Submissions

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Author Guidelines

Authors are invited to make a submission to this journal. All submissions will be assessed by an editor to determine whether they meet the aims and scope of this journal. Those considered to be a good fit will be sent for peer review before determining whether they will be accepted or rejected.

Before making a submission, authors are responsible for obtaining permission to publish any material included with the submission, such as photos, documents and datasets. All authors identified on the submission must consent to be identified as an author. Where appropriate, research should be approved by an appropriate ethics committee in accordance with the legal requirements of the study's country.

An editor may desk reject a submission if it does not meet minimum standards of quality. Before submitting, please ensure that the study design and research argument are structured and articulated properly. The title should be concise and the abstract should be able to stand on its own. This will increase the likelihood of reviewers agreeing to review the paper. When you're satisfied that your submission meets this standard, please follow the checklist below to prepare your submission.

Submission Preparation Checklist

All submissions must meet the following requirements.

  • This submission meets the requirements outlined in the Author Guidelines.
  • This submission has not been previously published, nor is it before another journal for consideration.
  • All references have been checked for accuracy and completeness.
  • All tables and figures have been numbered and labeled.
  • Permission has been obtained to publish all photos, datasets and other material provided with this submission.

Original Research Article

Publishes full-length applied AI/data science studies with clear data provenance, robust validation, meaningful baselines, appropriate metrics, and transparent limitations. Manuscripts undergo double-blind peer review.

Short Report / Technical Note

Publishes concise applied findings, technical improvements, or early results with adequate evaluation (including validation and baselines). Double-blind peer review applies.

Data Paper

Publishes well-documented datasets and data resources with clear collection/curation methods, quality checks, metadata, and access conditions. Double-blind peer review applies.

Methods & Pipeline Paper

Publishes methods, workflows, or analytical pipelines evaluated on real-world data with fair benchmarking and reproducible reporting. Double-blind peer review applies.

Applied Case Study / Implementation Report

Publishes real-world implementations, deployments, or operational evaluations of AI/data science solutions, including lessons learned, limitations, and practical impact. Double-blind peer review applies.

Systematic Review / Scoping Review

Publishes evidence syntheses with explicit methods (search strategy, eligibility criteria, and transparent reporting; PRISMA encouraged where applicable). Double-blind peer review applies.

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