Data Availability and Reproducibility Policy
Data Availability and Reproducibility Policy
MIJST is committed to research transparency and reproducibility, following the FAIR Data Principles, COPE guidance on research data, and the data-availability expectations of DOAJ, Scopus, and Web of Science.
1Data Availability Statement
Every submission must include a Data Availability Statement, stating where the underlying research data can be found — for example, in a public repository (with a persistent identifier), in the article's supplementary material, available from the authors on reasonable request, or not available with a stated reason (see Section 3). A missing or vague Data Availability Statement will be returned to the corresponding author for revision before the manuscript proceeds to peer review.
2Recommended Repositories
Authors are encouraged to deposit data in a recognized public repository appropriate to their field:
General-purpose: Zenodo, Figshare, Mendeley Data, Dryad
Discipline-specific: IEEE DataPort for engineering and computing, PhysioNet for biomedical and clinical signal data, or another registry recognized by the relevant field
Institutional: DSpace@MIST, where appropriate for datasets tied to MIST-affiliated research
Authors should cite deposited datasets in the reference list with their persistent identifier (DOI or equivalent).
3Exceptions to Data Sharing
Not all data can be shared openly. Acceptable reasons for restricting access include:
Confidentiality: Legal, contractual, or institutional restriction — including defense-related or government-sponsored research subject to institutional clearance
Participant privacy: Protection of research participants, consistent with MIJST's Confidentiality and Informed Consent policy
Third-party ownership: Data owned or controlled by a party other than the authors
Commercial or patent considerations: Ongoing intellectual property or licensing processes
Authors invoking an exception must state the reason in the Data Availability Statement rather than omitting it entirely.
4Code and Materials Availability
Where a submission's findings depend on custom software, code, models, or analysis scripts — including machine learning, simulation, and computational papers — authors are encouraged to make this code available through a public repository (e.g. GitHub, with an archived release via Zenodo) or state why it cannot be shared, using the exceptions listed in Section 3.
5Reporting Guidelines and Study Registration
Authors must follow the relevant discipline-specific reporting guideline where one exists, and disclose compliance in the manuscript:
CONSORT: Randomized controlled trials
PRISMA: Systematic reviews and meta-analyses
STROBE: Observational studies
ARRIVE: Research involving animals
Clinical trials must be registered in a recognized public trial registry before enrollment, with the registration number reported in the manuscript.
6Confidential Data During Review
Reviewers and Editors: may be given access to data necessary for verification during peer review. Such data must be treated as confidential, used only to evaluate the submission, and never retained, redistributed, or uploaded to any third-party tool, including generative AI tools.
7Non-Compliance
Submissions that do not include a Data Availability Statement, or that withhold data without a stated and reasonable justification, will not proceed to publication until this is resolved. Editors may request access to underlying data to verify a specific finding as part of the review process.
