Transactions on Data Science with Applications
The Transactions on Data Science with Applications (TDSA) is an international scholarly open access, interdisciplinary, fully refereed, and quarterly free of charge journal as a non-commercial publication. The publication start year of TDSA is 2026. All submitted manuscripts are checked for similarity through a trustworthy software named iThenticate to be assured about its originality and rigorously peer-reviewed by the international reviewers (The similarity rate must be below 20%).
TDSA considers original research articles, review articles, case studies, and technical notes addressing the data science lifecycle, from data acquisition and preparation to modelling, evaluation, interpretation, visualization, and decision-making. Its scope includes “statistical learning”, “machine learning”, “artificial intelligence”, “data mining”, “big-data analytics”, “deep learning”, “predictive and prescriptive analytics”, “optimization”, “data engineering”, “databases”, “cloud and distributed computing”, “natural language processing”, “computer vision”, “explainable artificial intelligence”, and “data-driven decision support”. Application-oriented studies may come from engineering, manufacturing, healthcare, finance, business, transportation, energy, environmental systems, the social sciences, and related fields. This journal respects the ethical rules in publishing and is subject to the rules of the Ethics Committee” for Publication (COPE) and committed to follow the executive regulations of the Law on Prevention and Combating Fraud in Scientific Works. This journal publishes fully open access journals, which means that all articles are available on the internet to all users immediately upon publication.
Aims & scope
The Transactions on Data Science with Applications (TDSA) publishes original research on the theoretical foundations, computational methods, technologies, and practical applications of data science. The journal provides an interdisciplinary forum for researchers and practitioners in computer science, statistics, mathematics, engineering, management, and other fields in which data are used to develop models, generate knowledge, and support decisions. TDSA considers studies covering the complete data science lifecycle, including data acquisition, preparation, integration, management, analysis, modelling, evaluation, interpretation, visualization, and communication. Submissions may present new theories, methods, algorithms, computational frameworks, or data-processing systems. The journal also considers application-oriented research that adapts or integrates established methods to address substantive problems, provided that the study demonstrates methodological rigor, appropriate evaluation, and a clear contribution to the application domain.
The journal welcomes research on topics including, but not limited to:
• Statistical methods and statistical learning
• Machine learning and artificial intelligence
• Deep learning and computational intelligence
• Data mining, pattern recognition, and knowledge discovery
• Big-data analytics and scalable data processing
• Predictive and prescriptive analytics
• Data engineering, database systems, and data architectures
• Data acquisition, preprocessing, cleaning, integration, and transformation
• Optimization and data-driven decision-making
• Natural language processing and text analytics
• Computer vision, image processing, and multimedia analytics
• Time-series analysis, forecasting, and anomaly detection
• Graph analytics, network analysis, and recommender systems
• Data visualization and human–data interaction
• Explainable, interpretable, and responsible artificial intelligence
• Data quality, governance, privacy, security, trust, and ethics
• Cloud, distributed, parallel, and high-performance computing
• Internet of Things, sensor data, and real-time analytics
• Reproducible data science, computational tools, and analytical workflows
• Mathematical and computational modelling of data-intensive systems
• Reproducible data science, computational tools, and analytical workflows
• Mathematical and computational modelling of data-intensive systems
TDSA also publishes research demonstrating the use of data science in engineering, manufacturing, supply chain and logistics, healthcare, finance, business and management, transportation, energy, environmental systems, agriculture, public services, education, and the social sciences. Application papers should explain the relevant domain problem, justify the selected analytical methods, report suitable validation procedures, and discuss the findings within the context in which the data were generated. The journal considers original research articles, review articles, case studies, and technical notes. Review articles should provide a critical and structured synthesis of a defined research area. Case studies should offer findings or methodological insights that extend beyond a single organization or setting. Technical notes should present a specific method, algorithm, dataset, software tool, or computational procedure with sufficient detail to permit assessment and, where possible, reproduction.
Editors-in-Chief
Salman Nazari-Shirkouhi
Associate Professor Department of Industrial and Systems Engineering, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Iran
Editorial Contact
ISSN
Pending