Aim & Scope

Aim

Data Science and Computational Intelligence provides a peer-reviewed venue for data science and computational intelligence. The journal is intended for data scientists, computational intelligence researchers, applied statisticians, and analytics teams. Its editorial goal is to publish articles that make the research question, method, evidence, and limitations visible enough for readers to evaluate and reuse.

Core Scope

The journal considers manuscripts in the following areas:

  • Original studies in data mining with transparent assumptions and evaluable evidence.
  • Research on computational intelligence that explains methods, data, and interpretation limits.
  • Applied work involving predictive modelling where practical relevance is supported by analysis rather than assertion.
  • Interdisciplinary work connecting data-centric AI to adjacent scientific, engineering, health, environmental, social, or policy questions.

Article Types Considered

The journal may consider research articles, data resource papers, methods papers, comparative studies, replication reports, and reviews. Article type should be selected according to the main contribution, not according to desired length or perceived prestige.

Method and Evidence Expectations

For this field, manuscripts should pay particular attention to:

  • data provenance
  • feature construction
  • statistical validation
  • model comparison
  • uncertainty and generalisability

Out of Scope

The journal does not consider manuscripts that are purely promotional, lack a research question, duplicate previously published work, make unsupported clinical or policy claims, present unverifiable results, or fall outside data science and computational intelligence. Manuscripts that are technically sound but do not fit the journal's subject identity may be returned before peer review.

Editorial Standard

The journal does not require spectacular novelty. It requires a clear contribution, appropriate citations, transparent methods, relevant ethical approvals where needed, and a limitations section. Reviewers and editors should ask whether the work is trustworthy and useful for its intended readership.