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@article{nokey,

title = {Tracking the featured topics of the International Science of Team Science conference series and their evolution during 2010-2019},

author = {Ruinan Li and Raf Guns and Tim C. E. Engels and Lin Zhang and Ying Huang},

url = {https://link.springer.com/article/10.1007/s11192-023-04651-3

https://www.researchgate.net/profile/Ying-Huang-137/publication/368301245_Tracking_the_featured_topics_of_the_International_Science_of_Team_Science_conference_series_and_their_evolution_during_2010-2019/links/63e1299962d2a24f920a754a/Tracking-the-featured-topics-of-the-International-Science-of-Team-Science-conference-series-and-their-evolution-during-2010-2019.pdf},

doi = {/10.1007/s11192-023-04651-3},

year  = {2023},

date = {2023-02-11},

journal = {Scientometrics},

issue = {128},

pages = {2447–2469},

abstract = {In this paper, we study how research topics and trends have evolved in the field of the science of team science (SciTS). Over the past 12 years, the International Science of Team Science conference has been making efforts to understand and enhance the processes and outcomes of collaborative team science. We argue that the sessions and papers in academic conferences are the best way to reflect the latest research trends. Based on the panel sessions and submitted papers in conference programs during 2010-2019, this study tracks the featured topics and how they have evolved in the field. We extracted terms from the titles of the sessions and papers, and visualized the research hotspots, research topics, and how they have evolved for analysis. We found that the research hotspots are constantly changing, and the research topics present evolutionary characteristics, such as continuation, split, and fusion. Additionally, we examined the models and case studies of team science, the characteristics and dynamics of teams, the interdisciplinary research dynamics in team science, team science education and training, and how team science is measured and evaluated. These results summarize the major research trends and opportunities in the SciTS field.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{Milia2022,

title = {Local emergence, global expansion: understanding the structural evolution of a bi-lingual national research landscape},

author = {Matias Federico Milia and Ariadna Nebot Giralt and Rigas Arvanitis},

url = {https://link.springer.com/article/10.1007/s11192-022-04403-9},

doi = {10.1007/s11192-022-04403-9},

year  = {2022},

date = {2022-06-21},

urldate = {2022-06-21},

journal = {Scientometrics},

abstract = {Research institutions organize their scientific activities in an increasingly diverse landscape. In matters of global interest, research relies on an ever-more cross-disciplinary background, which reveals intriguing questions concerning the local dynamics vs. global audiences. This paper proposes new methodological tools to assess, from a strategic perspective, the evolution of a given research landscape. It relies on the Global Research Institute of Paris’ recent experience, a new interdisciplinary Institute focusing on globalization topics beyond the usual economic meaning. The Institute leans on a broad and diverse set of research units of the Université de Paris and relates to the broad landscape of social sciences in France. This article charts the evolution of French authors’ scientific publications on the Institute’s thematic interests in French and English. It focuses on the structural features of the debate, namely the volume, the underlying historical semantic structure, and its main thematic domains. The paper offers significant evidence to understand knowledge circulation dynamics and links that non-speaking countries’ scientific literature builds with the English one.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bai2020,

title = {Visualizing research trends and research theme evolution in E‐learning field: 1999–2018},

author = {Yang Bai and Hongxiu Li and Yong Liu},

url = {https://link.springer.com/article/10.1007/s11192-020-03760-7},

doi = {https://doi.org/10.1007/s11192-020-03760-7},

year  = {2020},

date = {2020-11-19},

urldate = {2020-11-19},

journal = {Scientometrics},

abstract = {This paper aims to provide a comprehensive understanding of the evolution of major research themes and trends in e-learning research. A co-word analysis is applied for the analysis of the 21,656 keywords collected from 7214 articles published in 10 journals in the field of e-learning from the years 1999 to 2018. Specifically, a cluster analysis, social network analysis, strategic diagram, and graph theory were applied in the analysis for two time periods: 1999–2008 and 2009–2018. The study detects the bridging, popular, and core topics in e-learning research for the two periods. The research results indicate that e-learning research has undergone a health evolution over the past two decades. There is a temporal continuity of e-learning research because some research topics have kept their continuity over the studied 20 years. Meanwhile, the research traditions in the e-learning field are also continuously evolving with the development of new technologies. The results also offer useful hints on the future direction of how the field may evolve.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bordignon2020,

title = {A scientometric review of permafrost research based on textual analysis (1948-2020)},

author = {Frederique Bordignon},

url = {https://rdcu.be/b8FBh

http://dx.doi.org/10.17632/d8gvm96ykm.1

https://hal-enpc.archives-ouvertes.fr/hal-02963536v1},

doi = {https://doi.org/10.1007/s11192-020-03747-4},

year  = {2020},

date = {2020-10-17},

urldate = {2020-10-17},

journal = {Scientometrics},

abstract = {This article proposes an analysis of research dedicated to permafrost. Its originality is twofold: it covers a corpus (n=16,249) that has never been reviewed before and also makes use of a methodology based on successive textual analysis processes. With the text-mining of additional corpuses, we produce lists of qualified terms to fine-tune the indexing of the main corpus and isolate relevant terminology dedicated to infrastructure and soil properties. With these enrichments combined with other terminological extractions (such as place names recognition), we reveal the internal structure of permafrost research with the help of visual mapping and easily prove that permafrost research is multidisciplinary and multi-topical The semantic map and the diachronic analysis of terms clusters show that the interest had turned since the 1980s towards the role of climate change but also on China's needs for its highway and railway construction sites. The very strong and growing impact of Chinese research, focused on the Tibetan area, is one of the highlights of our data. Furthermore, we propose a focus on infrastructure vulnerability and use soil properties as a proxy to measure the existing interactions between two distinct research communities. The results suggest that research has mainly focused so far on the feasibility of building on frozen ground and exploiting soils, but remains at an early stage of addressing the impact of global warming on infrastructure degradation and its resilience. This study offers insights to permafrost experts, but also provide a methodology that could be reused for other investigations. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{bras2017oncology,

title = {Oncology research in late twentieth century and turn of the century Portugal: a scientometric approach to its institutional and semantic dimensions},

author = {Oriana Rainho Brás and Jean-Philippe Cointet and Alberto Cambrosio and Leonor David and João Arriscado Nunes and Fátima Cardoso and Carmen Jerónimo},

url = {https://doi.org/10.1007/s11192-017-2491-y},

doi = {10.1007/s11192-017-2491-y},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Scientometrics},

volume = {113},

number = {2},

pages = {867-888},

publisher = {Springer},

abstract = {This paper analyses the developmental dynamics of oncology research in Portugal during the second half of the twentieth century and early twenty first century. Grounding its conclusions in a scientometric analysis of a database of publications covering the period 1976–2015, the paper shows how the expansion of oncology research from the end of the 1990s through the 2000s is closely related to science and technology policy decisions in the country. The main actors of the institutional evolution of the field are public organizations, both hospital and academia/research-based, frequently working together. Portuguese oncology research focused especially on organ-based cancers, underlining the strong link between the laboratory and the clinic. Accordingly, translational research is a major trend in oncology research, as evidenced by the analysis of publications in major journals and inter-citation maps. Networks of institutional co-authorships show the importance of regional and international collaborations. The collaboration patterns over time reveal the importance of national and European collaborations during the initial years covered by our publication database, in line with the major impact of Portugal’s integration into the European Union, and a growing importance of regional collaborations, as well as with North and South American institutions in more recent years. Portugal provides a case study of how twentieth century policies at the national and European levels have impacted on the evolution of oncology research in countries from southern Europe.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}