Data Archiving Permissions
JCSR supports open science through responsible data archiving. Authors are encouraged to deposit underlying datasets, code, and supplementary materials in trusted repositories to improve transparency, reproducibility, and long term access.
Encouraging reproducibility and reuse
Data archiving is an essential component of rigorous scientific publishing. JCSR permits and encourages deposition of data in disciplinary or institutional repositories, and requests a clear data availability statement in every submission.
When data cannot be publicly shared due to privacy, legal, or ethical constraints, authors must describe the restrictions and provide controlled access pathways when possible.
Zenodo
General repository with DOI support
OSF
Open Science Framework projects
Figshare
Data, figures, and datasets
Dryad
Curated data archiving
Institutional Repos
University data repositories
Discipline Repos
Domain specific archives
What to include
Include a data availability statement in the manuscript that specifies where data are stored, how they can be accessed, and any reuse restrictions. Provide permanent identifiers such as DOIs to support citation and reuse.
For code and software, provide version details and licensing terms. For qualitative or sensitive datasets, describe anonymization procedures and access conditions.
Sensitive Data: If datasets involve human participants, protected locations, or proprietary materials, authors should de identify data and use controlled access repositories or data use agreements. Include ethics approval numbers and consent conditions.
Embargoes: Temporary embargoes may be permitted when justified by ongoing research or intellectual property considerations. Authors should state embargo duration and planned release date.
File Formats: Use non proprietary, reusable formats where possible, such as CSV, TXT, or standard open file types. Provide documentation or readme files to explain variables and methods.
How to cite and license data
Datasets should be cited in the reference list with a persistent identifier such as a DOI. Data citations should include authors, year, title, repository, version, and identifier to enable clear attribution and reuse.
When selecting licenses for data, consider open licenses that enable reuse while protecting sensitive information. Clearly state the license in the data availability statement.
Selecting the right archive
Repository selection checklist:
- Repository provides persistent identifiers and long term access.
- Data are stored in open, reusable file formats.
- Access controls are available for sensitive datasets.
- Repository supports versioning and updates.
- Metadata fields are complete and descriptive.
- Repository aligns with funder or institutional policies.
- Data can be cited directly in the manuscript.
Managing scale and provenance
For large datasets, consider repositories that support bulk uploads, version control, and long term storage. Provide a short data dictionary and link to any processing scripts used to generate analysis outputs.
When data are derived from multiple sources, document provenance clearly so readers can trace inputs and understand limitations.
Clear and specific wording
Example data availability statements should specify repository name, access URL, and any restrictions. For example, "Data are available in Zenodo at DOI:10.xxxx/xxxxx" or "Data available on request due to privacy". Clear statements reduce reviewer queries and improve transparency.
Improve reuse and citations
Well documented data sets increase citation potential and improve research reuse. Provide clear metadata and consistent naming conventions so others can interpret and build upon your work efficiently.
Protect sensitive information
For sensitive data, use repositories that support controlled access, de identification, and audit trails. Explain any access restrictions and provide contact points for data requests.
Document transformations
Describe any data processing steps clearly so others can replicate the workflow from raw data to final results.
Enable future studies
Provide clear reuse terms and cite datasets to encourage responsible follow on research.
Help others reuse data
Use consistent variable names and include a short readme file to explain the dataset structure and usage.
Track updates
Use repository versioning to document updates and corrections to datasets over time.
Restricted data
Provide a contact point for controlled access requests when data cannot be fully public.
