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Research Transparency & Reproducibility

Research transparency and reproducibility are fundamental components of scholarly integrity and scientific progress. Transparency allows readers, reviewers, and future researchers to understand how research was conducted, how conclusions were reached, and how findings may be evaluated, verified, challenged, or extended. Reproducibility strengthens confidence in published work and supports the cumulative advancement of knowledge across the operations research and management science community.

INFORMS recognizes that meaningful transparency may take different forms across methodologies, disciplines, and research settings. Quantitative empirical studies, simulations, computational experiments, qualitative fieldwork, theoretical modeling, laboratory experiments, archival analyses, and mixed-methods research each present different practical, legal, ethical, and disciplinary considerations. Accordingly, transparency expectations may vary by journal and by research context.

Authors are expected to engage in research and reporting practices that promote credibility, accountability, and appropriate transparency, while respecting legitimate constraints related to confidentiality, privacy, intellectual property, contractual obligations, participant protection, and researcher safety.

This page provides practical guidance regarding transparency and reproducibility. Ethical responsibilities relating to research integrity remain governed by INFORMS' Guidelines for Ethical Behavior in Publishing and applicable journal policies. Authors should also consult the editorial statement and any journal-specific transparency, disclosure, or reproducibility policies applicable to their submission.

General Principles

Accuracy & Integrity of the Scholarly Record

Authors are expected to present an accurate and complete account of the research performed. Data, analyses, computational procedures, experimental protocols, qualitative evidence, and findings should be represented honestly and transparently. Fabrication, falsification, selective misrepresentation, or knowingly inaccurate reporting constitute serious violations of publication ethics.

Research manuscripts should contain sufficient methodological detail, documentation, and referencing to permit informed evaluation of the work and, where feasible and appropriate, independent verification or replication.

Transparency Across Research Methodologies

INFORMS journals publish a wide range of research methodologies. Transparency expectations should therefore be interpreted in a manner appropriate to the type of research being conducted.

Examples may include:

  • sharing datasets, code, or computational workflows;
  • documenting simulation environments or parameter settings;
  • describing data collection and preprocessing procedures;
  • disclosing experimental instructions or study protocols;
  • documenting coding frameworks or qualitative analytical methods;
  • reporting inclusion, exclusion, and sampling decisions;
  • disclosing preregistration information or deviations from research plans;
  • providing supplemental appendices, data dictionaries, or replication materials; and
  • explaining limitations, uncertainties, and methodological tradeoffs.

Transparency is not limited to the public posting of data or code. In many cases, clear methodological explanation within the manuscript itself is equally important.

INFORMS recognizes that full public disclosure of all research materials is not always possible or appropriate. Legitimate constraints may include:

  • non-disclosure agreements (NDAs);
  • proprietary or licensed datasets;
  • personally identifiable or sensitive human-subject data;
  • institutional or governmental restrictions;
  • privacy, confidentiality, or security concerns;
  • legal or contractual limitations; or
  • risks to vulnerable populations, organizations, or researchers.

When such constraints exist, authors are expected to pursue alternative approaches that support transparency and verification to the greatest extent reasonably possible.

Depending on the circumstances, acceptable alternatives may include:

  • synthetic or transformed datasets;
  • masked or anonymized data;
  • controlled-access repositories;
  • delayed or embargoed public release;
  • detailed data dictionaries and methodological documentation;
  • distributional statistics sufficient for replication;
  • instructions for accessing licensed or proprietary data sources;
  • third-party verification arrangements; or
  • pseudocode or methodological descriptions in place of restricted source code.

The acceptability of alternative disclosure approaches remains subject to editorial review and journal-specific policy requirements.

Data Transparency

Authors are expected to maintain appropriate records of the data underlying their research and to retain such materials for a reasonable period following publication, sufficient to support editorial review, verification, or replication efforts where appropriate.

Authors should:

  • accurately describe the provenance and sources of data used in the research;
  • ensure they possess the legal and ethical right to use and publish the data;
  • disclose material preprocessing, cleaning, transformation, matching, or exclusion procedures;
  • explain missing data handling and sampling decisions where relevant; and
  • disclose material limitations or restrictions affecting data access or reuse.

When data are obtained from third-party, public, licensed, or proprietary sources, authors remain responsible for complying with applicable terms of use, contractual restrictions, privacy protections, and legal requirements.

Several INFORMS journals maintain additional data disclosure, archiving, or reproducibility requirements. Authors should consult journal-specific policies prior to submission.

Code & Computational Reproducibility

For research relying on computational analysis, simulation, optimization, machine learning, statistical estimation, or numerical experimentation, authors may be expected to provide code, scripts, computational instructions, configuration information, or related materials sufficient to support verification or replication of reported results.

Shared computational materials should:

  • reasonably correspond to the analyses reported in the manuscript;
  • contain sufficient documentation or annotation to permit understanding by knowledgeable researchers in the field;
  • identify relevant software environments, dependencies, packages, or licensed components where necessary; and
  • include instructions describing the progression from raw inputs to reported results where feasible.

Authors are not necessarily expected to provide production-quality software or extensive user support unless explicitly required by the journal.

Research Workflow, Disclosure & Provenance

Transparency may also include disclosure regarding the research process itself. Depending on the journal and methodology, authors may be asked to disclose:

  • preregistration information;
  • sequencing of studies or analyses;
  • deviations from original research plans;
  • related or overlapping studies;
  • contributor roles in data collection, coding, analysis, or interpretation;
  • provenance of research outputs and analytical workflows; and
  • use of generative AI or AI-assisted technologies in the research or manuscript preparation process.

Such disclosures help readers and reviewers understand how findings were developed and evaluated and support confidence in the integrity of the scholarly record.

Qualitative & Mixed-Methods Research

Transparency expectations apply to qualitative and mixed-methods research as well as quantitative and computational studies.

Authors of qualitative research are encouraged to provide sufficient detail regarding:

  • study settings and context,
  • participant selection and recruitment,
  • data collection methods,
  • coding and analytical frameworks,
  • interpretation processes,
  • evidentiary support for stated findings, and
  • limitations and constraints affecting disclosure.

INFORMS recognizes that confidentiality, anonymity, participant protection, and researcher safety considerations may place important limits on data sharing in qualitative research contexts.

Repositories, Supplemental Materials & Archiving

Some INFORMS journals require or encourage the use of:

  • institutional repositories,
  • discipline-specific repositories,
  • journal-managed repositories,
  • supplemental online appendices,
  • third-party archiving platforms, or
  • reproducibility workflows or replication packages.

Authors should consult journal-specific instructions regarding accepted repositories, formatting requirements, embargo options, supplemental materials, and archival expectations.

Journal-Specific Policies

Several INFORMS journals maintain additional policies or workflows relating to transparency, reproducibility, data disclosure, computational verification, or replication materials.

Examples include:

  • code and data disclosure policies,
  • reproducibility and replication package requirements,
  • data provenance certifications,
  • supplemental repository workflows,
  • transparency guidance for qualitative research,
  • computational reproducibility standards, and
  • preregistration guidance.

Authors are responsible for reviewing and complying with all journal-specific requirements applicable to their submission.

Additional guidance may be found in:

Updated July 18, 2026.

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