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@article{Autrive2023,

title = {Pratiques de l’analyse de réseau en géographie. Formations et emprunts disciplinaires},

author = {Élise Autrive and Laurent Beauguitte and Ninon Briot and Paul Gourdon},

url = {https://hal.science/hal-03920666v2

https://hal.science/hal-03920666v2/file/2023_10_ARCS_v2.pdf},

year  = {2023},

date = {2023-12-01},

journal = {ARCS},

abstract = {Three geographers who have defended their thesis in 2021 and who use network analysis methods from other disciplines (sociology, computer science, physics) explain the choice of methods, indicators and software used in their work.

They insist on both the weakness of initial training and the crucial role of self-training.

The strengths and weaknesses of network analysis and link-node visualizations are finally discussed.},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{León-Vargas2021,

title = {Two Decades of Research in Artificial Pancreas: Insights from a Bibliometric Analysis},

author = {Fabian León-Vargas and Jineth Andrea Arango Oviedo and Héctor Javier Luna Wandurraga},

url = {https://doi.org/10.1177/19322968211005500},

doi = {10.1177/19322968211005500},

year  = {2021},

date = {2021-04-15},

urldate = {2021-04-15},

journal = {Journal of Diabetes Science and Technology},

volume = {0},

number = {0},

pages = {19322968211005500},

abstract = {Artificial pancreas is a well-known research topic devoted to achieving better glycemic outcomes that has been attracting increasing attention over the years. However, there is a lack of systematic, chronological, and synthesizing studies that show the background of the knowledge generation in this field. This study implements a bibliometric analysis to recognize the main documents, type of publications, research categories, countries, keywords, organizations, and authors related to this topic.Methods:Web of Science core collection database was accessed from 2000 to 2020 in order to select high-quality scientific documents based on a specific search query. Bibexcel, MS Excel, Power BI, R-Studio, VOSviewer, and CorText software were used for a descriptive and network analysis based on the local database obtained. Bibliometric parameters as the h-index, frequencies, co-authorship and co-ocurrences were computed.Results:A total of 756 documents were included that show a growing scientific production on this topic with an increasing contribution from engineering. Outstanding authors, organizations, and countries were identified. An analysis of trends in research was conducted according to the scientific categories of the Web of Science database to identify the main research interests of the last 2 decades and the emerging areas with greater prominence in the coming years. A keyword network analysis allowed to identify the main stages in the development of the AP research over time.Conclusions:Results reveal a comprehensive background of the knowledge generation for the AP topic during the last 2 decades, which has been strengthened with international collaborations and a remarkable interdisciplinarity between endocrinology and engineering, giving rise to a growing number of research areas over time, where computer science and medical informatics stand out as the main emerging research areas.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Deng2020b,

title = {Mapping the interdisciplinarity in information behavior research: a quantitative study using diversity measure and co‐occurrence analysis},

author = {Shengli Deng and Sudi Xia},

doi = {https://doi.org/10.1007/s11192-020-03465-x},

year  = {2020},

date = {2020-04-11},

urldate = {2020-04-11},

abstract = {Information behavior research is an interdisciplinary field in essence due to the investiga- tion of interdisciplinary in previous work. To track the changes in interdisciplinarity of this field, more efforts should be put on basis of previous work. Based on publications searched from Web of Science from 2000 to 2018, we explored the interdisciplinarity of this field drawing on network analysis and diversity measure. Findings showed that although variety of disciplines in this field augmented significantly, the distribution of disciplines is unbal- anced and concentrated on some dominant disciplines such as computer science, engineer- ing, psychology, social science and medicine, etc. Relationships among disciplines have evolved over time and mainly focused on neighboring disciplines instead of distinct disci- plines. Computer science, engineering, psychology, health science and social science func- tion as intermediate disciplines connecting distinct disciplinary groups. Besides, the meas- urement using diversity measure shows that interdisciplinary degree of this field appears to decrease. This study contributes to the evolution and measurement of interdisciplinarity of information behavior research, which has implications for researchers and practitioners in this field.},

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}

}