Abstract
A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizations within a given field. With bibliometric analyses, researchers can acquire quantitatively valuable knowledge about a research area by using bibliographic information on academic publications provided by bibliographic data providers. Bibliometric analyses include the calculation of bibliometric networks to describe affiliations or similarities of bibliometric entities (e.g., authors) and group them into clusters representing subareas or communities. Calculating and visualizing bibliometric networks is a nontrivial and time-consuming data science task that requires highly skilled individuals. In addition to domain knowledge, researchers must often provide statistical knowledge and programming skills or use software tools having limited functionality and usability. In this paper, we present the ambalytics bibliometric platform, which reduces the complexity of bibliometric network analysis and the visualization of results. It accompanies users through the process of bibliometric analysis and eliminates the need for individuals to have programming skills and statistical knowledge, while preserving advanced functionality, such as algorithm parameterization, for experts. As a proof-of-concept, and as an example of bibliometric analyses outcomes, the calculation of research fronts networks based on a hybrid similarity approach is shown. Being designed to scale, ambalytics makes use of distributed systems concepts and technologies. It is based on the microservice architecture concept and uses the Kubernetes framework for orchestration. This paper presents the initial building block of a comprehensive bibliometric analysis platform called ambalytics, which aims at a high usability for users as well as scalability.
Highlights
The overall goal of science is to gather insights about reality through observation and experimentation and to predict events based on natural laws, i.e., to produce new knowledge about aspects of reality
This information is utilized by a quantitative approach for literature analysis, which is denoted with bibliometric analysis [1]
Toward the goal of filling this gap, we developed ambalytics, a web-based platform that simplifies conducting bibliometric network analyses and the visualization of results through the automation of parts of the required data science process
Summary
The overall goal of science is to gather insights about reality through observation and experimentation and to predict events based on natural laws, i.e., to produce new knowledge about aspects of reality Such knowledge is mainly articulated through the publication of documents, such as articles published in academic journals. While traditional literature reviews are a timeconsuming and manual task that only provides insights into a field of research on a sample basis, researchers can conduct a bibliometric analysis of a field of research to acquire quantitatively profound knowledge in a rather short period of time It is valuable—often indispensable—for academic researchers to have a deep understanding of a field of research
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