Data Modeling is an international, open access, peer-reviewed journal that publishes original research articles, reviews, methodological studies, and short communications addressing the development and application of mathematical, computational, and data-driven modeling approaches across diverse scientific domains.

The journal welcomes contributions that present theoretical developments, algorithmic innovations, and applied modeling studies involving complex systems, large-scale data, and computational frameworks. Submissions may include both foundational mathematical work and interdisciplinary applications that integrate modeling with real-world problems.

Areas of interest include, but are not limited to:

• Mathematical modeling and applied mathematics
• Computational modeling and simulation
• Data science, machine learning, and statistical modeling
• Bioinformatics and computational biology
• Computational genomics and systems biology
• Molecular modeling and biomolecular structure prediction
• Computational biochemistry and biophysics
• Multiscale and systems modeling
• Numerical methods and optimization techniques
• Network modeling and complex systems analysis
• Modeling in engineering, physical sciences, environmental systems, and health sciences

The journal considers studies involving analytical modeling, numerical simulations, data integration, and hybrid approaches that combine mathematical theory with computational techniques. Contributions addressing both domain-specific applications and cross-disciplinary methodologies are encouraged.