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VOL. 1, ISSUE 2 (2025)
Machine learning and big data analytics in scientific research
Authors
Ayo Nwabineli
Abstract
The integration of
machine learning (ML) and big data analytics into scientific research has
fundamentally transformed methodological approaches across disciplines. This
study examines the adoption patterns, implementation challenges, and research
outcomes associated with ML and big data technologies in contemporary
scientific inquiry. Using a mixed-methods approach, we analyzed 847 research
publications from 2019-2024 across multiple scientific domains, conducted
surveys with 312 researchers from 45 institutions, and performed quantitative
assessments of research impact metrics. Our analysis reveals that 68.3% of
surveyed researchers have incorporated ML techniques into their workflow, with
supervised learning methods being most prevalent (54.7%). Big data analytics
adoption showed strong correlation with institutional resources (r = 0.71, p
< 0.001) and research funding levels (r = 0.64, p < 0.001). Performance
analysis demonstrated that ML-enhanced research projects achieved significantly
higher citation rates (M = 23.4, SD = 8.9) compared to traditional
methodologies (M = 14.7, SD = 6.2; t(845) = 15.43, p < 0.001). However,
significant barriers persist, including data quality issues (reported by 73.1%
of respondents), computational resource limitations (61.4%), and expertise gaps
(58.7%). Implementation success correlated strongly with interdisciplinary
collaboration (β = 0.58, p < 0.001) and institutional support infrastructure
(β = 0.43, p < 0.001). These findings suggest that while ML and big data
analytics substantially enhance research capabilities and impact, successful
adoption requires comprehensive institutional frameworks, collaborative
networks, and targeted capacity-building initiatives. This research provides
evidence-based recommendations for research institutions, funding agencies, and
individual researchers seeking to leverage these transformative technologies
effectively.
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Pages:18-27
How to cite this article:
Ayo Nwabineli "Machine learning and big data analytics in scientific research". World Journal of Advanced Science, Vol 1, Issue 2, 2025, Pages 18-27
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