Bibliometric data from academic databases can be used to find relationships between metadata (authors, titles, citations etc.) and discover dominant topics. In this kernel, we'll use the Metaknowledge package and an information science and bibliometrics dataset from Web of Science/scopus to perform network analysis.
From: https://arxiv.org/ftp/arxiv/papers/2304/2304.14516.pdf
Exploratory Data Analysis (EDA) capabilities encompass the tools’ capacity for preliminary data analysis, pattern and trend identification, and data visualization, empowering researchers to uncover insights and formulate hypotheses. Network Analysis capabilities indicate the tools’ proficiency in examining and visualizing intricate relationships among entities such as authors, citations, and keywords, thereby aiding researchers in deciphering data structure and dynamics. Artificial Intelligence capabilities utilize Deep Learning techniques, like Topic Modeling, Embedding vectors, Text Summarization, and General NLP tasks, to augment the tools’ effectiveness.
The most common features these tools share include Citation Analysis, Collaboration Analysis, and World Collaboration Analysis, with many tools supporting Similarity Analysis and Topic Modeling. Both pyBibX and Scientopy offer a wide range of features.
Three of the most commonly used scientific databases are WoS, Scopus, and PubMed. While these databases share similarities in their coverage of scientific literature, they also have unique features that set them apart. Researchers working in a specific field, such as medicine, may find PubMed the most relevant, as it covers biomedical literature in-depth. On the other hand, researchers working in interdisciplinary fields may find WoS or Scopus more valuable, as they cover a broad range of subjects.
I'll try and answer the following questions:
Section 1 (i) How has the research domain evolved over the last decade? (ii) what are the major topics and trends within the data? (iii) how is research measuring the impact of accessibility to retail being conducted?
Section 2 (iv) How do researchers identify equitable access to retail? (methods and metrics) (v) What are the theoretical frameworks that guide research in access to retail? (vi) Which geographic scale is used by researchers to assess inequities in retail accessibility? (vii) What demographic groups are entitled to equitable access in research, and how are they involved in decision-making?