About the Journal

Data Science & Big Data Technology (DSBDT) (ISSN: 3070-345X) is an international, peer-reviewed, open-access academic journal published by the Institute of Advanced Technology and Green Innovation (INATGI). The journal provides a scholarly platform for the publication and dissemination of high-quality research on data science, big data technologies, artificial intelligence, machine learning, data engineering, intelligent systems, and their interdisciplinary applications.

DSBDT is dedicated to advancing both the theoretical foundations and practical development of data-driven technologies. The journal welcomes research that contributes new concepts, methods, algorithms, architectures, analytical frameworks, systems, tools, and empirical evidence for the acquisition, processing, management, analysis, interpretation, and responsible use of large-scale and complex data.

The journal recognizes that modern data science increasingly operates at the intersection of computing, engineering, management, economics, healthcare, social sciences, public governance, and other application domains. Accordingly, DSBDT encourages interdisciplinary research that demonstrates how data-intensive technologies can generate scientific knowledge, improve organizational decision-making, support intelligent systems, enhance operational performance, and address complex societal and technological challenges.

Scope and Research Areas

The scope of DSBDT includes, but is not limited to, the following areas:

Data science theories, methodologies, and computational frameworks;
Big data architectures, platforms, systems, and technologies;
Artificial intelligence and machine learning;
Deep learning, representation learning, and foundation models;
Generative artificial intelligence and large language models;
Data mining, knowledge discovery, and pattern recognition;
Statistical learning and advanced data analytics;
Predictive, prescriptive, and diagnostic analytics;
Data engineering, data integration, and data pipelines;
Database systems and large-scale data management;
Distributed, cloud, edge, and high-performance data processing;
Real-time, streaming, and event-based data analytics;
Graph data, knowledge graphs, and network analytics;
Natural language processing and text analytics;
Computer vision and multimodal data analytics;
Intelligent decision-support systems;
Recommender systems and personalized analytics;
Business intelligence and business analytics;
Data-driven management and organizational decision-making;
Industrial intelligence, smart manufacturing, and Industry 4.0;
Internet of Things, cyber-physical systems, and sensor data analytics;
Smart cities, digital infrastructure, and intelligent transportation;
Financial technology, computational finance, and risk analytics;
Healthcare analytics, biomedical data science, and medical artificial intelligence;
Environmental, energy, and sustainability analytics;
Social computing and computational social science;
Cybersecurity analytics, anomaly detection, and intelligent threat analysis;
Data privacy, security, and trustworthy data systems;
Responsible artificial intelligence, algorithmic accountability, and AI governance;
Data ethics, fairness, transparency, explainability, and regulatory issues;
Data quality, provenance, reproducibility, and research data management;
Emerging theories, technologies, and applications related to data-intensive intelligent systems.

Aims of the Journal

DSBDT aims to promote rigorous and impactful research that advances the development of data science and big data technologies while strengthening their responsible application in research, industry, organizations, and society.

The journal particularly seeks contributions that:

introduce innovative theories, models, algorithms, and computational methods;
develop novel data architectures, platforms, software systems, and analytical tools;
provide rigorous empirical evaluations of emerging data and AI technologies;
demonstrate meaningful applications of data science in real-world environments;
integrate data science with artificial intelligence, engineering, management, and domain knowledge;
address scalability, reliability, security, privacy, interpretability, fairness, and governance;
improve the reproducibility, transparency, and practical relevance of data-driven research;
bridge the gap between methodological innovation and operational implementation.

DSBDT welcomes research ranging from fundamental methodological studies to application-oriented investigations. Particular emphasis is placed on work that clearly explains its scientific contribution, methodological design, data sources, validation procedures, practical implications, and limitations.

Interdisciplinary Orientation

Data science and big data technology are inherently interdisciplinary. DSBDT therefore encourages collaboration among researchers and practitioners from computer science, artificial intelligence, information systems, statistics, engineering, business and management, economics, healthcare, public administration, environmental science, and related disciplines.

The journal is especially interested in studies that combine computational methods with substantive domain knowledge. Research should move beyond the simple application of existing algorithms and demonstrate a clear methodological, theoretical, technological, empirical, or practical contribution.

Studies involving real-world datasets, industrial systems, public-sector data, large-scale platforms, experimental environments, simulation systems, benchmark datasets, or reproducible computational workflows are particularly relevant to the journal.

