Data Sharing and Accessibility Policy

Research Transparency & Reproducibility

Data Sharing & Accessibility Policy

Digitus: Journal of Computer Science Applications supports responsible data sharing, research transparency, reproducibility, and FAIR data practices.

TRANSPARENCY REPRODUCIBILITY FAIR DATA

The journal encourages authors to make research data, source code, software, computational models, algorithms, experimental outputs, and other materials supporting their findings available whenever ethically, legally, and practically possible. Data sharing must respect participant privacy, confidentiality, intellectual property, software licensing, cybersecurity considerations, contractual obligations, and applicable laws.

1. Data Availability Statement

All research manuscripts must include a Data Availability Statement indicating whether supporting datasets, source code, software, models, scripts, experimental outputs, or other research materials are available, where they can be accessed, and any applicable conditions or restrictions.

Public Repository
The data and supporting materials for this study are available in [repository name] at [DOI/persistent identifier].
Available on Request
The data and supporting materials for this study are available from the corresponding author upon reasonable request.
Included in the Article
All data generated or analyzed during this study are included in this published article and its supplementary materials.
Restricted Data
The data or supporting materials are not publicly available due to privacy, ethical, legal, security, commercial, licensing, or third-party restrictions.

2. Data, Code Repositories & Documentation

Where research data, source code, software, scripts, computational models, trained models, or related materials can be shared, authors are encouraged to deposit them in a recognized disciplinary, general-purpose, software, or institutional repository that provides stable access and, preferably, a persistent identifier such as a DOI.

Appropriate repositories may include Zenodo, Figshare, Harvard Dataverse, Dryad, Open Science Framework (OSF), GitHub linked to Zenodo, or a recognized institutional or disciplinary repository.

Shared materials should be provided in appropriate, reusable, and preferably open or machine-readable formats. Authors should include sufficient metadata, README documentation, variable descriptions, software dependencies, version information, environment specifications, methodological details, and execution instructions to support interpretation, verification, reproducibility, and reuse.

3. Ethical, Legal, Security & Confidential Data

Data and code sharing must not compromise ethical obligations, participant confidentiality, personal information, cybersecurity, system security, commercially sensitive information, intellectual property, proprietary software, licensed datasets, or third-party agreements.

✓ De-identify or aggregate personal and sensitive data where appropriate. ✓ Respect informed consent, ethical approval, and data-use conditions.
✓ Explain legal, contractual, licensing, or proprietary restrictions. ✓ Avoid releasing credentials, exploitable vulnerabilities, or sensitive security information.
✓ State clearly how restricted data, code, or materials may be accessed, if possible. ✓ Respect third-party ownership of datasets, APIs, software, platforms, and digital resources.
Important: Authors are not required to make data, code, software, or other materials publicly available when doing so would violate ethical, legal, privacy, confidentiality, cybersecurity, intellectual-property, licensing, or contractual obligations.

4. Data Citation & FAIR Principles

Shared datasets, source code, software releases, models, and other research objects should be cited appropriately in the manuscript and reference list. Where available, authors should provide the creator(s), title, repository, year, version, DOI, software release, commit identifier, or other persistent identifier.

F
Findable		 A
Accessible		 I
Interoperable		 R
Reusable

FAIR does not necessarily mean that all data, source code, or digital research objects must be completely open. Where access is restricted, authors should provide clear metadata and explain the conditions under which access may be obtained.

5. Computational Reproducibility

For computational, algorithmic, artificial-intelligence, machine-learning, simulation, software-engineering, or data-science studies, authors are encouraged to provide sufficient information to allow qualified researchers to understand and, where feasible, reproduce the reported computational procedures.

✓ Programming language and software version ✓ Libraries, frameworks, packages, and dependencies
✓ Model architecture, parameters, and relevant configurations ✓ Dataset versions, preprocessing, and train/test procedures
✓ Source code, scripts, pseudocode, or executable workflow where possible ✓ Random seeds, hardware details, and environment information where relevant

When full computational reproducibility is not possible, authors should explain the relevant technical, legal, proprietary, security, or resource-related limitations transparently.

6. Editorial Oversight

The Editorial Team may request access to supporting datasets, source code, software, algorithms, models, scripts, outputs, or other underlying materials when necessary to verify reported results, investigate research-integrity concerns, evaluate computational reproducibility, or assess compliance with this policy.

Inaccurate or misleading data availability statements, unjustified refusal to provide underlying materials for confidential editorial verification, serious inconsistencies between the article and supporting data or code, or evidence that computational results cannot be substantiated may result in further editorial review and appropriate action under the journal's publication-ethics and post-publication policies.

Policy Alignment

This policy is informed by the COPE Core Practice on Data and Reproducibility, the COPE–DOAJ–OASPA–WAME Principles of Transparency and Best Practice in Scholarly Publishing, the FAIR Guiding Principles, and established international practices for responsible research-data, software, source-code, and computational-material sharing.