Post-Publication Discussion Policy

Mathematical and Data Analytics

1. Policy Statement

Mathematical and Data Analytics supports open, constructive, and evidence-based scholarly discussion of published research.

The journal recognizes that independent verification, examination of mathematical proofs, replication of statistical analyses, numerical experimentation, and methodological advances may generate findings that support, refine, or challenge previously published conclusions.

Post-publication discussions are managed in accordance with COPE principles, editorial independence, transparency, and academic integrity.

2. Scope of Post-Publication Discussion

Post-publication discussion may address mathematical reasoning, model assumptions, research methodology, dataset validity, sampling procedures, statistical analysis, computational implementation, interpretation of results, and the consistency of conclusions with the available evidence.

For theoretical and applied mathematical research, discussions may examine theorem statements, logical consistency, completeness of proofs, counterexamples, boundary conditions, model formulations, convergence properties, and the applicability of mathematical results.

For statistical and data analytics research, discussions may address data quality, missing data, sampling bias, statistical assumptions, uncertainty estimation, hypothesis testing, multiple comparisons, effect sizes, and the reliability of inferential or predictive claims.

For computational and machine learning research, discussions may examine numerical precision, algorithm implementation, dataset partitioning, data leakage, model validation, performance metrics, sensitivity analysis, and the reproducibility of comparative results.

All submissions must be supported by rigorous reasoning, relevant references, or verifiable evidence.

3. Submission of Comments and Critiques

Readers, researchers, academics, practitioners, and other interested parties may submit comments, critiques, clarifications, counterexamples, or replication findings concerning published articles.

Submissions should be sent to:

Editorial Contact: publikasi@itscience.org

Each submission should identify the relevant article by title, authors, and DOI or publication URL, and provide a clear description of the issue, supporting arguments, and appropriate evidence.

Supporting materials may include mathematical derivations, proofs, counterexamples, statistical outputs, analysis scripts, simulation configurations, datasets that may lawfully be shared, or relevant scholarly references.

Contributors should clearly distinguish demonstrated errors from alternative interpretations, methodological preferences, or questions requiring further clarification.

All communications must remain professional and free from personal attacks.

4. Editorial Assessment and Independent Review

The Editor-in-Chief or a designated editor will conduct an initial assessment of the submission’s relevance, scholarly merit, and supporting evidence.

When necessary, Mathematical and Data Analytics may invite independent reviewers with expertise in the relevant mathematical, statistical, or computational discipline.

Reviewers and editors must disclose relevant conflicts of interest.

Submissions that lack sufficient reasoning or evidence, fall outside the journal’s scope, or contain unsupported personal allegations may be declined.

Disagreement with the conclusions of a published article will not, by itself, constitute grounds for declining a well-supported scholarly contribution.

5. Authors’ Right to Respond

Authors whose published work is the subject of a post-publication discussion will normally be given a reasonable opportunity to respond.

Responses may include detailed proofs, clarification of assumptions, methodological explanations, model specifications, analysis scripts, additional statistical or numerical analyses, or other relevant supporting evidence.

Where appropriate and feasible, authors may provide source code, datasets, or technical information supporting the verification of their findings, subject to applicable legal, licensing, privacy, and confidentiality restrictions.

The editorial team will assess the critique and response objectively. An author’s decision not to respond will not, by itself, prevent publication of a contribution that meets the journal’s editorial standards.

6. Publication of Post-Publication Contributions

Submissions meeting the journal’s editorial standards may be published as Letters to the Editor, scholarly commentaries, author responses, or other appropriate forms of academic communication.

Published contributions will clearly identify the article under discussion and will be linked to the original article whenever technically feasible.

Where appropriate, critiques and author responses may be published together or linked to one another to help readers assess the exchange.

Publication of a commentary does not necessarily indicate editorial endorsement of the views expressed.

7. Reproducibility and Research Verification

Mathematical and Data Analytics encourages methodological transparency and the disclosure of sufficient information to facilitate independent research evaluation.

For theoretical research, relevant information may include explicit assumptions, precise definitions, complete theorem statements, sufficiently detailed proofs, and appropriate acknowledgment of prior results.

For statistical and computational research, relevant information may include data provenance, preprocessing procedures, model specifications, software versions, analysis scripts, random seeds, numerical precision, validation procedures, and simulation configurations.

Unsuccessful replication attempts should be evaluated in relation to the comparability of datasets, analytical procedures, and computational conditions before conclusions regarding research reliability are drawn.

For mathematical findings, an alleged counterexample or gap in a proof will be assessed against the precise assumptions and scope of the original claim.

Authors are not required to disclose confidential information, protected datasets, access credentials, or materials whose disclosure would violate applicable obligations. Where restrictions apply, the journal may consider appropriate alternatives, such as de-identified data, detailed methodological documentation, or authorized independent verification.

8. Suspected Research and Publication Misconduct

When post-publication discussion identifies credible concerns involving plagiarism, data fabrication, falsified statistical or numerical results, deliberate misrepresentation of mathematical findings, unauthorized dataset use, or other serious ethical violations, the matter will be addressed under the journal’s Allegations of Research Misconduct Policy.

Allegations must be assessed using verifiable evidence and must not be treated as established findings before appropriate examination.

Mathematical errors, differences in analytical approaches, and unsuccessful replication attempts do not automatically establish misconduct.

9. Corrections and Retractions

When post-publication discussions reveal material errors or serious concerns affecting the reliability of an article, Mathematical and Data Analytics may issue a correction, Expression of Concern, or retraction under its Correction and Retraction Policy.

Editorial action will be proportionate to the seriousness of the issue and the strength of the available evidence.

Errors that can be resolved without invalidating the principal findings may warrant a correction. Fundamental errors that render the principal findings unreliable may warrant retraction, regardless of whether they arose from honest error or deliberate misconduct.

10. Conflicts of Interest and Editorial Independence

Contributors, responding authors, reviewers, and editors involved in post-publication discussions must disclose relevant conflicts of interest.

Individuals whose impartiality may reasonably be compromised will not participate in the affected editorial assessment or decision.

When the Editor-in-Chief has a relevant conflict of interest, responsibility will be assigned to an independent editor or another appropriately qualified editorial representative.

Editorial decisions must not be improperly influenced by personal relationships, institutional affiliations, academic competition, commercial interests, or other inappropriate considerations.

11. Confidentiality and Fair Treatment

Mathematical and Data Analytics will protect confidential information and unpublished materials to the extent reasonably possible and consistent with applicable requirements.

Authors and contributors will be treated fairly and given appropriate opportunities to provide clarification.

The journal does not tolerate intimidation, personal attacks, or knowingly false accusations. Good-faith criticism supported by scholarly reasoning or evidence will be assessed on its merits.

12. Documentation and Appeals

The editorial office will maintain records of submissions, supporting evidence, author responses, reviews, and editorial decisions.

Records containing confidential information will be stored securely, with access restricted to authorized personnel.

Individuals who believe an editorial decision involved a material factual or procedural error may submit an appeal to:

Editorial Contact: [Insert the journal’s official editorial email address]

Appeals must include a clear explanation and relevant supporting evidence.

Where feasible, appeals will be reviewed by an editor or independent party who was not directly involved in the original decision.

Submitting an appeal does not automatically reverse or suspend the editorial decision.

13. Commitment to Scholarly Transparency

Mathematical and Data Analytics recognizes post-publication discussion as an essential component of continuous scholarly evaluation.

Through this policy, the journal aims to support open academic debate, strengthen mathematical rigor and research reproducibility, facilitate the correction of published errors, and maintain confidence in mathematical, statistical, and data analytics research.