Data Availability Policy

Learning Nexus of Computing (LNC) is committed to promoting transparency, reproducibility, and integrity in scholarly research. The journal encourages authors to make the data, software, code, and supporting materials underlying their published research available whenever possible, subject to ethical, legal, contractual, and privacy considerations.

Providing access to research data enhances scientific verification, facilitates collaboration, supports future research, and increases the visibility and impact of published work.

2. Purpose of the Policy

The purpose of this policy is to:

  • Promote transparency in scientific research.

  • Improve reproducibility and verification of research findings.

  • Encourage responsible data sharing.

  • Support long-term preservation of research outputs.

  • Increase the visibility and reusability of research data.

  • Align the journal with international best practices in scholarly publishing.

3. Scope

This policy applies to all manuscripts submitted to Learning Nexus of Computing, including:

  • Original Research Articles

  • Review Articles (where applicable)

  • Survey Papers

  • Technical Notes

  • Case Studies

  • Short Communications

Authors are encouraged to share any research data necessary to validate the findings presented in their manuscripts.

4. What is Research Data?

Research data may include, but is not limited to:

  • Experimental data

  • Simulation results

  • Observational data

  • Survey data

  • Images

  • Videos

  • Audio files

  • Source code

  • Software

  • Machine learning models

  • Algorithms

  • Configuration files

  • Benchmark datasets

  • Statistical analyses

  • Supplementary tables

  • Supplementary figures

  • Laboratory protocols

5. Data Availability Statement

Every manuscript should include a Data Availability Statement immediately before the References section.

The statement should clearly describe the availability of the data supporting the reported findings.

Example Statements

Publicly Available Data

Data Availability: The data supporting the findings of this study are publicly available in a recognized data repository and can be accessed through the repository link provided by the authors.

Data Available Upon Reasonable Request

Data Availability: The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Restricted Data

Data Availability: The research data cannot be made publicly available due to ethical, legal, privacy, or contractual restrictions. Access may be granted under appropriate conditions and with the necessary approvals.

No New Data

Data Availability: No new datasets were generated or analyzed during the current study.

6. Recommended Data Repositories

Authors are encouraged to deposit research data in recognized public repositories whenever possible.

Examples include:

  • Zenodo

  • Figshare

  • Dryad

  • Harvard Dataverse

  • Mendeley Data

  • GitHub (for source code)

  • GitLab

  • Institutional repositories

  • Domain-specific repositories appropriate to the research discipline

Where available, authors should provide the repository link and Digital Object Identifier (DOI).

7. Source Code and Software

Where software, source code, or computational models are essential to the reported research, authors are encouraged to provide public access through recognized repositories.

Authors should include:

  • Repository name

  • Version number

  • DOI (if available)

  • Permanent repository link

If the source code cannot be shared, authors should provide a brief explanation.

8. Data Citation

Datasets should be cited in the References section in the same manner as other scholarly resources.

A dataset citation should include:

  • Author(s)

  • Dataset title

  • Version (where applicable)

  • Repository

  • Year

  • DOI or persistent identifier

Authors are encouraged to use persistent identifiers such as DOIs whenever available.

9. FAIR Data Principles

Learning Nexus of Computing encourages authors to follow the FAIR Data Principles, ensuring that research data are:

  • Findable

  • Accessible

  • Interoperable

  • Reusable

Adherence to these principles enhances the long-term value and usability of research outputs.

10. Ethical and Legal Considerations

Authors must ensure that data sharing complies with:

  • Institutional policies

  • Funding agency requirements

  • Applicable laws and regulations

  • Privacy legislation

  • Confidentiality obligations

  • Intellectual property rights

Research data containing personal, confidential, or sensitive information should be appropriately anonymized before sharing.

11. Human Participant Data

For studies involving human participants, authors must protect participant privacy and confidentiality.

Personally identifiable information must not be disclosed unless:

  • Explicit informed consent has been obtained.

  • Disclosure is required by law.

  • Ethical approval permits public release.

Where data cannot be shared, the Data Availability Statement should clearly explain the reason.

12. Third-Party Data

Authors using datasets obtained from third parties must ensure that they have the necessary permissions to use and, where permitted, share those data.

Any restrictions imposed by third-party data providers should be clearly stated in the manuscript.

13. Data Integrity

Authors are responsible for ensuring that:

  • Research data are accurate.

  • Data have not been fabricated, falsified, or manipulated.

  • Shared datasets correspond to the reported analyses.

  • Appropriate documentation accompanies shared data where necessary.

The Editorial Office may request access to supporting data during editorial evaluation or peer review if clarification is required.

14. Editorial Review

Editors and reviewers may request access to supporting data where necessary to evaluate the validity, reproducibility, or integrity of the reported research.

Failure to provide requested supporting data without reasonable justification may affect the editorial decision.

15. Long-Term Preservation

Authors are encouraged to deposit datasets in repositories that provide long-term preservation, stable access, and persistent identifiers.

Long-term preservation improves research accessibility and supports future scholarly reuse.

16. Exceptions

Learning Nexus of Computing recognizes that unrestricted data sharing may not always be appropriate.

Exceptions may include:

  • National security considerations

  • Commercial confidentiality

  • Intellectual property protection

  • Patient privacy

  • Legal restrictions

  • Contractual obligations

  • Ethical limitations

Where data cannot be shared, authors should provide an appropriate explanation in the Data Availability Statement.

17. Compliance

Authors are expected to comply with this policy and any applicable requirements imposed by their institutions, funding agencies, or regulatory authorities.

Failure to comply with the journal's Data Availability Policy may result in requests for clarification, delays in editorial processing, or other editorial actions where appropriate.

18. Contact

Questions regarding data sharing, repository selection, or Data Availability Statements should be directed to the Editorial Office through the official communication channels available on the Learning Nexus of Computing website.

The Editorial Office will be pleased to provide guidance on the implementation of this policy.