The Journal of Computer and Data Science (JCODA) promotes transparency, reproducibility, and methodological rigor in computational and data-driven research. Authors are encouraged to provide sufficient information about their datasets, analytical methods, algorithms, software, and experimental procedures to enable other researchers to understand, evaluate, and, where feasible, reproduce the reported findings.
Reproducibility supports the verification of scientific results, strengthens confidence in published research, and facilitates further development of knowledge in computer science, data science, artificial intelligence, and related disciplines.
Authors are responsible for ensuring that the data, methods, and computational procedures described in their manuscripts are accurate, sufficiently documented, and consistent with the reported results. Where applicable, manuscripts should provide the following information:
Describe the origin, characteristics, collection procedures, selection criteria, preprocessing steps, and relevant limitations of the datasets used in the study.
Explain the research design, analytical methods, algorithmic procedures, model configurations, evaluation metrics, and experimental settings needed to interpret the results.
Identify relevant software, programming languages, libraries, frameworks, and versions. Where important, report hardware specifications, random seeds, dependencies, and computational environments.
Report results accurately, describe validation procedures, explain the evaluation process, and disclose relevant limitations or conditions that may affect reproducibility.
JCODA encourages authors to make the data supporting their findings available through appropriate repositories or other reliable means whenever legally, ethically, and practically possible. Shared materials should be accompanied by sufficient documentation to help other researchers understand and reuse them.
Authors should identify the source of publicly available datasets and provide a persistent link, DOI, accession number, or other suitable identifier whenever available.
Authors are encouraged to deposit original datasets and supporting materials in a trusted repository, subject to applicable consent, privacy, security, and intellectual property requirements.
When data cannot be shared publicly, authors should explain the restriction and, where possible, describe the conditions under which qualified researchers may request access.
Authors must not disclose confidential, personally identifiable, proprietary, or otherwise restricted information without appropriate authorization. Data sharing must comply with applicable laws, ethical approvals, participant consent, and third-party licensing conditions.
For studies that depend on custom software, scripts, computational models, or algorithms, authors are encouraged to share the relevant source code and implementation details whenever feasible. Shared code should be sufficiently documented to support inspection, execution, and independent verification of the reported results.
If code or software cannot be released, authors should state the reason, such as licensing restrictions, security considerations, confidentiality obligations, or institutional policies, and provide alternative details where possible.
Authors should include a data availability or reproducibility statement when relevant to their research. The statement should accurately describe the availability of the data, code, and other materials required to assess or reproduce the findings.
“The dataset and supporting code used in this study are publicly available at [repository name and URL or DOI]. The repository includes the relevant documentation and instructions for reproducing the reported analyses.”
“The data supporting this study are subject to privacy, confidentiality, or other access restrictions and cannot be made publicly available. Access may be considered upon reasonable request, subject to applicable ethical, legal, and institutional requirements.”
“No new datasets were generated or analyzed during this study. Data availability is therefore not applicable.”
Authors should select and adapt the statement that accurately reflects their study. These examples are provided as guidance and should not be used if they do not describe the actual availability of the research materials.
Editors and reviewers may assess whether the manuscript provides sufficient information to evaluate its methods, data sources, computational procedures, and reported findings. When necessary, the editorial team may request clarification, supporting documentation, access information, or additional details about the reproducibility of the study.
The availability of data or code does not by itself guarantee that results are reproducible. The quality of documentation, the appropriateness of the methods, the validity of the analysis, and the feasibility of independent verification may also be considered.
Authors must not fabricate datasets, misrepresent data availability, conceal material methodological limitations, or claim that experiments have been independently reproduced when they have not. Any correction to previously reported data or results should be disclosed promptly to the editorial team.
JCODA recognizes that complete public access to research data and software may not always be possible. Restrictions may arise from participant privacy, data protection laws, informed consent conditions, confidentiality agreements, intellectual property rights, commercial obligations, cybersecurity risks, or limitations imposed by third-party data providers.
In such cases, authors should provide a clear explanation of the limitations and make reasonable efforts to support transparency through alternative means, such as detailed methodological descriptions, anonymized or aggregated data, synthetic datasets, documented workflows, or controlled-access procedures, where appropriate.
The Journal of Computer and Data Science (JCODA) encourages responsible data management, transparent reporting, and reproducible research practices. Authors, reviewers, and editors share responsibility for promoting the reliability of computational and data-driven scholarship while respecting ethical obligations, privacy, security, intellectual property, and legitimate restrictions on data access.
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Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)Membangun Ekosistem Riset Digital yang Berkelanjutan |
