Data Archiving PermissionsInternational Journal of Neuroinformatics
IJNN supports data archiving that strengthens reproducibility and long term access while protecting ethical and legal obligations. This page outlines expectations for sharing neuroinformatics data, code, and materials.
Our Data Archiving Approach
We encourage authors to archive datasets, code, and protocols in trusted repositories whenever possible. Transparent data sharing improves scientific integrity, accelerates discovery, and supports secondary analyses in neuroinformatics and neuroscience.
When data cannot be shared due to confidentiality, proprietary restrictions, or third party agreements, authors must provide a clear explanation and describe any controlled access process.
All manuscripts must include a data availability statement that explains what is available, where it is archived, and any limitations on access.
Key Expectations
- Data availability statement in every article
- Use reputable repositories with persistent links
- De identify sensitive or patient data
- Provide code or analysis scripts when possible
- Document instruments and protocols
- Follow funder and ethics requirements
What to Archive
Datasets
Quantitative datasets that support neuroinformatics findings, including benchmark results, derived features, and model outputs.
Code and Scripts
Analysis scripts, statistical workflows, and computational models used to generate results.
Protocols
Imaging acquisition protocols, sequence parameters, and preprocessing steps.
Imaging Data
Raw and processed datasets with repository identifiers and clear file organization.
Supplementary Tables
Extended results, raw output tables, and supporting figures not in the main text.
Metadata
Codebooks, data dictionaries, and clear descriptions of variables and conditions.
Permissions and Compliance
Neuroinformatics research may involve human participants, clinical scans, or proprietary datasets. Archiving must respect ethical approvals, consent agreements, and legal constraints.
Remove identifiers and follow ethics committee approvals for participant data.
Do not share restricted datasets without written permissions from owners.
Obtain permissions before sharing data from external collaborators or vendors.
Apply clear data licenses that specify reuse conditions and attribution requirements.
Repositories and Documentation
Use repositories that provide persistent identifiers, version control, and long term access. Institutional repositories, discipline specific archives, and general purpose platforms can be appropriate if they meet these standards.
Ensure that archived files include a README, data dictionary, and clear citation guidance so that secondary users can interpret the data correctly.
When data access is restricted, provide a contact point and describe the request process, including any data use agreements.
Data Quality and Annotation
High quality annotation improves reuse and prevents misinterpretation of computational results. Authors should document units, normalization steps, and any transformations applied to raw data.
For datasets, include metadata, preprocessing parameters, and quality control outputs. For longitudinal studies, provide time points, harmonization steps, and validation procedures.
Version control is important when datasets are updated after publication. Clearly label revisions and maintain links to previous versions.
Annotation Checklist
- Units and measurement scales
- Normalization and preprocessing steps
- Sample metadata and batch IDs
- Quality control metrics
- Missing value handling
- Version identifiers and dates
Funder and Policy Alignment
Many funders require data management plans and repository deposition. Align your archiving approach with funder mandates early to avoid delays at publication.
For industry funded or collaborative projects, confirm intellectual property terms and any embargo periods before releasing data. Early planning prevents delays.
Data citation is essential for attribution and impact tracking. Provide recommended citation text and include data DOIs or accession numbers in the manuscript.
Compliance Steps
- Check funder data sharing policies
- Document data management plans
- Confirm embargo or access terms
- Provide data citations and DOIs
- Describe retention timelines
- Align with institutional policies
Author Responsibilities
Authors are responsible for securing permissions and ensuring that shared data complies with institutional and legal requirements. Include repository links and accession numbers in the data availability statement.
When data cannot be shared, provide a clear justification and describe how qualified researchers can request access. This ensures transparency without compromising ethical obligations.
The editorial office can answer questions about data sharing expectations, but authors should consult their ethics committee for final guidance. Document all approvals in the manuscript.
Need Guidance on Data Archiving?
Contact the editorial office for help aligning data sharing plans with neuroinformatics ethics and funder requirements before submission. We respond within 24 business hours.
Author Guidelines Contact the Editorial OfficeEmail: [email protected] | Response within 24 business hours | Data sharing support available
