Series Vol. 12 , 17 November 2023
* Author to whom correspondence should be addressed.
The Scopus database, which includes many open-access items, conference papers, funding details, and patent linkages, has developed as a vital resource within the dynamic social and economic environment. Gaining popularity in several fields, systematic reviews synthesize the relevant research literature in order to guide deliberative judgments. However, researchers require assistance in keeping up with the ever-increasing multidisciplinary nature of work and the ever-changing nature of information. Researchers need efficient methods to navigate and leverage the wealth of available knowledge for their systematic review processes as the number of scholarly production grows tremendously. This study employs descriptive statistics to examine and graphically present the bibliography (the list of sources cited in the text). This study was conducted in Dr. Jodi Schneider's lab and aims to identify trends in scholarly publishing and evaluate the overall content of scholarly works. Publication dates, item types, author lists, titles, and keywords are examined in the analysis, which takes CSV(Comma Separated Values), BibTeX, or RIS formats as input. Emerging research fields and patterns of collaboration can be better understood with the help of the descriptive statistics generated. Word clouds also help readers evaluate the quality and topic focus of the papers by providing a visual assessment of the paper's composition.
data visualization, bibliography, data analysis
1. Bannach-Brown, A. et al. 2018. “The Use of Text-Mining and Machine Learning Algorithms in Systematic Reviews: Reducing Workload in Preclinical Biomedical Sciences and Reducing Human Screening Error.” : 255760. https://www.biorxiv.org/content/10.1101/255760v1 (June 20, 2023).
2. Börner, Katy, Andreas Bueckle, and Michael Ginda. 2019. “Data Visualization Literacy: Definitions, Conceptual Frameworks, Exercises, and Assessments.” Proceedings of the National Academy of Sciences 116(6): 1857–64.
3. “Graphical Abstracts | Proceedings of the 33rd Annual International Conference on the Design of Communication.” https://dl.acm.org/doi/10.1145/2775441.2775465 (June 20, 2023).
4. Hsiao, Tzu-Kun, Yuanxi Fu, and Jodi Schneider. 2020. “Visualizing Evidence-Based Disagreement over Time: The Landscape of a Public Health Controversy 2002–2014.” Proceedings of the Association for Information Science and Technology 57(1): e315.
5. “Is There an Optimum Number Needed to Retrieve to Justify Inclusion of a Database in a Systematic Review Search? - Ross‐White - 2017 - Health Information & Libraries Journal - Wiley Online Library.” https://onlinelibrary-wiley-com.proxy2.library.illinois.edu/doi/full/10. 1111/hir.12185 (June 20, 2023).
6. Lehane, Elaine et al. 2019. “Evidence-Based Practice Education for Healthcare Professions: An Expert View.” BMJ Evidence-Based Medicine 24(3): 103–8.
7. Midway, Stephen R. 2020. “Principles of Effective Data Visualization.” Patterns 1(9): 100141.
8. O’Mara-Eves, Alison et al. 2015. “Using Text Mining for Study Identification in Systematic Reviews: A Systematic Review of Current Approaches.” Systematic Reviews 4(1): 5.
9. Prather, Kimberly A. et al. 2020. “Airborne Transmission of SARS-CoV-2.” Science 370(6514): 303–4.
10. “Scopus | The Largest Database of Peer-Reviewed Literature | Elsevier.” https://www.elsevier. com/en-gb/solutions/scopus (June 20, 2023).
11. Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. 2019. “How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-Analyses, and Meta-Syntheses.” Annual Review of Psychology 70: 747–70.
12. Trinquart, Ludovic, David Merritt Johns, and Sandro Galea. 2016. “Why Do We Think We Know What We Know? A Metaknowledge Analysis of the Salt Controversy.” International Journal of Epidemiology 45(1): 251–60.
13. Yu, Xiao, Quanquan Gu, Mianwei Zhou, and Jiawei Han. 2012. “Citation Prediction in Heterogeneous Bibliographic Networks.” In Proceedings of the 2012 SIAM International Conference on Data Mining (SDM), Proceedings, Society for Industrial and Applied Mathematics, 1119–30. https://epubs.siam.org/doi/abs/10.1137/1.9781611972825.96 (June 20, 2023).
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.