Version 1
: Received: 17 July 2024 / Approved: 18 July 2024 / Online: 18 July 2024 (11:28:19 CEST)
How to cite:
Chigarev, B. Visualization of Bibliometric Data Analysis on AI & Machine Learning Topic from the Scilit Abstract Database for 2021-2023. Preprints2024, 2024071464. https://doi.org/10.20944/preprints202407.1464.v1
Chigarev, B. Visualization of Bibliometric Data Analysis on AI & Machine Learning Topic from the Scilit Abstract Database for 2021-2023. Preprints 2024, 2024071464. https://doi.org/10.20944/preprints202407.1464.v1
Chigarev, B. Visualization of Bibliometric Data Analysis on AI & Machine Learning Topic from the Scilit Abstract Database for 2021-2023. Preprints2024, 2024071464. https://doi.org/10.20944/preprints202407.1464.v1
APA Style
Chigarev, B. (2024). Visualization of Bibliometric Data Analysis on AI & Machine Learning Topic from the Scilit Abstract Database for 2021-2023. Preprints. https://doi.org/10.20944/preprints202407.1464.v1
Chicago/Turabian Style
Chigarev, B. 2024 "Visualization of Bibliometric Data Analysis on AI & Machine Learning Topic from the Scilit Abstract Database for 2021-2023" Preprints. https://doi.org/10.20944/preprints202407.1464.v1
Abstract
Objectives. The aim of this study was to demonstrate the ability to visualize the results of the Scilit platform's bibliometric data analysis on the topic "AI & Machine Learning" to identify publications reflecting specific issues of the topic. Data source. Bibliometric records exported from the Scilit platform on the topic "AI & Machine Learning" for the years 2021–2023 were used. For each year, 6,000 records were downloaded in CSV and RIS format. Programs and utilities used. VOSviewer, Scimago Graphica, Inkscape, FP-growth utility, GSDMM algorithm. Used services: Elicit, QuillBot, Litmaps. Results. It has been shown that bibliometric data from the open access abstract database Scilit can serve as a quality alternative to subscription-only databases. Data exported from the Scilit platform require preprocessing to make them available in a format that can be processed by programs such as VOSviewer and Scimago Graphica. The use of GSDMM and FP-growth algorithms is effective for structuring bibliometric data for further visualization. The Scimago Graphica software provides wide possibilities for building compound diagrams, in particular, for representing the network of keywords in such important coordinates for bibliometric analysis as average year of publication and average normalized citation, as well as for building an alluvial diagram of co-occurrence of more than two keywords. The possibility of using such services as elicit.com, quillbot.com and app.litmaps.com to accelerate the selection of publications on the topic under study is shown.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
