Data archiving preserves the evidence base for chemistry research, supports reproducibility, and enables secondary analysis of spectra, structures, and reaction data. Archived datasets help reviewers and readers verify results and build on published work.

Authors are encouraged to deposit data, code, and related materials in trusted repositories whenever possible. Data sharing increases the value and impact of chemical research.

• Repositories should provide persistent identifiers (e.g., DOI or accession IDs) and support FAIR aligned metadata and access.
• Repositories should be stable, searchable, and widely used in the research community.
• Access conditions must be clearly described, including any restrictions.
• Data should be documented with clear file names, definitions, and variable descriptions.

For chemistry, this may include NMR, MS, IR, crystallographic files, computational inputs, and raw experimental data. Ensure repository terms align with institutional or grant requirements.

For large datasets, provide a summary table that describes file types and content.

All submissions must include a Data Availability Statement describing where data can be accessed, under what conditions, and how to request access if restrictions apply. The statement should be included in the manuscript under the "Statements and Declarations" section.

Example statements may include: data available in a public repository with DOI, data available upon reasonable request, or data restricted due to proprietary or legal constraints.

Authors are responsible for securing permissions for third party data or materials included in their datasets. If proprietary instruments or databases are used, ensure that sharing does not violate licensing terms.

When possible, datasets should be shared under open licenses that permit reuse with attribution. Ensure that data licenses align with institutional policies.

• Use non proprietary file formats when possible (e.g., CSV, TXT, JCAMP-DX).
• Include a README file with variable definitions and analytic notes.
• Document processing steps and version history.
• Ensure that files are labeled clearly and consistently.

Clear documentation improves reuse and reduces barriers for secondary analysis and computational reuse.

Datasets should be cited in the reference list with a persistent identifier. Include the repository name, dataset title, version (if applicable), and DOI or accession number. This ensures that data contributions are recognized and discoverable.