Authors should archive materials that enable validation and reuse of results. The following items are commonly expected:

• De identified field trial data or aggregated datasets when privacy is a concern.
• Analysis code, scripts, or modeling workflows.
• Study protocols, input rates, and management practices.
• Questionnaires or survey instruments when permissions allow.
• Supplementary tables, figure data, or raw sensor outputs when feasible.

Use trusted repositories that provide persistent identifiers, access controls, and long term preservation. Choose subject specific repositories when available for agriculture, soil, or crop data.

Every manuscript must include a data availability statement describing where data and code are stored, how they can be accessed, and any restrictions. Clear statements reduce reviewer queries and support indexing.

When datasets include personal, farm, or location specific information, apply de identification standards and obtain appropriate approvals. Controlled access or data use agreements are acceptable when they protect privacy or proprietary interests.

For animal studies, ensure welfare documentation and consent for farm data sharing when required. If precise geolocation could expose farm operations, provide aggregated or masked coordinates.

JF allows limited embargoes when justified by regulatory requirements, patent review, or contractual agreements. Authors should state the expected release date and the reason for restriction. Controlled access is acceptable when it protects participants or proprietary systems. Long embargoes should be justified clearly.

When possible, apply permissive data licenses that enable reuse with attribution. If third party rights apply, document restrictions and obtain permissions. Transparent licensing improves collaboration across farming systems and research groups.

Cite datasets in the reference list using the repository DOI or accession number. Proper data citation improves recognition for data creators and helps indexing services link articles to underlying resources.

If data are restricted, provide a clear process for access requests, including the contact point, eligibility criteria, and expected response time. This ensures transparency while protecting sensitive farm level information.

For computational studies, provide scripts, models, or configuration files that allow results to be reproduced. Include software versions, dependencies, and platform details when relevant. If code cannot be shared publicly, explain the limitation and indicate how reviewers can verify the analysis.

• Provides a persistent identifier such as a DOI.
• Supports long term preservation and stable access.
• Includes metadata fields for authors, funders, and keywords.
• Allows citation of datasets in the reference list.
• Offers access controls for sensitive farm data when needed.

Many funders require data management plans and open data policies. JF supports compliance by allowing authors to reference their plans and by accepting controlled access options when required by ethics boards or contracts.

Authors should confirm that their data sharing approach aligns with institutional review board guidance, partner agreements, and regional regulations. Early planning prevents delays at acceptance.

Transparent data practices improve reviewer confidence and help researchers validate results across regions and production systems. For farming research, data sharing accelerates replication, supports meta analyses, and strengthens evidence used by extension services and policy planners.

Clear documentation also enables future trials to build on existing field conditions and management practices. Well documented datasets can be reused in decision support tools and climate modeling. Shared data also supports extension training and farmer education and informs sustainable investment decisions in agriculture.