Research evolution of metal organic frameworks: A scientometric approach with human-in-the-loop
Article Category: Research Papers
Published Online: Jul 19, 2024
Page range: -
Received: Mar 19, 2024
Accepted: Jun 19, 2024
DOI: https://doi.org/10.2478/jdis-2024-0019
Keywords
© 2024 Xintong Zhao et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Purpose
This paper reports on a scientometric analysis bolstered by human-in-the-loop, domain experts, to examine the field of metal-organic frameworks (MOFs) research. Scientometric analyses reveal the intellectual landscape of a field. The study engaged MOF scientists in the design and review of our research workflow. MOF materials are an essential component in next-generation renewable energy storage and biomedical technologies. The research approach demonstrates how engaging experts, via human-in-the-loop processes, can help develop a comprehensive view of a field’s research trends, influential works, and specialized topics.
Design/methodology/approach
A scientometric analysis was conducted, integrating natural language processing (NLP), topic modeling, and network analysis methods. The analytical approach was enhanced through a human-in-the-loop iterative process involving MOF research scientists at selected intervals. MOF researcher feedback was incorporated into our method. The data sample included 65,209 MOF research articles. Python3 and software tool
Findings
The findings demonstrate the value of including domain experts in research workflows, refinement, and interpretation of results. At each stage of the analysis, the MOF researchers contributed to interpreting the results and method refinements targeting our focus on MOF research. This study identified influential works and their themes. Our findings also underscore four main MOF research directions and applications.
Research limitations
This study is limited by the sample (articles identified and referenced by the Cambridge Structural Database) that informed our analysis.
Practical implications
Our findings contribute to addressing the current gap in fully mapping out the comprehensive landscape of MOF research. Additionally, the results will help domain scientists target future research directions.
Originality/value
To the best of our knowledge, the number of publications collected for analysis exceeds those of previous studies. This enabled us to explore a more extensive body of MOF research compared to previous studies. Another contribution of our work is the iterative engagement of domain scientists, who brought in-depth, expert interpretation to the data analysis, helping hone the study.