Fourth Fifth International Conference on Science and Technology Metrics (STMet 2024)
 

 

Research Trend in the Field of Metagenomics: A Scientometric Analysis
Ramesha, Keshava
Senior Professor, Department of Library & Information Sc., Bangalore University, Bengaluru-560056, India., Professor, Department of Library & Information Sc. Tumkur University, Tumkuru-572103 India
Abstract: The study analyzed the scientific productivity of research output on metagenomics. The data for the study was retrieved from the Web of Science database from 2003 to 2024, using the keyword ‘metagenomics’. The study results reveal that the average RGR of publications increased gradually from 0.82 to 1.77. Meanwhile, the publication’s Doubling time (Dt) was 0.95, almost decreasing during the study period. The polynomial growth model fits well with the metagenomics research literature. Zhang Y (China) has the highest number (125) of research publications, followed by Delwart E with 122 (0.762%) publications and Wang Y with 119 (0.746%) publications. The National Natural Science Foundation (China) funded 1760 (10.99%) publications, followed by the United States Department of Health and Human Services, Washington, dedicated to enhancing the health and well-being of America has funded 1355(8.46%) publications.
Keywords: Metagenomics, Scientometric, Relative Growth Rate, Doubling Time, Document Forms Research Trend in the Field of Metagenomics: A Scientometric Analysis
DOI:https://doi.org/10.6025/stm/2024/5/185-197
Full_Text   PDF    Download:   48  times
References:


[1] Anandhalli, G. (2020). Modeling the growth of literature in the area of crystallography. Library Philosophy and Practice (e-
journal). 3813. http://digitalcommons.unl.edu/libphilprac/3813.

[2] Castellano, K. E., & Ho, A. D. (2013). A Practitioner’s Guide to Growth Models. Council of Chief State School Officers.

[3] Gupta, B. M., & Karisiddappa, C. R. (2000). Modelling the Growth of Literature in the Area of Theoretical Population Genetics. Scientometrics, 49(2), 321–355. https://doi.org/10.1023/A:1010577321082

[4] Rao, I. K. R., & Neelameghan, A. (2014). Scientometrics. Ess Ess Pubns.

[5] Thirumagal, A., & Sethukumari, S. N. (2013). Mapping of Scholarly Research in Cloud Computing: A Bibliometric Study. Journal of Information and Knowledge, 667–678. https://doi.org/10.17821/srels/2013/v50i5/43804

[6] Zhang, L., Chen, F., Zeng, Z., Xu, M., Sun, F., Yang, L., Bi, X., Lin, Y., Gao, Y., Hao, H., Yi, W., Li, M., & Xie, Y. (2021). Advances in Metagenomics and Its Application in Environmental Microorganisms. Frontiers in Microbiology, 12. https://doi.org/10.3389/fmicb.2021.766364

[7] Cheng, Saiyan & Wang, Bin (2012). An overview of publications on artificial intelligence research: A quantitative analysis of recent papers. In IEEE. Proceedings of fifth International Joint Conference of Computational Sciences and Optimization, 23-26 June 2012 at Harbin, China. IEEE Explore. doi.10.1109/cso.2012.156

[8] Darko A, Chan APC, Adabre MA et al (2020) Artificial intelligence in the AEC industry: scientometric analysis and visualisation of research activities. Automation in Construction, 112:103081. https://doi.org/10.1016/j.autcon.2020.103081

[9] Gupta, B., & Dhawan, S. (2018). Artificial Intelligence Research in India: A Scientometric Assessment of Publications Output during 2007-16. DESIDOC Journal of Library & Information Technology, 38(6), 416-422. https://doi.org/10.14429/djlit.38.6.12309

[10] Mahapatra, M. (1985). On the Validity of the Theory of Exponential Growth of Scientific Literature. In Proceedings of the 15th IASLIC Conference, Bangalore, 61-70.

[11] Niu J, Tang W, Xu F, Zhou X, Song Y. Global research on artificial intelligence from 1990-2014: spatially explicit bibliometric analysis. International Journal of Geo-Information. 2016;5(5):1-19. doi: 10.3390/ijgi5050066.

[12] Pandey, S., Verma, M. K., & Shukla, R. (2021). A Scientometric Analysis of Scientific Productivity of Artificial Intelligence Research in India. Journal of Scientometric Research, 10(2), 245–250. https://doi.org/10.5530/jscires.10.2.38

[13] Tjebane, M.M., Musonda, I., Okoro, C., Onososen, A. (2023). Artificial Intelligence (AI) in Sustainable Construction Management: A Scientometric Review. In: Haupt, T.C., Akinlolu, M., Simpeh, F., Amoah, C., Armoed, Z. (eds) Construction in 5D: Deconstruction, Digitalization, Disruption, Disaster, Development. Lecture Notes in Civil Engineering, vol 245. Springer, Cham. https://doi.org/10.1007/978-3-030-97748-1_12


Copyright © 2023 dline.info