiming Hu and Yin Zhang},
doi = {10.1007/s11192-017-2383-1},
year = {2017},
date = {2017-05-01},
urldate = {2017-05-01},
journal = {Scientometrics},
volume = {112},
pages = {91–109},
abstract = {Big Data is a research field involving a large number of collaborating disciplines. Based on bibliometric data downloaded from the Web of Science, this study applies various social network analysis and visualization tools to examine the structure and patterns of interdisciplinary collaborations, as well as the recently evolving overall pattern. This study presents the descriptive statistics of disciplines involved in publishing Big Data research; and network indicators of the interdisciplinary collaborations among disciplines, interdisciplinary communities, interdisciplinary networks, and changes in discipline communities over time. The findings indicate that the scope of disciplines involved in Big Data research is broad, but that the disciplinary distribution is unbalanced. The overall collaboration among disciplines tends to be concentrated in several key fields. According to the network indicators, Computer Science, Engineering, and Business and Economics are the most important contributors to Big Data research, given their position and role in the research collaboration network. Centering around a few important disciplines, all fields related to Big Data research are aggregated into communities, suggesting some related research areas, and directions for Big Data research. An ever-changing roster of related disciplines provides support, as illustrated by the evolving graph of communities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2016
Journal Articles
Raimbault, Benjamin; Cointet, Jean-Philippe; Joly, Pierre-Benoît
Mapping the emergence of synthetic biology Journal Article
In: PloS one, vol. 11, no. 9, pp. e0161522, 2016.
@article{raimbault2016mapping,
title = {Mapping the emergence of synthetic biology},
author = {Benjamin Raimbault and Jean-Philippe Cointet and Pierre-Benoît Joly},
url = {https://doi.org/10.1371/journal.pone.0161522},
doi = {10.1371/journal.pone.0161522},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {PloS one},
volume = {11},
number = {9},
pages = {e0161522},
publisher = {Public Library of Science},
abstract = {In this paper, we apply an original scientometric analyses to a corpus comprising synthetic biology (SynBio) publications in Thomson Reuters Web of Science to characterize the emergence of this new scientific field. Three results were drawn from this empirical investigation. First, despite the exponential growth of publications, the study of population level statistics (newcomers proportion, collaboration network structure) shows that SynBio has entered a stabilization process since 2010. Second, the mapping of textual and citational networks shows that SynBio is characterized by high heterogeneity and four different approaches: the central approach, where biobrick engineering is the most widespread; genome engineering; protocell creation; and metabolic engineering. We suggest that synthetic biology acts as an umbrella term allowing for the mobilization of resources, and also serves to relate scientific content and promises of applications. Third, we observed a strong intertwinement between epistemic and socio-economic dynamics. Measuring scientific production and impact and using structural analysis data, we identified a core set of mostly American scientists. Biographical analysis shows that these central and influential scientists act as “boundary spanners,” meaning that their importance to the field lies not only in their academic contributions, but also in their capacity to interact with other social spaces that are outside the academic sphere.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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 1050 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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