Data Availability Policy

Data Availability Policy

The Journal of Advanced Computer Science (JACS) promotes transparency, reproducibility, and research integrity. Authors are encouraged to make the data, software, and materials supporting their findings available to readers whenever possible, in line with international best practices in scholarly publishing.

This policy applies to all manuscripts submitted to and published in JACS.

1. Purpose

The purpose of this policy is to:

  • enhance research transparency and reproducibility

  • improve scientific credibility and trust

  • support open science practices in Computer Science research

  • enable reuse and verification of published results

2. Data Availability Requirement

Authors are encouraged to provide access to:

  • datasets used in experiments or evaluation

  • source code or scripts used for implementation

  • trained model weights (when possible)

  • algorithm configuration and hyperparameters

  • experimental environment details (software versions, hardware)

 If data cannot be shared, authors must clearly state the reason.

3. Data Availability Statement

All manuscripts must include a Data Availability Statement, which should appear in the manuscript (recommended section: before References or after Conclusion).

 Example Statements

Example 1 (Publicly available data):
The datasets used in this study are publicly available at: (URL / DOI).

Example 2 (Data available on request):
The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Example 3 (Restricted data):
The data are not publicly available due to confidentiality restrictions.

Example 4 (No data generated):
No new data were generated or analyzed in this study.

4. Recommended Data Repositories

Authors are encouraged to deposit data in trusted repositories such as:

  • institutional repositories

  • Zenodo

  • Figshare

  • Harvard Dataverse

  • Open Science Framework (OSF)

  • GitHub / GitLab (for code)

  • Kaggle (for datasets)

Repositories with DOI assignment are strongly recommended.

5. Software and Code Availability

For computational and AI-based research, authors should provide:

  • source code

  • libraries used

  • parameter settings

  • pre-processing steps

  • instructions for running the experiments

Authors should ensure that code is:

  • clearly documented

  • reproducible

  • released under an appropriate license when possible

6. Ethical and Legal Considerations

Authors must not share data that violates:

  • privacy and confidentiality agreements

  • national/institutional regulations

  • copyright restrictions

  • security-related sensitive information

For datasets involving human subjects, authors must ensure:

  • anonymization/de-identification

  • ethical approval if applicable

7. Editorial Verification

The editorial office may:

  • request access to datasets or code for verification

  • ask authors to clarify unavailable data

  • reject manuscripts if results are not verifiable or data claims are misleading

8. Exceptions

JACS understands that sharing may not always be possible. Exceptions include:

  • confidential data

  • restricted institutional datasets

  • proprietary/commercial data

  • national security restrictions

In such cases, authors must provide a clear explanation.