DATAMIND is a Gold Open Access journal and does not charge any article processing charges (APCs), submission fees, publication fees, page charges, or color figure fees.
DATAMIND (DM) publishes original research articles, review papers, and case studies in database systems, data engineering, AI data infrastructure, and cross-disciplinary applications of data analytics.
DM accepts original research articles, review articles, case studies, technical communications, perspectives, and data/resource papers.
Manuscripts must be submitted through the online system. Submissions must include a cover letter, anonymized manuscript, title page with author details, and conflict of interest statement.
Manuscripts must be written in clear academic English. Formatting: Times New Roman, 12 pt, 1.5 line spacing, 2.5 cm margins. Include Title, Authors, Abstract (150–250 words), Keywords (3–6), Introduction, Literature Review, Methodology, Results, Discussion, Conclusion, and References.
DM follows APA style. All references must include DOI where available.
Figures and tables must be numbered sequentially. High-resolution images (minimum 300 dpi) are required. Sources must be indicated if reproduced.
Authors are required to prepare their manuscripts according to the DATAMIND manuscript template before submission.
Last updated: 11 January 2026.
Authors must ensure originality, proper citation, and disclosure of conflicts of interest. Human and animal studies must comply with ethical standards and institutional approvals.
Authors must disclose any use of AI tools in manuscript preparation. AI tools cannot be listed as authors.
All submissions are screened using plagiarism detection software. Duplicate submission and data fabrication are strictly prohibited.
DM operates a double-blind peer review system with at least two independent reviewers. Editorial decisions include accept, minor revision, major revision, or reject.
To maintain transparency and accountability in the editorial process, DM publicly reports its editorial performance metrics.
Based on recent internal data analysis:
These values are calculated using the median rather than the mean to ensure robustness against outliers.
Authors are encouraged to provide data availability statements and repository links when applicable.
Authors retain copyright. Articles are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
DM follows COPE guidelines for corrections, retractions, and expressions of concern.
Editorial Office
DATAMIND
Email: datamind@inatgi.net