Aims & Scope

DATAMIND is an international, peer-reviewed, open-access journal focusing on the application of databases across diverse fields and the investigation of practical problems in database use and big data analytics. The journal provides an interdisciplinary platform for researchers and practitioners to examine how databases, large-scale datasets, and data-driven analytical methods can be used to understand real-world phenomena, support decision-making, improve organizational performance, and address complex social, economic, scientific, and industrial problems.

The journal primarily concentrates on the following two areas:

1. Database applications across different fields.

DATAMIND welcomes research that applies established, public, institutional, commercial, scientific, or administrative databases to investigate important questions in different disciplines and industries. The emphasis is placed on how databases can generate meaningful evidence, support practical decision-making, and improve the understanding of real-world problems.

Relevant application areas include, but are not limited to:

• Business, management, marketing, and organizational decision-making;
• Finance, accounting, banking, insurance, and financial risk analysis;
• Healthcare, public health, medicine, and biomedical research;
• Education, learning analytics, and academic performance;
• Supply chains, logistics, transportation, and operations management;
• Environmental governance, climate change, energy, and sustainability;
• Public policy, government administration, and social development;
• Digital platforms, social media, e-commerce, and online behavior;
• Agriculture, food systems, natural resources, and rural development;
• Science, technology, innovation, and interdisciplinary research.

Submissions may use a single mature database or integrate multiple databases from different sources. Studies should clearly explain the value of the selected database, the analytical process, the research findings, and the practical implications.

2. Practical problems and possible solutions in databases and big data analytics.

DATAMIND also publishes research that identifies, analyzes, and proposes solutions to practical problems arising from the collection, organization, integration, analysis, interpretation, governance, and use of databases and large-scale data.

Topics of interest include, but are not limited to:

• Data quality, data accuracy, incomplete data, missing values, and inconsistent records;
• Data cleaning, preprocessing, validation, and error correction;
• Integration of heterogeneous, multi-source, longitudinal, and cross-domain databases;
• Data comparability, standardization, classification, and measurement consistency;
• Database bias, sample imbalance, representativeness, and fairness;
• Privacy protection, data security, ethical data use, and responsible analytics;
• Data accessibility, transparency, openness, and reproducibility;
• Scalability and efficiency in large-scale data analysis;
• Real-time data analysis, monitoring, forecasting, and early-warning applications;
• Interpretability and reliability of results derived from large databases;
• Database governance, ownership, accountability, and regulatory compliance;
• Evaluation and comparison of analytical methods used in database and big data research;
• Practical frameworks, methodological improvements, and decision-support solutions for data-intensive problems.

The journal encourages studies that move beyond identifying data-related limitations and provide clear, feasible, and evidence-based solutions. Solutions may involve methodological improvements, analytical frameworks, governance mechanisms, data integration strategies, validation procedures, or practical recommendations for organizations and decision-makers.

DATAMIND welcomes original research articles, database application studies, empirical analyses, case studies, comparative studies, systematic reviews, methodological reviews, bibliometric studies, benchmark analyses, policy analyses, and perspective articles.

Manuscripts that focus only on abstract algorithms, software coding, or technical system construction without a clear database application, real-world analytical problem, or practical contribution are generally outside the primary scope of the journal.

Double-blinded Peer Review

Committed to scientific integrity and editorial excellence, DATAMIND employs a double-blinded peer-review process and adheres to the highest ethical standards as outlined by COPE. The journal embraces transparency, reproducibility, and accessibility, ensuring that all published content is freely available under an open-access model to maximize dissemination and global impact.

ISSN

• 3071-5601 (Online)