Data Sharing Policy

The Journal of Computing and AI (JCAI) supports open and responsible data sharing to enhance transparency, reproducibility, and the collective advancement of research in artificial intelligence, computing, and intelligent systems.

1. Data Availability

  • Authors are strongly encouraged to make the underlying data for their published articles publicly available at the time of publication, wherever possible.

  • Data should be deposited in recognized repositories relevant to the research domain, with persistent identifiers (e.g., DOI) and access links clearly stated in the article.

  • JCAI promotes the use of the FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable).

2. Exceptions and Restrictions

  • Data sharing may be limited in cases involving:

    • Ethical concerns (e.g., human subjects, confidentiality)

    • Legal or institutional restrictions

    • Proprietary or sensitive information

  • In such cases, authors must provide a clear justification for data unavailability and ensure that sufficient methodological transparency is provided for independent verification.

3. Author Responsibilities

  • Authors are responsible for:

    • Ensuring the accuracy and completeness of shared data

    • Providing detailed documentation of data collection, processing, and analysis methods

    • Including descriptive metadata to enable understanding and reuse

    • Indicating any software tools or scripts used in data generation or analysis

4. Support for Authors

  • JCAI understands the challenges of data sharing and encourages authors to:

    • Refer to institutional or funding body data policies

    • Use discipline-specific or general-purpose repositories (e.g., Zenodo, Figshare, GitHub, Kaggle, Dryad)

    • Contact the editorial office for guidance on data deposition and best practices

By fostering open data practices, JCAI contributes to the global advancement of AI and computing research through verifiable, transparent, and reusable knowledge.