当前位置: X-MOL 学术arXiv.cs.CY › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Studying the characteristics of scientific communities using individual-level bibliometrics: the case of Big Data research
arXiv - CS - Computers and Society Pub Date : 2021-06-10 , DOI: arxiv-2106.05581
Xiaozan Lyu, Rodrigo Costas

Unlike most bibliometric studies focusing on publications, taking Big Data research as a case study, we introduce a novel bibliometric approach to unfold the status of a given scientific community from an individual level perspective. We study the academic age, production, and research focus of the community of authors active in Big Data research. Artificial Intelligence (AI) is selected as a reference area for comparative purposes. Results show that the academic realm of "Big Data" is a growing topic with an expanding community of authors, particularly of new authors every year. Compared to AI, Big Data attracts authors with a longer academic age, who can be regarded to have accumulated some publishing experience before entering the community. Despite the highly skewed distribution of productivity amongst researchers in both communities, Big Data authors have higher values of both research focus and production than those of AI. Considering the community size, overall academic age, and persistence of publishing on the topic, our results support the idea of Big Data as a research topic with attractiveness for researchers. We argue that the community-focused indicators proposed in this study could be generalized to investigate the development and dynamics of other research fields and topics.

中文翻译:

使用个体层面的文献计量学研究科学共同体的特征:以大数据研究为例

与大多数专注于出版物的文献计量研究不同,以大数据研究为案例研究,我们引入了一种新颖的文献计量方法,从个人层面的角度展示给定科学界的状态。我们研究活跃于大数据研究的作者社区的学术年龄、生产和研究重点。人工智能 (AI) 被选为参考领域以进行比较。结果表明,“大数据”的学术领域是一个日益增长的话题,作者社区不断扩大,尤其是每年都有新作者。相较于人工智能,大数据吸引的是学龄较长的作者,在进入社区之前也算是积累了一定的出版经验。尽管两个社区的研究人员之间的生产力分布高度倾斜,与人工智能相比,大数据作者在研究重点和生产方面的价值更高。考虑到社区规模、整体学术年龄和在该主题上发表的持久性,我们的结果支持将大数据作为对研究人员具有吸引力的研究主题的想法。我们认为,本研究中提出的以社区为中心的指标可以推广到调查其他研究领域和主题的发展和动态。
更新日期:2021-06-11
down
wechat
bug