Rapid digitalization is reshaping the methodology and practice of natural-science research, moving inquiry beyond isolated manual experimentation toward data-intensive, model-driven, and networked science. This study aims to examine the role of information technologies (IT) in contemporary natural-science research, assess their effects on the efficiency of data collection, processing, and analysis, and explore the potential of digital tools for modeling, forecasting, and visualization. Methodologically, the work applies a systemic, interdisciplinary approach combining theoretical analysis of publications and institutional documents with comparative, systems, and case-study methods across three leading Ukrainian universities. Empirical indicators include the share of recent publications using IT, integration of classical methods with digital tools, laboratory computing capacity, access to licensed/open software, and connectivity to international databases, supplemented by domain-specific cases. Results show widespread but uneven digitalization: in publication practice, IT use reaches ~82% (KNU), 76% (LNU), and 88% (KhNU); integration of classical and digital methods is 64%, 59%, and 71%, respectively. Technical readiness varies, with modern computing in laboratories at 85% (KNU), 72% (LNU), and 89% (KhNU); departments’ access to licensed/open software at 78%, 67%, and 83%; and researcher access to international databases at 92%, 81%, and 95%. Domain specializations underline differentiated strengths - bioinformatics at KNU (≈68% of genetics studies), GIS at LNU (≈62% of ecological–geographical work), and computational modeling in physics/chemistry at KhNU (≈74%). The findings confirm IT as a determining factor across the research cycle, while also revealing constraints in infrastructure, software access, digital competence, and data governance. The study concludes with a policy agenda - targeted infrastructure upgrades, balanced provisioning of licensed and open platforms, continuous professional development, FAIR/reproducible workflows, and robust security/ethics—to convert clear digital gains into sustained research accuracy, productivity, and international visibility.
Krasnoshtan et al. (2025) studied this question.