Aims & Scope

DATAMIND publishes original, peer-reviewed research in data engineering, database systems, AI data infrastructure, and computational discovery systems. The journal prioritizes work that presents clear engineering contributions, including database architectures, data platforms, schemas, indexing strategies, metadata systems, data quality pipelines, benchmark datasets, APIs, reproducible software, and validated engineering applications.

Manuscripts that only apply generic machine learning models without a substantive data engineering, database, systems, or reproducibility contribution are normally considered outside the primary scope of the journal.

DATAMIND welcomes original research articles, comprehensive reviews, perspectives, and technical communications covering the full spectrum of data-driven research. The journal follows a quarterly publication schedule. Manuscript submissions are considered year-round, and accepted papers are assigned to the appropriate issue based on submission date.

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)