Data and Reproducibility

Journal of Midwifery (JOM) supports transparent, reusable, and reproducible research. This policy explains what data, code, and materials must be shared; how to document them; and when controlled access is appropriate. It applies to all article types that analyze or generate data, including quantitative, qualitative, and mixed-methods studies.

1. Introduction

  • Readers and reviewers should be able to understand, verify, and, where feasible, reproduce the reported findings.

  • JOM requires a Data Availability Statement (DAS) on every research article and encourages sharing of code, materials, and protocols with persistent identifiers.

2. Description

  • Data: all observations underlying analyses (e.g., numerical datasets, interview transcripts, images, audio/video, derived variables).

  • Code: software, analysis scripts, computational notebooks, and workflows used to process and analyze data.

  • Materials: questionnaires, interview guides, lab protocols, intervention manuals, training materials, and custom reagents/devices.

  • Metadata & documentation: codebooks, variable dictionaries, README files, version notes, and parameter settings that enable reuse.

3. Policy

3.1 Data Availability Statement (mandatory)

  • Each research article must include a DAS specifying:
    a) What data are available (raw/processed, subset, or none)
    b) Where they are hosted (repository name and persistent identifier, e.g., DOI/handle)
    c) Under what license/terms (e.g., CC0, CC BY, or controlled-access terms)
    d) Any restrictions (privacy, consent, legal, indigenous data sovereignty, commercial) and how qualified researchers can request access

Sample DAS (open): “De-identified participant-level data, codebook, and analysis scripts are available in Repository X at DOI: 10.xxxx/xxxx under CC BY 4.0.”
Sample DAS (controlled): “De-identified interview transcripts are available upon reasonable request via Repository Y (DOI: 10.xxxx/xxxx) under a Data Use Agreement due to confidentiality and consent restrictions.”

3.2 Repositories and identifiers

  • Deposit data and code in trusted repositories that issue persistent identifiers (e.g., DOI/handle) and support long-term preservation. Institutional repositories are acceptable if they provide stable identifiers and access controls.

  • Link dataset DOIs in the manuscript and cite them in the references.

3.3 Licensing

  • Prefer CC0 (data) or CC BY when reuse does not pose ethical/legal risks. For software/code, use a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0).

  • When open licensing is not appropriate (e.g., qualitative or clinical data), provide restricted/controlled access with clear conditions.

3.4 Code and computational reproducibility

  • Share analysis code and workflows (e.g., R, Stata, SPSS, Python notebooks), with a README that lists: software and package versions, OS, random seeds, and exact commands to reproduce figures/tables.

  • Archive code in a repository that mints a DOI (e.g., link a release archive) and reference that DOI in the manuscript.

3.5 Materials and protocols

  • Share study instruments (e.g., questionnaires, training manuals) and protocols. If copyright or proprietary constraints apply, share a detailed description and access conditions.

3.6 Reporting standards and preregistration

  • Use appropriate reporting guidelines (e.g., CONSORT, PRISMA, STROBE, CARE, ARRIVE, COREQ, TRIPOD, STARD) and state any trial/registry numbers in the manuscript.

  • JOM encourages preregistration of study plans and analysis protocols when feasible.

3.7 Image and figure integrity

  • Retain and provide original, unprocessed images (e.g., microscopy, blots) upon request. Any image adjustments must be applied to the entire image and described in Methods.

3.8 Exceptions and proportional sharing

  • If legal, ethical, or contractual constraints prevent full sharing, authors must:
    a) Provide a justification in the DAS;
    b) Share metadata/codebooks and, where possible, synthetic or aggregated data;
    c) Use a controlled-access repository with a documented request pathway;
    d) Confirm that consent forms and approvals support the chosen access model.

3.9 Compliance and enforcement

  • The editorial team checks the DAS, repository links, identifiers, and documentation at acceptance.

  • Failure to meet this policy may delay acceptance/publication or lead to corrections; misrepresentation may trigger the Allegations of Misconduct process.

4. Technicalities to Achieve and Materialise the Policies

4.1 Before submission (prepare a “Repro Pack”)

  • README (study overview, file map, reproduction steps)

  • Data files (open, non-proprietary formats preferred: CSV/TSV, JSON, TXT; for images: TIFF; audio: WAV)

  • Code/scripts (with requirements file and instructions)

  • Codebook/variable dictionary and data dictionary for qualitative codes

  • Protocols/instruments (questionnaires, interview guides)

  • Provenance notes (how raw data became analytic files)

  • License files (e.g., LICENSE, CITATION.cff) and dataset citation

4.2 During submission

  • Provide repository links/DOIs (temporary private links for peer review are acceptable)

  • Complete the DAS, funding, conflict of interest, and ethics/consent fields

  • Declare software versions and environments in Methods or Supplement

4.3 After acceptance (pre-publication checks)

  • Convert temporary links to public DOIs/handles or to controlled-access records with documented request procedures

  • Ensure references include dataset and code citations

  • Verify that licenses displayed in the repository match the DAS

4.4 Qualitative and sensitive data

  • De-identify transcripts; remove direct identifiers and minimize indirect identifiers; store keys separately.

  • Confirm consent covers data sharing or explain restrictions. Consider depositing topic guides and coding frameworks when transcripts cannot be shared.

  • Respect indigenous data sovereignty and community governance; use access controls and culturally appropriate terms of use.

4.5 Clinical and patient data

  • State ethics approval identifiers and consent procedures.

  • Share de-identified datasets or use controlled repositories; provide data dictionaries and statistical code.

4.6 Computational details

  • Provide software version info (e.g., R 4.x; Python 3.x + package versions), random seeds, and script order.

  • Where complex, include a container/environment file (e.g., Dockerfile, renv/requirements.txt) or a virtual environment export.

4.7 Data and code citation format (examples)

  • Dataset: Author(s). Title [dataset]. Repository; Year. DOI.

  • Code: Author(s). Title [code; version]. Repository; Year. DOI.

4.8 Post-publication updates

  • If datasets or code are updated, provide a new versioned DOI and request a correction notice in the article to maintain the scholarly record.

Related and supporting policies

Contact

Questions about data, code, and materials sharing: jom@med.unand.ac.id

Back to Publication Ethics main page: https://jom.fk.unand.ac.id/index.php/jom/ethics