All submissions require a data availability statement describing where data are stored, how they can be accessed, or why access is restricted.

• Public repositories with persistent identifiers
• Controlled access repositories for sensitive clinical data
• Data available from the corresponding author on reasonable request
• Data included within the article or supplementary files

Statements should specify access conditions, embargo periods, and any data use agreements or ethical limits.

Archive the data needed to reproduce the main findings, including derived datasets, codebooks, and analytic scripts when applicable.

• De identified participant level data where permitted
• Aggregate data used for tables and figures
• Analysis code and workflows with version details
• Study instruments and protocols

Select repositories that provide stable access, metadata quality, and clear reuse terms.

Remove personal identifiers and confirm consent for data sharing. Sensitive datasets should use controlled access with clear governance.

If embargo periods apply, state the timeline and access conditions in the data availability statement.

Provide a data dictionary that defines variables, coding, and missing value conventions. Include readme files that describe file structure and software requirements.

Use open and non proprietary formats when possible, such as CSV for tables and TXT or PDF for documentation.

Define access levels clearly, including open, controlled, and restricted access. When reuse requires approval, provide a clear application process and contact details.

For clinical datasets, include data use agreements and oversight processes that protect participant privacy while enabling reproducible research.

If data must remain under embargo due to ongoing analysis or regulatory requirements, provide a justified timeline and specify when data will become available.

Before deposit, review datasets for completeness, consistent variable naming, and missing data coding. Provide validation checks to support reuse and replication.

For large or multi site studies, a data management plan supports consistent storage, access control, and long term preservation. Planning early reduces compliance delays at submission.

Data management plans should align with funder requirements and institutional policies to avoid last minute revisions.

Specify any reuse limitations, required acknowledgments, and licensing terms so secondary users can comply with ethical and legal requirements.

• Indicate whether reuse is open or controlled
• List any embargo or access review steps
• Include citation requirements for datasets

For clinical or identifiable data, use controlled access repositories and remove direct identifiers. Describe governance procedures and approval requirements for secondary use.

Include a summary of consent language that supports data sharing and list any restrictions that apply to reuse.

Clear safeguards protect participants while enabling reproducible science.

Cite datasets in the reference list and include repository identifiers. Provide data dictionaries and readme files that define variables and coding.

Use open formats when possible and include analysis scripts or workflows that support replication.

• Data availability statement with repository links
• De identification and consent confirmation
• Data dictionary and readme documentation
• Code and workflow files with version details
• Access conditions and contact details if restricted

Clear reuse guidance supports responsible secondary analysis and encourages proper attribution of addiction research data.

When restrictions apply, provide a transparent process for qualified access requests.

This supports responsible collaboration and reuse.