Data and Reproducibility

The Journal of Diabetes, Metabolism and Endocrinology recognizes that transparent access to research data, methods, analytical procedures, and supporting materials is essential for reproducibility, verification, and advancement of scientific knowledge. The journal encourages authors to provide sufficient information and, where ethically and legally possible, access to the data and materials necessary to understand and reproduce their research.

1. Commitment to Reproducible Research

The journal encourages authors to report their research in sufficient detail to enable qualified researchers to understand, evaluate, and, where appropriate, reproduce the reported work.

Authors should provide clear information regarding:

• Research methodology.
• Data collection procedures.
• Statistical methods.
• Analytical approaches.
• Software and computational tools.
• Relevant research protocols.
• Data-processing procedures.
• Experimental materials and procedures.

2. Research Data Availability

Authors are encouraged to make the data underlying their published findings available whenever possible and appropriate.

Data may be made available through:

• Recognized disciplinary repositories.
• Institutional repositories.
• General-purpose research repositories.
• Supplementary files.
• Controlled-access repositories for sensitive data.

Where data cannot be publicly shared, authors should provide a clear explanation and, where feasible, describe how qualified researchers may request access.

3. Data Availability Statement

Authors should provide a Data Availability Statement where applicable.

The statement should clearly indicate where the supporting data can be accessed or explain why access is restricted.

Examples include:

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

or:

“The data supporting the findings of this study are openly available in [repository name] at [persistent identifier/DOI].”

4. Data Citation

The journal encourages authors to cite research datasets using standard scholarly reference-list practices.

Research data should be cited whenever they have contributed materially to the research or are publicly available and used to support the findings.

A complete data citation should, where available, include:

• Data creator(s).
• Dataset title.
• Repository or publisher.
• Publication or release year.
• Dataset version.
• Persistent identifier such as a DOI.

Data citation through the standard reference list provides readers with a clear and convenient mechanism for locating the underlying research data and supports reproducible research.

5. Persistent Identifiers

Where possible, authors are encouraged to use datasets and repositories that provide persistent identifiers, such as DOIs or accession numbers.

Persistent identifiers improve:

• Data discoverability.
• Citation accuracy.
• Long-term accessibility.
• Attribution.
• Reproducibility.

6. Human and Clinical Research Data

For research involving patients or human participants, data sharing must comply with:

• Informed-consent requirements.
• Ethical approval conditions.
• Institutional policies.
• Applicable privacy and data-protection requirements.

Identifiable patient information must not be publicly disclosed without appropriate authorization.

Where necessary, authors should use anonymization, de-identification, or controlled-access mechanisms.

7. Sensitive and Restricted Data

The journal recognizes that some research data cannot be openly released because of:

• Patient confidentiality.
• Privacy concerns.
• Ethical restrictions.
• Legal requirements.
• Security considerations.
• Intellectual-property restrictions.
• Third-party data-use agreements.

Such restrictions should be transparently described in the Data Availability Statement.

8. Reproducible Methods and Analysis

Authors are encouraged to provide sufficient methodological detail to allow independent researchers to understand the research process.

Where applicable, authors should consider sharing:

• Statistical analysis code.
• Computational scripts.
• Research protocols.
• Survey instruments.
• Questionnaires.
• Experimental procedures.
• Relevant supplementary information.
• Software versions and analytical parameters.

9. Computational and Statistical Reproducibility

For studies involving computational analysis, machine learning, bioinformatics, or statistical modelling, authors are encouraged to provide sufficient information to reproduce the reported analyses.

Where appropriate, this may include:

• Source code.
• Software and package versions.
• Model specifications.
• Analytical parameters.
• Data-processing procedures.
• Computational environment information.

Any shared code should be appropriately documented and, where possible, assigned a persistent identifier or deposited in a recognized repository.

10. Research Materials

Where feasible and ethically appropriate, authors are encouraged to make relevant research materials available to qualified researchers.

This may include:

• Protocols.
• Experimental materials.
• Measurement instruments.
• Questionnaires.
• Analytical scripts.
• Supplementary datasets.

Availability remains subject to copyright, intellectual-property, confidentiality, and ethical restrictions.

11. Verification of Published Findings

Editors and reviewers may request access to supporting data or materials when reasonably necessary to evaluate the validity of a manuscript.

Authors should cooperate with legitimate requests, subject to applicable ethical, legal, privacy, and confidentiality restrictions.

Failure to provide data without a legitimate justification may be considered when assessing concerns regarding research integrity.

12. Data Integrity

Authors are responsible for ensuring that shared datasets accurately represent the research described in the manuscript.

Data must not be:

• Fabricated.
• Falsified.
• Selectively manipulated to misrepresent findings.
• Mislabelled.
• Presented in a misleading manner.

Where serious concerns regarding underlying data arise after publication, the journal may investigate the matter under its Research Misconduct and Publication Ethics Policies.

13. Data and Supplementary Material

Where appropriate, supporting datasets and supplementary research materials may be published alongside the article or linked through an external repository.

Authors should ensure that repository links, accession numbers, and persistent identifiers are accurate and functional at the time of submission.

14. Reproducibility and Peer Review

Reviewers may use available datasets, supplementary materials, code, and methodological information to assess the reproducibility and robustness of the research.

The journal encourages reviewers to identify insufficient methodological information or data-access limitations that could affect the reproducibility of the reported findings.

15. Post-Publication Data Access

If supporting data become unavailable after publication, authors should inform the Editorial Office where the change materially affects the ability of readers to verify the published research.

The journal may update the Data Availability Statement or publish an appropriate editorial notice where necessary.

16. Commitment to Open and Reproducible Science

The Journal of Diabetes, Metabolism and Endocrinology believes that responsible data sharing and standardized data citation are important components of modern scholarly communication.

The journal therefore encourages authors to share research data whenever ethically, legally, and practically possible and to cite datasets through standard reference lists and persistent identifiers.

By making research data easier to locate, access, cite, and verify, the journal aims to strengthen transparency, reproducibility, research integrity, and the long-term value of published scientific research.