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@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}

}

@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},

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}

}

@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},

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}

}

@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},

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{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},

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}

}