Data & Code Availability Policy

The Journal of Intelligent Systems and Applied Sciences (JISAS) is committed to promoting transparency, reproducibility, and rigorous scientific practice in intelligent systems and applied computational research.

1. General Principles

Authors are expected to make data, code, and other relevant materials available to the extent possible, in accordance with ethical, legal, and privacy considerations. Transparency in data and code availability enhances reproducibility, verification, and long-term research impact.

2. Data Availability

Authors should clearly describe all datasets used in the study, including:

  • Data sources and collection methods

  • Dataset size and key characteristics

  • Preprocessing and cleaning procedures

Where possible, datasets should be deposited in recognized public repositories (e.g., Zenodo, Figshare, institutional repositories) and cited appropriately.

If data cannot be shared due to ethical, legal, or confidentiality constraints, authors must provide a clear justification in a Data Availability Statement.

3. Code and Software Availability

Authors are strongly encouraged to share:

  • Source code

  • Scripts for data processing and analysis

  • Configuration files or trained models, where applicable

Code should be provided via stable public repositories (e.g., GitHub with a persistent DOI via Zenodo) and include sufficient documentation to enable reuse or replication.

4. Data and Code Availability Statement

All submissions must include a Data and Code Availability Statement indicating:

  • What data and code are available

  • Where they can be accessed

  • Any restrictions on access and the reasons for such restrictions

Manuscripts lacking a clear statement may be returned for revision prior to peer review.

5. Exceptions and Ethical Considerations

JISAS recognizes that data and code sharing may not always be feasible. Legitimate exceptions include:

  • Protection of personal or sensitive data

  • Legal or contractual restrictions

  • Security or safety concerns

In such cases, authors should describe alternative means of verification or validation, such as synthetic data, controlled access, or detailed methodological documentation.

6. Editorial Review

Editors and reviewers may assess data and code availability as part of the peer-review process. While lack of open data or code does not automatically preclude publication, insufficient transparency may affect editorial decisions.