Research Data Sharing Policy

Journal of Responsible AI & Ethics (JRAIE)

The Journal of Responsible AI & Ethics (JRAIE) supports responsible research-data sharing as an important component of transparent, verifiable, and reproducible scholarly communication. The journal encourages authors to make research data supporting published findings available whenever such sharing is ethically appropriate, legally permissible, technically feasible, and consistent with privacy, confidentiality, intellectual-property, contractual, and participant-protection requirements.

This policy should be read together with the journal’s Data Availability Policy, Publication Ethics and Malpractice Statement, Privacy Policy, and Research Misconduct Policy.

1. Purpose and Scope

The purpose of this policy is to establish clear expectations regarding the responsible management, documentation, sharing, citation, and reuse of research data associated with manuscripts submitted to or published by JRAIE.

The policy applies to research data that are collected, generated, processed, analyzed, simulated, or used as evidence for scholarly findings.

Research data may include:

Quantitative and statistical datasets.
Survey and interview data.
Experimental observations.
Computational outputs.
Simulation datasets.
Artificial intelligence and machine-learning datasets.
Source data supporting tables and figures.
Analytical files.
Code, software, and computational resources.
Relevant metadata and documentation.

JRAIE recognizes that public sharing is not appropriate for every dataset. Data-sharing decisions should balance transparency and reproducibility with ethical, legal, privacy, security, and confidentiality requirements.

2. Data Sharing Principles

JRAIE encourages responsible data-management practices that support research transparency and reproducibility.

Where appropriate, authors should follow principles that support data being:

Findable.
Accessible.
Interoperable.
Reusable.

Authors should ensure that shared data are sufficiently documented, ethically appropriate, and relevant to understanding or verifying the reported research.

3. Data Availability Statement

Manuscripts involving research data should include an appropriate Data Availability Statement describing the status of the supporting data.

The statement should clearly indicate whether data are:

Publicly available through a repository.
Included within the article or supplementary materials.
Available from the corresponding author upon reasonable request.
Available through controlled access.
Obtained from third-party sources with access restrictions.
Unavailable due to ethical, legal, privacy, security, or contractual limitations.
Not applicable because no research dataset was used.

Authors should ensure that Data Availability Statements are accurate and do not create misleading expectations regarding access.

4. Data Repositories and Documentation

Authors are encouraged to deposit research data in appropriate institutional, disciplinary, or general-purpose repositories where possible.

Suitable repositories should provide:

Stable access.
Persistent identifiers.
Appropriate metadata.
Clear reuse conditions.
Long-term availability.

Where data are shared, authors should provide sufficient documentation, including where applicable:

Data descriptions.
Variable definitions.
Codebooks.
Data dictionaries.
Sampling information.
Processing procedures.
Methodological notes.
Version information.

5. Artificial Intelligence and Machine-Learning Data

For studies involving artificial intelligence, machine learning, generative AI, automated systems, or computational models, authors should provide appropriate transparency regarding datasets used for development, training, validation, testing, and evaluation.

Where applicable, authors should describe:

Dataset sources.
Data collection methods.
Inclusion and exclusion criteria.
Preprocessing procedures.
Annotation methods.
Training and evaluation procedures.
Dataset limitations.
Potential bias considerations.
Licensing or access restrictions.

Authors should avoid claims of generalizability, fairness, or reliability unless supported by appropriate evidence.

6. Synthetic and Simulated Data

Research based on synthetic, simulated, or artificially generated datasets must clearly disclose the nature of the data.

Authors should describe:

Data-generation methods.
Simulation assumptions.
Relevant parameters.
Limitations of interpretation.

Synthetic or simulated data should not be presented as real-world observational data.

7. Sensitive, Confidential, and Third-Party Data

JRAIE recognizes that some research data cannot be publicly released because of:

Participant privacy.
Confidentiality obligations.
Ethical restrictions.
Legal requirements.
Security concerns.
Intellectual-property restrictions.
Third-party licensing conditions.

Authors should clearly explain legitimate restrictions in the Data Availability Statement.

Where third-party data are used, authors remain responsible for ensuring appropriate permissions, accurate citation, and compliance with applicable conditions.

8. Research Software, Code, and Computational Materials

Where software, source code, models, scripts, computational workflows, or analytical tools contribute significantly to research findings, authors are encouraged to share these resources where technically and legally feasible.

Such materials may include:

Source code.
Statistical scripts.
AI model configurations.
Simulation procedures.
Computational workflows.
Analytical notebooks.

Where such resources cannot be shared because of proprietary, legal, security, or technical limitations, authors should disclose the relevant restrictions.

9. Data Citation and Reuse

Research datasets that contribute substantially to published findings should be cited appropriately.

Data citations should include available information such as:

Data creator or responsible organization.
Dataset title.
Repository name.
Publication or deposit year.
Persistent identifier.

Appropriate citation supports recognition of data creators, improves transparency, and enables responsible reuse of research resources.

Where research software or computational resources substantially contribute to findings, authors are encouraged to provide appropriate citation or acknowledgment of those resources.

10. Responsible AI Considerations in Data Sharing

For research involving AI systems and computational technologies, data-sharing decisions should consider responsible AI principles, including:

Transparency.
Fairness.
Accountability.
Privacy protection.
Prevention of unintended harm.

Authors should ensure that shared datasets, models, and computational resources are managed responsibly and in accordance with applicable ethical requirements.

11. Responsibilities of Authors

Authors are responsible for:

Providing accurate information regarding data availability.
Ensuring ethical and lawful data use.
Protecting confidential and sensitive information.
Obtaining necessary permissions for third-party data.
Providing sufficient documentation for shared data.
Maintaining consistency between reported methods and available resources.
Responding appropriately to legitimate research-data inquiries.

Authors should not claim that data are available if access cannot reasonably be provided.

12. Responsibilities of Editors and Reviewers

Editors may request clarification regarding data sources, availability, accessibility, or research integrity where necessary.

Reviewers may comment on whether data-related information is sufficient to support scholarly evaluation.

Editors and reviewers must maintain confidentiality and must not use unpublished research data obtained during peer review for unauthorized purposes.

13. Research Integrity and Misconduct

Fabrication, falsification, manipulation, or deliberate misrepresentation of research data is inconsistent with responsible scholarly publishing.

Concerns regarding inaccurate data statements, unavailable claimed datasets, or questionable data practices may be evaluated according to the journal’s Research Misconduct Policy and publication-ethics procedures.

The inability to share data because of legitimate ethical, legal, or confidentiality restrictions does not automatically indicate misconduct.

14. Policy Review and Commitment

JRAIE may periodically review this policy to reflect developments in research practices, artificial intelligence, data protection, repository infrastructure, and scholarly publishing standards.

The journal supports responsible data practices that strengthen transparency, reproducibility, accountability, and research integrity while respecting legitimate ethical, legal, privacy, and intellectual-property obligations.