Research Quality and Methodological Rigor

DSBDT emphasizes methodological rigor, transparency, reproducibility, and research integrity. Authors are expected to clearly describe the research problem, theoretical or technical contribution, methodology, data, experimental or empirical procedures, evaluation criteria, and limitations of the study.

For empirical and computational research, authors should provide sufficient methodological information to allow readers to understand and evaluate the reliability of the reported findings. Where appropriate, authors are encouraged to describe data preprocessing, model configuration, parameter settings, validation procedures, robustness checks, benchmark comparisons, and evaluation metrics.

The journal also welcomes research that critically evaluates existing methods, datasets, algorithms, platforms, and technological approaches. Negative findings, comparative evaluations, replication studies, and carefully designed validation studies may be considered when they make a meaningful contribution to the development of the field.

Responsible Data Science and Artificial Intelligence

As data-intensive technologies increasingly influence organizations, economies, public services, and individual decision-making, DSBDT recognizes the importance of responsible innovation.

The journal therefore welcomes research addressing issues such as:

data privacy and security;
algorithmic fairness and discrimination;
transparency and explainability;
responsible artificial intelligence;
ethical data collection and use;
data governance;
model accountability;
human oversight;
regulatory technology;
cybersecurity;
trustworthy machine learning;
social and organizational implications of artificial intelligence;
governance of generative AI and large-scale data systems.

Research involving sensitive data, human participants, organizational records, or other protected information should comply with applicable ethical, legal, privacy, and research-integrity requirements.

Types of Contributions

DSBDT welcomes a broad range of scholarly contributions, including:

Original Research Articles
Original theoretical, methodological, computational, empirical, or applied research presenting significant new findings.

Review Articles
Systematic, structured, critical, or state-of-the-art reviews that synthesize existing knowledge, identify research gaps, and propose future research directions.

Technical Articles
Research describing new algorithms, software frameworks, data infrastructures, computational architectures, technical systems, or engineering implementations.

Application Studies
Studies demonstrating rigorous and innovative applications of data science, big data, artificial intelligence, and related technologies in specific sectors or problem domains.

Data and Methodological Studies
Research introducing datasets, benchmarking frameworks, evaluation methods, reproducible analytical pipelines, or methodological resources that support further research.

Other forms of scholarly contribution may be considered when they are consistent with the aims and scope of the journal.

Peer Review

All manuscripts submitted to DSBDT are subject to editorial assessment and double-blind peer review in accordance with the journal's established editorial and peer-review policies.

Submissions are evaluated with particular attention to:

originality and significance;
relevance to the journal's scope;
methodological and technical rigor;
validity of analysis and conclusions;
clarity and completeness of presentation;
appropriate engagement with the relevant literature;
reproducibility and transparency where applicable;
ethical and responsible research practices;
potential scientific, technological, managerial, or societal contribution.

The final decision regarding publication is made on the basis of scholarly quality, relevance, reviewer evaluation, and compliance with the journal's editorial standards.

Open Access

DSBDT is committed to the broad dissemination of scholarly knowledge. Articles are made freely accessible online after publication, enabling researchers, practitioners, students, institutions, policymakers, and the wider public to access and use published research without subscription barriers.

Through its open-access publishing model, the journal seeks to improve the visibility, accessibility, circulation, and potential impact of research in data science and big data technology.

Intended Readership

The journal serves an international readership that includes:

researchers and academics in data science and artificial intelligence;
computer scientists and data engineers;
statisticians and quantitative researchers;
information systems scholars;
software and systems researchers;
engineering researchers;
business and management scholars;
data analysts and AI practitioners;
technology managers and industry professionals;
public-sector and policy researchers;
postgraduate students;
professionals working with data-intensive and intelligent systems.

Journal Mission

The long-term mission of Data Science & Big Data Technology is to contribute to the development of a rigorous, interdisciplinary, transparent, and socially responsible body of knowledge surrounding data-intensive technologies.

By bringing together advances in data science, big data, artificial intelligence, intelligent systems, and their applications, DSBDT seeks to provide an international forum in which theoretical innovation, technological development, empirical evidence, and practical implementation can inform and strengthen one another.

The journal ultimately aims to support the development of data-driven technologies that are not only computationally effective, but also reliable, interpretable, secure, responsible, and capable of generating meaningful value for science, industry, organizations, and society.

Data Science & Big Data Technology (DSBDT)
ISSN: 3070-345X
Published by the Institute of Advanced Technology and Green Innovation (INATGI).